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Open AccessJournal ArticleDOI

The single-cell transcriptional landscape of mammalian organogenesis

TLDR
A cell atlas of mouse organogenesis provides a global view of developmental processes occurring during this critical period, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
Abstract
Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. Data from single-cell combinatorial-indexing RNA-sequencing analysis of 2 million cells from mouse embryos between embryonic days 9.5 and 13.5 are compiled in a cell atlas of mouse organogenesis, which provides a global view of developmental processes occurring during this critical period.

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SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells.

TL;DR: This work investigates ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue and in cells derived from subsegmental bronchial branches by single nuclei and single cell RNA sequencing, suggesting increased vulnerability for SARS‐CoV‐2 infection.
Journal ArticleDOI

Eleven grand challenges in single-cell data science

David Lähnemann, +71 more
- 07 Feb 2020 - 
TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.
References
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Journal ArticleDOI

STAR: ultrafast universal RNA-seq aligner

TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Journal ArticleDOI

HTSeq—a Python framework to work with high-throughput sequencing data

TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Journal ArticleDOI

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Journal ArticleDOI

Notes on continuous stochastic phenomena.

TL;DR: Two problems arising in the two and three-dimensional cases of stochastic phenomena which are distributed in space of two or more dimensions are considered.
Posted Content

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

TL;DR: The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.
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