Biology subjects

Zheng, Y.

Publications and source records attributed to Zheng, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Digital pathological classification of peripheral neuroblastic tumors across multi-continent cohorts

Peripheral neuroblastic tumors (pNTs) are embryonal tumors of neural crest origin with highly variable clinical behaviors, ranging from spontaneous regression to aggressive and fatal disease. The International Neuroblastoma Pathology Classification (INPC) defines four diagnostic categories and stratifies tumors into Favorable Histology (FH) or Unfavorable Histology (UH) group based on age-linked assessment of tumor differentiation/maturation and the Mitosis-Karyorrhexis Index (MKI). Prognostic distinction according to INPC offers significant prediction of clinical outcomes, with patients harboring FH tumors demonstrating longer overall and event-free survival than those with UH tumor. Here, we assembled the largest multi-continental whole-slide histology imaging dataset of pNTs to date, comprising 2,030 tumors from the United States, Canada, Australia, New Zealand, Germany and Japan. Using this dataset, we developed NB-PathAssist, a computational pathology framework for automated INPC-based classification from digitized whole-slide images. NB-PathAssist integrates foundation-model-based feature extraction with an archetype-guided SummaryMixing architecture that compresses whole-slide morphology into representative histologic tokens for tumor-level classification. To automate MKI assessment, we manually annotated 14,049 nuclei, classifying them as mitotic-karyorrhectic (MK) tumor cells, non-MK tumor cells, or non-tumor cells. The resulting model was applied to phenotype cells in whole-slide images across 1,649 tissue sections. Across geographically and institutionally diverse cohorts, NB-PathAssist reproducibly assessed key diagnostic features and generated prognostic classifications concordant with reference INPC annotations. The average balanced accuracy for classifying tumor categories, grade of neuroblastic differentiation, MKI and prognostic groups achieved 0.84, 0.86, 0.89 and 0.96, respectively. We packaged the NB-PathAssist algorithms into an end-to-end online platform for scalable and standardized AI-assisted assessment of pNTs.

pathology↗

bioRxiv2026

Bioactivity foundation model for hit identification and hit-to-lead optimization from sequence data

Learning from large-scale bioactivity archives is complicated by heterogeneous assay readouts and differences in data reliability. Here we show that a model that predicts probability distributions over binding affinities from sequence data opens model training to vast and diverse troves of heterogeneous bioactivity data. We develop PSICHIC+, a bioactivity foundation model trained on approximately 82 million protein-ligand records using a distributional objective that preserves quantitative resolution while incorporating variation in data reliability. Given a protein sequence and ligand SMILES, PSICHIC+ predicts binding affinity in milliseconds. In zero-shot virtual-screening benchmarks without target-specific training data, it improves enrichment over regression- and classification-based approaches and performs competitively with leading structure-based methods. In lab-in-the-loop campaigns, distribution-guided acquisition improves recovery of high-activity compounds under fixed screening budgets. Combining PSICHIC+ with Boltz-2 improves hit-to-lead affinity prediction over Boltz-2 alone, with mean correlation approaching that of FEP+ on the evaluated benchmarks, without requiring high-resolution protein structures. These results support distributional learning as a scalable approach to using heterogeneous bioactivity data for hit identification and hit-to-lead optimization.

bioinformatics↗

bioRxiv2026

Representation learning of somatic missense mutation landscapes across cancer types

