Journal of Chemical Ecology

Animals balance staying in a favorable environment with exploring new ones. In C. elegans chemotaxis, the process by which worms migrate toward an attractant has been extensively studied. However, what happens after they reach it remains largely unexplored, partly because conventional assays immobilize worms at the point of arrival. Here, we quantitatively analyzed chemotactic behavior upon reaching an attractive odor source using an immobilization-free chemotaxis assay. We observed that 62% animals left the isoamyl alcohol region after initially approaching it, a behavior we termed "leaving behavior." Quantitative analysis revealed that leaving behavior represents a distinct locomotor state compared with free-moving, high-concentration odor avoidance, and approach behavior. To test whether leaving behavior is related to olfactory adaptation, we analyzed mutants in adaptation-related genes. The proportion of leaving behavior was significantly increased in egl-4 loss-of-function mutants compared with wild-type animals, whereas arr-1 mutants showed no significant difference. These results suggest that egl-4 negatively regulates leaving behavior, suggesting a role for this kinase in stabilizing post-arrival behavioral states beyond its known function in olfactory adaptation. Our findings indicate that chemotaxis involves dynamic behavioral transitions even after reaching an attractant, consistent with an exploration-exploitation trade-off framework.

Transgenerational plasticity can shape plant phenotype and influence plant response to environmental changes in interaction with the current conditions. While how past stress interact with either current optimal or stress conditions is increasingly documented within a single plant, transgenerational plasticity remains particularly poorly understood especially at the metabolome level. In our study, we investigated whether heat stress induces transgenerational metabolic and phenotypic modifications along two successive clonal ramet generations of the sub-Antarctic aquatic plant Limosella australis. We performed untargeted metabolomic approaches and measured morphologic and performance traits, to assess both transgenerational plasticity of the metabolome and the phenotype. We found that heat stress remodelled the metabolic profile and influenced the foraging strategy of our clonal plant, and that some of these metabolic changes persisted into the first clonal generation. This one therefore adopted an intermediate growth strategy, even though culture conditions were optimal. By comparing differentially accumulated features between daughter ramets from heat stressed mother ramets and from unstressed mother ramets, we identified common and specific metabolites accumulation to heat stress response, belonging to diverse compound families. However, we did not observe any adaptative advantage and any metabolic imprint during another heat stress applied on the second clonal generation. This work provides especially new clues into how plant metabolome integrates and transfers previous stressed clonal generation's information.

Heterochromatin protein 1 (HP1 is an intrinsic component of heterochromatin domains where it is involved in a diverse set of functions including heterochromatin spreading and organization, chromatin compaction and transcriptional silencing. It has been suggested that HP1 functions through a phase separation mechanism, a process that has been observed in vitro in the presence of N-terminal phosphorylation, nucleic acids and nucleosome arrays. HP1 can also interact with numerous binding partners that contain a specific motif called an HP1 access code (HAC). HACs recognize and bind to an interface formed by the chromoshadow (CSD) domains in the HP1 homodimer, the functional form of the protein. It has been shown that some HP1 binding partners can enhance its phase separation ability while others disrupt the process. Here, we focus on the interactions between HP1 and three binding partners, namely the p150 subunit of the chromatin assembly factor 1 (CAF-1), the N-terminal domain of the lamin B receptor (LBR), and the mitotic protein Shugoshin 1 (Sgo1). Using phase separation assays, we show that CAF-1 prevents HP1 phase separation while LBR and Sgo1 enhance it. Binding assays, mutational studies, NMR spectroscopy and computational analysis allow us to dissect the contributions of the HAC motifs, the charge patterns of the binding partner sequences and the role of N-terminal phosphorylation on HP1 in condensate formation. Our results demonstrate that each binding partner uniquely balances these contributions to modulate the properties of HP1, while electrostatic interactions dominate the regulation of phosphorylated HP1. These results suggest that HP1 binding partners play an important role in the modulation of its properties and the regulation of its functions in distinct biological contexts.

