Biology subjects

Ren, H.

Publications and source records attributed to Ren, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Establishing toxicologically relevant primary alveolar macrophage models to enable a realistic assessment of nanomaterials safety

Alveolar macrophages (AMs) are key sentinels of the distal lung, maintaining surfactant homeostasis and clearing inhaled particles through continuous surveillance and phagocytosis. Mechanistic studies of particle-cell interactions, important to assess the respiratory safety or toxicity of particles, however, are limited by the lack of suitable in vitro models that preserve the core features of resident AMs. Here, we benchmarked six murine AM models, including short-term cultured primary AMs (AMs 3 h, AMs 24 h), primary AMs maintained with GM-CSF, TGF-{beta}, and the PPAR-{gamma} agonist rosiglitazone (AMs GTR), bone marrow-derived AM-like cells (BM-AMs), the immortalized AM cell line MH-S, and the non-transformed fetal liver-derived AM-like cell line MPI, against in vivo AMs based on surface phenotype, transcriptomic identity, and their responses to three carbon-based nanomaterials (CBNs). Both cell lines showed limited AM identity, characterized by low CD11c and SiglecF but high CD11b surface expression. In contrast, AMs GTR and BM-AMs more closely preserved resident AM-like surface marker profiles. Transcriptomic features, although differing for alterations in metabolism-related pathways, particularly oxidative phosphorylation, revealed most in vivo-like for the primary AM models. Functional, toxicological assessment using soot-like carbon nanoparticles (CNP), double-walled carbon nanotubes (DWCNT) and multi-walled carbon nanotubes (MWCNT) demonstrated strong model-dependent responses. Consistent with MWCNT-induced reduction in AM numbers observed in vivo, AMs GTR and BM-AMs exhibited the strongest in vitro responses, including cytotoxicity and inflammatory cytokine release. MH-S cells in contrast stood out for their cytotoxic sensitivity supporting their use for high throughput cytotoxicity tests. Overall, these findings identify AMs GTR and BM-AMs as the closest models for studying AMs biology and AM-intrinsic responses to poorly soluble nanoparticles like CBNs, and with regard to availability of higher cell numbers, BM-AMs might provide a scalable platform for mechanistic and higher-throughput nanotoxicology studies.

pharmacology and toxicology↗

bioRxiv2026

Base-mismatched crRNA in CRISPR Enabled Universal One-pot Rapid Nucleic Acid Detection

CRISPR-Cas12a-based diagnostics are promising for field detection yet are often constrained by two step workflows or laborious optimization of one-pot reactions. Herein, we present a universal one-pot RPA/CRISPR-Cas12a strategy using base-mismatched crRNA in the seed region (positions 1-6). Introducing of single-nucleotide mismatches (complementary or error) into crRNA suppresses cis-cleavage activity, enabling amplification and detection in a single tube without PAM re-design or costly modified crRNA. Molecular dynamics (MD) simulations indicated that moderate potential energy (PE) variation correlates with improved screening performance, providing guidance for the selection of effective mismatches. This strategy successfully converted established two-step assays for Burkholderia pseudomallei and Brucella into one-pot formats, achieving detection of targets at 10 copies/L within 30 min with good specificity. Lyophilized pellets supported field testing on lithium-ion battery-powered devices, and experiments with simulated soil samples confirm the robustness of the approach. Together, this cost-effective, adaptable strategy can streamline RPA-CRISPR diagnostics for infectious disease surveillance.

