Cell Painting Meets Thermal Proteome Profiling to Decode How Drugs Work

One of the most stubborn problems in drug discovery is figuring out what a molecule actually does inside a cell. A compound may be designed to hit one protein, yet studies have shown that many cancer drugs in clinical trials kill cells through previously unknown off-target interactions rather than through their intended target. Understanding the full list of drug targets for a small molecule can explain adverse toxicity, drug tolerance, and hidden mechanisms of action that might otherwise doom a drug late in development. Now, a team of Swedish researchers led by Camilla Johansson and Erik T Jansson of Uppsala University has unveiled a computational strategy that fuses two of the most powerful high-throughput profiling technologies in chemical biology, and the results, published in Molecular Systems Biology, suggest the combined approach can pinpoint drug targets and mechanisms with an accuracy neither method achieves alone.

The two technologies at the heart of the study capture fundamentally different views of drug activity. The first, thermal proteome profiling, or TPP, grew out of the cellular thermal shift assay introduced in 2013. The principle is elegant: when a small molecule binds a protein, it often shifts the protein’s melting temperature, either stabilizing or destabilizing it. Researchers heat intact cells to a range of temperatures before lysis, then use mass spectrometry to measure how much of each protein remains soluble at each temperature. Coupling this to whole-proteome analysis, first demonstrated by Savitski and colleagues in 2014, allows an unbiased survey of thousands of drug-protein interactions in a single experiment. A typical whole-cell TPP experiment yields tens to hundreds of proteins with perturbed thermal stability, but these include not only direct drug targets but also proteins in complexes with, or downstream of, the true target, making it difficult to separate cause from consequence.

The second technology, Cell Painting, works from the opposite direction. Instead of measuring protein behavior directly, it captures the morphological fingerprint a drug imposes on a cell. Cells are treated with compounds and then stained with a fixed panel of fluorescent dyes labeling the nucleus, endoplasmic reticulum, nucleoli and cytoplasmic RNA, Golgi apparatus and actin cytoskeleton, and mitochondria. Software such as CellProfiler then extracts thousands of features per cell, producing a rich quantitative description of each treatment’s morphological perturbation. Because the assay is inexpensive and highly multiplexable, it can be applied to thousands of compounds at once. Compounds with similar mechanisms of action tend to induce similar morphological changes, so machine learning can infer the mechanism of an unknown compound by comparing it to well-annotated neighbors. The weakness is that small molecules often have many targets, and the accuracy of predictions depends heavily on the quality of available annotations.

The Uppsala team’s insight was that these two data types are complementary in a very specific way. TPP detects target engagement directly but misses proteins, sometimes because a peptide fails to ionize in the mass spectrometer, sometimes because a protein is more thermally stable than the tested temperature range, and sometimes because membrane-associated proteins sediment during centrifugation. Cell Painting, meanwhile, can suggest targets through compounds that produce similar shapes but cannot confirm physical binding. The researchers therefore built a pipeline that starts with the list of thermally stabilized or destabilized proteins from a TPP experiment and constructs a protein-protein interaction network using the STRING database, which aggregates experimentally scored interactions, curated pathway databases, and text mining. In parallel, the compound of interest is located within a Cell Painting dataset of 5259 compounds from the SPECS drug repurposing repository, profiled in U2OS bone cancer cells, and the known targets of its closest morphological neighbors are used to fill gaps in the network.

A key technical challenge was clustering the Cell Painting data reliably. Popular algorithms such as k-means and HDBSCAN are stochastic and can produce very different cluster structures on repeated runs, particularly on high-dimensional, poorly separated data like morphological profiles. The team turned to SC3s, a consensus clustering method originally developed for single-cell RNA sequencing, which combines principal component analysis with thousands of repeated k-means runs and summarizes the results into a consensus matrix. Running the algorithm 2000 times across five different cluster numbers, between 100 and 180, produced highly reproducible clusters for most test compounds. The cluster-derived target candidates were then merged with the TPP network, and betweenness centrality scores, a graph-theoretic measure of how often a node lies on shortest paths between others, were used to filter the merged network. Because drug targets have been shown to occupy central positions in interaction networks, the researchers reasoned that true targets would rank among the highest-scoring nodes.

To validate the approach, the team used publicly available TPP datasets for five well-characterized drugs: the BET bromodomain inhibitors (+)-JQ1 and I-BET151, the BRAF inhibitor vemurafenib, the ALK and MET inhibitor crizotinib, and the histone deacetylase inhibitor panobinostat. For (+)-JQ1, 67 proteins showed dose-dependent thermal shifts, and the resulting network correctly placed the known targets BRD4, BRD3, and BRD2, along with the documented off-target HADHA, among the top-ranked nodes. For I-BET151, 40 perturbed proteins yielded a network containing BRD4, BRD3, and BRD2. Intriguingly, although the two compounds share targets, only 11 perturbed proteins overlapped between them, suggesting they regulate different pathways downstream of target binding. Gene Ontology and Reactome enrichment analysis of the network communities recovered mechanisms consistent with the known biology of (+)-JQ, including lysine-acetylated histone binding and activation of cell death.

