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Draft:Optical Pooled Screening

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Single-cell Optical Pooled Screening (OPS) is a high-content pooled single-cell screening approach that profiles single cell phenotypes by optical microscopy and links each profile to a defined genetic perturbation identified by in situ genotyping. OPS provides sensitive and broad access to phenotypic responses of cells caused by user-defined genetic changes in a large-scale, systematic manner.

High-content pooled single-cell genetic screens have gained popularity in biological research over the last decade. CRISPR systems are a popular methodology for affecting genetic perturbations in OPS efforts, but the genetic modification can be of any kind, such as modifications in coding or regulatory sequences. The high-content nature of OPS data enables post-hoc discovery of live cell phenotypes not considered before the experiment and in-depth analysis of the primary screening data to classify and prioritize screening hits. As an intrinsically single-cell-resolved approach, OPS is also suited to identify perturbation effects on cellular heterogeneity. This allows researchers to visually assess how gene disruptions and other genetic perturbations cause changes in cellular characteristics like morphology, protein localization, regulatory accuracy or intracellular signaling. After the phenotype is recorded for each cell, the genotype of each cell is identified in situ, linking the image data to specific genetic alterations at the single-cell level. OPS is used in functional genomics, drug discovery, and disease research.

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. High-content single-cell functional genomic screens represent a significant advancement over previously established pooled genetic screening approaches relying on enrichment of perturbation identifier frequency in selected versus non-selected or original cell populations, which became very popular in tandem with the availability of highly specific and straightforwardly encodable CRISPR perturbations.[1] In contrast, high content single-cell screens like OPS with phenotypes and perturbation identifiers matched at the single-cell level enable characterization and possible classification of phenotypes post-hoc based on the primary screening data output. Perturbed cell phenotypes may be interpreted based on the nature of the perturbations enriched in a phenotypic class, or a quantitative trait can be directly mapped to genetic alteration in a regulatory or coding sequence.

The NGS approaches for high-content single-cell screening include single-cell RNA-seq known as Perturb-seq,[2][3] CRISP-seq,[4] or CROP-seq[5][6] in the perturbation screening context. Perturb-seq/CRISP-seq/CROP-seq provides assessment of transcript abundances which can be used to detect effects on transcriptional networks and the cell states these characterize, while OPS is naturally suited for readout of cellular structures, dynamic functionality when applied in a live cell setting, and can achieve high resolution of cell states. As an imaging method, OPS is uniquely suited for applications where spatial relationships are relevant, for example, the subcellular distribution or localization of organelles or molecular components, and spatial relationships among cells. The ability to relate cells spatially and/or temporally provides a basis to score cell non-autonomous phenotypes such as cell-cell interaction phenotypes or tissue context-dependent phenotypes. The possibility for live-cell imaging with single-molecule sensitivity makes it possible to characterize regulatory accuracy in timing, localization or expression as a function of genetic regulatory elements.

A wide range of methods have been reported for pooled enrichment screens for image-based phenotypes. These methods all work by segregating cell populations according to pre-specified single-cell image characteristics and reading out bulk perturbation identifier abundance in the segregated populations.[7][8][9][10][11][12][13][14]

History

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OPS was developed concurrently with Perturb-seq/CRISP-seq/CROP-seq[2][3][4][5][6]. The first reported OPS application was a small CRISPR interference screen that perturbed different components regulating a fluorescent reporter protein.[15] The live-cell phenotyping step was followed by hybridization-based readout of barcodes expressed by T7 RNA polymerase from the same plasmid as the single guide RNA (sgRNA). Simultaneously, Emanuel et al. performed an optical pooled screen with a bacterial library of mutated fluorescent proteins also followed by hybridization-based readout of barcodes.[16] Applications in human cells with CRISPR perturbations were subsequently reported with readout of thousands of sgRNA CRISPR perturbations by de novo in situ sequencing of sgRNA and barcode sequences amplified from mRNA using rolling circle amplification (RCA) and sequencing by synthesis chemistry[17][18] and >100 sgRNA perturbations by hybridization.[19] Protein epitopes have also been applied to encode genomic perturbations for enrichment[20] and in vivo optical pooled screens with readout from tissue sections.[21]

A genome-wide loss-of-function CRISPR screen in human cells was reported in 2023.[22] and included high-content phenotypes recorded from 10,366,390 cells assigned to one of 80,408 sgRNA. Several other genome-wide OPS datasets have been reported.[23][24][25] New protocols for nucleotide-level barcode readout leveraging "Zombie" in situ T7 in vitro transcription[26] for amplification[27][28] or pre-amplification[29][30] of OPS readout. A new application of OPS is genome wide tracking of chromosome loci over the cell cycle[31]

Background

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Pooled Genetic Screening

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Pooled genetic screening involves the introduction of a library of genetic alterations, such as CRISPR knockouts/knockdowns/base edits, RNA interference (RNAi) knockdowns, or ORFs (open reading frames encoding synthetic products) 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 are then studied. As a pooled screening method, OPS reduces batch effects and enables comparisons of perturbed, control, and differently perturbed cells within the same culture environment, and potentially in direct physical juxtaposition.

