The technical hurdle the researchers faced is deceptively simple to state. Adult organs are large, measured in millimeters to centimeters, while the cells that compose them are measured in micrometers. Conventional light microscopes cannot resolve every nucleus throughout a thick, intact organ, and sectioning the organ into slices destroys the spatial relationships that matter for understanding growth. Expansion microscopy, first developed in the mid-2010s, offered a way out by physically swelling tissue samples so that their features spread apart and become resolvable under standard optics. But applying the technique to an entire adult organ, rather than a thin slice, required a substantial engineering effort to make the method uniform, robust, and compatible with fluorescent reporters that label specific cell types.

The team’s solution was an integrated pipeline that combines tissue clearing, gel embedding, isotropic expansion, and high-speed light-sheet fluorescence imaging. They worked with transgenic zebrafish whose hepatocyte nuclei carry fluorescent tags, so every liver cell could be individually identified in the final images. After expansion and imaging, computational analysis stitched the data into a complete three-dimensional atlas of the liver, in which each hepatocyte could be located, counted, and measured. The result is a genuine whole-organ census: an intact adult zebrafish liver, spanning roughly five millimeters in thickness, was found to contain an average of 1,265,206 hepatocytes. That number, obtained by direct observation rather than extrapolation, sets a new benchmark for quantitative organ biology.

With the census in hand, the researchers turned to the central question of growth dynamics. They imaged livers at multiple stages across the post-embryonic period, from juvenile fish through fully mature adults, and charted how hepatocyte numbers changed over time. The pattern they found was strikingly non-linear. Rather than growing steadily in proportion to the body, the liver’s cell population increased by a remarkable 538-fold, and much of that increase was packed into a temporally concentrated burst of proliferation. In other words, the liver does not simply scale up with the rest of the animal; it follows its own growth schedule, with an intense phase of cell addition that is decoupled from overall body growth.

This decoupling is more than a curiosity. It suggests that organs possess intrinsic growth programs that operate on their own timelines, potentially governed by internal cues such as local signaling gradients, mechanical stress, or metabolic demand rather than by systemic growth factors alone. For developmental biologists, the finding reframes post-embryonic organ growth as an active, staged process rather than a passive consequence of the animal getting bigger. It also raises new questions about how a growing liver maintains its architecture, its blood supply, and its bile duct network while its cell population expands by more than two orders of magnitude in a relatively short window.

Counting cells, however, only tells part of the story. The deeper question is which cells are responsible for the growth. Are new hepatocytes generated broadly, with most existing cells dividing a little, or are they the descendants of a small subset of progenitors that divide a lot? To find out, the team integrated lineage tracing with their expansion microscopy workflow. In lineage tracing, a small number of cells are permanently labeled with a heritable fluorescent marker, and all of their descendants carry that same label. By combining this approach with whole-organ imaging, the researchers could trace clone territories, the spatial footprints of individual founding cells, across the entire adult liver.

The answer was unambiguous: liver growth is driven by a few hepatocytes undergoing drastic clonal expansion at the whole-organ scale. Instead of a democratic process in which every cell contributes equally, the growing liver is dominated by a small number of lineages whose progeny multiply extensively and come to occupy large territories within the organ. This clonal organization echoes principles seen in other self-renewing tissues, such as the intestinal epithelium and the blood system, where a limited pool of highly proliferative cells sustains tissue mass. Seeing the same logic play out across an entire intact vertebrate organ, rather than in a two-dimensional section, gives the observation a new level of rigor and visual force.

The clonal picture also has implications for how we think about liver disease and regeneration. The mammalian liver is famous for its ability to regrow after injury, and hepatocellular carcinoma is often discussed in terms of clonal expansion of mutated cells. If normal liver growth in zebrafish is naturally driven by a few dominant clones, then the boundary between physiological growth and pathological expansion may be governed by regulatory mechanisms that keep clonal proliferation in check. Understanding those mechanisms in a transparent, genetically tractable vertebrate could illuminate what goes wrong when clonal control fails in human disease.

