Institute of Chemistry, Academia Sinica, Taipei, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Genomics Research Center, Academia Sinica, Taipei, Taiwan.
Mol Cell Proteomics. 2024 Jul;23(7):100792. doi: 10.1016/j.mcpro.2024.100792. Epub 2024 May 27.
Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8, CD4 T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 μg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundance immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25-3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (such as C-type lectin receptor signaling, and chemokine signaling), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC model. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.
浸润肿瘤微环境 (TME) 的免疫细胞在塑造癌症发展以及影响临床结果和治疗反应方面发挥着关键作用。然而,在临床标本中获得对肿瘤浸润免疫的全面蛋白质组学快照通常受到小样本量和 TME 中免疫浸润细胞比例低的限制。为了能够对微尺度组织进行深入和高度敏感的分析,我们建立了一种免疫细胞富集文库辅助的数据非依赖性采集质谱 (DIA-MS) 策略。首先,从鼠肠系膜淋巴结 (MLN) 中分离的 CD8、CD4 T 淋巴细胞、B 淋巴细胞、自然杀伤细胞、树突状细胞和巨噬细胞的分化标记物中建立了 6 种免疫细胞亚型特异性光谱文库,涵盖了 7815 个具有表面标记物和免疫细胞富集蛋白的蛋白质组。通过单次 DIA 对来自鼠结直肠癌 (CRC) 模型的 1μg 组织蛋白进行了微尺度免疫蛋白质组学分析,证明了微尺度免疫蛋白质组学分析的可行性;与直接 DIA 分析相比 (6978 个蛋白质),免疫细胞富集文库增加了对 7419 个蛋白质的定量覆盖。这种增强作用使 841 个与免疫功能相关的蛋白质能够被映射,并且能够专门鉴定许多低丰度的免疫蛋白质,如 CD1D1 和 CD244,从而展示了对免疫景观分析的高灵敏度。该方法用于表征 CRC 模型中的 MLN,旨在阐明它们在 TME 中参与癌症发展的机制。即使在肿瘤中免疫细胞浸润的比例很低 (0.25-3%),我们的结果也表明适应性免疫信号通路 (如 C 型凝集素受体信号和趋化因子信号)、T 细胞受体信号和 Th1/Th2/Th17 细胞分化的下调,表明 CRC 模型 MLN 中的免疫抑制状态。使用免疫细胞富集文库的 DIA 方法展示了深度覆盖和高灵敏度,这可以促进对微尺度样本的免疫蛋白质组学景观的阐明。