Biology subjects

Zhong, H.

Publications and source records attributed to Zhong, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Distributed brain-spinal cord dynamics during micturition revealed by functional ultrasound imaging

Micturition relies on coordinated activity across distributed neural circuits spanning the brain and spinal cord, yet the spatiotemporal dynamics of these processes remain poorly understood. Disruption of these pathways in conditions such as spinal cord injury, Parkinson's disease, multiple sclerosis, and aging can lead to neurogenic bladder dysfunction. Here, we used functional ultrasound imaging (fUSI), a modality with high spatiotemporal resolution and single-trial sensitivity, to investigate central nervous system activity during micturition. In anesthetized rats undergoing controlled bladder filling and volitional voiding, we recorded power Doppler signals from either the brain or the lumbosacral spinal cord. We observed distinct, region-specific hemodynamic responses associated with micturition across multiple brain regions, including the thalamus, hypothalamus, midbrain, and neocortex - particularly sensorimotor and retrosplenial areas - as well as dorsal and ventral spinal cord circuits. Furthermore, population-level activity in these regions enabled accurate decoding of bladder pressure dynamics. These results provide a systems-level characterization of central nervous system activity during micturition and establish fUSI as a platform for developing neural interfaces to restore bladder control.

bioengineering↗

bioRxiv2026

iFLiP: accessible and low-cost lifetime fiber photometry for robust longitudinal and quantitative measurements of neural dynamics

Fluorescence lifetime measurements overcome the limitation of widely used intensity-based measurements in quantifying tonic activities and longitudinal changes in neural dynamics. However, while lifetime sensors are rapidly emerging, their applications have been limited by the complexity and expense of required hardware and software. Here, we report a simple, low-cost lifetime fiber photometry system, named iFLiP, that uses entirely off-the-shelf products and can be assembled without specialized expertise. We also develop software that enables turn-key operation. The integrated system can accurately monitor lifetime changes at up to 5000 Hz for days. We demonstrate the utility of iFLiP in monitoring the activity of protein kinase A (PKA) in vitro and in vivo, and uncover striatal subregion- and cell-type-specific PKA dynamics elicited by locomotion in mice. Additionally, multi-day continuous measurements in the basal forebrain reveal previously unappreciated PKA activity associated with sleep/wakefulness. Overall, iFLiP may make lifetime measurements broadly accessible in neuroscience research.

neuroscience↗

bioRxiv2026

WrapEvoFS enables auditable feature compression with regret-constrained representative locking

WrapEvoFS provides a deterministic, auditable rule for choosing one feature set from repeated stochastic searches. It retains candidates within a prespecified development-score gap, selects the most representative set by feature overlap, and records the decision before held-out evaluation. A corrected genetic objective preserves score ordering lost through zero truncation. In a fully nested four-class cancer analysis, all 30 locked signatures met the 0.01 tolerance, and 11 differed from the highest-scoring candidate; every changed selection increased mean feature overlap within the eligible pool. Paired differences in area under the receiver operating characteristic curve against recursive feature elimination with cross-validation all had 95% intervals crossing zero. No selector dominated matched comparisons on feature count and predictive performance, and one-time evaluation of frozen signatures at held-out centers yielded heterogeneous effects. WrapEvoFS standardizes selection within a fixed candidate bank under an empirical score-gap constraint, enabling reproducible reporting of performance, compression, and feature-agreement trade-offs without implying predictive superiority or stability under changes in participant composition.

bioinformatics↗

bioRxiv2026

A single-cell and multi-platform spatial atlas of the human pancreas resolves a transformation-associated epithelial axis and a recurrent boundary-organized tumour microenvironment

Most pancreatic ductal adenocarcinoma (PDAC) single-cell and spatial studies analyze one cohort or platform, obscuring recurrent biology. We assembled a human pancreas single-cell and single-nucleus reference of 1,186,130 cells from 19 studies and interpreted 176 Visium sections comprising 458,877 spots across non-diseased pancreas, chronic pancreatitis, PanIN, IPMN, primary PDAC and metastasis. Marker-supported labels were used after two RNA-based copy- number callers failed known-diploid controls. BANKSY domains, two reference-mapping methods and sample-level analyses resolved a cross-sectional epithelial axis extending from acinar-rich to malignant tissue. Five trajectory algorithms recovered similar ordering on a shared embedding; their consensus was interpreted as transformation-associated, not temporal or clonal. Malignant regions were globally segregated from fibroblast and myeloid compartments. Signed- distance analysis refined this pattern into a malignant core, a CAF/myeloid surround beginning at the tumour boundary and a more distal lymphoid compartment. Candidate extracellular-matrix communication, led by COLLAGEN, LAMININ and FN1, concentrated at the interface. Changes were reproduced in six patient-matched Normal-tumour pairs using exact patient-level tests. Visium HD resolved the same organization at single-cell resolution and showed that 8-um bins distorted immune-adjacency estimates. Xenium also revealed recurrent neighbourhoods but sample-specific stromal boundaries. We provide a confound-aware framework for identifying recurrent epithelial and microenvironmental organization in PDAC.

