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

Single-cell RNA sequencing has transformed biology by allowing researchers to measure the complete transcriptional activity of thousands of individual cells in a single experiment. This technology reveals extraordinary diversity within tissues, distinguishing cell types and even subtle cell states that bulk measurements would average away. However, scRNA-seq carries a fundamental limitation: during the dissociation process required to prepare samples, cells are physically separated from their tissue context. The resulting data describe what each cell is doing, but not where it was located. Spatial transcriptomics was developed precisely to solve this problem, preserving the spatial organization of cells within tissue sections while still capturing gene expression information. Yet even the most advanced spatial platforms typically profile fewer genes per cell or provide lower molecular resolution than dissociative single-cell methods, and most experiments capture only a single moment in time.

The new framework, developed by Mahdi Pursalim, Kaveh Kavousi, and Parisa Shooshtari, tackles the integration problem directly. The approach operates in two complementary stages. In the first stage, the method performs reference-based spatial deconvolution, a computational procedure that uses scRNA-seq data as a reference atlas to estimate which cell types are present in each spot or region of a spatial transcriptomics measurement. Because spatial platforms often measure mixtures of cells within each capture location, deconvolution is essential for untangling these mixed signals and recovering the underlying cellular composition. The second stage applies single-cell spatial mapping, transferring individual cells from the scRNA-seq dataset onto their most likely spatial positions within the tissue, guided by the gene expression similarities between the single-cell profiles and the spatial measurements.

What distinguishes this framework from previous integration efforts is its explicit treatment of time. The method is designed to work with time-series data—sets of scRNA-seq and spatial transcriptomics measurements collected at multiple, consecutive time points. By applying the deconvolution and mapping procedures independently at each time point and then comparing the results across consecutive stages, the framework quantifies how spatial cell-type distributions shift over the course of a biological process. These temporal changes are summarized using complementary abundance-weighted spatial descriptors, quantitative measures that capture both the proportion of each cell type and its spatial arrangement within the tissue. In parallel, the framework characterizes inferred cell-state dynamics by tracking changes in dominant cell-type assignments across consecutive time points, providing a computational estimate of how the state composition of the tissue evolves.

Recognizing that any computational method is only as trustworthy as its validation, the researchers subjected the spatial abundance component of their framework to rigorous simulation-based benchmarking. They generated synthetic spatial datasets with known, predefined cell-type compositions, using these simulated ground truths to quantitatively compare their approach against alternative spatial deconvolution and mapping methods. This evaluation strategy is critical in a field where real experimental data rarely come with complete knowledge of the true cellular makeup of every spatial location. The benchmarking results support the framework’s ability to recover simulated spatial cell-type abundance patterns, offering reassurance that the estimates it produces reflect genuine biological structure rather than computational artifacts.

To demonstrate the framework’s biological utility and generalizability, the team applied it to two systems at opposite ends of the biological spectrum: human glioblastoma organoids and embryonic chicken heart development. Glioblastoma organoids are laboratory-grown, three-dimensional structures derived from human cells that recapitulate key features of aggressive brain tumors, making them valuable models for studying cancer progression in a controlled setting. The framework revealed temporal patterns of spatial cell-type organization and inferred cell-state composition within these organoids that could not be detected through static analyses alone, offering a dynamic view of how the tumor-like cellular ecosystem reorganizes over time.

The second application, embryonic chicken heart development, represents one of the most dramatic examples of coordinated cellular behavior in biology. The heart forms through precisely choreographed movements of diverse cell populations, and disruptions to these processes underlie many congenital heart defects. When the researchers applied their framework to time-series data from developing chicken hearts, it captured temporal patterns in spatial cell-type redistribution and inferred cell-state dynamics that illuminate how the cellular architecture of this vital organ emerges during embryogenesis. Together, the two case studies—one disease-focused, one developmental—demonstrate that the framework is not tailored to a single biological context but can be applied broadly across systems where time-series single-cell and spatial data are available.

The authors are careful to frame their results with appropriate scientific caution, a nuance that matters for how the work should be interpreted. The framework captures what the researchers describe as spatiotemporal cell-type redistribution and inferred cell-state dynamics—computational measures derived from molecular data. These inferred quantities should be understood as descriptive computational measures rather than direct evidence of physical cell migration or lineage-validated state transitions. In other words, observing that the abundance of a cell type increases in a particular spatial region between two time points does not prove that cells physically traveled there; the change could also reflect differential proliferation, cell death, or changes in the sampling of the tissue. Similarly, apparent shifts in cell-state composition represent computational inferences rather than experimentally validated transitions of individual cells between states.

