Biology subjects

Chen, K.

Publications and source records attributed to Chen, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Primary Cilia Loss in Striatal Neurons Disrupts Synaptic Connectivity and Excitatory Transmission and Drives Metabolomic Remodeling

Disruption of striatal circuits is a central feature of many neurological and psychiatric disorders, yet the mechanisms that maintain afferent connectivity and synaptic function in striatal neurons remain incompletely defined. Primary cilia are signaling organelles present on almost all striatal medium spiny neurons that are enriched in neuromodulatory receptors, suggesting a role in coordinating striatal neuronal communication and biochemical state. Here, we show that conditional ablation of primary cilia from striatal neurons by AAV-Cre-mediated deletion of Ift88 disrupts the afferent connectivity, synaptic function, and chemical signature of the striatum. Monosynaptic rabies tracing revealed an approximately threefold reduction in brain-wide input convergence onto striatal neurons. Whole-cell recordings showed reduced miniature excitatory postsynaptic current amplitude and frequency together with a reduced NMDA:AMPA ratio, consistent with weakened glutamatergic synaptic transmission. Untargeted metabolomics revealed broad remodeling of the striatal chemical profile, predominantly toward decreased measured levels, with lipid-associated pathways most affected alongside reductions in polyamines, glutamate-related metabolites, and neuromodulatory, particularly excitatory, signaling molecules. By contrast, the cortex, which was not targeted by the AAV injection, showed fewer and directionally opposite molecular changes. These findings identify cilia as essential regulators of the structural, synaptic, and molecular integrity required for normal striatal circuit function.

neuroscience↗

bioRxiv2026

Interpretable Peripheral Blood Cell Classification via Vision-Language Concept Bottleneck and Soft Decision Tree

MotivationDeep learning classifiers for medical image analysis typically function as black boxes, disclosing neither the image features underlying their predictions nor the reasoning by which individual decisions are reached. Peripheral blood cell classification exemplifies this challenge: experienced laboratory professionals identify cell types through structured morphological criteria--nucleus shape, chromatin texture, nucleus-to-cytoplasm ratio, granularity, and staining properties--yet existing automated systems cannot express their reasoning in these same terms, impeding clinical audit and verification. ResultsWe present a two-stage interpretable pipeline that addresses both levels of opacity. In the first stage, a frozen domain-adapted vision-language model (ConceptCLIP) projects each cell image onto a 70-dimensional vector of morphological concept scores via zero-shot cosine similarity, eliminating the need for per-image concept annotations. In the second stage, a Soft Decision Tree (SDT) classifies cells solely on these concept scores, producing a deterministic, concept-based decision path for each prediction. On BloodMNIST (eight cell types, 3,421 test images), the full pipeline achieves 94.86% test accuracy--approximately 3 percentage points below the black-box ceiling--while providing fully traceable decision logic. Post-training histological annotation confirms that the learned routing logic aligns with established hematological morphology criteria and reveals an emergent separation of immature granulocyte subtypes (promyelocyte versus metamyelocyte) without subtype supervision, demonstrating that concept-based decision trees can recover clinically meaningful distinctions beyond the granularity of the training labels. Availability and implementationThe source code, trained SDT weights, precomputed concept score data, and inference scripts are publicly available at https://github.com/aquamarineaqua/CLIP-CBM-SoftDecisionTree.

bioinformatics↗

bioRxiv2026

IL-27 Signaling Protects Against Influenza-Associated Pulmonary Aspergillosis Through Inhibition of Type 2 Immunity and Enhanced Antifungal Immunity

