Biology subjects

Ma, S.

Publications and source records attributed to Ma, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-region two-photon calcium imaging in freely moving animals

Complex behaviors emerge from the coordinated activity of neural circuits distributed across widely separated brain regions. Existing optical approaches for monitoring neural activity lack the combined capability to record from anatomically distributed regions with high spatiotemporal resolution in freely moving animals. Here we introduce MINI2P-II, a 1.1-g 2P miniscope that achieves large-scale calcium imaging with high spatial resolution, deep imaging depth and dual-color imaging capability. Together with a dual-scope imaging system and a novel implantable periscope lens assembly, MINI2P-II enables simultaneous 2P imaging in widely separated or anatomically adjacent brain regions. In mice, we demonstrate inter-hemisphere coordination in bilateral motor cortex during climbing, egocentric boundary coding in simultaneous retrosplenial and posterior parietal cortex recordings, and in zebra finches, we showed large-scale behavior-dependent activity in both juvenile and adult individuals. MINI2P-II opens the door to multi-region 2P imaging across species and developmental stages in freely moving animals.

neuroscience↗

bioRxiv2026

Longitudinal diffusion tensor imaging identifies coordinated white matter remodeling during cocaine self-administration in mice

White matter microstructural alterations are increasingly recognized as a feature of cocaine use disorder (CUD), yet the longitudinal progression and biological underpinnings of these changes remain poorly understood. Although rodent intravenous self-administration (IVSA) provides a translational model of voluntary cocaine use, longitudinal diffusion tensor imaging (DTI) has not previously been combined with cocaine IVSA in mice. Here, we established a longitudinal IVSA-DTI framework to characterize white matter microstructural remodeling during cocaine self-administration and early withdrawal. Male C57BL/6 mice underwent DTI at baseline, following 11 consecutive days of cocaine (0.5 mg/kg/infusion) or saline IVSA, and after a 7-day withdrawal period immediately following a cocaine-seeking test (n = 4/group). Fractional anisotropy (FA) was evaluated longitudinally using atlas-based region-of-interest analyses and whole-brain diffusion maps. Cocaine-treated mice acquired robust operant responding and exhibited coordinated longitudinal FA alterations across interconnected hippocampal-associated white matter pathways. The most consistent changes occurred within the bilateral dorsal hippocampal commissure, fornix, and fimbria, with cocaine-treated mice demonstrating increased FA during self-administration relative to baseline while saline controls exhibited smaller or opposing longitudinal changes. Difference-in-differences maps further demonstrated cocaine-associated FA alterations within hippocampal pathways and localized regions of the corpus callosum after accounting for longitudinal changes in saline controls. These findings establish the feasibility of combining longitudinal DTI with cocaine IVSA in mice and demonstrate coordinated white matter microstructural remodeling during voluntary cocaine exposure. This framework provides a translational platform for future studies integrating longitudinal neuroimaging with genetically modified mouse models, tractography, and histological validation to investigate the mechanisms underlying cocaine-associated white matter plasticity.

neuroscience↗

bioRxiv2026

Facilitating Valid Statistical Inference in Biomedical Image Synthesis

Generative AI for medical image synthesis is increasingly used to impute missing data and augment biomedical datasets. Yet despite achieving high computational similarity to their real-world counterparts, synthetic images remain uncommon in clinical research practice, in part because of concerns about their biological validity. Here, we evaluate whether synthetic images support reliable downstream statistical analysis and biological inference in magnetic resonance imaging (MRI) and multiplexed immunofluorescence (mIF) imaging. We find that associations between patient phenotypes and synthesized images can be severely attenuated. This is because synthesis methods typically omit clinically relevant covariates and therefore generate images that, although visually realistic, mask key biological differences. Furthermore, naive analyses treat synthesized images as if they were observed, ignoring the statistical uncertainty introduced by synthesis. Drawing on multiple imputation theory, we introduce Phenotype Aware Multiple Synthesis (PAMS), an analysis framework that requires no model retraining and instead adjusts synthesized output to recover valid biological estimates with appropriately calibrated uncertainty. Applied to MRI and mIF data, PAMS reduced estimation bias and restored nominal confidence interval coverage. Because it is compatible with existing synthesis pipelines, PAMS offers a practical and rigorous route to statistically and biologically valid use of synthesized images in biomedical imaging research.

