Pooled genetic screens with image‐based profiling
2022; Springer Nature; Volume: 18; Issue: 11 Linguagem: Inglês
10.15252/msb.202110768
ISSN1744-4292
AutoresRussell T. Walton, Avtar Singh, Paul C. Blainey,
Tópico(s)CRISPR and Genetic Engineering
ResumoReview11 November 2022Open Access Pooled genetic screens with image-based profiling Russell T Walton Russell T Walton orcid.org/0000-0002-0546-474X Broad Institute of MIT and Harvard, Cambridge, MA, USA Department of Biological Engineering, MIT, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Avtar Singh Avtar Singh Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Paul C Blainey Corresponding Author Paul C Blainey [email protected] orcid.org/0000-0002-4889-8783 Broad Institute of MIT and Harvard, Cambridge, MA, USA Department of Biological Engineering, MIT, Cambridge, MA, USA Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Russell T Walton Russell T Walton orcid.org/0000-0002-0546-474X Broad Institute of MIT and Harvard, Cambridge, MA, USA Department of Biological Engineering, MIT, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Avtar Singh Avtar Singh Broad Institute of MIT and Harvard, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Paul C Blainey Corresponding Author Paul C Blainey [email protected] orcid.org/0000-0002-4889-8783 Broad Institute of MIT and Harvard, Cambridge, MA, USA Department of Biological Engineering, MIT, Cambridge, MA, USA Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA Contribution: Writing - original draft, Writing - review & editing Search for more papers by this author Author Information Russell T Walton1,2, Avtar Singh1,4 and Paul C Blainey *,1,2,3 1Broad Institute of MIT and Harvard, Cambridge, MA, USA 2Department of Biological Engineering, MIT, Cambridge, MA, USA 3Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA 4Present address: Department of Cellular and Tissue Genomics, Genentech, South San Francisco, CA, USA *Corresponding author. Tel: 617-714-7320; E-mail: [email protected] Molecular Systems Biology (2022)18:e10768https://doi.org/10.15252/msb.202110768 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single-cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy-based cellular profiling provides information complementary to next-generation sequencing (NGS) profiling and has only recently become compatible with large-scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale-up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy-based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships. Introduction The genetics and epigenetics of interacting cells over developmental time give rise to organisms and their characteristics. Understanding how genotypes give rise to phenotypes is the core objective of forward genetic screening, a set of approaches that systematically perturb the genome and record the phenotypic consequences (Fig 1A; Doench, 2018; Schuster et al, 2019). Genetic screens have a broad set of applications, including uncovering fundamental biology, characterizing the function of sequence variants, and identifying the molecular targets of drug candidates. The measurement of spatiotemporally resolved visual phenotypes in genetic screens, sampling the vast and dynamic structural complexity of biological systems, provides an information-rich basis to explore genotype–phenotype relationships. Figure 1. Approaches to genetic screening (A) Genetic screens seek to map genotypes to the phenotypes they produce. (B) Screening methodologies capture projections of cell phenotypes. Pooled profiling screens project individual cells into a multidimensional phenotypic space defined by the profiling method. Pooled enrichment screens project population averages into a unidimensional phenotypic space defined by the enrichment criteria. Arrayed screens can embody either of these phenotype–genotype associations. (C) Enrichment screens subject an initial cell library to an enrichment process to select for a phenotype of interest. Perturbation enrichment is determined by comparing the abundance of perturbation barcodes in the cell library before and after selection using next generation sequencing. (D) Cells can be enriched through a fitness advantage, fluorescence-activated cell sorting, or one of several approaches to isolate cells based on microscopy-defined features. (E) Profiling screens subject a complete cell library to profiling. Individual cells are assigned both perturbations and multidimensional phenotypic measurements. (F) Single cell profiling methods for genetic screening include single-cell sequencing approaches, CyTOF using protein barcodes, and microscopy-based phenotyping with in situ genotyping. FACS, fluorescence-activated cell sorting; IF, immunofluorescence; scRNA-seq, single-cell RNA