Influenza-associated pulmonary aspergillosis (IAPA) is a severe complication of influenza infection associated with substantial mortality. Influenza disrupts pulmonary host defenses and alters innate immune responses, predisposing patients to invasive fungal infection. Interleukin-27 (IL-27) is an immunoregulatory cytokine with context-dependent antiviral and antifungal effects; however, its role during IAPA remains undefined. A mouse model of IAPA was established by infecting wild-type and IL-27 receptor -deficient (Il27ra-/-) mice with influenza A, followed by Aspergillus fumigatus challenge. IL-27 and IL-27R expression were increased during IAPA. Single-cell RNA sequencing identified monocytes as the primary source of IL-27 and T cells as major IL-27r-expressing cells. Il27ra-/- mice exhibited significantly increased pulmonary fungal and influenza viral burden, enhanced type 2 immune responses characterized by elevated IL-4, IL-5, IL-9, IL-13, eosinophils, Th2 cells, pathogenic Th2 cells, and ILC2s. Despite increased eosinophil abundance, eosinophil-mediated conidial killing was impaired in Il27ra-/- mice. IL-27R deficiency also reduced macrophage abundance and impaired macrophage conidial uptake. Conversely, timed administration of rIL-27 enhanced fungal clearance, improved survival, and increased macrophage conidial uptake and augmented eosinophil killing capacity during IAPA. IL-27 signaling is a protective immunoregulatory cytokine during IAPA that limits pathological type 2 inflammation and enhances antifungal effector function of both eosinophils and macrophages. These findings identify IL-27 as a potential therapeutic in IAPA.

immunology↗

bioRxiv2026

Broad remodeling of the pulmonary immune landscape occurs during IAPA with specific functional deficits of neutrophil subsets

BackgroundInfluenza-associated pulmonary aspergillosis (IAPA) is a severe complication of influenza infection associated with prolonged intensive care unit stay and increased mortality. Although impaired antifungal immunity has been implicated in IAPA pathogenesis, the cell type-specific immune mechanisms driving susceptibility remain incompletely understood. We aimed to characterize the pulmonary immune landscape during IAPA using single-cell transcriptomics and functional neutrophil assays. MethodsMale C57BL/6 mice were assigned to naive control, influenza A/PR/8/34 (H1N1) infection, A. fumigatus (ATCC 42202) infection, or IAPA groups. Lung CD45+ immune cells underwent single-cell RNA sequencing with downstream clustering and CellChat ligand-receptor interaction analysis. Differential gene expression analyses were performed across myeloid, lymphoid, and neutrophil populations. Functional neutrophil responses were evaluated using flow cytometry, myeloperoxidase activity assays, and FLARE (fluorescent Aspergillus reporter) conidia to assess fungal conidia uptake and killing. Cross-species validation was performed using gene set enrichment analysis compared with published human IAPA transcriptomic datasets. ResultsIAPA broadly remodeled the pulmonary immune landscape across myeloid, lymphoid, and neutrophil compartments. Myeloid cells showed coordinated suppression of fungal pattern recognition receptors, lysosomal biogenesis programs, and inflammatory signaling. Lymphoid populations exhibited transcriptional signatures of T cell exhaustion and Th17 suppression. Within the neutrophil compartment, we identified two transcriptionally and functionally distinct populations, conventional and inflammatory neutrophils, with divergent antifungal effector capacities. Inflammatory neutrophils showed selective killing defects, while both subsets exhibited impaired phagocytic uptake during IAPA. Murine transcriptomic findings demonstrated strong concordance with immune dysfunction signatures identified in human IAPA. ConclusionIAPA susceptibility arises from coordinated transcriptional dysfunction spanning innate and adaptive immune compartments. Distinct neutrophil subset dysfunction, impaired fungal recognition pathways, and T-cell exhaustion signatures collectively contribute to defective fungal clearance, providing mechanistic insight into IAPA susceptibility and potential therapeutic targets.

immunology↗

bioRxiv2026

Assessing Microcirculation Impairment in Ischemic Stroke Mice Using Arteriovenous Co-fluctuation Analysis

Accurate assessment of cerebral hemodynamics impairment traditionally relies on arterial metrics, yet often overlooks venous drainage and arteriovenous dynamics, thereby limiting the evaluation of ischemia-induced microvascular dysfunction. To address this limitation, we implemented a signal-averaging framework, combined with co-fluctuation analysis, to extract predominantly arterial and venous hemodynamic signals and construct a dynamic arteriovenous co-fluctuation index that quantifies frame-by-frame coordination between arterial inflow and venous outflow activity. This time-resolved index enables spatial characterization of large-scale cortical arteriovenous coordination beyond conventional static correlation-based analyses. Comparative analyses between healthy controls and acute ischemic stroke mice demonstrated that the arteriovenous co-fluctuation index sensitively detects disruption of vascular coordination, revealing a slower state transition that occurs alongside distinct temporal abnormalities and regional heterogeneity between ischemic core and penumbral regions. These findings underscore the utility of arteriovenous coordination as a sensitive indicator of microcirculatory dysfunction, offering a practical analytical tool for assessing stroke-induced microvascular impairment.

