1. 引言
肿瘤微环境（Tumor Microenvironment, TME）是一个复杂且高度动态的生态系统，由肿瘤细胞、免疫细胞、基质细胞以及细胞外基质、血管和神经等多种组分构成，在肿瘤的发生、发展、侵袭、转移以及对治疗的响应中扮演着关键角色 12345。传统上，对TME的研究主要依赖于批量（bulk）组学技术，如批量RNA测序、基因组测序等。这些技术虽然提供了对组织样本整体分子特征的概览，但其固有的局限性在于无法区分不同细胞类型，平均化了异质性细胞群体的信号，从而掩盖了TME内部细胞之间以及细胞与环境之间复杂的相互作用和特异性分子机制 678。具体来说，传统bulk组学技术在以下几个方面存在明显不足：首先，分辨率不足，无法解析肿瘤内部及TME中细胞的精细亚群和功能状态，导致对细胞异质性的理解受限；其次，缺乏空间信息，无法揭示不同细胞类型在组织中的精确位置和空间分布模式，而空间位置对细胞功能和相互作用至关重要 26。
近年来，随着单细胞多组学技术的飞速发展，这些局限性正被逐步克服。单细胞多组学技术能够在单细胞水平上同时获取基因组、转录组、表观基因组、蛋白质组等多种分子信息，极大地提升了我们解析细胞异质性、识别稀有细胞类型和理解细胞间通讯网络的能力 8910。特别地，单细胞RNA测序（scRNA-seq）能够高分辨率地刻画TME中恶性细胞、免疫细胞和基质细胞等不同组分的亚群分类、功能状态及谱系演化规律 1011。与此同时，空间转录组（spatial transcriptomics）及其他空间多组学技术的兴起，使得研究人员能够在保持组织形态完整性的前提下，原位检测基因表达或蛋白质分布，从而揭示细胞在TME中的空间分布特征和邻域组成模式，进一步阐明空间位置与细胞功能及肿瘤进展之间的关联机制 26。
本综述旨在系统性地总结单细胞与空间多组学技术在肿瘤微环境研究中的最新进展和未来前景。我们将首先深入探讨scRNA-seq和空间转录组如何分别从细胞异质性和空间结构层面解析TME的核心特征。随后，我们将聚焦单细胞多组学如何促进对TME中免疫细胞亚群内部及跨细胞类型的调控网络和细胞互作异常介导的肿瘤免疫逃逸与治疗抵抗机制的理解。此外，本综述还将梳理肿瘤微环境研究中单细胞多组学数据整合方法的发展，包括单细胞转录组与空间转录组的跨模态整合，以及跨队列、泛癌单细胞多组学数据整合分析方法。最后，我们将重点讨论单细胞多组学在肿瘤微环境研究中的临床转化进展，包括肿瘤预后与治疗响应生物标志物的发掘以及治疗新靶点的开发，并对当前面临的挑战与未来的发展方向进行展望。
2. 单细胞与空间多组学技术对肿瘤微环境核心特征的解析
2.1 单细胞转录组测序（scRNA-seq）介导的肿瘤微环境细胞异质性刻画
单细胞RNA测序（scRNA-seq）技术的出现，彻底改变了我们对肿瘤微环境（TME）复杂性的认知，使其能够以单细胞分辨率剖析TME内各种细胞类型的异质性。通过对数万乃至数百万个单细胞的转录组进行分析，研究人员能够以前所未有的深度揭示TME中恶性细胞、免疫细胞和基质细胞的亚群分类、功能状态以及谱系演化规律，从而克服了传统批量测序技术无法捕捉细胞异质性的局限性。
在泛癌研究层面，scRNA-seq已广泛应用于揭示肿瘤内部细胞状态的异质性。例如，一项整合了1163个肿瘤样本、涵盖24种肿瘤类型的泛癌单细胞RNA测序数据集分析，识别出41个共识性的“元程序”（meta-programs），这些程序由数十个在许多肿瘤细胞亚群中协同上调的基因组成，涵盖了细胞周期、应激反应等通用过程以及谱系特异性模式，从而定义了转录组细胞内异质性的11个标志。该研究强调，大多数癌细胞的元程序与非恶性上皮细胞中发现的相似，表明很大一部分恶性细胞异质性程序在肿瘤发生前就已存在，反映了其细胞起源的生物学特性 12。另一项针对15种癌症类型的泛癌单细胞RNA测序分析则揭示了癌细胞状态在不同肿瘤类型中普遍存在，并且这些状态与肿瘤微环境形成了特定的相互作用，例如“干扰素反应”的癌细胞状态与TME中的T细胞和巨噬细胞相关联 13。
针对特定癌种，scRNA-seq同样取得了诸多突破性进展。在肝癌研究中，通过对124名患者和8只小鼠的189份肝癌样本进行scRNA-seq分析，研究人员对超过100万个细胞进行了解析，并将患者分为五种TME亚型，包括免疫激活型、髓系或基质细胞介导的免疫抑制型、免疫排斥型和免疫驻留型。值得注意的是，在髓系细胞富集亚型中富集的肿瘤相关中性粒细胞（TAN）与不良预后相关，并发现CCL4+TANs能够招募巨噬细胞，而PD-L1+TANs则能抑制T细胞细胞毒性，为靶向TANs的免疫疗法提供了潜在方向 14。在非小细胞肺癌（NSCLC）中，scRNA-seq揭示了TME中巨噬细胞的高度异质性，并发现组织驻留巨噬细胞在肿瘤早期形成中积累于肿瘤细胞附近，通过促进上皮-间充质转化和浸润以及诱导调节性T细胞反应来促进肿瘤生长。这些发现提示组织驻留巨噬细胞是早期肺癌预防和治疗的潜在靶点 15。胶质母细胞瘤的研究也利用scRNA-seq结合批量基因组和表达分析，识别出恶性细胞存在四种主要的细胞状态，这些状态模仿了不同的神经细胞类型，并受TME影响，表现出可塑性，且其相对频率受CDK4、EGFR、PDGFRA等基因拷贝数扩增和NF1突变的影响 16。
对于肿瘤微环境中的免疫细胞，scRNA-seq也提供了前所未有的细节。一项对来自19种主要癌症类型的649名患者的B细胞进行整合分析，揭示了肿瘤浸润B细胞（TIBs）在丰度和亚型组成上的显著异质性。该研究还识别出两种在肿瘤中富集且具有预后潜力的泛癌共有亚群：应激反应记忆B细胞和肿瘤相关非典型B细胞（TAABs），其中TAABs具有高克隆扩增和增殖能力，并与肿瘤中的活化CD4 T细胞密切相互作用，可预测免疫治疗反应 17。自然杀伤（NK）细胞作为先天免疫系统的重要组成部分，其在TME中的表型和功能多样性也通过整合24种癌症类型中716名患者的NK细胞scRNA-seq数据得以描绘。研究发现NK细胞组成具有肿瘤类型特异性，并识别出一组在肿瘤中富集、抗肿瘤功能受损且与不良预后和免疫治疗抵抗相关的肿瘤相关NK细胞，同时发现特定的髓系细胞亚群（特别是LAMP3+树突状细胞）可能介导NK细胞抗肿瘤免疫的调节 18。此外，对髓系细胞的scRNA-seq分析揭示了在非小细胞肺癌患者体内存在25种肿瘤浸润髓系细胞（TIM）状态，其中大部分在不同患者中可重复出现。通过与小鼠TIMs的比较，研究发现树突状细胞和单核细胞的群体结构几乎完全一致，中性粒细胞亚群保守，而巨噬细胞则存在物种差异 19。最新的泛癌研究进一步精细刻画了肿瘤浸润树突状细胞（DC）的单细胞图谱，识别出AXL+SIGLEC6+ DCs和朗格汉斯细胞样DCs等稀有亚群，并揭示了LAMP3+ DCs的多种细胞起源，为DC基础的免疫疗法开发提供了新见解 20。
基质细胞作为TME的重要组成部分，其异质性同样被scRNA-seq详细描绘。癌相关成纤维细胞（CAFs）是肿瘤微环境中最丰富的基质细胞类型之一。例如，在肝内胆管癌（ICC）中，scRNA-seq分析揭示了ICC细胞和血管癌相关成纤维细胞（vCAFs）之间复杂的相互作用，vCAFs通过分泌IL-6促进肿瘤进展，而ICC细胞则通过外泌体miR-9-5p诱导vCAFs高表达IL-6 21。另一项肝细胞癌（HCC）的研究则通过人鼠肿瘤的单细胞RNA测序揭示了CAF的异质性，并确定了一种显著的CD36+CAF亚群，其具有高水平的脂质代谢和巨噬细胞迁移抑制因子（MIF）的表达。CD36+CAFs通过招募CD33+髓源性抑制细胞（MDSCs）促进HCC进展 22。乳腺癌的scRNA-seq研究定义并功能注释了9种CAF表型和1种周细胞类型，并在其他四种癌症类型中验证了该分类系统 23。此外，结直肠癌的整合单细胞分析不仅全面刻画了TME中的转录重塑，还识别出一种罕见的肿瘤特异性内皮细胞亚群，该亚群具有T细胞招募潜力。基于TME异质性，患者可被分层，揭示了癌细胞利用不同免疫逃逸机制的TME亚型 24。胃腺癌（GAC）的进展研究也通过单细胞谱分析揭示了TME细胞状态和组成的改变，在癌前病变中存在大量IgA浆细胞，而在晚期GAC中则由免疫抑制性髓系和基质细胞亚群主导，并识别出与侵袭性表型和不良预后相关的SDC2高表达CAFs 25。这些研究共同展示了scRNA-seq在解析肿瘤微环境细胞异质性方面的强大能力，为理解肿瘤生物学和开发新型治疗策略奠定了基础。
2.2 空间转录组技术对肿瘤微环境空间结构的原位解析
尽管单细胞RNA测序（scRNA-seq）在细胞异质性解析方面取得了革命性进展，但它在组织解离过程中丢失了细胞原有的空间位置信息，而细胞的空间组织对于其功能发挥和细胞间相互作用至关重要 2627。空间转录组（Spatial Transcriptomics, ST）技术应运而生，通过在保留组织形态学信息的同时，测量组织切片中基因的表达水平，从而弥补了scRNA-seq的这一局限性 2628。这些技术能够揭示肿瘤微环境（TME）中不同细胞亚群的空间分布特征、细胞邻域组成模式，并进一步阐明空间位置与细胞功能、肿瘤进展之间的关联机制 226。
空间转录组技术大致可分为基于测序和基于成像两大类，它们共同为研究TME的空间复杂性提供了强有力的工具 26。基于测序的方法通常将组织切片放置在带有空间条形码的阵列上，然后对捕获到的mRNA进行测序，以重建基因表达的空间图谱。基于成像的方法则通过多重荧光原位杂交（multiplex FISH）或其他成像技术，直接在组织中检测和定位特定基因或分子的表达 29。这些技术的发展，使得研究人员能够以前所未有的精度，探究TME中各种细胞成分如何相互定位、形成“细胞邻域”（cellular neighborhoods）并协同作用，驱动肿瘤发生发展 230。
通过空间转录组技术，研究人员已经取得了诸多重要发现。例如，在非小细胞肺癌（NSCLC）的研究中，结合单细胞和空间转录组分析发现，肿瘤可分为富含淋巴细胞、富含髓系细胞和混合浸润亚型 31。在该研究中，研究人员还识别出髓系细胞诱导的CD14+CD4+T细胞，这些细胞通过“噬抱作用”（trogocytosis）获得非典型表型，并在淋巴细胞富集型肿瘤中高浸润，与患者预后不良相关 31。空间转录组进一步揭示，富含CD14+CD4+T细胞的肿瘤中肿瘤坏死因子-α（TNF-α）信号通路显著富集，且TNF-α能增强噬抱作用，促进CD14+CD4+T细胞的形成，从而揭示了TME中由TNF-α介导的免疫抑制机制和髓系-T细胞异常互作对NSCLC进展的贡献 31。
癌相关成纤维细胞（CAFs）是TME的关键组成部分，影响肿瘤生物学和治疗反应。一项整合了7个空间转录组和蛋白质组学平台、10种癌症类型、超过1400万个细胞的综合分析研究，发现了四种保守的空间CAF亚型，这些亚型在不同癌症类型和平台间均保持一致 30。这些CAF亚型表现出独特的空间组织模式、邻近细胞组成、相互作用网络和转录组特征。它们的丰度和组成在不同组织中有所不同，影响着TME的特性，如肿瘤浸润免疫细胞的水平、分布和状态组成、肿瘤免疫表型以及患者生存期，加深了对CAF空间异质性的理解 30。
空间转录组还被用于深入剖析肿瘤免疫微环境中的特定结构。例如，三级淋巴结构（Tertiary Lymphoid Structures, TLSs）作为异位淋巴聚集体，通常与免疫治疗响应增强和更好的临床结果相关 32。通过近单细胞分辨率的空间转录组图谱，研究人员全面分析了肝细胞癌（HCC）中不同成熟阶段的TLSs及其微环境 32。研究将未成熟TLSs分为“一致型”和“偏离型”两组，发现一致型TLSs与成熟TLSs类似，具有免疫治疗响应的生态位功能，而偏离型TLSs则不具备 32。更重要的是，研究发现恶性细胞通过色氨酸（tryptophan）代谢塑造的富含色氨酸的代谢微环境导致了TLSs成熟的偏离。抑制色氨酸代谢能促进肿瘤内TLSs的成熟，并增强肿瘤控制，与抗PD-1治疗协同增效 32。
此外，空间多组学技术也为肿瘤转移机制研究提供了新的视角。在骨肉瘤淋巴结转移的研究中，通过整合单细胞和空间转录组数据，揭示了骨肉瘤细胞与髓系细胞、CAF和NK/T细胞等多种细胞成分相互作用，重塑淋巴结微环境的机制 33。另一项针对宫颈癌的研究，结合单细胞RNA测序和空间转录组，全面刻画了疾病进展过程中TME的细胞和分子图景 34。研究特别强调了SPP1+巨噬细胞在宫颈癌中的显著富集及其通过SPP1-CD44信号轴与免疫细胞的广泛互作，进而导致免疫抑制微环境形成，并促进肿瘤细胞存活，且SPP1高表达与晚期肿瘤和不良预后相关 34。
这些研究充分展示了空间转录组和空间多组学技术在揭示TME空间异质性、细胞邻域构成以及空间结构与肿瘤进展和治疗响应之间关联方面的独特优势。通过整合空间信息，我们能够更全面地理解肿瘤的复杂生物学，为开发更精准的诊断和治疗策略提供坚实基础。
3. 单细胞多组学介导的肿瘤微环境细胞互作机制研究进展
3.1 免疫细胞亚群内部及跨细胞类型的调控网络解析
肿瘤微环境（TME）是一个高度复杂的生态系统，其中免疫细胞亚群之间、免疫细胞与其他非免疫细胞（如基质细胞和恶性细胞）之间的动态互作网络，是决定肿瘤进展和治疗响应的关键。单细胞多组学技术，特别是单细胞RNA测序（scRNA-seq），通过解析细胞特异性基因表达谱，结合配体-受体（ligand-receptor）推断算法，极大地加深了我们对这些精细调控网络的理解，揭示了核心免疫细胞的功能调控机制。
自然杀伤（NK）细胞是先天免疫系统的重要组成部分，在抗肿瘤免疫中发挥关键作用。然而，NK细胞在TME中的功能常常受到抑制。泛癌单细胞分析揭示，在24种癌症类型的716名患者中，肿瘤浸润NK细胞（TINKs）表现出显著异质性。研究发现，一部分肿瘤相关NK细胞（TANKs）在肿瘤中富集，其抗肿瘤功能受损，并与不良预后及免疫治疗抵抗相关。值得注意的是，特定的髓系细胞亚群，特别是LAMP3+树突状细胞，似乎在调节NK细胞的抗肿瘤免疫中起作用 18。LAMP3+树突状细胞因其在淋巴结中的迁移能力和调节多种淋巴细胞的潜力而被广泛关注 35。在肿瘤中，NKG2D配体的表达对NK细胞的激活至关重要。例如，在胶质母细胞瘤（GBM）中，肿瘤细胞上NKG2C/KLRC2的表达与髓源性抑制细胞数量的减少和肿瘤驻留淋巴细胞水平的升高相关，并能增强PD-1单克隆抗体治疗的抗肿瘤效果 36。此外，胃癌研究也发现，包括恶性肿瘤细胞、内皮细胞、MAIT细胞、T细胞样B细胞、浆细胞样树突状细胞、巨噬细胞、单核细胞和中性粒细胞在内的多种细胞，可能通过HLA-E-KLRC1/KLRC2相互作用调控细胞毒性T细胞和/或NK细胞的功能，为胃癌的免疫治疗提供了新的靶点 37。通过体外实验，研究也证实了高剂量放疗可以通过上调肝细胞癌（HCC）细胞上NK细胞激活配体（如NKG2D配体）的表达，从而增强CAR-NK细胞的抗肿瘤效应 38。这些发现共同指向TME中复杂的多细胞互作网络如何影响NK细胞的活性和功能。
树突状细胞（DCs）是启动和调节T细胞免疫应答的关键抗原呈递细胞。单细胞分析揭示了DC亚群的广泛异质性，并识别出多种具有独特功能和空间定位的DC亚群。例如，AXL+SIGLEC6+ DCs和朗格汉斯细胞样DCs等稀有亚群的鉴定，进一步丰富了我们对DC家族的理解。泛癌分析还深入探讨了LAMP3+ DCs的多种细胞起源，这些DCs因其在肿瘤引流淋巴结中激活T细胞的能力而备受关注。理解这些DC亚群的起源、功能和与其他细胞的互作，对于开发基于DC的免疫疗法至关重要。
肿瘤相关巨噬细胞（TAMs）是TME中最丰富的免疫细胞之一，通常表现出促肿瘤表型。单细胞分析揭示了TAMs的显著异质性和可塑性。在乳腺癌中，TAMs被精确分类为7个亚型，展示了抗肿瘤或促肿瘤的不同角色。一项研究首次鉴定了一种在乳腺癌中具有增殖和扩张能力的新型TAM亚型——TUBA1B TAMs，它在TAM多样性和肿瘤进展中发挥关键作用，并且是TAMs重塑和功能改变的起始点 39。另一项针对TNBC的研究发现，TAMs中CPVL和MSR1基因是重要的预后生物标志物，CPVL高表达与有利预后相关，可能通过抑制SPP1-CD44信号和促进CXCL9-CXCR3、C3-C3AR1信号来影响TAM与其他免疫细胞（如单核细胞、中性粒细胞和T细胞）的互作。MSR1则与不良预后相关，并与M2样TAMs极化相关联，提示这两种基因可能通过调节巨噬细胞极化参与对肿瘤微环境的塑造 40。在其他肿瘤类型中，TAMs也通过与其他细胞的互作促进肿瘤进展。例如，在前列腺癌中，FAP+成纤维细胞与SPP1+巨噬细胞之间存在显著的通讯，特别是通过CSF1/CSF1R和CXCL/ACKR1信号通路，共同促进免疫抑制性TME的形成 41。此外，肿瘤细胞与巨噬细胞之间的动态单细胞代谢组学研究也揭示了细胞间相互作用的复杂机制，并基于代谢特征识别出多种极化亚型的肿瘤相关巨噬细胞 42。这些研究强调了TAMs在TME中的关键调控作用及其作为潜在治疗靶点的巨大潜力。
除了上述免疫细胞，其他TME细胞类型也参与复杂的细胞互作网络。肿瘤浸润B细胞（TIBs）的泛癌单细胞图谱揭示了其在TME中的异质性和功能多样性。研究发现TIB亚群与检查点抑制剂治疗响应之间存在关联，并识别出特定B细胞亚群独特的配体-受体对，这些对在空间上得到了验证，表明B细胞和T细胞之间存在复杂的串扰 43。癌相关成纤维细胞（CAFs）作为TME中的主要基质细胞，也与免疫细胞形成重要的互作。一项泛癌研究通过整合分析发现了四种保守的空间CAF亚型，它们表现出独特的空间组织模式、邻近细胞组成和相互作用网络，并影响着TME特性，如肿瘤浸润免疫细胞的分布和肿瘤免疫表型 30。在肝内胆管癌中，血管癌相关成纤维细胞（vCAFs）通过分泌IL-6，诱导ICC细胞表观遗传学改变，特别是上调EZH2，从而增强恶性程度；同时，ICC细胞来源的外泌体miR-9-5p反过来诱导vCAFs高表达IL-6，形成一个促进肿瘤进展的恶性循环 21。
总而言之，单细胞多组学技术通过高分辨率的细胞分型和对细胞互作的深度解析，极大地拓展了我们对肿瘤微环境中免疫细胞亚群内部及跨细胞类型调控网络的理解，为开发更有效、更精准的肿瘤免疫治疗策略提供了坚实的基础。
3.2 细胞互作异常介导的肿瘤免疫逃逸与治疗抵抗机制
肿瘤微环境（TME）中细胞互作网络的失调是肿瘤免疫逃逸和治疗抵抗的关键驱动因素。单细胞多组学技术能够以前所未有的分辨率揭示这些异常互作的分子基础，为克服治疗抵抗提供了新的靶点和策略。
3.2.1 免疫抑制细胞的富集与功能失调
多种免疫抑制细胞在TME中的异常富集和功能增强是肿瘤免疫逃逸的核心机制。髓源性抑制细胞（MDSCs）是其中一类关键的免疫抑制细胞。胶质母细胞瘤（GBM）因其高度免疫抑制的微环境而难以治疗，MDSCs在其中扮演了重要角色，即使在总CD45+细胞中MDSCs仅占4%-8%，它们也通过复杂的机制促进免疫逃逸、肿瘤进展、血管生成、侵袭和转移 44。在非酒精性脂肪性肝炎相关肝细胞癌（NASH-HCC）中，N6-甲基腺苷阅读蛋白YTHDF1过表达，通过促进EZH2的翻译进而增加IL-6的分泌，IL-6则招募并激活MDSCs，导致CD8 T细胞功能障碍，从而介导免疫抑制 45。靶向YTHDF1可以增强抗PD-1治疗的抗肿瘤免疫应答，提示MDSCs与肿瘤细胞之间的互作是潜在的治疗靶点。此外，在肝细胞癌中，CXCL12肿瘤相关内皮细胞（CXCL12 TECs）通过分泌CXCL12抑制幼稚CD8 T细胞分化为细胞毒性T细胞，并招募MDSCs，从而促进免疫抑制 46。靶向CXCL12 TECs有望增强免疫治疗效果 46。
