Using single‐cell genomics to understand developmental processes and cell fate decisions

Griffiths, J. B.; Scialdone, Antonio; Marioni, John C.

doi:10.15252/msb.20178046

Cited by 212 publications

(151 citation statements)

References 95 publications

(104 reference statements)

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“…The ordering of cells along these paths is described by a pseudotime variable. While this variable is related to transcriptional distances from a root cell, it is often interpreted as a proxy for developmental time (Moignard et al, 2015;Haghverdi et al, 2016;Fischer et al, 2018;Griffiths et al, 2018). Since Monocle (Trapnell et al, 2014) and Wanderlust (Bendall et al, 2014) established the TI field, the number of available methods has exploded.…”

Section: Trajectory Analysis Trajectory Inferencementioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,597

1,386

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Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

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Section: Trajectory Analysis Trajectory Inferencementioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,597

1,386

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“…In fact, the authors evidenced that pluripotent ground-state should be characterized by female cells expressing XIST also having both X chromosomes active and female to male dosage compensation ( Figure 4B). Cellular fate and differentiation during development have been investigated by different researchers with a single-cell approach [160]. In this context, Rizvi and colleagues proposed an algorithm to study transcriptional regulation to identify cell identity over time and therefore cell differentiation in response to specific stimuli.…”

Section: Lncrnas In Embryo-derived Cellsmentioning

confidence: 99%

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

Alessio

Bonadio

Buson

et al. 2020

IJMS

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In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.Int. J. Mol. Sci. 2020, 21, 302 2 of 32 not achieved by transcription factors (TFs) alone because of their low number (approximately 1500 in mammals [4]) compared to genes to regulate (90% of the genome is transcribed in human [5]). A single TF is estimated to regulate only ten thousand genes according to Chromatin immunoprecipitation and DNA sequencing experiments (ChIp-seq) [6]. Moreover, the involvement of non-coding RNAs in gene expression regulation was evident [7,8]. The evidence that the non-protein coding component of the human genome is actively transcribed and carries out crucial functions limits the importance of coding genes in genome regulation. Non-coding transcripts become one of the stars of modern biology, especially because of their involvement in a wide range of regulatory processes and changed the association of non-coding regions to junk DNA [3].The genome of multicellular eukaryotes is mostly comprised of non-coding DNA (less than 2% of the human genome codes for proteins [9] while 90% of the human genome is transcribed). Non-coding DNA is transcribed into different classes of non-coding RNAs (ncRNAs) that include structural RNAs (rRNAs and tRNAs) and regulatory RNAs [10]. rRNAs and tRNAs are involved in mRNA translation and can be long or short in size. Regulatory RNAs are further divided into three classes: Small, medium, and long non-coding RNAs. Small non-coding RNAs are between 20 and 50 nucleotides long and include microRNAs (miRNAs) that participate in post-transcriptional regulation [11], small interfering RNAs (siRNAs) that are double-stranded RNAs involved in gene silencing through RNA interfering pathway [12], piwi interacting R...

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“…By ordering the cells according to pseudotimes, it is possible to identify the transcriptional changes that accompany developmental processes. This permits the detection of differentiation branching points during developmental continuous paths, the identification of crucial points of cellular decision‐making and the assessment of which genes are critical for driving these progressions in order to attempt the reconstruction of GRNs …”

Section: Single‐cell Transcriptome Analyses Characterize Differentiatmentioning

confidence: 99%

“…This permits the detection of differentiation branching points during developmental continuous paths, the identification of crucial points of cellular decision-making and the assessment of which genes are critical for driving these progressions in order to attempt the reconstruction of GRNs. [98][99][100] A few recent studies have used scRNA-seq to comprehensively characterize cell-specific transcriptomic states and differentiation trajectories throughout eye development. Hu and colleagues analysed 2421 individual cells of human NR and RPE, covering the developmental window between 5 and 24 fetal weeks.…”

Section: Single-cell Transcriptome Analyses Characterize Differentiatmentioning

confidence: 99%

Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs

Buono

Martı́nez-Morales

2020

BioEssays

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The ontogeny of the vertebrate retina has been a topic of interest to developmental biologists and human geneticists for many decades. Understanding the unfolding of the genetic program that transforms a field of progenitors cells into a functionally complex and multi‐layered sensory organ is a formidable challenge. Although classical genetic studies succeeded in identifying the key regulators of retina specification, understanding the architecture of their gene network and predicting their behavior are still a distant hope. The emergence of next‐generation sequencing platforms revolutionized the field unlocking the access to genome‐wide datasets. Emerging techniques such as RNA‐seq, ChIP‐seq, ATAC‐seq, or single cell RNA‐seq are used to characterize eye developmental programs. These studies provide valuable information on the transcriptional and cis‐regulatory profiles of precursors and differentiated cells, outlining the trajectories that connect each intermediate state. Here, recent systems biology efforts are reviewed to understand the genetic programs shaping the vertebrate retina.

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Using single‐cell genomics to understand developmental processes and cell fate decisions

Cited by 212 publications

References 95 publications

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Current best practices in single‐cell RNA‐seq analysis: a tutorial

A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs

Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs

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