Somatic missense mutations are pervasive in cancer genomes, yet existing studies have largely compressed them into mutation burden, mutation signatures, or driver gene annotations. Such representations obscure mutation-specific context and higher-order dependencies, leaving the latent biological structure of somatic mutation landscapes insufficiently characterized by systematic, data-driven approaches. Here we present MMRL, a pan-cancer Missense Mutation Representation Learning framework that learns the representations directly from somatic missense mutation profiles. By integrating mutation-associated features including gene identity, trinucleotide context, AlphaMissense pathogenicity, amino acid substitution and protein positional information, MMRL uses self-supervised learning to capture higher-order patterns among missense mutations across ~50,000 tumor samples from diverse cancer types. Across three pan-cancer cohorts, MMRL embeddings consistently achieved higher cross-validated prognostic discrimination than TMB and remained significantly associated with overall survival after adjustment for cancer type and TMB, as well as age where available. This prognostic value was also observed among patients treated with immune checkpoint inhibitors. Unsupervised clustering analysis of MMRL embeddings identified nine mutation subtypes in the TCGA pan-cancer cohort, with distinct survival outcomes, cancer-type compositions and mutational signature profiles, indicating that the learned embedding space captured clinically and biologically meaningful structure. Attention-based analyses further indicated that MMRL captured context-dependent mutation patterns beyond mutation frequency and predicted pathogenicity. Together, MMRL provides a data-driven strategy for decoding somatic missense mutation landscape and may facilitate precision oncology.

bioinformatics↗

bioRxiv2026

OTTER: Orchestrated Transcriptomic, Tumor-xenograft (PDX/CDX), and Epigenomic Reporting Workflow

Summary: Tumor xenograft sequencing libraries inevitably contain infiltrating murine host reads, but conventional deconvolution tools fail on bisulfite sequencing (BS-seq) because chemical C-to-T deamination collapses sequence entropy and destroys graft recall (< 0.015%). We present OTTER, an automated, compiled multi-omics workflow framework that decouples execution into static domain operators, an immutable reference registry, and a native strand-aware edit-distance engine, restoring bisulfite deconvolution (52%-54% recall across a 0%-99% compositional gradient) while preserving complete decision parity on transcriptomics. Availability and implementation: OTTER is implemented in Go and Rust, runs on x86_64 Linux (including CentOS 7 with glibc 2.17), and is freely available open-source under the MIT License at https://github.com/otterlab-bio/otter; test and validation data are provided at https://huggingface.co/datasets/fallingstar10/otter-data.

bioinformatics↗

bioRxiv2026

14-3-3 Enables β-Adrenergic Regulation of the Cardiac Sodium Channel at the Intercalated Disc

Background: Beta-Adrenergic stimulation enables the heart to adapt electrical activity to increased physiological demand. Although the cardiac sodium channel Nav1.5 is essential for impulse propagation, the mechanisms underlying its acute beta-adrenergic regulation, particularly within distinct cardiomyocyte membrane domains, remain poorly understood. We investigated whether the phosphorylation-sensitive adaptor protein 14-3-3 mediates this regulation. Methods and Results: Using automated patch-clamp electrophysiology in HEK293 cells and human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), we found that isoproterenol rapidly increased sodium current (INa) and promoted channel activation at more hyperpolarized potentials, whereas PKA inhibition reduced INa. Disruption of 14-3-3 function abolished these responses without substantially altering basal sodium current or Nav1.5 surface expression. Macropatch recordings from ventricular myocytes revealed that isoproterenol preferentially enhanced INa at the intercalated disc, an effect prevented by 14-3-3 inhibition. Proximity ligation assays further demonstrated increased Nav1.5/14-3-3 proximity following beta-adrenergic stimulation. Systematic mutagenesis identified S460 in the Nav1.5 DI-DII linker as a critical determinant of acute PKA-dependent regulation. Molecular modeling predicted that S460 phosphorylation stabilizes the Nav1.5/14-3-3 interaction. In CRISPR-engineered S460A iPSC-CMs, acute beta-adrenergic regulation of INa was abolished, whereas the isoproterenol-induced increase in conduction velocity was significantly attenuated. Computational simulations further predicted that enhanced intercalated-disc sodium current supports increased conduction velocity across varying conditions of electrical coupling and pacing rate. Conclusions: Our findings identify 14-3-3 and S460 as critical determinants of spatially selective beta-adrenergic regulation of Nav1.5 at the cardiac intercalated disc. This mechanism provides a molecular link between sympathetic signaling and cardiac sodium current regulation, supporting the concept that localized enhancement of Nav1.5 function contributes to the adaptation of cardiac conduction during increased sympathetic demand.