neuroscience↗

bioRxiv2026

A five-dimensional functional state space for fingerprinting disease transcriptomes

High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (5-D) functional state space that represents disease transcriptomes as coordinated activity patterns across major biological systems. The framework maps transcriptomic signals onto five functional systems, 14 subcategories, and a distinct infrastructure layer, and was implemented as a reproducible pipeline for functional scoring, cross-condition profiling, benchmarking, and large language model (LLM)-assisted interpretation. Applied to wound healing, sepsis, colorectal cancer-related datasets, an extended GEO atlas of 38 complete case-control disease fingerprints spanning diverse disease contexts, and a TCGA-COAD/READ stage benchmark, the approach recovered interpretable disease-state patterns and retained progression-related information under strong compression. It also improved the quantitative grounding of LLM-generated summaries. This framework provides a compact and auditable representation for comparing disease transcriptomes and supporting human-AI biological interpretation. TeaserA five-dimensional functional framework turns complex disease transcriptomes into compact, interpretable fingerprints for human-AI analysis.

bioinformatics↗

bioRxiv2026

PFOS aggravates atherosclerosis via Bacteroides caecimuris expansion-driven bile acid remodeling and subsequent intestinal FXR-TLR3 signaling cascade

BACKGROUNDPerfluorooctane sulfonate (PFOS) is a widely distributed persistent organic pollutant in the environment and has been associated with an increased risk of atherosclerosis. However, the underlying pathogenic mechanisms remain largely unclear. This study aimed to investigate the effects of PFOS on atherosclerosis and its associated gut-vascular axis. METHODSPseudo-germ-free mouse models and fecal microbiota transplantation (FMT) were used to determine the role of the gut microbiota in PFOS-induced atherosclerosis. Metagenomic sequencing was performed to characterize alterations in gut microbial composition following PFOS exposure, and targeted metabolomics was used to assess bile acid profiles in the ileum and plasma. Transcriptomic analysis of Bacteroides caecimuris (B.caecimuris) was conducted to explore the reasons for the increased abundance of B.caecimuris after PFOS exposure. In addition, intestinal transcriptomics and ChIP-qPCR were performed to validate transcriptional regulation within the FXR-TLR3 signaling axis. RESULTSAmong 127 participants with paired serum and fecal samples, including 82 patients undergoing coronary angiography with Gensini scores (GS score), fecal PFOS levels were significantly associated with lipid profiles and GS score, whereas serum PFOS showed no such association.Mechanistically, PFOS exposure promotes intestinal enrichment of B.caecimuris by upregulating its tolC gene, thereby enhancing efflux capacity.This microbial shift was accompanied by reduced levels of tauro-ursodeoxycholic acid (TUDCA) and aberrant activation of intestinal FXR signaling.Further analyses demonstrated that FXR activation upregulated TLR3 expression and promoted inflammatory responses and atherosclerosis progression via the TLR3-NF-{kappa}B signaling axis. Both intestinal epithelial-specific FXR deficiency (Fxr{Delta}IE) and TUDCA supplementation significantly suppressed pathway activation and alleviated disease phenotypes.Functional experiments identified TLR3 as a key downstream effector of FXR. Overexpression of TLR3 abolished the protective effects observed in Fxr{Delta}IE mice. Moreover, pharmacological inhibition of TLR3 using CU CPT-4a significantly improved established atherosclerotic lesions in vivo. CONCLUSIONSThis study identifies a gut microbiota-driven FXR-TLR3 signaling axis that mediates PFOS-induced atherosclerosis. These findings provide new mechanistic insights into environmentally induced cardiovascular disease and suggest potential targets for risk assessment and therapeutic intervention.

