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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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17 recordsLinked to original sources

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗

bioRxiv2026

Cell-type separability predicts annotation accuracy and outweighs algorithm choice: a factorial benchmark across seven scRNA paradigms

Automated cell-type annotation is a prerequisite for most single-cell RNA-sequencing (scRNA-seq) analyses, but the rapid proliferation of methods spanning marker-based, correlation-based, classical machine-learning, deep-learning, semi-supervised, large-language-model (LLM), and transformer foundation-model paradigms has outpaced head-to-head evaluation. Existing benchmarks rely on convenience samples of real datasets in which cell count, class imbalance, cell-type number, and differential-expression strength co-vary uncontrollably, precluding causal attribution of performance to any dataset property. To resolve this, we benchmarked 63 tools across seven paradigms using a Taguchi L9(34) orthogonal array that varies four dataset properties independently, progressively reconfiguring experimental control across five phases: fully controlled simulation, within-platform and cross-platform real-data validation, database-connected and LLM-based annotation under ontology-aware scoring, and fine-tuned foundation models. Using standardized oracle inputs and Cohen's {kappa}, we found that, within the ranges tested, the major paradigms achieved comparable accuracy. Accuracy was predicted near-linearly by the separability of cell types in a shared expression embedding, measured as k-nearest-neighbor (kNN) purity, a relationship that held across sequencing platforms and in fine-tuned foundation models. We attributed the vast majority of {kappa} variance to dataset structure and only a small share to tool identity. Computational cost traded against workflow accessibility rather than accuracy: accessible correlation-based and LLM-based approaches performed competitively, while foundation models matched them only after fine-tuning. Because our oracle design isolates algorithmic capability from upstream noise, these results reframe how methods should be selected: the field's near-term gains lie in strengthening infrastructure--prioritizing tool accessibility, standardized evaluation, and robustness to pipeline variation.

bioinformatics↗

bioRxiv2026

Ex vivo human tumor slices more accurately predict patient responses to an oncolytic virus than in vivo mouse models

Immunotherapies, including oncolytic viruses (OV), are promising therapies that can enhance anti-tumor immune responses. However, preclinical success of immunotherapies in mouse models has not always translated to clinical benefit in cancer patients. This study compared preclinical efficacy and mechanism of action for ASP9801, a vaccinia virus expressing IL-7 and IL-12, using mouse models of colorectal cancer (CRC) in vivo and in human organotypic tumor slice models ex vivo. The murine surrogate for ASP9801 significantly reduced tumor volumes in treated and abscopal tumors in two different CRC models in vivo (MC38 and RO100). Treatment efficacy was accentuated when combined with anti-PD1 treatment, and single-cell RNA sequencing analysis revealed depletion of tumor cells and increased T cell infiltration and activation in both treated and abscopal tumors. However, human tissue analysis ex vivo (E-slices) using PDX models and patient samples showed that ASP9801 is not effective in CRC, consistent with clinical trial results. On the other hand, ASP9801 was highly effective in GBM, indicating indication-specific efficacy of ASP9801, and how E-slice assays can be used to identify treatment-sensitive indications. This study demonstrates the superiority of E-slices over mouse models for predicting clinical response and its utility in planning clinical trials.

cancer biology↗

bioRxiv2026

Compression Sequencing enables ultra-sensitive and scalable scRNA-seq

Current sequencing methods are inefficient and bottlenecked by repeated sampling of highly abundant molecules, which dominate sequencing reads, limit assay throughput and sensitivity for rare targets. For example, single-cell RNA sequencing (scRNA-seq) can profile up to millions of cells, but remains severely constrained by sequencing cost, resulting in shallow gene coverage and high dropout rate. Here we report an information science-inspired method, Compression Sequencing, that tackles this fundamental inefficiency and enables highly improved (>100x) sequencing power. Our method works by performing an accurate and unbiased logarithmic transform on molecular abundances over a wide (5 logs) dynamic range, thus suppressing high-abundance targets and enriching rare ones, while maintaining quantitative accuracy. Applied to scRNA-seq libraries, our method allows ultra-sensitive detection of low-abundance transcripts (2-5x more UMIs), ultra-low sequencing cost (200x reduction), preserves accurate cell types and differential expression analysis over a 500-2,000 gene panel. In AML clinical samples, Compression Sequencing reproduces clinical diagnosis and additionally allows transcriptomic profiling at affordable cost (est. $10 per sample). Our approach thus enables ultra-sensitive and scalable single-cell analysis for large-scale functional genomics studies, drug discovery screens, AI cell model training, as well as affordable single-cell disease diagnostics.

