New computational method reveals protein rhythms hidden in yeast cell cycle

For decades, biologists have known that a dividing cell is not a static factory running at a constant pace. Instead, the machinery of life rises and falls in waves, with enzymes, structural proteins and regulatory molecules accumulating and dissipating in carefully timed rhythms that mirror the stages of the cell division cycle. Yet capturing these oscillations at the scale of the entire proteome has remained one of the most stubborn technical challenges in molecular biology. Now, a team of researchers at the University of Groningen, working with colleagues at the University of Basel, has unveiled a computational framework that recovers cell cycle-dependent protein concentration trajectories from ordinary bulk proteomics data, offering the most detailed picture yet of how thousands of proteins pulse through the division cycle of budding yeast. The work, published in Molecular Systems Biology, addresses a problem that has limited proteomics experiments for years: the inherent imperfection of cell synchronisation.

The difficulty lies in a fundamental mismatch between what scientists can measure and what they want to know. Ideally, researchers would track protein concentrations in single cells as they progress through the cycle, but single-cell proteomics currently lacks the sensitivity required for the tiny volumes of yeast cells, and unlike RNA, proteins cannot be amplified to compensate for minute sample amounts. Fluorescence microscopy with tagged proteins offers one alternative, but it suffers from incomplete fluorophore degradation, artefacts from the tags themselves, and limited temporal resolution. The practical compromise has long been to synchronise a population of cells chemically or mechanically and then measure protein abundances across the culture over time using mass spectrometry. The trouble is that synchronisation is never perfect. Cells drift out of alignment, especially in budding yeast, where asymmetric division gives newborn daughters a longer G1 phase than their mothers, and intrinsic biochemical stochasticity progressively scatters the population across cell cycle stages.

This desynchronisation acts like a blur filter, smearing out the true oscillations in protein concentration and systematically underestimating how dynamic the proteome really is. To undo that blur, the Groningen team, led by Andre Zylstra and Matthias Heinemann, turned to computational deconvolution, a mathematical technique that solves the inverse problem of reconstructing an underlying signal from an observed, distorted one. Their approach models each bulk measurement as a weighted mixture of cell cycle stage-specific concentrations, where the weights reflect how the sampled cell volume is distributed across the cycle at the moment of sampling. Expressed as a matrix equation, the relationship between the true single-cell dynamics and the population-averaged measurements is captured by a convolution matrix that encodes the blurring effect of desynchronisation specific to each experiment.

Estimating that convolution matrix accurately was the central technical hurdle. The researchers built a sophisticated computational model that simulates the temporal evolution of a yeast population, cell by cell, tracking each individual cycle through its phases of early G1, late G1, S/G2, anaphase and telophase, along with cell volume growth in G1 and after budding. Crucially, the parameters for these simulations were not invented but measured. Using time-lapse fluorescence microscopy of cells growing in microfluidic devices, the team tracked 192 complete cell cycles, identifying key events such as START, budding, karyokinesis and cytokinesis with the help of fluorescently tagged histone H2A and the cell cycle inhibitor Whi5. From these data they extracted distributions of phase durations, birth volumes and growth rates, fitted as multivariate log-normal distributions that capture the natural variability between cells. Because budding yeast divides asymmetrically, the model treats mother and daughter cycles distinctly, with daughter early-G1 phases lasting a median of sixty minutes compared with just fifteen minutes for mothers.

The model was then fine-tuned to each specific proteomics experiment using independent measurements of cell cycle phase distributions and cell volume distributions taken from the same cultures. When the standard model was compared with experimental populations, simulated cells reached START roughly twenty to fifty minutes earlier than their real counterparts, likely reflecting stress from the elutriation procedure or differences between shake flasks and microfluidic chambers. By adjusting the log-normal parameters with particle swarm optimisation, the team brought simulations into close agreement with the measured populations, producing convolution matrices that faithfully represented the desynchronisation present in each replicate time course. This volume-aware approach marked a significant advance over earlier deconvolution studies, which relied mainly on DNA content or budding index data and largely ignored the substantial influence of cell size on population-averaged concentration measurements.