The researchers did not stop at cell numbers and clones. They also asked how the liver’s three-dimensional shape emerges during growth, and whether shape and cell number are controlled by the same or different mechanisms. Their target was the extracellular matrix, specifically laminins, a family of proteins that form a structural scaffold around tissues and are known to influence cell behavior. When the team disrupted laminin function, they observed a striking dissociation: the liver’s overall shaping was perturbed even as total cell number increases continued. This decoupling suggests that tissue architecture and cell proliferation are regulated independently, with the extracellular matrix acting as an architectural control layer that is at least partly separate from the growth control layer. For bioengineers trying to grow organs in the lab, the lesson is that building the right structure requires more than making enough cells; the scaffold matters on its own terms.

Perhaps the most exciting aspect of the study is its generality. The authors showed that WAO-ExM is not limited to healthy livers. They applied the workflow to diseased livers, where it could resolve pathological changes at single-cell resolution across the whole organ, and to other adult zebrafish organs, including the heart and the pancreas. In each case, the platform delivered the same promise: a complete, single-cell-resolved view of an intact adult vertebrate organ. Because zebrafish are transparent, genetically manipulable, and widely used in biomedical research, the method is likely to spread quickly through labs studying regeneration, cancer, diabetes, and congenital heart disease. The technique bridges micrometer-scale cell behaviors with centimeter-scale organ growth, closing a persistent gap in biology between what we can see in a dish and what happens in a living body. For a field that has long had to choose between seeing everything and seeing clearly, whole-organ expansion microscopy offers, for the first time, both at once.

Subject of Research: Single-cell mapping of growth dynamics and clonal organization in the developing and adult zebrafish liver

Article Title: Whole-organ single-cell mapping defines growth dynamics and clonal organization in developing and adult zebrafish livers

Article References: Roan, H.-Y., Tian, X., Chu, W.-C., Lee, C.-M., Chen, H., Santoso, F., Wang, C.-H., Liu, Y.-H., Kumar, U., Hsiao, H.-Y., Yuh, C.-H., Chen, B.-C., & Chen, C.-H. (2026). Whole-organ single-cell mapping defines growth dynamics and clonal organization in developing and adult zebrafish livers. PLOS Biology, 24(9), e3004007. https://doi.org/10.1371/journal.pbio.3004007

Image Credits: AI Generated

DOI: 10.1371/journal.pbio.3004007

Keywords: zebrafish, liver, expansion microscopy, hepatocytes, clonal expansion, organ growth, lineage tracing, extracellular matrix, laminin, single-cell imaging, PLOS Biology, regeneration

The authors begin from a striking statistic: only about one percent of the 3.2 billion base pairs of the human genome codes for functional proteins, yet roughly 80.4 percent of the genome participates in non-protein-coding regulation of gene expression in at least one cell type. This enormous regulatory investment underscores how vital epigenetic gene regulation is at every stage of single- and multicellular life. Epigenetic modifications, chemical changes to DNA and the histone proteins around which it is wrapped, fold the genome into permissive and restrictive chromatin structures and nuclear arrangements. When these systems go awry, they contribute to the initiation and maintenance of diseased states. Yet while whole-genome mapping has revealed the landscape of these marks, the dynamics and variability of their interactions at the level of individual cells remain far less understood.

That variability is what biologists call transcriptional noise, the cell-to-cell variance in messenger RNA content, often quantified by the coefficient of variation, a mean-normalised standard deviation. Noise comes in two flavours. Extrinsic noise arises from subtle differences in the cellular microenvironment that affect processes such as growth rate and cell volume, and it tends to influence all gene expression within a given cell uniformly. Intrinsic noise, by contrast, stems from the randomness of biochemical reactions involving small molecule numbers, such as transcription factors binding to their cognate motifs, and it affects each gene uniquely. The distinction was famously established in bacteria by the Elowitz laboratory, which used two reporter genes expressing yellow and cyan fluorescent proteins under the same promoter: fluctuations in the combined signal reflected extrinsic noise, while divergence between the two colours within a single cell revealed intrinsic noise. The greatest source of intrinsic noise, the review emphasises, is the stochastic switching of gene promoters between active and inactive states.