bioinformatics↗

bioRxiv2026

Deciphering Spatial Cellular Organization through RNA-Anchored Multi-Omics Deconvolution

Spatial technologies that jointly profile RNA and additional molecular layers provide a comprehensive view of tissue organization, yet their limited resolution results in measurements that reflect mixtures of multiple cell types. While RNA-based deconvolution methods are well established, incorporating co-profiled non-transcriptomic modalities without compromising cell-type identifiability remains an open challenge, as reliable references are largely restricted to transcriptomic data. Here we present SMODER, an RNA-anchored framework for spatial multi-omics deconvolution that treats transcriptomes as the primary determinant of cell identity while incorporating complementary molecular modalities in a constrained manner. By adopting an asymmetric integration strategy, SMODER preserves RNA-driven cell-type resolution and enables robust estimation of cell-type compositions across spatial locations. Across simulations and diverse spatial multi-omics datasets spanning protein, chromatin accessibility and histone modification assays, SMODER consistently outperforms existing methods in both accuracy and spatial coherence. Beyond deconvolution, SMODER denoises multi-omic signals and enables cell-type-aware analysis of regulatory programs. Application to spatial CUT\&Tag--RNA-seq and {RNA--ATAC-seq} data reveals spatially resolved epigenetic repression and cis-regulatory relationships linked to gene expression.

bioinformatics↗

bioRxiv2026

Disrupted chromatin architecture blocks B lymphopoiesis in Down syndrome

Human fetal haematopoiesis requires developmental potential to be encoded before lineage identity becomes transcriptionally apparent, but whether three-dimensional genome architecture provides such information is unknown. Here we develop ChromE-seq, a scalable single-cell assay that jointly profiles chromatin conformation and transcription, and integrate it with chromatin accessibility and DNA methylation to generate a multimodal atlas of ~400,000 haematopoietic cells from disomic and trisomy 21 (T21) human fetal liver. Chromatin contacts frequently preceded lineage-specific transcription or persisted after transcription declined, revealing 3D chromatin priming and memory. Unexpectedly, B-lineage and erythroid differentiation followed opposing architectural trajectories, with progressive loss and gain of chromatin connectivity, respectively. T21 disproportionately disrupted B-lineage architecture, redistributed interactions towards shorter genomic distances and weakened an EBF1-centred regulatory circuit. Deletion of a T21-sensitive EBF1 loop anchor reproduced the CLP-to-B-progenitor bottleneck observed in T21. These findings establish 3D genome architecture as an organizing layer of human fetal haematopoiesis that links developmental potential with lineage-specific vulnerability to genomic perturbation.

genomics↗

bioRxiv2026

DEPICT enables sample-centric therapeutic prioritization across evolving and spatially heterogeneous tumor states

Accurate treatment selection remains a major challenge in precision oncology because tumors with similar genomic alterations can respond differently to therapy. Most drug-response models evaluate individual drugs or drug-sample pairs, whereas clinical decision-making requires therapies to be prioritized within each tumor context. Here we present DEPICT (Drug Efficacy Prediction via Integrated ConText), a sample-centric deep learning framework that reframes drug-response prediction as the relative prioritization of therapies within an individual tumor context. DEPICT integrates transcriptomic and mutational features with prior alteration-therapy relationship and was trained on 231,880 cell line-drug pairs comprising 941 cancer cell lines and 289 compounds. Without retraining, DEPICT retained predictive performance in independently profiled cell lines, experimentally induced drug-resistant states and matched primary lung tumors and patient-derived organoids. Representations learned exclusively from bulk pharmacogenomic data further captured progressive drug-response states at single-cell resolution and outperformed existing single-cell drug-response inference methods. Applied to spatial transcriptomic data, DEPICT resolved clone-associated therapeutic heterogeneity within individual lung tumors, and predicted regional differences were recapitulated in matched multiregional organoid drug-response assays. These findings establish sample-centric therapeutic prioritization as a transferable strategy for modeling dynamic and spatially heterogeneous drug vulnerabilities across biological scales.

