Biology subjects

Nguyen, E.

Publications and source records attributed to Nguyen, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Dysregulation of epithelial differentiation architecture underlies field cancerization in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) has exceptionally high recurrence rates, exceeding 50%, contributing to mortality and morbidity. This recurrence rate is proposed to be due to field cancerization, a phenomenon where tumor-adjacent "field" tissue that appears histologically normal harbors molecular changes that prime it for subsequent tumor growth. Current models attribute field cancerization to shared driver mutations between tumors and adjacent at-risk tissue, but this assumption has never been directly tested. To comprehensively characterize this phenomenon, we profiled patient-matched tumor, normal-adjacent field (NAF), contralateral normal, and blood samples from HPV-negative HNSCC patients using whole genome sequencing, targeted driver gene sequencing, single-nucleus multiome profiling, spatial transcriptomics and functional proliferation assays. While histologically normal NAF tissues did harbor expanded mutant clones, these mutations rarely overlapped with paired tumors and were not shared across patients, suggesting molecular features beyond mutations are altered in NAF. Single-cell multiomics revealed that NAF epithelium loses its normal organizational hierarchy: basal stem cell markers persist in differentiated cells, while terminal differentiation programs fail to activate, with reconfiguration of both gene regulatory networks and cell-cell interactions. Proliferating cells appeared in tissue layers normally restricted to post-mitotic cells, a pattern typically seen only in overt dysplasia. Within this disrupted hierarchy, a distinct epithelial state arose in NAF that expressed both normal epithelial and tumor-associated markers, which we localized to the parabasal layers using immunofluorescence and spatial transcriptomics. Together, our findings demonstrate that field cancerization operates through epithelial hierarchy disorganization that disrupts differentiation, generates cells with tumor-like features, and may create a permissive environment for the expansion of cancer-associated mutations, all within tissue that appears histologically normal.

cancer biology↗

bioRxiv2026

Impact of opioid-based versus opioid-free anaesthesia on postoperative recovery, circulating inflammatory cytokines and brain-derived neurotrophic factor.

Background Opioid-free anaesthesia (OFA) has emerged as an alternative to opioid-based anaesthesia (OBA), but its benefit over OBA for postoperative quality of recovery (QoR) remains unclear. To address this question while limiting variability inherent in clinical studies, such as analgesic regimens and patient psychosocial factors, we investigated the effects of OFA versus OBA on postoperative recovery in a controlled preclinical mouse model. Methods Adult male and female C57BL/6J mice underwent laparotomy or sham procedures under isoflurane anaesthesia, and were randomly assigned to saline (control), fentanyl (OBA), or dexmedetomidine (OFA) as intraoperative analgesic adjuncts. All mice received multimodal postoperative analgesia, including carprofen. We adapted a standardised multidimensional recovery model using six behavioural endpoints reflecting human QoR domains: body weight, food intake, locomotor activity, mechanical sensitivity, sucrose preference, and social interaction. These endpoints were assessed on postoperative days 1, 2, 3, and 7. Plasma inflammatory cytokine and brain-derived neurotrophic factor (BDNF) concentrations were also measured in parallel as potential biological correlates of recovery. Results Over the study period, we observed no treatment effect across behavioural endpoints in laparotomised mice, including body weight (P=0.157), food intake (P=0.884), locomotor activity (P=0.864), mechanical sensitivity (P=0.628), sucrose preference (P=0.615), and social interaction (P=0.442). Composite QoR scores did not differ between OFA and OBA groups (P=0.569), with no differences in postoperative circulating inflammatory cytokine or BDNF concentrations. Conclusions Dexmedetomidine-based OFA and fentanyl-based OBA produced similar postoperative behavioural recovery, circulating inflammatory cytokine and BDNF trajectories in laparotomised mice receiving multimodal postoperative analgesia.