The consequence of this switching is that messenger RNA is produced in short discrete bursts interspersed with longer periods of relative inactivity, a process occurring on timescales ranging from minutes to hours. Cell-to-cell variability results from the burst size, the number of RNA molecules produced during the active state; the burst frequency, how often transitions to the active state occur; and the degradation rate of the resulting transcripts. Remarkably, recent work has shown that fluctuations in gene expression can persist for several cell divisions, in line with the idea that bursting may drive phenotypic divergence of otherwise identical cells. On shorter timescales, bursting provides a way for cells to respond rapidly to stimuli. Understanding its regulation matters greatly for fields such as oncology, where heterogeneity in gene expression is associated with poor prognosis and drug resistance.

Here the review makes its most provocative conceptual move. The traditional two-state model, in which promoters flip between a single ON and a single OFF state, is increasingly challenged by evidence for more than two promoter states. The authors propose, in agreement with others, that bursting should be understood as a multi-scale phenomenon: during a macroscopic ON state, multiple small microbursts occur, corresponding to convoys of RNA polymerase II traversing the gene, separated by short and shallow OFF states lasting roughly 1.5 minutes, while deep OFF states extend far longer, around 9 to 34 minutes. The accumulation of these microbursts within each ON state produces a larger macroburst. This distinction matters because different experimental techniques operate at different scales. Single-cell transcriptomics generally captures the macroscale, lacking time resolution and fitting pre-determined models to distribution data, whereas live-cell imaging using MS2 or PP7 bacteriophage coat protein reporter systems offers very high temporal resolution and therefore probes the microscale. Even gene length matters: imaging a long gene captures macroburst dynamics as multiple polymerase convoys progress simultaneously, while a short gene reveals individual microbursts.

Armed with this framework, the authors turn to the epigenetic machinery. DNA methylation, long viewed as a stable, repressive mark, turns out to be far more dynamic than generally appreciated. It responds rapidly to extracellular stimuli, as seen in immune cells following bacterial infection, primarily at distal enhancers, and in post-mitotic neurons, where 1.4 percent of assessed CpG sites rapidly underwent demethylation or de novo methylation after stimulation. Intriguingly, 87 percent of differentially methylated CpG sites showed decreased methylation after an associated increase in transcription factor binding, suggesting that changes in DNA methylation are often a consequence rather than a cause of altered gene expression. Multiple transcription factors can recruit the methyltransferases DNMT3A or DNMT3B to trigger promoter hypermethylation, and methyl-CpG-binding proteins such as MeCP2 recruit repressive complexes, although MeCP2 has also been shown to activate genes and induce extensive chromatin unfolding in models of Rett syndrome.

The genomic location of methylation determines its effect on noise. Promoter methylation has been implicated with higher levels of transcriptional noise, apparently acting as a trigger that forces ON-to-OFF state transitions and increases the burstiness of genes, while unmethylated promoters can remain active and quieter. Gene body methylation, by contrast, is associated with reduced noise, a finding replicated in Arabidopsis thaliana and the sea anemone Exaiptasia pallida, and may work by suppressing transcription from cryptic intragenic promoters or by inhibiting deposition of the histone variant H2A.Z, which is itself linked to increased noise. In mouse embryonic stem cells, DNA methylation was identified as a key regulator of stochastic phenotypic state switching, although studies of Polycomb-targeted active genes found no such effect, underscoring the context dependency of these mechanisms.