bioinformatics↗

bioRxiv2026

Structural streamlining of axonemal doublet microtubules in Plasmodium male gametes

Male gametogenesis in malaria parasites produces up to eight flagellated gametes within approximately 15 min, requiring de novo construction of motile axonemes on an unusually short timescale. Here we combine cellular cryo-electron tomography, high-resolution cryo-EM, proteomics, and parasite genetics to define the molecular architecture and assembly states of the Plasmodium male gamete axoneme. The doublet microtubules retain the principal surface-associated motility modules but contain a markedly reduced luminal microtubule inner protein network. Their inner junction lacks PACRG and is instead organized by CFAP20 and CFAP52, whose combined loss disrupts doublet integrity and exflagellation. We further identify a calmodulin protein at the outer dynein arm docking region and show that outer dynein arms accumulate progressively during gametogenesis. These findings establish distinct form of axonemal remodeling in Plasmodium and reveal the ordered assembly of structural and motor modules during rapid male gamete formation.

cell biology↗

bioRxiv2026

Low-Noise Finite-Bandgap FET Biosensors in the Nonlinear Gouy-Chapman-Stern Region: A Universal Calibration Curve

Finite-bandgap charge-based FET biosensors are known to be insensitive to electronic tunneling noise in the semiconductor and interference from weakly-charged proteins in physiological samples. However, their signal is still corrupted by pH fluctuations and condensing counterions in the bulk electrolyte. Here, we demonstrate that electrolyte noise is screened by the Stern layer beyond the linear Debye-Huckel limit, when the interfacial potential drop is larger than the thermal voltage. Using a fully nonlinear Gouy-Chapman-Stern (GCS) theory, a universal calibration curve is derived and shown to collapse the low-noise FET signals for miRNA, extracellular vesicles (EV), and protein, captured either directly or through silica and magnetic nanoparticle charge reporters, over eleven decades of analyte concentration. The theory involves the dissociation constant KD and a gain parameter {chi}, which represents the competing effects of the reporter charge and buffer Debye screening that must be large to produce the low-noise GCS condition. The limit of detection (LOD) is shown to be KD/{chi}, which can be optimized by varying the probe density, charge reporter, and buffer solution. An optimized hybridization buffer then yields {chi} ~ 22 (LOD ~1 pM) for miRNA detection, and multivalent capture reduces KD by four orders of magnitude for EV detection (relative to protein detection) with {chi} ~ 34 (LOD ~10 fM).

biophysics↗

bioRxiv2026

Climate Warming Patterns Predict Potential Residency Shifts

Introduction and Aim: Future climate instability creates obstacles for organisms across taxa, especially in areas that experience higher stochasticity. Wetland wading bird populations are a prime example of this scenario, as the hydrologic cycles that drive population success in these habitats depend heavily on the predictability of wet and dry periods. When fluctuations in wet and dry periods shift excessively, populations may be forced to relocate or shift migration routes. Our goal is to predict if worsening environmental conditions could drive increases in facultative migration and subsequent residency shifts Location: State of Florida, USA. Methods: We developed an individual-based movement model motivated by wading birds in southern Florida to determine if this concept of climate-driven range or residency shift is likely to occur in the future. To further interpret the outputs of this individual-based model, we compared habitat suitability for the American wood stork across Southeast of the United States between 2010 and 2025, 2055 and 2085. Results: Our IBM model predicted that a northward residency shift is predicted to occur given the environmental scenario of harsher fluctuation via climate instability in the Everglades. Moreover, our findings suggest that wood storks with partial and/or facultative migration may be more likely to adapt to these fluctuations. Our suitability maps showed spatially heterogeneous responses, with notable increases in northern Georgia, and the Carolinas, and consistent declines in northern Florida, indicating a gradual northward shift in suitable breeding habitat. Main Conclusions: Conserving networks of suitable wetlands across broader spatial scales, may be critical for facilitating movement and persistence under hydrologic instability. Mechanistic models such as the one presented in this study can help anticipate these shifts and inform proactive conservation planning. This model can also be adapted to fit similar wetland systems, or other landscapes with various population and movement phenotypes present.