molecular biology↗

bioRxiv2026

Self-guided search in immersive VR reveals active hierarchical planning

Despite the ubiquitous nature of visual search in our daily lives, our knowledge of the underlying mechanisms is almost entirely derived from studies using simple, synthetic stimuli briefly flashed on a computer monitor. In real life, however, visual search occurs in three-dimensional environments over much longer timescales and involves the dynamic coordination of attention, memory, and action planning, generating novel predictions that cannot be tested with traditional approaches. Here, we examined how self-guided visual search unfolds under naturalistic conditions that extend over space and time. We asked participants (N=80) to find specific objects (e.g., a green lamp, an orange shelf) in an immersive, visually rich virtual reality environment using range-limited teleportation (2 m steps). Consistent with our hypotheses, we found that self-guided search engaged a hierarchical information-seeking policy characterized at each stage by recurring plan-and-execute cycles paired with subgoal-specific attentional priorities. Subgoal priorities progressed from wayfinding to identification of clusters of target-similar objects, and then to specific target features. These search priorities progressively narrowed the search space through coordinated eye, head, and body movements that recurred throughout the task. We conclude that naturalistic search is hierarchical, built from sequential subgoals with shifting attentional priorities that single-level tasks cannot capture. Moreover, immersive VR with eye-head-body tracking dissociates motor planning from execution moments and reveals how looking content shifts with each task subgoal while the person moves through space, something desktop tasks cannot capture.

animal behavior and cognition↗

bioRxiv2026

PartitionFinder-mAIC: Phylogenetic Partitioning using Marginal Akaike Information Criterion

Partition models are widely used in phylogenomic analyses to account for heterogeneous evolutionary processes across different regions or loci of a sequence alignment. How alignment regions are grouped into partitions (the partitioning scheme) affects both the degree of over- or under-parameterization and the accuracy of downstream phylogenetic inferences. PartitionFinder is a widely adopted framework for selecting an optimal partitioning scheme. Using Akaike information criterion (AIC) and Bayesian information criterion (BIC), PartitionFinder merges partitions with similar evolutionary processes to avoid model overfitting. However, AIC and BIC are based on conditional likelihoods that treat partition assignments as fixed, whereas the recently introduced marginal AIC (mAIC) averages site likelihoods over the models of all partitions, providing a more appropriate criterion for inferring global parameters such as tree topology and branch lengths (Susko et al. 2026). Here, we implement mAIC for partition models in IQ-TREE 3 and integrate it into the PartitionFinder algorithms. Using a range of simulated and empirical DNA and protein datasets, we show that PartitionFinder-mAIC yields partitioning schemes with fewer partitions than those selected by AIC or BIC, and additionally improves phylogenetic inference at most key branches of the green plant evolution. The new PartitionFinder-mAIC is available in IQ-TREE version 3.1.4 with the command-line option -merit mAIC.

evolutionary biology↗

bioRxiv2026

OmniSyn unifies target-aware molecular generation and optimization within a synthesis-native LLM framework across the human proteome

Designing target-specific bioactive molecules with actionable synthesis routes for the human proteome holds enormous potential for expanding therapeutic discovery, but remains a challenge. Existing target-aware generative models often depend on protein structures and generate molecules before assessing synthetic feasibility. Here we present OmniSyn, a protein-sequence-conditioned Mixture-of-Experts (MoE) language model that couples a task-conditioned interaction module with a synthesis-action decoder to generate molecules with explicit synthesis traces, thereby unifying de novo ligand generation, synthesizability projection and hit-to-lead (H2L) optimization within synthesis-traceable chemical space. OmniSyn is pre-trained with self-distillation and post-trained with task-specific reinforcement learning (RL) to adapt expert routing and optimize molecular properties across design modes. On unseen protein targets from MolGenBench, a real-world drug-discovery benchmark, OmniSyn achieves state-of-the-art performance across de novo design and H2L optimization, including target-awareness and hit-rediscovery metrics, despite relying only on protein sequences rather than three-dimensional (3D) pocket structures. By embedding synthesis planning into the design process, OmniSyn shifts molecular generation from a generate-then-filter paradigm toward design-with-synthesis paradigm, transforming virtual predictions into experimentally actionable candidates. Independent AiZynthFinder evaluation yielded retrosynthetic success rates of 68.47% for de novo generation and 71.92% for H2L optimization, improving over the strongest baselines by 61.3% and 184.2%, respectively, and supporting the synthetic feasibility of OmniSyn-generated molecules. In synthesizability projection, OmniSyn further converts outputs from external generative models into close analogues with improved retrosynthetic feasibility while preserving molecular similarity. Having established strong benchmark performance and external retrosynthetic feasibility, we next applied OmniSyn at human-proteome scale, spanning more than 21,000 targets. Rapid sequence-conditioned sampling enabled the construction of, to our knowledge, the largest human-proteome-scale generative virtual library, comprising 2.7 billion target-specific molecules, each accompanied by model-derived synthesis traces and target-specific prioritization scores. By enabling scalable target-specific molecular design with synthesis-aware generation across the human proteome, OmniSyn opens new opportunities for exploring previously inaccessible therapeutic targets, including those lacking experimentally resolved structures.