molecular biology↗

bioRxiv2026

RiboBridge couples RNA backbone generation to sequence design through a learned interaction topology

Designing ribonucleic acid (RNA) molecules with prescribed structures and functions is central to RNA engineering. Backbone-first approaches generate three-dimensional geometry before assigning a nucleotide sequence, yet leave open how that geometry should be represented for downstream sequence design. Here we introduce RiboBridge, which infers a probabilistic residue-pair state from backbone geometry, recycles binary pair probabilities through a second pass and transfers binary and typed signals as soft attention biases for sequence decoding. On 550 generated backbones, pair-type decoding achieved joint refolding validity of 0.451 versus 0.335 for an architecture-matched topology-free decoder. On a matched 110-backbone intervention cohort, replacing aligned pair states with exact-length donors matched on pair-state statistics reduced joint validity from 0.464 to 0.214, showing backbone alignment matters. Pair-confidence ranking enriched the joint-validity rate from 0.451 overall to 0.596 among the top 248 before external refolding, with the same ranking signal transferring to independently generated RFDpoly and RNA-FrameFlow backbones. On 53 experimentally determined backbones, pair-coupled decoding improved secondary-structure agreement relative to residue-wise argmax. These results support a learned relational interface between sequence-free backbone generation and sequence design.

bioinformatics↗

bioRxiv2026

Structural Basis of γδ T-Cell Receptor Crosslinking and Activation by a Spontaneously Dimerized Fab

The gamma delta T-cell receptor ({gamma}{delta} TCR) mediates {gamma}{delta} T-cell responses that are crucial for immune defense, tissue surveillance, and homeostasis. While recent studies suggest that {gamma}{delta} TCRs engage ligands multivalently, how multivalent engagement activates the {gamma}{delta} TCR remains elusive. Here, we present the structures of the V{gamma}9V{delta}2 (clone G115) TCR in both the inactive and the agonist-crosslinked active states. In the {gamma}{delta} TCR lipoprotein complex, cholesterol and phospholipids form a "barrier" that isolates CD3{zeta}{zeta} from the other subunits and likely prevents autoactivation. As an agonist, the Fab fragment of the UCHT1 antibody dimerizes upon binding the V{gamma}9V{delta}2 TCR, thereby crosslinking and activating the receptor. Cryo-EM analysis of Fab-crosslinked V{gamma}9V{delta}2 TCR identifies three Fab dimer interfaces, one between the two light chains and the other two between light and heavy chains. These Fab-Fab interfaces, together with the Fab-CD3{epsilon} interface, mediate the assembly of the V{gamma}9V{delta}2 TCR and UCHT1 Fab into 2:2 tetramers. Upon activation, the V{gamma}9V{delta}2 TCR becomes conformationally plastic and loses enriched lipids, including cholesterol and phosphatidylethanolamine. Our findings reveal a dimerization-driven activation mechanism, inform the design of rigid TCR-crosslinking agonists, and suggest multivalency-induced clustering as a general paradigm for {gamma}{delta} TCR activation.

immunology↗

bioRxiv2026

Charting sex divergence in the human brain across the lifespan

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

neuroscience↗

bioRxiv2026

Small GTPases RhoA and RhoC coordinately regulate cardiomyocyte size in the mouse heart

