High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Stossi, Fabio; Singh, Pankaj K.; Safari, Kazem; Marini, Michela; Labate, Demetrio; Mancini, Michael A.

doi:10.1016/j.bcp.2023.115770

Cited by 10 publications

(4 citation statements)

References 189 publications

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“…Information extraction from CP images involves two main steps: firstly, identifying regions of interest (e.g., cellular substructures) and extracting relevant features, typically done using open-source ( i.e., CellProfiler, (13,14)) or commercial software; secondly, performing feature reduction and representation (1,15–17) to facilitate further downstream analyses such as clustering and classification. Due to its biological and analytical relevance, single cell data in HT imaging-based campaigns is now widely used both for data quality control and for hit identification associated with various treatments based upon clear phenotypic differences (6,18–23). Nonetheless, while CP approaches have entered the mainstream for phenotypic screening, there is still a very active research effort to enhance robustness, processing speed, and sensitivity to best capture cell population heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…A notable exception is the work from Pearson et al, (18) that demonstrated the potential advantage of interrogating single cell information by analyzing the statistical distribution of data within cellular populations. While the traditional per-well average approach has proven to be successful and sufficient for hit calling from single end point assays in large scale screening campaigns, it ignores the inherent phenotypic heterogeneity in a cell population and the fact that many biological responses do not follow a normal distribution (6,19,23).…”

Section: Introductionmentioning

confidence: 99%

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SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Stossi,

Singh,

Marini

et al. 2024

Preprint

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Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software (i.e., CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe (SwiftPhenotypicAnalysis ofCells), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover’s Distance – EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Stossi,

Singh,

Marini

et al. 2024

Preprint

Self Cite

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“…For example, CRISPR-Cas9, siRNA, and cDNA screens are used to identify genes and proteins involved in specific pathways and processes and are also applied in academia and pharmaceutical companies for target identification. 9,10 HCS is also used in drug discovery to screen for novel compounds, and to better understand the biological effects of compounds. For example, compounds identified through traditional screening could be profiled further using phenotypic assays to investigate selectivity and toxicity, such as in-vitro micronuclei formation assays to identify compounds that could potentially damage DNA.…”

Section: Introductionmentioning

confidence: 99%

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

Seal,

Trapotsi,

Spjuth

et al. 2024

Preprint

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High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting’s ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

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“…28 These high-content representations have mostly been used for drug discovery 29,30 , but recent research has applied this approach for basic research studies, such as predicting cell health, toxicity, and cancer cell resistance. [30][31][32][33][34] While the most common image-based profiling approach is to aggregate single-cell measurements per well, single-cell approaches enable more systematic analyses and are becoming increasingly common. 35 For example, Schorpp et al developed a machine-learning approach to predict apoptosis and necrosis from Cell Painting data at single-cell resolution.…”

Section: Introductionmentioning

confidence: 99%

A morphology and secretome map of pyroptosis

Lippincott,

Tomkinson,

Bunten

et al. 2024

Preprint

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Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and healthy cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

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High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Cited by 10 publications

References 189 publications

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

A morphology and secretome map of pyroptosis

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