Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ canno...

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Veröffentlicht in:BioEssays Jg. 44; H. 11; S. e2200084 - n/a
Hauptverfasser: Ke, May, Elshenawy, Badran, Sheldon, Helen, Arora, Anjali, Buffa, Francesca M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge Wiley Subscription Services, Inc 01.11.2022
Schlagworte:
bioinformatics
biomedical research
Cardiovascular diseases
complex disease
computational biology
Computer applications
Context
diabetes
Diabetes mellitus
Disease
disease progression
Disease resistance
evolution
Gene sequencing
Heterogeneity
Medical research
neoplasms
New technology
Pipelines
Populations
Questions
RNA
scRNA‐seq
sequence analysis
single‐cell RNA sequencing
therapeutics
transcriptome
Transcriptomes
transcriptomics
Tumors
tumour microenvironment
ISSN:0265-9247, 1521-1878, 1521-1878
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Abstract Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research. Single‐cell RNA sequencing compares the transcriptomes of individual cells to study their function, evolution and interactions. We review emerging technologies and their applications, and highlight the limitations and the need for reproducible experimental protocols and robust computational pipelines.
AbstractList Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research. Single‐cell RNA sequencing compares the transcriptomes of individual cells to study their function, evolution and interactions. We review emerging technologies and their applications, and highlight the limitations and the need for reproducible experimental protocols and robust computational pipelines.
Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.
Author Arora, Anjali
Sheldon, Helen
Elshenawy, Badran
Ke, May
Buffa, Francesca M
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  email: francesca.buffa@oncology.ox.ac.uk, francesca.buffa@unibocconi.it
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Notes May Ke and Badran Elshenawy, Anjali Arora and Francesca M. Buffa contributed equally to this study.
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PublicationDate November 2022
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PublicationDate_xml – month: 11
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  text: November 2022
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PublicationTitle BioEssays
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Publisher Wiley Subscription Services, Inc
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SecondaryResourceType review_article
Snippet Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this...
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crossref
wiley
SourceType Aggregation Database
Enrichment Source
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StartPage e2200084
SubjectTerms bioinformatics
biomedical research
Cardiovascular diseases
complex disease
computational biology
Computer applications
Context
diabetes
Diabetes mellitus
Disease
disease progression
Disease resistance
evolution
Gene sequencing
Heterogeneity
Medical research
neoplasms
New technology
Pipelines
Populations
Questions
RNA
scRNA‐seq
sequence analysis
single‐cell RNA sequencing
therapeutics
transcriptome
Transcriptomes
transcriptomics
Tumors
tumour microenvironment
Title Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fbies.202200084
https://www.proquest.com/docview/2726905731
https://www.proquest.com/docview/2711307377
https://www.proquest.com/docview/2811976007
Volume 44
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