The incidence of early-onset colorectal cancer (CRC) has risen sharply in recent decades1, yet the biological basis underlying the distinct behavior of tumors arising in young versus aged tissues remains poorly understood. Here we show that aging reprograms the epigenetic landscape of the colon, restricting colon tumor growth through stable silencing of developmental and fetal gene programs. We find that colon tumors arising in aged mice are intrinsically less proliferative than those arising in young animals. Multi-omic profiling of normal colon and colon tumors reveals that aging drives DNA hypermethylation, loss of Polycomb-associated chromatin states, and reduced chromatin accessibility at a defined set of developmental genes that are bivalent (marked by both H3K27me3 and H3K4 methylation), transcriptionally active in colon tumors from young animals and repressed in both tumors and normal tissue from old animals. Among the genes most strongly repressed in old animals is Tacstd2 (Trop2), a regulator of fetal intestinal programs and epithelial stemness. Pharmacologic inhibition of DNA methylation reactivates the aging-silenced gene network in organoids from old animals, whereas genetic disruption of Tacstd2 suppresses growth and developmental transcriptional programs in young tumor organoids. TACSTD2, fetal gene signatures, and the aging-associated bivalent gene program are likewise repressed in late-onset vs. early-onset human colorectal cancers. Collectively, these findings identify age-associated epigenetic silencing of developmental gene programs as a causal mechanism that constrains colorectal tumor growth and provide a mechanistic framework for understanding the distinct biology of early-onset colorectal cancer.

Extrahepatic delivery of lipid nanoparticles (LNPs) to non-phagocytic cells is a major challenge, with the leading strategy involving surface functionalization with target-specific monoclonal antibody (mAb) ligands. We investigate the stability of mAb-conjugated LNPs using two anchoring systems: the commonly used DSPE-PEG2kDa-maleimide and a block copolymer, PCL5kDa-b-PEG2kDa -maleimide, with the hypothesis that conjugation to a 150,000 Da antibody could overwhelm the relatively small ~600 Da aliphatic anchor on the PEG-lipid in vivo. Shedding of the mAB would compromise targeting. Conjugation integrity following IV injection was assessed by tagging LNPs and mAbs with metal ion tracers that could be quantified by ICP-MS. Results show that DSPE-PEG-mAb rapidly (within 1h) dissociates from LNPs in blood, leading to accelerated LNP clearance. In contrast, mAbs conjugated using PCL-b-PEG remained stably associated with the LNP over the 24h circulation and clearance of the construct. Results are connected to a thermodynamic model that reproduces experimental findings for PEG-anchor(-mAb) shedding in vitro and in vivo. This study identifies anchoring strength as a critical, unconsidered parameter for in vivo performance when conjugating mAbs to LNPs for extrahepatic delivery.

Blockade by sedimentary particles, such as mineral crystals, is a continuous risk the kidney tubule faces. To prevent that, kidney resident macrophages form transepithelial protrusions and remove intratubular sedimentary particles, a behavior particularly prevailing in the medulla over the cortex. However, the molecular mechanisms underlying this characteristic behavior of medulla macrophages are incompletely understood. In this study, we identified that the medulla had higher mechanical stiffness than the cortex in steady state, which was further elevated when kidney stone formed. Increased tissue rigidity was sensed by medulla macrophages via mechanoreceptor Piezo1, which promoted macrophage protrusion formation and their ability to clean the tubules. Loss of Piezo1 expression in kidney macrophages predisposed mice to intratubular accumulation of mineral crystal in steady state and accelerated kidney stone formation during oxalate intake challenge. Signaling via Piezo1 mobilized molecules involved in cell adhesion and protrusion assembly, including Talin2 and focal adhesion kinase (FAK). Finally, we developed a first-of-its-kind cell-based therapy for the treatment of experimental nephrolithiasis by exploiting macrophage Piezo1 activity, and this strategy shows great promise for future translational research.

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

Temporal niche partitioning is a strategy for reducing interspecies competition and strengthening reproductive isolation. It relies on animals confining their daily activity to distinct diurnal, crepuscular, or nocturnal windows. However, a hardwired temporal niche is only advantageous under stable, predictable ecological regimes; surviving dynamic environments demands behavioral flexibility. Yet, it remains unclear how animals override rigid biological constraints to rapidly exploit transiently available fitness-critical time windows. To address this, we leveraged the twilight-active, species-rich Drosophila genus and monitored their daily activity under naturalistic conditions. Here, we show that intense sociosexual interactions rapidly drive a species-specific reformatting of their canonical crepuscular niche. The dominant sensory modality used for sexual communication predicts niche shift direction: reliance on chemosensation for courtship redirects behavioral activity into the night, while visual reliance shifts it into the day. This temporal plasticity bypasses the circadian clock, instead operating via a conserved dopaminergic pathway. Dopamine operates a dual-output brain circuit that simultaneously inhibits sleep and sustains sexual motivation. Our results reveal how mating imperatives decouple behavioral timing from circadian command, enabling conditional colonization of otherwise restricted temporal windows. Ultimately, by driving the divergence of previously overlapping niches, sociosexually induced temporal plasticity provides a powerful mechanism for sympatric coexistence in crowded environments.

