T cell exhaustion was first characterized in the context of chronic viral infections, where researchers observed that T cells exposed to persistent antigen stimulation lost their ability to produce key inflammatory molecules such as interleukin-2 and tumour necrosis factor. Over time, these cells also lost cytotoxic function, the very machinery they use to kill infected or malignant cells. Cancer, particularly solid tumours, creates a similar situation of chronic antigen exposure. Tumour cells continuously present mutated or overexpressed proteins that T cells can recognize, but instead of a swift, decisive attack, the interaction stretches into months or years. This perpetual stimulation, combined with a suppressive tissue environment, drives T cells into increasingly dysfunctional states.

The tumour microenvironment amplifies this process through multiple converging pressures. Solid tumours are frequently hypoxic, meaning oxygen levels are low, which restricts the metabolic activity T cells require to sustain an energetic response. Nutrient competition is fierce, as rapidly dividing cancer cells consume glucose and amino acids such as glutamine, leaving T cells starved of fuel. Lactic acid secreted by tumours acidifies the surroundings and further impairs immune metabolism. On top of these metabolic constraints, tumour cells and associated stromal cells release immunosuppressive signalling molecules, including transforming growth factor beta and prostaglandins, while recruiting regulatory T cells and myeloid-derived suppressor cells that actively dampen immune attack. Each of these forces contributes to the progressive erosion of T cell function.

A crucial insight from recent research is that exhaustion is not a single uniform state but a spectrum of differentiation. Studies using single-cell RNA sequencing and T cell receptor tracking have revealed that exhausted populations contain both progenitor-like cells and terminally exhausted cells. Progenitor exhausted T cells retain a limited capacity to proliferate and can persist over time, serving as a reservoir from which other exhausted cells arise. Terminal exhausted cells, by contrast, are locked into a dysfunctional program marked by the loss of proliferative potential and reduced effector cytokine production. This distinction matters enormously for therapy, because checkpoint blockade immunotherapies appear to depend heavily on reinvigorating the progenitor compartment rather than resurrecting the terminal cells directly.

Central to the molecular identity of exhausted T cells is the transcription factor TOX, which becomes highly expressed as exhaustion deepens. TOX does not act alone; it works within broader gene regulatory networks that reshape the cell’s identity. Exhausted T cells express inhibitory receptors such as PD-1, TIM-3, LAG-3 and TIGIT on their surface, which serve as markers of the exhausted state and, in some cases, actively transmit suppressive signals. They also shift their metabolic profile, relying more heavily on fatty acid oxidation and oxidative phosphorylation rather than the glycolytic metabolism that characterizes robustly activated T cells. These changes are not merely consequences of a hostile environment; they reflect a fundamental reprogramming of cellular identity.

That reprogramming is epigenetic in nature, and this is one of the most consequential findings in the field. Exhausted T cells accumulate stable chromatin modifications that lock in their dysfunctional gene expression patterns. Enhancer regions that once supported the expression of effector molecules are remodelled and silenced, while new regulatory elements are opened to sustain inhibitory receptor expression. The result is a state that resists simple reversal. Even when the source of chronic antigen stimulation is removed, exhausted T cells often fail to return to their original functional program, because the epigenetic landscape that governed it has been irreversibly altered. This epigenetic rigidity helps explain why some patients respond spectacularly to immune checkpoint inhibitors while others derive little benefit.

Immune checkpoint blockade, exemplified by antibodies against PD-1 and CTLA-4, has transformed the treatment of melanoma, lung cancer, kidney cancer and several other malignancies. These therapies work in part by interrupting the inhibitory signals that exhaust T cells receive. Yet the overall response rates across cancer types remain far from universal, and the review underscores that the depth of exhaustion within a patient’s tumour infiltrating lymphocytes is a major determinant of success. Tumours with abundant progenitor exhausted T cells that still retain proliferative capacity tend to respond better, whereas tumours dominated by terminal exhaustion or lacking T cell infiltration altogether, sometimes described as cold tumours, respond poorly. This understanding has fuelled efforts to combine checkpoint inhibitors with other interventions that can broaden and deepen immune responses.