cancer biology↗

bioRxiv2026

ARID3a-Expressing Naive B Cells in SLE have an Activated Phenotype and Transiently Express Surface CD68

Numbers of ARID3a (AT-Rich Interaction Domain 3a) -expressing B lymphocytes from patients with systemic lupus erythematosus (SLE) are associated with increased disease activity. Normally, ARID3a-expressing circulating naive B cells are rare, but in SLE naive B cells dramatically increase ARID3a expression. We found that in vitro stimulation of B lymphocytes from healthy individuals with a cocktail of cytokines and agonists induced ARID3a in a subset of activated naive B cells and in IgD-CD27- double negative B cells previously associated with autoimmunity. Single cell RNA-seq of isolated naive B cells from ten SLE patients, with varying frequencies of ARID3a-expressing cells, revealed that ARID3a-associated genes included activation markers. Moreover, our data revealed the unexpected co-expression of the scavenger receptor CD68 with ARID3a, at both the transcript and protein level, in activated subsets of naive B cells. Inhibition of ARID3a in stimulated B cell cultures blocked naive B cell activation and CD68 expression. Together, these data identify ARID3a and CD68 as markers of naive B cell precursors associated with autoimmunity in SLE.

immunology↗

bioRxiv2026

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

Immune checkpoint inhibitors (ICI) are central to the treatment of metastatic clear cell renal cell carcinoma (ccRCC), yet only a subset of patients derive durable benefit, and clinically deployable predictive biomarkers remain an unmet need. RNA-based T-effector signatures capture cytotoxic immune biology and have been associated with ICI response in clinical trial cohorts; however, their clinical implementation is limited by the marked spatial heterogeneity of ccRCC, as well as cost, long turnaround time, sample quality requirements, and limited accessibility. Here, we developed a visually interpretable deep learning (DL) model that predicts a T-cell-enriched immune score directly from hematoxylin and eosin (H&E)-stained whole-slide images. To overcome the inability of H&E morphology alone to distinguish lymphocyte subsets, we trained the model using multimodal spatial supervision from CD8, PAX8, and ERG IHC, which respectively identified cytotoxic T-cell-rich regions, tumor cells, and endothelial cells, thereby constraining immune predictions to relevant tumor microenvironmental niches. The resulting H&E DL Immune score was validated by pathologist review, comparison with held-out CD8 IHC annotations, and independent datasets. The H&E DL Immune score correlated with T-effector RNA scores across independent institutional and IMmotion150 clinical trial cohorts (spearman correlations of 0.726; p=5.90x10-15 and 0.706; p=4.04x10-19). As a proof of principle, the score was used to characterize associations with key biological features across large cohorts, including sarcomatoid differentiation, BAP1 and PBRM1 mutation status, and additional transcriptomic signatures. In IMmotion150 clinical trial cohort, a median-dichotomized H&E DL Immune score, similar to RNA-based T-effector score, was significantly associated with clinical benefit from atezulumab therapy. In contemporary institutional cohorts of patients treated with frontline ipilimumab plus nivolumab or in initial 3 lines of nivolumab monotherapy, patients in the top quartile of H&E DL Immune score had significantly longer progression-free survival. Collectively, these findings support a scalable and interpretable H&E-based biomarker that captures T-effector biology and can help identify patients with ccRCC more likely to benefit from ICIs.

bioinformatics↗

bioRxiv2026

Honey bee (Apis mellifera) gut microbiome associations with viruses and pesticides across Canadian agroecosystems