Histone variants and nucleosome density add another layer. The H3.3 variant has a turnover time of approximately two hours, strikingly similar to the interval between transcription bursts for hormone-responsive genes, suggesting that H3.3 occupancy could act as a form of transcriptional memory; indeed, H3.3 and its methylatable fourth lysine residue are essential for retaining memory of gene activity states after nuclei transplantation. The chaperone HIRA deposits H3.3 at decondensed, transcriptionally active promoters and enhancers, while DAXX deposits it in condensed inactive chromatin, recruiting histone deacetylase II to maintain repression. The variant macroH2A, when paired with the transcription factor NRF-1, reduces noise by maintaining the ON state, whereas macroH2A nucleosomes lacking NRF-1 may promote stochastic ON-OFF switching. Independently of variant identity, a higher density of promoter-proximal nucleosomes was found to lower burst frequency and increase expression noise, slowing the rate of OFF-to-ON transitions.

Histone post-translational modifications show equally nuanced effects. Broadly, histone acetylation marks active genes, and evidence is accumulating that it mainly regulates burst size: the HDAC inhibitors trichostatin and SAHA increased the burst size of latent HIV production and, in a PP7-tagged cell line, predominantly affected burst size. Yet site-specific epigenetic rewriting using CRISPR-based dead Cas9 fusion proteins has revealed that targeted promoter acetylation can predominantly influence burst frequency, as at the circadian Bmal1 promoter, while enhancer acetylation increased burst duration of the FOS gene in neurons, mediated by the reader protein BRD4 and the release of paused RNA polymerase II. Histone methylation appears more tied to transcriptional memory: H3K4 methylation persists along gene bodies in yeast for over an hour after transcription ceases, transmits burst frequency between mother and daughter Dictyostelium cells, and in breast cancer cells, broad H3K4me3 peaks were associated with more homogeneous expression, while narrow peaks accompanied heterogeneous expression and drug resistance via the demethylase KDM5B.

Finally, the three-dimensional organisation of the genome and its enhancer networks constrain these dynamics. Macro-level features such as A and B compartments and topologically associated domains appear tied to stable, lineage-specific expression rather than acute bursting, although TAD disruption can cause developmental malformations by rewiring enhancer contacts. Enhancer-promoter loops, which can span up to three megabases, are far more dynamic: deleting specific CTCF sites in T helper 2 cells increased gene expression noise by destabilising loops, while forced looping and stronger enhancers in Drosophila increased burst frequency without changing burst size. Genome-wide analyses confirm that enhancers primarily modulate burst frequency, and the mediator complex drives the rapid succession of initiation events that produces a burst of polymerase activity. The authors conclude that resolving the true genome-wide effects of epigenetics on bursting will require high-throughput imaging of thousands of loci under epigenetic perturbation, but with such techniques on the horizon and single-cell transcriptomics exploding, the field stands on the verge of finally connecting the epigenetic code to the pulsating rhythm of gene activity.

Subject of Research: Epigenetic regulation of transcriptional bursting and gene expression noise in eukaryotic cells

Article Title: Transcription bursting and epigenetic plasticity: an updated view

Article References: Beckman, W. F., Jiménez, M. Á. L., & Verschure, P. J. (2021). Transcription bursting and epigenetic plasticity: an updated view. Epigenetics Communications, 1(1), Article 6. https://doi.org/10.1186/s43682-021-00007-1

Image Credits: AI Generated

DOI: 10.1186/s43682-021-00007-1

Keywords: transcription bursting, epigenetics, gene expression noise, DNA methylation, histone variants, histone modifications, chromatin architecture, enhancers, RNA polymerase II, topologically associated domains, transcriptional memory, single-cell imaging

Mass spectrometry imaging has become one of the most powerful tools in modern biology because it can map hundreds of molecules directly within a tissue slice, revealing where lipids, metabolites, proteins and other biomolecules reside without labels or stains. Yet the technique has long been constrained by its spatial resolution. Conventional instruments raster a laser across a tissue section with a defined step size, and features smaller than that step blur together. Boosting resolution usually demands costly instrumentation such as specialized lasers, transmission-mode optics or next-generation ion optics, placing subcellular molecular imaging out of reach for many laboratories.