bioinformatics↗

bioRxiv2026

Inorganic Nitrogen Availability Drives Metabolic Specialization and Adaptive Strategies in Vibrio harveyi and Vibrio parahaemolyticus

Nitrogen availability is a key factor shaping microbial metabolism, ecological adaptation, and nitrogen cycling in aquatic environments. Members of the genus Vibrio are ubiquitous heterotrophic bacteria in marine and aquaculture ecosystems, yet their responses to different inorganic nitrogen sources remain poorly understood. Here, we systematically compared the growth characteristics, nitrogen transformation capacity, and molecular responses of Vibrio harveyi and Vibrio parahaemolyticus under ammonium (NH4+), nitrate (NO3-), and nitrite (NO2-) conditions using physiological assays, comparative genomic analysis, and transcriptomic profiling. V. harveyi exhibited broader nitrogen utilization capacity and was able to grow under all three nitrogen conditions, whereas V. parahaemolyticus showed a strong preference for NH4+ and limited growth under NO3- and NO2- conditions. Moreover, V. harveyi displayed rapid population expansion accompanied by reduced long-term viability, while V. parahaemolyticus maintained greater population stability. Both species showed NO3- accumulation during growth despite lacking canonical nitrification genes under NH4+ condition, suggesting the potential involvement of non-canonical heterotrophic nitrification processes. Transcriptomic analysis revealed nitrogen source-dependent metabolic specialization in V. harveyi. NH4+ availability promoted motility-associated responses and metabolic overflow, whereas NO3- induced iron acquisition-related pathways and NO2- activated assimilatory nitrite reduction coupled with oxidative stress adaptation. These findings demonstrate that inorganic nitrogen availability drives divergent metabolic and adaptive strategies in Vibrio, providing new insights into their nitrogen metabolic potential and ecological roles in aquatic environments.

microbiology↗

bioRxiv2026

SemVac: A Semantic Vaccinology Paradigm Powered by LLMs for Antigen Discovery

Reverse vaccinology identifies vaccine antigens from pathogen genomes, yet existing methods rely mainly on sequence and structure and overlook the functional and immunological knowledge recorded in the published literature. We introduce semantic vaccinology, a paradigm in which large language models (LLMs) reason over literature-derived protein descriptions to predict protective antigens. Implemented as SemVac, the workflow retrieves publications linked to each protein through PaperBLAST, condenses the evidence into a structured semantic profile, and prompts an LLM to return an antigenicity probability. Benchmarked against a curated 246-protein bacterial benchmark and the specialized protein-language and geometric-deep-learning predictor PLGDL, the best of 14 general-purpose LLMs matched or exceeded the precision of PLGDL; the open-weight Kimi K2 0905 offered the strongest performance-cost balance. Predictions were robust to masking of vaccine keywords, reproducible across repeated inference, and generalized to a 1,200-protein cross-pathogen dataset. Surprisingly, explicit chain-of-thought reasoning increased recall but lowered precision in every model tested, revealing over-reasoning in biological scoring. Applied to the mpox virus proteome, SemVac recovered the established mpox antigen repertoire and prioritized uncharacterized candidates. Two of these, A30L and C19L, received independent experimental support in recent orthopoxvirus vaccine development, providing external validation. For one candidate, B20R, the model generated a coherent but false TNF-decoy narrative unsupported by curated annotations, demonstrating that LLM confabulation can be detected when reasoning traces are cross-checked against curated resources. Semantic vaccinology therefore establishes the literature as an explicit, auditable third modality alongside sequence and structure, while making its failure modes transparent and correctable.