调节性T细胞（Tregs）也是重要的免疫抑制细胞亚群。在结直肠癌（CRC）中，一项研究发现，抗PD-1治疗抵抗的患者中，耗竭性T细胞（Tex）、GZMK+ T细胞、应激反应T（TSTR）细胞、Treg细胞和γδ T细胞都与治疗抵抗相关 47。食管鳞状细胞癌（ESCC）中，对免疫检查点抑制剂（ICI）抵抗的患者表现出祖细胞CD8 Tex和超活化Treg（hyper-Treg）之间的串扰。这种hyper-Treg通过CLEC2C-KLRB1轴调节CD8 T细胞的活化，形成以hyper-Treg为主导的抑制性细胞互作网络，导致TME的整体免疫抑制状态 48。
肿瘤相关巨噬细胞（TAMs）同样是TME中高度异质性的髓系细胞，通常具有促肿瘤和免疫抑制功能。在结直肠癌肝转移（CRLM）中，SPHK1在TAMs中高表达，导致SPHK1 TAMs通过IL-1β信号通路，诱导CD8 T细胞耗竭并建立免疫抑制微环境，从而导致抗PD-1治疗效果不佳 49。
3.2.2 T细胞功能耗竭与免疫检查点阻断抵抗
T细胞耗竭是肿瘤免疫逃逸的重要特征，表现为T细胞效应功能受损、增殖能力下降以及持续高表达免疫抑制受体（如PD-1、TIM-3）。单细胞多组学研究深入揭示了T细胞耗竭的分子机制和调控网络。在透明细胞肾癌（ccRCC）中，TRIM28被确定为免疫逃逸的关键调节因子。TRIM28通过稳定PARP1并促进其SUMO化，从而通过NAD-SIRT1-p65信号通路增强PD-L1的表达；同时，TRIM28消耗TME中的NAD，限制CD8 T细胞的NAD供应，损害其呼吸和效应功能，导致CD8 T细胞功能障碍 50。这揭示了肿瘤内在代谢重编程与CD8 T细胞功能失调相结合的免疫抑制机制。
癌相关成纤维细胞（CAFs）在T细胞功能耗竭中也扮演了关键角色。在人类结肠腺癌和三阴性乳腺癌组织中，AEBP1的表达与T细胞功能障碍呈正相关，并预示着不良的患者预后。单细胞RNA测序进一步确定CAFs是AEBP1的主要来源。CAF特异性AEBP1缺失可增强小鼠体内的T细胞细胞毒性并抑制肿瘤生长。其机制在于自泌的AEBP1与CAF上的CKAP4结合，激活AKT/PD-L1信号通路，从而驱动T细胞功能障碍 51。
PD-L1的异常上调是肿瘤免疫逃逸的常见机制。泛素特异性加工酶2（USP2）被鉴定为PD-L1稳定性的新型调节剂。USP2通过直接与PD-L1相互作用并去泛素化其K48连接的多聚泛素化，从而增加PD-L1的丰度。USP2的缺失导致PD-L1的内质网相关降解，减弱PD-L1/PD-1相互作用，并使癌细胞对T细胞介导的杀伤敏感。USP2缺失还能增强小鼠的抗肿瘤免疫力，增加CD8 T细胞浸润并减少MDSCs和Tregs的免疫抑制性浸润 52。
3.2.3 宿主因素与治疗抵抗
除了肿瘤细胞和TME细胞间的直接互作，宿主本身的生理状态，如衰老，也会影响TME并导致治疗抵抗。衰老会导致T细胞免疫重塑，与癌症等年龄相关疾病的不良临床结果相关 53。在衰老过程中，肿瘤微环境中CD8 T细胞的初始激活受限，其影响甚至超过了细胞内在缺陷，从而限制了肿瘤控制能力 54。年轻小鼠的T细胞转移到老年小鼠体内也无法恢复肿瘤控制，因为老年TME会迅速诱导T细胞功能障碍。衰老TME中的细胞外信号驱动肿瘤浸润性年龄相关功能障碍（Tage）细胞状态，该状态在功能、转录和表观遗传学上都与典型的T细胞耗竭不同 54。靶向髓系细胞可以重振常规1型树突状细胞，改善肿瘤控制并恢复衰老个体中的CD8 T细胞免疫，提示衰老TME的重塑是潜在的干预靶点 54。
3.2.4 放化疗抵抗机制
细胞互作异常不仅影响免疫治疗，也影响传统放化疗的效果。癌相关成纤维细胞（CAFs）和中性粒细胞在化疗抵抗中发挥重要作用。在结直肠癌中，炎性CAFs（iCAFs）与新辅助化放疗反应不佳相关。研究发现，辐射后白介素-1α（IL-1α）不仅使CAFs向炎性表型极化，还触发氧化性DNA损伤，从而使iCAFs易于发生p53介导的治疗诱导性衰老，进而导致化放疗抵抗和疾病进展 55。抑制IL-1、预防iCAFs衰老或进行衰老细胞清除治疗，可使小鼠对辐射敏感，并且直肠癌患者血清中较低的IL-1受体拮抗剂水平与不良预后相关 55。
化疗还可诱导中性粒细胞募集和中性粒细胞胞外陷阱（NET）的形成，从而降低乳腺癌肺转移小鼠模型中的治疗反应。化疗处理的癌细胞分泌IL-1β，进而触发NETs的形成。整合素-αvβ1和基质金属蛋白酶9这两种NET相关蛋白是诱导化疗抵抗所必需的，它们通过捕获并激活潜在的TGF-β，导致癌细胞发生上皮-间充质转化，并与化疗抵抗相关 56。靶向IL-1β-NET-TGF-β轴有望减少或预防转移性化疗抵抗 56。
此外，肿瘤细胞本身通过细胞间通讯诱导的耐药性也是化疗抵抗的重要机制。乳腺癌耐药细胞分泌的细胞外囊泡（EVs）可上调敏感细胞中多种转运蛋白的表达，从而增加敏感细胞对化疗药物的存活率。这些EVs还可能下调免疫系统相关因子以逃避免疫检测，并通过阻止细胞凋亡来增加敏感乳腺癌细胞的存活。暴露于耐药EVs的人巨噬细胞会触发促炎细胞因子分泌，表明EVs在介导耐药性和调节TME炎症反应中的作用 57。
综上所述，单细胞多组学技术为我们揭示了TME中复杂的细胞互作异常如何驱动肿瘤免疫逃逸和治疗抵抗提供了深度洞察。这些研究不仅阐明了MDSCs、Tregs、TAMs、CAFs和特定T细胞亚群的功能失调，还揭示了宿主因素（如衰老）以及肿瘤细胞与TME其他组分之间在放化疗抵抗中的关键作用。这些发现为开发靶向特定细胞互作通路、重塑免疫抑制TME并克服治疗抵抗的新策略奠定了坚实基础。
4. 肿瘤微环境研究中单细胞多组学数据整合方法的发展
单细胞多组学技术，特别是单细胞RNA测序（scRNA-seq）和空间转录组（Spatial Transcriptomics, ST）技术，在解析肿瘤微环境（TME）的细胞异质性和空间结构方面取得了巨大成功。然而，scRNA-seq在细胞解离过程中丢失了空间信息，而目前的空间转录组技术往往在单细胞分辨率上存在局限性，或者无法捕捉全转录组信息 5859。为了充分利用这两种技术的优势，并克服各自的局限性，开发有效的跨模态数据整合方法变得至关重要。这些整合方法旨在将scRNA-seq的高分辨率细胞类型信息映射到空间转录组数据上，从而在保持空间上下文的同时实现对组织中细胞类型的精细定位和功能解析。
4.1 单细胞转录组与空间转录组的跨模态整合方法
将单细胞RNA测序（scRNA-seq）数据与空间转录组（ST）数据进行有效整合是当前肿瘤微环境研究领域的热点和挑战。这种整合的目标是将scRNA-seq在单细胞水平上识别出的精细细胞亚群和分子特征，准确地映射到具有空间位置信息的组织切片上，从而在保留空间背景的同时，实现接近单细胞分辨率的空间基因表达分析和细胞功能解析。目前，主流的整合方法主要围绕细胞类型反卷积（deconvolution）、数据对齐（alignment）和深度学习等技术展开，每种方法都有其适用场景和技术优劣势。
4.1.1 细胞类型反卷积（Deconvolution）方法
细胞类型反卷积是目前将scRNA-seq数据整合到空间转录组数据中的核心策略之一。由于许多空间转录组技术（如Visium）每个空间捕获点（spot）可能包含多个细胞 58，其测量的基因表达是这些细胞的混合信号。反卷积方法通过利用scRNA-seq数据作为参考，估计每个空间捕获点内不同细胞类型的比例或数量。
代表性算法及原理：
- 例如，SPOTlight、CellDART、Stereoscope等算法通过不同的统计模型或机器学习方法，学习scRNA-seq数据中各细胞类型的基因表达特征，然后将这些特征应用于空间转录组数据，推断每个spot中的细胞组成。
- Redeconve是最新发展的一种算法，它能够以单细胞分辨率反卷积空间转录组数据，实现对数千种细微细胞状态的解释。该算法在准确性、分辨率、鲁棒性和速度方面表现出优越性。例如，Redeconve被应用于人类胰腺癌数据集，揭示了癌克隆特异性T细胞浸润，并在淋巴结样本中识别出IgA+和IgG+区域之间差异的细胞毒性T细胞，为肿瘤免疫学和抗体类别转换的调控机制提供了新见解 60。
- scResolve则专注于从多细胞分辨率的空间转录组数据中恢复单细胞表达谱。该方法能够准确地恢复单个细胞在其位置的表达谱，这是细胞类型反卷积无法实现的。scResolve已被应用于人类乳腺癌和肺病数据，使得在不同组织背景下进行细胞类型特异性差异基因表达分析和稀有细胞群体鉴定成为可能，从而促进了更灵活和精确的空间分析 61。
优势： 这类方法能够揭示空间位置上的细胞组成异质性，并估计各细胞类型在空间上的丰度分布，有助于理解特定细胞类型在TME中的聚集或排斥模式。
局限性： 多数反卷积方法只能估计细胞类型比例，无法直接获得每个空间捕获点内单个细胞的精确表达谱，也难以处理参考scRNA-seq数据中未包含的稀有细胞类型。此外，不同细胞类型的混合可能会导致模型准确性下降。
4.1.2 数据对齐与映射（Alignment and Mapping）方法
数据对齐方法旨在将scRNA-seq细胞直接或间接地映射到空间转录组的物理位置上，从而为每个空间位置的细胞提供单细胞分辨率的转录组信息。
代表性算法及原理：
- ST-seq/scRNA-seq整合：一些方法如Seurat V4的CCA（Canonical Correlation Analysis）或Harmony等，通过学习scRNA-seq和空间转录组数据之间共享的潜在结构，将两种模态的数据对齐，使得scRNA-seq中的细胞能够被“投影”到空间转录组的位置上。
- optimal transport (OT)：一些算法如SCOT (Single-Cell Alignment Using Optimal Transport) 利用最优传输理论来整合单细胞多组学数据。SCOT是一个无监督算法，通过构建k近邻图来保留局部几何结构，并找到一个概率耦合矩阵，使域内距离差异最小化，最终通过重心的投影将一个单细胞数据集映射到另一个数据集上。这种方法能够有效地对齐不同模态的单细胞数据，即使在缺乏正交对齐信息的情况下也能进行参数调整 62。
- 参考图谱构建：构建细胞类型参考图谱，并利用相关模型将空间数据中的表达模式与参考图谱进行匹配，从而预测空间位置上的细胞类型 63。
优势： 能够提供更精细的空间细胞类型定位，有时甚至能推断出单细胞水平的基因表达空间分布。
局限性： 对于空间分辨率较低的ST技术，这种映射可能仍然是近似的。同时，数据的批次效应、技术平台差异以及不同样本间的生物学变异都可能影响对齐的准确性。
4.1.3 深度学习（Deep Learning）方法
随着人工智能技术的发展，深度学习模型在整合多模态单细胞数据方面展现出强大潜力。这些方法能够学习复杂的非线性关系，处理大规模和高维度数据，并在一定程度上克服数据稀疏性问题 64。
- 代表性算法及原理： 深度学习模型，如变分自编码器（VAE）或生成对抗网络（GAN），可以用于学习scRNA-seq和空间转录组数据的联合嵌入空间。在这个共享空间中，来自不同模态的数据点可以被有效地比较和整合。一些模型甚至可以直接利用组织图像信息，结合基因表达数据进行空间细胞类型推断和基因表达预测 64。
- 优势： 具有强大的特征学习能力，能够处理噪声和复杂模式，可能在空间分辨率提升和细胞状态预测方面优于传统方法。
- 局限性： 模型训练通常需要大量数据，且解释性相对较差，有时难以理解模型做出预测的具体依据。
4.1.4 整合分析在提升空间数据分辨率、挖掘细胞空间功能特征中的作用
单细胞转录组与空间转录组的整合分析具有重要的生物学意义和应用价值：
- 提升空间分辨率： 通过将高分辨率的scRNA-seq细胞类型信息映射到空间转转录组数据上，可以有效克服现有空间转录组技术在单细胞分辨率上的不足，有助于在组织切片上实现准单细胞甚至单细胞水平的细胞类型识别和定位，例如scResolve可恢复单细胞表达谱 61。这使得研究人员能够更准确地识别肿瘤微环境中的稀有细胞亚群，并分析其在特定空间区域的分布。
- 揭示细胞空间邻域关系： 整合数据能够更精确地定义TME中的“细胞邻域”，即在空间上相互靠近并可能进行功能互作的细胞群体 65。例如，通过识别肿瘤细胞与免疫细胞、基质细胞在空间上的共定位模式，可以推断潜在的细胞间通讯轴，如配体-受体互作关系，从而揭示肿瘤进展、免疫逃逸和治疗抵抗的机制 6667。
- 挖掘空间功能异质性： 结合空间信息，可以更深入地理解同一细胞类型在不同空间位置上的功能差异。例如，某种免疫细胞在肿瘤核心和边缘区域可能表现出不同的激活状态或免疫抑制功能。整合分析有助于识别这些空间特异性的功能特征，例如，在人类肾脏疾病的研究中，多模态方法定义了28种在肾损伤中发生变化的细胞状态，并利用空间转录组在损伤邻域内定位了这些状态，揭示了与肾功能下降相关的适应不良状态 65。
- 辅助生物标志物发现与靶点识别： 通过高分辨率的空间功能分析，可以识别与特定疾病状态（如肿瘤转移、治疗响应）密切相关的空间特异性细胞亚群或基因表达模式，从而为发现新型诊断生物标志物和开发精准治疗靶点提供线索 5966。例如，在肝细胞癌（HCC）的研究中，通过整合scRNA-seq和ST数据，发现了S100A6促转移亚型肿瘤细胞，并揭示了其与成纤维细胞通过SPP1-CD44和CCN2/TGF-β-TGFBR1相互作用的反馈循环，抑制该循环可抑制转移 67。
- 动态过程的时空重构： 虽然现有技术多为静态快照，但通过整合不同时间点的单细胞和空间数据，有望重构肿瘤发生发展或治疗响应过程中的细胞动态变化及其空间演变轨迹 68。例如，在结直肠癌的进展研究中，通过整合scRNA-seq和Visium HD数据，识别了在正常组织向腺瘤和癌变转变过程中，浆细胞、中性粒细胞和成纤维细胞中显著的hallmark通路变化，并发现了NOTUM+上皮细胞和IGHG1/3+浆细胞集中于正常组织与腺瘤边界，提示它们可能参与形态学转变 69。
综上所述，单细胞转录组与空间转录组的跨模态整合方法是未来TME研究的关键方向。通过不断优化整合算法，并结合多组学信息，我们将能够获得对肿瘤微环境更为全面和精细的理解，加速肿瘤生物学研究向临床转化的进程。
4.2 跨队列、泛癌单细胞多组学数据整合分析方法
单细胞测序技术的快速发展导致了海量单细胞数据的积累，这些数据通常来源于不同的研究机构、技术平台、实验批次甚至不同物种。对这些异质性数据进行有效的整合分析，对于识别保守的细胞亚群、发现通用的调控机制、以及开展泛癌研究具有重要意义。然而，数据整合面临的主要挑战是批次效应（batch effect），即由非生物学因素（如实验条件、试剂差异、操作人员等）引入的系统性变异，这些变异可能掩盖真实的生物学信号，导致错误的结论 70。因此，开发稳健的批次校正和数据整合方法是泛癌单细胞多组学研究成功的关键。
4.2.1 批次校正方法
批次效应是单细胞数据整合中最突出的问题。为了去除批次效应，研究人员开发了多种计算方法，大致可分为以下几类：
线性模型/降维方法：
- ComBat： 最初用于批量RNA-seq数据，后被应用于单细胞数据。ComBat通过拟合线性模型，估计并去除批次特定的加性（additive）和乘性（multiplicative）效应。
- LIGER (Linked Inference of Genomic Experimental Relationships)： LIGER使用集成因子分析（integrative non-negative matrix factorization, iNMF）来识别共享和批次特异性的表达模式，通过迭代优化过程实现数据整合，并能发现跨数据集共享的细胞类型 70。
- CCA (Canonical Correlation Analysis) / Seurat 3： Seurat V3及后续版本引入的整合策略，通过识别不同数据集中共享的“锚点”（anchors），然后使用这些锚点进行数据校正和整合。CCA可以识别不同数据集中共享的变异源，帮助对齐细胞群，但可能对大的批次效应敏感。Seurat 3的整合方法在处理不同条件、技术和物种的数据集时表现出色 7071。
图论/最近邻方法：
- Harmony： Harmony是一种迭代的、基于软聚类的方法，它通过调整细胞嵌入（embeddings）来最大化不同批次中相似细胞之间的连接，同时最小化批次间的差异。Harmony在处理大规模数据集时计算效率高，并被推荐为首选的批次整合方法之一 70。
- scAlign： scAlign利用暹罗神经网络（Siamese neural networks）学习一种共同的低维表示，使得来自不同批次但生物学上相似的细胞在该共享空间中彼此靠近。
深度学习方法：
- autoencoders： 自动编码器（autoencoders）作为一种非线性降维方法，可以通过学习数据的低维表示来去除批次效应。例如，一些方法使用条件自动编码器（conditional autoencoders）将批次信息作为输入，从而生成批次校正后的嵌入空间 72。
- Conos： Conos通过构建相互最近邻图，并利用这些图在不同数据集之间建立连接，从而实现数据整合，同时保留生物学变异。
其他方法：
- FastMNN： FastMNN是一种基于互近邻（mutual nearest neighbors, MNN）对齐的方法，通过识别不同批次中相互的最近邻细胞对，并使用这些MNN对来计算批次校正向量。
对14种批次校正方法进行的一项基准测试显示，Harmony、LIGER和Seurat 3在计算时间、处理大规模数据集的能力以及批次效应校正效果方面表现良好，同时能有效保留细胞类型纯度。其中，Harmony因其显著更短的运行时间而被推荐为首选尝试方法 70。
4.2.2 统一注释和跨样本比较
批次校正后，下一步是进行统一的细胞类型注释和跨样本比较。这通常包括：
- 聚类与细胞类型识别： 对整合后的数据进行聚类，识别出具有相似基因表达特征的细胞群。然后，通过已知的标记基因（marker genes）或机器学习方法，对这些细胞群进行生物学注释，确定其细胞类型。
- 参考图谱构建与查询： 构建高质量的细胞类型参考图谱（cell atlas），然后可以将新的单细胞数据集与这些图谱进行比对，从而实现自动化的细胞类型注释和标准化比较。
- 差异基因表达分析： 在去除批次效应后，可以更可靠地进行跨样本或跨条件之间的差异基因表达分析，识别在不同状态下发生改变的基因。最新的工具如scDist，基于混合效应模型，能够稳健地识别单细胞RNA-seq数据中受扰动的细胞类型，即便在存在个体间和队列间变异的情况下也能有效减少假阳性发现，并成功应用于COVID-19和免疫治疗数据集，揭示了新的疾病机制和治疗响应洞察 73。
4.2.3 泛癌单细胞多组学整合研究的应用价值
泛癌单细胞多组学整合分析在挖掘保守细胞亚群、通用调控机制以及临床转化方面展现出巨大的潜力：
- 识别保守细胞亚群和通用调控机制： 通过整合来自不同癌症类型的大量单细胞数据，研究人员可以识别出在多种癌症中普遍存在的保守细胞亚群，如特定的免疫细胞（NK细胞 18、B细胞 43）、基质细胞（CAFs 30）或恶性细胞状态 12。这些保守亚群可能代表了肿瘤发生发展中的共同驱动力或抵抗治疗的通用机制。例如，一项泛癌单细胞图谱研究揭示了在肿瘤中富集、抗肿瘤功能受损且与不良预后及免疫治疗抵抗相关的肿瘤相关NK细胞，并指出特定髓系细胞亚群（LAMP3+树突状细胞）可能介导其功能调节 18。另一项针对肿瘤浸润B细胞的泛癌分析识别出两种保守的泛癌共有亚群：应激反应记忆B细胞和肿瘤相关非典型B细胞（TAABs），其中TAABs具有高克隆扩增和增殖能力，并与肿瘤中的活化CD4 T细胞密切相互作用，可预测免疫治疗反应 43。
- 克服样本量限制： 单个研究往往受限于样本量，难以捕捉稀有细胞类型或全面的生物学变异。泛癌整合分析通过汇集大量数据，有效增加了统计功效，有助于发现低丰度但重要的细胞群，并提高统计推断的可靠性。