Rho family GTPases regulate cardiomyocyte growth and cytoskeletal organization, yet the overlapping and individual function of the two primary isoforms RhoA and RhoC, remain largely unknown in the heart. Here, we examined cardiac structure and function at baseline and in response to mechanical and neurohumoral stress in mice with cardiomyocyte-specific deletion of Rhoa, global loss of Rhoc, or combined deficiency of both isoforms. Deletion of either Rhoa or Rhoc increased myocardial expression of the other isoform, showing reciprocal compensation. At baseline, Rhoc deficiency produced elongated, narrower cardiomyocytes, whereas additional loss of Rhoa restored cell width but length remained longer. Combined Rhoa/Rhoc deficiency also disrupted intercalated disc protein expression and organization and caused age-dependent cardiac hypertrophy with mild pulmonary congestion despite preserved systolic function. Following pressure overload, loss of either isoform increased hypertrophy and fibrosis compared with control groups, while combining Rhoa/Rhoc deficiency significantly worsened cardiac remodeling and systolic dysfunction. Neurohumoral stimulation with angiotensin II and phenylephrine similarly revealed cooperative control of hypertrophic growth in Rhoa/Rhoc double deleted cardiomyocytes versus either single deletion. These findings identify RhoA and RhoC as partially compensatory effectors of cardiomyocyte growth and ventricular remodeling with stress stimulation, although their individual contributions to hypertrophic growth, structural integrity, and cardiac function depend on both the nature and duration of cardiac stress.

cell biology↗

bioRxiv2026

Multi-omics reveals accumulation mechanisms underlying differences in sesquiterpenoid content across different growth environments of Atractylodes macrocephala rhizome

This study aimed to explore mechanisms driving differential sesquiterpenoid accumulation in Atractylodes macrocephala rhizome (AMR) under different growth environments. Root and rhizosphere soil samples were collected from wild, imitated wild, one- and two-year cultivated AMR. Combined metabolomic, transcriptomic and microbiomic profiling was carried out to investigate alterations in sesquiterpenoid levels, root-associated microbiota, and terpenoid biosynthetic genes under different growth environments. Metabolomics revealed that wild AMR exhibited higher pyruvate consumption and accumulated more atractylone and atractylenolides. Transcriptomic analysis demonstrated that the genes encoding key MEP pathway enzymes (DXS, DXR, MCT, HDS) and the genes encoding the sesquiterpenoid biosynthetic enzyme GDS were significantly upregulated in wild AMR. In contrast, the genes encoding triterpenoid synthetic enzymes (SQS, SQE, {beta}-AS) were downregulated, redirecting metabolic precursors toward sesquiterpenoid synthesis. Microbial community structures differed substantially across groups. Further analysis suggested rhizosphere microorganisms were important contributors to AMR medicinal quality, and sesquiterpenoid metabolites accumulated in AMR roots, together with the rhizosphere microenvironment, jointly shape specific rhizosphere fungal assembly. This study systematically revealed the synergistic regulatory relationships among sesquiterpenoids metabolism, gene expression, growth environment, and root-associated microbes, providing multi-omics evidence for clarifying the mechanism by which these factors coordinately regulate the quality of AMR.

plant biology↗

bioRxiv2026

Sparse dynamic graphical models for longitudinal non-Gaussian data

Longitudinal non-Gaussian data are common in biomedical and microbiome studies, where responses may not be normally distributed. However, many existing dynamic graphical models are developed for Gaussian responses, while many non-Gaussian graphical models focus on cross-sectional dependence and do not explicitly estimate temporal transition effects. We propose a latent Gaussian dynamic graphical model for longitudinal non-Gaussian responses, including binary, count, and compositional outcomes. The proposed framework jointly estimates the contemporaneous conditional dependence network and the lag-one temporal dependence structure. We further develop a penalized Monte Carlo expectation maximization procedure for estimation. Simulation studies across multiple graph structures and outcome types show that the proposed method can recover both dependence structures, with more stable recovery for the lag-one temporal dependence structure and more structure-dependent recovery for the contemporaneous conditional dependence network. We apply the proposed method to longitudinal data from the Integrative Human Microbiome Project (HMP2) using a multinomial observation model. The results reveal persistent lag-one temporal dependence and contemporaneous conditional dependence among dominant microbial taxa, demonstrating the utility of the proposed framework for dynamic network analysis of longitudinal non-Gaussian biomedical data.