pathology↗

bioRxiv2026

TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition

T cell receptor recognition of peptide-MHC depends on sequence, interface chemistry, and three-dimensional geometry, but docking geometry is often summarized at the whole-receptor level, leaving CDR3-local pose difficult to compare across structures. We introduce TCR-FramePose, a local-frame descriptor set that represents each TCR-pMHC complex as three bodies -- whole TCR, CDR3, and CDR3{beta} -- measured relative to a pMHC groove frame. For each body, FramePose decomposes the native pose into reach, offset direction on S2, and orientation on SO(3); for tangent-space analyses, these components are mapped to six coordinates per body and 18 coordinates per complex. Applied to 378 curated {beta}TCR-pMHC crystal structures, FramePose recovers known class-associated receptor-placement differences and additionally resolved whole-TCR and CDR3{beta} orientation shifts that were not captured by crossing angle. The same orientation coordinates identified reverse-polarity and off-axis outliers as distinct modes. In cross-validated association analyses, FramePose added nonredundant BSA- and affinity-associated information beyond conventional descriptors, and the modest affinity gain was concentrated in CDR3 orientation blocks which were least recoverable from conventional descriptors. Biological grouping analyses showed that shared receptor pose over peptide-MHC was organized primarily by germline V-region framework. TCRs recognizing the same peptide-MHC target favors shared FramePose geometries rather than strong receptor-specific divergence, whereas CDR3 sequence did not detectably reposition the rigid-body pose after antigen context and germline framework were fixed. MHC allele and peptide length contributed smaller adjustments, localized mainly to CDR3{beta} and groove-normal orientation axes. Finally, interface analyses showed that affinity tracked interface burial, with CDR3{beta} reach linking FramePose geometry to binding through buried surface area. Within engineered panels, mutation-level effects were panel-specific, with CDR3{beta} remodeling localizing to a recurrent interface region but varying in direction across receptors. These properties enable FramePose to serve as a geometric filter for in silico TCR-pMHC models and as a feature layer for structure-guided TCR engineering. Together, TCR-FramePose provides a nonredundant geometric layer for structure-guided TCR-pMHC analysis, linking germline-scaffolded recognition, CDR3-local pose, and interface organization without replacing sequence, contact, or energetic descriptors.

bioinformatics↗

bioRxiv2026

Endothelial Baf60c in BPD-Associated Pulmonary Hypertension

BACKGROUNDBronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) complicates prematurity and carries substantial morbidity in extremely preterm infants. Pulmonary microvascular endothelial cell (PMVEC) dysfunction promotes capillary rarefaction and vascular remodeling, but epigenetic mechanisms after neonatal hyperoxia are poorly defined. Baf60c (SMARCD3), a SWI/SNF subunit supporting vascular homeostasis, and Smarcc2 (BAF170), a PBAF scaffold subunit linked to proliferative signaling, have not been studied together in BPD-PH. METHODSNeonatal C57BL/6 mice were exposed to 85% oxygen for 14 days. Right ventricular systolic pressure (RVSP), right ventricular hypertrophy, lung weight index, and pulmonary histopathology were assessed; PMVEC proliferation, migration, and invasion were measured. Transcriptome sequencing with GO/KEGG analyses, siRNA knockdown, LY294002 inhibition, coimmunoprecipitation, and Western blotting mapped the Baf60c-Smarcc2-PI3K-Akt-mTOR axis. A Tie1-driven, lung-tropic adeno-associated virus delivered by superficial facial vein injection at postnatal day 1 enabled PMVEC-specific Baf60c overexpression. RESULTSHyperoxia increased RVSP, right ventricular hypertrophy, and lung weight index, impaired alveolarization, reduced capillary density, and promoted arteriolar remodeling. PMVEC function was impaired, with PI3K-Akt pathway enrichment and suppressed signaling. Hyperoxia decreased Baf60c and increased Smarcc2. Baf60c knockdown upregulated Smarcc2, suppressed PI3K-Akt-mTOR, and phenocopied hyperoxia; Smarcc2 knockdown had opposite effects. Baf60c bound Smarcc2 but not PI3K. PMVEC-specific Baf60c overexpression attenuated pulmonary hypertension and right ventricular hypertrophy and partially improved alveolar and microvascular injury. CONCLUSIONSHyperoxia-induced BPD-PH is associated with reduced Baf60c, increased Smarcc2, and suppressed PI3K-Akt-mTOR signaling in PMVECs. Baf60c may indirectly regulate this pathway through Smarcc2. Endothelial Baf60c is a potential therapeutic target in BPD-PH. Novelty and RelevanceO_ST_ABSWhat Is New?C_ST_ABSA neonatal hyperoxia BPD-PH model was characterized with adverse hemodynamic and pulmonary vascular endpoints together with PMVEC functional impairment. Hyperoxia downregulates Baf60c, upregulates Smarcc2, and suppresses PI3K-Akt-mTOR signaling in PMVECs. Baf60c interacts with Smarcc2 without direct PI3K binding. PMVEC-specific Baf60c overexpression via superficial facial vein AAV delivery ameliorates pulmonary hypertension. What Is Relevant?BPD-PH is a developmental pulmonary hypertension phenotype in which pulmonary vascular resistance and right ventricular load are measurable with RVSP and Fulton index, consistent with Group 3 PH biology. SWI/SNF-mediated chromatin remodeling in pulmonary endothelium may link neonatal oxygen injury to impaired angiogenic endothelial function. Clinical/Pathophysiological Implications?Endothelial Baf60c deficiency may contribute to pulmonary vascular rarefaction after neonatal hyperoxia. Restoring endothelial Baf60c may improve pulmonary hemodynamics in BPD-PH, although structural lung injury may be only partially reversible.