- 构建高质量的细胞图谱： 整合后的泛癌数据可以作为构建全面、高质量细胞图谱的基础，为后续的单细胞研究提供标准化的参考。例如，通过整合不同数据集，可以构建出更完善的细胞类型分类系统，并发现新的细胞状态或谱系。
- 指导治疗策略的开发： 识别出的保守细胞亚群和通用调控机制可以作为开发广谱抗癌治疗策略的潜在靶点。例如，如果某种免疫抑制性细胞亚群在多种癌症中普遍存在并发挥关键作用，那么针对该亚群的治疗干预可能对广泛的肿瘤类型有效。前列腺癌M2巨噬细胞的单细胞分析整合了多项GEO和TCGA数据，最终筛选出9个M2巨噬细胞相关的预后基因（如SMOC2, PLPP1, HES1等），并构建了风险评分模型，验证了M2巨噬细胞及其相关基因在前列腺癌发生发展和转移中的关键作用，并可作为有效的预后预测因子 74。
- 揭示不同肿瘤类型的共性与特异性： 通过泛癌整合分析，不仅可以发现不同肿瘤之间的共性机制，还可以揭示特定肿瘤类型独有的生物学特征，从而更好地理解肿瘤的异质性，并为个性化治疗提供依据。
总而言之，跨队列、泛癌单细胞多组学数据整合分析是单细胞研究领域的重要发展方向。通过有效的批次校正、统一注释和深入比较，这些方法能够从海量数据中挖掘出深刻的生物学洞察，加速我们对肿瘤微环境的理解，并最终推动精准医疗的发展。
5. 单细胞多组学在肿瘤微环境研究中的临床转化进展
单细胞多组学技术不仅极大地深化了我们对肿瘤微环境（TME）复杂生物学的理解，也为肿瘤的临床诊断、预后评估和治疗策略的开发带来了革命性的机遇。通过对肿瘤组织、外周血等临床样本进行高分辨率分析，研究人员能够识别出与肿瘤进展、治疗响应及预后密切相关的细胞亚群和分子特征，从而推动精准医疗的发展。
5.1 肿瘤预后与治疗响应预测生物标志物发掘
单细胞多组学技术凭借其识别稀有细胞亚群和精细分子特征的能力，成为发掘肿瘤预后和治疗响应预测生物标志物的强大工具。这些生物标志物可以指导临床医生为患者选择最合适的个性化治疗方案。
5.1.1 预后预测生物标志物
- 髓系细胞亚群的预后价值： 肿瘤浸润髓系细胞（TIMs）在肿瘤生长中扮演关键角色。一项针对人非小细胞肺癌（NSCLC）患者的单细胞RNA测序（scRNA-seq）研究，揭示了25种TIM状态，其中大多数在患者间具有可重复性。这些发现为TIMs作为免疫治疗靶点的未来研究奠定了基础，也提示特定TIM亚群可能成为预后标志物 19。在透明细胞肾癌（ccRCC）中，通过单细胞蛋白活性分析和多光谱免疫荧光验证，研究人员发现了一个以TREM2/APOE/C1Q上调为特征的肿瘤特异性巨噬细胞亚群。在一个大型临床验证队列中，这些标记物在术后复发的患者肿瘤中显著富集，表明TREM2/APOE/C1Q阳性巨噬细胞浸润是ccRCC复发的潜在预后生物标志物，也是候选治疗靶点 75。此外，妇科恶性肿瘤（包括输卵管卵巢癌、子宫内膜癌和宫颈癌）的单细胞免疫微环境分析发现，一个促血管生成巨噬细胞亚群与不良临床结局相关，而一个干扰素预处理的巨噬细胞亚群则通过募集T细胞与改善生存率相关 76。
- 其他免疫细胞的预后指标： 肿瘤浸润B细胞（TIBs）的泛癌单细胞图谱揭示了其在丰度和亚型组成上的显著异质性。研究识别出两种在肿瘤中富集且具有预后潜力的泛癌共有亚群：应激反应记忆B细胞和肿瘤相关非典型B细胞（TAABs），其中TAABs具有高克隆扩增和增殖能力，并与肿瘤中的活化CD4 T细胞密切相互作用，可预测免疫治疗反应 43。自然杀伤（NK）细胞的泛癌分析也识别出一组在肿瘤中富集、抗肿瘤功能受损且与不良预后和免疫治疗抵抗相关的肿瘤相关NK细胞 18。在肝细胞癌（HCC）中，通过整合多组学数据，研究人员识别出5种具有不同临床预后、干性特征、免疫景观和治疗反应的转录组亚型。其中，Class 1表现出炎症表型和更好的临床结果，而Class 5和Class 3则指示抑制性肿瘤免疫微环境，可能对免疫检查点阻断和靶向治疗敏感 77。
- 基质细胞的预后作用： 癌相关成纤维细胞（CAFs）是TME的重要组成部分，其亚群的特异性特征可作为预后标志物。在非小细胞肺癌（NSCLC）中，一项研究通过scRNA-seq和空间转录组学分析，鉴定出一种肌成纤维细胞样CAF亚群——POSTN CAFs，该亚群在晚期肿瘤中显著富集，并与细胞外基质重塑、肿瘤侵袭和免疫抑制相关的基因表达特征相关。POSTN CAFs与SPP1巨噬细胞密切共定位，并与T细胞耗竭和浸润减少相关。POSTN的表达或POSTN CAFs的丰度与NSCLC患者的不良预后相关 78。
5.1.2 治疗响应预测生物标志物
- 免疫检查点抑制剂（ICI）响应： scRNA-seq在预测ICI治疗响应方面展现出巨大潜力。胃印戒细胞癌（GSRCC）对免疫治疗响应较差，其免疫微环境（TIME）表现出免疫无反应性，CD4和CD8 T细胞难以被激活，进而损害B细胞功能。研究发现，由滤泡辅助性T细胞、T辅助性17型细胞和耗竭性CD8 T细胞主要产生的CXCL13是TIME转变的关键协调因子，其表达水平可预测GC患者对ICI的响应 79。在膀胱癌（BLCA）中，整合scRNA-seq和批量RNA-seq数据构建的预后模型，发现高风险评分的患者预后较差，且对免疫治疗的响应可能性较低，这与更高的免疫细胞浸润和肿瘤突变负荷（TMB）相关 80。
- 化疗响应： 单细胞多组学可以识别与化疗抵抗相关的细胞亚群和通路。胰腺导管腺癌（PDAC）的单细胞RNA测序分析揭示了基底样恶性导管细胞亚群的比例是化疗响应和患者预后的关键决定因素。该研究发现，PDAC克隆进化过程中，伴随着与癌相关基因剂量改变相关的亚型特异性变化，通过形成免疫抑制微环境来进展和转移 81。
- 新型靶向治疗响应： scRNA-seq也可以识别对特定新型疗法有响应的标志物。例如，肝细胞癌（HCC）的分子分型研究，将患者分为五种TME亚型，发现某些亚型对免疫检查点阻断和靶向治疗敏感，而另一些则对经导管动脉化疗栓塞（TACE）治疗响应更佳，为指导HCC的精准治疗提供了依据 77。
5.2 肿瘤微环境靶向治疗新靶点开发
通过深入解析TME中细胞互作和功能调控机制，单细胞多组学技术为开发新的治疗靶点提供了丰富的线索。
- 靶向免疫抑制细胞： 在胶质瘤中，一项研究利用单细胞转录组学和谱系细胞术分析了48例人胶质瘤的免疫图谱，识别出22种不同的免疫细胞类型，并发现IDH野生型胶质瘤中抗原提呈细胞样小胶质细胞和细胞毒性CD8+ T细胞更为丰富 82。鉴于髓系细胞在构建免疫抑制微环境中的关键作用，针对它们的靶向策略备受关注。在结肠炎（UC）的研究中，单细胞分析揭示了色氨酸代谢在UC中具有显著的细胞类型异质性，巨噬细胞和成纤维细胞中活性最高。CTSS、S100A11和TUBB等关键基因在巨噬细胞中显著上调，提示它们作为生物标志物和治疗靶点的潜力 83。这一发现可能为其他炎症相关疾病甚至肿瘤中巨噬细胞的靶向提供启示。
- 靶向基质细胞以重塑TME： 癌相关成纤维细胞（CAFs）及其不同亚型已成为重要的治疗靶点。在结直肠癌中，CTHRC1+ CAFs通过WNT5A信号通路促进EMT（上皮-间充质转化）并增强肿瘤细胞侵袭性，上调邻近恶性上皮细胞中的MSLN表达。靶向CTHRC1+ CAF-WNT5A-MSLN信号轴为晚期结直肠癌患者提供了有前景的治疗策略 84。在非小细胞肺癌中发现的POSTN CAFs与SPP1巨噬细胞协同作用，促进促纤维化微环境形成和免疫抑制，其与不良预后相关，提示POSTN CAFs可能是新的治疗靶点 78。
- 增强免疫效应细胞功能： S100A4被鉴定为胶质瘤免疫抑制性T细胞和髓系细胞的调节因子。删除非癌细胞中的S100A4足以重塑免疫景观并显著改善生存率，表明S100A4是一个潜在的免疫治疗靶点 85。在类风湿关节炎（RA）的研究中，整合单细胞RNA-seq、批量RNA-seq、孟德尔随机化和eQTL分析揭示了T细胞异质性。ICOS和IL6ST被鉴定为与RA T细胞相关的诊断特征，为RA免疫治疗策略提供了新思路 86。这些发现也可能为肿瘤T细胞功能障碍的逆转提供借鉴。
- 靶向细胞间通讯通路： 肿瘤细胞与TME中其他细胞的复杂通讯网络提供了许多潜在的靶点。例如，在肝内胆管癌（ICC）中，血管癌相关成纤维细胞（vCAFs）通过分泌IL-6促进肿瘤进展，而ICC细胞则通过外泌体miR-9-5p诱导vCAFs高表达IL-6，形成一个促肿瘤的恶性循环。中断这种循环可以成为新的治疗策略。
- 结合多模态信息开发新策略： 空间肿瘤学通过将分子分析与微环境特征相结合，为理解细胞相互作用和组织结构提供了关键的原位背景 87。这使得在TME中识别出具有临床相关性的特征，如神经周围侵犯、三级淋巴结构和肿瘤-基质界面等，为增强治疗和诊断方法提供了新的策略。
综上所述，单细胞多组学技术正在从分子层面揭示肿瘤微环境的复杂性，并在此基础上开发出更精准的预后和治疗响应预测生物标志物，以及针对免疫抑制微环境和特定细胞亚群的创新治疗靶点。这些进展有望显著改善肿瘤患者的诊疗结局。
6. 挑战与未来展望
单细胞多组学技术在肿瘤微环境研究中取得了突破性进展，极大地深化了我们对肿瘤异质性、细胞互作和免疫逃逸机制的理解，并为临床转化带来了新的希望。然而，这项新兴技术在走向成熟和广泛应用的过程中，仍然面临诸多挑战。
6.1 当前挑战
技术局限性与成本：
- 通量与分辨率的权衡： 尽管单细胞测序技术通量不断提高，但同时实现高通量、高分辨率（例如全长转录本测序）以及检测稀有转录本或低表达基因仍然是一个挑战。空间转录组技术在空间分辨率、灵敏度、多重能力、通量和覆盖范围等方面仍有待发展 88。
- 多组学数据的获取： 同时在单细胞水平上获取高质量的多组学数据（如基因组、转录组、蛋白质组、表观基因组、代谢组）仍然具有技术难度和高成本。虽然多组学联合分析能够提供更全面的细胞状态信息 9，但目前实现起来仍需克服许多技术瓶颈。
- 样本处理与细胞完整性： 肿瘤组织的异质性、纤维化程度以及细胞间的紧密连接，使得细胞解离过程可能导致细胞应激、基因表达改变，甚至丢失部分脆弱细胞类型。这可能影响数据的真实性和代表性。
- 数据量巨大与计算挑战： 单细胞多组学数据量庞大且复杂，需要强大的计算资源和高效的生物信息学算法进行数据处理、整合、降噪和解释。人工智能已在处理多组学数据方面展现出能力，但仍需高效算法来提取有价值的见解 89。
数据分析与整合的复杂性：
- 批次效应与数据整合： 来自不同平台、不同批次甚至不同实验室的数据存在显著的批次效应，这给跨研究、泛癌或多中心数据整合带来了巨大挑战。尽管已有多种批次校正算法（如Harmony, Seurat V3, LIGER）问世，但仍需不断优化，以在去除技术误差的同时，最大限度地保留生物学变异 10。
- 细胞类型注释的标准化： 缺乏统一的细胞类型命名和注释标准，导致不同研究之间难以进行直接比较和结果验证。例如，针对T细胞状态，已有研究通过整合多项研究建立了参考图谱和投影算法，以实现T细胞异质性描述的一致性 90。建立高质量的细胞类型参考图谱和通用的注释框架是解决这一问题的关键。
- 细胞互作推断的准确性： 基于配体-受体表达推断细胞间互作的方法存在局限性，例如无法区分配体是分泌型还是膜结合型，也无法捕获所有可能的细胞间通讯模式（如膜接触依赖性、胞外囊泡介导等）。此外，空间信息缺失或分辨率不足也限制了互作推断的准确性。
- 多模态数据整合的挑战： 将scRNA-seq与空间转录组、单细胞ATAC-seq等不同模态的数据进行有效整合，以全面揭示基因表达、染色质可及性和空间定位之间的复杂关系，仍然是计算生物学领域的前沿难题 10。
临床转化面临的挑战：
- 生物标志物的验证与标准化： 虽然单细胞多组学技术能够发现潜在的预后和治疗响应生物标志物，但这些标志物在临床应用前需要大规模、多中心、前瞻性的临床试验进行严格的验证。此外，如何将单细胞发现转化为可用于常规临床检测的标准化、低成本方法也是一个难题。
- 样本异质性与可及性： 肿瘤样本的异质性（如取样部位、肿瘤内不同区域）以及有限的样本可及性（如活检组织量小），使得全面获取具有代表性的单细胞多组学数据面临挑战。
- 治疗靶点的可药性与安全性： 基于单细胞数据发现的潜在治疗靶点，需要进一步评估其可药性，并进行严格的临床前和临床安全性与有效性测试。靶向特定细胞亚群可能导致脱靶效应和毒副作用。
6.2 未来展望
尽管面临诸多挑战，单细胞多组学技术及其在肿瘤微环境研究中的应用仍充满巨大的发展潜力。
技术创新与集成化平台：
- 更高通量、更高分辨率、更低成本的技术： 未来将出现能够同时实现更高通量、更高空间分辨率（亚细胞级）、更深层分子信息（如蛋白质、代谢物、表观遗传修饰）检测的集成化多组学平台。例如，将空间蛋白质组学、空间代谢组学与空间转录组学结合，以提供更全面的空间分子图谱 9。
- 活体单细胞分析技术： 发展能够在活体状态下对单个细胞进行长期追踪和分子分析的技术，这将有助于实时监测肿瘤微环境的动态变化、细胞谱系演进以及治疗响应过程，而非仅仅是静态快照。
- 原位多组学技术： 进一步发展无需解离组织即可同时获取多种分子信息（转录组、蛋白质组、表观基因组）的原位多组学技术，从而最大限度地保留细胞的天然状态和空间信息。
计算方法与人工智能的融合：
- 更强大的数据整合与分析算法： 深度学习和人工智能技术将发挥越来越重要的作用，发展更智能的算法来有效整合异质性多组学数据，准确校正批次效应，识别稀有细胞亚群，并推断复杂的细胞互作网络。例如，利用图神经网络、注意力机制等技术，更精准地捕捉细胞间空间距离和分子交流。
- 预测模型与数字病理： 将单细胞多组学数据与影像学（如病理切片、医学影像）信息结合，开发集成化的预测模型。通过机器学习从数字病理图像中提取特征，并与分子数据结合，实现更精准的疾病诊断、预后评估和治疗响应预测 89。
- 计算模拟与虚拟组织： 发展基于单细胞多组学数据的计算模拟模型，构建“虚拟肿瘤组织”，预测不同治疗干预对TME的影响，加速药物研发进程。
临床转化的加速：
- 标准化流程与数据库建设： 建立全球统一的单细胞多组学数据生成、处理和分析标准，并构建大规模、高质量、可共享的肿瘤单细胞多组学数据库和参考图谱。这将为全球研究者提供宝贵的资源，促进泛癌研究和生物标志物发现。
- 液体活检的单细胞多组学应用： 将单细胞多组学技术应用于液体活检样本（如循环肿瘤细胞、循环肿瘤DNA、外泌体），实现肿瘤的早期诊断、实时监测和复发预测，为患者提供非侵入性的个性化管理方案。
- 新型靶点与个性化治疗： 基于单细胞多组学发现的免疫抑制细胞亚群、关键细胞互作轴、代谢重编程等，开发针对性的免疫治疗（如新型检查点抑制剂、CAR-T/NK细胞疗法）、靶向治疗和联合治疗方案。例如，针对泛癌中保守的免疫抑制机制开发广谱的抗肿瘤策略，同时根据患者特异性的TME特征进行精准治疗分层。
- 药物重定位与联合用药： 利用单细胞数据识别现有药物对特定细胞亚群的潜在作用，从而实现药物重定位，并探索最佳的药物联合方案，克服肿瘤耐药性。
总之，单细胞多组学技术正处于快速发展和变革之中。尽管当前仍面临诸多挑战，但随着技术的不断革新、计算方法的日益成熟以及多学科的交叉融合，这项技术必将在肿瘤微环境研究领域发挥更核心的作用，为最终实现肿瘤的精准诊断和个性化治疗贡献里程碑式的力量。
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参考文献
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Borros Arneth
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BACKGROUND AND OBJECTIVES: The tumor microenvironment has been widely implicated in tumorigenesis because it harbors tumor cells that interact with surrounding cells through the circulatory and lymphatic systems to influence the development and progression of cancer. In addition, nonmalignant cells in the tumor microenvironment play critical roles in all the stages of carcinogenesis by stimulating and facilitating uncontrolled cell proliferation. AIM: This study aims to explore the concept of the tumor microenvironment by conducting a critical review of previous studies on the topic. Methods: This review relies on evidence presented in previous studies related to the topic. The articles included in this review were obtained from different medical and health databases. RESULTS AND DISCUSSION: The tumor microenvironment has received significant attention in the cancer literature, with a particular focus on its role in tumor development and progression. Previous studies have identified various components of the tumor microenvironment that influence malignant behavior and progression. In addition to malignant cells, adipocytes, fibroblasts, tumor vasculature, lymphocytes, dendritic cells, and cancer-associated fibroblasts are present in the tumor microenvironment. Each of these cell types has unique immunological capabilities that determine whether the tumor will survive and affect neighboring cells. CONCLUSION: The tumor microenvironment harbors cancer stem cells and other molecules that contribute to tumor development and progression. Consequently, targeting and manipulating the cells and factors in the tumor microenvironment during cancer treatment can help control malignancies and achieve positive health outcomes.