We showed before that the endogenous peptide Nociceptin blocks the premature differentiation of oligodendrocytes (OLGs), preventing untimely precocious myelination in the developing brain. Consistent with this early function, Nociceptin brain expression is developmentally regulated, sharply decreasing with the initiation and progression of myelination. However, we now found that at difference with controls and relapsing-remitting multiple sclerosis (RRMS), Nociceptin levels are highly elevated in cerebrospinal fluid from patients with the most severe progressive MS (PMS) forms. This questioned whether Nociceptin early developmental effects could be latter recapitulated, interfering with remyelination in PMS. This possibility was tested by inducing experimental autoimmune encephalomyelitis in older mice, at an age equivalent to that with increased risk of RRMS transition into PMS. Older animals develop persistently highly debilitating clinical symptoms, and display both brain and spinal cord demyelination. Importantly, these mice exhibit elevated brain Nociceptin levels, and their treatment with an antagonist of the Nociceptin receptor (NOR) elicits a regression of clinical scoring that is accompanied by higher ratios of OLGs/OLG progenitor cells, increased myelination, and reduction of reactive astrocytes. These findings suggest that Nociceptin may be a crucial player in the age-related progression of MS; interfering with OLG maturation and remyelination, and perhaps further exacerbating neurological dysfunction by targeting astrocyte populations. The upregulation of Nociceptin secretion by human astrocytes in response to proinflammatory cytokines, also points to this peptide as a mediator of microglia-astrocyte interactions supporting MS progression with aging. NOR may offer a novel pharmacological target for ameliorating the devastating effects of MS progression.

Social media users often feel that time vanishes while scrolling, but real feeds confound novelty, rewards, social signals, and self-paced control, leaving the driver of this distortion unclear. We tested whether self-paced visual exploration is sufficient to compress subjective time by comparing active scrolling with passive, yoked viewing and a static baseline. Twenty-three adults viewed sequences of natural images under three within-subject conditions: Scrolling (self-paced mouse clicks), Watching (a passive, yoked replay of their own scrolling sequence), and a Baseline (a static image). Participants estimated the elapsed duration of each block. Subjective duration was most compressed under Scrolling (48% of elapsed time), followed by Watching (51%) and Baseline (65%). Two sources separated these effects. Adding back the empty inter-image fixations brought the image-rich conditions to within seconds of the Baseline, showing that observers barely counted the blank gaps; the Scrolling--Watching difference, by contrast, was independent of these shared gaps, isolating self-paced control as a second source of compression. Electrophysiology linked that control to anticipatory neural states and the timing of early visual responses, with no amplified encoding of individual images. The results favor an attention-weighted account of timing, on which subjective duration tracks how much attention reaches the clock, a resource that a self-paced stream and its uncounted gaps both draw away.

Medical foundation models compress biomedical data into embeddings that support diverse downstream clinical tasks. However, successful model deployment is hampered by performance degradation on external data. It is recognized that embeddings capture acquisition signatures, such as hardware and technical differences, in addition to biology. Effective harmonization must remove the acquisition signature while preserving biological signals, a trade-off that current methods fail to balance adequately. Input-level normalization fails to eliminate acquisition signatures from embeddings, whereas embedding-level methods adjust features in an untargeted manner. We present FEATMAP, a harmonization approach that models acquisition signatures as geometric distortions between manifolds of similarly arranged embeddings. Using paired data that isolate the effect of acquisition signatures, FEATMAP fits a single global affine transformation per foundation model to correct acquisition signatures directly in the embedding space. This targeted, reusable correction aims to preserve biological and demographic variation while harmonizing across acquisition signatures. Across scanner and foundation-model harmonization in digital pathology and field-strength harmonization in brain MRI, FEATMAP improves cross-condition embedding similarity, reduces performance gaps without retraining, and suggests potential for the alignment of disparate embedding spaces.