Among the most promising strategies is the combination of checkpoint blockade with therapies that reshape the tumour microenvironment itself. Agents that block transforming growth factor beta signalling, deplete regulatory T cells, or reprogramme myeloid suppressor cells may relieve some of the pressures driving exhaustion in the first place. Metabolic interventions, such as drugs that improve oxygen delivery or alter nutrient availability, represent another frontier. Oncolytic viruses and radiation therapy can convert cold tumours into inflamed ones by releasing tumour antigens and provoking innate immune activation, drawing fresh waves of T cells into the tumour that have not yet undergone exhaustion. Adoptive cell therapies, including chimeric antigen receptor T cells and tumour infiltrating lymphocyte therapy, introduce freshly armed immune cells but face the same risk of becoming exhausted once they encounter the suppressive tumour milieu, prompting efforts to engineer them with enhanced fitness and resistance to suppression.

Looking ahead, the review highlights the potential of manipulating the epigenetic and transcriptional programs that define exhaustion. Drugs targeting DNA methylation and histone modification are already approved for certain cancers, and researchers are investigating whether such agents can loosen the epigenetic locks that keep exhausted T cells dysfunctional. More precise approaches may one day selectively reprogramme the enhancer landscape of exhausted T cells, restoring effector function while preserving the cells’ tumour specificity. Single-cell and spatial profiling technologies continue to refine the map of exhaustion states within tumours, enabling clinicians to stratify patients according to the immunological character of their disease and to monitor how therapies shift T cell states over time.

Decoding T cell exhaustion in the tumour microenvironment is ultimately about recovering a lost weapon. The immune system already possesses cells capable of eliminating cancer; the challenge is that tumours have learned to wear them down through chronic stimulation and environmental hostility. By dissecting the transcriptional, epigenetic and metabolic architecture of exhaustion, researchers are converting what once seemed like an irreversible defeat into a set of addressable molecular mechanisms. Each layer of understanding brings the field closer to combination therapies that can prevent exhaustion, reverse it in its earlier stages, or work around it when it has become entrenched, offering new hope for patients whose cancers have so far resisted the immune system’s grasp.

Subject of Research: T cell exhaustion in the tumour microenvironment and its implications for cancer immunotherapy

Article Title: Decoding T cell exhaustion in the tumour microenvironment

Article References: Park, J. A., Im, J., & Hwang, S.-M. (2026). Decoding T cell exhaustion in the tumour microenvironment. Experimental & Molecular Medicine, 58(8), 2590-2602. https://doi.org/10.1038/s12276-026-01809-w

Image Credits: AI Generated

DOI: 10.1038/s12276-026-01809-w

Keywords: T cell exhaustion, tumour microenvironment, immunotherapy, PD-1, checkpoint blockade, TOX, epigenetics, cancer, inhibitory receptors, single-cell sequencing, Decoding, cell

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

The researchers set out to test what happens when so-called bystander T cells, which recognize viral rather than tumor targets, are reactivated within the tumor microenvironment. Using a mouse model of melanoma, they transferred memory CD8+ T cells specific to an irrelevant viral antigen and then delivered the matching viral peptide directly into the tumor, alongside anti-PD-L1 checkpoint blockade. The result was striking: tumors were cleared even though the activated T cells could not, in any conventional sense, see the cancer. In experiments where mice lacked any tumor-specific TCRαβ+ T cells whatsoever, tumor elimination still proceeded, demonstrating that classical recognition-dependent killing was not required.

The mechanism, the authors show, is paracrine. Activated T cells flood the tumor microenvironment with effector cytokines, chiefly interferon-γ and tumor necrosis factor, which act on surrounding cells rather than on the tumor directly through T cell receptors. These signals recruit waves of innate immune cells, including Ly6c-high monocytes and neutrophils, and induce the enzyme iNOS in myeloid cells, driving local production of nitric oxide. The combination of interferon-γ, TNF and nitric oxide proved lethal to tumor cells, triggering caspase-dependent death pathways that recapitulated melanoma clearance observed in living animals.

Technical detail from the single-cell work reinforces the picture. Using CITE-seq, the team profiled tens of thousands of cells from the tumor microenvironment before and after treatment, mapping how activated virus-specific T cells reshape the entire cellular ecosystem. The adhesion molecule VCAM-1 emerged as essential, apparently by anchoring and coordinating the influx of myeloid cells, and depletion experiments confirmed that innate leukocytes, not just the cytokines themselves, are indispensable to the killing program. Notably, natural killer cells were not required, pointing instead to recruited monocytes and neutrophils as the critical innate effectors.