sequencing; scATAC-seq, single-cell assay for transposase-accessible chromatin using sequencing; CyTOF, cytometry by time-of-flight. Download figure Download PowerPoint Today, many screening approaches apply targeted genotypic perturbations across an otherwise constant genetic background, such that differential phenotypes can be directly attributed to the perturbation. For example, consider a CRISPR-Cas9 gene knockout (CRISPR KO) screen in which a Cas9 nuclease is directed by a sequence-programmable guide RNA (gRNA) to a complementary genomic target, generating mutations that ablate function of the target gene. Here, the genotype of a cell is defined by the gene that has been targeted for loss-of-function. Following perturbation, the objective is to understand how each genotype influences cell phenotype. The measured phenotype could take the form of a unidimensional measurement to capture a specific feature of interest, such as relative cell fitness in a population, or a high-dimensional measurement to capture multiple aspects of cell phenotype, like a visual phenotype or transcriptional state (Fig 1B). In the example CRISPR KO screen, each gene loss-of-function could be connected to cellular abundance as a proxy for gene essentiality. Genetic screening approaches fundamentally differ in the way perturbations and phenotypes are associated (Fig 1B). Strategies can be classified into three groups: arrayed, pooled enrichment, and pooled profiling screens. In arrayed screens, perturbations are identified by position in a multiwell plate and phenotypic measurements are made for each well. The logistics of working with hundreds to tens of thousands of individual samples pose a major challenge to many researchers' ability to implement large-scale arrayed screens. Pooled screens offer a solution to this problem by introducing a large number of perturbations into a single sample. In pooled enrichment screens, cells of interest are then enriched (e.g., by survival) and next-generation sequencing (NGS) is used to compare the abundance of "perturbation barcodes"—sequences that encode perturbation identity—before and after enrichment (Fig 1C and D). In CRISPR screens, the gRNA itself may conveniently function as a perturbation barcode. Finally, in pooled profiling screens, phenotypic features and perturbation barcodes are measured in each individual cell in the mixed population (Fig 1E). While image-based "visual" phenotypes have largely been inaccessible in pooled genetic screening formats, technological advances now provide options for assaying microscopy-defined phenotypes in pooled screens. In this review, we discuss technological advances that enable studies of genotype-to-phenotype relationships with microscopy-based imaging. We provide an overview of approaches for arrayed, pooled enrichment, and pooled profiling screens with visual phenotypes and focus on the current suite of perturbation technologies and microscopy-based phenotyping approaches, in particular as they apply to pooled profiling screens. Finally, we suggest a roadmap for continued development and application of pooled profiling screens to extend the impact of microscopy-based genetic screening. Microscopy-based genetic screening maps genotypes to visual phenotypes Arrayed screens Arrayed screens allow the greatest flexibility in choice of perturbation and phenotyping approaches thanks to the simplicity of perturbation association to cell sample by position in the arrayed layout, for example a multiwell plate (Fig 1B). This is an important contrast with pooled screens (discussed in the following section) where more complex designs and extra steps are necessary to deconvolute the pooled perturbations. While maintaining compatibility with barcoded perturbations that are required for pooled screens, arrayed screens can additionally employ RNA perturbants without DNA precursors, such as small interfering RNA (siRNA) or CRISPR ribonucleoproteins, and chemical perturbants (Chia et al, 2010; Serçin et al, 2019). Phenotypic measurements may be perturbation-averaged, by taking a bulk measurement of all cells in a well, or single-cell resolution, via microscopy or single-cell sequencing approaches. Such measurements can span dimensionality from a single fluorescent reporter to molecular omics measurements. The simplicity and flexibility of implementation make arrayed screens an attractive approach at relatively small scales. However, generation and maintenance of large arrayed cell libraries is challenging, expensive, and requires particular care to limit plate-position and plate-to-plate statistical biases. Further, when control cells and perturbed cells are segregated in different wells, confounding epiphenomena may obscure perturbation-specific effects. At large