immunology↗

bioRxiv2026

Conversion of a Viral Glycan Shield into a Binding Anchor via Causal-Driven Antibody Optimization

Therapeutic antibodies are challenged by rapidly evolving pathogens that exploit glycosylation to shield epitopes. SARS-CoV-2 JN.1 exemplifies this, escaping antibodies through the N354-linked glycan. However, targeting glycosylated epitopes remains vacant, as scarce and heterogeneous glycan structures render existing approaches ineffective. Here, we introduce the Antibody Evolution Nexus with Causal-Driven Simulation (AENCS), integrating molecular simulation with causal inference. Applying AENCS to restore S309 efficacy against JN.1, we identified ACC01, exhibiting [~]24-fold improved neutralization. With limited prior knowledge of the N354 glycosylation site, ACC01 stabilized this glycan conformation, facilitating the determination of its cryo-EM structure. Causal dissection revealed how this glycan shield is functionally inverted into a binding anchor through multi-layered interactions. This mechanistic conversion, combined with the conservation of N354 glycosylation, enabled ACC01 to maintain potent activity against the latest variant NB.1.8.1. Collectively, AENCS demonstrates causal-driven antibody engineering can illuminate cryptic glycosylated epitopes, providing viable paradigms for exploring this vacant frontier.

bioengineering↗

bioRxiv2026

Ventricular Expansion Couples Hyperosmotic Stress to Thirst

Thirst is thought to arise from osmotic signals detected by circumventricular organs, yet how hyperosmotic stress activates mechanosensitive channels remains unclear because cell shrinkage should reduce membrane tension. Here we identify a brain-scale mechanical signal that couples systemic osmotic stress to drinking. In mice, hypertonic saline rapidly increased serum and cerebrospinal fluid osmolality, expanded the lateral ventricles, and promoted water intake. Relieving or blocking ventricular deformation attenuated drinking without eliminating osmotic gradients. Hyperosmotic challenge also produced localized deformation of periventricular cells, where spatial transcriptomics revealed candidate mechanosensitive channels, including Tmem63b and Piezo1. Thus, ventricular expansion provides a mechanical component of osmotic thirst, uncovering an osmo-mechanical layer of interoceptive regulation.

neuroscience↗

bioRxiv2026

Genos-m: a foundation model for human-associated microbial genomes

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

bioinformatics↗

bioRxiv2026

LAT condensation gates PLCγ1 activation via bimodal LAT phosphorylation

T cells can respond to even a single molecular binding event of antigen to a TCR. A key step in the TCR signaling pathway that definitively exhibits this single molecule response is the initiation of calcium influx by activation of PLC{gamma}1 in the LAT protein condensate. Here, we describe detailed kinetic measurements examining how protein condensation of LAT regulates activation of PLC{gamma}1 using a reconstituted membrane system. The results reveal that membrane recruitment of PLC{gamma}1 is tightly controlled by the LAT phosphorylation state, with no measurable independent recruitment to PIP2 or PIP3 lipids via the PLC{gamma}1 PH domains. We further observe PLC{gamma}1 is rapidly activated by membrane-associated kinase upon recruitment, irrespective of the LAT condensation state. These studies also revealed a crosstalk mechanism in which the TEC family kinases responsible for PLC{gamma}1 activation also phosphorylate LAT. This interaction establishes a positive feedback loop in LAT phosphorylation, mediated through LAT condensation, which drives a bimodal LAT phosphorylation response to TCR activation. Kinetic modeling reveals how this LAT phosphorylation response can cooperatively gate PLC{gamma}1 activation from a single TCR. These results suggest the LAT condensate facilitates both signal amplification and noise suppression in PLC{gamma}1 activation through a bimodal switch affecting LAT phosphorylation. Significance StatementT cells are sensitive sensors capable of detecting and responding to trace amounts of foreign antigen. Understanding how they achieve such sensitivity while maintaining accurate antigen discrimination remains a key challenge. Here, through detailed kinetic measurements of PLC{gamma}1 activation, we identify a cross reactivity in which kinases responsible for PLC{gamma}1 phosphorylation also phosphorylate LAT. This creates a bimodal switch controlling LAT phosphorylation levels, which gates PLC{gamma}1 activation from single TCR signals. We suggest this mechanism plays a key role in the signal amplification and noise suppression required for T cells to detect single antigen molecules.

