Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on s...

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Published in:Trends in molecular medicine Vol. 27; no. 12; pp. 1135 - 1158
Main Authors: Shen, Sophie, Sun, Yuliangzi, Matsumoto, Maika, Shim, Woo Jun, Sinniah, Enakshi, Wilson, Sean B., Werner, Tessa, Wu, Zhixuan, Bradford, Stephen T., Hudson, James, Little, Melissa H., Powell, Joseph, Nguyen, Quan, Palpant, Nathan J.
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.12.2021
Subjects:
cell differentiation
Cell Differentiation - genetics
Genomics
Humans
pluripotent stem cell
Pluripotent Stem Cells - physiology
scRNA-seq
Single-Cell Analysis - methods
single-cell genomics
Transcriptome
single-cell genomics
cell differentiation
scRNA-seq
pluripotent stem cell
ISSN:1471-4914, 1471-499X, 1471-499X
Online Access:Get full text
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Abstract Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation. Single-cell genomics is a growing technology platform that is poised to dramatically upscale the discovery and translation of stem cell science through the use of emerging wet and dry laboratory tools.Novel sample multiplexing strategies for single-cell transcriptomic assays are enabling efficient generation of large datasets to study stem cell biology and accelerate the development and optimization of induced pluripotent stem cell (iPSC) protocols.Emerging next-generation computational strategies harness growing data consortia to deduce regulatory factors controlling differentiation, intercellular communication, and lineage relationships between cells.Unsupervised strategies to integrate and compare cell types between datasets provide a means to leverage existing comprehensive atlases of in vivo development to annotate and benchmark cell types derived from in vitro differentiation protocols.
AbstractList Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation. Single-cell genomics is a growing technology platform that is poised to dramatically upscale the discovery and translation of stem cell science through the use of emerging wet and dry laboratory tools.Novel sample multiplexing strategies for single-cell transcriptomic assays are enabling efficient generation of large datasets to study stem cell biology and accelerate the development and optimization of induced pluripotent stem cell (iPSC) protocols.Emerging next-generation computational strategies harness growing data consortia to deduce regulatory factors controlling differentiation, intercellular communication, and lineage relationships between cells.Unsupervised strategies to integrate and compare cell types between datasets provide a means to leverage existing comprehensive atlases of in vivo development to annotate and benchmark cell types derived from in vitro differentiation protocols.
Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.
Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.
Author Sinniah, Enakshi
Werner, Tessa
Wilson, Sean B.
Little, Melissa H.
Palpant, Nathan J.
Wu, Zhixuan
Powell, Joseph
Hudson, James
Shen, Sophie
Sun, Yuliangzi
Shim, Woo Jun
Matsumoto, Maika
Nguyen, Quan
Bradford, Stephen T.
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  givenname: Joseph
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  givenname: Nathan J.
  orcidid: 0000-0002-9334-8107
  surname: Palpant
  fullname: Palpant, Nathan J.
  email: n.palpant@uq.edu.au
  organization: Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
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Keywords single-cell genomics
cell differentiation
scRNA-seq
pluripotent stem cell
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Snippet Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review...
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SubjectTerms cell differentiation
Cell Differentiation - genetics
Genomics
Humans
pluripotent stem cell
Pluripotent Stem Cells - physiology
scRNA-seq
Single-Cell Analysis - methods
single-cell genomics
Transcriptome
Title Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1471491421002549
https://dx.doi.org/10.1016/j.molmed.2021.09.006
https://www.ncbi.nlm.nih.gov/pubmed/34657800
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