animal behavior and cognition↗

bioRxiv2026

When DL-Based Prescreening Meets Synthon-Based Docking: Target-Adapting PharmacoNet via MEL-Steered Correction

As chemical libraries expand into the trillions of molecules, Virtual SYNthon Hierarchical Enumeration Screening (V-SYNTHES) has emerged as a leading strategy for making gigascale virtual screening computationally tractable. In V-SYNTHES, a Minimal Enumeration Library (MEL) of chemical fragments is docked against a target first, and only the top-scoring fragments are expanded into full ligands for large-scale docking. However, among the large number of comparably well-docked fragments, only a small fraction can be expanded under a fixed docking budget, leaving most similarly promising fragments unexplored. General-purpose prescreening tools can be adopted to address this constraint, reallocating the same docking budget across a larger pool of fragments' enumerated full ligands by their proxy score. However, such tools are applied without accounting for target-specific pocket environments. One such method, PharmacoNet, predicts interaction hotspots from a protein structure and ranks candidates via graph matching against a fixed set of interaction-type weights. We recognize that V-SYNTHES's initial fragment-docking step, ordinarily used only for selection of best fragments for expansion, already reveals which of these hotspots and interaction types a given pocket actually favors, and we can recover this signal to fine-tune PharmacoNet accordingly. We introduce MEL-Steered PharmacoNet, a parameter-efficient adaptation framework that specializes PharmacoNet to a given target through two composable mechanisms: (i) empirical density-map steering of predicted pharmacophore hotspots, and (ii) empirical fine-tuning of interaction-type scoring weights. Across three structurally distinct GPCR targets (CB2, GPR91, 5-HT2AR), MEL-Steered PharmacoNet achieves substantial enrichment factor (EF100) gains over a random baseline, and improves EF100 over PharmacoNet by 8.94x, 6.87x, and 1.69x, respectively. The fitted per-target weights further reveal distinct, chemically interpretable interaction profiles that PharmacoNet's generic fixed weights fail to capture. These results show that fragment-docking data already generated by the standard V-SYNTHES pipeline can adapt a general-purpose pharmacophore prescreening method to an individual target, significantly improving its performance while retaining its ultra-fast screening ability, with no additional experimental data or model retraining.

bioinformatics↗

bioRxiv2026

Voluntary exercise during weight loss attenuates adipose CD8+ T cell exhaustion that persists after weight regain

BackgroundWeight gain and loss induce adipose CD8+ T cell exhaustion, which persists and may worsen glucose tolerance following weight regain. Because exercise can reduce T cell exhaustion in the blood, we hypothesized that exercise during weight loss would attenuate adipose CD8+ T cell exhaustion and glucose tolerance following weight regain. MethodsMale C57Bl/6J mice were fed low-fat or high-fat diets over 8 to 9-week cycles to generate lean, obese, weight loss, or weight cycled groups. Additional weight loss and weight cycled groups were provided exercise wheels during the weight loss phase. ResultsAs expected, weight loss increased total and exhausted CD8+ T cells by flow cytometry. Mice that ran the most during weight loss had the lowest proportion of exhausted CD8+ T cells. Notably, exercise reduced the proportion of exhausted CD8+ T cells even after the cessation of exercise and weight regain in all mice. However, exercise did not improve glucose tolerance or macrophage inflammation following weight regain. Moreover, exercise did not affect the induction of innate immune memory in adipose macrophages following weight loss. ConclusionThe addition of exercise to a weight loss intervention remarkably reduced exhausted CD8+ T cells in the adipose tissue even after the cessation of exercise and weight regain. While exercise did not affect macrophage inflammation or glucose tolerance following weight regain, these results illuminate new questions about the persistence and mechanisms by which exercise reduces tissue CD8+T cell exhaustion and the direct role of macrophages in modulating glucose tolerance with weight cycling.

immunology↗

bioRxiv2026

Single-nucleus Transcriptomics Reveals Precystic Dysfunction in Polycystic Kidney Disease