bioengineering↗

bioRxiv2026

Microbial valerate is associated with CAR T dysbiosis and its supplementation enhances CAR T function in B-cell lymphoma

Anaerobe-depleting antibiotic exposure is associated with inferior progression-free survival after CD19 CAR T-cell therapy in large B-cell lymphoma, yet the cellular mechanisms linking gut dysbiosis to the CAR T-cell product and whether this imprint is reversible have remained undefined. In two independent CAR-T candidate cohorts, low stool valerate at the time of CAR-T eligibility identified a multi-metabolite-deficient dysbiotic gut microbiome state marked by depletion of fiber-fermenting commensals and loss of carbohydrate-fermentation, SCFA-biosynthesis, and amino-acid metabolism pathways. Reanalysis of single-cell RNA sequencing from 42 lymphoma patients stratified by piperacillin-tazobactam/imipenem/meropenem (PIM) exposure revealed that PIM-exposed CAR T-cell products were CD4-skewed, with significantly elevated AP-1/immediate-early gene (IEG) and cellular activation signatures that together predicted inferior progression-free survival. Ex vivo conditioning of CAR T-cells with valerate produced a chromatin and transcription factor program distinct from butyrate or propionate, characterized by KLF/SP/EGR family engagement, KLF4 promoter opening, and broad induction of AP-1/IEG and MHC class II transcripts, whereas butyrate drove broader chromatin remodeling with TBX21/EOMES/NF-{kappa}B gains and KLF2 promoter closure, and propionate induced an NFY-centered program with preferential commitment to low-mitochondrial-content states. Untargeted metabolomics confirmed valerate uptake and mitochondrial {beta}-oxidation in CAR T-cells, while dietary sodium valerate supplementation in meropenem-treated mice bearing A20 lymphoma significantly reduced tumor burden and extended survival compared with CAR T-cells alone. These findings identify stool valerate as a bedside-deployable biomarker of dysbiosis-imprinted CAR T-cell dysfunction and support ex vivo or dietary valerate supplementation as a clinically tractable strategy to improve CAR-T anti-tumor function in patients with disrupted gut microbiomes.

cancer biology↗

bioRxiv2026

Distinct roles for partially redundant transcription factors in Caenorhabditis elegans mesoderm lineage development

Developmental transcription factors often have overlapping functions, making it difficult to define the distinct roles of individual factors during lineage specification. We investigated the partially redundant transcription factors TBX-35 and CEH-51 in the Caenorhabditis elegans embryonic MS mesodermal lineage using 4D lineage tracing, reporter imaging, genetics, and single-cell RNA sequencing. In tbx-35 mutants, MS descendants showed progressively slower cell cycles and a division pattern that increasingly resembled the cousin C lineage. Fate-regulator expression also shifted toward C-like features, including ectopic pal-1 and expanded HLH-1 expression, although mutant cells did not simply adopt normal C-lineage positions. Loss of tbx-35 also impaired a later MS-dependent Notch induction in the AB lineage while leaving an earlier induction intact. CEH-51 showed a different pattern of activity whereby its protein became enriched in anterior MS daughters, and ceh-51 mutants produced later, more restricted lineage defects that were strongest in descendants of cells with higher CEH-51 levels. Single-cell profiling identified overlapping but nonidentical sets of genes dependent on the two factors. TBX-35-dependent changes were strongest at earlier stages, whereas CEH-51-dependent genes became more prominent later and were enriched in anterior MS sublineages. Finally, temperature-shift experiments determined that the severity and onset of tbx-35 mutant phenotypes depend on the maternal temperature environment and cannot be explained by differences in residual CEH-51 expression. These findings reveal that TBX-35 and CEH-51 contribute differently across the MS lineage and that reliable mesoderm development is supported by overlapping zygotic and maternal regulatory inputs.

