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Updated: Nov 20, 2025
Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
Published on: June 30, 2017
High Content Analysis Across Signaling Modulation Treatments for Subcellular Target Identification Reveals
1Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India.
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.
Insights
This study uses computational image analysis to identify drug targets by analyzing cellular responses to compounds. The findings reveal organelle-specific sensitivities and signaling plasticity, aiding in understanding cellular phenotypes.
Area of Science:
- Computational biology
- Cellular imaging
- Drug discovery
Background:
- Cellular phenotypes arise from downstream effects of bioactive compounds.
- Understanding these downstream targets is crucial for deciphering cellular responses.
Purpose of the Study:
- To computationally identify subcellular targets of bioactive compounds.
- To understand the basis of cellular phenotypes observed in response to treatments.
- To explore organelle-specific sensitivities and signaling heterogeneity.
Main Methods:
- Utilized cell-painting-based light microscopy to capture cellular phenotypes.
- Generated morphological profiles from multiple cellular organelles across ~270 diverse treatments.
- Employed multivariate regression for predicting cellular response based on organelle profiles.
Main Results:
- Demonstrated phenotypic diversity across treatments based on image-based profiles.
- Identified the endoplasmic reticulum as a frequently targeted organelle.
- Validated a prediction model for cellular response using ~1,000 new compounds.
- Quantified signaling heterogeneity and identified evidence of signaling plasticity.
Conclusions:
- Image-based profiling facilitates subcellular target identification in drug discovery and disease states.
- The approach provides insights into the hallmarks of cellular response and signaling plasticity.
- Revealed varying effects of compounds on cellular integrity despite specific modulation outcomes.
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