scales, arrayed screens require complex and costly automation, large teams, and extensive validation procedures; for smaller teams, pooled screens may be the only feasible option. Despite the challenges, genome-wide arrayed screens have produced valuable data through large-scale efforts. For example, Boutros et al (2004) conducted a genome-wide growth and viability screen in Drosophila cell lines, identifying hundreds of essential genes. And, in a genome-wide arrayed siRNA screen in human embryonic stem cells using a fluorescent reporter of pluripotency, Chia et al (2010) identified genes responsible for the maintenance of pluripotency. More recently, genome-wide arrayed CRISPR-KO screens have been performed in primary kidney fibroblasts to identify relevant factors in kidney disease (Turner et al, 2020). Arrayed screens in their diverse forms have been reviewed in greater detail elsewhere (Zanella et al, 2010; Boutros et al, 2015). Pooled screens The major advantages of pooled screens over arrayed formats are that cell libraries can be generated, maintained, and screened as single samples, and that perturbation effects are determined using robust within-sample comparisons. Pooled oligo libraries encoding genetic perturbation reagents are commercially available at flexible scales from a range of vendors and enable a straightforward and cost-effective path to realizing a specified cell library. In a typical workflow, these oligos can be cloned into lentiviral packaging vectors, prepared as a lentiviral library, and transduced into the screening cell line to generate the cell library, each step in a single pooled reaction. This cell library can then be maintained and screened as a single culture. In addition to the reduced experimental burden of pooled screens, the handling of fewer individual cultures and the presence of internal controls in the mixed cell populations help reduce batch variability, avoid confounds, and improve statistical power. Mixing differently perturbed cells throughout the same sample is a key advantage for profiling studies where the comparison of perturbations against one another is often of interest. Despite these advantages, achieving sufficient scale to provide reliable estimates of genotype–phenotype associations yet challenges many pooled screening efforts. A typical CRISPR KO screen of 20,000 genes (roughly every single-gene knockout in the human genome) with four gRNAs per gene with an average coverage of 200 cells per gRNA requires obtaining data from 16 million cells. The way that pooled screens extract perturbation-phenotype associations from a mixed population is the critical distinguishing factor between the two categories of pooled screens: enrichment screens and profiling screens. Pooled enrichment screens Pooled enrichment screens employ selection for a pre-defined phenotype of interest to yield a scalar enrichment score for each perturbation in a library. Enrichment scores are determined by NGS of perturbation barcodes to compare their abundance in the starting and enriched cell libraries (Fig 1B and C). As the phenotype of interest determines the experimental enrichment strategy, it is necessary to strictly define the phenotype prior to screening. While many individual cells receive each perturbation, the enrichment score reflects an average of the enrichment across individual cells. Thus, enrichment scores describe population-averaged phenotypes, in contrast to phenotypes measured in individually genotyped cells. Enrichment screens can select for complex phenotypes, including complex multiparametric image-based phenotypes; however, they necessarily project these phenotypes into unidimensional space represented by the enrichment score. For example, viability screens typically compress several phenotypes including cell division rate, cell–cell signaling, tolerance of various cellular stresses, and even adherence to cultureware, to a single "fitness score" determined by the endpoint NGS guide abundance measurement. Genome-scale pooled enrichment screens have become routine using straightforward enrichment methods including those targeting fitness/viability effects and fluorescence-activated cell sorting (FACS) on the scalar signals of reporter genes or antibody stains. For example, the Cancer Dependency Map project encompasses genome-wide pooled fitness enrichment screens performed in 501 and 908 cancer cell lines with RNAi and CRISPR KO perturbations, respectively (Tsherniak et al, 2017; Pacini et al, 2021). There exists a growing set of methods to enable the enrichment of cells on more complex functional, molecular, and morphological axes (Fig 1D). In addition to the now-conventional fitness advantage and FACS methodologies, recent advances in microscopy-based approaches have