physiology↗

bioRxiv2026

Structural assemblies for an RNA world

Protein homo-oligomerization widely generates symmetric assemblies for encapsulation and scaffolding. Conversely, natural RNA quaternary structures appear limited, with known RNA-only multimers predominantly forming dimers or dihedral assemblies from long transcripts. Whether short RNAs can access broader higher-order assembly space has remained unclear. Here, using cryo-EM, we show that RNAs under 200 nucleotides form three distinct structural classes: a 60-subunit viral-capsid-like icosahedron, non-caged oligomers including a trimer and a strand-exchanged dimer, and a continuous filament assembled from a 57-nucleotide RNA. These structures demonstrate that assembly complexity does not simply scale with RNA length; compact RNAs can specify architectures traditionally associated with proteins. Our findings broaden the known RNA quaternary repertoire, strengthen the structural plausibility of higher-order organization in an RNA world, and establish foundational reference architectures to guide future RNA-based self-assembling nanoparticle design. HighlightsO_LIShort RNAs reveal protein-like assembly principles for an RNA world C_LIO_LIThe first natural RNA-only T=1 icosahedral cage is a viral-capsid-like 60-mer C_LIO_LICompact RNA-RNA interfaces encode global curvature, valency and propagation C_LIO_LIRNA motifs assemble into closed cages, finite oligomers and extended filaments C_LI

evolutionary biology↗

bioRxiv2026

Mesothelial Plasticity Specifies a Pleural Immune Circuit that Orchestrates Lung Regeneration

Following injury, predisposition towards regenerative repair and away from degenerative remodeling is central to organismal health, yet the upstream determinants that instruct this fate choice remain poorly understood. Here, we identify mesothelial cell plasticity as a central determinant between regeneration and degeneration by comparing mouse models of pneumonectomy (PNX) versus chronic lung allograft dysfunction (CLAD). While mesothelial cells expand in both settings, following PNX, these cells undergo differentiation through multiple transitional states, culminating in an inflammatory population that orchestrates monocyte recruitment and subsequent tissue regeneration. In contrast, in CLAD, mesothelial cells enrich in an extracellular matrix-enriched state that lacks pro-regenerative signaling capacity. Using single cell epigenomic profiling and in vivo genetics, we define a mesothelium-specific TWIST1-CCL2 axis that governs mesothelium plasticity and signaling, monocyte recruitment and lung regrowth. Together, these findings demonstrate that context-dependent reprogramming of a pleural population, known to protect organs, can be leveraged to drive regeneration.