developmental biology↗

bioRxiv2026

Genetic Disruption at the CIP2A Locus Modulates T Cell Responses and Attenuates Experimental Autoimmune Encephalomyelitis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) driven by pathogenic T cell-mediated inflammation. Fingolimod (FTY720), an approved therapy for MS, is an established activator of protein phosphatase 2A (PP2A). However the contribution of PP2A in autoimmune neuroinflammation remains incompletely understood. Here, we addressed this question using experimental autoimmune encephalomyelitis (EAE), a murine model of MS, in mice carrying a genetic disruption of the locus encoding cancerous inhibitor of protein phosphatase 2A (CIP2A), an endogenous inhibitor of PP2A. Mice with disruption of the CIP2A locus, the knock out (KO) mice, exhibited attenuated EAE severity compared with wild-type (WT) controls. Histological and flow-cytometric analyses revealed markedly reduced infiltration of mononuclear cells, including CD4 and CD4CXCR6 encephalitogenic T cells, in the CNS of diseased KO mice. Reduced numbers of these T cell populations were also observed in peripheral lymphoid organs of the Cip2a-deficient mice during EAE, while T cell abundance was comparable under steady-state conditions, suggesting impaired activation-induced expansion rather than altered homeostasis or migration. Single-cell RNA sequencing of CNS and lymph node immune cells revealed changes in cell-type abundance and gene expression. Notably, Il17a expression was reduced in CNS CD8+ T cells and showed a similar trend in {gamma}{delta} T cells. Together, our findings reveal that genetic disruption at the CIP2A locus attenuates EAE, possibly by limiting the expansion and accumulation of encephalitogenic T cell populations in CNS. These results identify the CIP2A locus as a previously unrecognized regulator of T cell-driven autoimmune neuroinflammation and provide new insights into mechanisms that restrain pathogenic T cell responses during EAE.

immunology↗

bioRxiv2026

Lipid-ASO therapeutics exhibit differential tissue targeted delivery upon systemic or local CNS administration

Antisense oligonucleotides (ASOs) are a powerful therapeutic modality, but their full potential is hindered by pharmacokinetic properties that affect tissue and cellular delivery. Lipid conjugation is increasingly used to modulate ASO's biodistribution and promote extrahepatic activity, yet lipid dependent effects on in vivo functional delivery, particularly in the central nervous system (CNS), remain less explored. Here, we performed a side by side in vivo comparison of cholesterol, palmitic acid (C16:0), docosanoic acid (C22:0), and eicosapentaenoic acid (C20:5) conjugated to a fully phosphorothioated 3 10 3 LNA gapmer ASO targeting the Malat1 long non coding RNA. Lipid-ASO conjugates were administered systemically or locally in the brain of mice and evaluated for tissue level and cellular level distribution by imaging, qPCR and single-cell RNA sequencing, simultaneously annotating cell origin and global transcriptional changes within the cell. Following systemic administration in mice, lipid conjugation improved overall multi organ efficacy compared to unconjugated ASO, but with pronounced tissue specific differences. Single cell sequencing of liver and heart transcriptomes revealed lipid dependent cellular uptake patterns and transcriptional responses distinct from administration of unconjugated ASO. After intracerebroventricular administration, selected fatty acid conjugates enhanced silencing in deep brain regions such as the striatum, whereas cholesterol conjugation impaired functional delivery despite increased CNS retention. Light-sheet microscopy showed restricted parenchymal penetration of cholesterol ASOs compared with broader but heterogeneous distribution of palmitic acid conjugate. Together, these findings demonstrate that lipid identity critically determines ASO efficacy, productive cellular uptake, and regional CNS engagement, emphasizing the need for context specific lipid design in ASO therapeutic development.