Polycystic kidney disease (PKD) is the most common cause of end stage renal disease with a known genetic etiology. This disease is characterized by the progressive development and expansion of kidney cysts. While recent studies have shed light on cell types and states contributing to PKD progression following cyst formation, the biological processes at work prior to cyst formation are relatively unexplored. To better understand mechanisms contributing to cystogenesis, we analyze pre-cystic kidneys from Pkd1R3277C/R3277Cmice across multiple early timepoints, generating a transcriptomic atlas of nearly 1 million single nucleus transcriptomes. Activation of a small subset of genes in a precystic signaling pathway drives changes in both the distal convoluted tubule and proximal tubule cells. This pathway overlaps with a recently described "failed repair" transcriptomic signature despite the lack of clear changes in tissue morphology at these early stages of nascent cystogenesis. We identify Creb5 as a critical driver for cystogenesis. This single cell transcriptomic analysis of nascent cystogenesis reveals previously unrecognized cellular signaling at the earliest assessed points in precystic kidneys and provides a foundation for the development of high definition early diagnostic and therapeutic approaches prior to observable cysts in PKD.

Developmental Biology↗

bioRxiv2026

Polymer-based flexible multi-shank probes for simultaneous intracortical microstimulation and two-photon calcium imaging

Intracortical microstimulation is an essential tool for basic neuroscience and sensory restoration, yet the spatiotemporal effects of advanced multielectrode stimulation strategies on cortical networks remain poorly understood. This study presents a versatile experimental platform featuring polymer-based, flexible multi-shank electrode arrays integrated with a custom high-density neurostimulator. The polyimide penetrating probes contain densely spaced iridium oxide microelectrodes on thin, flexible shanks with sharp tips designed to minimize mechanical mismatch, tissue damage, and brain dimpling. The system was validated in transgenic GCaMP6 mice through simultaneous two-photon calcium imaging and electrical stimulation in layer 2/3 of the visual cortex. Monopolar stimulation reliably evoked robust, parameter-dependent neuronal activation, with neuronal recruitment increasing as a function of current intensity and pulse duration. Bipolar stimulation produced activation patterns distinct from monopolar stimulation, with neuronal responses that were more spatially confined when bipolar electrodes were positioned in close proximity. Furthermore, static current steering--implemented by varying the current ratio between the selected electrode pair--successfully shifted the centroid of activated neuronal populations, enabling fine spatial control over the site of activation. The developed platform provides a flexible framework for investigating local neuronal responses to complex electrical stimulation paradigms, facilitating the development of neural prostheses with higher spatial precision.

