SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data

Welch, Joshua D.; Hartemink, Alexander J.; Prins, Jan F.

doi:10.1101/047845

Cited by 48 publications

(69 citation statements)

References 24 publications

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“…To date, several computational methods have been reported that profile developmental processes, such as Monocle (Trapnell et al 2014), Wanderlust (Bendall et al 2014), Wishbone (Setty et al 2016), SLICER (Welch et al 2016), Diffusion Pseudotime , Destiny (Angerer et al 2016), and SCUBA (Marco et al 2014). These methods attempt to order cells into smooth continuous spatiotemporal trajectories to model development.…”

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confidence: 99%

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

et al. 2017

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The stochastic dynamics and regulatory mechanisms that govern differentiation of individual human neural precursor cells (NPC) into mature neurons are currently not fully understood. Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC subtypes and critical developmental stages of alternative lineage specifications. This study comprises an unsupervised, high-resolution strategy for identifying cell developmental bifurcations, tracking the stochastic transcript kinetics of the subpopulations, elucidating regulatory networks, and finding key regulators. Our data revealed the bifurcation and developmental tracks of the two NPC subpopulations, and we captured an early (24 h) transition phase that leads to alternative neuronal specifications. The consequent up-regulation and down-regulation of stageand subpopulation-specific gene groups during the course of maturation revealed biological insights with regard to key regulatory transcription factors and lincRNAs that control cellular programs in the identified neuronal subpopulations.

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confidence: 99%

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

et al. 2017

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“…Interesting future directions of research include extending the model to align manifolds with dimensionality higher than one, as well as adapting the method for cell populations whose cells fall into discrete clusters rather than along one continuous spectrum. In addition, our model does not explicitly account for branching trajectories, which can arise in biological processes with multiple outcomes [3,9].…”

Section: Discussionmentioning

confidence: 99%

“…Using SLICER, a method we previously developed [9], we visualized each type of data as a twodimensional projection and inferred a one-dimensional ordering for the cells. The 2D projections show All rights reserved.…”

Section: Single Cell Transcriptome and Epigenome Data Show Common Modmentioning

confidence: 99%

“…In such experiments, each sequenced cell is assumed to be at one point in the process, and sequencing enough cells can reveal the progression of gene expression changes that occur during the process [8,9]. More recently, several experimental techniques for performing single cell epigenetic have been developed [10][11][12][13][14][15][16][17], and initial analyses have demonstrated that single cell epigenetic data can be used to elucidate the series of changes in a sequential process [16,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Welch

Hartemink

Prins

2017

Preprint

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Single cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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“…networks , diffusion maps , TSCAN (Ji and Ji, 2016), SLICER (Welch et al, 2016) and SCOUP (Matsumoto and Kiryu, 2016). These various types of pseudotime analyses allow the identification of regulators of temporal processes and of transient events that are obscured by bulk-derived data.…”

Section: The Basics Of Scrna-seq Analysismentioning

confidence: 99%

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

2017

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In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how singlecell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

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SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data

Cited by 48 publications

References 24 publications

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

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

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