bioinformatics↗

bioRxiv2026

Rank-Preserving Alignment Enables Cross-platform Learning and Phenotyping for Single-Cell and Spatial Proteomics

Decades of antibody-based protein profiling have generated valuable cohorts across evolving cytometry, sequencing and spatial imaging platforms. Differences in marker coverage, signal scale and antibody performance hinder joint analysis and reuse of cohorts with long-term clinical follow-up. Widely used RNA-based integration methods prioritize shared embeddings rather than directly comparable protein measurements. To fill in the gap, we developed RAMP (Rank-preserving Alignment of Multi-platform Proteomics), an unsupervised framework that aligns shared proteins without cell-type annotations. It combines sample-specific anchoring and cross-platform cell matching with bounded monotone transformations. The corrected values retain each protein marker's expression order across cells within a sample. Across seven integration tasks, RAMP achieved the strongest overall balance of batch correction and cell-type preservation. RAMP also improved marker-threshold transfer while preventing expression-order reversals observed with some comparison methods. These reversals made cell types with originally high marker expression appear lower-expressing than other populations. Such overcorrection can mislead cell identification and protein comparisons, even when the integrated embedding shows improved dataset mixing. By preserving each marker's expression order within a sample, RAMP protects the relationships needed to interpret normalized protein measurements. To investigate whether better harmonized inputs improve downstream learning, we used linear, nearest-neighbor and small neural-network models as a controlled, small-scale testbed. This approach tests an input quality question relevant to proteomic foundation models without undertaking large-scale pretraining. Under matched supervision, RAMP improved cross-platform annotation across all three model families and improved missing-marker prediction over unaligned inputs. These gains demonstrate the value of harmonized inputs for predictive learning and motivate evaluating RAMP as preprocessing for proteomic foundation models. In bone marrow, we used high-quality single-cell protein measurements to impute the failed CODEX CD19 channel, recovering B-cell spatial distributions. The completed panel improved B-cell separation from natural killer cells in the joint embedding. In melanoma, a proliferating CD8 T-cell state near tumor cells was associated with longer survival. Transfer such a spatial information to CITE-seq connected tumor proliferation and infiltration state to an adhesion and activation program involving PD-1, LFA-1 and CD2. In lung cancer, we used single-cell CyTOF measurements to predict unmeasured HLA-ABC states in vascular cancer-associated fibroblasts profiled by spatial imaging mass cytometry (IMC). Higher predicted scores were associated with lower immune-cell fractions and worse disease-free survival in the IMC cohort. In head and neck cancer, transferring a fibroblast/macrophage spatial niche score to CITE-seq identified a CD39/TIGIT-associated CD8 T-cell phenotype. Protein and RNA profiles supported checkpoint-associated features, and transfer back to CODEX showed correspondence with measured LAG3. RAMP enables existing single-cell and spatial cohorts to support new discoveries by connecting molecular programs, tissue organization and clinical outcomes across platforms.

bioinformatics↗

bioRxiv2026

π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection

The high taxonomic and strain-level diversity of microbial communities makes it difficult for reference databases to fully represent the protein sequences present in metaproteomic samples, limiting database-dependent peptide identification. De novo peptide sequencing can recover peptide sequences directly from tandem mass spectra without relying on reference databases, providing complementary peptide evidence for metaproteomics. However, most existing de novo sequencing models were developed largely from non-microbial proteomic data and lack specific adaptation to microbial spectra. Here, we established a dedicated microbial spectral resource comprising more than 10 million annotated high-quality tandem mass spectra from 72 cultured microbial isolates, with peptide digests from each isolate separated into five high-pH reversed-phase fractions to increase the opportunity for deeper and more diverse peptide sampling. Using this resource, we developed {pi}-MNovo, which combines adaptation to microbial spectra with evidence-guided candidate selection to improve full-length peptide sequencing precision. On a seven-species external benchmark, {pi}-MNovo achieved 66.30% complete-peptide recall under the same residue-mass-based complete-peptide matching criterion applied to all models, exceeding four published models by 6.9-21.7% in relative recall, with consistent gains across species and peptide-length groups. In two independent metaproteomic datasets, {pi}-MNovo increased reference-supported taxonomic coverage, while synthetic-community analysis recapitulated the designed abundance ranking. From pFind-unidentified spectra, {pi}-MNovo recovered 1,587 pFind-unreported, reference-matched peptides that passed spectrum-evidence filtering. These results show that {pi}-MNovo expands recoverable peptide evidence and enhances the utility of de novo sequencing for metaproteomic analysis.