Developmental Biology↗

bioRxiv2026

Complete elucidation and heterologous reconstruction of the biosynthetic pathway of camptothecin

Camptothecin derivatives are first-line anticancer drugs used worldwide for the treatment of diverse malignant tumors. However, the biosynthetic pathway of camptothecin has remained elusive for five decades. Here, we fully map its entire biosynthetic route. We discovered five key missing enzymes (OpCAR, OpSDR11, OpCS, OpGH1, and OpSTR) via the combination of MALDI mass spectrometry imaging, single-cell RNA sequencing and co-expression analysis. Meanwhile, we demonstrated a free flavin mononucleotide triggered the non-enzymatic 6-5-6 to 6-6-5 fused-ring skeleton rearrangement, filling the last gap in camptothecin biosynthesis. Finally, we validated this identified pathway and achieved the de novo biosynthesis of camptothecin in Saccharomyces cerevisiae. These discoveries uncover the long-standing mystery underlying camptothecin and pave the way for manufacturing camptothecin and its derivatives through synthetic biology approaches.

synthetic biology↗

bioRxiv2026

Programming T cells for intercellular genome editing

Therapeutic genome editing requires delivery of editing molecules to defined cell types, but targeting specificity and efficiency are currently limited. We hypothesized that properties inherent to immune cells, including tissue infiltration and programmed cell recognition, could be harnessed to engineer a cell-based delivery system. We show here that T cells can both produce and transfer editing machinery to target cells. In response to a programmable ligand, engineered T-lymphoid cells can transfer enzymes using complex spatiotemporal logic and deliver cargo in a cell contact-dependent or -independent manner. We demonstrate feasibility of this approach in primary human T cells, establishing a customizable genetic circuit for macromolecular delivery controlled by intercellular interactions.

bioengineering↗

bioRxiv2026

Robust integration of weakly anchored spatial multi-omics

Spatial multi-omics holds great promise for dissecting complex biological processes, though inherent technical constraints continue to limit its widespread adoption. Currently, most studies therefore measure distinct omics features on separate tissue sections, necessitating spatial diagonal integration. An emerging practical solution is to leverage hematoxylin and eosin (H&E) images as an integration anchor, given their ubiquity, low cost, and compatibility across tissue preparations. However, this anchor is frequently compromised in real-world settings by variations in H&E staining style, absence of reliable histological landmarks, and mismatches in spatial resolutions across omics modalities. To address this, we introduce SpaWeaver, a computational framework that couples a pathology foundation model with a graph Transformer and a latent feature aligner module, providing a highly robust solution for weakly anchored spatial omics data diagonal integration. Extensive experiments demonstrate that SpaWeaver exhibits superior robustness against isolated or synergistic weak-anchoring factors. The spatial multi-omics profiles generated by SpaWeaver link molecular features originally separated on two sections, unlocking diverse downstream analyses once exclusive to co-assayed spatial multi-omics data, including niche-aware cell-cell communication inference and multi-omics resolved cell state. In this study, it unveils tumor-distance-dependent fibroblast-CD4{square}T-cell signaling in human colon adenocarcinoma and identifies a hypoxic glycolytic tumor state with pyknotic nuclei in human ovarian cancer. Overall, our approach bridges readily accessible single-omics measurements across weakly anchored tissue sections, enabling unified spatial multi-omics characterization and system-level tissue analysis.

bioinformatics↗

bioRxiv2026

Somatic variant detection in normal tissues from single-cell sequencing data

A crucial advantage of single-cell sequencing (SCS) is its ability to identify somatic variants in individual cells, enabling phylogenetic analysis of cellular populations within bulk tissues. While identifying somatic variants in tumor tissues via SCS has become a common practice, doing so in normal tissues remains challenging due to the rarity of somatic variants in normal cells. To evaluate the feasibility of somatic variant calling from widely available single-nucleus RNA-seq (snRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) data, we profiled a Cell-line mix of six HapMap samples prepared by the SMaHT consortium using 10x Genomics 5 snRNA-seq (12k cells with 36k mean reads per cell) and snATAC-seq (11k cells with 14k median high-quality fragments per cell) for variant calling. PacBio long-read whole genome sequencing (WGS) data (109x) generated from individual cell lines were used as ground truth. Two computational tools, Monopogen and SComatic, were used for somatic variant calling from the SCS data. Monopogen achieved single nucleotide variant (SNV) detection accuracies of 93.30% in the snRNA-seq and 99.64% in the snATAC-seq data, both of which outperformed SComatic (74.35% and 94.29%, respectively). Monopogen also consistently detected somatic SNVs at cellular fractions as low as 0.5% (2.54% in snRNA and 0.81% in snATAC) in individual samples. Notably, snATAC-seq exhibited higher genomic coverage breadth and larger number of variants detected than snRNA-seq. While the SCS data have lower overall genome coverage than that of the bulk WGS, the single-cell level variant resolution allows Monopogen to assign variants to their cells of origin with over 80% accuracy in both RNA and ATAC modalities, thereby facilitating studies of clonal evolution and cell-type-specific mutagenesis. Other benchmarking methods were also evaluated (DeepVariant, Cellsnp-lite and Mutect2) for comparison. In conclusion, our study demonstrated the feasibility of performing reliable single-cell somatic mutation calling in a cell-line mixture and discussed the strengths and limitations of current computational methods when applied to normal tissues.

