Pooled genetic screens with image‐based profiling

Walton, Russell T.; Singh, Avtar; Blainey, Paul C.

doi:10.15252/msb.202110768

Cited by 11 publications

(6 citation statements)

References 222 publications

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“…Similar batch effect removal approaches have been important for meta‐analysis in other fields and data types, such as from RNA‐sequencing and peptide‐centered proteomics via mass spectrometry (Leek et al , 2010 ; Gregori et al , 2012 ; Tran et al , 2020 ). The batch effect removal strategy has also started to be applied in image‐based screening (Chandrasekaran et al , 2021 ; Walton et al , 2022 ). However, no standard batch effect method has yet been widely established for image analysis.…”

Section: Discussionmentioning

confidence: 99%

Multisite assessment of reproducibility in high‐content cell migration imaging data

Serra-Picamal

Bakker³

et al. 2023

Molecular Systems Biology

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High‐content image‐based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high‐quality open‐access data sharing and meta‐analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta‐analysis of results from live‐cell microscopy, have not been systematically investigated. Here, using high‐content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta‐analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image‐based datasets of perturbation experiments. Thus, reproducible quantitative high‐content cell image analysis of perturbation effects and meta‐analysis depend on standardized procedures combined with batch correction.

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Section: Discussionmentioning

confidence: 99%

Multisite assessment of reproducibility in high‐content cell migration imaging data

Serra-Picamal

Bakker³

et al. 2023

Molecular Systems Biology

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“…While high-content transcriptomic data from scRNA-seq can be measured from pooled CRISPR screens, the cost of achieving high-content readout as such from a genome-wide CRISPR screen can be unfeasibly high [ 114 ]. To overcome this hurdle, image-based profiling can provide high-content morphological readout for CRISPR screens at the genome scale [ 33 , 115 , 116 ]. Notably, optical-pooled CRISPR screens [ 117 ] can be combined with image-based profiling to create a genome-wide perturbation atlas and to construct a gene function network based on the uncovered genotype–phenotype relationships [ 115 , 118 , 119 ].…”

Section: Novel Applications Of Morphological Profiling In Drug Discoverymentioning

confidence: 99%

“…To overcome this hurdle, image-based profiling can provide high-content morphological readout for CRISPR screens at the genome scale [ 33 , 115 , 116 ]. Notably, optical-pooled CRISPR screens [ 117 ] can be combined with image-based profiling to create a genome-wide perturbation atlas and to construct a gene function network based on the uncovered genotype–phenotype relationships [ 115 , 118 , 119 ]. For example, Ramezani et al.…”

Section: Novel Applications Of Morphological Profiling In Drug Discoverymentioning

confidence: 99%

Morphological profiling for drug discovery in the era of deep learning

Tang,

Ratnayake,

Seabra

et al. 2024

Briefings in Bioinformatics

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Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

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“…Pooled screens have had a transformative effect on our ability to link genetic perturbations with their phenotypic outcomes at scale. While historically limited to a bulk readout of a few parameters 1,2 , typically through cell enrichment by viability or FACS, pooled screens have recently been integrated with high-dimensional phenotypic readouts through molecular profiling [3][4][5] (Perturb-Seq) or optical imaging 6 . These approaches have made it possible to study gene function and regulatory circuits at unprecedented resolution.…”

Section: Introductionmentioning

confidence: 99%

Highly multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Kudo,

Meireles,

Moncada

et al. 2023

Preprint

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Optical pooled screening (OPS) is a highly scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture, due to limitations associated within situsequencing (ISS) of perturbation barcodes. Here, we developed PerturbView, an OPS technology that leveragesin vitrotranscription (IVT) to amplify barcodes prior to ISS, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in iPSC-derived neurons, primary immune cells, and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NFκB signaling. Furthermore, we combined PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way toin vivoscreens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

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Pooled genetic screens with image‐based profiling

Cited by 11 publications

References 222 publications

Multisite assessment of reproducibility in high‐content cell migration imaging data

Multisite assessment of reproducibility in high‐content cell migration imaging data

Morphological profiling for drug discovery in the era of deep learning

Highly multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

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