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

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.

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

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.

scholarly journals XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

2021 ◽  
Vol 7 (17) ◽  
pp. eabg4755
Author(s):  
Youjin Lee ◽  
Derek Bogdanoff ◽  
Yutong Wang ◽  
George C. Hartoularos ◽  
Jonathan M. Woo ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spatial Organization ◽  
Cell Types ◽  
Mouse Tumor ◽  
Spatially Resolved ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
A Cell ◽  
Anatomical Space

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.

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

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.

Optical Cell Tagging for Spatially Resolved Single‐Cell RNA Sequencing

2021 ◽  
Author(s):  
Qi Tang ◽  
Lu Liu ◽  
Yilan Guo ◽  
Xu Zhang ◽  
Shaoran Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Optical Cell ◽  
Spatially Resolved ◽  
Single Cell Rna Sequencing

scholarly journals Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly

Cell Research ◽  
2020 ◽  
Vol 30 (9) ◽  
pp. 763-778 ◽  
Author(s):  
Xianwen Ren ◽  
Guojie Zhong ◽  
Qiming Zhang ◽  
Lei Zhang ◽  
Yujie Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Self Assembly ◽  
Spatial Organization ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Rna Degradation ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cell Rna Sequencing ◽  
A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

scholarly journals Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Rna Degradation ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cell Rna Sequencing ◽  
A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

scholarly journals Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

2017 ◽  
Author(s):  
Jiarui Ding ◽  
Anne Condon ◽  
Sohrab P. Shah
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spatial Organization ◽  
Mapping Function ◽  
Cell Types ◽  
Generative Models ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Data Points ◽  
Low Dimensional

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data, or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbour structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.

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