extended enrichment screens to complex optical phenotypes including subcellular localization of biomolecules and cell morphology. And several such approaches maintain live cells following enrichment, an additional attractive feature enabling further characterization of the population of interest. One set of approaches leverages photochemical reactions to selectively label individual cells as they are imaged with fluorescence microscopy. This labeling can enable enrichment of cells based on a microscopy-defined phenotype. The Photostick method relies on a photochemical crosslinker that enables selected cells to remain adhered while unselected cells are enzymatically removed (Chien et al, 2015). Several groups have also developed approaches using selective photoconversion of fluorescent proteins based on visual phenotypes measured with microscopy for subsequent enrichment with FACS (Kuo et al, 2016; Hasle et al, 2020; Kanfer et al, 2021; Yan et al, 2021). Hasle et al (2020) employed a photoconversion method termed visual cell sorting to screen 346 SV40 nuclear localization sequence variants across about 638,000 cells, identifying variants with improved nuclear localization relative to the wild-type sequence. Kanfer et al (2021) used a photoactivatable fluorescent protein to label cells for FACS isolation with TFEB localization phenotypes in a genome wide screen, imaging over 12 million cells. Yan et al (2021) screened over 11 million cells in a library of about 6,000 perturbations, isolating cells displaying nuclear size phenotypes through a photoactivatable fluorescent protein and subsequent cell sorting. In addition to microscopy-based approaches, specialized cell sorters have been developed to reconstruct fluorescence or Raman microscopy images of cells and sort cells on image-based criteria in real time (Nitta et al, 2018, 2020; preprint: Salek et al, 2022; Schraivogel et al, 2022). These approaches have been demonstrated with diverse phenotypic measurements, including fluorescent reporter localization and surface epitope immunofluorescence (IF) in live cells and intracellular IF in fixed cells. Schraivogel et al (2022) leveraged fluorescence image-based cell sorting to perform a genome-wide screen in HeLa cells to identify factors regulating the localization of p65, a key component of the nuclear factor κB pathway. At the demonstrated flow rate, this approach would enable genome-wide screens with 3 gRNAs per gene and 100× coverage per gRNA in just 9 h of sorting time. Robotic cell picking, microraft arrays, and optical trapping in microfluidic chips have also been used to mechanically isolate cells based on optical phenotypes (Piatkevich et al, 2018; Luro et al, 2020; Wheeler et al, 2020). Piatkevich et al (2018) developed a robotic cell picking approach to select cells based on visual phenotypes and employed the system to perform directed evolution of fluorescent proteins. Wheeler et al (2020) used automated confocal microscopy and microraft arrays to screen over 12,000 perturbations in about 120,000 cells, identifying RNA binding proteins involved in stress granule formation. Luro et al (2020) used a microfluidic chip to screen genetic circuits in Escherichia coli, making live-cell measurements of circuit activity with microscopy and using optical trapping to isolate selected cells for genotyping. While enrichment screens represent phenotype with an enrichment score, a unidimensional and population-averaged metric, some experimental designs for enrichment screening can expand these capabilities. Subjecting the same cell library to multiple distinct enrichment and readout steps will yield multiple enrichment scores (Surdziel et al, 2017). However, obtaining each distinct enrichment score set essentially requires performing a complete additional screen. An exception could include the use of picking or photoconversion methods with multiple sorting bins, though the number of bins and/or capacity of the sorter would place a limit on the number of simultaneous enrichments that could be performed (Hasle et al, 2020). Additionally, the resolution of perturbation-phenotype association can be improved by pairing perturbation barcodes with randomized clonal barcodes. Clonal barcodes uniquely identify the original perturbed cells such that the enrichment score of each clone can be measured separately to segregate clonal effects and provide some distribution-level information. Clonal barcodes have been implemented with CRISPR-Cas9-based perturbations through the addition of a randomized barcode to the gRNA (Schmierer et al, 2017; Zhu et al, 2019). Clonal barcoding offers a compromise between perturbation-averaged and single-cell resolution phenotypes, distinguishing among some sources of cell variability, such as genetic