The single cell transcriptional landscape of mammalian organogenesis

By Junyue Cao, M. Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, Stefan Mundlos, Lena Christiansen, Frank Steemers and 2 more. 2019

3,661 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019, methodology): “For instance, t-SNE is widely used in the bioinformatics community in areas such as single-cell transcriptomics (Macosko et al. 2015; Cao et al. 2019; Tasic et al. 2018), human genetics (Hirata et al. 2019), metagenomic assembly (Beaulaurier et al. 2018), spatial organization of microbial…”
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays (2021, positive): “These stages includemost of the key events inmouse organogenesis (Cao et al., 2019).”
The specious art of single-cell genomics (2021, methodology): “Trajectory Inference & Continuous Relationships Trajectory inference and pseudotime tasks, such as in RNA velocity [23] or Monocle [22, 24] workflows, focus on local, continuous relationships for inference and calculating pseudotime coordinates.”
Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “Monocle3 [40] was employed to perform trajectory inference on the same DLPFC slice (#151,673) with the low-dimensional representations from Seurat, stLearn, DeepST, STAGATE, and SEDR (Additional file 1: Supplementary methods).”
A human cell atlas of fetal gene expression (2020, methodology): “Below, we leverage integrationwith amouse atlas of organogenesis (11) to investigate the conserved program of blood cell specification and developmental origins of microglia and macrophages.”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, positive): “Speci ﬁ cally, PSTS constructed more meaningful trajectories compared to Slingshot and Monocle3 (the method used by SPATA 31 ) (Fig.”
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data (2022, methodology): “Cell type annotation methods can be categorized into three types: (1) annotation using marker genes, (2) annotation using correlation-based methods and (3) annotation by supervised classification(5).”
A molecular cell atlas of the human lung from single cell RNA sequencing (2019, background): “The 58 identified cell populations included 15 epithelial (clusters 1-15), 9 endothelial (16-24), 9 stromal (25-33), and 25 immune (34-58) populations, greater than the number of classical cell types in each compartment (11 epithelial, 5 endothelial, 7 stromal, 20 immune, and 2 neuronal cell types) ”