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Ofer Elhanani, Raz Ben-Uri, Leeat Keren
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Armando Rojas, Paulina Araya, Ileana Gonzalez, et al.
Adv Exp Med Biol. 2020;1226:23-35. doi: 10.1007/978-3-030-36214-0_2.
A compelling body of evidence has demonstrated that gastric cancer has a very particular tumor microenvironment, a signature very suitable to promote tumor progression and metastasis. Recent investigations have provided new insights into the multiple molecular mechanisms, defined by genetic and epigenetic mechanisms, supporting a very active cross talk between the components of the tumor microenvironment and thus defining the fate of tumor progression. In this review, we intend to highlight the role of very active contributors at gastric cancer TME, particularly cancer-associated fibroblasts, bone marrow-derived cells, tumor-associated macrophages, and tumor-infiltrating neutrophils, all of them surrounded by an overtime changing extracellular matrix. In addition, the very active cross talk between the components of the tumor microenvironment, defined by genetic and epigenetic mechanisms, thus defining the fate of tumor progression, is also reviewed.
4Nerves in cancer.PubMed
Ali H Zahalka, Paul S Frenette
Nat Rev Cancer. 2020 Mar;20(3):143-157. doi: 10.1038/s41568-019-0237-2. Epub 2020 Jan 23.
The contribution of nerves to the pathogenesis of malignancies has emerged as an important component of the tumour microenvironment. Recent studies have shown that peripheral nerves (sympathetic, parasympathetic and sensory) interact with tumour and stromal cells to promote the initiation and progression of a variety of solid and haematological malignancies. Furthermore, new evidence suggests that cancers may reactivate nerve-dependent developmental and regenerative processes to promote their growth and survival. Here we review emerging concepts and discuss the therapeutic implications of manipulating nerves and neural signalling for the prevention and treatment of cancer.
5Turning cold into hot: emerging strategies to fire up the tumor microenvironment.PubMed
Kaili Ma, Lin Wang, Wenhui Li, et al.
Trends Cancer. 2025 Feb;11(2):117-134. doi: 10.1016/j.trecan.2024.11.011. Epub 2024 Dec 26.
The tumor microenvironment (TME) is a complex, highly structured, and dynamic ecosystem that plays a pivotal role in the progression of both primary and metastatic tumors. Precise assessment of the dynamic spatiotemporal features of the TME is crucial for understanding cancer evolution and designing effective therapeutic strategies. Cancer is increasingly recognized as a systemic disease, influenced not only by the TME, but also by a multitude of systemic factors, including whole-body metabolism, gut microbiome, endocrine signaling, and circadian rhythm. In this review, we summarize the intrinsic, extrinsic, and systemic factors contributing to the formation of 'cold' tumors within the framework of the cancer-immunity cycle. Correspondingly, we discuss potential strategies for converting 'cold' tumors into 'hot' ones to enhance therapeutic efficacy.
6Advances in Spatial Multi-Omics in Gastric Cancer.PubMed
Hongfei Yan, Yang Liu
Cells. 2026 Mar 17;15(6):535. doi: 10.3390/cells15060535.
Gastric cancer (GC) remains a major global health burden, with its unfavorable prognosis primarily driven by extensive tumor heterogeneity. Traditional bulk omics, while informative, are inherently limited by the averaging effect of diverse cell populations and fail to capture the critical spatial molecular disparities within the tumor and its microenvironment (TME). Single-cell omics can capture cellular heterogeneity but lack spatial context. Therefore, there is an urgent clinical need for spatial multi-omics to provide a high-definition dissection of GC heterogeneity and to optimize therapeutic efficacy. This review first outlines briefly the evolution of spatial technologies, including transcriptomics, proteomics, metabolomics, genomics and epigenomics, and their transformative applications in GC research. We further explore how these platforms refine molecular classification beyond traditional models, identify next-generation biomarkers, and decode the intricate cellular interactions governing immune evasion and metastasis. Next, we highlight the pivotal role of spatial profiling in unravelling the multidimensional mechanisms of resistance to chemotherapy, targeted therapy and immunotherapy. Finally, we address current technical bottlenecks and discuss prospects for clinical translation.
7An Overview on Single-Cell Technology for Hepatocellular Carcinoma Diagnosis.PubMed
Sheik Aliya, Hoomin Lee, Munirah Alhammadi, et al.
Int J Mol Sci. 2022 Jan 26;23(3):1402. doi: 10.3390/ijms23031402.
Hepatocellular carcinoma is a primary liver cancer caused by the accumulation of genetic mutation patterns associated with epidemiological conditions. This lethal malignancy exhibits tumor heterogeneity, which is considered as one of the main reasons for drug resistance development and failure of clinical trials. Recently, single-cell technology (SCT), a new advanced sequencing technique that analyzes every single cell in a tumor tissue specimen, aids complete insight into the genetic heterogeneity of cancer. This helps in identifying and assessing rare cell populations by analyzing the difference in gene expression pattern between individual cells of single biopsy tissue which normally cannot be identified from pooled cell gene expression pattern (traditional sequencing technique). Thus, SCT improves the clinical diagnosis, treatment, and prognosis of hepatocellular carcinoma as the limitations of other techniques impede this cancer research progression. Application of SCT at the genomic, transcriptomic, and epigenomic levels to promote individualized hepatocellular carcinoma diagnosis and therapy. The current review has been divided into ten sections. Herein we deliberated on the SCT, hepatocellular carcinoma diagnosis, tumor microenvironment analysis, single-cell genomic sequencing, single-cell transcriptomics, single-cell omics sequencing for biomarker development, identification of hepatocellular carcinoma origination and evolution, limitations, challenges, conclusions, and future perspectives.
8Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy.PubMed
Zaoqu Liu, Huanyun Li, Qin Dang, et al.
Cell Mol Life Sci. 2022 Oct 31;79(11):577. doi: 10.1007/s00018-022-04608-4.
Recently, immunotherapy has gained increasing popularity in oncology. Several immunotherapies obtained remarkable clinical effects, but the efficacy varied, and only subsets of cancer patients benefited. Breaking the constraints and improving immunotherapy efficacy is extremely important in precision medicine. Whereas traditional sequencing approaches mask the characteristics of individual cells, single-cell sequencing provides multiple dimensions of cellular characterization at the single-cell level, including genomic, transcriptomic, epigenomic, proteomic, and multi-omics. Hence, the complexity of the tumor microenvironment, the universality of tumor heterogeneity, cell composition and cell-cell interactions, cell lineage tracking, and tumor drug resistance mechanisms are revealed in-depth. However, the clinical transformation of single-cell technology is not to the point of in-depth study, especially in the application of immunotherapy. The newly discovered vital cells and tremendous biomarkers facilitate the development of more efficient individualized therapeutic regimens to guide clinical treatment and predict prognosis. This review provided an overview of the progress in distinct single-cell sequencing methods and emerging strategies. For perspective, the expanding utility of combining single-cell sequencing and other technologies was discussed.
9Methods and applications for single-cell and spatial multi-omics.PubMed
Katy Vandereyken, Alejandro Sifrim, Bernard Thienpont, et al.
Nat Rev Genet. 2023 Aug;24(8):494-515. doi: 10.1038/s41576-023-00580-2. Epub 2023 Mar 2.
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
10A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication.PubMed
Changde Cheng, Wenan Chen, Hongjian Jin, et al.
Cells. 2023 Jul 30;12(15):1970. doi: 10.3390/cells12151970.
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
11Integrative single-cell transcriptomic analysis deciphers heterogeneous characteristics of gastrointestinal tract cancer.PubMed
Chuwen Sun, Tong Li, Xin Jin, et al.
Clin Transl Med. 2025 Aug;15(8):e70415. doi: 10.1002/ctm2.70415.
BACKGROUND: Gastrointestinal tract cancer (GIC), including oesophageal cancer (EC), gastric cancer (GC) and colorectal cancer (CRC), is characterised with high global incidence and mortality rates, with similar tumourigenic processes. However, the common and heterogeneous molecular features among GIC at single-cell level remain poorly characterised. METHODS: Single-cell RNA-seq data of more than one million high-quality annotated cells from 577 specimens, including 121 ECs, 182 GCs and 254 CRCs, were integrated to systematically decipher the heterogeneous characteristics of GIC. Non-negative matrix factorisation (NMF) was employed to identify epithelial cell meta-programs (MPs), and cell-cell communication analysis was conducted to investigate regulatory interactions between the tumour microenvironment (TME) and these MPs. Additionally, cell lineage inference analysis was performed to identify metaplastic signatures in EC and GC. RESULTS: We identified 24 consensus MPs from epithelial cells and 42 distinct subtypes from non-epithelial cells thus offering a comprehensive overview of heterogeneous characteristic in GIC. Notably, we observed that EC exhibited unique features, including heightened activity in stress-related programs and a more exhausted TME, enriched with CD4+ Tregs and CD8+ exhausted T cells. In contrast, epithelial cells in GC displayed increased expression of epithelial-mesenchymal transition (EMT)-related signatures and an activated immune phenotype, marked by enrichment of NK cells and CD8+ effector T cells. Moreover, samples with metaplastic signatures in GC and EC showed similarities to CRC, including elevated expression of metabolism-associated signatures and an abundance of CD4+ helper-like T cells. Finally, we identified the potential regulatory roles of the TME in shaping epithelial cell behaviour. CONCLUSIONS: Our findings provide insights into the common and specific cellular and molecular patterns associated with GIC tumourigenesis and TME remodelling. We also elucidate the similarity between GC/EC with metaplastic signature and CRC, which advancing our understanding of these malignancies. KEY POINTS: A comprehensive single-cell atlas of gastrointestinal tract cancer (GIC) was constructed. GICs exhibit distinct epithelial features and specific tumour microenvironment (TME) patterns, forming diverse niches. GC and EC exhibiting metaplastic features show elevated metabolism-associated signatures and share similarities with CRC.
12Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.PubMed
Avishai Gavish, Michael Tyler, Alissa C Greenwald, et al.
Nature. 2023 Jun;618(7965):598-606. doi: 10.1038/s41586-023-06130-4. Epub 2023 May 31.
Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.
13Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment.PubMed
Dalia Barkley, Reuben Moncada, Maayan Pour, et al.
Nat Genet. 2022 Aug;54(8):1192-1201. doi: 10.1038/s41588-022-01141-9. Epub 2022 Aug 5.
Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.
14Liver tumour immune microenvironment subtypes and neutrophil heterogeneity.PubMed
Ruidong Xue, Qiming Zhang, Qi Cao, et al.
Nature. 2022 Dec;612(7938):141-147. doi: 10.1038/s41586-022-05400-x. Epub 2022 Nov 9.
The heterogeneity of the tumour immune microenvironment (TIME), organized by various immune and stromal cells, is a major contributing factor of tumour metastasis, relapse and drug resistance, but how different TIME subtypes are connected to the clinical relevance in liver cancer remains unclear. Here we performed single-cell RNA-sequencing (scRNA-seq) analysis of 189 samples collected from 124 patients and 8 mice with liver cancer. With more than 1 million cells analysed, we stratified patients into five TIME subtypes, including immune activation, immune suppression mediated by myeloid or stromal cells, immune exclusion and immune residence phenotypes. Different TIME subtypes were spatially organized and associated with chemokine networks and genomic features. Notably, tumour-associated neutrophil (TAN) populations enriched in the myeloid-cell-enriched subtype were associated with an unfavourable prognosis. Through in vitro induction of TANs and ex vivo analyses of patient TANs, we showed that CCL4 TANs can recruit macrophages and that PD-L1 TANs can suppress T cell cytotoxicity. Furthermore, scRNA-seq analysis of mouse neutrophil subsets revealed that they are largely conserved with those of humans. In vivo neutrophil depletion in mouse models attenuated tumour progression, confirming the pro-tumour phenotypes of TANs. With this detailed cellular heterogeneity landscape of liver cancer, our study illustrates diverse TIME subtypes, highlights immunosuppressive functions of TANs and sheds light on potential immunotherapies targeting TANs.
15Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells.PubMed
María Casanova-Acebes, Erica Dalla, Andrew M Leader, et al.
Nature. 2021 Jul;595(7868):578-584. doi: 10.1038/s41586-021-03651-8. Epub 2021 Jun 16.
Macrophages have a key role in shaping the tumour microenvironment (TME), tumour immunity and response to immunotherapy, which makes them an important target for cancer treatment. However, modulating macrophages has proved extremely difficult, as we still lack a complete understanding of the molecular and functional diversity of the tumour macrophage compartment. Macrophages arise from two distinct lineages. Tissue-resident macrophages self-renew locally, independent of adult haematopoiesis, whereas short-lived monocyte-derived macrophages arise from adult haematopoietic stem cells, and accumulate mostly in inflamed lesions. How these macrophage lineages contribute to the TME and cancer progression remains unclear. To explore the diversity of the macrophage compartment in human non-small cell lung carcinoma (NSCLC) lesions, here we performed single-cell RNA sequencing of tumour-associated leukocytes. We identified distinct populations of macrophages that were enriched in human and mouse lung tumours. Using lineage tracing, we discovered that these macrophage populations differ in origin and have a distinct temporal and spatial distribution in the TME. Tissue-resident macrophages accumulate close to tumour cells early during tumour formation to promote epithelial-mesenchymal transition and invasiveness in tumour cells, and they also induce a potent regulatory T cell response that protects tumour cells from adaptive immunity. Depletion of tissue-resident macrophages reduced the numbers and altered the phenotype of regulatory T cells, promoted the accumulation of CD8 T cells and reduced tumour invasiveness and growth. During tumour growth, tissue-resident macrophages became redistributed at the periphery of the TME, which becomes dominated by monocyte-derived macrophages in both mouse and human NSCLC. This study identifies the contribution of tissue-resident macrophages to early lung cancer and establishes them as a target for the prevention and treatment of early lung cancer lesions.
16An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.PubMed
Cyril Neftel, Julie Laffy, Mariella G Filbin, et al.
Cell. 2019 Aug 8;178(4):835-849.e21. doi: 10.1016/j.cell.2019.06.024. Epub 2019 Jul 18.
Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.
17Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes.PubMed
Yu Yang, Xueyan Chen, Jieying Pan, et al.
Cell. 2024 Aug 22;187(17):4790-4811.e22. doi: 10.1016/j.cell.2024.06.038. Epub 2024 Jul 23.
Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.
18A pan-cancer single-cell panorama of human natural killer cells.PubMed
Fei Tang, Jinhu Li, Lu Qi, et al.
Cell. 2023 Sep 14;186(19):4235-4251.e20. doi: 10.1016/j.cell.2023.07.034. Epub 2023 Aug 21.
Natural killer (NK) cells play indispensable roles in innate immune responses against tumor progression. To depict their phenotypic and functional diversities in the tumor microenvironment, we perform integrative single-cell RNA sequencing analyses on NK cells from 716 patients with cancer, covering 24 cancer types. We observed heterogeneity in NK cell composition in a tumor-type-specific manner. Notably, we have identified a group of tumor-associated NK cells that are enriched in tumors, show impaired anti-tumor functions, and are associated with unfavorable prognosis and resistance to immunotherapy. Specific myeloid cell subpopulations, in particular LAMP3 dendritic cells, appear to mediate the regulation of NK cell anti-tumor immunity. Our study provides insights into NK-cell-based cancer immunity and highlights potential clinical utilities of NK cell subsets as therapeutic targets.
19Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species.PubMed
Rapolas Zilionis, Camilla Engblom, Christina Pfirschke, et al.
Immunity. 2019 May 21;50(5):1317-1334.e10. doi: 10.1016/j.immuni.2019.03.009. Epub 2019 Apr 9.
Tumor-infiltrating myeloid cells (TIMs) comprise monocytes, macrophages, dendritic cells, and neutrophils, and have emerged as key regulators of cancer growth. These cells can diversify into a spectrum of states, which might promote or limit tumor outgrowth but remain poorly understood. Here, we used single-cell RNA sequencing (scRNA-seq) to map TIMs in non-small-cell lung cancer patients. We uncovered 25 TIM states, most of which were reproducibly found across patients. To facilitate translational research of these populations, we also profiled TIMs in mice. In comparing TIMs across species, we identified a near-complete congruence of population structures among dendritic cells and monocytes; conserved neutrophil subsets; and species differences among macrophages. By contrast, myeloid cell population structures in patients' blood showed limited overlap with those of TIMs. This study determines the lung TIM landscape and sets the stage for future investigations into the potential of TIMs as immunotherapy targets.
20Pan-Cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells.PubMed
Tianyi Ma, Xiaojing Chu, Jinyu Wang, et al.
Cancer Res. 2025 Oct 1;85(19):3596-3613. doi: 10.1158/0008-5472.CAN-24-3595.
UNLABELLED: Dendritic cells (DC) are pivotal orchestrators of antitumor immunity. DC-based antitumor treatments are being actively developed, but effective clinical responses have not yet been achieved. Further exploration of DC heterogeneity in the tumor microenvironment and across cancer types could provide insights for developing DC-based immunotherapies. In this study, we integrated single-cell RNA sequencing data of DCs from more than 2,500 samples across 33 cancer types and established a comprehensive blueprint of human DCs. Several rare subsets of DCs infiltrated the tumors, including AXL+SIGLEC6+ DCs and Langerhans cell-like DCs, and displayed functional potentials marked with distinct transcriptomic characteristics. Computational analyses demonstrated that the Langerhans cell-like subset could be an additional cellular origin of tumor-enriched LAMP3+ DCs and that distinct cellular origins are associated with the pleiotropic functional potentials of LAMP3+ DCs. Furthermore, this DC atlas enabled the development of a machine learning model to guide DC annotation for subsequent single-cell analysis and prioritization of a valuable target for enhancing antitumor DC vaccination. This integrative resource provides a panoramic view to unravel the complexity of tumor-infiltrating DCs and offers valuable insights for developing therapies targeting DCs. SIGNIFICANCE: The comprehensive dissection of tumor-infiltrating dendritic cells redefined cell subsets with different regulations, tissue preferences, and functional potentials and provided an atlas as a rich resource with promising applications. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
21Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma.PubMed
Min Zhang, Hui Yang, Lingfei Wan, et al.
J Hepatol. 2020 Nov;73(5):1118-1130. doi: 10.1016/j.jhep.2020.05.039. Epub 2020 Jun 5.
BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (ICC) is the second most common liver malignancy. ICC typically features remarkable cellular heterogeneity and a dense stromal reaction. Therefore, a comprehensive understanding of cellular diversity and the interplay between malignant cells and niche cells is essential to elucidate the mechanisms driving ICC progression and to develop therapeutic approaches. METHODS: Herein, we performed single-cell RNA sequencing (scRNA-seq) analysis on unselected viable cells from 8 human ICCs and adjacent samples to elucidate the comprehensive transcriptomic landscape and intercellular communication network. Additionally, we applied a negative selection strategy to enrich fibroblast populations in 2 other ICC samples to investigate fibroblast diversity. The results of the analyses were validated using multiplex immunofluorescence staining, bulk transcriptomic datasets, and functional in vitro and in vivo experiments. RESULTS: We sequenced a total of 56,871 single cells derived from human ICC and adjacent tissues and identified diverse tumor, immune, and stromal cells. Malignant cells displayed a high degree of inter-tumor heterogeneity. Moreover, tumor-infiltrating CD4 regulatory T cells exhibited highly immunosuppressive characteristics. We identified 6 distinct fibroblast subsets, of which the majority were CD146-positive vascular cancer-associated fibroblasts (vCAFs), with highly expressed microvasculature signatures and high levels of interleukin (IL)-6. Functional assays indicated that IL-6 secreted by vCAFs induced significant epigenetic alterations in ICC cells, particularly upregulating enhancer of zeste homolog 2 (EZH2) and thereby enhancing malignancy. Furthermore, ICC cell-derived exosomal miR-9-5p elicited high expression of IL-6 in vCAFs to promote tumor progression. CONCLUSIONS: Our single-cell transcriptomic dataset delineates the inter-tumor heterogeneity of human ICCs, underlining the importance of intercellular crosstalk between ICC cells and vCAFs, and revealing potential therapeutic targets. LAY SUMMARY: Intrahepatic cholangiocarcinoma is an aggressive and chemoresistant malignancy. Better understanding the complex transcriptional architecture and intercellular crosstalk of these tumors will help in the development of more effective therapies. Herein, we have identified important interactions between cancer cells and cancer-associated fibroblasts in the tumor stroma, which could have therapeutic implications.
