Pooled optical screens in human cells

Feldman, David; Singh, Avtar; Schmid-Burgk, Jonathan L.; Mezger, Anja; Garrity, Anthony J.; Rj, Carlson; Zhang, Feng; Pc, Blainey

doi:10.1101/383943

Cited by 56 publications

(93 citation statements)

References 48 publications

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“…In our pooled image-based screen, we analyzed approximately one million cultured human cells, ultimately recovering ~650,000. This throughput is ~1,000-fold more than what could be achieved using other photoconvertible fluorophore-based methods [10][11][12][13] , ~20-fold more than current MERFISH pooled screens 5 , and similar to in situ sequencing-based screens 16 ( Supplementary Table 1). Thus, Visual Cell Sorting enables the analysis of thousands of genetic variants in a single experiment.…”

Section: Discussionmentioning

confidence: 99%

“…High content imaging 1 , in situ sequencing methods [2][3][4][5][6][7][8][9] , and other approaches [10][11][12][13][14] have revolutionized the investigation of how genetic variants and gene expression programs dictate cellular morphology, organization and behavior. One important application of these methods is visual genetic screening, in which a library of genetic variants is introduced into cells and the effect of each variant on a visual phenotype is quantified.…”

Section: Introductionmentioning

confidence: 99%

“…In a classical high content visual genetic screen, each genetic perturbation occupies a separate well. New in situ methods, which employ sequencing by repeated hybridization of fluorescent oligo probes [2][3][4][5][6] or direct synthesis [7][8][9]15 to visually read out nucleic acid barcodes, permit hundreds of perturbations to be assessed in a pooled format. For example, multiplexed fluorescent in-situ hybridization was used to assess the effect of 210 CRISPR sgRNAs on RNA localization in ~30,000 cultured human U-2 OS cells 5 ; and in situ sequencing was used to measure the effect of 952 sgRNAs on the localization of an NFkB reporter in ~400,000 cells 16 .…”

Section: Introductionmentioning

confidence: 99%

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Visual Cell Sorting: A High-throughput, Microscope-based Method to Dissect Cellular Heterogeneity

Hasle

Cooke

Srivatsan

et al. 2019

Preprint

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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. Visual Cell Sorting uses automated imaging and phenotypic analysis to direct 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 the effect of 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 use Visual Cell Sorting to recover cells that retain normal nuclear morphologies after paclitaxel treatment, then derive their single cell transcriptomes to identify multiple pathways associated with paclitaxel resistance in human 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual Cell Sorting: A High-throughput, Microscope-based Method to Dissect Cellular Heterogeneity

Hasle

Cooke

Srivatsan

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Critically, our framework for probing and understanding genetic interactions is compatible both with the many emerging tools for perturbing the state of individual cellsgenome editing, base editors, epigenome editing-and the many tools for measuring ittranscriptome, chromatin state, proteome, imaging etc. (63). The power of these tools is further enhanced by new computational methods that allow improved design of experiments, such as recommender systems (62) and compressed sensing (64,65).…”

Section: Discussionmentioning

confidence: 99%

Exploring genetic interaction manifolds constructed from rich phenotypes

Norman

Horlbeck

Replogle

et al. 2019

Preprint

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Synergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds. One Sentence Summary:Principles and mechanisms of genetic interactions are revealed by rich phenotyping using single-cell RNA sequencing.

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“…Deep characterization of purified resistant clones can be useful in identifying resistant drivers, and through perturbational approaches, the association between these putative drivers and phenotype can be defined. The sgRNA-barcodes can also be readily adapted to existing high-throughput single-cell readouts developed for CRISPR screens, such as single-cell gene expression [18], [19] and optical screening [20].…”

Section: Discussionmentioning

confidence: 99%

CloneRetriever: retrieval of rare clones from heterogeneous cell populations

Feldman

Tsai

Garrity

et al. 2019

Preprint

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Conclusions 44CloneRetriever provides a means to track and access specific and rare clones of 45 interest across dynamic changes in population structure to comprehensively explore the 46 basis of these changes. 47 48 Keywords 49 Cellular heterogeneity, Viable clone-specific cells recovery, Clonal fitness tracking, 50 CRISPR sgRNA-barcode DNA library 51 52 causal drivers of clone fitness could give rich insights into the molecular mechanisms of 62 selection and suggest potential interventions. 63 Heritable and plastic cellular features can drive selection outcomes. For example, 64 genetic features can change with mutagens, such as DNA-damaging chemotherapies, 65 and epigenetic states can rapidly shift in response to drug exposure [5]or environment 66 [6]. Metastatic clones may alter their epigenetic profiles upon seeding a metastatic site 67[7], obscuring the preexisting features that enabled them to metastasize. However, 68 existing methods to identify these features tend to rely on comparing populations in bulk 69 before and after selection, which limits their usefulness in detecting pre-existing features 70 that changed during selection. A useful alternative approach would be to identify clones 71 based upon their response to selective pressure, and then isolate representative 72 untreated cells from each clone for genomic and functional characterization. 73 Genomically integrated DNA barcodes provide a scalable methodology to track rare 74 clones by measuring relative barcode abundance over time [8]. However, relative clone 75 fitness alone cannot elucidate mechanisms of selection. Single-cell technologies can 76 provide genomic profiles of heterogeneous cells within a population. Clone identity can 77 be incorporated into single-cell RNA-seq (scRNA-seq) profiles by capturing transcribed 78 barcodes, linking clonal history and cell fate [9]. However, single-cell genomic profiling 79 is inherently destructive. Both DNA barcoding and single-cell approaches have a limited 80 ability to probe functional differences between clones, whereas retrieval of viable cells 81 from clones would enable a wide range of genomic and functional analysis. 82 488 R.B. and P.B. receive grant funding from the Novartis Institute of Biomedical Research 489 for an unrelated project. C.M.J. is currently a full-time employee and stockholder of 490

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Pooled optical screens in human cells

Cited by 56 publications

References 48 publications

Visual Cell Sorting: A High-throughput, Microscope-based Method to Dissect Cellular Heterogeneity

Visual Cell Sorting: A High-throughput, Microscope-based Method to Dissect Cellular Heterogeneity

Exploring genetic interaction manifolds constructed from rich phenotypes

CloneRetriever: retrieval of rare clones from heterogeneous cell populations

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