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Draft:Optical Pooled Screening

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  • Comment: There is a LOT of unsourced essay like commentary here and original research (Future Directions section). Theroadislong (talk) 22:21, 15 January 2025 (UTC)
  • Comment: "segregated populations.[7][8][9][10][11][12][13][14]" is a prime example of WP:CITEKILL. Instead we need one excellent reference per fact asserted. If you are sure it is beneficial, two, and at an absolute maximum, three. Three is not a target, it's a limit. Aim for one. A fact you assert, once verified in a reliable source, is verified. More is gilding the lily. Please choose the very best in each cas 🇺🇦 FiddleTimtrent FaddleTalk to me 🇺🇦 17:56, 13 November 2024 (UTC)

Single-cell Optical Pooled Screening (OPS) is a high-content and pooled type of single-cell screening that profiles single cell phenotypes by optical microscopy and links the profile of each cell to genetic perturbation(s) identified by in situ genotyping. OPS identifies phenotypic responses of cells to genetically-defined variation in a large-scale, systematic manner. High-content single-cell screening and OPS have been adopted by the biotechnology industry for applications in drug development.[1][2][3]

High-content pooled single-cell genetic screens became available as a functional genomics technique starting circa 2016.[4] While the genetic intervention (also known as a "genetic perturbation" in CRISPR screening) can be of any type that can be associated with a genetic sequence in the cell, including modifications in protein-coding or regulatory sequences,[5] CRISPR systems are the most common methodology for affecting genetic perturbations in OPS efforts.[6] Similar to the post-hoc identification of cellular phenoypes in the high content imaging method Cell Painting,[7][8] the high-content nature of OPS data enables screens for cellular phenotypes not considered prior to data generation and in-depth analysis of the primary screening data to classify and prioritize screening hits.[4] As an intrinsically single-cell-resolved approach, OPS is recognized as capable of identifying perturbation effects on the distribution of single-cell phenotypes across cells in addition to population-average phenotypes based on central tendency statistics.[9][10] Researchers use OPS to visually assess how gene disruptions and other genetic perturbations cause changes in cellular characteristics like morphology,[11] protein localization,[12] or intracellular signaling via transduction of signals detected by biochemical receptors in the cell.[13] OPS requires in situ genotyping,[14] for example by in situ sequencing[15][16] the perturbation in each cell or a nucleotide sequence "barcode" (analogous to the UPC barcode) that links image-based cell phenotypes to specific genetic alterations at the single-cell level. OPS is used in functional genomics,[6] drug discovery,[7] and disease research.[17]

Context

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OPS is one of two approaches (the other being single-cell next-generation sequencing (NGS)) available to generate high-content single-cell screening data.[18] High-content single-cell functional genomic screens[4] differ from previously established pooled genetic screening approaches relying on enrichment of perturbation identifier frequency in selected versus non-selected or original cell populations.[19][20] In contrast, high content single-cell screens like OPS match cell phenotypes and perturbation identifiers at the single-cell level, enabling characterization and possible classification of phenotypes post-hoc based on the primary screening data output.[18] Perturbed cell phenotypes are interpreted based on the nature of the perturbations enriched in a phenotypic class,[11] or a quantitative trait can be directly mapped to genetic alteration in a regulatory or coding sequence.[21]

The NGS approaches for high-content single-cell screening include single-cell RNA-seq screening methods known as Perturb-seq,[22][23] CRISP-seq,[24] or CROP-seq[25][26] in the perturbation screening context. Perturb-seq/CRISP-seq/CROP-seq provide assessment of transcript abundances which can be used to detect effects on transcriptional networks and the cell states these characterize, while OPS directly reads out cellular structures, dynamic molecular/cellular functionality in live cell settings, and can achieve high resolution of cell states.[18] As an imaging method, OPS is applicable where spatial relationships are relevant, for example, the subcellular distribution or localization of organelles or molecular components,[27] and spatial relationships among cells.[28] Imaging assays can also score cell non-autonomous phenotypes such as cell-cell interaction phenotypes, tissue context-dependent phenotypes, and the effect genes have outside the cell.[29][30] In one study, live-cell imaging with single-molecule sensitivity applied to characterize regulatory accuracy in timing, localization or expression as a function of genetic regulatory elements.[14]

A wide range of approaches including robotic picking[31], Visual Cell Sorting[32], CRISPR-based microRaft followed by guide RNA identification (CRaft-ID)[33], single-cell isolation following time-lapse imaging (SIFT)[34], AI-photoswitchable screening (AI-PS)[35], optical enrichment[36], image-enabled cell sorting (ICS)[37], and Photopick[38] have been reported for pooled enrichment screens for image-based cell phenotypes. These methods all work by segregating cell populations according to pre-specified single-cell image characteristics and bulk readout perturbation identifier abundance in the segregated populations.