The tumor cell death observed was not a quiet, orderly apoptosis alone. The researchers found evidence of panoptotic pathways, the interconnected family of inflammatory death programs that includes pyroptosis, necroptosis and apoptosis, converging on caspase-dependent execution. This matters because inflammatory cell death can further amplify immune recruitment, potentially converting a localized activation event into a self-reinforcing tumoricidal cascade. The synergy of interferon-γ and TNF in driving this form of death echoes findings from other recent studies linking cytokine cooperation to inflammatory tumor cell killing.

Perhaps the most clinically provocative result came from translational analysis. The gene expression signatures associated with this bystander-activation response in mice were predictive of survival among human patients with melanoma, suggesting that the same biology operates, or at least leaves traces, in human disease. In vitro, the cytokine-and-nitric-oxide cocktail killed human melanoma cell lines, including A375 and SK-MEL-2 cells, through the same caspase-dependent mechanism, bolstering the case that the mouse findings are not an artifact of the model system.

The study builds on a growing body of work showing that tumors are infiltrated by large numbers of T cells that have nothing to do with the cancer. Earlier research established that virus-specific memory T cells populate tumors and can be repurposed for immunotherapy, and that bystander CD8+ T cells are abundant and phenotypically distinct in human tumor infiltrates. Strategies have already been proposed to exploit this, from oncolytic viruses carrying tumor-irrelevant epitopes to lipid nanoparticle RNA approaches that leverage SARS-CoV-2-specific immunity for cancer treatment. The new work provides the mechanistic foundation for why such approaches might succeed: productive activation, not antigen specificity, is the trigger.

The implications for immunotherapy design are considerable. Current approaches such as personalized neoantigen vaccines, adoptive T cell transfer and checkpoint blockade all aim, in different ways, to generate or rescue tumor-specific T cell responses, an endeavor that is expensive, slow and often thwarted by tumor immune evasion. If intratumoral T cell activation alone can suffice, then simpler strategies become conceivable: delivering activation signals directly into tumors to wake up whatever unexhausted bystander T cells happen to be present, and letting the paracrine storm of cytokines, nitric oxide and recruited innate cells do the killing. Intratumoral CpG oligonucleotides and STING agonists, which already show clinical promise, may partly work through exactly this kind of bystander mechanism.

Cautions remain. The experiments were performed largely in mouse melanoma models, and the requirement for VCAM-1, myeloid cells and specific cytokine combinations may vary across tumor types and tissue contexts. The balance between tumoricidal inflammation and harmful tissue damage will also need careful calibration, particularly given the known role of interferon-γ and TNF synergy in cytokine shock syndromes. Still, the conceptual shift is profound: the tumor microenvironment may be less a fortress requiring a precisely targeted key and more a tinderbox awaiting a spark, provided enough activated T cells are standing by inside it.

For a field that has spent decades chasing tumor antigens, the message of this study is liberating and unsettling in equal measure. Immunotherapy, the authors conclude, may not need to induce or rescue cancer-specific responses at all. Triggering productive T cell activation within tumors can be sufficient, and the immune system’s own inflammatory machinery will handle the rest.

Subject of Research: Paracrine tumor killing by activated bystander T cells independent of tumor antigen recognition

Article Title: Intratumoral T cell activation kills tumors regardless of T cell specificity

Article References: Ghirardelli Smith, O. C., Dao, T. T., Gavil, N. V., O’Flanagan, S. D., Rubin, A. J., Nguyen, S., Watowich, M. B., Liu, N., Weyu, E., Quarnstrom, C. F., Soerens, A. G., Joag, V., Rosato, P. C., Krummel, M. F., Geller, M. A., Miller, J. S., Giubellino, A., Vezys, V., Shalek, A. K., & Masopust, D. (2026). Intratumoral T cell activation kills tumors regardless of T cell specificity. Nature Immunology. https://doi.org/10.1038/s41590-026-02642-z

Image Credits: AI Generated

DOI: 10.1038/s41590-026-02642-z

Keywords: T cell activation, bystander T cells, tumor immunology, interferon-gamma, nitric oxide, melanoma, checkpoint blockade, innate immune cells, panoptosis, immunotherapy, Intratumoral, cell

Standard organoid culture media typically include a basal formulation such as DMEM/F12 supplemented with factors that promote neural differentiation, including N2 and B27 supplements, and often retinoic acid at later stages to encourage photoreceptor maturation. Variations among laboratories in the choice of basal medium, the concentration of supplements, the presence or absence of serum components, and the timing of factor additions have long been recognized as sources of heterogeneity, but the systematic consequences of these choices for downstream applications have been less thoroughly characterized. The finding that media alter both adeno-associated virus transduction and retinal ganglion cell survival suggests that seemingly minor formulation differences can propagate into functional outcomes that matter enormously for translational work.

