The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Cole Trapnell,Davide Cacchiarelli,Davide Cacchiarelli,Jonna Grimsby,Prapti Pokharel,Shuqiang Li,Michael A. Morse,Michael A. Morse,Niall J. Lennon,Kenneth J. Livak,Tarjei S. Mikkelsen,Tarjei S. Mikkelsen,John L. Rinn,John L. Rinn,John L. Rinn +14 more
TLDR
Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.Abstract:
Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.read more
Citations
More filters
Journal ArticleDOI
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Journal ArticleDOI
Spatial reconstruction of single-cell gene expression data
TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.
Journal ArticleDOI
SCANPY: large-scale single-cell gene expression data analysis
TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
Journal ArticleDOI
Reversed graph embedding resolves complex single-cell trajectories.
TL;DR: Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner, is applied to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.
Journal ArticleDOI
A survey of best practices for RNA-seq data analysis
Ana Conesa,Pedro Madrigal,Pedro Madrigal,Sonia Tarazona,David Gomez-Cabrero,Alejandra Cervera,Andrew McPherson,Michał Wojciech Szcześniak,Daniel J. Gaffney,Laura L. Elo,Xuegong Zhang,Ali Mortazavi +11 more
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
References
More filters
Journal ArticleDOI
Fast gapped-read alignment with Bowtie 2
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Journal ArticleDOI
BEDTools: a flexible suite of utilities for comparing genomic features
Aaron R. Quinlan,Ira M. Hall +1 more
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Journal ArticleDOI
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions
Daehwan Kim,Daehwan Kim,Geo Pertea,Cole Trapnell,Cole Trapnell,Harold Pimentel,Kelley Ryan Matthew,Steven L. Salzberg,Steven L. Salzberg +8 more
TL;DR: TopHat2 is described, which incorporates many significant enhancements to TopHat, and combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes.
Journal ArticleDOI
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Cole Trapnell,Adam Roberts,Loyal A. Goff,Loyal A. Goff,Loyal A. Goff,Geo Pertea,Daehwan Kim,Daehwan Kim,David R. Kelley,David R. Kelley,Harold Pimentel,Steven L. Salzberg,John L. Rinn,John L. Rinn,Lior Pachter +14 more
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Related Papers (5)
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more