History

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OPS was developed concurrently with Perturb-seq,[22][23] CRISP-seq,[24], and CROP-seq[25][26]. In 2017, the first report of an OPS described a small CRISPR interference screen that perturbed different components regulating a fluorescent reporter protein.[14] In this study, the live-cell phenotyping step was followed by FISH-based readout of barcodes expressed by T7 RNA polymerase from the same plasmid as the CRISPR single guide RNA (sgRNA). Another early report described an OPS with a bacterial library of mutated fluorescent proteins also followed by FISH-based readout of barcodes.[39] Applications in human cells with CRISPR perturbations were subsequently reported with readout of thousands of sgRNA CRISPR perturbations by in situ sequencing[15] of sgRNA and barcode sequences amplified from mRNA using rolling circle amplification (RCA) and sequencing by synthesis chemistry;[12] and in another example, readout of >100 sgRNA perturbations by FISH.[40] Protein epitopes have also been applied to encode genomic perturbations for enrichment[41] and in vivo OPS with readout from tissue sections.[42][43]

A genome-wide scale loss-of-function CRISPR OPS in human cells was reported in 2023 and included high-content phenotypes recorded from 10,366,390 cells assigned to one of 80,408 sgRNA perturbations.[13] Other genome-wide OPS datasets were reported for infection of human cells by filoviruses,[10] cell signaling,[44] and morphological characterization under different culture conditions.[45] New protocols for nucleotide-level barcode readout incorporate "Zombie" in situ T7 RNA polymerase-driven in vitro transcription[46] for amplification[47][48] or pre-amplification[49][50] of OPS readout. A recent application of OPS is genome-wide tracking of chromosome loci over the cell cycle.[51]

Background

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Pooled Genetic Screening

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Pooled genetic screening involves the introduction of a library of engineered genetic perturbations, such as CRISPR knockouts/knockdowns/base edits, RNA interference (RNAi) knockdowns, or open reading frames (ORFs) encoding synthetic proteins into a population of cells. Each cell in the population is targeted with one or more genetic modifications from the library, and the effects of these modifications on cell phenotype is then studied. Pooled screening methods reduce batch effects compared with arrayed screening approaches where perturbations are evaluated individually in microplate wells[52] and enable comparisons of perturbed, control, and differently perturbed cells that are mixed together within the same culture environment.

Optical Imaging Technologies

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OPS utilizes optical microscopy techniques to generate images that represent cell phenotypes and perturbation genotypes. High content single-cell screening applications require data from millions of cells,[53] and a need for rapid image generation in the OPS setting. The requirement for the application of different reagents to visualize cellular phenotypes and genotypes make automation of reagent dispensing and image acquisition highly desirable.[6]

Epifluorescence "wide field" microscopy is the technique most commonly applied to generate OPS data.[21] Fast confocal methods including automated commercial screening microscopes used for high content screening are also suitable and may be preferable for phenotypes that require 3-dimensional analysis and samples with high fluorescence background.[54] OPS can be carried out using different microscopy techniques for phenotyping and/or genotyping when the images produced can be registered to match cells across techniques.[21]

Common phenotyping assays used for OPS include cell morphology/cell painting, localization of specific biomolecules using protein tags, immunofluorescence, or nucleic acid probes,[40] and reporter systems that convert cellular activities to optical signals. OPS has been applied to read out phenotypes by live cell imaging[55][11] including the dynamic activities of living cells. For OPS, live cell imaging is carried out before in situ readout of perturbation genotypes, as the in situ protocols applied require cell fixation.[21]

Increasing the number of readout channels for phenotyping, for example by localizing a large number of different biomolecules in cells, increases the resolution of the biological phenotype.[56] A recent preprint reported the use of iterative indirect immunofluorescence imaging (4i)[57] as part of a readout panel totaling 11 markers.[44]

Microfluidics

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The first application of OPS was conducted with bacteria in microfluidic devices for the flow of reagents for fixation, permeabilization, and genotyping over the cells after optical phenotyping.[58][59] The microstructured devices also enabled exponential growth over multiple generations while maintaining the initial clonal representation independent of growth rate variation across lineages, a key benefit when some phenotypes are linked to fitness advantages or disadvantages.

