From Genes to Images: the Unexpected Power of Cell Images to Decode Drug Mechanism

Introduction
The search for new medicines has long been dominated by two main strategies: target-based and phenotypic screening. While target-based approaches focus on a single, well-understood biomarker, phenotypic screening takes a broader view, testing drugs for their ability to produce a desired change in a cell.
The advent of "omics" sciences, with gene, protein, and metabolite profiling, has provided the field of phenotypic drug discovery with powerful tools for elucidating drug mechanisms. With a more detailed understanding of phenotypic changes through molecular profiling, scientists were able to “evolve from serendipity to a structure–activity relationship-based approach that can minimize safety risks while […] optimizing phenotypic activity to increase the chances of clinical success” (Vincent et al., 2024).
But what if the most powerful of those tools was an image?
This post will introduce you to image-based phenotypic profiling—a powerful approach that complements and, in some cases, surpasses traditional omics methods.
Figure 1: Time-lapse imaging of ChromaLIVE, a data-rich, non-toxic dye for image-based profiling.
The omics revolution: a molecular view of the cell
To understand the power of images, we first have to appreciate the established omics methods for phenotypic profiling. Traditional omics methods provide a detailed molecular view:
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Genomics/Transcriptomics: Methods like RNA-seq measure the expression of thousands of genes to determine what a cell is “planning” to do. This is a foundational tool for identifying drug targets and understanding regulatory pathways.
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Proteomics: Mass spectrometry-based proteomics, or novel higher-throughput methods that measure secreted proteins (Nomic Bio) measure the levels and modifications of proteins, which are the cell's functional workhorses. This provides a more direct readout of cellular activity than gene expression.
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Metabolomics: This method measures the small-molecule metabolites (e.g., sugars, lipids), providing a real-time snapshot of the cell's metabolic state and its response to perturbations.
While these methods provide valuable molecular data, they all share a critical limitation: they provide data as a list of molecules and their quantities, and therefore, don’t necessarily capture a collective outcome of disease or drug effects.
The rise of image-based profiling: the power of seeing
Image-based profiling uses high-throughput microscopy to capture detailed visual information from cells. By staining different cellular compartments (ex. cytoplasm, mitochondria, nucleus), this method quantifies visual features of the cell, including its size, shape, and texture, as well as the size, shape, and spatial relationships of its internal organelles. The result is a rich, multi-dimensional profile of a cell’s phenotype, which gives scientists the opportunity to distinguish between what often are subtle phenotypes, like the phenotypes of specific diseases, or of specific drugs!
Multiple leading drug discovery and academic groups have adopted image-based profiling in the last decade, because of its unique advantages, which other omics methods cannot provide on their own.
Here are the 4 reasons why:
1. Capturing the ultimate manifestation of disease and drug effects
Cellular biology is more than the sum of its parts. A drug's effect is often the result of a cascade of interactions across different molecular layers—from gene expression to protein function to metabolic changes. Image-based profiling can capture the ultimate manifestation of the biological response in a single, holistic assay. The method measures the collective outcome of all these molecular events, making it a highly sensitive and unbiased readout that can reveal a phenotype even when individual molecular changes are subtle (Caicedo et al., 2017).
2. Spatial and contextual information
Image-based profiling provides data with spatial context. Although scientists don’t need to “see” changes with their eyes, the standard image analysis tools can capture spatial changes that are relevant to disease — for instance, if mitochondria have fragmented or elongated, how a cell's shape changed in response to a perturbation, or the broader organization of cells as populations. Other -omics methods lack this visual and spatial information, which are crucial for understanding a drug's mechanism of action. Recent research has shown that integrating morphological data from images with other omics data, such as spatial transcriptomics, significantly improves the accuracy and interpretability of cellular analysis (Laubscher et al., 2023).
3. Unbiased and hypothesis-free
Similar to the omics sciences, image-based profiling is an unbiased approach. It doesn't require a pre-existing hypothesis about a drug's target or mechanism. By measuring hundreds of morphological features, it can reveal unexpected or novel phenotypes. This makes it an ideal tool for large-scale, exploratory screens to efficiently identify compounds of interest before investing in more expensive, targeted assays (Rohban et al., 2017).
4. Cost-effective and high-throughput
Once an imaging protocol is established, it becomes a scalable and cost-effective method for screening large compound libraries. A single assay can generate a massive amount of data, making it a powerful first-pass screen to triage compounds and focus resources on the most promising ones.
Figure 2: "Cube-omics": visual representation of the different layers of molecular profiling in a cell, with images on top as the ultimate manifestation of biological processes.
Applications of image-based profiling for drug discovery…
…in the “omics” context
Modern drug discovery recognizes that the most powerful approach is to combine these different technologies in a multi-omics approach.
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Image-based profiling (e.g., with ChromaLIVE) is an ideal first-pass screen. It is high-throughput, cost-effective, and provides a holistic view. It helps identify gene perturbations or drugs that induce an interesting phenotype without needing to know the target.
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Genomics or Proteomics can then be used on the "hits" from the image-based screen. This provides the molecular data that image-based profiling lacks, in order to deconvolute the mechanism of action revealed by the images.
…in broader drug discovery
Image-based profiling has been successfully used in drug discovery across multiple stages:
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Target identification and gene annotations: Image-based profiling can be used with genetic perturbations (e.g., CRISPR knockout or RNAi knockdown) to understand the function of a gene. By observing the phenotypic changes that occur when a gene is silenced, researchers can infer its role in cellular biology and find compounds that produce a similar effect.