Adeno-associated virus vectors are the leading platform for retinal gene therapy, with approved products demonstrating that subretinal or intravitreal delivery can produce durable clinical benefit in inherited retinal degenerations. The success of AAV-mediated gene transfer depends on a cascade of events: vector particles must reach the target cells, bind to cell surface receptors, undergo endocytosis, traffic through the cytoplasm, enter the nucleus, uncoat, and convert their single-stranded genome into a transcriptionally competent double-stranded form. Each step can be influenced by the physiological state of the target cell, including membrane composition, endosomal trafficking dynamics, proteasome activity, and the expression of factors that second-strand synthesis. If culture media shift cells into states that favor or hinder any of these steps, then organoid-based assessments of vector tropism and potency will yield results that are artifacts of the culture condition rather than faithful predictions of clinical behavior.

This consideration is particularly acute because organoids are increasingly used as preclinical screening platforms for vector engineering. Researchers seeking capsids with improved photoreceptor tropism, or with the ability to penetrate the inner limiting membrane after intravitreal injection, frequently validate their designs in retinal organoids before advancing to animal studies. A capsid that appears highly efficient in organoids maintained in one medium might underperform in organoids maintained in another, not because the capsid has changed but because the cellular context has. Standardizing media composition, or at minimum reporting it comprehensively and testing key findings across multiple formulations, would strengthen the predictive value of such screens and reduce the risk of pursuing vector designs whose apparent advantages do not survive a change of culture conditions.

The effects on retinal ganglion cell survival are equally consequential. Retinal ganglion cells are the projection neurons of the visual system, conveying visual information from the retina to the brain through the optic nerve, and their degeneration underlies glaucoma and other optic neuropathies. In organoid culture, ganglion cells are notoriously fragile; they are among the first cell types generated, they reside in the innermost layer of the tissue, and they depend on trophic support that is difficult to reproduce in a dish. Their progressive loss during long-term organoid culture is a well-documented limitation, and it complicates any effort to model ganglion cell diseases or to test neuroprotective strategies. If specific medium components can substantially extend ganglion cell survival, this opens two important avenues: first, the creation of longer-lived organoid models in which disease-relevant cell types remain available for study; and second, the identification of the trophic factors and metabolic conditions that ganglion cells require, which may themselves point toward therapeutic targets.

The mechanistic links between medium composition and cell survival likely involve several intersecting pathways. Oxidative stress is a prominent candidate, since retinal neurons are metabolically demanding and vulnerable to reactive oxygen species, and the antioxidant capacity of medium supplements such as those in B27 varies with formulation and with the degradation of components over time in culture. Energy metabolism is another: the retina is among the most oxygen-consuming tissues in the body, and photoreceptors in particular rely on aerobic glycolysis, a metabolic mode whose support depends on glucose and pyruvate availability in the medium. Growth factor signaling, including pathways involving BDNF, CNTF, GDNF, and insulin-like growth factors, also modulates ganglion cell survival, and the presence, stability, and concentration of such factors differ across media formulations. Even the buffering system and the resulting pH stability can influence neuronal health, as can osmolarity and the accumulation of metabolic waste products between medium changes.

For AAV transduction specifically, medium composition might act through effects on the cell surface. The glycocalyx, the dense layer of sugars coating the plasma membrane, provides attachment points that many AAV seruses exploit, and its composition is sensitive to culture conditions, including the availability of specific sugars and the activity of glycosyltransferases. Heparan sulfate proteoglycans serve as primary attachment receptors for several AAV serotypes, and sialic acid residues are critical for others. Media that alter glycosaminoglycan synthesis or sialylation could therefore change the efficiency of the initial binding step. Downstream, intracellular trafficking depends on the cytoskeleton and on endosomal pH, both of which can be modulated by medium components such as ammonium chloride accumulation, chloroquine-like compounds, or simply the energetic state of the cell. These mechanisms offer plausible, testable explanations for how the same vector applied to the same organoid type can perform differently across media.