neuroscience↗

bioRxiv2026

Designing AI-programmable therapeutics with the EDEN family of foundation models

The ability to interpret, modify, and design DNA has driven many of the most significant advances in modern medicine, from diagnostics, biologics, and vaccines to cell and gene therapies. However, the inherent complexity of biological systems means that most modern medicines are still engineered using bespoke, labor-intensive processes. To address the need for a generalisable and programmable approach to therapeutic design, we introduce the EDEN (environmentally-derived evolutionary network) family of metagenomic foundation models, including a 28 billion parameter model trained on 9.7 trillion nucleotide tokens from BaseData1. This dataset, at the time of training, contained more than 10 billion novel genes from over 1 million new species, and is intentionally enriched for environmental and host-associated metagenomes, phage sequences, and mobile genetic elements, enabling the model to learn from diverse and novel cross-species evolutionary mechanisms and apply them to key challenges in human health. EDEN achieves state-of-the-art performance across a series of predictive and generative genomic and protein benchmarks. To demonstrate the models broad applicability across biology, we evaluate EDENs capacity for programmable therapeutic design by challenging a single architecture to design biological novelty across three distinct therapeutic modalities, disease areas and biological scales: (i) large gene insertion, (ii) antibiotic peptide design, and (iii) microbiome design. First, we demonstrate AI-programmable Gene Insertion (aiPGI), in which EDEN designs de novo large serine recombinases (LSRs) capable of inserting large pieces of DNA at desired target sites in the human genome when prompted only on 30 nucleotides of DNA sequence from the desired target site. In low-N experimental validation, EDEN generated multiple active recombinases for all tested disease-associated genomic loci (ATM, DMD, F9, FANCC, GALC, IDS, P4HA1, PHEX, RYR2, USH2A) and 4 potential safe harbor sites in the human genome. EDEN achieves an overall functional hit rate of 63.2% across diverse DNA prompts when prompted on only 30bp of DNA from outside the training data. 50% of EDEN-generated LSRs were active in human cells, achieving therapeutically relevant levels of CAR insertion in primary human T cells. We also show that EDEN can generate active bridge recombinases when prompted on the associated guide RNA alone, with sequence identities to training and public data as low as 65%. These results pave the way for a new generation of cell and gene therapies by opening the door to rapid, programmable and site-specific integration of large genetic payloads without double-strand breaks. This offers an alternative to the safety, efficiency and payload limitations inherent in viral or nuclease-based editing at thousands of currently intractable human therapeutic targets. Second, we use the same model to generate a focused low-N library of novel antimicrobial peptides where 97% showed activity, with top candidates achieving single-digit micromolar potency against critical-priority multidrug-resistant pathogens. Third, to demonstrate that EDEN captures inter-genomic features, we design a gigabase-scale microbiome with over 94,000 synthetic metagenomic assemblies, including prophage genomes and correct cross-species metabolic pathway completions. The EDEN-generated synthetic microbiome covers 9,067 species with a biome-specific taxonomic accuracy of 99%. Over 1,500 of the generated species were outside the fine-tuning dataset while retaining the correct microecological properties and biome association, thus significantly expanding genetic and taxonomic diversity. Together, these results establish a new strategic direction for AI-programmable therapeutics, in which a single foundation model architecture designs candidate therapeutics across diverse modalities and disease areas. This suggests that the combination of billions of years of evolutionary data with specific therapeutic records offers a clear, scaling-driven path to making therapeutic design a predictable engineering discipline. O_FIG O_LINKSMALLFIG WIDTH=141 HEIGHT=200 SRC="FIGDIR/small/699009v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@68c20borg.highwire.dtl.DTLVardef@19b8e8corg.highwire.dtl.DTLVardef@1ab9362org.highwire.dtl.DTLVardef@1592cb7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

MDS-associated SF3B1 mutations promote aberrant fate choice of hematopoietic stem cell via mis-splicing of mediator kinase module component CDK8

Mutations in RNA splicing factor SF3B1 are among the most common in MDS and are strongly associated with MDS with ring sideroblasts (MDS-RS). While aberrant splicing of terminal erythroid regulators has been implicated in MDS pathogenesis, the impact of SF3B1 mutations on early hematopoietic progenitor function remains unclear. Here, we identify CDK8, a key kinase of the mediator complex involved in transcriptional regulation, as a recurrent mis-spliced target in SF3B1-mutant MDS. Mutant SF3B1 induces cryptic 3' splice site selection in CDK8, leading to loss of CDK8 mRNA and protein. Using primary human HSPCs, our study identifies CDK8 as an important regulator of HSPC homeostasis and cell fate determination. CDK8 depletion results in expansion of HSPCs and shifts differentiation toward the erythroid and myeloid lineages, mirroring phenotypes observed in SF3B1-mutant MDS. Lastly, functional restoration of CDK8 rescues early erythroid phenotypes in SF3B1-mutant cells. These findings implicate CDK8 mis-splicing as a mechanistic driver of altered progenitor fate and dysplasia in SF3B1-mutant MDS, linking aberrant splicing to transcriptional dysregulation and hematopoietic lineage commitment.

cancer biology↗

bioRxiv2025

A small periplasmic protein governs broad physiological adaptations in Vibrio cholerae via regulation of the DbfRS two-component system

