Protein Assembly, Not Abundance, Explains Why Cells Behave So Differently

For decades, biologists have treated protein abundance as the closest available proxy for what a cell is actually doing. Measure how much of each protein a cell produces, the reasoning went, and you have a reasonable snapshot of its functional state. A new study published in Molecular Systems Biology challenges that assumption at a fundamental level, demonstrating that the same protein molecule can exist in dramatically different functional states depending on whether it is floating free as a monomer or locked into a multi-protein complex. Led by George Rosenberger, Peng Xue, Ruedi Aebersold, Andrea Califano, and Yansheng Liu, an international team spanning ETH Zurich, Columbia University, and Yale University has built a computational and experimental framework that reads out not just how much of a protein a cell contains, but what that protein is doing — and the results reveal hidden mechanisms behind phenotypic diversity that abundance data alone simply cannot see.

The study exploited an unusual natural experiment: fourteen HeLa cell line variants, collected from thirteen laboratories around the world, that have drifted genetically over decades of independent culture. These lines display strikingly different behaviors — some grow faster, some are more invasive, and some are far more susceptible to Salmonella infection — despite descending from the same original tumor. Because this genetic drift generated a diverse landscape of genotypes, proteomes, and phenotypes without any deliberate manipulation, the panel serves as a natural perturbation system, ideal for tracing how molecular differences cascade into functional differences. Previous work on these cells had profiled their genomes, transcriptomes, and total proteomes, but never their protein complexes, leaving a critical gap between molecular measurements and cellular behavior.

The team closed that gap using SEC-SWATH-MS, a technique that physically separates proteins by size before quantifying them by mass spectrometry. Because protein complexes elute from the size-exclusion column at positions corresponding to their molecular weight, while monomeric proteins appear at their individual sizes, the method physically distinguishes the assembled and unassembled fractions of the same protein. Applying this approach to the two most behaviorally divergent lines — the invasive, infection-prone HeLa CCL2 and the faster-growing HeLa Kyoto — across 420 mass spectrometry runs, the researchers generated a quantitative matrix covering 7,175 proteins. A computational toolkit called SECAT then translated these co-elution profiles into six distinct metrics per protein: total abundance, assembled abundance, monomer abundance, complex abundance, interactor abundance, and interactor ratio.

The most striking finding concerns a long-standing hypothesis in molecular biology: that protein complex assembly buffers cells against variation in gene copy number and transcription. Earlier studies had inferred this buffering indirectly from bulk correlations between mRNA and protein levels, but never demonstrated it by physically separating the two states of the same molecule. The new data provide exactly that direct evidence. For proteins present in both monomeric and assembled forms — the so-called paired state — the monomeric fraction tracked gene dosage far more tightly than the assembled fraction. Spearman correlations with copy number variation were 0.322 for monomers versus 0.198 for assembled proteins, and with mRNA abundance 0.574 versus 0.484, differences that reached statistical significance. In other words, when copy number or transcription fluctuates, the excess unassembled subunits appear to be selectively degraded by cellular quality-control pathways, while the functional complex-bound pool remains stable.

This buffering is not a passive curiosity but appears central to cellular fitness. When the researchers cross-referenced their protein classifications with CRISPR gene dependency scores from the DepMap project, they found that paired-state proteins — those subject to assembly buffering — are significantly more essential for cell survival than proteins detected only as monomers, with a p-value of 1.35 × 10⁻²⁹ and median dependency probabilities of 0.065 versus 0.032. Complex assembly, the authors conclude, functions as a protective shield insulating core cellular machinery from the noise of genetic drift. This is precisely the kind of mechanistic insight that total protein abundance measurements, however accurate, cannot deliver, because they average together the monomeric and complexed pools into a single number.