ecology↗

bioRxiv2026

PURELIGHT: a quantitative photon-counting framework unifying intensity and lifetime imaging at video rate across detector technologies

Quantitative fluorescence microscopy requires photon-efficiency, speed and accurate intensity and lifetime measurements. Time-correlated single-photon counting (TCSPC) simultaneously captures intensity and lifetime, but photon pile-up distorts both signals at high count rates, preventing fast acquisitions. Existing corrections discard photons, distort intensity, or require specialized detectors. Here we introduce PURELIGHT, an integrated hardware and software framework that simultaneously recovers undistorted intensities and lifetimes at count rates far beyond conventional pile-up limits. PURELIGHT works with hybrid photodetectors, silicon photomultipliers and photomultiplier tubes while retaining over three times more photons than alternative approaches. Using two-photon imaging, we showcase PURELIGHT's superior accuracy and spatial contrast, demonstrating video-rate subcellular lifetime imaging in awake mice, a unique lifetime-calibrated ratiometric modality and crosstalk-free temporal multiplexing. By removing the limits that have confined TCSPC to low-signal applications, PURELIGHT promotes the adoption of quantitative, photon-efficient microscopy across the life sciences.

neuroscience↗

bioRxiv2026

Neutrophil remodeling is associated with human meibomian gland dysfunction and enables IFN-γ- and PAD4-dependent gland obstruction in mice

Meibomian gland dysfunction (MGD), a disorder of the eyelid's modified sebaceous glands, is the leading cause of dry eye disease and ocular surface morbidity, yet the immune mechanisms driving gland obstruction remain poorly defined. In a cross-sectional study of 66 patients with ocular surface inflammation, we used meibography and spectral flow cytometry of tear washes to identify a disease-associated, remodeled neutrophil state whose abundance is associated with gland atrophy. Using single-cell transcriptomics in a murine model of immune-mediated MGD, we revealed a disease-associated neutrophil state that exhibited ocular surface-enrichment, CD14 and ICAM-1 expression, and elevated IFN-{gamma} response and inflammatory signatures. Spatial transcriptomics localized IFN-{gamma} signaling and neutrophil migration signatures to the periglandular compartment. The remodeled neutrophils exhibited PAD4-dependent histone citrullination, with Padi4 deletion reducing NET-associated obstructive plugging, thus identifying PAD4-dependent NETotic activity as their disease-producing output. Inhibition of IFN-{gamma} signaling phenocopied Padi4 deficiency, yet combined disruption of these pathways provided no additive protection, indicating that IFN-{gamma} and PAD4 function as separable required inputs. Remodeled neutrophils accumulated under both conditions, uncoupling disease severity from cell abundance alone. Our findings support immune-mediated obstructive MGD as a mechanistic endotype driven by the IFN-{gamma}- and PAD4-dependent effector output of a remodeled neutrophil state.

immunology↗

bioRxiv2026

A cross-modal generative model for incomplete and degradedprostate MRI with multicentre clinical validation

Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.

bioengineering↗

bioRxiv2026

MG2Act: A Mechanism-Inspired Sequential Attention Framework for Molecular Glue Degradation Prediction