22CD36 cancer-associated fibroblasts provide immunosuppressive microenvironment for hepatocellular carcinoma via secretion of macrophage migration inhibitory factor.PubMed
Gui-Qi Zhu, Zheng Tang, Run Huang, et al.
Cell Discov. 2023 Mar 6;9(1):25. doi: 10.1038/s41421-023-00529-z.
Hepatocellular carcinoma (HCC) is an immunotherapy-resistant malignancy characterized by high cellular heterogeneity. The diversity of cell types and the interplay between tumor and non-tumor cells remain to be clarified. Single cell RNA sequencing of human and mouse HCC tumors revealed heterogeneity of cancer-associated fibroblast (CAF). Cross-species analysis determined the prominent CD36 CAFs exhibited high-level lipid metabolism and expression of macrophage migration inhibitory factor (MIF). Lineage-tracing assays showed CD36CAFs were derived from hepatic stellate cells. Furthermore, CD36 mediated oxidized LDL uptake-dependent MIF expression via lipid peroxidation/p38/CEBPs axis in CD36 CAFs, which recruited CD33myeloid-derived suppressor cells (MDSCs) in MIF- and CD74-dependent manner. Co-implantation of CD36 CAFs with HCC cells promotes HCC progression in vivo. Finally, CD36 inhibitor synergizes with anti-PD-1 immunotherapy by restoring antitumor T-cell responses in HCC. Our work underscores the importance of elucidating the function of specific CAF subset in understanding the interplay between the tumor microenvironment and immune system.
23Cancer-associated fibroblast classification in single-cell and spatial proteomics data.PubMed
Lena Cords, Sandra Tietscher, Tobias Anzeneder, et al.
Nat Commun. 2023 Jul 18;14(1):4294. doi: 10.1038/s41467-023-39762-1.
Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future.
24Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms.PubMed
Xiaojing Chu, Xiangjie Li, Yu Zhang, et al.
Nat Cancer. 2024 Sep;5(9):1409-1426. doi: 10.1038/s43018-024-00807-z. Epub 2024 Aug 15.
The tumor microenvironment (TME) considerably influences colorectal cancer (CRC) progression, therapeutic response and clinical outcome, but studies of interindividual heterogeneities of the TME in CRC are lacking. Here, by integrating human colorectal single-cell transcriptomic data from approximately 200 donors, we comprehensively characterized transcriptional remodeling in the TME compared to noncancer tissues and identified a rare tumor-specific subset of endothelial cells with T cell recruitment potential. The large sample size enabled us to stratify patients based on their TME heterogeneity, revealing divergent TME subtypes in which cancer cells exploit different immune evasion mechanisms. Additionally, by associating single-cell transcriptional profiling with risk genes identified by genome-wide association studies, we determined that stromal cells are major effector cell types in CRC genetic susceptibility. In summary, our results provide valuable insights into CRC pathogenesis and might help with the development of personalized immune therapies.
25Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression.PubMed
Ruiping Wang, Shumei Song, Jiangjiang Qin, et al.
Cancer Cell. 2023 Aug 14;41(8):1407-1426.e9. doi: 10.1016/j.ccell.2023.06.005. Epub 2023 Jul 6.
Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1-6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and survival outcomes. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation.
26Spatial transcriptomics in development and disease.PubMed
Ran Zhou, Gaoxia Yang, Yan Zhang, et al.
Mol Biomed. 2023 Oct 9;4(1):32. doi: 10.1186/s43556-023-00144-0.
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
27Dissecting mammalian reproduction with spatial transcriptomics.PubMed
Xin Zhang, Qiqi Cao, Shreya Rajachandran, et al.
Hum Reprod Update. 2023 Nov 2;29(6):794-810. doi: 10.1093/humupd/dmad017.
BACKGROUND: Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. OBJECTIVE AND RATIONALE: We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. SEARCH METHODS: The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. OUTCOMES: First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. WIDER IMPLICATIONS: Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.
28Exploring tissue architecture using spatial transcriptomics.PubMed
Anjali Rao, Dalia Barkley, Gustavo S França, et al.
Nature. 2021 Aug;596(7871):211-220. doi: 10.1038/s41586-021-03634-9. Epub 2021 Aug 11.
Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
29Emerging Techniques in Spatial Multiomics: Fundamental Principles and Applications to Dermatology.PubMed
Bojing B Jia, Bryan K Sun, Ernest Y Lee, et al.
J Invest Dermatol. 2025 May;145(5):1017-1032. doi: 10.1016/j.jid.2024.09.006. Epub 2024 Nov 5.
Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity.
30Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics.PubMed
Yunhe Liu, Ansam Sinjab, Jimin Min, et al.
Cancer Cell. 2025 May 12;43(5):905-924.e6. doi: 10.1016/j.ccell.2025.03.004. Epub 2025 Mar 27.
Cancer-associated fibroblasts (CAFs) are a multifaceted cell population essential for shaping the tumor microenvironment (TME) and influencing therapy responses. Characterizing the spatial organization and interactions of CAFs within complex tissue environments provides critical insights into tumor biology and immunobiology. In this study, through integrative analyses of over 14 million cells from 10 cancer types across 7 spatial transcriptomics and proteomics platforms, we discover, validate, and characterize four distinct spatial CAF subtypes. These subtypes are conserved across cancer types and independent of spatial omics platforms. Notably, they exhibit distinct spatial organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic profiles. Their abundance and composition vary across tissues, shaping TME characteristics, such as levels, distribution, and state composition of tumor-infiltrating immune cells, tumor immune phenotypes, and patient survival. This study enriches our understanding of CAF spatial heterogeneity in cancer and paves the way for novel approaches to target and modulate CAFs.
31TNF-⍺-mediated myeloid-instructed CD14CD4 T cells are associated with poor survival in lung adenocarcinoma.PubMed
Claire Marceaux, Ilariya Tarasova, Daniel Batey, et al.
Cell Rep Med. 2026 Feb 17;7(2):102593. doi: 10.1016/j.xcrm.2026.102593. Epub 2026 Feb 9.
The tumor microenvironment is composed of diverse immune populations that can either support anti-tumor immunity or promote tumor progression. Myeloid cells are major drivers of immunosuppression, yet therapies targeting them have shown limited success. To uncover mechanisms underlying myeloid-driven immune suppression, we performed spatial multi-omics analyses of non-small cell lung cancer (NSCLC). Independent of oncogenic driver status, tumors stratify into lymphoid-enriched, myeloid-enriched, and mixed immune-infiltrated subtypes. In tumor and adjacent non-malignant lungs, we identify myeloid-instructed CD14CD4 T cells. These cells arise through trogocytosis adopting an atypical phenotype. In lymphoid-enriched tumors, high infiltration of CD14CD4 T cells correlates with poor patient survival. Spatial transcriptomics reveal enrichment of tumor necrosis factor alpha (TNF-α) signaling in CD14CD4-T-cell-rich tumors. Functional assays demonstrate that TNF-⍺ enhanced trogocytosis, promoting the formation of CD14CD4 T cells. These findings uncover a TNF-⍺-mediated mechanism of immunosuppression in the TME and highlight aberrant myeloid-T cell interactions as contributors to NSCLC progression.
32Spatial transcriptomics reveals tryptophan metabolism restricting maturation of intratumoral tertiary lymphoid structures.PubMed
Zhonghui Tang, Yinqi Bai, Qi Fang, et al.
Cancer Cell. 2025 Jun 9;43(6):1025-1044.e14. doi: 10.1016/j.ccell.2025.03.011. Epub 2025 Apr 3.
Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates found in numerous cancers, often linked to enhanced immunotherapy responses and better clinical outcomes. However, the factors driving TLS maturation are not fully understood. Using near single-cell spatial transcriptomic mapping, we comprehensively profile TLSs under various maturation stages and their microenvironment in hepatocellular carcinoma (HCC). Based on their developmental trajectories, we classify immature TLSs into two groups: conforming and deviating TLSs. Our findings indicate that conforming TLSs, similar to mature TLSs, possess a niche function for immunotherapy responses, while deviating TLSs do not. We discover that the tryptophan-enriched metabolic microenvironment shaped by malignant cells contributes to the deviation of TLS maturation. Inhibiting tryptophan metabolism promotes intratumoral TLS maturation and enhances tumor control, synergizing with anti-PD-1 treatments. Therefore, promoting TLS maturation represents a potential strategy to improve antitumor responses and immunotherapy outcomes.
33Single-cell and spatial transcriptomics reveal metastasis mechanism and microenvironment remodeling of lymph node in osteosarcoma.PubMed
Yun Liu, Mingwei He, Haijun Tang, et al.
BMC Med. 2024 May 17;22(1):200. doi: 10.1186/s12916-024-03319-w.
BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor and is highly prone to metastasis. OS can metastasize to the lymph node (LN) through the lymphatics, and the metastasis of tumor cells reestablishes the immune landscape of the LN, which is conducive to the growth of tumor cells. However, the mechanism of LN metastasis of osteosarcoma and remodeling of the metastatic lymph node (MLN) microenvironment is not clear. METHODS: Single-cell RNA sequencing of 18 samples from paracancerous, primary tumor, and lymph nodes was performed. Then, new signaling axes closely related to metastasis were identified using bioinformatics, in vitro experiments, and immunohistochemistry. The mechanism of remodeling of the LN microenvironment in tumor cells was investigated by integrating single-cell and spatial transcriptomics. RESULTS: From 18 single-cell sequencing samples, we obtained 117,964 cells. The pseudotime analysis revealed that osteoblast(OB) cells may follow a differentiation path from paracancerous tissue (PC) → primary tumor (PT) → MLN or from PC → PT, during the process of LN metastasis. Next, in combination of bioinformatics, in vitro and in vivo experiments, and immunohistochemistry, we determined that ETS2/IBSP, a new signal axis, might promote LN metastasis. Finally, single-cell and spatial dissection uncovered that OS cells could reshape the microenvironment of LN by interacting with various cell components, such as myeloid, cancer-associated fibroblasts (CAFs), and NK/T cells. CONCLUSIONS: Collectively, our research revealed a new molecular mechanism of LN metastasis and clarified how OS cells influenced the LN microenvironment, which might provide new insight for blocking LN metastasis.
34Deciphering the cellular and molecular landscape of cervical cancer progression through single-cell and spatial transcriptomics.PubMed
Peng Xia, Juanhong Zhou, Rong Shen, et al.
NPJ Precis Oncol. 2025 May 28;9(1):158. doi: 10.1038/s41698-025-00948-z.
Cervical cancer represents a significant global health challenge, with complex cellular and molecular mechanisms driving its progression from HPV infection to invasive malignancy. This study employed an integrated approach combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (stRNA-seq) to comprehensively characterize the tumor microenvironment (TME) across different stages of cervical cancer development. Through analysis of samples from normal cervix, HPV-infected normal cervix, high-grade squamous intraepithelial lesions (HSIL), and invasive cervical cancer, we identified distinct cellular populations and their dynamic changes during disease progression. Our findings revealed significant heterogeneity in immune cell populations, particularly highlighting the role of SPP1+ macrophages that were substantially enriched in cervical cancer compared to precancerous and normal tissues. Cell-cell communication networks and spatial mapping demonstrated that SPP1+ macrophages interact extensively with immune cells through the SPP1-CD44 signaling axis. This interaction contributes to an immunosuppressive microenvironment through modulation of T cell function and promotion of tumor cell survival. Furthermore, high expression of SPP1 correlated with advanced tumor stages and poor overall survival in cervical cancer patients, highlighting its potential as a prognostic biomarker. Our comprehensive characterization of the cellular landscape and intercellular communication networks in cervical cancer progression provides valuable insights for the development of targeted therapeutic strategies aimed at modulating the TME, particularly through disruption of the SPP1-CD44 axis. These findings establish a foundation for more effective personalized approaches to improve clinical outcomes in cervical cancer patients.
35Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma.PubMed
Qiming Zhang, Yao He, Nan Luo, et al.
Cell. 2019 Oct 31;179(4):829-845.e20. doi: 10.1016/j.cell.2019.10.003.
The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45 immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. A cluster of LAMP3 dendritic cells (DCs) appeared to be the mature form of conventional DCs and possessed the potential to migrate from tumors to LNs. LAMP3 DCs also expressed diverse immune-relevant ligands and exhibited potential to regulate multiple subtypes of lymphocytes. Of the macrophages in tumors that exhibited distinct transcriptional states, tumor-associated macrophages (TAMs) were associated with poor prognosis, and we established the inflammatory role of SLC40A1 and GPNMB in these cells. Further, myeloid and lymphoid cells in ascites were predominantly linked to tumor and blood origins, respectively. The dynamic properties of diverse CD45 cell types revealed by this study add new dimensions to the immune landscape of HCC.
36NKG2C/ tumor cell expression enhances immunotherapeutic efficacy against glioblastoma.PubMed
Olaya de Dios, M Angeles Ramírez-González, Irene Gómez-Soria, et al.
J Immunother Cancer. 2024 Aug 30;12(8):e009210. doi: 10.1136/jitc-2024-009210.
BACKGROUND: Activating and inhibitory receptors of natural killer (NK) cells such as NKp, NKG2, or CLEC are highly relevant to cold tumors including glioblastoma (GBM). Here, we aimed to characterize the expression of these receptors in GBM to gain insight into their potential role as modulators of the intratumoral microenvironment. METHODS: We performed a transcriptomic analysis of several NK receptors with a focus on the activating receptor encoded by NKG2C, among bulk and single-cell RNA sequencing GBM data sets. We also evaluated the effects of KLRC2-overexpressing GL261 cells in mice treated with or without programmed cell death protein-1 (PD-1) monoclonal antibody (mAb). Finally, we analyzed samples from two clinical trials evaluating PD-1 mAb effects in patients with GBM to determine the potential of NKG2C to serve as a biomarker of response. RESULTS: We observed significant expression of several inhibitory NK receptors on GBM-infiltrating NK and T cells, which contrasts with the strong expression of KLRC2 on tumor cells, mainly at the infiltrative margin. Neoplastic expression was associated with a reduction in the number of myeloid-derived suppressor cells and with a higher level of tumor-resident lymphocytes. A stronger antitumor activity after PD-1 mAb treatment was observed in NKG2C-expressing tumors both in mouse models and patients with GBM whereas the expression of inhibitory NK receptors showed an inverse association. CONCLUSIONS: This study explored the role of neoplastic NKG2C/ expression in shaping the immune profile of GBM and suggests that it is a predictive biomarker for positive responses to immune checkpoint inhibitor treatment in patients with GBM. Future studies could further validate this finding in prospective trials.
37Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing.PubMed
Haiping Jiang, Dingyi Yu, Penghui Yang, et al.
Clin Transl Med. 2022 Feb;12(2):e730. doi: 10.1002/ctm2.730.
BACKGROUND: Deciphering intra- and inter-tumoural heterogeneity is essential for understanding the biology of gastric cancer (GC) and its metastasis and identifying effective therapeutic targets. However, the characteristics of different organ-tropism metastases of GC are largely unknown. METHODS: Ten fresh human tissue samples from six patients, including primary tumour and adjacent non-tumoural samples and six metastases from different organs or tissues (liver, peritoneum, ovary, lymph node) were evaluated using single-cell RNA sequencing. Validation experiments were performed using histological assays and bulk transcriptomic datasets. RESULTS: Malignant epithelial subclusters associated with invasion features, intraperitoneal metastasis propensity, epithelial-mesenchymal transition-induced tumour stem cell phenotypes, or dormancy-like characteristics were discovered. High expression of the first three subcluster-associated genes displayed worse overall survival than those with low expression in a GC cohort containing 407 samples. Immune and stromal cells exhibited cellular heterogeneity and created a pro-tumoural and immunosuppressive microenvironment. Furthermore, a 20-gene signature of lymph node-derived exhausted CD8 T cells was acquired to forecast lymph node metastasis and validated in GC cohorts. Additionally, although anti-NKG2A (KLRC1) antibody have not been used to treat GC patients even in clinical trials, we uncovered not only malignant tumour cells but one endothelial subcluster, mucosal-associated invariant T cells, T cell-like B cells, plasmacytoid dendritic cells, macrophages, monocytes, and neutrophils may contribute to HLA-E-KLRC1/KLRC2 interaction with cytotoxic/exhausted CD8 T cells and/or natural killer (NK) cells, suggesting novel clinical therapeutic opportunities in GC. Additionally, our findings suggested that PD-1 expression in CD8 T cells might predict clinical responses to PD-1 blockade therapy in GC. CONCLUSIONS: This study provided insights into heterogeneous microenvironment of GC primary tumours and organ-specific metastases and provide support for precise diagnosis and treatment.
38Radiotherapy enhances the anti-tumor effect of CAR-NK cells for hepatocellular carcinoma.PubMed
Xiaotong Lin, Zishen Liu, Xin Dong, et al.
J Transl Med. 2024 Oct 13;22(1):929. doi: 10.1186/s12967-024-05724-4.
BACKGROUND: Chimeric antigen receptor (CAR)-NK cell therapy has shown remarkable clinical efficacy and safety in the treatment of hematological malignancies. However, this efficacy was limited in solid tumors owing to hostile tumor microenvironment (TME). Radiotherapy is commonly used for solid tumors and proved to improve the TME. Therefore, the combination with radiotherapy would be a potential strategy to improve therapeutic efficacy of CAR-NK cells for solid tumors. METHODS: Glypican-3 (GPC3) was used as a target antigen of CAR-NK cell for hepatocellular carcinoma (HCC). To promote migration towards HCC, CXCR2-armed CAR-NK92 cells targeting GPC3 were first developed, and their cytotoxic and migration activities towards HCC cells were evaluated. Next, the effects of irradiation on the anti-tumor activity of CAR-NK92 cells were assessed in vitro and in HCC-bearing NCG mice. Lastly, to demonstrate the potential mechanism mediating the sensitized effect of irradiation on CAR-NK cells, the differential gene expression profiles induced by irradiation were analyzed and the expression of some important ligands for the NK-cell activating receptors were further determined by qRT-PCR and flow cytometry. RESULTS: In this study, we developed CXCR2-armed GPC3-targeting CAR-NK92 cells that exhibited specific and potent killing activity against HCC cells and the enhanced migration towards HCC cells. Pretreating HCC cells with irradiation enhanced in vitro anti-HCC effect and migration activity of CXCR2-armed CAR-NK92 cells. We further found that only high-dose (8 Gy) but not low-dose (2 Gy) irradiation in one fraction could significantly enhanced in vivo anti-HCC activity of CXCR2-armed CAR-NK92 cells. Irradiation with 8 Gy significantly up-regulated the expression of NK cell-activating ligands on HCC cells. CONCLUSIONS: Our results indicate the evidence that irradiation could efficiently enhance the anti-tumor effect of CAR-NK cells in solid tumor model. The combination with radiotherapy would be an attractive strategy to improve therapeutic efficacy of CAR-NK cells for solid tumors.
39Single-cell transcriptional atlas of tumor-associated macrophages in breast cancer.PubMed
Yupeng Zhang, Fan Zhong, Lei Liu
Breast Cancer Res. 2024 Sep 4;26(1):129. doi: 10.1186/s13058-024-01887-6.
