Biology subjects

Mantri, M.

Publications and source records attributed to Mantri, M..

3 recordsLinked to original sources

bioRxiv2020

Strength in numbers: Large-scale integration of single-cell transcriptomic data revealsrare, transient muscle progenitor cell states in muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not provide spatial information that is needed to understand the context in which myogenic differentiation occurs. Here, we demonstrate how large-scale integration of new and public single-cell and spatial transcriptomic data can overcome these limitations. We created a large-scale single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 79 public single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting compendium includes nearly 350,000 cells and spans a wide range of ages, injury, and repair conditions. Combined, these data enabled identification of the predominant cell types in skeletal muscle with robust, consensus gene expression profiles, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors marked by stem potential, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem-cell quiescence to myofiber maturation and identified rare, short-lived transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. This analysis identified the temporal variation in the colocalization of immune cell subtype interactions with myogenic progenitors during injury recovery. We provide a public web tool to enable interactive exploration and visualization of this rich single-cell transcriptomic resource. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

genomics↗

bioRxiv2020

Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Single-cell RNA sequencing (scRNA-seq) enables the study of cell biology with high resolution. scRNA-seq expression analyses rely on the availability of a high quality annotation of genes in the genome. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, gene annotations often fail to cover the full transcriptome of every cell type at every stage of development, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant information beyond the scope of the best available gene annotation. This is achieved by performing single-cell expression analysis on any region in the genome for which transcriptional products are detected. Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

bioinformatics↗

bioRxiv2020

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

Single-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.

genomics↗