TEMI takes a different path. Instead of refining the instrument, the team enlarges the tissue itself. Drawing on principles from expansion microscopy, a technique introduced more than a decade ago, the researchers embed tissue in a water-rich hydrogel polymer network. When the gel swells, it carries the tissue along with it, spreading the molecules apart and effectively magnifying the sample before any mass spectrometry takes place. A laser raster that would have been too coarse for the original tissue becomes fine enough to resolve individual cells in the expanded version, yielding more than a 3.5-fold improvement in effective imaging resolution with standard equipment.

The critical challenge was chemistry. Traditional expansion protocols often rely on harsh denaturation conditions, including high heat and strong detergents, to homogenize tissue and allow gels to stretch uniformly. Those conditions would destroy the very molecules mass spectrometry aims to detect. The TEMI workflow solves this by using a re-embedding strategy that expands tissue under mild, harsh-condition-free conditions, preserving the chemical integrity of lipids, metabolites, N-glycans, peptides and proteins. The result is a sample that is both physically enlarged and chemically faithful to its original molecular composition.

The published protocol walks readers through every stage of the process. It begins with constructing a gelation chamber, then describes hydrogel-based tissue gelation and expansion, followed by cryosectioning of the expanded tissue-hydrogel composite. Because expanded samples are soft and hydrated, cutting them into thin sections requires careful handling, and the protocol provides the specific parameters that make reproducible sectioning possible. Detailed guidance covers matrix application, data acquisition and visualization pipelines, along with troubleshooting tips accumulated through the team’s extensive experimentation.

One of the most striking capabilities of TEMI is multiomics mapping on a single tissue section. The protocol describes a sequential workflow in which N-glycans are released in situ by treatment with the enzyme PNGase F, after which proteins are digested with trypsin and imaged, all following lipid and metabolite analysis on the same expanded section. This means a single slice of brain tissue can yield spatially resolved maps of multiple molecular classes, each anchored to the same anatomical context, an efficiency that conventional workflows struggle to match.

Quality control receives particular attention. The protocol includes a dedicated workflow for measuring deformation maps and expansion factors, which quantify how uniformly the gel has expanded. Uneven expansion would distort molecular maps, so the researchers provide computational tools, released through a public code repository, that quantify tissue expansion non-uniformity. They also demonstrate that analyte delocalization during sample preparation is minimal: lipid and small-metabolite signals were detected exclusively in tissue regions, with no corresponding signals in adjacent blank hydrogel areas, confirming that molecules stay where they belong during the swelling process.

The demonstration experiments showcase the method’s power on the mouse cerebellum, a tissue with exquisitely organized layers of cells. Comparing an unexpanded control cerebellum imaged with a 50-micrometer laser raster against a double-embedded, expanded sample at the same step size reveals a dramatic difference in molecular detail. Pushing further, the team performed TEMI after three cycles of gel embedding and expansion with a 10-micrometer raster step, resolving biomolecular heterogeneity that remains invisible in unexpanded tissue. The protocol also demonstrates mapping of small metabolites, N-glycans and proteins across cerebellar structures, illustrating the breadth of the multiomics capability.

The work builds on the team’s primary research paper published in Nature Methods, which introduced TEMI and established its foundational performance, and on a rich history of expansion microscopy methods developed by collaborators at the Howard Hughes Medical Institute’s Janelia Research Campus, including protein-retention expansion microscopy and ten-fold robust expansion. Raw datasets from the protocol’s figures are publicly available in the MassIVE repository, and the deformation-measurement code is hosted on GitHub, lowering barriers for laboratories that want to adopt or adapt the approach.