BACKGROUND: The internal heterogeneity of breast cancer, notably the tumor microenvironment (TME) consisting of malignant and non-malignant cells, has been extensively explored in recent years. The cells in this complex cellular ecosystem activate or suppress tumor immunity through phenotypic changes, secretion of metabolites and cell-cell communication networks. Macrophages, as the most abundant immune cells within the TME, are recruited by malignant cells and undergo phenotypic remodeling. Tumor-associated macrophages (TAMs) exhibit a variety of subtypes and functions, playing significant roles in impacting tumor immunity. However, their precise subtype delineation and specific function remain inadequately defined. METHODS: The publicly available single-cell transcriptomes of 49,141 cells from eight breast cancer patients with different molecular subtypes and stages were incorporated into our study. Unsupervised clustering and manual cell annotation were employed to accurately classify TAM subtypes. We then conducted functional analysis and constructed a developmental trajectory for TAM subtypes. Subsequently, the roles of TAM subtypes in cell-cell communication networks within the TME were explored using endothelial cells (ECs) and T cells as key nodes. Finally, analyses were repeated in another independent publish scRNA datasets to validate our findings for TAM characterization. RESULTS: TAMs are accurately classified into 7 subtypes, displaying anti-tumor or pro-tumor roles. For the first time, we identified a new TAM subtype capable of proliferation and expansion in breast cancer-TUBA1B TAMs playing a crucial role in TAMs diversity and tumor progression. The developmental trajectory illustrates how TAMs are remodeled within the TME and undergo phenotypic and functional changes, with TUBA1B TAMs at the initial point. Notably, the predominant TAM subtypes varied across different molecular subtypes and stages of breast cancer. Additionally, our research on cell-cell communication networks shows that TAMs exert effects by directly modulating intrinsic immunity, indirectly regulating adaptive immunity through T cells, as well as influencing tumor angiogenesis and lymphangiogenesis through ECs. CONCLUSIONS: Our study establishes a precise single-cell atlas of breast cancer TAMs, shedding light on their multifaceted roles in tumor biology and providing resources for targeting TAMs in breast cancer immunotherapy.
40Single-cell Atlas reveals core function of CPVL/MSR1 expressing macrophages in the prognosis of triple-negative breast cancer.PubMed
Xinan Wang, Li Lin, Xue Zhang, et al.
Front Immunol. 2024 Dec 24;15:1501009. doi: 10.3389/fimmu.2024.1501009. eCollection 2024.
BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with the worst prognosis among all subtypes. The impact of distinct cell subpopulations within the tumor microenvironment (TME) on TNBC patient prognosis has yet to be clarified. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) integrated with bulk RNA sequencing (bulk RNA-seq), we applied Cox regression models to compute hazard ratios, and cross-validated prognostic scoring using a GLMNET-based Cox model. Cell communication analysis was used to elucidate the potential mechanisms of CPVL and MSR1. Ultimately, RNA interference-mediated gene knockdown was utilized to validate the impact of specific genes on the polarization of tumor-associated macrophages (TAMs). RESULTS: Our findings revealed that the function of immune cells is more pivotal in prognosis, with TAMs showing the strongest correlation with TNBC patient outcomes, compared with other immune cells. Additionally, we identified CPVL and MSR1 as critical prognostic genes within TAMs, with CPVL expression positively correlated with favorable outcomes and MSR1 expression associated with poorer prognosis. Mechanistically, CPVL may contribute to favorable prognosis by inhibiting the SPP1-CD44 ligand-receptor and promoting CXCL9-CXCR3, C3-C3AR1 ligand-receptor, through which TAMs interact with other cells such as monocytes, neutrophils, and T cells. Moreover, cytokines including IL-18, IFNγR1, CCL20, and CCL2, along with complement-related gene like TREM2 and complement component CFD, may participate in the process of CPVL or MSR1 regulating macrophage polarization. Furthermore, RT-PCR experiments confirmed that CPVL is positively associated with M1-like TAM polarization, while MSR1 is linked to M2-like TAM polarization. Finally, the prognostic significance of these two genes is also validated in HER2-positive breast cancer subtypes. CONCLUSIONS: CPVL and MSR1 are potential biomarkers for macrophage-mediated TNBC prognosis, suggesting the therapeutic potential of macrophage targeting in TNBC.
41Intercellular communication between FAP+ fibroblasts and SPP1+ macrophages in prostate cancer via multi-omics.PubMed
Tingting Wu, Xinyu Li, Fei Zheng, et al.
Front Immunol. 2025 May 14;16:1560998. doi: 10.3389/fimmu.2025.1560998. eCollection 2025.
BACKGROUND: Prostate cancer (PCa) presents substantial heterogeneity and unpredictability in its progression. Despite therapeutic advancements, mortality from advanced PCa remains a significant challenge. Understanding the intercellular communication within the tumor microenvironment (TME) is critical for uncovering mechanisms driving tumorigenesis and identifying novel therapeutic targets. METHODS: We employed an integrative approach combining bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to investigate interactions between FAP+ fibroblasts and tumor-associated macrophages in PCa. Key findings were validated using immunohistochemical and immunofluorescence staining techniques. RESULTS: Analysis of 23,519 scRNA-seq data from 23 prostate samples revealed a pronounced accumulation of FAP+ fibroblasts in tumor tissues. Spatial transcriptomics and bulk RNA sequencing demonstrated strong associations between FAP+ fibroblasts and SPP1+ macrophages. Notably, tumor-specific intercellular signaling pathways, such as CSF1/CSF1R and CXCL/ACKR1, were identified, highlighting their potential role in fostering an immunosuppressive TME. CONCLUSION: Our findings unveil a distinct pattern of crosstalk between FAP+ fibroblasts and SPP1+ macrophages in PCa, shedding light on potential therapeutic targets for advanced PCa.
42Dynamic single-cell metabolomics reveals cell-cell interaction between tumor cells and macrophages.PubMed
Yi Zhang, Mingying Shi, Mingxuan Li, et al.
Nat Commun. 2025 May 16;16(1):4582. doi: 10.1038/s41467-025-59878-w.
Single-cell metabolomics reveals cell heterogeneity and elucidates intracellular molecular mechanisms. However, general concentration measurement of metabolites can only provide a static delineation of metabolomics, lacking the metabolic activity information of biological pathways. Herein, we develop a universal system for dynamic metabolomics by stable isotope tracing at the single-cell level. This system comprises a high-throughput single-cell data acquisition platform and an untargeted isotope tracing data processing platform, providing an integrated workflow for dynamic metabolomics of single cells. This system enables the global activity profiling and flow analysis of interlaced metabolic networks at the single-cell level and reveals heterogeneous metabolic activities among single cells. The significance of activity profiling is underscored by a 2-deoxyglucose inhibition model, demonstrating delicate metabolic alteration within single cells which cannot reflected by concentration analysis. Significantly, the system combined with a neural network model enables the metabolomic profiling of direct co-cultured tumor cells and macrophages. This reveals intricate cell-cell interaction mechanisms within the tumor microenvironment and firstly identifies versatile polarization subtypes of tumor-associated macrophages based on their metabolic signatures, which is in line with the renewed diversity atlas of macrophages from single-cell RNA-sequencing. The developed system facilitates a comprehensive understanding single-cell metabolomics from both static and dynamic perspectives.
43A pan-cancer single-cell RNA-seq atlas of intratumoral B cells.PubMed
Evelyn Fitzsimons, Danwen Qian, Andrei Enica, et al.
Cancer Cell. 2024 Oct 14;42(10):1784-1797.e4. doi: 10.1016/j.ccell.2024.09.011.
Tumor-infiltrating B cells play a significant role in tumor development, progression, and prognosis, yet a comprehensive classification system is lacking. To address this gap, we present a pan-cancer single-cell RNA sequencing (scRNA-seq) atlas of tumor-infiltrating B and plasma cells across a large sample cohort. We identify key B cell subset signatures, revealing distinct subpopulations and highlighting the heterogeneity and functional diversity of these cells in the tumor microenvironment. We explore associations between B cell subsets and checkpoint inhibitor therapy responses, finding subset-specific effects on overall response. Additionally, we examine B and T cell crosstalk, identifying unique ligand-receptor pairs for specific B cell subsets, spatially validated. This comprehensive dataset serves as a valuable resource, providing a detailed atlas that enhances the understanding of B cell complexity in tumors and opens new avenues for research and therapeutic strategies.
44Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives.PubMed
Hao Lin, Chaxian Liu, Ankang Hu, et al.
J Hematol Oncol. 2024 May 8;17(1):31. doi: 10.1186/s13045-024-01544-7.
Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45 cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.
45Targeting N6-methyladenosine reader YTHDF1 with siRNA boosts antitumor immunity in NASH-HCC by inhibiting EZH2-IL-6 axis.PubMed
Lina Wang, Lefan Zhu, Cong Liang, et al.
J Hepatol. 2023 Nov;79(5):1185-1200. doi: 10.1016/j.jhep.2023.06.021. Epub 2023 Jul 17.
BACKGROUND & AIMS: RNA N-methyladenosine (mA) reader protein YTHDF1 has been implicated in cancer; however, its role in hepatocellular carcinoma (HCC), especially in non-alcoholic steatohepatitis-associated HCC (NASH-HCC), remains unknown. Here, we investigated the functional role of YTHDF1 in NASH-HCC and its interplay with the tumor immune microenvironment. METHODS: Hepatocyte-specific Ythdf1-overexpressing mice were subjected to a NASH-HCC-inducing diet. Tumor-infiltrating immune cells were profiled with single-cell RNA-sequencing, flow cytometry, and immunostaining. The molecular target of YTHDF1 was elucidated with RNA-sequencing, mA-sequencing, YTHDF1 RNA immunoprecipitation-sequencing, proteomics, and ribosome-profiling. Ythdf1 in NASH-HCC models was targeted by lipid nanoparticle (LNP)-encapsulated small-interfering Ythdf1. RESULTS: YTHDF1 is overexpressed in tumor tissues compared to adjacent peri-tumor tissues from patients with NASH-HCC. Liver-specific Ythdf1 overexpression drives tumorigenesis in dietary models of spontaneous NASH-HCC. Single-cell RNA-sequencing and flow cytometry revealed that Ythdf1 induced accumulation of myeloid-derived suppressor cells (MDSCs) and suppressed cytotoxic CD8 T-cell function. Mechanistically, Ythdf1 expression in NASH-HCC cells induced the secretion of IL-6, which mediated MDSC recruitment and activation, leading to CD8 T-cell dysfunction. EZH2 mRNA was identified as a key YTHDF1 target. YTHDF1 binds to mA-modified EZH2 mRNA and promotes EZH2 translation. EZH2 in turn increased expression and secretion of IL-6. Ythdf1 knockout synergized with anti-PD-1 treatment to suppress tumor growth in NASH-HCC allografts. Furthermore, therapeutic targeting of Ythdf1 using LNP-encapsulated small-interfering RNA significantly increased the efficacy of anti-PD-1 blockade in NASH-HCC allografts. CONCLUSIONS: We identified that YTHDF1 promotes NASH-HCC tumorigenesis via EZH2-IL-6 signaling, which recruits and activates MDSCs to cause cytotoxic CD8 T-cell dysfunction. YTHDF1 may be a novel therapeutic target to improve responses to anti-PD-1 immunotherapy in NASH-HCC. IMPACT AND IMPLICATIONS: YTHDF1, a N-methyladenosine reader, is upregulated in patients with non-alcoholic steatohepatitis (NASH)-associated hepatocellular carcinoma (HCC); however, its role in modulating the tumor immune microenvironment in NASH-HCC remains unclear. Here, we show that Ythdf1 mediates immunosuppression in NASH-HCC and that targeting YTHDF1 in combination with immune checkpoint blockade elicits robust antitumor immune responses. Our findings suggest novel therapeutic targets for potentiating the efficacy of immune checkpoint blockade in NASH-HCC and provide the rationale for developing YTHDF1 inhibitors for the treatment of NASH-HCC.
46CXCL12 tumor-associated endothelial cells promote immune resistance in hepatocellular carcinoma.PubMed
Yajie Lu, Yunpeng Liu, Xiaoshuang Zuo, et al.
J Hepatol. 2025 Apr;82(4):634-648. doi: 10.1016/j.jhep.2024.09.044. Epub 2024 Oct 9.
BACKGROUND & AIMS: The tumor microenvironment (TME) plays a crucial role in the limited efficacy of existing treatments for hepatocellular carcinoma (HCC), with tumor-associated endothelial cells (TECs) serving as fundamental TME components that substantially influence tumor progression and treatment efficacy. However, the precise roles and mechanisms of TECs in HCC remain inadequately understood. METHODS: We employed a multi-omics profiling strategy to investigate the single-cell and spatiotemporal evolution of TECs within the microenvironment of HCC tumors, showcasing varied responses to immunotherapy. Through an analysis of a clinical cohort of patients with HCC, we explored the correlation between TEC subpopulations and immunotherapy outcomes. The influence of TEC subsets on the immune microenvironment was confirmed through comprehensive in vitro and in vivo studies. To further explore the mechanisms of distinct TEC subpopulations in microenvironmental modulation and their impact on immunotherapy, we utilized TEC subset-specific knockout mouse models as well as humanized mouse models. RESULTS: In this study, we identified a new subset of CXCL12 TECs that exert a crucial role in immune suppression within the HCC TME. Functionally, CXCL12 TECs impede the differentiation of CD8 naïve T cells into CD8 cytotoxic T cells by secreting CXCL12. Furthermore, they attract myeloid-derived suppressor cells (MDSCs). A bispecific antibody was developed to target both CXCL12 and PD1 specifically, showing significant promise in bolstering anti-tumor immune responses and advancing HCC therapy. CONCLUSIONS: CXCL12 TECs are pivotal in mediating immunosuppression within the HCC microenvironment and targeting CXCL12 TECs presents a promising approach to augment the efficacy of immunotherapies in patients with HCC. IMPACT AND IMPLICATIONS: This investigation reveals a pivotal mechanism wherein CXCL12 tumor-associated endothelial cells (TECs) emerge as crucial modulators of immune suppression in the tumor microenvironment of hepatocellular carcinoma (HCC). The discovery of CXCL12 TECs as inhibitors of CD8 naïve T cell activation and recruiters of myeloid-derived suppressor cells significantly advances our grasp of the dynamic between HCC and immune regulation. Moreover, the development and application of a bispecific antibody precisely targeting CXCL12 and PD1 has proven to enhance immune responses in a humanized mouse HCC model. This finding underscores a promising therapeutic direction for HCC, offering the potential to amplify the impact of current immunotherapies.
47Single-cell transcriptome analysis reveals regulatory programs associated with tumor resistance during immunotherapy in colorectal cancer.PubMed
Yan Chen, Tao Liu, Guangtao Min, et al.
Int J Surg. 2026 Jan 1;112(1):694-708. doi: 10.1097/JS9.0000000000003459. Epub 2025 Sep 9.
BACKGROUND: Colorectal cancer (CRC), a predominant cancer type globally, is one of the most common cancers worldwide. Immune-checkpoint inhibitors have robust efficacy in the treatment of patients with metastatic DNA mismatch repair (dMMR) CRC; however, some of the patients among them demonstrate resistance to immunotherapy, and the underlying molecular mechanisms remain elusive. METHODS: Using single-cell sequencing data, we identified and annotated cell populations, systematically comparing cell proportions and gene expression changes across different groups. Gene set enrichment analysis was conducted to identify significantly enriched signaling pathways in T-cell subpopulations. Furthermore, we assessed the impact of transcriptional dysregulation in stress response stated T (TSTR) and γδ T cells on tumor immunotherapy responses and drug resistance. The potential role of T-cell subset interactions in modulating drug resistance and sensitivity was investigated through comprehensive cell communication analysis. Lastly, clinical samples from 190 CRC patients were collected, with paired adjacent normal tissues. Immunohistochemistry was performed using antibodies against FOS and KLRB1. RESULTS: T cells were identified as the predominant cell population influencing immunotherapy outcomes. T-cell subsets exhibited distinct functional characteristics, with notable differences in their distribution between resistant and sensitive tumor groups. Specifically, exhausted T cells (Tex), GZMK+ T, TSTR, regulatory T cells (Treg), and γδ T cells were associated with therapeutic resistance. For Tex and GZMK+ T cells, resistance was correlated with the activation of antigen processing and presentation pathways, whereas oxidative stress pathways were downregulated. In contrast, γδ T cells in the sensitive group exhibited the activation of protein folding pathways, which may contribute to anti-tumor immune responses. Transcriptional network dysregulation in TSTR and γδ T cells was observed in the drug-resistant group. Cell-to-cell communication analysis showed stronger interactions among T-cell subpopulations, with changes in key signaling pathways linked to treatment resistance. Additionally, downregulation of the CD69-KLRB1 signaling pathway was identified as a potential mechanism of drug resistance in CRC. Lastly, high expression of FOS was significantly associated with a worse prognosis, whereas high expression of KLRB1 predicted improved clinical outcomes, including prolonged overall survival and progression-free survival, and emerged as an independent prognostic factor for CRC patients. CONCLUSION: This study highlights the pivotal role of T-cell subsets in patients with metastatic dMMR CRC who resistant to anti-PD-1 therapy, revealing that transcriptional dysregulation and impaired cell communication networks are central mechanisms underlying drug resistance. Notably, KLRB1 has been identified as a promising biomarker for immunotherapy response in CRC patients.
48Progenitor CD8 T cells and hyper-Treg crosstalk: a driver of immune checkpoint inhibitor resistance in esophageal squamous cell carcinoma.PubMed
Suning Huang, Jingwei Yan, Wenqi Liu, et al.
Apoptosis. 2026 Jan 12;31(1):44. doi: 10.1007/s10495-025-02256-0.
Despite the crucial role of antigen presentation in immune checkpoint inhibitor (ICI) efficacy, its contribution and the mechanism in esophageal squamous cell carcinoma (ESCC) remain unclear, representing a key knowledge gap in overcoming immune escape in its immunotherapy. This study explores how tumor antigen presentation and intercellular interactions in the tumor microenvironment (TME) drive ICI resistance using single-cell RNA sequencing (scRNA-seq). Publicly available scRNA-seq data from 24 ESCC patients treated with chemotherapy and ICIs were analyzed. Cell clustering, transcription factor regulation, cell-cell communication analysis, and KEGG/GO enrichment analyses were used to examine malignant cell heterogeneity, the relationship between antigen-presenting cells and ICI responses, and cell-cell interactions influencing anti-tumor response. Spatial relationships were validated through multiplex immunofluorescence. Malignant cells were classified by enrichment analyses into cNMF_1, cNMF_2, cNMF_3, and cNMF_4, with cNMF_4 showing antigen-presenting traits. Based on ICI response groups, cell-cell communication analysis revealed that in poor responders, the antigen presentation ability of tumors induced by treatment was enhanced, and mainly enriched in the MHC-I pathway. The crosstalk between Progenitor CD8 Tex and hyper-Treg in the TME drove ICI resistance. Hyper-Treg likely regulated CD8 T activation through the CLEC2C-KLRB1 axis, forming an inhibitory cell interaction network dominated by hyper-Treg, resulting in an overall strong immune suppression state of the TME in this population. The antigen-presenting malignant epithelial cells of ESCC exhibit significant interactions with various T cells in the TME. ICI resistance is closely associated with the crosstalk between progenitor CD8 Tex and hyper-Treg, representing a promising target for personalized ESCC therapy.
49Targeting SPHK1 in macrophages remodels the tumor microenvironment and enhances anti-PD-1 immunotherapy efficacy in colorectal cancer liver metastasis.PubMed
Yizhi Zhan, Jinsong Xu, Zhanqiao Zhang, et al.
Cancer Commun (Lond). 2025 Oct;45(10):1203-1228. doi: 10.1002/cac2.70047. Epub 2025 Jul 16.
BACKGROUND: Colorectal cancer liver metastasis (CRLM) is characterized by an immunosuppressive microenvironment and a blunted response to immunotherapy. Notably, tumor-associated macrophages (TAMs) play a critical role in modulating immune responses and exhibit significant heterogeneity in CRLM. Sphingosine kinase 1 (SPHK1) serves as a pivotal kinase in maintaining the balance between ceramide and sphingosine-1-phosphate (S1P) levels. However, the effects of SPHK1 within TAMs on tumor immune evasion during CRLM remain elusive. This study aimed at investigating the role of TAM-intrinsic SPHK1 in tumor immunosuppressive microenvironment in CRLM. METHODS: SPHK1 expression levels in TAMs were estimated by immunofluorescence and bioinformatics analysis. Several animal models were established to elucidate the role of SPHK1 in tumor immunity reprogramming in vivo. Flow cytometry, cytokine assay, and transwell assay were conducted to investigate the effects of SPHK1 in TAMs in cell-cell communication in vitro. RNA-sequencing, Western blotting, and quantitative real-time polymerase chain reaction were used to explore the molecular mechanism by which SPHK1 activated NLR family pyrin domain containing 3 (NLRP3) inflammasome in TAMs. RESULTS: We found that SPHK1 was mainly expressed in TAMs and identified SPHK1 TAMs as associated with CRLM and diminished efficacy of immunotherapy in human patients. These SPHK1 TAMs exhibited strong immunosuppressive activities by inducing CD8 T cell exhaustion with high programmed cell death 1 (PD-1) expression via the interaction between TAMs and CRC cells. Mechanistically, SPHK1-produced S1P exerted an autocrine effect to activate NLRP3 inflammasome and interleukin 1 beta (IL-1β) release via nuclear factor-kappa B (NF-κB) and hypoxia inducible factor 1 subunit alpha (HIF-1α) signaling in TAMs. Paracrine IL-1β then upregulated the expression of monocyte chemoattractants and ADAM metallopeptidase domain 17 (ADAM17) sheddase in CRC cells, resulting in TAM infiltration and CD8 T cell dysfunction in the liver microenvironment. Furthermore, combining SPHK1-targeting treatments with anti-PD-1 therapy or radioimmunotherapy largely stalled liver metastasis and caused a significant extension of lifespan in preclinical mouse models. CONCLUSIONS: Our findings highlighted the role of SPHK1 of TAMs in facilitating CRLM by promoting CD8 T cell dysfunction and immunosuppressive microenvironment. Combining SPHK1 blockade with anti-PD-1 therapy may be a promising treatment regimen for patients with CRLM.