pharmacology and toxicology↗

bioRxiv2026

A COJEC-chemotherapy resistant model of Th-ALK(F1174L)/MYCN neuroblastoma offers insights into tumour immune evasion and development of the bone marrow metastatic niche

Multi-agent COJEC chemotherapy is the main-stay of induction treatment for patients diagnosed with high-risk neuroblastoma. However, at least 10% of patients will be primary refractory to chemotherapy and only 50% achieve 5-year overall survival. The bone marrow is the most frequent site of metastasis in these patients. Novel approaches are required to improve response rates but the inter- and intra- patient tumour heterogeneity and dynamics of the neuroblastoma immune microenvironment makes anticipation of resistance phenotypes incredibly challenging. We present here a novel immunocompetent C57 Bl/6 model of Th-ALK(F1174L)/MYCN neuroblastoma, in which spontaneous abdominal tumours are driven by expression of mutant Anaplastic Lymphoma Kinase and over-expression of Mycn in the neural crest. We have used this model to generate a personalised dosing schedule inducing COJEC-chemotherapy resistance, in which individual mice receive chemotherapy cycles dependent upon the progression of their neuroblastoma tumours. Using both single cell RNA sequencing and spatial immunophenotyping gave us extraordinary precision in our comprehensive analysis of the tumour intrinsic and microenvironmental factors associated with COJEC resistance. We found that the resistance phenotype was driven by Cdk8 upregulation in adrenergic and mesenchymal tumour cells. Infiltration of immunosuppressive myeloid-derived immune cells and remodeling of the tumour-associated stroma further contributed to COJEC resistance. In the bone marrow we observed expansion of neutrophils and evidence of NETosis associated with micro-metastatic disease. Our results further endorse the development of CDK8-targeting therapeutics for neuroblastoma patients which might boost the anti-tumour immune response. Additional studies will be required to define the roles of neutrophils and neutrophil NETosis in neuroblastoma progression and metastasis. Our C57 Bl/6 model will be pivotal in future preclinical studies of immune-modulating therapeutics.

cancer biology↗

bioRxiv2026

Heterogeneous and conserved radiation responses reveal FOXM1-dependent regulation of microcephaly genes in glioblastoma

Glioblastoma (GBM) is characterized by marked heterogeneity, glioma stem-like cells (GSCs), and resistance to therapy. Because GSCs share features with neural progenitor cells (NPCs), we investigated whether neurodevelopmental programs contribute to their response to irradiation. Transcriptional profiling of four patient-derived GSC lines revealed cell line-specific responses, with radiosensitivity correlating with the magnitude of p53 activation and basal expression of its negative regulator, MDM2. Despite this heterogeneity, radiation consistently activated p53-dependent pathways and suppressed cell-cycle programs. Among these, genes associated with primary hereditary microcephaly (MCPH) that regulate NPC proliferation were coordinately repressed. Single-cell RNA sequencing localized this response to G2/M-cycling cells. FOXM1 was similarly reduced following irradiation, emerged as a candidate regulator of a subset of MCPH genes, and correlated with their expression in GBM tumors. Pharmacological inhibition of FOXM1 reduced expression of selected MCPH genes and enhanced radiosensitivity in U251 cells. Together, these findings identify coordinated suppression of a FOXM1-associated MCPH program as part of the GBM radiation response, while suggesting that the radiosensitizing effects of pharmacological FOXM1 inhibition extend beyond this transcriptional axis.

cancer biology↗

bioRxiv2026

Microglia drive demyelination via multiple sclerosis antibodies and BTK signaling

Microglia are the predominant immune cells in multiple sclerosis (MS) demyelinating lesions, where they phagocytose myelin, but whether they destroy myelin or merely scavenge its debris is unknown. Here, we explore whether pathogenic autoantibodies found in MS may induce the phagocytic destruction of myelin by microglia. Applying patient-derived, myelin-targeting antibodies to the mouse cortex, we developed an in vivo model of MS with focal demyelination that depended on epitope specificity and Fc gamma receptor and complement binding. Longitudinal monitoring of microglia-myelin interactions using in vivo two-photon microscopy revealed rapid microglial envelopment of intact myelin driving myelin loss, while single-cell RNA sequencing identified a demyelination-associated microglial signature. Parallel changes were observed in human MS lesions, where microglia enveloped intact myelin and similar genes were upregulated. Inhibition of Brutons tyrosine kinase (BTK) limited microglial transcriptional changes and prevented myelin loss following microglial envelopment. These findings directly implicate microglia in pathological myelin loss and support BTK inhibition as a therapeutic strategy to prevent demyelination by modulating microglia behavior.