biophysics↗

bioRxiv2026

A New Information Theoretic Approach Shows that Mixture Models Outperform Partitioned Models for Phylogenetic Analyses of Amino Acid Data

Partitioned and mixture models are widely employed in Maximum Likelihood phylogenetic analyses of large genomic datasets. Comparing the fit of the two types of models has been challenging, because standard information-theoretic approaches cannot be applied. Mixture models are increasingly popular for the analysis of amino acid datasets and can lead to different conclusions compared to partitioned models. This raises an important question - which type of model tends to perform better? Susko et al. (2026) recently introduced the marginal Akaike information criterion (mAIC), which allows mixture models and partitioned models to be directly compared for the first time. Here, we use the mAIC and a range of other approaches to compare the fit of mixture and partitioned models across a diverse set of empirical datasets. We show that mixture models are universally favoured on amino acid datasets. This has important implications for interpreting empirical analyses and suggests that continued development of mixture models is an important avenue for future research.

evolutionary biology↗

bioRxiv2026

Application of the Nicking Loop™ targeted library preparation method to DNBSEQ™ sequencing

Nicking Loop is a PCR-free targeted library preparation method that combines conversion of linear target DNA into circular single-stranded DNA (CssDNA) library with early sample indexing in a single step. The resulting CssDNA libraries can be either directly sequenced or optionally amplified, offering maximum flexibility across sequencing applications. This study demonstrates the compatibility of Nicking Loop circular libraries with a MGIs DNBSEQ platform. Compatibility was evaluated against established linear Nicking Loop libraries sequenced on Illumina MiSeq platform. Using synthetic reference samples with defined variant allele frequencies, Nicking Loop method demonstrated matching performance across both library formats and sequencing platforms. Key quality metrics, including unique molecular identifier (UMI) distributions, error profiles and VAF detection, were all highly consistent. Both library types generated over 97% singleton UMIs, indicating uniform template sampling, and VAF measurements were strongly concordant across platforms (Spearmans {rho} = 0.939). Collectively, these findings demonstrate that Nicking Loop method is directly applicable to circular NGS platforms, such as DNBSEQ, strongly supporting its use as a platform-agnostic library preparation strategy for targeted sequencing applications.

molecular biology↗

bioRxiv2026

Pancreatic Duct Cells as a Potential Source for Human Islet Neogenesis: Insights from Imaging Mass Cytometry

The question of whether islet neogenesis occurs in adult humans has been a subject of long-standing debate. To explore the characteristics of islet endocrine cells associated with pancreatic ducts, we employed imaging mass cytometry to examine pancreatic tissues from individuals across different age groups, including those with prediabetes or type 2 diabetes (T2D). Our analysis revealed the presence of all five pancreatic islet endocrine cell types, along with two types of non-hormone-expressing endocrine cells, located within or immediately adjacent to the ducts. These cells were most abundant in infancy, with a gradual decline observed through adulthood. Notably, ductal {beta} cells predominated in infancy, whereas ductal cells became more prevalent in adulthood, and significantly increased in the group aged over 60 years. Obesity further increased the ductal {beta} cells in the subjects aged over 60 years. Under prediabetic and T2D conditions, an increase in all duct-related endocrine cells was observed. These findings indicate that ductal cells may serve as a reservoir for new pancreatic endocrine cells, offering potential insights into the promotion of endogenous {beta} cell regeneration in diabetic patients. Highlights{bigcirc} Characterization of various islet endocrine cell types related to ducts in human pancreas. {bigcirc}The insulin-positive cells are the dominant cells among all duct-related islet endocrine cell types during the infancy period, however, the glucagon-positive cells become the dominant cells in adulthood. {bigcirc}T2D, Obesity, and aging are involved in the increase in the number of duct-related endocrine cells.