Spectral bleed-through remains a persistent practical problem in multichannel fluorescence microscopy. Signal from one fluorophore can be recorded in the detection channel of another, thereby biasing intensity measurements, inflating apparent colocalization, and complicating the interpretation of dynamic microscopy data. Although many correction strategies exist, routine workflows often remain fragmented across ad hoc scripts, manually tuned graphical procedures, or method-specific blind-unmixing implementations with limited provenance. Here we present spectral-unmixing, an open-source Python package for reproducible linear spectral unmixing in multidimensional microscopy stacks. The package unifies directed two-channel correction with multiple alpha-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a 2 x 2 mixing model, and PICASSO-family blind unmixing for multichannel data. Microscopy inputs are normalized at the API boundary to canonical TZCY X stacks, allowing the same unmixing code to be applied across file formats without manual axis handling. Machine-readable sidecar reports preserve the effective processing configuration and estimated coefficients for every output, so that workflows can be audited and reproduced. Synthetic and real-data-derived benchmarks show that the implemented workflows accurately estimate and correct bleed-through when their model assumptions are satisfied. In fixed-alpha two-channel simulations, the mean-ratio and linear-fit estimators recovered {approx} 0.283 for a ground-truth value of 0.28 and reduced target-channel normalized root mean squared error from approximately 0.029 to 0.003. In time-varying simulations, per-time-point estimation tracked coefficient drift substantially better than reference-time-point estimation. Bidirectional inversion recovered reciprocally mixed channels accurately when coefficients were known or well estimated. PICASSO-family benchmarks further showed a practical trade-off between reducing residual inter-channel dependence and preserving fluorophore identity, with MATLAB-style workflows behaving more conservatively and source-sink formulations providing stronger dependence suppression when meaningful directional priors are available. Together, these elements make spectral-unmixing a practical, transparent, and extensible platform for reproducible spectral unmixing of fluorescence microscopy data in neuroscience and other quantitative bioimage-analysis settings.

Recent advances in spatially resolved transcriptomics (SRT) enabled measurement of sets of pathway genes activity within tissues. However, existing gene set activity scoring methods overlook spatial dependencies among tissue locations, restricting their ability to capture region-specific pathway activities associated with disease pathology or cellular communication. Moreover, these methods lack significance-level inference for activity scores, provide limited interpretability of gene-level contribution to a pathway, and scale poorly to advanced large-size SRT datasets. To address these limitations, we present GESSO (Gene sEt activity Score analysis with Spatial lOcation), a spatially informed gene set scoring method adaptable to diverse SRT platforms. GESSO models gene set activity levels through a graph-regularized matrix decomposition algorithm, jointly inferring spatially coherent gene set activity scores (GASs) and interpretable metagene weights that capture gene-level contributions. It further implements a permutation-based local significance test and a stratified low-resolution approximation that scales to high-resolution SRT datasets such as Visium HD, Stereo-seq, and Xenium Prime. Across 13 datasets from five SRT platforms, GESSO outperformed all existing methods in accuracy, calibration, interpretability, and scalability. Applications revealed novel biological programs, including spatially confined EMT activation within tumor-stroma interfaces, developmental signaling gradients across embryonic tissues, and coordinated B-cell, T-cell, and signaling pathways within germinal centers of human lymph node tissue, revealing the spatial organization of immune function at subregional resolution.

Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has focused on anatomical and physiological factors, here we observed a fundamental contribution from the computations performed by the underlying neural network. Handcrafted and trained recurrent neural networks supporting continuous representations respond to seizure perturbations with higher activity and earlier performance decline relative to matched networks stabilizing discrete, well-separated states. Consistent with this prediction, in vivo recordings revealed that medial entorhinal cortex, whose grid cells exhibit continuous attractor dynamics, drives acute epileptiform discharges with stronger involvement and smoother state trajectories compared to CA3, a hippocampal subfield associated with discrete memory storage. Moreover, selective synaptic silencing demonstrated that this difference in seizure responses depends on intact entorhinal connectivity. Thus, the computations that enable neural networks to process information also influence their vulnerability to pathological transitions.