To capture additional layers of protein function beyond structure, the team deployed two network-based inference algorithms. VIPER infers transcription factor activity from transcriptomic data by measuring the enrichment of a regulator’s known target genes among differentially expressed transcripts. VESPA applies the same enrichment logic to phosphoproteomic data, estimating kinase and phosphatase activities from the phosphorylation states of their substrates. Applied to the HeLa panel, msVESPA identified 73 of 549 kinases and phosphatases as differentially active between CCL2 and Kyoto cells, while msVIPER flagged 2,009 differentially active regulatory proteins out of 6,156 tested. The researchers then validated these inferred activities against three independent reference datasets: CRISPR dependency scores, subcellular localization annotations from the Human Protein Atlas, and microRNA perturbation responses, confirming that the inferences prioritize essential genes, map to coherent cellular structures, and track phenotypic outcomes.

Bringing all three layers together through network diffusion analysis, the study surfaced two major mechanistic themes distinguishing the cell lines. The first centers on invasion and infection. The actin-nucleating Arp2/3 complex, which drives the formation of invadopodia — protrusive structures that both degrade surrounding tissue and admit bacterial entry — is more abundant in CCL2 cells. Critically, the regulatory proteins WIPF1 and WIPF2, which control Arp2/3 activation through the WASP family, show a decisive stoichiometric shift: WIPF1 predominates in CCL2, promoting invadopodium initiation, while WIPF2 dominates in Kyoto, where it blocks initiation and preserves a stable cortical actin network. This WIPF1/WIPF2–Arp2/3 axis plausibly explains why CCL2 cells are both more invasive and more susceptible to Salmonella, since the same membrane-ruffling machinery serves pathogen and tumor cell alike.

The second theme involves immune adaptation. SECAT revealed that while total proteasome subunit abundance barely differs between the lines, CCL2 cells assemble both constitutive proteasomes and immunoproteasomes — an inflammatory variant that generates different peptide fragments for MHC class I antigen presentation. Kyoto cells, by contrast, rely exclusively on the constitutive form, with higher levels of proteasome assembly chaperones. Such an immunoproteasome switch, invisible to conventional abundance profiling because PSMB8 and PSMB9 showed no significant total-abundance changes, represents a form of immune phenotypic plasticity with obvious implications for how tumor cells evade immune surveillance. Similar abundance-invisible mechanisms appeared throughout the data: the IQGAP2–CDC42 scaffolding complex and COP9 signalosome–DDB1/2 supercomplexes differed in stoichiometry between the lines while neither partner changed detectably in total amount.

The broader significance of the work lies in its reframing of what a protein measurement should mean. A protein, the authors argue, is not a single attribute but a bundle of context-dependent states — its abundance, its assembly, its phosphorylation, and its regulatory activity — each captured by a different molecular network. By reconstructing context-specific protein-protein interaction, kinase-substrate, and gene regulatory networks from the data itself and propagating signals across them, the framework identifies functional drivers rather than abstract variance components, complementing latent-factor approaches such as MOFA. The nominated mechanisms are directly testable: LIMK1 activity can be probed with kinase assays, SCF complex reconfiguration by co-immunoprecipitation, and the WIPF1/WIPF2 ratio by live-cell imaging of invadopodia. The authors acknowledge limitations — the approach still demands substantial sample and instrument time, and reference databases remain biased toward well-studied biology — but they envision the framework as a stepping stone toward artificial intelligence virtual cell models capable of adaptive, mechanism-based predictions of cell state. For now, the message is clear: to understand why two genetically similar cells behave so differently, look past how much protein they make, and ask what that protein is actually doing.

Subject of Research: Multi-omics inference of protein complex assembly and activity states as determinants of cellular phenotypic variability

Article Title: Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability

Article References: Rosenberger, G., Xue, P., Bludau, I., Martelli, C., Williams, E., Collins, B. C., Califano, A., Liu, Y., & Aebersold, R. (2026). Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00228-3

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00228-3

Keywords: proteomics, protein complexes, multi-omics, SEC-SWATH-MS, SECAT, HeLa cells, protein activity, network diffusion, copy number variation, immunoproteasome, invadopodia, systems biology