Molecular glue degraders act by inducing productive proximity between an E3 ligase and a substrate protein. For most characterized degradative glues, a small molecule first engages the E3, conditions its substrate-recognition surface, and only then enables recruitment of a compatible neo-substrate. This directionality is rarely encoded explicitly in computational models, which typically fuse molecule, E3 and target representations simultaneously. We present MG2Act, a structure-independent framework that translates this two-step logic into sequential cross-attention, using CRBN-mediated degradation as the most data-rich representative system. Starting from a curated continuous-valued benchmark of 1,207 pairs across 47 targets, a refined subset of 1,159 pairs was selected to train MG2Act after excluding rare targets. On identical processed data, MG2Act consistently outperforms machine learning baselines, robustly generalizes under strict redundancy-filtering, and responds coherently to mechanism-based perturbations. Prospective screening and zero-shot target-conditioned prioritization identified nanomolar degraders of IKZF1, CK1 and CDK4, including the non-classical IMiD-core CDK4 degrader SWC-202.

bioinformatics↗

bioRxiv2026

Structural characterization of stochastic detailed balance in chemical reaction networks

The global potential of a chemical reaction network has many applications and is closely related to the stochastic detailed balance. However, many fundamental questions concerning stochastic detailed balance remain unresolved, such as whether it depends on the system volume and how to construct new systems that satisfy it. In this paper, we show that stochastic detailed balance may depend on the system volume. We therefore introduce four types of stochastic detailed balance according to their dependence on volume and rate constants, and systematically investigate the relationships among them. Our results distinguish detailed balance arising from particular choices of volume and parameters from that enforced by network structure, and identify conditions under which detailed balance at one volume extends to all volumes. We further obtain a class of networks satisfying stochastic detailed balance for every volume and every positive choice of rate constants, and construct new systems whose global potentials exhibit double-well structures.

systems biology↗

bioRxiv2026

Multidimensional control of ingestive behavior by lateral hypothalamic neurotensin neurons

Consumption of food and water is regulated by interactions between neural circuits that govern motivational drive states, arousal, motor activity, and reward signaling, among other factors. Neurons in the lateral hypothalamus that express the neuropeptide neurotensin (LH-Nts neurons) are known to influence many of these elements, but their precise role in regulating specific aspects of ingestive behavior remains unclear. Utilizing a tightly controlled head-fixed task, we find that LH-Nts neurons strongly encode the rate of licking for water and sweet solutions, with weaker modulation of activity by solution identity and restriction state. Silencing LH-Nts neurons reduces water intake but has little direct effect on hunger or satiety. Instead, we find that these neurons impact multiple underlying behavioral components necessary for food consumption, including arousal and engagement with a novel food source, and also influence thermoregulation and metabolism. Together, these data establish tonic activity of LH-Nts neurons as a critical signal supporting exploration and volitional movement, while also delineating a role for these neurons in driving active consumption, particularly of water. Furthermore, quantitative projection mapping revealed widespread innervation of structures linked to thirst, arousal, reward, metabolism, and facial motor control, suggesting that these neurons play an important role coordinating neural circuits governing multiple different aspects of ingestion.

neuroscience↗

bioRxiv2026

Uhrf1 loss disrupts Ctcf-associated chromatin organization during early mouse embryogenesis