The validation extended further. For panobinostat, TPP alone detected only three of eleven known targets, but Cell Painting cluster analysis contributed eight more, and the combined network placed six targets among the top-ranked proteins. For vemurafenib, the primary target BRAF was entirely absent from the TPP data, while the off-target FECH was missing from the Cell Painting cluster, so only the integrated network captured both. The method correctly identified known targets and mechanisms for four of the five compounds. The exception, crizotinib, proved instructive rather than disappointing: its true targets ALK, ROS1, and MET are transmembrane proteins that classical TPP protocols rarely detect, and neither U2OS nor K-562 cells expressed ALK or ROS1. Instead, the analysis highlighted the Bcr-Abl pathway through the off-targets ABL1 and BCR, a biologically plausible finding given the high expression of these proteins in the K-562 leukemia cells used for TPP. The team then scaled the validation to a public Proteome Integral Solubility Alteration, or PISA, dataset covering 49 compounds, a high-throughput TPP variant in which all temperature treatments are pooled before mass spectrometry. The combined model achieved target prediction with a ROC AUC of at least 0.7 for 21 compounds, compared with 15 for PISA alone and 14 for Cell Painting alone, and outperformed the individual methods in the majority of cases.

As a proof of principle, the researchers applied the pipeline to sinomenine, a plant-derived alkaloid used in China to treat rheumatoid arthritis and pain, whose mechanism of action has remained largely obscure. They generated their own PISA data in U2OS cells treated with 10 and 30 micromolar sinomenine, finding 175 and 121 significant thermal shifts respectively, with 94 proteins affected at both doses. The integrated network revealed a strikingly multimodal profile. Among the highest centrality scores were the beta-2 adrenergic receptor ADRB2 and the NMDA receptor subunit GRIN2C, both endogenously expressed in U2OS cells, supporting an action on nervous system receptors consistent with the compound’s analgesic use and with previous mouse studies showing elevated GABA signaling. Enrichment analysis also flagged neurotransmitter receptor activity and chemical synaptic transmission. Equally intriguing was the appearance of cyclin-dependent kinase 2, CDK2, alongside pathways governing RNA binding, DNA replication, chromosome organization, and double-strand break repair, pointing to potential anti-tumor effects that corroborate earlier sinomenine studies. Additional communities implicated actin cytoskeleton organization, membrane trafficking, RHO GTPase signaling, and metabolic processes including the citric acid cycle, with the structural protein ACTB and the mitochondrial transporter SFXN1 scoring highly.

The authors are careful to note the method’s limits. It cannot detect a target unless that target shows thermal shifts or is annotated for morphologically similar compounds, and the workflow deliberately trusts TPP-derived nodes over Cell Painting-derived ones, which meant that in 7 of 15 cases a target seen only in imaging data failed to enter the final network. Cell line choice matters enormously, as the crizotinib case demonstrated, and the team recommends using the same cell line for both assays when investigating a compound with unknown targets. Clustering reproducibility also faltered for vemurafenib, the compound with the weakest morphological signal. Even so, the payoff is substantial: a typical mass spectrometry run covers only about half the proteome, roughly 10,000 of 20,000 canonical proteins, and TPP alone can leave researchers with 35 to 64 candidate targets to chase. The integrated workflow narrows that list to ten or fewer, promising to slash the time and cost of follow-up validation experiments. With the full pipeline written in Python and released on GitHub, the Uppsala team has handed chemical biologists a practical tool for turning two imperfect lenses on drug action into one considerably sharper picture, one that could accelerate both drug development and drug repurposing for compounds whose secrets have resisted decryption for decades.

Subject of Research: Integration of Cell Painting morphological profiling and thermal proteome profiling for drug target identification and mechanism of action inference

Article Title: Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action

Article References: Johansson, C., Johansson, M., Kasi, P. B., Larsson, M., Jakobsson, P.-J., Göransson, U., Carreras Puigvert, J., Spjuth, O., & Jansson, E. T. (2026). Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action. Molecular Systems Biology, 22(8), 1270-1291. https://doi.org/10.1038/s44320-026-00214-9

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00214-9

Keywords: Cell Painting, thermal proteome profiling, drug discovery, mechanism of action, target deconvolution, protein-protein interaction networks, proteomics, sinomenine, PISA, mass spectrometry, chemical biology, Uppsala University