The broader lesson resonates with a recurring theme in stem cell biology: the environment is part of the experiment. Organoids are often described as miniaturized versions of human tissues, but they are better understood as products of a continuous dialogue between intrinsic developmental programs and the culture environment. Small differences in oxygen tension, media exchange schedules, matrix composition, and the physical handling of cultures have all been shown to affect organoid morphology and cell type composition. The present findings add media formulation to this list in a way that directly touches two of the most translationally important readouts: gene delivery efficiency and survival of a clinically critical neuron.

From a practical standpoint, laboratories working with retinal organoids for gene therapy applications should consider several measures. Detailed documentation of medium composition, including lot numbers of supplements whose activity varies between batches, would improve reproducibility across the field. Cross-validation of key results in at least two distinct media formulations would reveal whether findings are robust or condition-dependent. Where possible, matching the metabolic and trophic environment of the organoid to the physiological state of the target tissue in vivo would improve the clinical relevance of preclinical testing. For ganglion cell studies specifically, optimizing media for survival may need to be balanced against the goal of photoreceptor maturation, since conditions that favor one cell class may not favor another, and the developmental timing of these requirements may differ.

There are also implications for disease modeling. Many inherited retinal diseases are cell-type specific, and the value of an organoid model depends on maintaining the relevant cells in a state that resembles their in vivo counterpart. Ganglion cell loss in culture has limited the use of organoids for modeling optic neuropathies such as those caused by mutations in OPA1 or other genes affecting mitochondrial function. If optimized media extend ganglion cell survival substantially, models of these diseases become feasible, enabling the study of pathogenesis in a human developmental context and the screening of candidate neuroprotective compounds. Similarly, for glaucoma research, where the interplay between elevated intraocular pressure, axonal transport disruption, and somal survival is difficult to disentangle in animal models, longer-lived organoid systems with robust ganglion cell populations would provide a complementary human platform.

The intersection with AAV biology deserves particular attention as the gene therapy field matures. Dose-limiting toxicity, immune responses, and the challenge of achieving pan-retinal transduction after intravitreal delivery remain central obstacles. Organoids offer a human-relevant system in which to evaluate candidate capsids, promoters, and expression cassettes, but their utility depends on the transduction results reflecting what would occur in a patient retina. The finding that media promote or suppress transduction suggests that part of the variability reported across organoid studies of AAV tropism may be attributable to culture conditions rather than to genuine differences in vector performance. Disentangling these variables will require systematic comparisons in which identical vectors are applied to organoids raised in parallel under different media conditions, with careful quantification of both transduction efficiency and the cell-type composition of the tissues.

It is also worth considering how these findings fit into the larger regulatory and manufacturing landscape. As retinal organoids move toward use in potency assays and release testing for cell and gene therapy products, the dependence of their properties on media composition becomes a matter of product consistency. Regulatory frameworks emphasize the characterization of critical quality attributes, and for organoid-based assays, the culture medium is arguably a critical reagent whose composition must be controlled with the same rigor as the biological material itself. Manufacturers of media and supplements may need to provide more detailed specifications, and users may need to implement qualification procedures for each new lot, particularly for supplements such as B27 whose complex composition includes components with variable biological activity.

Looking forward, the systematic mapping of how individual medium components affect retinal organoid physiology could yield a design framework for culture conditions tailored to specific applications: media optimized for photoreceptor maturation for studies of inherited photoreceptor degenerations, media optimized for ganglion cell survival for optic neuropathy models, and media that support efficient AAV transduction for vector validation studies. Such an approach would treat the medium as an engineering variable rather than a fixed convention, transforming a source of uncontrolled variability into a tool for shaping organoid properties. The present work, by demonstrating that culture media alter both AAV transduction and retinal ganglion cell survival in retinal organoids, provides both a caution about the interpretation of existing organoid studies and a constructive starting point for this more deliberate approach to organoid culture design.