Two-component signaling pathways allow bacteria to sense and respond to environmental changes, yet the sensory mechanisms of many remain poorly understood. In the pathogen Vibrio cholerae, the DbfRS two-component system controls the biofilm lifecycle, a critical process for environmental persistence and host colonization. Here, we identified DbfQ, a small periplasmic protein encoded adjacent to dbfRS, as a direct modulator of pathway activity. DbfQ directly binds the sensory domain of the histidine kinase DbfS, shifting it toward phosphatase activity and promoting biofilm dispersal. In contrast, outer membrane perturbations, caused by mutations in lipopolysaccharide biosynthesis genes or membrane-damaging antimicrobials, activate phosphorylation of the response regulator DbfR. Transcriptomic analyses reveal that DbfR phosphorylation leads to broad transcriptional changes spanning genes involved in biofilm formation, central metabolism, peptidoglycan synthesis, and cellular stress responses. Constitutive DbfR phosphorylation imposes severe fitness costs in an infection model, highlighting this pathway as a potential target for anti-infective therapeutics. We find that dbfQRS-like genetic modules are widely present across bacterial phyla, underscoring their broad relevance in bacterial physiology. Collectively, these findings establish DbfQ as a new class of periplasmic regulator that influences two-component signaling and bacterial adaptation.

microbiology↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

Semantic mining of functional de novo genes from a genomic language model

Generative genomics models can design increasingly complex biological systems. However, effectively controlling these models to generate novel sequences with desired functions remains a major challenge. Here, we show that Evo, a 7-billion parameter genomic language model, can perform function-guided design that generalizes beyond natural sequences. By learning semantic relationships across multiple genes, Evo enables a genomic "autocomplete" in which a DNA prompt encoding a desired function instructs the model to generate novel DNA sequences that can be mined for similar functions. We term this process "semantic mining," which, unlike traditional genome mining, can access a sequence landscape unconstrained by discovered evolutionary innovation. We validate this approach by experimentally testing the activity of generated anti-CRISPR proteins and toxin-antitoxin systems, including de novo genes with no significant homology to any natural protein. Strikingly, in-context protein design with Evo achieves potent activity and high experimental success rates even in the absence of structural hypotheses, known evolutionary conservation, or task-specific fine-tuning. We then use Evo to autocomplete millions of prompts to produce SynGenome, a first-of-its-kind database containing over 120 billion base pairs of AI-generated genomic sequences that enables semantic mining across many possible functions. The semantic mining paradigm enables functional exploration that ventures beyond the observed evolutionary universe.

synthetic biology↗

bioRxiv2024

Synaptonemal complex protects double-Holliday junctions during meiosis

Chromosomal linkages formed through crossover recombination are essential for accurate segregation of homologous chromosomes during meiosis1. The DNA events of recombination are linked to structural components of meiotic chromosomes2. Imperatively, the biased resolution of double-Holliday junction intermediates (dHJs) into crossovers3,4 occurs within the synaptonemal complex (SC), the meiosis-specific structure that mediates end-to-end synapsis of homologs during the pachytene stage5,6. However, the SCs roles in crossover-specific dHJ resolution remains unclear. Here, we show that key SC components function through dependent and interdependent relationships to protect dHJs from aberrant "dissolution" into noncrossover products. Conditional ablation experiments reveal that cohesin, the core of SC lateral elements, is required to maintain both synapsis and dHJ-associated crossover recombination complexes (CRCs) during pachytene. The SC central region transverse-filament protein is also required to maintain CRCs. Reciprocally, stability of the SC central region requires the continuous presence of CRCs, thereby coupling synapsis and desynapsis to dHJ formation and resolution. However, dHJ protection and maintenance of CRCs can occur without end-to-end homolog synapsis mediated by the central element of the SC central region. We conclude that local ensembles of SC components are sufficient to enable crossover-specific dHJ resolution and thereby ensure the linkage and segregation of homologous chromosomes.

