High‐throughput, microscope‐based sorting to dissect cellular heterogeneity

Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual ph...

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Published inMolecular systems biology Vol. 16; no. 6; pp. e9442 - n/a
Main Authors Hasle, Nicholas, Cooke, Anthony, Srivatsan, Sanjay, Huang, Heather, Stephany, Jason J, Krieger, Zachary, Jackson, Dana, Tang, Weiliang, Pendyala, Sriram, Monnat, Raymond J, Trapnell, Cole, Hatch, Emily M, Fowler, Douglas M
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.06.2020
EMBO Press
John Wiley and Sons Inc
Springer Nature
Subjects
Annotations
Apoptosis
Automation
Cell Line
Cell Nucleus Shape - drug effects
Cells - cytology
EMBO22
Experiments
Flow Cytometry
Fluorescence
Gene expression
Genetic engineering
genetic screening
Genetic Testing
Genotype & phenotype
Genotypes
Heterogeneity
Humans
Identification methods
Localization
Method
Methods
microscopy
Microscopy, Fluorescence - instrumentation
Morphology
Nuclear Localization Signals - metabolism
Paclitaxel
Paclitaxel - pharmacology
pharmacology
Phenotype
Phenotypes
Proteins
Reagents
Ribonucleic acid
RNA
subcellular localization
Transcriptome - drug effects
Transcriptome - genetics
transcriptomics
transcriptomics
genetic screening
microscopy
subcellular localization
pharmacology
Online AccessGet full text
ISSN1744-4292
1744-4292
DOI10.15252/msb.20209442

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Abstract Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs. Synopsis This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells. Expression of the photoactivatable fluorescent protein Dendra2 permits selective, irreversible, and high‐throughput labeling of cells exhibiting different visual phenotypes. These labeled cell subpopulations can be sorted and thus subject to diverse downstream genomics assays. Photoactivation using a digital micromirror device affixed to a 405 nm laser is accurate, non‐toxic, and can be tuned to produce four discrete levels of fluorescence. Human cells expressing sequence variant libraries can be sorted according to a visual phenotype followed by sequencing, which provides sequence‐function maps for phenotypes such as protein subcellular localization. Cell populations that respond in a visually heterogeneous fashion to drug treatment can be sorted and subject to transcriptomic analyses, revealing the molecular states associated with complex drug responses. Graphical Abstract This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells.
AbstractList Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs. This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells.
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs. Synopsis This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells. Expression of the photoactivatable fluorescent protein Dendra2 permits selective, irreversible, and high‐throughput labeling of cells exhibiting different visual phenotypes. These labeled cell subpopulations can be sorted and thus subject to diverse downstream genomics assays. Photoactivation using a digital micromirror device affixed to a 405 nm laser is accurate, non‐toxic, and can be tuned to produce four discrete levels of fluorescence. Human cells expressing sequence variant libraries can be sorted according to a visual phenotype followed by sequencing, which provides sequence‐function maps for phenotypes such as protein subcellular localization. Cell populations that respond in a visually heterogeneous fashion to drug treatment can be sorted and subject to transcriptomic analyses, revealing the molecular states associated with complex drug responses. Graphical Abstract This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells.
Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs. Synopsis This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells. Expression of the photoactivatable fluorescent protein Dendra2 permits selective, irreversible, and high‐throughput labeling of cells exhibiting different visual phenotypes. These labeled cell subpopulations can be sorted and thus subject to diverse downstream genomics assays. Photoactivation using a digital micromirror device affixed to a 405 nm laser is accurate, non‐toxic, and can be tuned to produce four discrete levels of fluorescence. Human cells expressing sequence variant libraries can be sorted according to a visual phenotype followed by sequencing, which provides sequence‐function maps for phenotypes such as protein subcellular localization. Cell populations that respond in a visually heterogeneous fashion to drug treatment can be sorted and subject to transcriptomic analyses, revealing the molecular states associated with complex drug responses. This study describes an imaging‐based approach for pooled genetic screening and morphology‐based transcriptomics that uses high‐throughput photoactivation followed by FACS to separate differentially labeled cells.
Abstract Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence‐activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single‐cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.
Author Tang, Weiliang
Hasle, Nicholas
Jackson, Dana
Stephany, Jason J
Pendyala, Sriram
Monnat, Raymond J
Cooke, Anthony
Krieger, Zachary
Srivatsan, Sanjay
Huang, Heather
Fowler, Douglas M
Hatch, Emily M
Trapnell, Cole
AuthorAffiliation 2 Leica Microsystems Buffalo Grove IL USA
5 Department of Bioengineering University of Washington Seattle WA USA
4 Department of Pathology University of Washington Seattle WA USA
1 Department of Genome Sciences University of Washington Seattle WA USA
3 Divisions of Basic Sciences and Human Biology Fred Hutchinson Cancer Research Center Seattle WA USA
AuthorAffiliation_xml – name: 2 Leica Microsystems Buffalo Grove IL USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32500953$$D View this record in MEDLINE/PubMed
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Issue 6
Keywords transcriptomics
genetic screening
microscopy
subcellular localization
pharmacology
Language English
License Attribution
2020 The Authors. Published under the terms of the CC BY 4.0 license.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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32543109 - Mol Syst Biol. 2020 Jun;16(6):e9640. doi: 10.15252/msb.20209640
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– reference: 32543109 - Mol Syst Biol. 2020 Jun;16(6):e9640. doi: 10.15252/msb.20209640
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Snippet Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains...
Abstract Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale...
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SubjectTerms Annotations
Apoptosis
Automation
Cell Line
Cell Nucleus Shape - drug effects
Cells - cytology
EMBO22
Experiments
Flow Cytometry
Fluorescence
Gene expression
Genetic engineering
genetic screening
Genetic Testing
Genotype & phenotype
Genotypes
Heterogeneity
Humans
Identification methods
Localization
Method
Methods
microscopy
Microscopy, Fluorescence - instrumentation
Morphology
Nuclear Localization Signals - metabolism
Paclitaxel
Paclitaxel - pharmacology
pharmacology
Phenotype
Phenotypes
Proteins
Reagents
Ribonucleic acid
RNA
subcellular localization
Transcriptome - drug effects
Transcriptome - genetics
transcriptomics
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Title High‐throughput, microscope‐based sorting to dissect cellular heterogeneity
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