Transformer-based deep learning models have shown great potential for decoding visual EEG signals. However, their internal attention mechanisms are often evaluated primarily on optimization objectives, leaving their alignment with biological brain connectivity an open question. This study empirically evaluates how variations in EEG preprocessing strategies affect these attention representations using the Adaptive Thinking Mapper (ATM) model as a framework. We compared a baseline pipeline (MVNN only) against a comprehensive cleaning pipeline integrating ICA and notch filtering. The models were evaluated through cross-generalization, noise robustness, and spectral-temporal ablation analyses. Furthermore, we investigated the structural correspondence between the model's data-driven attention weights and neurophysiological reference networks (GPDC, PDC, and DTF) using Node Strength Correlation and Representational Similarity Analysis (RSA). The results show that the comprehensive preprocessing successfully suppresses non-neural artifacts, such as frontal noise and electrical interference, while maintaining comparable decoding accuracy and baseline robustness. Alignment analyses revealed that the broad spatial organization of the learned attention patterns remains highly stable across pipelines, capturing key directed connectivity dynamics with subtle, metric-dependent variations in global representational geometry. This work provides an empirical exploration into bridging data-driven attention weights with neurophysiological consistency, offering insights toward more transparent brain-computer interfaces.

Representations of the same physical stimulus vary between individuals. Characterizing individual differences has practical implications, but is challenging because these representations are not directly observable. Given a model of how representations vary within a population, we propose a Bayesian adaptive procedure for estimating an individual observer's representation from a series of targeted perceptual discrimination judgments. A key component of our approach is using Fisher information to identify stimulus distortions that efficiently differentiate observers in the population. As a proof of concept, we focus on individual differences in color perception and simulate observers with cone fundamentals drawn from an individual colorimetric observer model. We demonstrate that our approach can recover key aspects of a sampled observer's cone fundamentals using simulated three-alternative forced-choice oddity judgments with approximately 500 trials, corresponding to an experimental duration of approximately one hour. Our Bayesian adaptive framework provides a promising and generalizable approach to efficiently link behavioral measurements to individual differences in sensory representations.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.

Whether different pathogens acquire resistance to antimicrobials through the same mutations is a major question in evolution and microbiology. Most antifungal drugs are used to treat infections caused by multiple fungal species, many of which have diverged for millions of years. If the evolution of resistance was to converge onto the same set of mutations across species, knowing the mechanism of resistance in one would allow us to predict and track it in others. The extent of this convergence remains unknown due to the lack of systematic data on resistance mutations. Here, we quantify the conservation of resistance mutations in the cytosine deaminase (CD), a protein responsible for resistance to flucytosine, one of the oldest antifungal drugs. By comparing the crystal structures of this enzyme through 600 My of evolution, we show that the CD structure is highly conserved. We compared the full CD mutational resistance spectrum of resistance from an ascomycete and a basidiomycete. We found that resistance mutations in one ortholog can be used to predict resistance in the other at a high level of accuracy. However, because of epistasis, around 10% of mutations have distinct effects in the two orthologs, which imposes an upper limit to the transferability of the knowledge of resistance mutations from one species to another. Using biochemical assays and by structural characterization of several mutants, we identify distinct mechanisms of epistasis, one important being that the local physiochemical environment of some position has evolved in a way that makes the same substitutions destabilizing or entirely inactivating in an ortholog-specific manner. Our results show that resistance mutations can be conserved in fungi across hundreds of millions of years of evolution but that epistasis eventually limits the accuracy of these predictions.

Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.

Epithelial cells can encode prior damage into lasting epigenetic and functional states, enabling a primed response to future insults. In the pancreas, acute injury induces reversible acinar cell reprogramming toward a progenitor-like identity that persists beyond repair, supporting resilience to recurrent injury but creating a permissive state for malignant transformation. Given the central role of the tissue niche in stem cell regulation, we investigated microenvironmental adaptations that sustain this primed epithelial state. Using genetic mouse models and ex vivo organoid co-cultures, we identify a sex-specific sensory neural memory after pancreatitis that sustains long-term epithelial plasticity through a CGRP-dependent neuron-epithelial axis. We show that sex differences in acute inflammation drive neutrophil-dependent suppression of neural activation in females, decoupling neural memory from epithelial plasticity after repair. In males, neural memory promotes post-injury plasticity, revealing tissue memory as coordinated adaptation between epithelial progenitors and their niche.