Biology subjects

Jin, K.

Publications and source records attributed to Jin, K..

At least 19 recordsLinked to original sources

bioRxiv2026

MERFISH-based pooled genetic screening reveals cell non-autonomous phenotypes in intact tissue

Genetic perturbations can reshape both the state of a targeted cell and the behavior of neighboring cells. Measuring these effects systematically in intact tissues requires pooled screening methods that jointly identify perturbations, molecular phenotypes, and spatial context of individual cells. Here, we report an imaging-based pooled screening method that detects perturbation identities and endogenous RNAs without enzymatic amplification. Relative to our previous enzymatic-amplification-based workflow, amplification-free MERFISH detection with direct hybridization increased recovery of perturbation barcodes by ~10-fold and endogenous transcripts by 2.5-fold, improving perturbation assignment as well as molecular profiling of both perturbed and neighboring cells. Applying this method to a genetic mosaic mouse liver screen targeting ~200 genes in hepatocytes, we profiled 1.5 million cells in intact tissues and mapped both cell-autonomous responses of perturbed hepatocytes and cell non-autonomous changes in neighboring immune and vascular populations. These included localized accumulation of pDC-like cells near hepatocytes with impaired V-ATPase function and angiogenic responses in vascular cells near Vhl-perturbed hepatocytes with hypoxia activation. Our method provides a simplified, scalable approach to linking genetic perturbations with cell state, multicellular response, and tissue organization.

genomics↗

bioRxiv2026

Rac1 and CHK1 Converge on Abi-1 to Regulate DNA Repair Dependency and Treatment Response in Human Cancers

Radiation therapy (RT) resistance remains a major clinical challenge, yet biomarkers guiding precision radiosensitization are lacking. We previously demonstrated that Rac1 promotes RT resistance in glioblastoma (GBM) by inducing Abi-1-S323 dephosphorylation and enhancing non-homologous end joining (NHEJ). Here, we identify Abi-1-S323 as a key regulator of DNA repair states and a determinant of therapeutic efficacy in human cancers. Clinically, loss of Abi-1-S323 phosphorylation was associated with poor outcomes in patients with RT-treated GBM. Bioinformatic analyses revealed that non-small cell lung cancer (NSCLC) and head and neck cancer (HNC) are among the cancers with frequent RAC1 amplification, suggesting that these tumor types may have increased Rac1-Abi-1 signaling activity. Loss of Abi-1-S323 phosphorylation also predicted poor outcomes in patients with RT-treated HNC. Consistent with these clinical observations, high Rac1 activity and low Abi-1-S323 phosphorylation were associated with enhanced DNA double-strand break repair and radioresistance in NSCLC and HNC models, whereas genetic or pharmacological inhibition of this signaling impaired DNA repair and radiosensitized tumors in vitro and in vivo. Mechanistically, we identified CHK1 as a kinase that phosphorylates Abi-1 at S323 and defines an alternative homologous recombination (HR)-dependent repair state. Tumors with high Rac1-Abi-1 signaling exhibited elevated NHEJ capacity and were selectively radiosensitized by Rac1 inhibition, whereas tumors with low Rac1-Abi-1 signaling displayed high CHK1 activity, preferentially relied on HR, and were selectively radiosensitized by CHK1 inhibition. These findings establish Abi-1-S323 as a biomarker defining therapeutically distinct DNA repair states and provide a framework for precision radiosensitization.

cancer biology↗

bioRxiv2026

AINN-Express: A Leakage-Aware, Sequence-Only Predictor of VHH Antibody Expression Built on the AINN-P1 Protein Foundation Model

Expression -- whether an antibody can be produced at usable yield -- is one of the earliest and most expensive filters in therapeutic discovery. We present AINN-Express, a sequence-only predictor of VHH single-domain antibody (nanobody) expression built on AINN-P1, Ainnocences protein foundation model. AINN-Express encodes a VHH with a frozen AINN-P1 encoder and scores it with a lightweight gradient-boosted classifier: it takes only an amino-acid sequence, returns an expression probability, and needs no structure and no per-task model training. Under a leakage-safe, leave-program-out evaluation, AINN-Express reaches ROC-AUC 0.87 within known antibody programs and 0.81 on entirely new programs -- well above the majority baseline -- making it a practical tool for prioritizing candidates before wet-lab work. We further justify the encoder choice with a controlled, leakage-aware benchmark against general-purpose protein language models: on this task, AINN-P1 (167 M parameters) generalizes to unseen programs far better than a general-purpose ESM2 (650 M) -- 0.81 versus 0.68 new-program ROC-AUC -- and matches a domain-finetuned ESM2 (0.83) with no task-specific finetuning, at roughly one-quarter of the parameters. The gap is invisible under a random split, where all encoders score [~]0.88; only leave-program-out evaluation reveals that general-purpose embeddings largely encode program identity rather than transferable determinants of expression. Purpose-built representation quality, not parameter count, is what makes AINN-Express generalize.

