Abstract
Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular response. This response is a readout of cellular phenotypes captured from cell-painting-based light microscopy images. The readouts are morphological profiles measured simultaneously from multiple cellular organelles. Cellular profiles generated from roughly 270 diverse treatments on bone cancer cell line form the high content screen used in this study. Phenotypic diversity across these treatments is demonstrated, depending on the image-based phenotypic profiles. Furthermore, the impact of the treatments on specific organelles and associated organelle sensitivities are determined. This revealed that endoplasmic reticulum has a higher likelihood of being targeted. Employing multivariate regression overall cellular response is predicted based on fewer organelle responses. This prediction model is validated against 1,000 new candidate compounds. Different compounds despite driving specific modulation outcomes elicit a varying effect on cellular integrity. Strikingly, this confirms that phenotypic responses are not conserved that enables quantification of signaling heterogeneity. Agonist-antagonist signaling pairs demonstrate switch of the targets in the cascades hinting toward evidence of signaling plasticity. Quantitative analysis of the screen has enabled the identification of these underlying signatures. Together, these image-based profiling approaches can be employed for target identification in drug and diseased states and understand the hallmark of cellular response.
Highlights
Measurement of biological activity upon small molecule-based treatment has the potential to illustrate the mechanisms of action by comparing it with profiles of known compounds (Hughes et al, 2000; Lamb et al, 2006; Feng et al, 2009)
To annotate the treatment compounds with the relevance of the induced phenotype or the respective mechanism of action (MoA), the “ground truth” of the image data (Corsello et al, 2017) made available as part of the BBBC036 (Bray et al, 2017) from the Broad Bioimage Benchmark Collection (Ljosa et al, 2012) has been used
The script developed for the analysis presented in this study is done using MatLab
Summary
Measurement of biological activity upon small molecule-based treatment has the potential to illustrate the mechanisms of action by comparing it with profiles of known compounds (Hughes et al, 2000; Lamb et al, 2006; Feng et al, 2009). These measurements from high-throughput target-directed screens have been widely used for their potential application in drug discovery. A mechanism of action (MoA) usually refers to biochemical interaction through which the drug acts to induce pharmacological effect and phenotypic changes (Kandaswamy et al, 2016), which can be studied based on the phenotype
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