A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.

By Sonia Nestorowa, Fiona Kathryn Hamey, Blanca Pijuan Sala, E. Diamanti, Mairi S. Shepherd, E. Laurenti, Nicola K. Wilson, D. Kent, Berthold Göttgens. Blood, 2016

688 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c0bf21af5044b74c43b1214dca736e5975f2b303

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Citations in the corpus

Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “We applied PAGA to simulated data (Supplemental Note 5) for this system and three experimental datasets: 2,730 cells measured using MARS-seq [24], 1,654 cells measured using Smart-seq2 [25] and 44,802 cells from a 10x Genomics protocol [26].”
Integrative single-cell analysis (2019, background): “Paul et al. 41 and Nestorowa et al. 42 applied this workflow to profile early murine haematopoietic progenitors, revealing the immunophenotypes of transcriptionally defined cell-type clusters.”
Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “We obtained a read count matrix for the SMART-Seq2 data set (Nestorowa et al. ) 37 under the GEO accession GSE81682, and considered 765 annotated progenitors cells expressing at least 4,000 genes.”
Fundamental limits on dynamic inference from single-cell snapshots (2017, result): “This contrasts with previous scRNA-seq datasets of hematopoietic progenitors that included a composite of many subpopulations (38) or used complex FACS gates to exclude early progenitors (29).”
Large-scale simultaneous measurement of epitopes and transcriptomes in single cells (2017, unknown): “Reverse transcription and template switch is performed in bulk after emulsion breakage (7).”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “The two data batches of this dataset were acquired by Nestorowa et al. [44] using the SMART-seq2 protocol (GSE81682, 1920 cells), and Paul et al. [45] using the MARS-seq protocol (GSE72857, 10,368 cells).”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, methodology): “[12]: GSE81682, MARS-seq platform haematopoietic data by Paul et al.”
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama (2019, methodology): “One of the panoramas identified by Scanorama consists of two datasets of hematopoietic stem cells (HSCs) 18,19 which, once corrected for batch effects and plotted along the first two principal components, reconstruct the expected HSC differentiation hierarchy (Supplementary Fig.”
Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, background): “However, recent state maps from scSeq (9), as well as clonal studies using barcodes (10) and single-cell culture (11), suggest that the traditional intermediate cell types are internally heterogeneous in state and fate potential, with HSPCs lying along a continuum of states rather than a stepwise hi”
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2019, unknown): “We applied PAGA to simulated data (Additional file 1: Note 5) for this system and three experimental datasets: 2730 cells measured using MARS-seq [24], 1654 cells measured using Smart-seq2 [25], and 44,802 cells from a 10× Genomics protocol [26].”
The Human Cell Atlas (2017, background): “…differentiation (Bendall et al., 2014), and from single-cell RNA expression during myogenesis in vitro (Trapnell et al., 2014), early hematopoiesis (Nestorowa et al., 2016), neurogenesis in vivo (Habib et al., 2016; Shin et al., 2015), and reprogramming from fibroblasts to neurons (Treutlein et…”