bioinformatics↗

bioRxiv2026

Frozen Protein Foundation-Model Embeddings Improve Antibody-Antigen ΔΔG Ranking

We investigate whether representations from AINN-P1--a protein foundation model trained autoregressively on tens of millions of natural protein sequences--transfer to the task of ranking antibody-antigen pairs by binding affinity. Casting affinity maturation as a learning-to-rank problem over the change in binding free energy ({Delta}{Delta}G), we compare a task-specific sequence model trained end-to-end from scratch against lightweight downstream heads built on top of frozen AINN-P1 embeddings, all evaluated under an identical five-fold cross-validation protocol. A regularized linear probe on the frozen embeddings already surpasses the from-scratch baseline, and an optimized lightweight head raises the mean Spearman rank correlation from 0.42 to 0.53--a relative improvement of approximately 28%-- while training in seconds and without any fine-tuning of the foundation model. Because a linear probe alone exceeds the fully trained end-to-end baseline, the gain is attributable to representation quality rather than to added downstream-model capacity. These results position frozen foundation-model embeddings as a strong, data-efficient default for affinity ranking in antibody engineering and establish a conservative lower bound that task-adaptive fine-tuning is expected to exceed.

bioinformatics↗

bioRxiv2026

Conserved Cell Type Signatures Across the Brainstem and Spinal Cord in the Mouse Central Nervous System

Understanding how cell types are organized across the central nervous system (CNS) is key to uncovering neural function. Here, we integrate single-nucleus Multiome (RNA+ATAC) sequencing, spatial transcriptomics, and computational analyses to map conserved cell type signatures in the adult mouse brainstem and spinal cord. We identify a shared core of neuronal and non-neuronal cell types, alongside region-specific specializations reflecting distinct functions. Spatial data reveal conserved cellular niches across the brainstem-spinal cord boundary, indicating a continuous organizational logic. Cross-region comparisons uncover recurrent gene expression modules and signaling programs that may support shared circuit features. Chromatin accessibility profiling highlights cell-type-specific regulatory programs and implicates Hox transcription factors in positional identity. Notably, cell-type and positional identities are largely orthogonal, with varying regional influence across neuronal classes: motor neurons show strong positional coupling, whereas glutamatergic and GABAergic interneurons show minimal entrainment. This work provides a reference for the shared molecular architecture of these CNS regions.

neuroscience↗

bioRxiv2026

AM-DMF-SCP-pro: An Ultrafast and Streamlined Nanolitre-Scale Lab-on-a-Chip Platform for Single-Cell Proteomics and Tumor Microenvironment Profiling

Single-cell proteomics (SCP) remains constrained by slow sample preparation and background interference in low-input, multistep workflows, limiting its broader practical use in complex biological samples. Here we report AM-DMF-SCP-pro, a streamlined active-matrix digital microfluidic workflow that enables rapid on-chip lysis and digestion in 6 nL droplets within a closed nanolitre-confined microenvironment. AM-DMF-SCP-pro reduces total sample-preparation time to as little as 30 min, including a 15 min digestion step, while maintaining quantitative reproducibility and digestion performance. Systematic optimization of mass spectrometry acquisition and nanolitre-scale digestion conditions enables deeper, more sensitive and lower-background proteome profiling from ultra-low peptide inputs. In murine tumor samples, AM-DMF-SCP-pro reliably resolves immune and tumor compartments, identifies treatment-associated tumor cell states and enables quantitative analysis of neutrophil-associated proteomic signatures. These results establish AM-DMF-SCP-pro as a practical workflow for rapid, low-background single-cell proteome profiling in complex biological samples.

systems biology↗

bioRxiv2026

Gut microbiome-dependent IL-1 signaling is a mediator of ACVR1R206H-driven heterotopic ossification

