AI Insight
Researchers developed CellMAPP, a high-throughput imaging platform that uses iterative antibody staining to study how genetic perturbations affect cellular architecture across multiple organelles simultaneously. By analyzing 17 cellular markers across 524 genetic perturbations in breast cancer cells, they demonstrated that combining multiple markers provides complementary information about cellular responses to genetic changes. They also created CHART, an analysis framework that uses archetype analysis and large language models to automatically identify and annotate distinct morphological states, revealing coordinated patterns of organelle reorganization that respond to genetic perturbations.
Why it matters
This approach enables more interpretable and comprehensive analysis of cellular responses in genetic screens, potentially accelerating drug discovery and our understanding of gene function. The framework can systematically identify genes that regulate specific organelle states and reveal complex multi-organelle coordination patterns that might be missed by single-marker approaches.
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⚠️ Preprint – Noch nicht peer-reviewed
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High-content imaging-based screens with small molecule or genetic perturbations rely on rich optical phenotypes, yet how distinct subcellular markers contribute complementary biological information remains unclear. Here, we introduce CellMAPP, a multiplexed organelle imaging platform that uses optical pooled screens with iterative antibody staining to map cellular architecture under perturbation. Using 17 markers across 524 genetic perturbations in MCF-7 cells, we systematically examined how individual, and combinations of, markers capture diverse morphological responses. We find that markers provide complementary views of perturbation effects, with increased multiplexing improving signal and the breadth of hit recovery, although some phenotypes are better recovered from a subset of features rather than a full profile. Because non-molecular image-based profiles can be challenging to link with interpretable biology, we developed CHART (available at https://github.com/Genentech/CHART), an archetype analysis framework that identifies distinct reference morphological states for each marker and uses multimodal large language models (MLLMs) to automatically annotate them, connecting cellular images to recognizable organelle phenotypes. Integrating archetypes across multiple markers revealed modular morphological programs that describe coordinated organelle reorganization in response to perturbation. This framework identified regulators of defined organelle states such as micronuclei, nominated novel gene functions and revealed complex patterns of multi-organelle reposition. Together, optical profiling with MAPP and CHART provides an interpretable, scalable framework for image-based screens that sheds light on rules governing cellular structural reprogramming.