50TRIM28 drives immune evasion via PARP1 SUMOylation and NAD depletion in clear cell renal cell carcinoma.PubMed
Xiangpeng Zhan, Hongji Hu, Yang Liu, et al.
J Immunother Cancer. 2025 Oct 23;13(10):e013025. doi: 10.1136/jitc-2025-013025.
BACKGROUND: Immune checkpoint blockade (ICB) therapy has demonstrated significant clinical potential in a variety of cancers; however, its efficacy in clear cell renal cell carcinoma (ccRCC) remains suboptimal. In ccRCC, an increased infiltration of CD8 T cells does not necessarily correlate with improved prognosis, indicating the presence of unique immune evasion mechanisms within the tumor microenvironment (TME). METHODS: Tripartite motif-containing 28 (TRIM28) was identified as a potential therapeutic target through single-cell transcriptomics (GSE159115) and Geneformer-based perturbation screening. Functional validation was performed by constructing shTRIM28 and overexpression cell models to assess tumor proliferation, CD8 T cell co-cultures, flow cytometry, and patient-derived xenograft models. Co-immunoprecipitation and GST pull-down assays were used to analyze the TRIM28-poly (ADP-ribose) polymerase 1 (PARP1) interaction. SUMOylation/ubiquitination studies elucidated the mechanism regulating PARP1 stability, and chromatin immunoprecipitation-quantitative PCR identified the transcriptional regulation of programmed death-ligand 1 (PD-L1). High-throughput screening was conducted with RNA-seq, liquid chromatography-tandem mass spectrometry, and metabolomics. Virtual screening identified the TRIM28 inhibitor Eltrombopag, which was tested in combination with anti-programmed cell death protein-1 (PD-1) therapy for in vivo efficacy and metabolic reprogramming. RESULTS: We identified TRIM28 as a central regulator of immune evasion in ccRCC. Using high-throughput gene knockout screening, we demonstrated that TRIM28 depletion reprograms malignant epithelial cells toward a less aggressive phenotype and significantly enhances tumor cell susceptibility to cytotoxic T lymphocyte killing. Mechanistically, TRIM28 promotes immune resistance through dual immunometabolic mechanisms: first, by stabilizing PARP1 and promoting its SUMOylation, which in turn amplifies PD-L1 expression via NAD-SIRT1-p65 signaling; second, by depleting NAD in the TME, limiting NAD availability for CD8 T cells and impairing their respiration and effector function. These findings provide a novel mechanistic framework for TRIM28-driven immune suppression, integrating tumor-intrinsic metabolic reprogramming with CD8 T cell dysfunction. Notably, we identified Eltrombopag as a candidate TRIM28 inhibitor, which synergized with anti-PD-1 therapy to enhance antitumor immunity and overcome ICB resistance in murine models. CONCLUSIONS: This study reveals that TRIM28 is a key regulator of PD-L1 expression and T cell dysfunction in ccRCC through PARP1 stabilization and NAD metabolic reprogramming. Targeting TRIM28/PARP1/PDL1 with Eltrombopag reshapes the immunosuppressive TME and enhances checkpoint blockade efficacy, providing a novel combinatorial strategy for ccRCC immunotherapy.
51AEBP1 drives fibroblast-mediated T cell dysfunction in tumors.PubMed
Xiaoyu Wang, Jie Li, Daqiang Song, et al.
Nat Commun. 2025 Sep 1;16(1):8171. doi: 10.1038/s41467-025-63659-w.
T cell dysfunction enables tumor immune evasion, understanding its mechanism is crucial for improving immunotherapy. Here we show, by RNA-sequencing analysis of human colon adenocarcinoma and triple-negative breast cancer tissues, that expression of Adipocyte Enhancer-Binding Protein 1 (AEBP1) positively correlates with T cell dysfunction and indicative of unfavorable patient outcomes. Subsequent single-cell RNA sequencing identifies cancer-associated fibroblasts (CAF) as the primary AEBP1 source. Fibroblast-specific AEBP1 deletion in mice enhances T cell cytotoxicity and suppresses tumor growth. Mechanistically, autocrine AEBP1 binds CKAP4 on CAFs, activating AKT/PD-L1 signaling to drive T cell dysfunction. By molecular-docking-based virtual screening we identify Chem-0199, a drug that disrupts the interaction between AEBP1 and CKAP4, thereby enhancing antitumor immunity. Both genetic and pharmacological AEBP1 inhibition synergize with immune checkpoint blockade in syngeneic models. Our study establishes AEBP1 as a key regulator of CAF-mediated T cell dysfunction and a therapeutic target.
52USP2 promotes tumor immune evasion via deubiquitination and stabilization of PD-L1.PubMed
Zean Kuang, Xiaojia Liu, Na Zhang, et al.
Cell Death Differ. 2023 Oct;30(10):2249-2264. doi: 10.1038/s41418-023-01219-9. Epub 2023 Sep 5.
The abnormal upregulation of programmed death ligand-1 (PD-L1) on tumor cells impedes T-cell mediated cytotoxicity through PD-1 engagement, and further exploring the mechanisms regulation of PD-L1 in cancers may enhance the clinical efficacy of PD-L1 blockade. Here, using single-guide RNAs (sgRNAs) screening system, we identify ubiquitin-specific processing protease 2 (USP2) as a novel regulator of PD-L1 stabilization for tumor immune evasion. USP2 directly interacts with and increases PD-L1 abundance in colorectal and prostate cancer cells. Our results show that Thr288, Arg292 and Asp293 at USP2 control its binding to PD-L1 through deconjugating the K48-linked polyubiquitination at lysine 270 of PD-L1. Depletion of USP2 causes endoplasmic reticulum (ER)-associated degradation of PD-L1, thus attenuates PD-L1/PD-1 interaction and sensitizes cancer cells to T cell-mediated killing. Meanwhile, USP2 ablation-induced PD-L1 clearance enhances antitumor immunity in mice via increasing CD8 T cells infiltration and reducing immunosuppressive infiltration of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), whereas PD-L1 overexpression reverses the tumor growth suppression by USP2 silencing. USP2-depletion combination with anti-PD-1 also exhibits a synergistic anti-tumor effect. Furthermore, analysis of clinical tissue samples indicates that USP2 is positively associated with PD-L1 expression in cancer. Collectively, our data reveal a crucial role of USP2 for controlling PD-L1 stabilization in tumor cells, and highlight USP2 as a potential therapeutic target for cancer immunotherapy.
53Age-associated remodeling of T cell immunity and metabolism.PubMed
SeongJun Han, Peter Georgiev, Alison E Ringel, et al.
Cell Metab. 2023 Jan 3;35(1):36-55. doi: 10.1016/j.cmet.2022.11.005. Epub 2022 Dec 5.
Aging results in remodeling of T cell immunity and is associated with poor clinical outcomes in age-related diseases such as cancer. Among the hallmarks of aging, changes in host and cellular metabolism critically affect the development, maintenance, and function of T cells. Although metabolic perturbations impact anti-tumor T cell responses, the link between age-associated metabolic dysfunction and anti-tumor immunity remains unclear. In this review, we summarize recent advances in our understanding of aged T cell metabolism, with a focus on the bioenergetic and immunologic features of T cell subsets unique to the aging process. We also survey insights into mechanisms of metabolic T cell dysfunction in aging and discuss the impacts of aging on the efficacy of cancer immunotherapy. As the average life expectancy continues to increase, understanding the interplay between age-related metabolic reprogramming and maladaptive T cell immunity will be instrumental for the development of therapeutic strategies for older patients.
54The aged tumor microenvironment limits T cell control of cancer.PubMed
Alex C Y Chen, Sneha Jaiswal, Daniela Martinez, et al.
Nat Immunol. 2024 Jun;25(6):1033-1045. doi: 10.1038/s41590-024-01828-7. Epub 2024 May 14.
The etiology and effect of age-related immune dysfunction in cancer is not completely understood. Here we show that limited priming of CD8 T cells in the aged tumor microenvironment (TME) outweighs cell-intrinsic defects in limiting tumor control. Increased tumor growth in aging is associated with reduced CD8 T cell infiltration and function. Transfer of T cells from young mice does not restore tumor control in aged mice owing to rapid induction of T cell dysfunction. Cell-extrinsic signals in the aged TME drive a tumor-infiltrating age-associated dysfunctional (T) cell state that is functionally, transcriptionally and epigenetically distinct from canonical T cell exhaustion. Altered natural killer cell-dendritic cell-CD8 T cell cross-talk in aged tumors impairs T cell priming by conventional type 1 dendritic cells and promotes T cell formation. Aged mice are thereby unable to benefit from therapeutic tumor vaccination. Critically, myeloid-targeted therapy to reinvigorate conventional type 1 dendritic cells can improve tumor control and restore CD8 T cell immunity in aging.
55Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer.PubMed
Adele M Nicolas, Marina Pesic, Esther Engel, et al.
Cancer Cell. 2022 Feb 14;40(2):168-184.e13. doi: 10.1016/j.ccell.2022.01.004. Epub 2022 Feb 3.
Standard cancer therapy targets tumor cells without considering possible damage on the tumor microenvironment that could impair therapy response. In rectal cancer patients we find that inflammatory cancer-associated fibroblasts (iCAFs) are associated with poor chemoradiotherapy response. Employing a murine rectal cancer model or patient-derived tumor organoids and primary stroma cells, we show that, upon irradiation, interleukin-1α (IL-1α) not only polarizes cancer-associated fibroblasts toward the inflammatory phenotype but also triggers oxidative DNA damage, thereby predisposing iCAFs to p53-mediated therapy-induced senescence, which in turn results in chemoradiotherapy resistance and disease progression. Consistently, IL-1 inhibition, prevention of iCAFs senescence, or senolytic therapy sensitizes mice to irradiation, while lower IL-1 receptor antagonist serum levels in rectal patients correlate with poor prognosis. Collectively, we unravel a critical role for iCAFs in rectal cancer therapy resistance and identify IL-1 signaling as an attractive target for stroma-repolarization and prevention of cancer-associated fibroblasts senescence.
56Neutrophil extracellular traps formed during chemotherapy confer treatment resistance via TGF-β activation.PubMed
Alexandra Mousset, Enora Lecorgne, Isabelle Bourget, et al.
Cancer Cell. 2023 Apr 10;41(4):757-775.e10. doi: 10.1016/j.ccell.2023.03.008.
Metastasis is the major cause of cancer death, and the development of therapy resistance is common. The tumor microenvironment can confer chemotherapy resistance (chemoresistance), but little is known about how specific host cells influence therapy outcome. We show that chemotherapy induces neutrophil recruitment and neutrophil extracellular trap (NET) formation, which reduces therapy response in mouse models of breast cancer lung metastasis. We reveal that chemotherapy-treated cancer cells secrete IL-1β, which in turn triggers NET formation. Two NET-associated proteins are required to induce chemoresistance: integrin-αvβ1, which traps latent TGF-β, and matrix metalloproteinase 9, which cleaves and activates the trapped latent TGF-β. TGF-β activation causes cancer cells to undergo epithelial-to-mesenchymal transition and correlates with chemoresistance. Our work demonstrates that NETs regulate the activities of neighboring cells by trapping and activating cytokines and suggests that chemoresistance in the metastatic setting can be reduced or prevented by targeting the IL-1β-NET-TGF-β axis.
57Extracellular vesicles from human breast cancer-resistant cells promote acquired drug resistance and pro-inflammatory macrophage response.PubMed
Patrick Santos, Caroline P Rezende, Renan Piraine, et al.
Front Immunol. 2024 Oct 15;15:1468229. doi: 10.3389/fimmu.2024.1468229. eCollection 2024.
INTRODUCTION: Breast cancer is a significant public health problem around the world, ranking first in deaths due to cancer in females. The therapy to fight breast cancer involves different methods, including conventional chemotherapy. However, the acquired resistance that tumors develop during the treatment is still a central cause of cancer-associated deaths. One mechanism that induces drug resistance is cell communication via extracellular vesicles (EVs), which can carry efflux transporters and miRNA that increase sensitive cells' survivability to chemotherapy. METHODS: Our study investigates the transcription changes modulated by EVs from tamoxifen- and doxorubicin-resistant breast cancer cells in sensitive cells and how these changes may induce acquired drug resistance, inhibit apoptosis, and increase survivability in the sensitive cells. Additionally, we exposed human macrophages to resistant EVs to understand the influence of EVs on immune responses. RESULTS: Our results suggest that the acquired drug resistance is associated with the ability of resistant EVs to upregulate several transporter classes, which are directly related to the increase of cell viability and survival of sensitive cells exposed to EVs before a low-dose drug treatment. In addition, we show evidence that resistant EVs may downregulate immune system factors to evade detection and block cell death by apoptosis in sensitive breast cancer cells. Our data also reveals that human macrophages in contact with resistant EVs trigger a pro-inflammatory cytokine secretion profile, an effect that may be helpful for future immunotherapy studies. DISCUSSION: These findings are the first transcriptome-wide analysis of cells exposed to resistant EVs, supporting that resistant EVs are associated with the acquired drug resistance process during chemotherapy by modulating different aspects of sensitive cancer cells that coffer the chemoresistance.
58Computational solutions for spatial transcriptomics.PubMed
Iivari Kleino, Paulina Frolovaitė, Tomi Suomi, et al.
Comput Struct Biotechnol J. 2022 Sep 1;20:4870-4884. doi: 10.1016/j.csbj.2022.08.043. eCollection 2022.
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
59Clinical and translational values of spatial transcriptomics.PubMed
Linlin Zhang, Dongsheng Chen, Dongli Song, et al.
Signal Transduct Target Ther. 2022 Apr 1;7(1):111. doi: 10.1038/s41392-022-00960-w.
The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
60Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.PubMed
Zixiang Zhou, Yunshan Zhong, Zemin Zhang, et al.
Nat Commun. 2023 Dec 1;14(1):7930. doi: 10.1038/s41467-023-43600-9.
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
61Recovering single-cell expression profiles from spatial transcriptomics with scResolve.PubMed
Hao Chen, Young Je Lee, Jose A Ovando-Ricardez, et al.
Cell Rep Methods. 2024 Oct 21;4(10):100864. doi: 10.1016/j.crmeth.2024.100864. Epub 2024 Sep 25.
Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.
62Single-Cell Multiomics Integration by SCOT.PubMed
Pinar Demetci, Rebecca Santorella, Björn Sandstede, et al.
J Comput Biol. 2022 Jan;29(1):19-22. doi: 10.1089/cmb.2021.0477. Epub 2022 Jan 5.
Although the availability of various sequencing technologies allows us to capture different genome properties at single-cell resolution, with the exception of a few co-assaying technologies, applying different sequencing assays on the same single cell is impossible. Single-cell alignment using optimal transport (SCOT) is an unsupervised algorithm that addresses this limitation by using optimal transport to align single-cell multiomics data. First, it preserves the local geometry by constructing a -nearest neighbor (-NN) graph for each data set (or domain) to capture the intra-domain distances. SCOT then finds a probabilistic coupling matrix that minimizes the discrepancy between the intra-domain distance matrices. Finally, it uses the coupling matrix to project one single-cell data set onto another through barycentric projection, thus aligning them. SCOT requires tuning only two hyperparameters and is robust to the choice of one. Furthermore, the Gromov-Wasserstein distance in the algorithm can guide SCOT's hyperparameter tuning in a fully unsupervised setting when no orthogonal alignment information is available. Thus, SCOT is a fast and accurate alignment method that provides a heuristic for hyperparameter selection in a real-world unsupervised single-cell data alignment scenario. We provide a tutorial for SCOT and make its source code publicly available on GitHub.
63Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.PubMed
Gunsagar S Gulati, Jeremy Philip D'Silva, Yunhe Liu, et al.
Nat Rev Mol Cell Biol. 2025 Jan;26(1):11-31. doi: 10.1038/s41580-024-00768-2. Epub 2024 Aug 21.
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
64Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.PubMed
Shixin Li, Tianxiang Xiao, Yuanyuan Lan, et al.
Adv Sci (Weinh). 2026 Jan;13(5):e18949. doi: 10.1002/advs.202518949. Epub 2025 Dec 12.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized the study of cellular heterogeneity and tissue organization. However, the increasing scale and complexity of these data demand more powerful and integrative computational strategies. Although conventional statistical and machine learning methods remain effective in specific contexts, they face limitations in scalability, multimodal integration, and generalization. In response, artificial intelligence (AI) has emerged as a transformative force, enabling new modes of analysis and interpretation. In this review, we survey AI applications across the transcriptomic analysis workflow-from initial preprocessing through key downstream analyses such as trajectory inference, gene regulatory network reconstruction, and spatial domain detection. For each analytical task, we trace the developmental trajectory and evolving trends of AI models, summarize their advantages, limitations, and domain-specific applicability. We also highlight key innovations, ongoing challenges, and future directions. Furthermore, this review provides practical guidance to assist researchers in model selection and support developers in the design of novel AI tools. An online companion supplement providing an in-depth look at all methods discussed: https://zhanglab-kiz.github.io/review-ai-transcriptomics.
65An atlas of healthy and injured cell states and niches in the human kidney.PubMed
Blue B Lake, Rajasree Menon, Seth Winfree, et al.
Nature. 2023 Jul;619(7970):585-594. doi: 10.1038/s41586-023-05769-3. Epub 2023 Jul 19.
Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
66Integration Analysis of Single-Cell Multi-Omics Reveals Prostate Cancer Heterogeneity.PubMed
Xiaojie Bian, Wenfeng Wang, Mierxiati Abudurexiti, et al.
Adv Sci (Weinh). 2024 May;11(18):e2305724. doi: 10.1002/advs.202305724. Epub 2024 Mar 14.
Prostate cancer (PCa) is an extensive heterogeneous disease with a complex cellular ecosystem in the tumor microenvironment (TME). However, the manner in which heterogeneity is shaped by tumors and stromal cells, or vice versa, remains poorly understood. In this study, single-cell RNA sequencing, spatial transcriptomics, and bulk ATAC-sequence are integrated from a series of patients with PCa and healthy controls. A stemness subset of club cells marked with SOX9AR expression is identified, which is markedly enriched after neoadjuvant androgen-deprivation therapy (ADT). Furthermore, a subset of CD8CXCR6 T cells that function as effector T cells is markedly reduced in patients with malignant PCa. For spatial transcriptome analysis, machine learning and computational intelligence are comprehensively utilized to identify the cellular diversity of prostate cancer cells and cell-cell communication in situ. Macrophage and neutrophil state transitions along the trajectory of cancer progression are also examined. Finally, the immunosuppressive microenvironment in advanced PCa is found to be associated with the infiltration of regulatory T cells (Tregs), potentially induced by an FAP fibroblast subset. In summary, the cellular heterogeneity is delineated in the stage-specific PCa microenvironment at single-cell resolution, uncovering their reciprocal crosstalk with disease progression, which can be helpful in promoting PCa diagnosis and therapy.
67Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts.PubMed
De-Zhen Guo, Xin Zhang, Sen-Quan Zhang, et al.
Mol Cancer. 2024 Aug 2;23(1):157. doi: 10.1186/s12943-024-02062-3.
BACKGROUND: Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. METHODS: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. RESULTS: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1 metabolism subtype (Metab-subtype), TOP2A proliferation phenotype (Prol-phenotype), and S100A6 pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. CONCLUSION: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6 tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.
68Spatial multi-omic map of human myocardial infarction.PubMed
Christoph Kuppe, Ricardo O Ramirez Flores, Zhijian Li, et al.
Nature. 2022 Aug;608(7924):766-777. doi: 10.1038/s41586-022-05060-x. Epub 2022 Aug 10.
Myocardial infarction is a leading cause of death worldwide. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.