Inflammatory diseases cause significant morbidity and mortality, but their pathobiology is often difficult to dissect due to complex genetic-environmental interactions. Genetic forms of heterotopic ossification, such as fibrodysplasia ossificans progressiva (FOP), reduce genetic variability, allowing careful dissection of non-genetic drivers of inflammation. While >95% of FOP patients harbor the ACVR1R206H mutation, patients exhibit significant variability in disease progression, suggesting a role of environmental drivers. Here, we identify the gut microbiome as a regulator of inflammation-driven HO in FOP. Metagenomic profiling of cohabitating FOP/unaffected sibling pairs revealed a pathogenic gut microbiome profile in FOP patients (Bray-Curtis, p < 0.05). In Pdgfr-Cre/Acvr1R206H (FOP) mice, gut microbiome ablation by antibiotics reduced spontaneous HO formation (47.4% reduction, p < 0.05) and reduced plasma IL-1 pathway activity. IL-1{beta} blockade in FOP mice suppressed trauma-induced HO formation. These findings identify a gut microbiome-IL-1-HO axis with modifiable targets for developing treatments for HO and related inflammatory conditions. One Sentence SummaryAntibiotic disruption of the gut microbiome reduces HO in FOP mice via an IL-1 mediated pathway.

immunology↗

bioRxiv2026

AINN-P1: A Compact Sequence-Only Protein Language Model Achieves Competitive Fitness Prediction on ProteinGym

Protein language models (PLMs) are increasingly central to protein engineering and drug discovery. Many high-performing systems, however, rely on large parameter counts, multiple sequence alignments (MSAs), explicit structural inputs, or computationally intensive attention mechanisms, limiting their accessibility and throughput. Here we present AINN-P1, a 167M-parameter protein language model trained exclusively on raw UniRef amino-acid sequences using an autoregressive next-token prediction objective. AINN-P1 employs a multiplicative LSTM (mLSTM) architecture--an attention-free, recurrent design that scales linearly with sequence length and avoids growing key-value caches during inference. We evaluate AINN-P1 on ProteinGym fitness prediction tasks spanning activity, binding, expression, and stability using a frozen-encoder protocol with lightweight few-shot regression heads. Under this protocol, AINN-P1 achieves an average Spearman{rho} of 0.441 across four task categories and a Spearman{rho} of 0.625 on stability--the highest among sequence-only models in our comparison set. Because our evaluation uses few-shot supervised regression rather than the zero-shot scoring employed by most ProteinGym leaderboard baselines, direct numerical comparison requires caution; we discuss this methodological distinction throughout. Beyond benchmark performance, AINN-P1 emphasizes practical deployability: its recurrent architecture avoids quadratic memory scaling, supports fixed-state inference on long sequences, and enables rapid adaptation through frozen embeddings rather than costly end-to-end fine-tuning. We discuss when sequence-only models are sufficient when structural information remains beneficial and how compact foundation models can serve as efficient front-end filters in drug discovery workflows.

bioinformatics↗

bioRxiv2026

Metabolic adaptation to maternal hyperglycemia via ACLY-dependent acetyl-CoA production drives epigenetic remodeling and dysregulated placental development

AbstractsGestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy that is paradoxically associated with both fetal overgrowth and fetal growth restriction (FGR). While maternal hyperglycemia is widely presumed to drive macrosomia through excessive nutrient supply, the mechanisms underlying FGR remain poorly understood. Here, using a mouse model that recapitulates the small-for-gestational-age (SGA) phenotype observed in human GDM pregnancies, we identify placental underdevelopment as a principal driver of FGR. Despite systemic nutrient abundance, hyperglycemic placentas exhibit reduced mass and an increased fetal-to-placental weight ratio, indicative of placental insufficiency. Mechanistically, maternal hyperglycemia induces anabolic metabolic rewiring while suppressing oxidative phosphorylation (OXPHOS), accompanied by upregulation and nuclear redistribution of ATP-citrate lyase (ACLY). ACLY converts glucose-derived carbon into acetyl-CoA in the cytosol and nucleus, thereby coupling glycolytic flux to lipid and hexosamine biosynthesis as well as to global histone hyperacetylation. This hyperacetylation-associated epigenetic reprogramming activates metabolic, innate immune, and inflammatory gene programs while repressing pro-proliferative and anti-apoptotic pathways. Consequently, placental growth is compromised despite nutrient excess. Importantly, activation of the ACLY-acetyl-CoA axis and global histone hyperacetylation is consistently observed in human GDM placentas across diverse birth outcomes, suggesting a conserved metabolic-epigenetic adaptation to maternal hyperglycemia. Together, these findings identify ACLY-dependent acetyl-CoA production as a central metabolic node linking maternal hyperglycemia to chromatin remodeling and placental development control, thereby reshaping fetal growth trajectories.