With the forward model in place, the team confronted the second major obstacle: deconvolution is mathematically ill-conditioned, meaning that even small amounts of noise in the input data can explode into dramatic distortions in the reconstructed trajectories. The researchers demonstrated this vividly with synthetic data, showing that a simple non-negative least squares approach fails catastrophically when a modest amount of Gaussian noise is added to an otherwise perfect signal. Their solution was a regularised least squares algorithm that penalises roughness in the reconstructed concentration profile, favouring smooth solutions consistent with the expectation that protein concentrations do not fluctuate wildly between adjacent points in the cell cycle. The strength of this penalty, governed by a regularisation coefficient, was selected individually for each protein using leave-one-out cross-validation across three replicate experiments, balancing the competing risks of over-smoothing genuine dynamics and overfitting measurement noise.

The experimental foundation for the analysis came from time course proteomics experiments in which yeast cultures were synchronised in early G1 by centrifugal elutriation and then sampled every twenty minutes for nearly five hours. Protein concentrations were quantified for 3373 proteins using sixteen-plex tandem mass tag labelling and liquid chromatography-tandem mass spectrometry. After removing 101 proteins known to be asymmetrically distributed between mother and daughter cells, a violation of a key modelling assumption, the team applied their deconvolution to 3272 proteins. To separate genuine dynamics from artefacts, they filtered for solutions with both high signal-to-noise ratios and high peak-to-trough ratios, ultimately identifying 539 proteins with high-amplitude cell cycle-dependent behaviour, with concentration swings ranging from roughly 1.3-fold to as much as 32-fold.

Validation against established yeast biology lent strong credibility to the results. Hierarchical clustering of the 539 trajectories produced five groups with coherent functional enrichments: proteins involved in DNA replication and chromosome organisation peaked around S and G2 phases, respiratory and ATP synthesis proteins peaked near S/early-G2 consistent with known oxygen consumption patterns, amino acid biosynthesis enzymes peaked around START, and carbohydrate metabolism proteins peaked during phases requiring cell wall synthesis. Individual case studies were equally convincing. The deconvolved trajectory of Acc1, the rate-limiting enzyme of fatty acid synthesis, showed a sharp rise peaking in S/G2, matching prior Western blot data and coinciding with the known peak in lipid biosynthesis. Ergosterol synthesis enzymes peaked in G1, ribosomal proteins and ribosome biogenesis factors peaked around late G1, and proteins such as Hsl1, Mcd1 and Pds1 showed dynamics identical to decades of classical literature.

Beyond reconstructing concentrations, the team leveraged their trajectories to infer cell cycle-dependent transcription factor activity by combining the deconvolved data with documented regulatory relationships from the YEASTRACT database. The analysis recovered known regulators such as Ace2, whose activity peaked in daughter early G1, and Yox1, most active during S/G2, while also proposing intriguing new candidates. Activity of the stress-responsive factors Msn2 and Msn4 showed pronounced peaks in daughter early G1 and S/G2, a finding supported by independent time-lapse imaging of Msn2 nuclear localisation, and the Hap4 and Hap5 components of the Hap complex peaked around S/G2, potentially contributing to the metabolic switching between fermentation and respiration observed during the cycle. The researchers have released their complete dataset, computer code and uncertainty estimates as openly accessible resources, anticipating that the cell cycle-resolved proteome will become a key reference for future investigations into how metabolic oscillations emerge and how they exert control over the fundamental process of cell division.

Subject of Research: Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics

Article Title: Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics

Article References: Zylstra, A., Rovetta, M., Vedelaar, S. R., Bleischwitz, C., Fülleborn, J. A., van Oppen, Y., Markus, H. P., Korbeld, K. T., Calzati, E., Milias-Argeitis, A., Buczak, K., Schmidt, A., & Heinemann, M. (2026). Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00241-6

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00241-6

Keywords: budding yeast, cell cycle, proteomics, deconvolution, Saccharomyces cerevisiae, mass spectrometry, protein dynamics, metabolic oscillations, transcription factors, systems biology, Cell, cycle-dependent