Optical Cell Tagging for Spatially Resolved Single‐Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

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SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics

10.1101/2021.04.08.438978 ◽

2021 ◽

Author(s):

Michael E Nelson ◽

Simone G Riva ◽

Ann Cvejic

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Marker Gene ◽

Marker Genes ◽

Sequencing Data ◽

Computational Framework ◽

Data Set ◽

Spatially Resolved ◽

Single Cell Rna Sequencing ◽

The Given

Spatial transcriptomics is revolutionising the study of single-cell RNA and tissue-wide cell heterogeneity, but few robust methods connecting spatially resolved cells to so-called marker genes from single-cell RNA sequencing, which generate significant insight gleaned from spatial methods, exist. Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA sequencing data for spatial transcriptomics approaches. SMaSH extracts robust and biologically well-motivated marker genes, which characterise the given data-set better than existing and limited computational approaches for global marker gene calculation.

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Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis

Nature Communications ◽

10.1038/s41467-021-21892-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Madhav Mantri ◽

Gaetano J. Scuderi ◽

Roozbeh Abedini-Nassab ◽

Michael F. Z. Wang ◽

David McKellar ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cellular Differentiation ◽

Spatial Organization ◽

Spatial Information ◽

Cellular Interactions ◽

Chicken Heart ◽

Spatially Resolved ◽

Expression Of Genes ◽

Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about tissue morphology and cellular interactions. Here, we combine single-cell and spatial transcriptomics with algorithms for data integration to study the development of the chicken heart from the early to late four-chambered heart stage. We create a census of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions in cardiac lineages defines transcriptional differences between epithelial and mesenchymal cells within the epicardial lineage. Using spatially resolved expression analysis, we identify anatomically restricted expression programs, including expression of genes implicated in congenital heart disease. Last, we discover a persistent enrichment of the small, secreted peptide, thymosin beta-4, throughout coronary vascular development. Overall, our study identifies an intricate interplay between cellular differentiation and morphogenesis.

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Faculty Opinions recommendation of XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.739974427.793585290 ◽

2021 ◽

Author(s):

Vijay Tiwari ◽

Deborah Lavin

Keyword(s):

Tumor Microenvironment ◽

Single Cell ◽

Rna Sequencing ◽

Spatially Resolved ◽

Single Cell Rna Sequencing

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Spatiotemporal single-cell RNA sequencing of developing hearts reveals interplay between cellular differentiation and morphogenesis

10.1101/2020.05.03.065102 ◽

2020 ◽

Author(s):

Madhav Mantri ◽

Gaetano J. Scuderi ◽

Roozbeh Abedini Nassab ◽

Michael F.Z. Wang ◽

David McKellar ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cellular Differentiation ◽

Spatial Organization ◽

Spatial Information ◽

Cardiac Development ◽

Cellular Interactions ◽

Chicken Heart ◽

Spatially Resolved ◽

Single Cell Rna Sequencing

ABSTRACTSingle-cell RNA sequencing is a powerful tool to study developmental biology but does not preserve spatial information about cellular interactions and tissue morphology. Here, we combined single-cell and spatial transcriptomics with new algorithms for data integration to study the early development of the chicken heart. We collected data from four key ventricular development stages, ranging from the early chamber formation stage to the late four-chambered stage. We created an atlas of the diverse cellular lineages in developing hearts, their spatial organization, and their interactions during development. Spatial mapping of differentiation transitions revealed the intricate interplay between cellular differentiation and morphogenesis in cardiac cellular lineages. Using spatially resolved expression analysis, we identified anatomically restricted gene expression programs. Last, we discovered a stage-dependent role for the small secreted peptide, thymosin beta-4, in the coordination of multi-lineage cellular populations. Overall, our study identifies key stage-specific regulatory programs that govern cardiac development.

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