developmental biology↗

bioRxiv2026

SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.

cell biology↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

Virtual spatial transcriptomics (ST) methods predict gene expression or cell types from histology images, extending molecular readouts beyond the limited regions or samples directly measured by ST platforms. However, the statistical reliability of these predictions remains unclear. Here, we present UTOPIA, a model-agnostic framework for multiscale confidence quantification in virtual ST. UTOPIA assigns statistically calibrated confidence scores to predictions across spatial resolutions and biological granularities, ranging from single genes to metagenes and from specific cell types to broader cell classes. UTOPIA controls false discovery rates for detecting genes, metagenes, or cell types while accounting for local tissue context. We show that prediction confidence depends critically on both spatial resolution and biological granularity, with reliable inference often emerging only at coarser, biologically meaningful scales. Across multiple ST platforms and in both in-sample and out-of-sample settings, UTOPIA enhances interpretability, prevents false biological conclusions, and enables more trustworthy downstream analyses of virtual ST.

genomics↗

bioRxiv2026

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Understanding spatial organization of cells is critical for deciphering tissue function and disease. Single-cell RNA-sequencing (scRNA-seq) profiles transcriptomes at scale but loses spatial context, while spatial transcriptomics (ST) preserves spatial information but is constrained by cost and gene coverage. Here, we present REMAP, a deep learning framework that integrates gene expression with neighborhood-level gene-gene covariance to reconstruct multi-scale spatial organization of scRNA-seq data using one or multiple ST references. Across 2D and 3D mouse brain, human fetal cortex, and seven human cancer types, REMAP consistently outperformed existing approaches. Applied to a human multiple sclerosis atlas, REMAP resolved microglial neighborhood heterogeneity, and identified a rare pro-inflammatory microglia-astrocyte subpopulation. Across diverse cancers, REMAP recovered conserved spatially-defined cancer-associated fibroblast subtypes with known prognostic significance. By transforming cost-efficient single-cell datasets into spatially interpretable tissue maps, REMAP enables spatial hypothesis generation, microenvironment discovery, and population-scale inference of conserved and perturbed architectural principles in human disease.

genomics↗

bioRxiv2025

Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia.

Anesthesia recovery is critical for resuming normal physiological and neuronal functions; however, the mechanisms involved remain elusive. Here, we identify a female-selective corticosterone-mediated microglia-neuron interaction in vivo during ketamine anesthesia recovery, absent in males. This microglia-neuron interaction induces plastic and functional neuronal changes, as evidenced by increased spine density and mEPSC frequency, which is occluded upon microglia depletion. We show that this process is driven through upregulation of the stress-responsive co-chaperone Fkbp5 mRNA and its protein, FKBP51, in female microglia. Fkbp5/FKBP51 is a key intermediary in a corticosteroid-induced stress response, and its involvement points towards a critical interface between endocrine signaling and microglia. Thus, to counteract the observed KXA-mediated corticosterone increase in the blood, we remove the primary source of corticosterone through adrenalectomy. Close microglia-neuron interaction was absent, but was reinstated after corticosterone injection. Our findings offer a new mechanism of microglia-mediated neuronal plasticity during anesthesia recovery, which is mediated through corticosterone, enhancing our understanding of sex differences in brain function.

neuroscience↗

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

Evidence for hierarchical representations of written and spoken words from an open-science human neuroimaging dataset

Reading and speech recognition rely on multi-level processing that builds from basic visual or sound features to complete word representations, yet details of these processing hierarchies (in particular those for spoken words) are still poorly understood. We re-analyzed the functional magnetic resonance imaging (fMRI) data provided in the Mother Of all Unification Studies (MOUS) open-science dataset by using parametric regressions of word frequency and sublexical unit (bigram or syllable) frequency during reading and speech listening tasks in order to elucidate lexical processing hierarchies in the visual and auditory modalities. We first validated our approach in the written word domain, where the technique identified significant correlations for word frequency in the left mid-fusiform cortex (at the location of the Visual Word Form Area) with a left occipital region tracking bigram frequency, compatible with prior reports. During listening, low-frequency spoken words elicited greater responses in a left mid-superior temporal region consistent with the recently-described Auditory Word Form Area (AWFA), while a more posterior region of the superior temporal gyrus was sensitive to syllable frequency. Activation in the left inferior frontal gyrus correlated with both written and spoken word frequency. These findings demonstrate parallel hierarchical organizations in the anteroventral visual and auditory streams, with modality-specific lexica and upstream sublexical representations that converge in higher-order language areas.