cell biology↗

bioRxiv2024

Necessity and Impact of Specialization of Large Foundation Model for Medical Segmentation Tasks

BackgroundLarge foundation models, such as the Segment Anything Model (SAM), have shown remarkable performance in image segmentation tasks. However, the optimal approach to achieve true utility of these models for domain-specific applications, such as medical image segmentation, remains an open question. Recent studies have released a medical version of the foundation model MedSAM by training on vast medical data, who promised SOTA medical segmentation. Independent community inspection and dissection is needed. PurposeThis study assesses the performance of off-the-shelf medical foundation model MedSAM for the segmentation of anatomical structures in pelvic MR images. We also evaluate the dependency on prompting scheme and demonstrate the gain of further specialized fine-tuning. MethodsMedSAM and its lightweight version LiteMedSAM were evaluated out-of-the-box on a public MR dataset consisting of 589 pelvic images split 80:20 for training and testing. An nnU-Net model was trained from scratch to serve as a benchmark and to provide bounding box prompts for MedSAM. MedSAM was evaluated using different quality bounding boxes, those derived from ground truth labels, those derived from nnU-Net, and those derived from the former two but with 5-pixel isometric expansion. Lastly, LiteMedSAM was refined on the training set and reevaluated on this task. ResultsOut-of-the-box MedSAM and LiteMedSAM both performed poorly across the structure set, especially for disjoint or non-convex structures. Varying prompt with different bounding box inputs had minimal effect. The mean Dice score and mean Hausdorff distances (in mm) for obturator internus using MedSAM and LiteMedSAM were {0.251 {+/-} 0.110, 0.101 {+/-} 0.079} and {34.142 {+/-} 5.196, 33.688 {+/-} 5.306}, respectively. Fine-tuning of LiteMedSAM led to significant performance gain, improving Dice score and Hausdorff distance for the obturator internus to 0.864 {+/-} 0.123 and 5.022 {+/-} 10.684, on par with nnU-Net with no significant difference in evaluation of most structures. All segmentation structures all benefited significantly from specialized refinement, at varying improvement margin. ConclusionOur study alludes to the potential of deep learning models like MedSAM and lite MedSAM for medical segmentation but also highlight the need for specialized refinement and adjudication: it is quite likely that off-the-shelf use of such large foundation models may be suboptimal, and specialized fine-tuning can significantly enhance segmentation accuracy.

bioengineering↗

bioRxiv2024

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

synthetic biology↗

bioRxiv2023

Single cell spatial transcriptomic profiling of childhood-onset lupus nephritis reveals complex interactions between kidney stroma and infiltrating immune cells.

Children with systemic lupus erythematosus (SLE) are at increased risk of developing kidney disease, termed childhood-onset lupus nephritis (cLN). Single cell transcriptomics of dissociated kidney tissue has advanced our understanding of LN pathogenesis, but loss of spatial resolution prevents interrogation of in situ cellular interactions. Using a technical advance in spatial transcriptomics, we generated a spatially resolved, single cell resolution atlas of kidney tissue (>400,000 cells) from eight cLN patients and two controls. Annotated cells were assigned to 35 reference cell types, including major kidney subsets and infiltrating immune cells. Analysis of spatial distribution demonstrated that individual immune lineages localize to specific regions in cLN kidneys, including myeloid cells trafficking to inflamed glomeruli and B cells clustering within tubulointerstitial immune hotspots. Notably, gene expression varied as a function of tissue location, demonstrating how incorporation of spatial data can provide new insights into the immunopathogenesis of SLE. Alterations in immune phenotypes were accompanied by parallel changes in gene expression by resident kidney stromal cells. However, there was little correlation between histologic scoring of cLN disease activity and glomerular cell transcriptional signatures at the level of individual glomeruli. Finally, we identified modules of spatially-correlated gene expression with predicted roles in induction of inflammation and the development of tubulointerstitial fibrosis. In summary, single cell spatial transcriptomics allows unprecedented insights into the molecular heterogeneity of cLN, paving the way towards more targeted and personalized treatment approaches.