developmental biology↗

bioRxiv2026

A spatial-temporal atlas of human islet pathophysiology identifies a size-dependent trajectory from compensation to decompensation

Type 2 diabetes (T2D) is characterized by progressive islet dysfunction, yet the transition from functional adaptation to failure remains poorly defined within the native tissue architecture. Using multiplex imaging mass cytometry, we systematically analyzed human pancreatic islets across a spectrum of non-diabetic (ND), prediabetic (PreD), and T2D donors, revealing that pathological remodeling is profoundly size-dependent. This remodeling is manifested as a coordinated evolution of subpopulation abundance, endocrine cell proportions, structural integrity, and protein expression profiles, revealing that islets of different sizes undergo divergent fates during disease progression. We identified a size-dependent vulnerability spectrum where medium-and large-sized islets (>100 m in diameter) serve as the primary histopathological correlates of glycemic failure (HbA1c), exhibiting early density loss and structural disintegration. In contrast, small islets (30-100 {micro}m in diameter) exhibited compensatory hormone upregulation during PreD. Notably, diverging from the death of the small paradigm in Type 1 Diabetes, our data reveal a significant expansion of extra-islet endocrine clusters (EECs) that initiates during the compensatory stage, effectively preceding the onset of overt hyperglycemia. Finally, t-SNE clustering reconstructed a continuous trajectory of islet remodeling, capturing the phenotypic evolution of islets from normoglycemia through compensation to clinical decompensation. This study provides a high-resolution atlas of islet pathophysiology, offering new insights into T2D progression. HighlightsO_LIIslet remodeling during T2D progression is critically dependent on islet size. C_LIO_LIMedium and large islets density negatively correlates with HbA1c. C_LIO_LISmall islets exhibit resilience to metabolic stress during T2D progression. C_LIO_LIEECs expansion suggests a process of -cell-biased neogenesis. C_LIO_LIt-SNE maps the islet trajectory from compensation to decompensation. C_LI

pathology↗

bioRxiv2026

Light-inducible proximity labelling in vivo captures sex-specific RNA at excitatory synapses

Local RNA regulation is essential for synaptic plasticity, yet the full repertoire of RNA species and associated isoforms within specific synaptic compartments in vivo has yet to be determined. Existing RNA profiling approaches lack the spatial and temporal precision needed to resolve RNA repertoires restricted to cell-type specific synaptic compartments. To overcome this challenge, we developed PSD-95-Halo-seq, a light-induced proximity labelling technique that, when combined with long-read sequencing, selectively captures all post-synaptic full length RNA species in the excitatory post-synaptic compartment. Here, we applied this approach to investigate sex differences in RNA expression within excitatory synapses following exposure to an associative fear learning task in C57/Bl6 mice. We found dramatic sex differences in pseudogene and protein coding RNA expression, which are most abundant in females, with males exhibiting multiple noncoding RNA classes, including lncRNA, snoRNA, and rRNA. Females generally showed more 3' UTR expression, and there was widespread differential exon usage following fear conditioning, including 179 isoforms in males, 69 in females, with no significant gene-level differential expression, indicating that behavioural state modifies sex-specific isoform usage rather than overall transcript abundance. Our discovery that synaptic RNA composition is dynamic, sexually dimorphic, and profoundly shaped by experience, offers new insight into previously inaccessible mechanisms underlying sex differences in fear-related learning and memory. PSD-95-Halo-seq is therefore a powerful method for the precise spatiotemporal identification of compartment-and cell-type-specific RNA. One sentence take-awaySynapse-targeted proximity labelling and Long-Read sequencing demonstrate that excitatory post-synapses encode experience through sex-specific, isoform-level RNA remodeling.

