Understanding development and stem cells using single cell-based analyses of gene expression

Kumar, Pavithra; Tan, Yuqi; Cahan, Patrick

doi:10.1242/dev.133058

Cited by 118 publications

(80 citation statements)

References 136 publications

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“…Thus, improved resolution when characterizing gene expression patterns at the single cell level in the CNS may be translated into a better understanding of the brain in both health and disease. The desired, extensive gene expression lists are now technologically possible to obtain via single cell transcriptomics, as evidenced by recent scRNA-seq studies of the mouse cortex [57, 58]. …”

Section: Molecular Taxonomy Of the Brain At The Single Cell Levelmentioning

confidence: 99%

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Ofengeim

Γιαγτζόγλου

Huh

et al. 2017

Trends in Molecular Medicine

118

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Single cell RNA sequencing (scRNA-seq) is an exciting new technology allowing the analysis of transcriptomes from individual cells, and is ideally suited to address the inherent complexity and dynamics of the central nervous system. ScRNA-seq has already been applied to the study of molecular taxonomy of the brain. This work has paved the way to expanding our understanding of nervous systems and to providing insights into cellular susceptibilities and molecular mechanisms in neurological and neurodegenerative diseases. Here, we discuss the recent progress and the challenges in applying this technology to advance our understanding of the brain. We favor the application of scRNA-seq in the discovery of targets and biomarkers as a new approach in developing novel therapeutics for the treatment of neurodegenerative diseases.

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Section: Molecular Taxonomy Of the Brain At The Single Cell Levelmentioning

confidence: 99%

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Ofengeim

Γιαγτζόγλου

Huh

et al. 2017

Trends in Molecular Medicine

118

View full text Add to dashboard Cite

show abstract

“…Principal component analysis (PCA) is widely used to identify genes that vary across the sampled single cells. Cell relationships can then be visualized in two-dimensional space based on the expression of these genes, for example using tSNE or forcedirected graphs, and clustered using a variety of different algorithms, such as BackSPIN, K-means and others (Kumar et al, 2017;Kolodziejczyk et al, 2015). A major challenge in deconstructing cell composition is to understand the source of heterogeneity in the dataset.…”

Section: Defining the Cellular Composition Of Organoidsmentioning

confidence: 99%

“…In this section, we summarise the most common methods for cell capture, for preparation of the cDNA and for cell barcoding. For greater detail on each capture and chemistry method, we refer the reader to two recent reviews (Kumar et al, 2017;Kolodziejczyk et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Human organomics: a fresh approach to understanding human development using single-cell transcriptomics

Camp

Treutlein

2017

Development

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Innovative methods designed to recapitulate human organogenesis from pluripotent stem cells provide a means to explore human developmental biology. New technologies to sequence and analyze single-cell transcriptomes can deconstruct these 'organoids' into constituent parts, and reconstruct lineage trajectories during cell differentiation. In this Spotlight article we summarize the different approaches to performing single-cell transcriptomics on organoids, and discuss the opportunities and challenges of applying these techniques to generate organ-level, mechanistic models of human development and disease. Together, these technologies will move past characterization to the prediction of human developmental and disease-related phenomena.

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“…A single scRNA-seq snapshot can be used to infer lineage relationships between cell types and states 2 . Snapshot scRNAseq studies have been used to investigate multiple aspects of development, including the early embryo, blood, differentareas of the brain and more 3 . Even though snapshot scRNAseq can provide novel insights into development, it has recognized limits 4 , including that cell populations that appear earlier or later than the sampling time cannot be studied.…”

Section: Introductionmentioning

confidence: 99%

Tempora: cell trajectory inference using time-series single-cell RNA sequencing data

Tran

Bader

2019

Preprint

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Single-cell RNA sequencing (scRNAseq) can map cell types, states and transitions during dynamic biological processes such as development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNAseq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNAseq data. In performance comparison tests, Tempora accurately inferred developmental lineages in human skeletal myoblast differentiation and murine cerebral cortex development, beating state of the art methods. Tempora uses biological pathway information to help identify cell type relationships and can identify important time-dependent pathways to help interpret the inferred trajectory. Our results demonstrate the utility of time information to supervise trajectory inference for scRNA-seq based analysis.

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Understanding development and stem cells using single cell-based analyses of gene expression

Cited by 118 publications

References 136 publications

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Human organomics: a fresh approach to understanding human development using single-cell transcriptomics

Tempora: cell trajectory inference using time-series single-cell RNA sequencing data

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