bioinformatics↗

bioRxiv2026

Temporal TOC1-CLV3-WUS cascade controls organogenesis and rhythmic stem development

Coordinated organogenesis and rhythmic stem development determine shoot morphogenesis, which governs distinct plant architecture and crop yield. The shoot apical meristem (SAM) drives organ initiation and stem growth, with its activity sustained by the spatially defined CLAVATA3 (CLV3)-WUSCHEL (WUS) feedback loop. However, how the temporal CLV3-WUS module modulates SAM activity remains elusive. Here, we demonstrate that both CLV3 and WUS are required for circadian stem elongation. The clock component TIMING OF CAB EXPRESSION1 (TOC1) directly activates and sustains the circadian expression of CLV3, and this regulatory process is controlled by the APETALA1-CIRCADIAN CLOCK-ASSOCIATED1 module. Notably, the rhythmic expression pattern of CLV3, rather than its transcript abundance, plays a more important role in regulating WUS expression and sustaining SAM activity, and CLV3 confers circadian rhythmicity to WUS protein accumulation. Transcriptome and ChIP seq analyses reveal that WUS modulates multiple developmental programs, including meristem homeostasis, phytohormone responses, and cell wall remodeling. Mechanistically, WUS directly activates PECTIN METHYLESTERASE5 expression to promote stem elongation. Collectively, our findings uncover a spatiotemporal regulatory cascade that fine-tunes SAM activity to coordinate rhythmic organogenesis and stem development, thereby establishing optimal phyllotactic patterning in plants.

plant biology↗

bioRxiv2026

A fibroblast-rich transcriptomic axis reflects stromal remodeling within the CMS4 phenotype in colorectal cancer

Bulk-tumor expression signatures can reflect cellular composition as well as tumor-cell programs. We evaluated a frozen, equal-weight 87-gene score in colorectal cancer using 11 Gene Expression Omnibus (GEO) cohorts, TCGA COAD/READ, and E-MTAB-12862. In GEO (1,783 tumors; 441 progression-related events), the hazard ratio per score standard deviation was 1.33 (95% confidence interval, 1.19-1.49). The estimate was similar in seven disease-free-survival cohorts. In 376 TCGA tumors, the hazard ratio was 1.25 (1.03-1.52) for progression-free interval. The association did not reproduce in E-MTAB-12862: among 948 stage I-III tumors with 436 recurrence-free-survival events, the hazard ratio was 1.05 (0.95-1.16). The frozen score correlated with CMS4, cancer-associated fibroblast, and stromal measures. The stromal-remodeling module produced a GEO estimate similar to the full score, whereas removing that module attenuated the association. Composition residualization did not detect additional prognostic information after accounting for the measured CMS4 and stromal components. Single-cell and spatial analyses localized the gene program to fibroblast-rich compartments. The 87-gene score mainly reflects a fibroblast-rich, matrix-remodeling component of CMS4 rather than an independent prognostic subtype. Its prognostic association varied across cohorts, and current evidence does not support clinical use.