The implications extend across basic biology and medicine. Single-cell spatial resolution for untargeted molecular imaging could illuminate how metabolic gradients shape tissue function, how lipid compositions vary between neighboring cells in the nervous system, and how disease states such as cancer or neurodegeneration alter molecular architecture at scales previously accessible only to antibody-based imaging. Because TEMI requires no instrument modifications, it promises broad accessibility: laboratories with standard mass spectrometry imaging systems can now reach a resolution regime once reserved for a handful of specialized facilities. The protocol’s authors, spanning the University of Wisconsin-Madison and HHMI Janelia, and supported in part by the National Institutes of Health, position the method as a practical bridge between the spatial-omics revolution and the everyday mass spectrometry lab, inviting a wide research community to stretch its view of tissue, quite literally, into sharper focus.

Subject of Research: Tissue expansion combined with mass spectrometry imaging for high-spatial-resolution multiomics molecular mapping

Article Title: Tissue expansion mass spectrometry imaging (TEMI) for high-spatial-resolution multiomics molecular mapping

Article References: Tissue expansion mass spectrometry imaging (TEMI) for high-spatial-resolution multiomics molecular mapping. (n.d.). https://doi.org/10.1038/s41596-026-01427-w

Image Credits: AI Generated

DOI: 10.1038/s41596-026-01427-w

Keywords: mass spectrometry imaging, tissue expansion, TEMI, spatial omics, single-cell imaging, lipidomics, metabolomics, N-glycans, proteomics, hydrogel expansion, Nature Protocols, multiomics

The research focuses on nuclear factor kappa B, or NFκB, the master transcription factor that activates immune genes in macrophages. Decades of work have established that NFκB does not simply switch on and off. Instead, it shuttles in and out of the nucleus with distinctive temporal patterns — oscillatory for some stimuli, transient for others — and these dynamic signatures, sometimes called signaling codons, carry information about which ligand triggered the response. Six such codons have been characterized: activation speed, peak amplitude, duration of signaling, total integral activity, the balance of early versus late activity, and oscillatory content. Together, they form something like a Morse code of immunity, allowing the cell to distinguish, for example, a cytokine signal from a bacterial lipopolysaccharide encounter.

The problem with most previous studies, the authors note, is that they examined one ligand at a time. Nature rarely cooperates so neatly. During an E. coli infection, a macrophage may first encounter LPS from the bacterial envelope, then CpG DNA from the ruptured genome, and simultaneously inflammatory TNF produced by neighboring cells. In the gut, immune cells confront a rolling storm of microbial and host-derived signals. The combinatorial space of possible ligand mixtures is astronomically large, making comprehensive experimental exploration essentially impossible. The UCLA team’s solution was to build a virtual laboratory: a mechanistic mathematical model of NFκB signaling, comprising 52 ordinary differential equations, 101 reactions, and 133 parameters, organized into five receptor modules corresponding to the TNF, TLR2, TLR4, TLR9, and TLR3 pathways, all converging on a shared IKK–IκBα–NFκB core.

Crucially, the model was not trained on mixture data. It was fitted to live-cell imaging trajectories of hundreds of individual macrophages responding to each of five ligands — TNF, LPS, Pam3CSK4, CpG, and poly(I:C) — at multiple doses. To extend the model into combinatorial territory, the researchers developed an elegant statistical workaround. Because each simulated cell’s parameters had only been estimated for one receptor module plus the shared core, they imputed the missing receptor parameters using nearest-neighbor hot-deck imputation, anchoring each match on the core module parameters that all pathways share. In this way, the team generated virtual cell populations capable of responding to any ligand combination — thirty-one conditions in total, spanning single ligands through the full five-ligand cocktail.

The first validation was encouraging. Experimental responder fractions, measured by live-cell microscopy of fluorescently tagged RelA, rose with the number of ligands present and plateaued much as the model predicted, suggesting that signaling from multiple ligands generally combines in an integrative fashion and that non-responding cells reflect receptor-level heterogeneity rather than cellular ill-health. But one prediction failed in a telling way. The model expected the CpG plus poly(I:C) combination to produce a stronger response than either ligand alone; the experiments showed the opposite, with the pair dampening signaling below the CpG-only level. That discrepancy became the thread the researchers pulled to unravel a previously unknown mechanism.