neuroscience↗

bioRxiv2026

Hindbrain explants enable multimodal and longitudinal analysis of the developing olivo-cerebellar circuit at single-cell resolution

Experimental models that preserve native mammalian CNS circuitry while enabling longitudinal analysis of circuit assembly at single-cell resolution remain scarce, limiting mechanistic studies and therapeutic discovery. Here, we establish embryonic mouse hindbrain explants as a scalable in vitro model that maintains the long-range olivo-cerebellar circuit while providing direct experimental access to both pre- and postsynaptic neurons. The preparation supports repeated live imaging, targeted single-cell manipulation and labelling, electrophysiology, ultrastructural analysis, and single-cell RNA sequencing during circuit assembly. Hindbrain explants faithfully recapitulate key features of olivo-cerebellar organization and development, including cytoarchitecture, synaptic organization and maturation, neuronal differentiation, and spontaneous network activity while preserving developmental glial features. By combining developmental and physiological fidelity with longitudinal multimodal accessibility, this resource bridges the gap between reductionist cultures and technically demanding in vivo approaches, providing a versatile and ethical model for investigating the molecular and cellular mechanisms of cerebellar circuit assembly and disease.

neuroscience↗

bioRxiv2026

Sphingolipid metabolism-related genes as key regulatory hubs in white smoke inhalation induced lung injury

Objective White smoke inhalation injury (WSI) causes severe acute lung damage with no specific therapy currently available. Sphingolipid metabolism is implicated in pulmonary inflammation, but its transcriptional regulatory landscape in WSI remains unexplored. This study aimed to identify key sphingolipid metabolism related genes and evaluate their regulatory roles and therapeutic potential in WSI. Methods We established a rat model of WSI and performed integrated bulk RNA sequencing, weighted gene coexpression network analysis (WGCNA), and single-cell RNA sequencing (scRNAseq) to screen for differentially expressed sphingolipid metabolism-related genes (DESRGs). Protein-protein interaction (PPI) network with four centrality algorithms was used to prioritize hub genes. In silico gene knockout and molecular docking were conducted to assess regulatory functions and identify potential drug candidates. Results We identified 22 DESRGs that were predominantly enriched in DNA replication and cell cycle pathways rather than canonical sphingolipid metabolic processes. PPI consensus prioritized three hub genes--Top2a, Ttk, and Ccna2--with Top2a exhibiting the highest expression in epithelial cells and significant downregulation after smoke exposure. ScRNAseq revealed immune cell infiltration and epithelial differentiation trajectories. Virtual knockout showed that Top2a depletion affected the largest transcriptomic fraction (~0.4%) and was enriched in lysosome biogenesis, innate immunity, phagocytosis, and lipid catabolism. Molecular docking identified thalidomide as a high affinity ligand for Top2a (Vina score: -8.5 kcal/mol). Conclusion Our multiomics integrative framework identifies Top2a as a central regulatory hub linking sphingolipid associated inflammation to epithelial responses in WSI, and nominates thalidomide as a potential drug repurposing candidate. These findings provide prioritized targets for future translational investigation.

bioinformatics↗

bioRxiv2026

Scaling recipes for single-cell RNA sequencing foundation models: when do scaling laws hold?