heterogeneity or semi-random perturbation outcomes, but not others, like cell cycle stage, local spatial context, and other sources of biological noise. Pooled profiling screens Pooled profiling screens capture perturbation barcodes and high-dimensional phenotypes of individual cells in a population (Fig 1B and E). The three broad approaches to pooled profiling screens employ single-cell "omic" sequencing, mass spectrometry, and microscopy as their foundational technologies (Fig 1F). Single-cell sequencing screens adapt single-cell RNA sequencing (scRNA-seq) or single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to recover perturbation barcodes alongside the phenotypic measurements (Adamson et al, 2016; Dixit et al, 2016; Jaitin et al, 2016; Datlinger et al, 2017; Rubin et al, 2019; Replogle et al, 2020). Mass spectrometry approaches require encoding perturbation barcodes at the protein level as unique epitope combinations are required for readout using methods including cytometry by time of flight (CyTOF) and multiplexed ion beam imaging by time of flight (MIBI-TOF) (Keren et al, 2019) to characterize both genotype and phenotype at the protein level (Wroblewska et al, 2018; Dhainaut et al, 2022). Microscopy-based pooled profiling screens use a variety of barcoding and imaging approaches to measure both cell phenotype and genotype in situ (Fig 2A–D; Emanuel et al, 2017; Lawson et al, 2017; Feldman et al, 2019; Wang et al, 2019; Shi et al, 2020; Dhainaut et al, 2022). Three general approaches for in situ genotyping have been demonstrated: fluorescence in situ hybridization (FISH), in situ sequencing (ISS), and iterative IF (Fig 2B–D). Figure 2. Approaches to measure perturbation barcodes in situ (A) Perturbation barcodes are genetically encoded and may be transcribed to RNA or transcribed and translated to protein epitopes. (B) Fluorescence in situ hybridization approaches measure RNA barcodes through iterative hybridization, imaging, and stripping of fluorescent probes. Diverse encoding schemes may be used. (C) In situ sequencing approaches clonally amplify barcode sequences in situ and read out barcodes through iterative cycles of sequencing/imaging. (D) Iterative immunofluorescence approaches detect protein barcodes—unique combinations of epitopes—by iteratively staining, imaging, and destaining with fluorescently labeled antibodies. Epitopes are used combinatorially in each barcode and diverse encoding schemes may be used. Download figure Download PowerPoint In FISH genotyping approaches, perturbation barcodes are transcribed in live cells, a signal amplification step generating many barcode copies in each cell (Fig 2A). Cells are then fixed and barcodes are measured by iteratively hybridizing fluorescent probes, imaging cells, stripping probes, and repeating with subsequent probe sets until all barcodes can be decoded (Fig 2B). Alternatively, transcriptionally inactive barcodes have also been measured following in vitro transcription in fixed cells (Askary et al, 2019). The first microscopy-based pooled screening methods were described in two 2017 E. coli screening studies that measured perturbation barcodes with FISH (Emanuel et al, 2017; Lawson et al, 2017). Lawson et al (2017) conducted a screen of three E. coli variants and measured complex phenotypes including over 4 h of dynamics by tracking bacteria in a microfluidic chip. Emanuel et al (2017) screened 60,000 fluorescent protein variants for brightness and stability while retrieving perturbation barcodes by multiplexed FISH. Though characterizing a simple phenotype, the screen profiled 20 million individual bacteria. And in a screen of lncRNA localization, Wang et al (2019) knocked out 54 genes encoding RNA-binding proteins and profiled about 30,000 human osteosarcoma cells, characterizing both phenotype and genotype with multiplexed FISH. In an alternative approach for highly multiplexed barcode detection, Shi et al (2020) developed a hyperspectral imaging-based FISH method, termed HiPR-FISH, using 10 fluorophores to barcode over 1,000 genotypes using a single (non-iterative) hybridization. HiPR-FISH was implemented to identify 1,023 distinct E. coli isolates across about 65,000 single cells. In situ sequencing approaches also rely on transcribed perturbation barcodes (Fig 2A). Briefly, following fixation of cells, RNA barcodes are reverse transcribed to cDNA, and a padlock probe is used to copy the barcode into a circular single-stranded DNA molecule, which serves as a template for rolling circle amplification (RCA) to clonally amplify the barcode sequence. Following amplification, barcodes are sequenced in situ, as demonstrated by several groups, with sequencing-by-synthesis (SBS) or sequencing-by-ligation chemistry (Fig 2C; Ke et al, 2013; Payne, 