Optical Imaging Technologies

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OPS utilizes standard microscopy techniques to generate images that represent cell phenotypes and perturbation genotypes. Many OPS applications require the imaging of millions to tens of millions of cells, and require high-throughput microscopy to enable data generation in an acceptable amount of time. The large amount of imaging data that must be generated and requirement for the application of different reagents to visualize cellular phenotypes and genotypes make automation of reagent dispensing and image acquisition highly desirable.

Epi-fluorescence "wide field" microscopy is the technique most commonly applied to generate OPS data. Fast confocal methods are also suitable and may be preferable for phenotypes that require 3-dimensional analysis and samples with high fluorescence background. 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.

Common phenotyping assays used for OPS include cell morphology profiling or "cell painting", localization of specific biomolecules of interest including proteins (ie as visualized by fusion to fluorescent proteins/tags or immunofluorescence) and RNA (ie as visualized by hybridization to synthetic probes), and reporter systems that convert cellular activities to optical signals. OPS is compatible with phenotyping of live cells and the identification of dynamic activity of live cells across a broad range of time scales. Live cell OPS applications typically record live-cell phenotypes before in situ readout of perturbation genotypes, as the in situ protocols applied require cell fixation.

A frontier in OPS is the expansion of the number of readout channels for phenotyping, with different channels often corresponding to readout of different biomolecules in cells. The integration of high-multiplexity techniques able to provide images representing the distribution of many different biomolecules is an active area of research. A recent preprint reported the use of iterative indirect immunofluorescence imaging (4i) [Kramer 2023] as part of a readout panel totalling 11 markers.[32]

Microfluidics

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The first application of OPS was conducted with bacteria in microfluidic devices designed to provide a fluidic interface for flowing reagents for fixation, permeabilization, and genotyping over the cells after optical phenotyping[33] and enables automated exchange of reagents.[34] 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/CRISP-seq/CROP-seq.[2][3][4][5][6] and previous enrichment screening approaches[1][2][3][4][6][18].[35][36] 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, attention must be paid to the construct design and protocols used to maintain the intended linkage[18][37][38][36]. Errors in component synthesis, procedures for production of DNA or viruses, and processes occurring in the screening population can de-link elements and degrade screening performance[reference needed to Hedge et al 2018][36]

Bacterial libraries for OPS have been generated using episomal and chromosomally integrated genomic perturbations. A preferred method is to expressing sgRNA or ORFs from plasmids that also encode T7-expressed RNA barcodes.[39] Strain libraries based on chromosomal mutations have been constructed using the phage lambda-derived Red recombination system.[40] 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.[41][31]

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. 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.

Primary analysis of phenotyping 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[42][22][23]. For live cell imaging the segmented cells are tracked in time lapse movies and time-dependent phenotypes can be additionally scored.

Primary analysis of in situ genotyping data (eg from sequential FISH hybridization or sequencing-by-synthesis) 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 genotyping images may involve a range of computational approaches including machine learning approaches.[43]

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[44] and for the optimal design of OPS experiments[45] are currently under 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 related to cellular processes and dynamics.
  • Drug Discovery: By identifying genes that regulate key disease-associated cellular pathways/phenotypes/states, and the gene functions that must be intact for a drug to act, OPS helps researchers discover new drug targets and better understand the molecular mechanisms of drugs.
  • Disease Research: OPS is used to investigate the etiology and pathophysiology of diseases including cancer, neurodegenerative conditions, and infectious diseases. 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 that the phenotypic susceptibility has been for each cell.[30]

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. OPS is more than 10-fold higher in throughput and lower in cost than alternative single-cell NGS readouts. The estimated cost per cell including commercial instrumentation, commercially available reagents, and labor using a protocol first reported in 2018 for 12 cycles of in situ SBS in human cells was $0.001/cell.[17][18][35]
  • 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.
  • 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.[2], CRISP-seq[4], CROP-seq[5][6], and CROPseq-multi[36]
  • 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.
  • 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.
  • 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.
  • 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, 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. 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.
  • Perturbation efficacy: OPS is impacted by limitations of the perturbation methodology used. 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, potentially limiting the accessibility of OPS.
  • Data complexity: The vast amount of imaging data generated by OPS requires substantial computational power and advanced software for processing and analysis, incurring costs and the need for expert attention.

Future Directions

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Several emerging trends are poised to further advance OPS:

  • Deployment for a wider range of biological research models
  • Use together with imaging technology advancements including super-resolution microscopy and methods for live-cell imaging
  • Integration with a wider range of image-based assays of live and fixed cells including the use of advanced reporter systems, tagging strategies, and probes/staining reagents
  • Scaling and Automation: Improvements in automation of wet-lab and computational steps will make even larger-scale OPS experiments tractable.
  • Integration with Other Technologies: Combining OPS with other methods, such as single-cell RNA sequencing or proteomics, could provide further information about gene function and cellular phenotypes
  • Machine learning for primary data analysis (pixels to matrices) and secondary analysis/integration with other biological data and broader knowledge will speed up the analysis phase of projects, make biological connections that may otherwise be missed, and produce human-interpretable result summaries based on extensive data gathering and comparison

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(Specific references would include key studies on CRISPR, RNAi, OPS, and related optical imaging technologies.)

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