The honey bee (Apis mellifera) gut microbiome plays a central role in host health, yet its variation across agricultural landscapes remains poorly resolved. This study investigates how major environmental stressors, particularly pesticide exposure and RNA virus loadings, shape the honey bee gut microbiome in a large-scale field study conducted across Canada, spanning diverse agroecosystems from British Columbia to Quebec. We identify consistent associations between specific bacterial taxa and major RNA viruses, including enrichment of Serratia marcescens with SBV and depletion of Bombella intestini with BQCV. Pesticide exposure is likewise linked to reproducible shifts in key microbial taxa. Together, these findings reveal that interacting stressors jointly shape the bee gut microbiome and enable prediction of microbiome responses in agroecosystems. HighlightsDistinct associations identified between gut bacteria and major bee RNA viruses (BQCV, SBV, LSV, IAPV) Pesticide exposure is linked to reproducible shifts in key microbial taxa Combined virus-pesticide effects form coordinated clusters that predict microbiome variation and specific bacterial responses Integrated modeling demonstrates that environmental stressors can jointly explain microbiome structure beyond crop effects Graphical abstractSchematic overview of potential links between pesticide exposure and RNA virus infection and their effects on the bee gut bacterial community. Solid arrows indicate associations supported by the present study, whereas dashed arrows indicate hypothesized or unresolved interactions. Associations between the presence of specific bee RNA viruses (left) or pesticide residues (right) and changes in the relative abundance of particular gut taxa (pink {uparrow}, increased; blue {downarrow}, decreased). The pesticide subtype is indicated by the icon in the cell (leaf - herbicide, hyphae - fungicide and insect - insecticide). Several bacterial taxa showed reproducible associations with specific viral or pesticide variables, including Bombella intestini, Serratia marcescens, Melissococcus plutonius, Paenibacillus alvei, Apibacter sp. wkB309, and Gilliamella sp. A7. Abbreviations: BQCV Black queen cell virus; LSV, Lake Sinai virus; SBV, Sacbrood virus; IAPV, Israeli acute paralysis virus. (p/n/b) indicate the sample matrix in which the pesticide was detected, namely pollen, nectar, and bee tissue, respectively. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/731697v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@cb92a4org.highwire.dtl.DTLVardef@1087045org.highwire.dtl.DTLVardef@102cabforg.highwire.dtl.DTLVardef@4ce2f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

nanoASM: Long-Read Allele-Specific DNA Methylation Profiling Enables Functional Annotation of Regulatory Noncoding Variants in Human Prostate Tissues

Long-read nanopore sequencing enables simultaneous detection of germline variation and native DNA base modifications on individual DNA molecules, providing a unique opportunity to investigate allele-specific epigenetic regulation. Here, we performed whole-genome nanopore sequencing on normal and tumor prostate tissues to characterize differential methylation, methylation entropy, and allele-specific methylation (ASM) associated with noncoding genetic variants. Genome-wide analysis identified extensive cancer-associated differentially methylated regions (DMRs), with hypermethylated DMRs significantly enriched near transcription start sites and transcriptional regulatory regions. Integration with transcriptomic datasets revealed strong inverse relationships between promoter methylation and gene expression, while 5-hydroxymethylcytosine (5hmC) levels positively correlated with transcriptional activity across gene bodies. Using fragment-level methylation patterns enabled by long-read sequencing, we further quantified methylation entropy incorporating both 5mCG and 5hmCG states. Cancer-hypermethylated DMRs exhibited markedly reduced entropy, consistent with clonal fixation of methylation states during tumor progression. Entropy profiling across chromatin annotations demonstrated maximal epigenetic heterogeneity at partially modified enhancer-associated regions. To investigate cis-regulatory genetic effects, we developed a simple ASM framework (nanoASM) that can partition sequencing reads by allelic state and identifies allele-specific DMRs directly from long-read data. Compared with conventional population-level mQTL analysis, ASM demonstrated substantially improved statistical efficiency by leveraging within-individual contrasts and reducing sample-level heterogeneity. Although germline single nucleotide polymorphisms (SNPs) were largely shared between normal and tumor tissues, ASM patterns differed substantially, with tumor-associated ASM regions displaying significantly larger genomic span and stronger allelic methylation differences. Comparative analysis with TCGA prostate mQTL and GTEx prostate eQTL datasets demonstrated substantial concordance between ASM directionality and downstream transcriptional effects, particularly for variants located within DMRs and near transcription start sites. At the IRX4 prostate cancer risk locus, ASM identified an androgen-responsive regulatory domain overlapping AR ChIP-seq and H3K27ac peaks, nominating rs6885084 as a candidate functional variant. At the PSCA locus, ASM anchored by rs4736369 was associated with allele-specific methylation, chromatin activation, transcript abundance, and isoform usage. Together, these findings establish nanopore-based ASM analysis as a powerful approach for resolving functional noncoding variants and their regulatory domains they control in prostate cancer.