neuroscience↗

bioRxiv2025

High-throughput Single-cell Proteomics Enabled by an Integrated Hyperplexing and Automatic Labelling Approach without Fractionation

Single-cell proteomics (scProteomics) enables comprehensive analysis of protein composition, expression and functions at the single-cell level. While label-free techniques have generated promising results, achieving high-throughput scProteomics remains a substantial challenge. In this study, we present an approach that increases scProteomics throughput by approximately 30-fold through the true integration of isobaric tags (IBT) with tandem mass tags (TMT), a method we term Integral-Hyperplex. Due to remarkable differences in signal response between TMT16 and IBT16 reporter ions, we implemented two normalization strategies to ensure accurate and integrated quantification: (1) the inclusion of a normalization channel in both IBT and TMT groups to serve as internal standards; and (2) the generation of protein-specific conversion factors between IBT16 and TMT16 signals. We validated the quantification accuracy of the Integral-Hyperplex method by labeling HeLa digests at varying ratios. Furthermore, we demonstrated fully automated labeling on the active-matrix digital microfluidics chip, consuming only 12 nL of labeling reagent per single cell and with a reaction volume as low as 20 nL. Our approach was validated through proof-of-principle quantification experiments across three types of single cells. Approximately 2,000 protein groups were quantified per cell, and the three cell types can be well clustered, confirming the reliability of the Integral-Hyperplex approach. Approximately 300 samples can be analyzed per day due to insufficient resolution of timsTOF SCP. Throughput can be further enhanced using TMT32, additional labeling reagents, and more advanced mass spectrometers such as Orbitrap Astral and timsTOF Ultra 2, and our approach is theoretically capable of analyzing up to [~]2,000 single cells per day.

biochemistry↗

bioRxiv2025

Desmoplakin Haploinsufficiency Underlies Cell-Cell Adhesion Failure in DSP Cardiomyopathy and is Rescued by Transcriptional Activation

BackgroundTruncating variants in desmoplakin (DSPtv), are a leading cause of arrhythmogenic cardiomyopathy (ACM), often presenting with early fibrosis and arrhythmias disproportionate to systolic dysfunction. DSP is critical for cardiac mechanical integrity, linking desmosomes to the cytoskeleton to withstand contractile forces. While loss-of-function is implicated, direct evidence, both for DSP haploinsufficiency in human hearts and for the impact of mechanical stress on cardiomyocyte adhesion, has been limited, leaving the pathogenic mechanism unclear. MethodsWe analyzed explanted human heart tissue from patients with DSPtv (N=3), titin truncating variants (TTNtv, N=5), and controls (N=5) using RNA-sequencing and mass spectrometry. We generated human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) harboring patient-derived or CRISPR-Cas9 engineered DSPtv to model a range of DSP expression levels. Using a 2D cardiac muscle bundle (CMB) platform enabling live visualization of cell junctions, we developed an assay to assess cell-cell adhesion upon heightened contractile stress in response to the contractile agonist endothelin-1. CRISPR-interference (CRISPRi) was used to confirm the role of DSP loss, and CRISPR-activation (CRISPRa) was tested for therapeutic rescue. ResultsCompared to both control and TTNtv hearts, DSPtv human hearts exhibited reduced DSP at both the mRNA and protein level, as well as broadly disrupted desmosomal stoichiometry. Transcriptomic and proteomic analyses implicated cell adhesion, extracellular matrix, and inflammatory pathways. iPSC-CM models recapitulated DSP haploinsufficiency and desmosomal disruption. DSPtv CMBs showed normal baseline contractile function. However, they displayed marked cell-cell adhesion failure with contractile stress (75% failure vs. 8% in controls, p<0.001). Adhesion failure was prevented by the myosin inhibitor, mavacamten. CRISPRi-mediated DSP knockdown replicated this susceptibility to adhesion failure. Conversely, CRISPRa robustly increased DSP expression and rescued cell-cell adhesion failure in DSPtv CMBs (9% failure post-CRISPRa, p<0.001 vs. un-treated). Rescue occurred even when only the DSPII isoform was upregulated in a model with biallelic DSP transcript 1 loss of function. ConclusionsDSP haploinsufficiency is the major cause of DSP cardiomyopathy with a primary consequence of conferring vulnerability to cardiomyocyte cell-cell adhesion failure under heightened contractile stress. Transcriptional activation of DSP reverses this defect in preclinical models, establishing proof-of-concept for a potential therapeutic strategy in DSP cardiomyopathy.

genetics↗