With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly. Spatial transcriptomics provides spatial location and pattern information about cells in tissue sections at single cell resolution. Cell type classification of spatially-resolved cells can also be inferred by matching the spatial transcriptomics data to reference single cell RNA-sequencing (scRNA-seq) data with cell types determined by their gene expression profiles. However, robust cell type matching of the spatial cells is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that while matching results of individual algorithms vary to some degree, they also show agreement to some extent. We present two ensembl meta-analysis strategies to combine the individual matching results and share the consensus matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spot-based spatial data analysis using SSAM, allowing segmentation-free cell type assignment.
Tissue biology involves an intricate balance between cell – intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single – cell profiling methods, such as single – cell RNA – seq (scRNA – seq), and histology imaging data, such as Hematoxylin – and – Eosin (H&E) stains. While single – cell profiles provide rich molecular information, they can be challenging to collect routinely and do not have spatial resolution. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage adversarial machine learning to develop SCHAF (Single – Cell omics from Histology Analysis Framework), to generate a tissue sample ′ s spatially – resolved single – cell omics dataset from its H&E histology image. We demonstrate SCHAF on two types of human tumors – from lung and metastatic breast cancer – training with matched samples analyzed by both sc/snRNA – seq and by H&E staining. SCHAF generated appropriate single – cell profiles from histology images in test data, related them spatially, and compared well to ground – truth scRNA – Seq, expert pathologist annotations, or direct MERFISH measurements. SCHAF opens the way to next – generation H&E2.0 analyses and an integrated understanding of cell and tissue biology in health and disease.
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