genetics↗

bioRxiv2026

Comparative Proteomics Across Tissues and Crop Agroecosystems Reveals Agricultural Stressor Responses in the Western Honey Bee

Maintaining honey bee health in crop production systems is increasingly difficult because worker bees encounter multiple chemical and biological pressures from pesticides and pathogens. How these field-realistic pressures affect molecular physiology across functionally distinct tissues remains poorly understood. Here, we tested whether tissue-resolved proteomics could separate stable tissue-specific patterns from crop-associated molecular changes. To do this, we profiled abdomen, gut, and head proteomes from honey bees collected across four Canadian crop ecosystems over two consecutive years, and integrated these data with pesticide-residue and pathogen-load measurements. Proteomic variation was structured by both tissue identity and crop environment. Tissue-specific proteomic profiles were characterized across samples, whereas crop-associated effects were detected in both years and were stronger in 2021, the second year of the study. Tissue-specific enrichment and network analyses linked the abdomen to lipid catabolism and ubiquitin-proteasome proteostasis, the gut to central carbon metabolism, membrane transport, vesicle trafficking, and cytoskeletal organization, and the head to neurosensory and mitochondrial functions, together with amino-sugar metabolism and vesicle-associated quality-control modules. Among the measured pesticide residues, boscalid was the most reproducible chemical correlate of proteomic variation, with the strongest signal in the gut. Cross-year validation associated boscalid exposure with reduced abundance of gut proteins involved in mitochondrial metabolism, protein quality control, vesicle trafficking, nutrient transport, and biosynthetic pathways. Additionally, integrated proteome-transcriptome-microbiome factor analysis further identified gut-centered components associated with measured stressor variables and linked protein-level variation to coordinated transcriptomic and microbial shifts. Independent-year validation showed that compact crop-associated protein signatures detected in 2020 were also present in 2021. Together, these results show that honey bee tissues maintain stable proteomic identities while showing tissue- and year-specific responses to pesticide and pathogen pressures encountered in crop ecosystems. The gut proteome may specifically provide a sensitive molecular indicator of pesticide-associated perturbation under field conditions.

bioinformatics↗

bioRxiv2026

Cell-type-specific transposable element transcription tracks symbiosis and calcification programs in the reef-building coral Acropora hemprichii

Transposable elements (TEs) are pervasive components of eukaryotic genomes and major drivers of genome evolution, yet their contribution to cell-type-specific regulatory landscapes remains poorly understood, particularly in non-model marine invertebrates. Here, we integrated single-cell RNA sequencing with pseudo-aligned TE expression profiling to examine how TE transcription relates to cell type identity in the reef-building coral Acropora hemprichii. We constructed a cell atlas comprising 4,716 cells across eight major cell types. Notably, TE expression alone was sufficient to accurately resolve all major cell types, indicating that cell-type-specific transcriptional states are robustly reflected in TE activity patterns. We identified 9,759 expressed TEs, of which 333 exhibited strong cell-type-specific activity. These differentially expressed TE features were associated with nearby expressed genes and transcription factor loci, suggesting a relationship between cell-type-specific TE activity and local gene regulatory programs. Genes associated with cell-type-specific TEs were enriched for core coral physiological processes, including calcification, metabolite transport, and symbiosis-related functions. Together, these findings indicate that TE transcription is structured along coral cell-type identity and physiological specialization. Our study provides a single-cell-resolved framework for investigating TE-gene relationships in early-diverging metazoans and a community resource for future functional interrogation in reef-building corals.

bioinformatics↗

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

Recovering biological structure in sparse single-cell proteomics with GIRAFI

Single-cell proteomics (SCP) based on liquid-chromatography mass-spectrometry resolves protein-level cellular heterogeneity, but interpretation remains limited by detection-linked sparsity. SCP profiles continuous, peptide-derived intensities and has lower throughput than single-cell RNA sequencing, making denoising methods for large-scale, count-based transcriptomics difficult to apply. Here we present GIRAFI, a graph-informed statistical learning framework that imputes missing values and reveals reproducible cell states by constraining inference to dataset-aware, prior-knowledge-informed protein neighborhoods. We evaluated GIRAFI across SCP datasets spanning diverse biological/technical contexts. In masking-based recovery experiments and cell-type-specific protein-protein interaction inference, GIRAFI outperformed existing methods, and matched bulk proteomics comparisons corroborated recovery accuracy and ablations supported the graph-informed design. Beyond reduced replicate- and source-associated technical structure, GIRAFI recovered ground-truth cell-type annotations, improved cell state-resolved pathway analysis, and enabled trajectory inference consistent with known time courses. These results establish graph-constrained imputation as an effective strategy for improving SCP robustness, biological structure, interpretation, and cross-dataset comparability.