immunology↗

bioRxiv2023

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90,000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

neuroscience↗

bioRxiv2023

A hybrid RNA FISH immunofluorescence protocol on Drosophila polytene chromosomes

ObjectivesInvestigating protein-DNA interactions is imperative to understanding fundamental concepts such as cell growth, differentiation, and cell development in many systems. Sequencing techniques such as ChIP-seq can yield genome-wide DNA binding profiles of transcription factors; however this assay can be expensive, time-consuming, may not be informative for repetitive regions of the genome, and depend heavily upon antibody suitability. Combining DNA fluorescence in situ hybridization (FISH) with immunofluorescence (IF) is a quicker and inexpensive approach which has historically been used to investigate protein-DNA interactions in individual nuclei. However, these assays are sometimes incompatible due to the required denaturation step in DNA FISH that can alter protein epitopes, hindering primary antibody binding. Additionally, combining DNA FISH with IF may be challenging for less experienced trainees. Our goal was to develop an alternative technique to investigate protein-DNA interactions by combining RNA FISH with IF. ResultsWe developed a hybrid RNA FISH and IF protocol for use on Drosophila melanogaster polytene chromosome spreads in order to visualize colocalization of proteins and DNA loci. We demonstrate that this assay is sensitive enough to determine if our protein of interest, Multi-sex combs (Mxc), localizes to single-copy target transgenes carrying histone genes. Overall, this study provides an alternative, accessible method for investigating protein-DNA interactions at the single gene level in Drosophila melanogaster polytene chromosomes.

genetics↗

bioRxiv2023

The mitochondrial regulation of smooth muscle cell proliferation in type 2 diabetes

BackgroundType 2 diabetes (T2D) is associated with a strongly increased risk for restenosis after angioplasty driven by proliferation of vascular smooth muscle cells (VSMCs). Here, we sought to determine whether and how mitochondrial dysfunction in T2D drives VSMC proliferation with a focus on ROS and intracellular [Ca2+] that both drive cell proliferation, occur in T2D and are regulated by mitochondrial activity. MethodsUsing a diet-induced mouse model of T2D, the inhibition of the mitochondrial Ca2+/calmodulin-dependent kinase II (mtCaMKII), a regulator of Ca2+ entry via the mitochondrial Ca2+ uniporter selectively in VSMCs, we performed in vivo phenotyping after mechanical injury and established the mechanisms of excessive proliferation in cultured VSMCs. ResultsIn T2D, the inhibition of mtCaMKII reduced both neointima formation after mechanical injury and the proliferation of cultured VSMCs. VSMCs from T2D mice displayed accelerated proliferation, reduced mitochondrial Ca2+ entry and membrane potential with elevated baseline [Ca2+]cyto compared to cells from normoglycemic mice. Accelerated proliferation after PDGF treatment was driven by activation of Erk1/2 and its upstream regulators. Hyperactivation of Erk1/2 was Ca2+-dependent rather than mitochondrial ROS-driven Ca2+-dependent and included the activation of CaMKII in the cytosol. The inhibition of mtCaMKII exaggerated the Ca2+ imbalance by lowering mitochondrial Ca2+ entry and increasing baseline [Ca2+]cyto, further enhancing baseline Erk1/2 activation. With inhibition of mtCaMKII, PDGF treatment had no additional effect on cell proliferation. Inhibition of activated CaMKII in the cytosol decreased excessive Erk1/2 activation and reduced VSMC proliferation. ConclusionsCollectively, our results provide evidence for the molecular mechanisms of enhanced VSMC proliferation after mechanical injury by mitochondrial Ca2+ entry in T2D.

molecular biology↗