Matrix Biology PlusSystems BiologyPeer-reviewed journal article
Extracellular matrix gene expression signatures as cell type and cell state identifiers
Fabio SacheriD, Christian FeregrinoiD, Patrick TschoppiD, Collin Y. EwaldiD
Abstract
Transcriptomic signatures based on cellular mRNA expression profiles can be used to categorize cell types and states. Yet whether different functional groups of genes perform better or worse in this process remains largely unexplored. Here we test the core matrisome - that is, all genes coding for structural proteins of the extracellular matrix - for its ability to delineate distinct cell types in embryonic single-cell RNA-sequencing (scRNA-seq) data. We show that even though expressed core matrisome genes correspond to less than 2% of an entire cellular transcriptome, their RNA expression levels suffice to recapitulate important aspects of cell type-specific clustering. Notably, using scRNA-seq data from the embryonic limb, we demonstrate that core matrisome gene expression outperforms random gene subsets of similar sizes and can match and exceed the predictive power of transcription factors. While transcription factor signatures generally perform better in predicting cell types at early stages of chicken and mouse limb development, i.e., when cells are less differentiated, the information content of the core matrisome signature increases in more differentiated cells. Our findings suggest that each cell type produces its own unique extracellular matrix, or matreotype, which becomes progressively more refined and cell type-specific as embryonic tissues mature. HighlightsO_LICell types produce unique extracellular matrix compositions C_LIO_LIDynamic extracellular matrix gene expression profiles hold predictive power for cell type and cell state identification C_LI
- organism: mouse
- organism: multiple species
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 23
- FWCI
- 1.41
- Field percentile
- top 19%
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