bioinformatics↗

bioRxiv2026

SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification

Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck in neuroscience. While advances in fluorescent labeling and imaging can generate vast datasets, analysis is often the limiting factor. Several deep learning-based tools have been proposed to ameliorate these issues. However, existing applications primarily focus on dendritic spines and lack robust solutions for segmenting synaptic puncta in dense tissue preparations. To address this, we introduce SynAPSeg, which encompasses an open-source framework for deep learning-based analysis and, to the best of our knowledge, the first large-scale, publicly available instance segmentation dataset specifically curated for synaptic puncta. We use this dataset to train deep learning models that reach the performance of human experts across a unique benchmark dataset. SynAPSeg integrates these models into an interactive interface, with support for multi-dimensional data, enabling fully automated segmentation and quantification pipelines alongside an annotation module for refinement and validation. We demonstrate the frameworks scalability by performing the first comprehensive mapping of nearly 4 million excitatory postsynaptic PSD95 puncta within inhibitory interneurons across the dorsal hippocampus, revealing regional differences in synapse properties. Finally, we show SynAPSegs utility for 3D quantification by applying these models to study aging-associated synaptic changes in CA1 parvalbumin (PV)-positive inhibitory neurons. Through this approach, we uncover a reduction in PSD95 density along PV dendrites in the aged CA1, indicating reduced glutamatergic recruitment of PV neurons which could contribute to age-related cognitive decline. Collectively, these results demonstrate that SynAPSeg provides a scalable solution for comprehensively studying synaptic architecture in health and disease.

neuroscience↗

bioRxiv2026

Sensitive and Selective Next-Generation FRET-based PKA Biosensors

The cyclic AMP (cAMP)/protein kinase A (PKA) signaling pathway regulates diverse cellular processes through precise spatiotemporal control across subcellular compartments. Forster resonance energy transfer (FRET)-based A-kinase activity reporters (AKARs) have enabled live-cell visualization of PKA activity, but their limited dynamic range constrains detection of subtle or compartment-specific signaling events. Here, we present a suite of sixth-generation cyan/yellow FRET-based PKA sensors (the AKAR6 series) with substantially enhanced sensitivity and improved selectivity. Systematic optimization of the FRET donor-acceptor pair and sensor backbone yields superior performance versus previous best-in-class FRET-based AKARs, enabling robust detection of subtle PKA activity changes across diverse experimental modalities, including flow cytometry, fluorescence lifetime-based FRET, and two-photon imaging in brain slices. We further leverage kinome atlas data to engineer a variant with improved selectivity for more accurate visualization of nuclear PKA activity. Using the AKAR6 toolkit, we showed that in contrast to strong GPCR-induced PKA activities across all tested compartments in PC12 cells, growth factors stimulated a significant PKA activity at the trans-Golgi network but no detectable activity at cis-Golgi, signifying highly compartmentalized PKA signaling at the sub-organelle level. Furthermore, NGF and EGF induced sustained and transient PKA activity, respectively, across various intracellular compartments, including the nucleus, suggesting that growth factor-specific temporal controls are maintained across subcellular compartments. Together, the AKAR6 toolkit provides a sensitive, selective, and versatile platform for dissecting compartmentalized PKA signaling across cells and tissues.

bioengineering↗

bioRxiv2026

Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and Expanded Framework for Data Evaluation

Single-cell proteomics (SCP) offers direct insight into functional protein states that drive cellular heterogeneity, complementing genomic and transcriptomic analyses. Although recent reports have demonstrated improved proteome coverage, their reliance on specialized instrumentation limits broader adoption. Additionally, current evaluation practices remain largely centered on protein and peptide identification counts, which alone do not fully reflect data quality or biological interpretability. Here, we describe an accessible, label-free SCP workflow which implements easily accessible laboratory equipment: a single-cell dispenser, conventional multiwell plates, and an incubator with water-bath-based humidity control. Using trapped ion mobility spectrometry-time-of-flight mass spectrometry (timsTOF), we systematically optimize key sample preparation variables, including trypsin concentration, incubation time, reduction/alkylation, digestion conditions, and plate types, which together maximize data quality and reproducibility. We further introduce a data-quality framework that moves beyond identification counts, emphasizing quantitative consistency and biological interpretability via individual protein coverage completeness across cells, coefficients of variation across technical replicates, peptide-to-protein ratios, and single-cell-to-bulk correlations. Collectively, our approach lowers technical barriers to accessing SCP while enabling more rigorous, interpretable, and scalable SCP analysis across diverse research contexts.