UHRF1 is a chromatin-binding protein essential for maintaining DNA methylation and histone modification states, yet its integrated role in vivo remains incompletely understood. To define its function, we generated conditional Uhrf1 knockout embryonic stem cells (ESCs) and embryos. Uhrf1-/- ESCs exhibited near-complete loss of 5mC and 5hmC but maintained pluripotency, whereas Uhrf1-null embryos developed normally until E8.5 and then failed to develop further by E9.5, phenocopying Dnmt1 loss. Single-cell multi-omic (ME-seq) profiling of E8.5 embryos revealed impaired lineage stabilization, widespread hypomethylation, and disrupted chromatin architecture. Uhrf1 loss was associated with altered CTCF-associated chromatin signal, broad remodeling of chromatin contacts, altered cis-regulatory relationships, and reduced predicted BMP-related ligand-receptor communication, particularly within neural crest populations. These findings identify Uhrf1 as a central regulator that tightly couples DNA methylation maintenance to 3D genome organization during gastrulation, thereby, directing early lineage specification and positioning Uhrf1 as a pivotal mediator of epigenetic information transfer during early embryogenesis. HighlightsO_LIUhrf1 knockout ESCs show global loss of 5mC/5hmC but maintain pluripotency. C_LIO_LIUhrf1-null embryos develop normally until E8.5 but die by E9.5 with severe defects. C_LIO_LISingle-cell multi-omics revealed disrupted chromatin, transcription, and lineage stability upon knockout. C_LIO_LILoss of Uhrf1 alters CTCF-associated chromatin signal, predicted BMP-related communication, and cis-regulatory relationships. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/738670v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@15bf31dorg.highwire.dtl.DTLVardef@1b601ecorg.highwire.dtl.DTLVardef@386018org.highwire.dtl.DTLVardef@12ce99f_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

Evaluating the cross-species transferability and scaling of sequence-to-function predictions in AlphaGenome

Deep learning models that predict molecular phenotypes directly from DNA sequence offer a powerful framework for interpreting genomic variation. Recently, AlphaGenome was introduced as a deep sequence-to-function architecture capable of predicting observations that historically required experiments. While the model has shown high accuracy, it was primarily evaluated on human variants scored against a reference genome. Here, we test performance on mouse data, the other species AlphaGenome was trained on although with fivefold fewer features than human (1,128 versus 5,930). We demonstrate that AlphaGenomes predictive performance varies considerably depending on the functional task. Specifically, predicted quantitative expression effects are directionally weak and compressed roughly 100-fold relative to empirical benchmarks across both reconstructed-haplotype and single-variant regimes. In contrast, canonical splice-site disruptions are recognized with near-identical accuracy in mouse and human (AUC 0.96 versus 0.98), displaying no cross-species divergence in predicted effect magnitude. We developed a scoring-approach for AI-agents to autonomously assess AlphaGenome prediction confidence and accurately differentiate between AlphaGenomes robust sequence-level recognition across species and its current limitations when interpreting un-fine-mapped regulatory variants. This demonstrates how GenAI innovations that are still under development can safely be harnessed by wrapping a responsible AI layer around the call to intercept flawed results, thereby adhering to international standards, such as the Australian Voluntary AI Safety Standard (VAISS).

bioinformatics↗

bioRxiv2026

A molecular integrator of sleep duration and interruption

Sleep is regulated across multiple timescales. Transitions between sleep and wake happen within seconds1,2; individual sleep bouts last minutes to hours3; and homeostatic sleep need has classically been tracked across multiple bouts4. Rapid sleep-to-wake transitions are driven by identified neurons, circuits, and neuromodulators1, while slow wave activity correlates with sleep need across hours5,6. However, no signal has been shown to encode sleep history within individual sleep bouts, the timescale at which the brain must continuously monitor how much sleep has occurred and how likely waking is at any given moment. Biochemical signals downstream of sleep/wake-associated neuromodulators display slower dynamics than the neuromodulators themselves7,8, making them candidate encoders of within-bout sleep history. Here, by measuring protein kinase A substrate phosphorylation (PKA-SP) in real time in freely behaving mice7-9, we show that membrane PKA-SP decreases exponentially within each sleep bout with consistent kinetics across bouts, integrates sleep duration and sleep interruption, and continuously forecasts moment-to-moment waking probability. Following sleep deprivation, PKA-SP reaches lower levels at the end of sleep bouts, correlating with increased sleep need dissipation. These findings identify a molecular signal encoding within-bout sleep history, revealing how biochemical dynamics bridge fast arousal circuits and the slow timescale of classical sleep homeostasis.

neuroscience↗