Both CpG and poly(I:C) are sensed by Toll-like receptors located in endosomes, and both depend on the same endosomal transport machinery to reach those receptors. The original model had treated these transport processes as independent, but reality is stingier: the cell has a finite capacity for hauling cargo into endosomes. By reformulating the transport reactions with competitive Michaelis–Menten kinetics — adding no new parameters — the researchers captured the bottleneck. The result was a negative correlation between CpG–TLR9 and pIC–TLR3 signaling complexes: because poly(I:C) signals relatively weakly, it effectively poisons the stronger CpG response by saturating their shared transport pathway. The revised model accurately reproduced the non-integrative experimental result.

With the model refined, the team asked whether ligand identity remains readable within mixtures. Using Wasserstein distance analysis and machine learning classifiers trained on the six signaling codons, they found that specificity does degrade as ligands pile up — the five-ligand condition was hardest to classify — but it never vanished. Even the five-ligand mixture was recognized well above chance. More strikingly, binary classifiers asked a biologically natural question: is this ligand present in the mixture at all? For TNF, the answer could be read from NFκB dynamics with an ROC area under the curve of 0.94; LPS and Pam followed closely, and experimental data confirmed the pattern. Macrophages, it appears, can partially detect specific threats even in a molecular crowd.

To map synergy and antagonism systematically, the team simulated roughly 360,000 single-cell trajectories across all ten ligand pairs at six doses each. Synergy emerged mainly at low doses and traced back to ultrasensitive IKK activation: in cells with low receptor abundance and constrained TAK1 signaling, two weak inputs could combine to push the ultrasensitive IKK switch over threshold, amplifying NFκB peaks. Antagonism, by contrast, arose at high doses through resource competition. For the LPS–Pam pair, the shared co-receptor CD14 proved to be the limiting resource. Pam binds and releases CD14 more rapidly, peaking early and sequestering the co-receptor before LPS signaling can fully mature, thereby diverting CD14 away from productive TLR4 signaling and weakening the downstream NFκB response.

The most consequential discovery concerned what these interactions do for stimulus-response specificity. When the researchers compared a model lacking CpG–pIC antagonism with the updated model that includes it, the antagonistic version showed significantly greater separability between conditions in Wasserstein distance analyses. Multidimensional scaling visualizations made the point vividly: without competition, the CpG–pIC condition drifted into the cluster of other mixtures, becoming just another blended signal. With competition included, it stood apart, carrying a distinctive dynamic fingerprint. Antagonism, born of a mundane biochemical bottleneck, was actively manufacturing uniqueness. Synergy, though dramatic at the single-cell level, did not confer any similar benefit — it boosted peak responses without improving the distinguishability of the informative signaling codons.

The implications reach well beyond this particular pair of ligands. Resource-limited antagonism — competition for endosomal transport, for co-receptors like CD14, or for shared kinase modules — may represent a general design principle by which immune signaling networks preserve information under combinatorial overload. The computational workflow itself is a contribution: by combining mechanistic ordinary differential equation models with nonlinear mixed-effects parameter estimation and anchor-based statistical matching, the team demonstrated a path toward virtual cell models that retain single-cell heterogeneity while extending predictions far beyond their training data. With all code and data released openly, the framework is ready to absorb additional pathways such as AP1, p38, and the interferon axis, and to explore sequential stimulation over longer timescales. For now, the message is clear: in the immune system’s crowded molecular marketplace, a little rivalry among signals may be exactly what keeps the message intelligible.

Subject of Research: How signaling pathway antagonism shapes macrophage stimulus-response specificity to mixtures of immune ligands

Article Title: Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism

Article References: Guo, X., Sen, S., Gonzalez, J., & Hoffmann, A. (2026). Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00230-9

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00230-9

Keywords: macrophages, NF-kB, signaling dynamics, Toll-like receptors, stimulus-response specificity, ligand mixtures, mathematical modeling, systems biology, innate immunity, antagonism, synergy, single-cell imaging