Methodology

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Creation and Use of Genetic Libraries

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OPS requires genetically perturbed cell populations similar to Perturb-seq,[22][23] CRISP-seq[24], and CROP-seq[25][26] and enrichment[20] screens. In mammalian systems, viral transduction is commonly used to introduce elements of the genetic perturbation system such as sgRNAs into cells. For perturbation constructs depending on linkage between sgRNA and barcode elements or among sgRNA or barcodes,[60] attention must be paid to the construct design and protocols used to maintain the intended linkage[61][62]. Errors in component synthesis, procedures for production of DNA or viruses, and processes occurring in the screening population can de-link elements, and require strategies for mitigation[63] to maintain screen performance, particularly for systems capable of multiple[64] perturbations.

Bacterial libraries for OPS have been generated using episomal and chromosomally integrated genomic perturbations. A preferred method is to express sgRNA or ORFs from plasmids that also encode T7-expressed RNA barcodes.[14] Strain libraries based on chromosomal mutations have been constructed using the phage lambda-derived Red recombination system.[65] For chromosomally expressed barcodes, Zombie in situ T7 in vitro transcription pre-amplification can achieve the target concentration required for RCA and combinatorial FISH genotyping.[66][51]

Data Analysis Methods

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OPS data analysis requires the extraction of phenotype parameter scores from each cell and matching these scores with perturbation genotype identifiers extracted from each cell.[21] Then, the distributions of phenotype parameter scores can be determined for each perturbation genotype and tested against the distributions observed for cells receiving control perturbations or a different perturbation genotype.[45]

Primary analysis of phenotype images comprises two major steps. First, cell segmentation and the alignment of segmentation masks across all the available images. Second, feature identification and extraction of feature scores from the pixel level data. Primary analysis of phenotyping images may involve a range of computational approaches including machine learning approaches such as support vector machines, PCA, and low-dimensional embedment with clustering,[11] and deep learning.[13][10] For live cell imaging the segmented cells are tracked in time lapse movies and time-dependent phenotypes can be additionally scored.[11]

Primary analysis of in situ genotype data (eg from sequential FISH or in situ sequencing) also comprises two major steps. First, identification of signal loci and association of loci with cells and analysis of signal sequences. Second, assignment of perturbation identifiers to signal loci and cells. Primary analysis of genotype images may involve a range of computational approaches including modern machine learning approaches.[67]

Primary analysis concludes with the merging of single-cell phenotypes and genotypes and identification of the set of cells with high-quality matched single-cell phenotype scores and genotype identifiers. Secondary analysis entails testing for perturbation effects and integration with other data resources and biological knowledge. New machine learning approaches for the identification and interpretation of perturbation effects from OPS datasets[68] and for the optimal design of OPS experiments[69] are active areas of development.

Applications

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OPS is applicable across multiple research areas and approaches, including:

  • Functional Genomics and Cell Biology: OPS facilitates large-scale functional studies by revealing how specific genetic changes affect a wide range of cellular characteristics and processes[11]
  • Drug Discovery: By identifying genes that regulate key disease-associated cellular pathways/phenotypes/states,[44] and the gene functions that must be intact for a drug to act, OPS helps researchers discover new drug targets[10] and better understand the molecular mechanisms of drugs[54]
  • Disease Research: OPS is used to investigate the etiology and pathophysiology of diseases including cancer,[30] cell models used to study neurodegenerative conditions,[49] and infectious diseases.[13] By identifying genes and alleles associated with disease phenotypes in research models, and exploring the impact in research models of genes and alleles known to be associated with clinically-defined disease in humans, OPS can contribute to the fundamental understanding of disease manifestation.
  • Diagnostics: OPS has been used combined with antibiotic susceptibility testing to identify the species in a mixed sample after the phenotypic susceptibility has been determined for each cell[70]

