Khan Muiz, Arslanturk Suzan, Draghici Sorin
Department of Computer Science, Wayne State University, Detroit, 48202 Michigan, United States.
Advaita Bioinformatics, Ann Arbor, 48105 Michigan, United States.
Nucleic Acids Res. 2025 Jun 20;53(12). doi: 10.1093/nar/gkaf536.
Spatial data acquisition technologies enable high-throughput quantification of molecular expression in tissue sections maintaining spatial context information. However, performing downstream analysis on a whole tissue section requires the alignment and integration of multiple tissue slices. This is a nontrivial task due to tissue heterogeneity and plasticity. Although manual solutions exist, they are time-consuming and require technical expertise. Hence, automated and robust alignment and integration of multiple slices within and across datasets, individuals, and experiments becomes essential. This study aims to (i) present a comprehensive review of methodologies for spatial transcriptomics (ST) data alignment and integration, (ii) explain the problem, its scope and challenges, and (iii) propose a general pipeline. We review 24 tools addressing multi-slice ST alignment and integration, and tackling key challenges through downstream validation. Tools focusing solely on single-slice ST analyses or multi-omics integration are excluded. We categorize these approaches by methodology (statistical mapping, image processing and registration, and graph-based) in accordance with the generalized pipeline. We evaluate their strengths, limitations, and real-world applications based on task scope and their potential to advance biological insights. Despite improved spatial resolution and 3D tissue reconstruction, significant challenges persist in achieving robust alignment and integration across heterogeneous tissue slices.
空间数据采集技术能够在保持空间上下文信息的情况下，对组织切片中的分子表达进行高通量定量分析。然而，对整个组织切片进行下游分析需要对多个组织切片进行对齐和整合。由于组织的异质性和可塑性，这是一项具有挑战性的任务。虽然存在手动解决方案，但它们既耗时又需要专业技术知识。因此，在数据集、个体和实验内部及之间对多个切片进行自动化且可靠的对齐和整合变得至关重要。本研究旨在：（i）全面综述空间转录组学（ST）数据对齐和整合的方法；（ii）解释该问题、其范围和挑战；（iii）提出一个通用流程。我们综述了24种用于多切片ST对齐和整合的工具，并通过下游验证来应对关键挑战。仅专注于单切片ST分析或多组学整合的工具被排除在外。我们根据广义流程，按方法（统计映射、图像处理与配准以及基于图的方法）对这些方法进行分类。我们根据任务范围及其推进生物学见解的潜力，评估它们的优势、局限性和实际应用。尽管空间分辨率和三维组织重建有所改进，但在实现跨异质组织切片的可靠对齐和整合方面，仍存在重大挑战。