Subject of Research: Culture media alter retinal organoid physiology promoting AAV transduction and retinal ganglion cell survival

Article Title: Culture media alter retinal organoid physiology promoting AAV transduction and retinal ganglion cell survival

Article References: O’Hara-Wright, M., Lim, B. Y., M. Mangala, M., Kaiser, V., Wong, E., Aubin, D., Nemeruck, V., Reynisson, H., Doroudian, F., Chan, O. P. Y., Aryamanesh, N., A. Paulo, J., Palomba, S., Mirzaei, M., Ginn, S. L., & Gonzalez-Cordero, A. (2026). Culture media alter retinal organoid physiology promoting AAV transduction and retinal ganglion cell survival. Gene Therapy. https://doi.org/10.1038/s41434-026-00642-0

Image Credits: AI Generated

DOI: 10.1038/s41434-026-00642-0

Keywords: Culture, media, alter, retinal, organoid, physiology, promoting, transduction, ganglion, cell, survival, scientific research

Much of the current understanding of cell fate comes from single-cell molecular measurements, particularly RNA sequencing. These methods can provide detailed profiles of gene expression and reveal regulatory networks associated with distinct cell states. Yet each measurement is usually a snapshot: it captures the molecular condition of a cell after the sample has been collected, rather than continuously observing the transition that produced it. Computational methods such as pseudotime analysis can arrange cells along an inferred developmental trajectory, but an inferred sequence is not the same as a direct record of change. Morphodynamics offers a complementary perspective by measuring features that can be observed repeatedly in living cells. Those features may include cell area, shape, movement, position within a tissue, nuclear geometry and the relationship between cellular structures. In principle, a time-resolved record of these variables can reveal transitional states that molecular sampling misses. The objective is not to replace molecular profiling, but to add the physical and behavioral dimension needed to understand how cell states are established in real tissues.

Tolonen and colleagues selected the Xenopus mucociliary epithelium because it differentiates rapidly and produces a complex, multilayered tissue. The model begins with an ectodermal cap that can be excised from an embryo and attached to a fibronectin-coated glass-bottom dish. Under culture conditions, the tissue proceeds through much of its differentiation program while remaining sufficiently thin for high-resolution, long-term live imaging. Over approximately 22 hours, it develops into a mature bilayer containing several specialized cell types. These include multiciliated cells, which help move material across an epithelial surface; small secretory cells; ionocytes; goblet cells; and basal stem cells. The cell types differ in their morphology and movement trajectories, creating observable physical signatures that can be measured during development. Because the explant reproduces important features of mucociliary epithelial development and resembles aspects of mammalian airway epithelium, it provides a tractable system for studying how cell behavior contributes to tissue organization.

To follow individual cells, the researchers used embryos injected with fluorescent markers labeling nuclei and cell membranes. Live imaging then captured the developing epithelium in three dimensions, while segmentation and tracking tools converted the image sequence into individual cell trajectories. Segmentation assigns image pixels or voxels to a particular cell, creating a digital mask that defines its boundaries. Tracking links those masks across successive frames, allowing researchers to estimate how each cell moves and changes over time. The analysis faced practical challenges. Cell shapes varied, the tissue was compact, and the available resolution along the imaging axis was limited. Membrane boundaries could therefore be difficult to identify consistently. Nuclear labeling provided a more reliable anchor, enabling accurate lineage tracking even when the surrounding cell geometry was ambiguous. From these trajectories, the team extracted morphometric and dynamic measurements and used them to define a morphodynamic state for each cell. This concept parallels a molecular state defined by gene expression, but it is based on physical features and behavior recorded in living tissue.

The first analysis produced an instructive negative result. When individual cellular features were considered without broader lineage information, the cells did not form sharply separated clusters corresponding to their eventual identities. The absence of clear clusters suggests that the relevant differences in this tissue are subtle rather than dramatic. Epithelial cells are also subject to physical constraints: they must pack together, share boundaries and maintain tissue integrity, which can make distinct cell types look similar at particular moments. Differentiation may therefore be encoded not in one conspicuous feature, but in combinations of modest changes distributed across time. To address this problem, the researchers turned to supervised machine-learning models. They generated a ground-truth dataset by fixing and immunostaining tissues at the endpoint, assigning final cell identities and then tracing those cells backward through their recorded lineages. This provided the models with known outcomes against which earlier morphodynamic patterns could be tested, transforming subtle physical trends into measurable associations with fate.