Advantages and Limitations

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Advantages

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  • Causality: As a genetic method, OPS provides a basis for direct causal inference based on results of genomic perturbations/interventions
  • Phenotype discovery: Exploratory analysis of OPS datasets enables post-hoc discovery of new cell phenotypes - for example from unsupervised machine learning methods - and subsequent analysis of gene perturbation effects on such novel phenotypes
  • Direct phenotypic readout: OPS provides direct visual assessment of generalized and disease-associated cellular phenotypes
  • High Throughput: OPS enables the screening of thousands of genetic perturbations in a single experiment with fast and low-cost optical readout. The estimated cost per cell including commercial instrumentation, commercially available reagents, and labor using a protocol[21] first reported in 2018[71] for 12 cycles of in situ SBS in human cells was $0.0005/cell.[12]
  • CRISPR Precision: compatibility with CRISPR allows OPS to benefit from highly specific genetic perturbations affected by CRISPR systems including Cas9-based methods.
  • Perturbation technology compatibility: OPS is compatible with the same perturbation technologies (eg CRISPR methods) and perturbation/cell libraries used for many other screening approaches, facilitating integrative analysis across OPS datasets and across OPS and other screening dataset types. OPS is also compatible with approaches requiring or electing the use of multiple perturbations or guide RNAs to be delivered to each cell.[53][64]
  • Perturbation readout compatibility: OPS is compatible with standard perturbation constructs and readout of barcode or guide RNA sequences from RNA or DNA, examples include Perturb-seq[22], CRISP-seq[24], CROP-seq[25][26], and CROPseq-multi[64]
  • Phenotyping compatibility: OPS poses few fundamental limitations on the types of samples or cellular imaging assays. Phenotyping can be carried out using any imaging assay and any optical hardware compatible with imaging before or after genotyping that provides cellular throughput sufficient to meet the requirement of the screen designed. OPS protocols using Zombie in situ T7 RNA polymerase pre-amplification of DNA identifiers pose few restrictions on prior sample processing.[49][50]
  • High hit rate: when multiple molecular markers are used for readout and analysis scores many cellular features, a large fraction of perturbations result in reproducible phenotypic effects[11]
  • Live cell phenotypes: Phenotyping of live cells avoids fixation artifacts and enables studies of dynamic molecular and cellular phenomena across a wide range of time scales[11][12]
  • High statistical power and hit validation rate: the pooled format of OPS reduces interference from batch effects; matched single-cell genotyping and phenotyping allows stringent quality filtering to restrict analysis to cells with high-quality genotypes and phenotypes; image features can be scored for all cells without feature dropout,[27] reducing score variability
  • High interpretability: fine-scale classification of phenotypic hits sets novel hits in the biological context of co-classified perturbations with known functions.[11] This remains the case when no interpretation is available for the scored image features themselves.

Limitations

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  • Assay development: While some imaging assays like Cell Painting have been substantially standardized, a wide variety assay protocols are relevant to OPS, and some require specialized staining reagents and procedures that may have compatibility conflicts with in situ genotyping protocols[50]
  • Perturbation efficacy: OPS is impacted by limitations of the perturbation methodology used.[72] For example, limited perturbation efficiency or specificity will degrade statistical power to detect phenotypic affects associated with the intended perturbation
  • Data generation cost: High-content imaging systems and the reagents consumed in processing genetic libraries have significant costs,[21] potentially limiting the accessibility or scalability of OPS
  • Data complexity: The high quantity of imaging data generated by OPS requires substantial computational power and advanced software for storage, processing, and analysis, incurring costs and the need for expert attention

Future Directions

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Ongoing developments in related areas are described as creating new opportunities for OPS technology and applications:

  • Deployment for a wider range of biological research models[73]
  • Use together with imaging technology advancements including methods for live-cell imaging[74]
  • Integration with a wider range of image-based assays of live and fixed cells including the use of advanced reporter systems[75], tagging strategies[76], and probes/staining reagents
  • Machine learning for primary analysis (pixels to matrices) of genotyping[67] and phenotyping[10] data
  • Machine learning for secondary analysis and integration with other biological data and broader knowledge[77] may speed up the analysis phase[6] of projects

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