bioinformatics↗

bioRxiv2026

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

Werner syndrome helicase (WRN) is a synthetic-lethal vulnerability in microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) cancers1-5, and non-covalent and covalent WRN inhibitors are now entering clinical development6-9. A central unresolved question is whether resistance to one WRN inhibitor class inevitably compromises the target, or instead creates actionable vulnerabilities to an alternate modality. Here we generated ten stable acquired-resistance models across three MSI-H cell lines using the non-covalent inhibitor HRO761 and the covalent inhibitor VVD-214. Whole-exome sequencing, biochemical reconstitution and isogenic knock-in models identified recurrent on-target WRN missense mutations as dominant resistance drivers, but with sharply modality-specific spectra: HRO761 resistance clustered at G729/F730/I852, whereas VVD-214 resistance concentrated at E846. These mutations impaired inhibitor engagement and abolished the canonical WRN inhibitor (WRNi)-induced pharmacodynamic cascade, including WRN reduction, DNA damage response (DDR) activation and G2/M arrest. Crucially, most resistance mutations retained biochemical, cellular and in vivo sensitivity to the alternate inhibitor modality, revealing a functional orthogonality that enabled a 7-day cyclic alternating regimen to delay tumour regrowth in xenografts. We further identified F730L as an engineered cross-resistant bottleneck model and used structure-guided, artificial intelligence (AI)-enabled optimization to generate GBA-007, a proof-of-concept mutation-tolerant WRN inhibitor candidate with promising activity against F730L in vitro and in vivo. Thus, clinically relevant WRN inhibitor classes impose distinct on-target resistance trajectories that can be exploited through schedule design, while cross-resistant bottlenecks can be addressed by rapid mutation-aware inhibitor engineering.

Cancer Biology↗

bioRxiv2026

Repurposing of the assembly chaperone PAC1-PAC2 defines a specialized proteasome landscape in mature mammalian sperm

Mature mammalian sperm rely on active proteasomes containing the testis-specific subunit 4s for capacitation, acrosomal exocytosis and fertilization, but how these proteasomes are structurally specialized remains unclear. We determine the endogenous proteasome landscape of mature bovine spermatozoa and testis using cryo-electron microscopy. We resolve five sperm proteasome assemblies: PA200-20S, free 20S, a previously unrecognized PA200-20S-PAC1/2 hybrid, and PAC1/2-20S complexes capped at one or both ends. Unexpectedly, the PAC1/2-bound complexes are mature and POMP-free, selectively incorporated for 4s, lack 2, and adopt a PAC1/2 orientation and gate configuration distinct from known assembly intermediates. Together with elevated proteolytic activity, these features indicate that PAC1/2 is repurposed from a transient assembly chaperone into a stable post-assembly regulator in mature sperm. 4s-containing proteasomes assemble through the canonical PAC1-PAC4/POMP pathway yet may follow {beta}2-{beta}3-{beta}1 incorporation ordering; functionally, 4s incorporation remodels the sCP surface and enhances peptidase activities, definining the spermatoproteasomes as a hyperactive 20S variant. PAC1/2-bound proteasomes are absent from testis, where PA200-20S and 26S proteasomes are present, while 26S proteasomes are nearly lost in mature sperm. Our findings define a sperm-specific, ATP-independent proteasome landscape, expand the functional repertoires of 4s and PAC1/2, and provide a structural framework for proteasome specialization in male fertility.

cell biology↗

bioRxiv2026

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

pathology↗