69Integrating scRNA-seq and Visium HD for the analysis of the tumor microenvironment in the progression of colorectal cancer.PubMed
Chun Wang, Mengying Lu, Cuimin Chen, et al.
Int Immunopharmacol. 2025 Jan 3;145:113752. doi: 10.1016/j.intimp.2024.113752. Epub 2024 Dec 6.
BACKGROUND: Colorectal cancer (CRC) development is a complex, multi-stage process, transitioning from normal to adenomatous tissue, and then to invasive carcinoma. Despite research, there's a knowledge gap on using high-resolution spatial omics to understand CRC's tumor microenvironment dynamics. METHODS: We used single-cell transcriptomics to study major biological changes and cell interactions in CRC progression. Additionally, high-resolution spatial transcriptomics helped us examine the spatial distribution of cells with significant pathway changes, offering insights into the tumor microenvironment's development throughout CRC stages. RESULTS: In the progression of CRC, plasma cells, neutrophils, and fibroblasts exhibit the most significant changes in hallmark pathways, while epithelial cells show the most pronounced alterations in metabolic pathways. We also identified a population of NOTUM + epithelial cells and IGHG1/3 + plasma cells that are concentrated at the boundary between normal tissue and adenomas. Pathway analysis further suggests that these NOTUM + cells activate numerous cancer-related pathways, despite the absence of significant pathological morphological changes. Additionally, we conducted a targeted drug prediction analysis to identify potential therapeutic agents for NOTUM-expressing epithelial cells. CONCLUSIONS: Analyzing scRNA-seq and Visium HD data, we found that IGHG1/3 + plasma cells and tumor-associated neutrophil (TANs) may significantly affect colorectal tissue transformation from normal to adenoma and carcinoma. These cells are concentrated at the transition between normal and adenomatous tissue. We also found NOTUM-expressing cells at the edge of normal and adenomatous areas, possibly indicating a morphological transition as normal cells evolve into adenoma cells.
70A benchmark of batch-effect correction methods for single-cell RNA sequencing data.PubMed
Hoa Thi Nhu Tran, Kok Siong Ang, Marion Chevrier, et al.
Genome Biol. 2020 Jan 16;21(1):12. doi: 10.1186/s13059-019-1850-9.
BACKGROUND: Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. RESULTS: We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. CONCLUSION: Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
71Integrating single-cell transcriptomic data across different conditions, technologies, and species.PubMed
Andrew Butler, Paul Hoffman, Peter Smibert, et al.
Nat Biotechnol. 2018 Jun;36(5):411-420. doi: 10.1038/nbt.4096. Epub 2018 Apr 2.
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
72Representation learning of single-cell RNA-seq data.PubMed
Constantin Ahlmann-Eltze, Florian Barkmann, Jan Lause, et al.
RNA. 2026 Mar 16;32(4):504-519. doi: 10.1261/rna.080889.125.
Single-cell RNA sequencing (scRNA-seq) has become a cornerstone experimental technique in tissue biology, with gene expression data for over 100 million cells available in public repositories. The high dimensionality, sparsity, and technical noise inherent to scRNA-seq data have motivated the development of a broad spectrum of representation learning approaches. These methods learn compressed, lower-dimensional representations of single-cell transcriptomes that are meant to preserve essential variation while reducing noise, and can be used for clustering, visualization, trajectory inference, and other downstream tasks. Furthermore, methods have emerged that aim to integrate data from multiple experiments by learning a common latent representation. In this review, we frame factor models, autoencoders, contrastive learning approaches, and transformer-based foundation models as distinct instances of the representation learning paradigm for scRNA-seq. We provide a coherent taxonomy of these methods that articulates their conceptual foundations, shared assumptions, and key distinctions. We also discuss benchmarking and identify major challenges and open questions that will shape the future of the field.
73Robust identification of perturbed cell types in single-cell RNA-seq data.PubMed
Phillip B Nicol, Danielle Paulson, Gege Qian, et al.
Nat Commun. 2024 Sep 1;15(1):7610. doi: 10.1038/s41467-024-51649-3.
Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.
74Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer.PubMed
Yitian Ou, Chengxing Xia, Chunwei Ye, et al.
Ann Med. 2024 Dec;56(1):2398195. doi: 10.1080/07853890.2024.2398195. Epub 2024 Sep 2.
BACKGROUND: Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages' prognosis value in PCa. METHODS: Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. RESULTS: A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa ( < 0.05). CONCLUSIONS: We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.
75Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages.PubMed
Aleksandar Obradovic, Nivedita Chowdhury, Scott M Haake, et al.
Cell. 2021 May 27;184(11):2988-3005.e16. doi: 10.1016/j.cell.2021.04.038. Epub 2021 May 20.
Clear cell renal carcinoma (ccRCC) is a heterogeneous disease with a variable post-surgical course. To assemble a comprehensive ccRCC tumor microenvironment (TME) atlas, we performed single-cell RNA sequencing (scRNA-seq) of hematopoietic and non-hematopoietic subpopulations from tumor and tumor-adjacent tissue of treatment-naive ccRCC resections. We leveraged the VIPER algorithm to quantitate single-cell protein activity and validated this approach by comparison to flow cytometry. The analysis identified key TME subpopulations, as well as their master regulators and candidate cell-cell interactions, revealing clinically relevant populations, undetectable by gene-expression analysis. Specifically, we uncovered a tumor-specific macrophage subpopulation characterized by upregulation of TREM2/APOE/C1Q, validated by spatially resolved, quantitative multispectral immunofluorescence. In a large clinical validation cohort, these markers were significantly enriched in tumors from patients who recurred following surgery. The study thus identifies TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker for ccRCC recurrence, as well as a candidate therapeutic target.
76Single-cell landscape of the tumour immune microenvironment in human gynaecologic malignancies.PubMed
Simin Yin, Sen Li, Mengyan Tu, et al.
Clin Transl Med. 2025 Nov;15(11):e70538. doi: 10.1002/ctm2.70538.
BACKGROUND: The immune microenvironment of the three most common gynaecological malignancies-tubo-ovarian cancer, endometrial cancer and cervical cancer-has not been systematically studied, limiting clinical application. METHODS: This study analyses 272 389 CD45+ immune cells by integrating publicly available single-cell RNA sequencing (scRNA-seq) data from 111 tumour and non-malignant tissue samples. We identified distinct subsets within immune cells: 11 for monocytes/macrophages, six for CD4 T cells, eight for CD8 T cells and five for B cells, detailing their distribution, prevalence and distinct functions. RESULTS: A pro-angiogenic macrophage subset linked to NF-κB signalling was associated with worse clinical outcomes and an interferon-primed macrophage subset correlated with improved survival by recruiting T cells through CXCL9/10/11 secretion, as confirmed by multi-colour immunohistochemistry. T cells exhibited dynamic roles in tubo-ovarian cancer, with CD8 Tex cells contributing to immune dysfunction and poor prognosis, while CD8 Trm cells in early-stage tumours supported immune surveillance. Additionally, we identified co-stimulatory and co-inhibitory receptor interactions and classified distinct B cell subsets with varying prognostic implications. CONCLUSIONS: This comprehensive analysis of the tumour immune microenvironment in gynaecological malignancies provides new insights into immune cell composition and function offering potential for optimising immunotherapies and improving clinical outcomes in these cancers.
77Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.PubMed
Yihong Chen, Xiangying Deng, Yin Li, et al.
Hepatology. 2024 Sep 1;80(3):536-551. doi: 10.1097/HEP.0000000000000869. Epub 2024 Mar 27.
BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS: We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC. CONCLUSIONS: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.
78Single-cell and spatial transcriptomics reveal POSTN cancer-associated fibroblasts correlated with immune suppression and tumour progression in non-small cell lung cancer.PubMed
Chao Chen, Qiang Guo, Yang Liu, et al.
Clin Transl Med. 2023 Dec;13(12):e1515. doi: 10.1002/ctm2.1515.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are potential targets for cancer therapy. Due to the heterogeneity of CAFs, the influence of CAF subpopulations on the progression of lung cancer is still unclear, which impedes the translational advances in targeting CAFs. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on tumour, paired tumour-adjacent, and normal samples from 16 non-small cell lung cancer (NSCLC) patients. CAF subpopulations were analyzed after integration with published NSCLC scRNA-seq data. SpaTial enhanced resolution omics-sequencing (Stereo-seq) was applied in tumour and tumour-adjacent samples from seven NSCLC patients to map the architecture of major cell populations in tumour microenvironment (TME). Immunohistochemistry (IHC) and multiplexed IHC (mIHC) were used to validate marker gene expression and the association of CAFs with immune infiltration in TME. RESULTS: A subcluster of myofibroblastic CAFs, POSTN CAFs, were significantly enriched in advanced tumours and presented gene expression signatures related to extracellular matrix remodeling, tumour invasion pathways and immune suppression. Stereo-seq and mIHC demonstrated that POSTN CAFs were in close localization with SPP1 macrophages and were associated with the exhausted phenotype and lower infiltration of T cells. POSTN expression or the abundance of POSTN CAFs were associated with poor prognosis of NSCLC. CONCLUSIONS: Our study identified a myofibroblastic CAF subpopulation, POSTN CAFs, which might associate with SPP1 macrophages to promote the formation of desmoplastic architecture and participate in immune suppression. Furthermore, we showed that POSTN CAFs associated with cancer progression and poor clinical outcomes and may provide new insights on the treatment of NSCLC.
79Single-Cell Profiling of Tumor Immune Microenvironment Reveals Immune Irresponsiveness in Gastric Signet-Ring Cell Carcinoma.PubMed
Jing Chen, Kuai Liu, Yikai Luo, et al.
Gastroenterology. 2023 Jul;165(1):88-103. doi: 10.1053/j.gastro.2023.03.008. Epub 2023 Mar 14.
BACKGROUND & AIMS: Gastric cancer (GC) is a major cancer type characterized by high heterogeneity in both tumor cells and the tumor immune microenvironment (TIME). One intractable GC subtype is gastric signet-ring cell carcinoma (GSRCC), which is associated with poor prognosis. However, it remains unclear what the GSRCC TIME characteristics are and how these characteristics may contribute to clinical outcomes. METHODS: We enrolled 32 patients with advanced GC of diverse subtypes and profiled their TIME using an immune-targeted single-cell profiling strategy, including (1) immune-targeted single-cell RNA sequencing (n = 20 patients) and (2) protein expression profiling by a targeted antibody panel for mass cytometry (n = 12 patients). We also generated matched V(D)J (variable, diversity, and joining gene segments) sequencing of T and B cells along CD45 immunocytes. RESULTS: We found that compared to non-GSRCC, the GSRCC TIME appears to be quiescent, where both CD4 and CD8 T cells are difficult to be mobilized, which further impairs the proper functions of B cells. CXCL13, mainly produced by follicular helper T cells, T helper type 17, and exhausted CD8 T cells, is a central coordinator of this transformation. We show that CXCL13 expression can predict the response to immune checkpoint blockade in GC patients, which may be related to its effects on tertiary lymphoid structures. CONCLUSIONS: Our study provides a comprehensive molecular portrait of immune cell compositions and cell states in advanced GC patients, highlighting adaptive immune irresponsiveness in GSRCC and a mediator role of CXCL13 in TIME. Our targeted single-cell transcriptomic and proteomic profiling represents a powerful approach for TIME-oriented translational research.
80Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model.PubMed
Zhiyong Tan, Xiaorong Chen, Jieming Zuo, et al.
J Transl Med. 2023 Mar 27;21(1):223. doi: 10.1186/s12967-023-04056-z.
BACKGROUND: The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA. METHODS: BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated. RESULTS: scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy. CONCLUSION: By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.
81Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications.PubMed
Joo Kyung Park, Hyoung-Oh Jeong, Hyemin Kim, et al.
Mol Cancer. 2024 May 3;23(1):87. doi: 10.1186/s12943-024-02003-0.
BACKGROUND: Intratumoral heterogeneity (ITH) and tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) play important roles in tumor evolution and patient outcomes. However, the precise characterization of diverse cell populations and their crosstalk associated with PDAC progression and metastasis is still challenging. METHODS: We performed single-cell RNA sequencing (scRNA-seq) of treatment-naïve primary PDAC samples with and without paired liver metastasis samples to understand the interplay between ITH and TME in the PDAC evolution and its clinical associations. RESULTS: scRNA-seq analysis revealed that even a small proportion (22%) of basal-like malignant ductal cells could lead to poor chemotherapy response and patient survival and that epithelial-mesenchymal transition programs were largely subtype-specific. The clonal homogeneity significantly increased with more prevalent and pronounced copy number gains of oncogenes, such as KRAS and ETV1, and losses of tumor suppressor genes, such as SMAD2 and MAP2K4, along PDAC progression and metastasis. Moreover, diverse immune cell populations, including naï ve SELL regulatory T cells (Tregs) and activated TIGIT Tregs, contributed to shaping immunosuppressive TMEs of PDAC through cellular interactions with malignant ductal cells in PDAC evolution. Importantly, the proportion of basal-like ductal cells negatively correlated with that of immunoreactive cell populations, such as cytotoxic T cells, but positively correlated with that of immunosuppressive cell populations, such as Tregs. CONCLUSION: We uncover that the proportion of basal-like subtype is a key determinant for chemotherapy response and patient outcome, and that PDAC clonally evolves with subtype-specific dosage changes of cancer-associated genes by forming immunosuppressive microenvironments in its progression and metastasis.
82Immune landscape of isocitrate dehydrogenase-stratified primary and recurrent human gliomas.PubMed
Pravesh Gupta, Minghao Dang, Shivangi Oberai, et al.
Neuro Oncol. 2024 Dec 5;26(12):2239-2255. doi: 10.1093/neuonc/noae139.
BACKGROUND: Human gliomas are classified using isocitrate dehydrogenase (IDH) status as a prognosticator; however, the influence of genetic differences and treatment effects on ensuing immunity remains unclear. METHODS: In this study, we used sequential single-cell transcriptomics on 144 678 and spectral cytometry on over 2 million immune cells encompassing 48 human gliomas to decipher their immune landscape. RESULTS: We identified 22 distinct immune cell types that contribute to glioma immunity. Specifically, brain-resident microglia (MG) were reduced with a concomitant increase in CD8+ T lymphocytes during glioma recurrence independent of IDH status. In contrast, IDH-wild type-associated patterns, such as an abundance of antigen-presenting cell-like MG and cytotoxic CD8+ T cells, were observed. Beyond elucidating the differences in IDH, relapse, and treatment-associated immunity, we discovered novel inflammatory MG subpopulations expressing granulysin, a cytotoxic peptide that is otherwise expressed in lymphocytes only. Furthermore, we provide a robust genomic framework for defining macrophage polarization beyond M1/M2 paradigm and reference signatures of glioma-specific tumor immune microenvironment (termed GlioTIME-36) for deconvoluting transcriptomic datasets. CONCLUSIONS: This study provides advanced optics of the human pan-glioma immune contexture as a valuable guide for translational and clinical applications.
83Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.PubMed
Guorong Chen, Hongying Qi, Li Jiang, et al.
J Transl Med. 2024 Dec 20;22(1):1121. doi: 10.1186/s12967-024-05934-w.
BACKGROUND: Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic disruptions. Tryptophan metabolism has been believed as a significant factor in UC pathogenesis, with specific metabolites influencing immune modulation and gut microbiota interactions. However, the precise regulatory mechanisms and key genes involved remain unclear. METHODS: AUCell, Ucell, and other functional enrichment algorithms were utilized to determine the activation patterns of tryptophan metabolism at the UC cell level. Differential analysis identified key genes associated with tryptophan metabolism. Five machine learning algorithms, including Random Forest, Boruta algorithm, LASSO, SVM-RFE, and GBM were integrated to identify and categorize disease-specific characteristic genes. RESULTS: We observed significant heterogeneity in tryptophan metabolism activity across cell types in UC, with the highest activity levels in macrophages and fibroblasts. Among the key tryptophan metabolism-related genes, CTSS, S100A11, and TUBB were predominantly expressed in macrophages and significantly upregulated in UC, highlighting their involvement in immune dysregulation and inflammation. Cross-analysis with bulk RNA data confirmed the consistent upregulation of these genes in UC samples, highly indicating their relevance in UC pathology and potential as targets for therapeutic intervention. CONCLUSIONS: This study is the first to reveal the heterogeneity of tryptophan metabolism at the single-cell level in UC, with macrophages emerging as key contributors to inflammatory processes. The identification of CTSS, S100A11, and TUBB as key regulators of tryptophan metabolism in UC underscores their potential as biomarkers and therapeutic targets.
84Single-cell and spatial transcriptome profiling reveal CTHRC1+ fibroblasts promote EMT through WNT5A signaling in colorectal cancer.PubMed
Yunfei Lu, Yang Chen, Zhenling Wang, et al.
J Transl Med. 2025 Mar 6;23(1):282. doi: 10.1186/s12967-025-06236-5.
BACKGROUND: Cancer-associated fibroblasts (CAFs), known for facilitating the progression and metastasis of colorectal cancer (CRC), have become a promising therapeutic target. However, the significant heterogeneity of CAFs and their intricate crosstalk with tumor cells present substantial challenges in the development of precise and effective therapeutic strategies. METHODS: Single-cell RNA sequencing (scRNA-seq) technology was used to identify various cell subtypes. Spatial transcriptomics (ST) was employed to map the spatial niches and colocalization patterns of these cell subtypes. Cell-cell interactions among these subtypes were analysed via CellChat and NicheNet software. Tumor cell invasion, migration, and proliferation were assessed through wound healing assays, transwell assays, colony formation assays, and xenograft mouse models. RESULTS: We identified a significant spatial colocalization between CTHRC1+ CAFs and a distinct subtype of malignant epithelial cells, both residing within the EMT-active spatial niche. Our results demonstrate that CTHRC1+ CAFs, as a major source of WNT5A, promote epithelial-mesenchymal transition (EMT) and enhance tumor cell invasiveness by upregulating MSLN expression in adjacent malignant epithelial cells. This signaling axis contributes significantly to CRC progression and metastasis. CONCLUSIONS: Targeting the CTHRC1+ CAF-WNT5A-MSLN signaling axis presents a promising therapeutic strategy for advanced CRC patients. Our study provides new insights into the role of CAFs in CRC progression and offers potential avenues for developing targeted therapies to disrupt this pathway.
85Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target.PubMed
Nourhan Abdelfattah, Parveen Kumar, Caiyi Wang, et al.
Nat Commun. 2022 Feb 9;13(1):767. doi: 10.1038/s41467-022-28372-y.
A major rate-limiting step in developing more effective immunotherapies for GBM is our inadequate understanding of the cellular complexity and the molecular heterogeneity of immune infiltrates in gliomas. Here, we report an integrated analysis of 201,986 human glioma, immune, and other stromal cells at the single cell level. In doing so, we discover extensive spatial and molecular heterogeneity in immune infiltrates. We identify molecular signatures for nine distinct myeloid cell subtypes, of which five are independent prognostic indicators of glioma patient survival. Furthermore, we identify S100A4 as a regulator of immune suppressive T and myeloid cells in GBM and demonstrate that deleting S100a4 in non-cancer cells is sufficient to reprogram the immune landscape and significantly improve survival. This study provides insights into spatial, molecular, and functional heterogeneity of glioma and glioma-associated immune cells and demonstrates the utility of this dataset for discovering therapeutic targets for this poorly immunogenic cancer.
86Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.PubMed
Qiang Ding, Qingyuan Xu, Yini Hong, et al.
Front Immunol. 2024 Jun 19;15:1399856. doi: 10.3389/fimmu.2024.1399856. eCollection 2024.
OBJECTIVE: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. METHODS: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. RESULTS: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. CONCLUSION: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.
87Spatial oncology: Translating contextual biology to the clinic.PubMed
Dennis Gong, Jeanna M Arbesfeld-Qiu, Ella Perrault, et al.
Cancer Cell. 2024 Oct 14;42(10):1653-1675. doi: 10.1016/j.ccell.2024.09.001. Epub 2024 Oct 3.
Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.
88Spatial Transcriptomic Technologies.PubMed
Tsai-Ying Chen, Li You, Jose Angelito U Hardillo, et al.
Cells. 2023 Aug 10;12(16):2042. doi: 10.3390/cells12162042.
Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.
89Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.PubMed
Xiujing He, Xiaowei Liu, Fengli Zuo, et al.
Semin Cancer Biol. 2023 Jan;88:187-200. doi: 10.1016/j.semcancer.2022.12.009. Epub 2022 Dec 31.
With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.
90Interpretation of T cell states from single-cell transcriptomics data using reference atlases.PubMed
Massimo Andreatta, Jesus Corria-Osorio, Sören Müller, et al.
Nat Commun. 2021 May 20;12(1):2965. doi: 10.1038/s41467-021-23324-4.
Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.