biochemistry↗

bioRxiv2025

Oxa1L-Mediated Co-translational Protein Insertion Maintains Mitochondrial Function in Parkinson's Disease Models

Mitochondrial respiratory chain integrity relies on the coordinated synthesis and membrane insertion of mitochondrially encoded proteins. How limitations in this process contribute to mitochondrial dysfunction under stress remains poorly understood. Here, we identify the mitochondrial insertase Oxa1L as a key buffering factor that modulates mitochondrial vulnerability under Parkinsonian stress conditions. Transcriptomic analyses and cellular stress models reveal pronounced disruption of oxidative phosphorylation pathways accompanied by reduced Oxa1L abundance. Functional gain- and loss-of-function analyses demonstrate that Oxa1L actively influences mitochondrial membrane potential, reactive oxygen species levels, and ATP production during stress. Notably, Oxa1L selectively governs the abundance of mitochondrially encoded respiratory chain subunits, indicating that co-translational membrane insertion represents a rate-limiting step in mitochondrial protein biogenesis. Structural analyses position Oxa1L at the mitoribosomal exit site, providing a spatial framework for coupling translation to inner membrane insertion. Together, our findings uncover a critical layer of mitochondrial quality control at the level of co-translational insertion and establish Oxa1L as a determinant of mitochondrial resilience under stress.

cell biology↗

bioRxiv2025

SPIN: Inkjet-Driven Nanowell Workflow for Scalable and Sensitive Single-Cell Proteomics

Single-cell proteomics is emerging as a powerful approach to resolve cellular heterogeneity, yet sample processing remains challenging due to limited input material and the absence of protein amplification. We present a protocol centered on an image-guided, machine-learning-driven inkjet single-cell printer integrated with a dew-point-controlled nanowell chip to reduce loss, increase throughput, and improve reproducibility. The system dispenses single cells at >1 Hz into sealed nanoliter wells with minimal surface contact, virtually eliminating evaporation; a high-thermal-conductivity aluminum substrate and precise environmental control further ensure exceptional reproducibility. Relative to a commercial dispenser, the workflow yields significantly higher protein and peptide recovery without bias toward high-abundance species, delivering uniformly deep coverage. Biological pathway analysis emphasizes the robustness of this workflow, as there is a near 100% protein completeness detected among the enzymes in the Kreb cycle in both A549 and astrocytes, suggesting the consistency across all samples evaluated. This platform addresses core processing bottlenecks and enables reliable, scalable single-cell proteomics.

systems biology↗

bioRxiv2025

Lymphatics unload VEGFR3 in response to the endothelial stress upon VEGFR2 insufficiency

Vascular endothelial growth factor receptor 2 (VEGFR2) is a key target for regulating the endothelial cell lineage and angiogenesis. It is also expressed by lymphatic endothelial cells (LEC) while its participation in the process of lymphangiogenesis remains inadequately characterized. We show in this study that VEGFR2 is highly expressed in dermal initial lymphatic vessels and valves. The induced deletion of pan-endothelial Vegfr2 at the neonatal stage produced a potent suppression of dermal lymphatic growth, including the thinner lymphatic diameter, the decrease of LEC number and lymphatic valves. Mechanistically, the VEGFR2 insufficiency led to a dramatic decrease of lymphatic VEGFR3, a key regulator mediating signals for lymphatic growth and remodeling. The RNA sequencing analysis revealed that GO terms enriched for the downregulated genes included biological processes related to the EC development while pathways related to the hematopoiesis and immune responses were upregulated in the skin of Vegfr2 mutants compared with the littermate controls. This was further confirmed by the RNA-seq analysis of dermal tissues 48 hours after the endothelial Vegfr2 deletion. Consistently, an inhibitory effect on the dermal lymphatic growth was also observed by targeting Vegfr2 in PROX1+ cells, manifesting a similarly altered transcriptome signature. The alteration of lymphatic gene expression was further validated by the siRNA mediated knockdown of Vegfr2 in primary lymphatic endothelial cells, showing a transcriptional trend of LEC to hematopoietic transition. Findings from this study imply that VEGFR2 is required for the maintenance of endothelial identity and its insufficiency may trigger the alteration of lymphatic transcriptional programs to diminish the VEGFR3-mediated lymphangiogenesis.

developmental biology↗