Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development DOI Creative Commons
Sueli Marques, David van Bruggen, Darya Vanichkina

et al.

Developmental Cell, Journal Year: 2018, Volume and Issue: 46(4), P. 504 - 517.e7

Published: Aug. 1, 2018

Pdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis the central nervous system (CNS). It is unclear whether there a correlation between these and different (OL) states at adult stages. Here, we present bulk single-cell transcriptomics resources providing insights on how transitions occur. We found that post-natal OPCs from brain spinal cord similar transcriptional signatures. Moreover, OPC progeny of E13.5 electrophysiological profiles to derived subsequent waves, indicating pre-OPCs rewire their network development. Single-cell RNA-seq lineage tracing indicates subset originates pericyte lineage. Thus, our results indicate embryonic CNS give rise cell lineages, including with convergent regions.

Language: Английский

Integrating single-cell transcriptomic data across different conditions, technologies, and species DOI
Andrew Butler, Paul Hoffman, Peter Smibert

et al.

Nature Biotechnology, Journal Year: 2018, Volume and Issue: 36(5), P. 411 - 420

Published: April 2, 2018

Language: Английский

Citations

11253

Multi-omics approaches to disease DOI Creative Commons
Yehudit Hasin-Brumshtein, Marcus M. Seldin, Aldons J. Lusis

et al.

Genome biology, Journal Year: 2017, Volume and Issue: 18(1)

Published: May 5, 2017

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field "integrative genetics". Other omics technologies, such as proteomics metabolomics, are now often incorporated into everyday methodology biological researchers. In this review, we provide an overview focus on their integration across multiple layers. As compared a single type, multi-omics offers opportunity understand flow information that underlies disease.

Language: Английский

Citations

1980

RNA sequencing: the teenage years DOI
Rory Stark, Marta Grzelak, James Hadfield

et al.

Nature Reviews Genetics, Journal Year: 2019, Volume and Issue: 20(11), P. 631 - 656

Published: July 24, 2019

Language: Английский

Citations

1586

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap DOI
Jüri Reimand, Ruth Isserlin, Véronique Voisin

et al.

Nature Protocols, Journal Year: 2019, Volume and Issue: 14(2), P. 482 - 517

Published: Jan. 21, 2019

Language: Английский

Citations

1561

Differential analysis of RNA-seq incorporating quantification uncertainty DOI
Harold Pimentel, Nicolas Bray,

Suzette Puente

et al.

Nature Methods, Journal Year: 2017, Volume and Issue: 14(7), P. 687 - 690

Published: June 5, 2017

Language: Английский

Citations

1426

Gene co-expression analysis for functional classification and gene–disease predictions DOI Creative Commons
Sipko van Dam, Urmo Võsa, Adriaan van der Graaf

et al.

Briefings in Bioinformatics, Journal Year: 2016, Volume and Issue: unknown, P. bbw139 - bbw139

Published: Dec. 8, 2016

Gene co-expression networks can be used to associate genes of unknown function with biological processes, prioritize candidate disease or discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, constructed from RNA sequencing data also enable the inference functions associations for non-coding splice variants. Although gene typically do not provide information about causality, emerging methods differential analysis are enabling identification underlying various phenotypes. Here, we introduce guide researchers through a (differential) analysis. We an overview tools create analyse expression data, explain how these identify role disease. Furthermore, discuss integration other types offer future perspectives

Language: Английский

Citations

960

Deciphering cell–cell interactions and communication from gene expression DOI Open Access
Erick Armingol, Adam Officer, Olivier Harismendy

et al.

Nature Reviews Genetics, Journal Year: 2020, Volume and Issue: 22(2), P. 71 - 88

Published: Nov. 9, 2020

Language: Английский

Citations

921

Transcriptomics technologies DOI Creative Commons
Rohan G. T. Lowe, Neil J. Shirley, Mark R. Bleackley

et al.

PLoS Computational Biology, Journal Year: 2017, Volume and Issue: 13(5), P. e1005457 - e1005457

Published: May 18, 2017

Transcriptomics technologies are the techniques used to study an organism's transcriptome, sum of all its RNA transcripts. The information content organism is recorded in DNA genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures snapshot time total transcripts present cell. first attempts whole began early 1990s, technological advances since late 1990s have made transcriptomics widespread discipline. has been defined by repeated innovations that transform field. There two key contemporary field: microarrays, which quantify set predetermined sequences, sequencing (RNA-Seq), uses high-throughput capture sequences. Measuring expression genes different tissues, conditions, or points gives on how regulated reveals details biology. It can also help infer functions previously unannotated genes. Transcriptomic analysis enabled gene changes organisms instrumental understanding human disease. An entirety allows detection broad coordinated trends cannot be discerned more targeted assays.

Language: Английский

Citations

918

Towards a complete map of the human long non-coding RNA transcriptome DOI
Barbara Uszczyńska-Ratajczak, Julien Lagarde, Adam Frankish

et al.

Nature Reviews Genetics, Journal Year: 2018, Volume and Issue: 19(9), P. 535 - 548

Published: May 23, 2018

Language: Английский

Citations

539

Piercing the dark matter: bioinformatics of long-range sequencing and mapping DOI
Fritz J. Sedlazeck, Hayan Lee, Charlotte A. Darby

et al.

Nature Reviews Genetics, Journal Year: 2018, Volume and Issue: 19(6), P. 329 - 346

Published: March 29, 2018

Language: Английский

Citations

498