Fatemi Michael Y, Lu Yunrui, Diallo Alos B, Srinivasan Gokul, Azher Zarif L, Christensen Brock C, Salas Lucas A, Tsongalis Gregory J, Palisoul Scott M, Perreard Laurent, Kolling Fred W, Vaickus Louis J, Levy Joshua J
medRxiv. 2023 Oct 9:2023.10.09.23296700. doi: 10.1101/2023.10.09.23296700.
The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.
将深度学习方法应用于空间转录组学，在揭示基因表达模式与组织结构之间的复杂关系（这些关系与各种病理状况相关）方面已显示出前景。能够直接从组织组织形态学推断基因表达模式的深度学习方法，可以扩展在组织切片中识别空间分子标记的能力。然而，目前利用这些技术的方法受到组织制备和特征方面显著变异性的困扰，这可能会阻碍这些工具的更广泛应用。此外，在小型研究队列中使用空间转录组学训练深度学习模型仍然是一项成本高昂的工作。需要新颖的组织制备过程来提高检测的可靠性、分辨率和可扩展性。本研究调查了一种增强的样本处理工作流程对促进基于深度学习的空间转录组学评估的影响。增强的工作流程利用了Visium CytAssist检测的灵活性，允许对组织切片进行自动苏木精-伊红（H&E）染色（例如徕卡邦德染色）、40倍分辨率的全切片成像，以及在单个捕获区域内对来自多个患者的组织切片进行多重分析以进行空间转录组学分析。我们使用一组13名pT3期结直肠癌（CRC）患者，比较了在使用增强工作流程制备的玻片上训练的深度学习模型与利用手动组织染色和组织切片标准成像的传统工作流程的效果。利用Inceptionv3神经网络，我们旨在预测跨匹配连续组织切片的基因表达模式，每个切片都源自不同的工作流程，但基于持久的组织结构进行对齐。研究结果表明，增强的工作流程明显优于传统的空间转录组学工作流程。从增强组织切片预测的基因表达谱也产生了与真实情况在拓扑结构上更一致的表达模式。这导致在确定与不同空间结构相关的生物标志物时统计精度提高。这些见解有可能通过扩大与转移和复发相关的空间分子标记范围来提高诊断和预后生物标志物的检测。未来的工作将进一步探索这些发现，以丰富我们对各种疾病的理解，并更细致地揭示分子途径。将深度学习与空间转录组学相结合为丰富我们对肿瘤生物学的理解和改善临床结果提供了一条引人注目的途径。然而，为了获得最高保真度的结果，有效的样本处理至关重要，促进组织技术人员、病理学家和基因组学专家之间的合作对于开启这个由空间转录组学驱动的癌症研究新时代至关重要。
