Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial informatio...

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Vydané v:	BioEssays Ročník 42; číslo 10; s. e1900221 - n/a
Hlavní autori:	Asp, Michaela, Bergenstråhle, Joseph, Lundeberg, Joakim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Wiley Subscription Services, Inc 01.10.2020
Predmet:	Gene expression labor New technology RNA RNA‐sequencing single cells Spatial data spatial omics spatial transcriptomics spatially resolved transcriptomics tissue heterogeneity transcriptomics RNA-sequencing tissue heterogeneity RNA spatial transcriptomics single cells spatially resolved transcriptomics spatial omics gene expression
ISSN:	0265-9247, 1521-1878, 1521-1878
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Abstract	Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method‐specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue‐type dependence, and limited capacity to obtain detailed single‐cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon. In this review, current spatial transcriptomics methods are surveyed. These methods detect RNA molecules while retaining information of where the molecules are located in the tissue. The advantages and drawbacks of existing methods are discussed.
AbstractList	Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method‐specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue‐type dependence, and limited capacity to obtain detailed single‐cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon. Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method‐specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue‐type dependence, and limited capacity to obtain detailed single‐cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon. In this review, current spatial transcriptomics methods are surveyed. These methods detect RNA molecules while retaining information of where the molecules are located in the tissue. The advantages and drawbacks of existing methods are discussed. Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.
Author	Asp, Michaela Bergenstråhle, Joseph Lundeberg, Joakim
Author_xml	– sequence: 1 givenname: Michaela surname: Asp fullname: Asp, Michaela organization: Science for Life Laboratory – sequence: 2 givenname: Joseph surname: Bergenstråhle fullname: Bergenstråhle, Joseph organization: Science for Life Laboratory – sequence: 3 givenname: Joakim orcidid: 0000-0003-4313-1601 surname: Lundeberg fullname: Lundeberg, Joakim email: joakim.lundeberg@scilifelab.se organization: Science for Life Laboratory
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32363691$$D View this record in MEDLINE/PubMed
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Snippet	Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly...
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SubjectTerms	Gene expression labor New technology RNA RNA‐sequencing single cells Spatial data spatial omics spatial transcriptomics spatially resolved transcriptomics tissue heterogeneity transcriptomics
Title	Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration
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