2017; Chen et al, 2018; Feldman et al, 2019). We describe the experimental procedure for ISS using SBS at length in a recent protocol publication (Feldman et al, 2022). In our initial demonstration, we studied 952 gene knockouts, measuring p65 localization in about 6 million cells in a series of screens and taking time course measurements of live cells for over 400,000 cells in targeted downstream screening (Feldman et al, 2019). Recently, we extended this approach to screen about 20,000 gRNAs targeting 5,072 essential genes, profiling DNA content, DNA damage, and microtubule and F-actin subcellular organization across 31 million cells (Funk et al, 2022). Lastly, in iterative IF approaches, perturbation barcodes encode unique protein epitope combinations that are transcribed and translated in live cells (Fig 2A; Wroblewska et al, 2018; Rovira-Clavé et al, 2021). Following fixation, protein barcodes can be decoded through iterative IF measurements (Fig 2D). In an in vivo screen of 35 CRISPR KOs, Dhainaut et al (2022) used iterative IF to recover protein barcodes from about 1,750 tumor lesions in mouse tissue sections. By using protein barcode epitopes combinatorially, as many as 120 unique combinations have been demonstrated as distinguishable and greater barcoding complexity may be achievable by deploying additional orthogonal epitopes, higher order epitope combinations, and/or multiple barcodes with distinct subcellular localization (Wroblewska et al, 2018; Dhainaut et al, 2022; Kudo et al, 2022). Methods for genetic perturbation and barcoding Pooled genetic screens are one of several approaches based on cellular barcoding, strategies that use molecular barcodes to enable recovery of information about cellular contents or identities (Fig 3A; Kebschull & Zador, 2018). These barcodes are most commonly DNA or RNA sequences read by sequencing at an experimental endpoint, though protein barcodes can be decoded with mass spectrometry or IF measurements (Wroblewska et al, 2018; Dhainaut et al, 2022). In the context of pooled genetic screens, cellular barcodes encode cellular genotype, for example, the gene target of a gRNA in a CRISPR KO screen. However, the applications of cellular barcoding extend beyond laboratory perturbations and engineered genetic differences that typify genetic screens. Figure 3. Cellular barcoding and perturbation (A) Applications of cellular barcoding. In pooled genetic screens, genetic perturbation libraries are used to introduce one or more genetic perturbations to cells with the same genetic background. Pooled cell models use barcodes to distinguish cells of different genetic backgrounds. Static clonal barcoding experiments use barcodes to track the progeny of individual clones. Dynamic subclonal barcoding approaches use dynamic barcodes to determine subclonal relationships between cells. (B) Approaches for programmable perturbation. Genetic perturbations, or changes to DNA sequence, include gene knockouts, introduction of new DNA sequences, or precise sequence changes. Epigenetic perturbations include changes to DNA accessibility, transcription factor recruitment, DNA methylation, histone modifications, and 3D genome structure. Transcriptomic perturbations include gene knockdown and precise sequence changes. DSBs, double strand breaks; HDR, homology-directed repair; CRISPRa, CRISPR-mediated activation of transcription; CRISPRi, CRISPR-mediated interference of transcription; RNAi, RNA interference. Download figure Download PowerPoint In contrast to delivering a specific perturbation, pooled cell models leverage naturally occurring genetic and epigenetic diversity between cell models. In these approaches, cells barcoded according to their origin, such as cell lines from different individuals or tissue origins, can be assayed in a pooled setting. Pooled cell models have been used to identify anticancer reagents and characterize metastatic potential (Yu et al, 2016; Corsello et al, 2020; Jin et al, 2020). Cellular barcoding can also be used to track populations of cells over time. Static clonal barcoding is used to identify cells that originate from a single barcoded ancestor in a mixed population, and subclonal barcoding approaches use dynamic barcodes to identify subclonal relationships between cells (Kebschull & Zador, 2018). While this review is focused on genetic screening and the association of perturbation barcodes to resulting phenotypes, our discussion of methods to pair cellular barcodes to phenotypes is further relevant to this broader set of cellular barcoding methods. In genetic screening approaches popular today, pooled cells differ in the sequence-programmable perturbation each receives, and the perturbation sequence ide
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