The supervised analysis used multivariate, multiclass prediction methods, including XGBoost and multinomial logistic regression implemented with scikit-learn. Rather than asking whether one measurement alone identified a cell type, these models evaluated combinations of features and their contribution to classification. XGBoost, an ensemble method based on decision-tree boosting, produced a mean cell-fate prediction accuracy of approximately 80 percent in the reported analysis. The most influential feature was the cell’s position along the Z axis. That result is biologically plausible because certain differentiated cell types occupy the apical surface of the multilayered epithelium. The model also identified the offset between nuclear and membrane centroids as informative. This measurement can reflect changes in cell polarity, shape and spatial organization during morphogenetic events such as radial intercalation, when cells move between tissue layers or rearrange relative to their neighbors. These signals were not necessarily strong enough to identify fate in isolation. Their predictive value emerged when the model considered several measurements together and interpreted them in the context of a cell’s lineage.

The findings illustrate why time-resolved phenomics could become an important partner to single-cell omics. Molecular data can show which genes and regulatory pathways are associated with a transition, whereas morphodynamic data can reveal when a cell changes position, how it reshapes itself and whether its movements are coordinated with those of nearby cells. Such information is especially relevant in epithelia, where fate is linked to tissue architecture, mechanical forces and collective behavior. A cell’s final identity may depend partly on the physical environment it experiences as it moves through a crowded, curved or multilayered tissue. Live imaging preserves this context, while computational pipelines make it possible to quantify many cells across extended developmental windows. Previous work has shown that single-cell phenomics can expose behavioral and mechanical heterogeneity during tissue remodeling. The current analysis extends that idea by showing that dynamic physical measurements can be scaled into a predictive framework resembling an omics workflow, even when individual features are relatively weak and cell states change continuously.

Several limitations remain before morphodynamics can provide a complete account of cell fate. The imaging system depends on fluorescent labeling, reliable segmentation and sufficient spatial and temporal resolution, and the authors note that variable morphology and limited Z resolution can affect the resulting masks. Prediction accuracy also depends on the quality and scope of the ground-truth dataset used for training. A model trained in one developmental system may not transfer directly to another tissue, species or disease state. Future studies could combine live morphodynamic measurements with molecular profiling of the same cells or closely matched lineages. Such integration may identify the precise time points at which physical changes coincide with decisive regulatory events. The approach could also help establish baseline patterns for biomedical phenotyping and early disease detection. In cancer research, for example, a detailed understanding of how normal cells change shape, position and behavior during differentiation could make it easier to recognize abnormal departures from that program. The broader significance is that cell fate may be read not only in molecular snapshots, but also in the evolving geometry and motion of living cells.

An important conceptual shift in this work is the treatment of morphology as a state variable rather than merely an endpoint description. A cell’s location, geometry and movement can be recorded repeatedly, preserving the order in which changes occur. This makes it possible to ask whether a physical feature precedes the appearance of a mature marker, rather than simply correlating the two after differentiation has finished. The distinction is especially valuable for identifying transition windows in which a cell may still be responsive to its environment or susceptible to developmental perturbation.

The study also shows why lineage information is central to interpreting phenotypic measurements. Cells sharing a tissue compartment may appear similar at one time point even when their later outcomes diverge. Conversely, the same feature may have different implications depending on where a cell came from and how it has moved. Linking measurements across a trajectory therefore supplies context that a collection of unrelated images cannot provide. Endpoint immunostaining served as the reference for assigning outcomes, while the preceding live record supplied the evidence used for prediction. This combination connects retrospective identity measurements with prospective dynamics without assuming that every visible difference is fate-determining.

Prediction should nevertheless be distinguished from mechanism. An informative feature, such as apical position or nuclear–membrane displacement, may report a process that accompanies fate commitment without causing it. The predictive pipeline can reveal when and where such associations occur, but perturbation experiments would be needed to test their functional importance. The framework could consequently serve as a way to prioritize developmental time points, cellular behaviors or physical transitions for experimental intervention. In this sense, morphodynamic analysis is not only a classification strategy: it can organize the complex sequence of events that connects progenitor behavior to the architecture of a differentiated epithelium.

Subject of Research: Using live-cell morphodynamics to predict cell fate during epithelial differentiation

Article Title: Tracking cell fate through morphodynamics

Article References: Dorr, J., & Mitchell, B. J. (2026). Tracking cell fate through morphodynamics. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00244-3

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00244-3

Keywords: cell fate, morphodynamics, live-cell imaging, epithelial development, Xenopus, single-cell phenomics, machine learning, cell differentiation, Tracking, cell, fate, scientific research