cancer biology↗

bioRxiv2026

FFPE-CUTAC: A Single Assay, Multiple Layers

Background: Archival biobanks of formalin-fixed paraffin-embedded (FFPE) specimens represent a vast and underused resource for retrospective molecular studies linked to clinical follow-up. However, fixation-induced chemical modification and nucleic acid fragmentation limit scalable genomic and transcriptomic profiling. FFPE-CUTAC (Cleavage Under Targeted Accessible Chromatin) is an RNA polymerase II-targeted DNA assay specifically designed to profile regulatory activity in FFPE tissue. Here, we assess its archival robustness, information content, compatibility with RNA-seq, and ability to recover copy-number alterations. Results: In meningioma and breast cancer cohorts, we benchmarked FFPE-CUTAC against matched fresh-frozen RNA-seq, FFPE RNA-seq and FFPE whole-genome sequencing. For FFPE specimens spanning up to 25 years, FFPE-CUTAC showed no systematic collection-year-associated decline in library yield or signal-quality metrics. In contrast, FFPE RNA-seq is more affected by the FFPE tissue degradation and specimen age on read composition, gene-body coverage, and gene detection. By measuring RNA polymerase II occupancy on chromatin rather than mature RNA abundance, FFPE-CUTAC is less constrained by transcript half-life, polyadenylation or transcript-capture design. FFPE-CUTAC extends beyond gene regions to non-coding regulatory elements, while the shared gene-level signals supported its integration with existing RNA-seq cohorts for large-scale, long-term clinical association study. FFPE-CUTAC libraries also retain DNA dosage information and can be used to recover chromosome-arm gain, loss and intact states with 96% concordance to matched whole-genome sequencing. Conclusions: By converting routinely preserved pathology sections into integrated regulatory and copy-number profiles, FFPE-CUTAC provides a practical foundation for constructing clinically annotated disease maps to support molecular stratification, prognostic modeling and treatment-association studies.

cancer biology↗

bioRxiv2026

Adaptive introgression in Tibetans spans focal and distributed architectures

The Denisovan-derived EPAS1 haplotype is a paradigmatic example of adaptive introgression, but whether archaic ancestry contributed beyond such large-effect loci remains unclear. We analyzed genomes from 1,039 Tibetans, Han Chinese, archaic hominins and ancient Tibetan Plateau individuals. Denisovan-derived adaptive variation was highly localized around EPAS1, whereas Neanderthal-derived variation was distributed across the genome; temporal and elevational patterns reinforced this contrast. Despite individual-level heterogeneity, Neanderthal-introgressed gene repertoires repeatedly converged on immune, metabolic and signal-transduction pathways. A Tibetan-enriched Neanderthal PPARGC1A haplotype was associated with lower fasting glucose and showed concordant support from an independent East Asian type 2 diabetes GWAS. These findings reveal that adaptive introgression can contribute to complex human adaptation through functionally convergent combinations of archaic variants, rather than only through shared large-effect haplotypes.

evolutionary biology↗

bioRxiv2026

MorphCell learns reconstructable shape representations with explicit scale control for 3D cell morphology

Three-dimensional (3D) cell morphology provides a measurable phenotype of cellular state and function, but representing it across biological systems and imaging modalities remains difficult. Existing approaches do not jointly provide transferable representations, complete surface reconstruction, geometric interpretation and explicit control of physical scale. Here we introduce MorphCell, a self-supervised framework that uses cell-surface point clouds to learn shape-driven representations independently of physical scale. MorphCell combines cross-view reconstruction pretraining with spherical self-reconstruction. The former captures geometric relationships between surface regions, whereas the latter recovers complete 3D morphology from individual representations. Pretrained on non-biological object surfaces, MorphCell transfers to cellular datasets without biological task-specific training and outperforms existing point-cloud representations in morphological classification. The learned representations capture both global contour and local surface variation, enabling geometric interpretation through reconstruction, biophysical descriptors and saliency analysis. By retaining physical scale as a separate variable for controlled fusion, MorphCell further reveals that shape and scale contribute differently across biological distinctions. This framework provides a general approach for representing, reconstructing and interpreting 3D cellular morphology across imaging modalities and biological contexts.

cell biology↗