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Identifying disease phenotypes: The method can be used to identify and quantify unique morphological signatures associated with specific disease states or genetic perturbations. This allows for screening compounds that can either revert the diseased phenotype back to a healthy state or mimic a beneficial gene perturbation.
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Mechanism of action (MoA) elucidation: This is the primary and most significant application. Researchers use the image-based profiles generated to compare the effects of a novel compound to a library of compounds with known MoAs. If the unknown compound's profile is a close match to a known one, it suggests a similar MoA. This is a powerful, unbiased way to infer how a drug works.
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Toxicity profiling: The assay can detect subtle morphological changes that are indicative of various types of cellular stress and toxicity. By comparing a compound's profile to a database of known toxic agents, researchers can assess its potential for adverse effects early in the discovery process.
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Lead hopping and drug repurposing: The assay can identify structurally dissimilar compounds that produce a similar phenotypic effect. This allows chemists to find new chemical starting points for a drug (lead hopping) or discover new uses for existing drugs (repurposing).
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Assay outcome prediction: Image-based profiling can be used to reduce and replace traditional biological HTS assays in pharma drug discovery settings, as it has been shown to boost screening hit-rates and compound diversity when compared with multiple assay types, technologies, disease areas and target classes traditionally used in drug discovery. Initial expenses of image-based profiling screens would be easily recouped relative to those traditional assays (Haslum et al., 2024)
Figure 3: Image-based profiling workflow with ChromaLIVE-stained cells: 1) imaging, 2) segmentation, 3) feature extraction, and 4) ChromaLIVE image profiles.
Table 1: Comparison of Phenotypic Profiling Methods in Drug Discovery
Criteria |
Image-Based Profiling |
Transcriptomics (RNA-seq) |
Proteomics (Mass Spec, secreted proteins) |
Metabolomics (Mass Spec/NMR) |
Primary Information |
Spatial & Morphological Phenotype: Cell/organelle shape, size, texture, and spatial relationships. |
Gene Expression: Levels of all mRNA transcripts. |
Protein Expression & Modifications: Levels of all proteins and PTMs (e.g., phosphorylation). |
Metabolic Profile: Levels of small molecule metabolites (sugars, lipids, etc.). |
What It Measures |
The collective outcome of cellular biology. |
The instructions for cellular machinery. |
The functional machinery itself. |
The final output of cellular function. |
Key Advantage |
Provides spatial context and visual data; captures a more holistic view. |
Comprehensive, genome-wide view of gene activity; good for target ID. |
Measures the functional molecules of the cell; captures PTMs directly. |
Provides a direct view of a cell's metabolic state; very sensitive to environmental changes. |
Key Limitation |
Indirect molecular information. |
Lacks spatial and temporal context; mRNA doesn't always equal protein activity. |
Lacks spatial and temporal context; technically complex; expensive. |
Lacks spatial and temporal context; technically difficult; requires destructive sampling. |
Throughput & Scalability |
Highest. Highly scalable to HTS, single-cell resolution. |
High. Scalable to HTS (e.g., L1000, bulk RNA-seq); single-cell is lower throughput. |
Lowest. Primarily used for hit validation; not for large-scale screening. |
Low. Often used for hit validation; not for large-scale screening. |
Cost & Resources |
Lowest. Relatively low cost per data point; requires imaging hardware; |
Moderate. Sequencing is a significant cost; requires bioinformatics support. |
Highest. Requires expensive mass spectrometry hardware and expert staff. |
High. Requires expensive MS or NMR hardware and expert staff. |
Real-time data |
Yes (only with ChromaLIVE), can capture kinetics. |
None |
None |
None |
Workflow |
Non-destructive only with ChromaLIVE; fixation required otherwise. |
Destructive (cells must be lysed). |
Destructive (cells must be lysed). |
Destructive (cells must be lysed). |
In summary
Image-based profiling is a powerful and complementary partner to other omics methods. Its integration with genomic, proteomic, and metabolomic data provides a truly comprehensive view of how a drug affects a cell, from the molecular instructions to the collective outcome on cells that image-based profiles can provide.
By embracing image-based profiling, we can accelerate drug discovery, uncover new mechanisms of action, and, most importantly, deliver safer and more effective drugs to patients. The ability to "see" the cellular response is a game-changer, and it's time for images to take their rightful place as a central tool in the drug discovery toolkit.
References:
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5 Reasons Why Drug Discovery Leaders Choose Live Cells for their Cell Painting Assay
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Vincent, F., et al. (2022). “Phenotypic drug discovery: recent successes, lessons learned and new directions.” Nature Reviews Drug Discovery.
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Caicedo, J. C., et al. (2017). "Morphological profiling of drug-induced cellular phenotypes using a cell painting assay." Molecular Systems Biology.
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Laubscher, E., et al. (2023). "Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning." bioRxiv.
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Rohban, M. H., et al. (2017). "Screening thousands of drugs in cell painting image-based assays to map their mechanism of action." Bioinformatics.
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Seal, S., et al. (2024). “Cell Painting: A Decade of Discovery and Innovation in Cellular Imaging.” Nature Methods.
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Haslum, J. F., et al. (2024). “Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.” Nature Communications.
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