Deep learning models exhibit empirical scaling laws whereby performance changes predictably with model size, dataset size, and training compute. Although these relationships are well established in domains such as language and image modelling, their applicability to biological data remains unclear. Here, we investigate scaling behaviour in foundation models trained on large collec tions of single-cell transcriptomes. We show that pre-training loss decreases systematically with model capacity and training compute, exhibiting a power law dependence on model size. The strength and regularity of these trends differ between model formulations. We identify and quantify empirical relationships linking the optimal learning rate and depth-to-width ratio to model size and depth or compute. These results demonstrate that scaling principles extend to transcriptomic modelling. More broadly, they provide a quantitative framework for estimating the expected returns from additional resources and selecting suit able hyperparameters and architectures, thereby supporting the development of increasingly capable foundation models for omics data.

bioinformatics↗

bioRxiv2026

Single-Cell Profiling of Dynamic Epicardial Cell States During Myocardial Infarction

Background: The epicardium is reactivated after myocardial infarction (MI); however, the gene expression profiles of post-MI adult epicardial subpopulations remain incompletely defined. Methods: Single-cell RNA sequencing was performed on lineage-traced Wt1+ epicardial cells from Wt1CreERT2/+; R26tdT/+; PdgfranGFP/+ adult mice after sham surgery or at 7 and 14 days after permanent artery ligation to induce MI. Immunostaining was performed on Wt1-lineage-traced cardiac tissue to validate spatial expression after ischemic injury. Results: Unbiased clustering identified nine transcriptionally distinct epicardial populations, encompassing mesothelial, fibroblast/mesenchymal, transitional, and proliferative phenotypes. Fibroblast-like epicardial cells (Wt1+/Pdgfra+) showed time-dependent expression profiles associated with upregulation of epithelial-to-mesenchymal transition (EMT) and extracellular matrix (ECM) gene programs. At 7 days post-MI, there was notable enrichment of genes related to chemokines and Wnt components. By 14 days post-MI, the expression profile shifted toward immune regulation. In contrast, a Wt1high/Msln+ population showed minimal upregulation of EMT gene programs but enhanced paracrine signaling related to wound healing and semaphorins, suggesting reactivation of reparative and angiogenic functions akin to those of the epicardium during embryonic development. Immunostaining and in situ hybridization fluorescence analyses validated laminar epicardial cell placement after MI, comprising a surface Msln+ sheet, an overlapping Wt1-lineage band, and a subadjacent PDGFR+ and Periostin+ compartment that expands 7-14 days after MI and regresses by day 28 post-ischemia. Conclusions: Our data define epicardial gene programs in which a signaling epithelial cell surface overlays an effector mesenchymal cell stroma to coordinate angiogenesis, leukocyte recruitment, and ECM remodeling. This study presents the first integrated single-cell atlas of epicardial-derived cells across multiple post-ischemic timepoints, offering new insights into their reparative potential and dynamic signaling diversity in the injured adult heart.

molecular biology↗

bioRxiv2026

Basophilic Erythroblast Emerges as the Key Turning Point in Polycythemia Vera

Abstract Polycythemia vera (PV) is a rare, chronic myeloproliferative neoplasm driven by the JAK2V617F mutation and characterized by uncontrolled erythroid proliferation. Although the mutation arises in hematopoietic stem cells, the differentiation stage at which its transcriptional consequences first become biologically meaningful has remained undefined. Using a multi-layer transcriptomics integration approach that combined differential gene expression, NicheNet ligand-receptor analysis, pseudotime trajectory inference, and CNV profiling on scRNA seq data, alongside bulk transcriptome validation, we identified basophilic erythroblasts as the critical transition point at which JAK2V617F shifts from a genomically present but transcriptionally silent state to an actively trajectory-altering and treatment-responsive disease driver. Differential expression revealed a qualitatively distinct disease signature at this stage, including ERFE-mediated iron dysregulation, MAP2K2-driven RAS/MAPK co-activation, and epigenetic reprogramming. NicheNet showed the establishment of a TGF{beta} superfamily and chemokine-driven niche-remodeling axis, and pseudotime analysis demonstrated that basophilic erythroblasts are the first erythroid population to exhibit condition-dependent trajectory divergence, whereas earlier progenitors showed none despite carrying the mutation. Interferon- treatment showed its broadest counterresponse at this stage but declined sharply thereafter, identifying basophilic erythroblasts as both the principal therapeutic target and the point of maximum vulnerability in PV.

bioinformatics↗