This caution reflects a broader truth about computational modeling of biological dynamics. Single-cell and spatial transcriptomics are destructive measurements—each sample is consumed in the process of profiling it—so reconstructing temporal behavior necessarily involves stitching together observations from different specimens assumed to represent the same underlying process. Despite this inherent limitation, the value of such models is considerable. They generate testable hypotheses about which cell populations move where, when specific state transitions occur, and how the spatial organization of tissues changes during development or disease. These hypotheses can then guide targeted follow-up experiments, such as live imaging or lineage tracing, that directly observe the behaviors the models predict.

The publication arrives at a moment when the experimental technologies underpinning the framework are advancing rapidly. Spatial transcriptomics platforms continue to increase in resolution, throughput, and affordability, and time-course studies combining scRNA-seq with spatial profiling are becoming increasingly common in developmental biology, cancer research, and regenerative medicine. What has lagged behind is the computational toolkit for integrating these data modalities across time in a principled, quantitative, and benchmarked way. By providing a broadly applicable framework with validated spatial abundance estimation, temporally resolved descriptors of cellular organization, and demonstrated performance across biologically distinct systems, the researchers offer the community a practical resource for systematic characterization of dynamic cellular behaviors. The work points toward a future in which the choreography of cells—their movements, positions, and transformations—can be reconstructed computationally from molecular snapshots, deepening understanding of how tissues build themselves, how tumors evolve, and how these processes might ultimately be modulated for therapeutic benefit.

The methodological choices behind the framework reflect practical constraints inherent to current spatial transcriptomics platforms. Reference-based deconvolution is necessary because many widely used spatial assays capture expression from groups of cells within each measured location, meaning the raw signal represents a mixture rather than a single cell. By anchoring this unmixing step to scRNA-seq references, the framework leverages the superior molecular resolution of dissociative profiling while retaining the positional information that spatial measurements provide. The complementary use of single-cell spatial mapping then adds a second layer of inference, assigning individual cells from the reference to plausible tissue positions based on expression similarity.

The benchmarking strategy deserves particular attention from readers evaluating the method. Simulation-based validation, in which synthetic spatial datasets are generated with known cell-type compositions serving as ground truth, allows quantitative comparison against alternative deconvolution and mapping approaches in a way that real tissue data cannot. Because no experimental measurement of a real tissue comes with a complete, verified inventory of every cell type at every location, simulations provide the only setting where estimation accuracy can be measured directly. The reported performance of the spatial abundance component against competing methods therefore offers a meaningful, if partial, assessment of reliability.

The two biological applications also illustrate the diversity of questions the framework can address. Glioblastoma organoids and the embryonic chicken heart differ not only in species and biological context but in the character of their cellular dynamics: one models the progressive reorganization of a tumor-like cellular ecosystem, while the other captures the choreographed emergence of organ architecture during embryogenesis. That a single computational pipeline can extract temporally resolved patterns of spatial cell-type organization and inferred cell-state composition from both systems suggests the approach is not dependent on tissue-specific assumptions, an important property for a tool intended for broad community use.

The authors’ framing of their results as descriptive computational measures rather than direct evidence of physical cell migration or lineage-validated state transitions is a notable feature of the work. Such interpretive restraint is uncommon and valuable in a field where language about cell movement can easily outpace what molecular snapshot data can actually demonstrate. The framework is published as open access under a Creative Commons license, and the article appeared as a citable, peer-reviewed accepted manuscript carrying a permanent DOI, with a final Version of Record to follow. The research was supported in part by the Children’s Health Research Institute and the Ontario Institute for Cancer Research.

Subject of Research: Integrative computational modeling of cell migration and tissue organization using time-series single-cell RNA sequencing and spatial transcriptomics data

Article Title: Systems-level modeling of cell migration using spatially-resolved single-cell data

Article References: Pursalim, M., Kavousi, K., & Shooshtari, P. (2026). Systems-level modeling of cell migration using spatially-resolved single-cell data. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06649-z

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06649-z

Keywords: cell migration, spatial transcriptomics, single-cell RNA sequencing, spatiotemporal modeling, deconvolution, cell-state dynamics, glioblastoma organoids, chicken heart development, time-series analysis, computational biology, tissue organization, cell types