neuroscience↗

bioRxiv2025

Tirzepatide Synergizes with Leptin on Weight Loss and Restoring Metabolic Homeostasis in Diet-induced Obesity Model

Leptin resistance limits anti-obesity efficacy. We identified a leptin-sensitizing mechanism through tirzepatide (TZP), a glucagon-like peptide-1 receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) dual-agonist. Our tirzepatide clinical trial revealed that circulating leptin levels at baseline correlated with weight loss efficacy in patients with obesity, suggesting leptin and tirzepatide could interact to achieve stronger effects on weight loss. Next, we utilized the diet-induced obesity (DIO) mice and demonstrated the synergistic effects of tirzepatide and leptin combination (TZP+Lep) on weight loss. TZP+Lep treatment further improved hepatic insulin sensitivity and upregulated thermogenetic gene expression in brown adipose tissue. Metabolic profiling under thermoneutrality revealed TZP+Lep treatment further reduced food intake and increased energy expenditure. Tirzepatide sensitized leptin signaling in hypothalamic pro-opiomelanocortin (POMC) and GLP-1R expressing neurons. TZP+Lep synergistically increased POMC neuronal firing by decreasing the inhibitory postsynaptic input. Together, our work showed combining tirzepatide and leptin as a potential way for better maintenance of metabolic homeostasis in obesity management. HighlightsO_LITirzepatide and leptin synergistically promote weight loss through reduced food intake and increased energy expenditure. C_LIO_LITirzepatide and leptin synergistically improve insulin sensitivity and metabolic homeostasis with altered hepatic and adipose gene expression. C_LIO_LITirzepatide sensitizes leptin signaling in hypothalamic GLP-1R and POMC neurons. C_LIO_LITirzepatide and leptin synergistically increase POMC neuronal firing by decreasing inhibitory postsynaptic input. C_LI

neuroscience↗

bioRxiv2025

Density-based longitudinal neuron tracking in high-density electrophysiological recordings

Tracking single neurons across days in high-density extracellular recordings is essential for establishing neural mechanisms of learning, memory, and post-injury recovery. However, in weeks-long recordings, identifying cross-day matches among thousands of units is confounded by changes in spike waveforms, unit turnover, and representational drift in neural responses. We introduce DANT (Density-based Across-day Neuron Tracking), an unsupervised framework that jointly estimates probe motion and neuron identity by alternating between density-based clustering in feature space and probe-motion correction inferred from provisional matches. Estimated drift is used to re-register spike waveforms across sessions, after which clustering is recomputed; this iterative loop continues until the set of matches stabilizes. In parallel, DANT learns a decision boundary from match and non-match assignments derived from the clustering results, enabling it to reject low-similarity candidate pairs. Applied to weeks-long Neuropixels recordings from the cortex and striatum in freely moving rats during stable behavior and task switching, DANT substantially increases match yield and reduces false negatives while maintaining a low false-positive rate relative to existing approaches. Together, these results indicate that DANT provides a general, unsupervised solution for longitudinal tracking in chronic, high-density recordings. The bigger pictureModern high-density probes such as Neuropixels record hundreds of units per session. When chronically implanted, many of these units can be stably recorded over extended periods, opening the door to tracking how large neural populations change over days to weeks, a question central to learning, memory, internal states, and recovery from brain injury. Yet, small shifts in probe position, changes in spike waveforms, and drifts in firing properties mean that linking units across sessions is challenging. DANT (Density-based Across-day Neuron Tracking) addresses this problem by providing an unsupervised framework that combines density-based clustering and iterative motion correction to identify cross-day matches with minimal subjective decisions. Applied to chronic Neuropixels recordings from freely moving, task-performing rats, DANT recovers longer and more complete chains of putative same neurons than existing approaches, while keeping the false-positive rate low. By making cross-day tracking more sensitive, scalable, and reproducible, DANT enables a wider range of neuroscience studies to examine the longitudinal behavior of single neurons and neural populations.

neuroscience↗