Biology subjects

Hu, T.

Publications and source records attributed to Hu, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Scaling genetic discovery through automated post-GWAS interpretation

Genome-wide association studies (GWAS) have made association discovery systematic and cumulative, yet evidence for interpreting associated loci remains selective, heterogeneous and difficult to accumulate across studies. Here we present Post-GWAS Intelligence (PGI), which couples context-dependent post-GWAS analysis to a common representation of analysis-derived evidence. Its multi-agent engine, VariantAgent, executes workflows, records tool-derived results in standardized variant-centred units and generates trait-level interpretations linked to their analytical basis and evidential status. Complementary benchmarks showed that VariantAgent outperformed tested general-purpose and biomedical agents, with its largest advantage when success required both a correct answer and a valid analytical trace. Across 34 published GWAS, VariantAgent achieved a mean per-study recovery of 82.1% for directly comparable findings. The largest gains lay downstream of association mapping, where uniform analysis extended published interpretations and identified regulatory relationships at established risk loci not previously reported in the source studies. Analysis of 1,041 GWAS yielded 530,949 evidence units and corresponding reports, which PGI organized into a cross-trait catalogue. Catalogue interrogation resolved an apparent cross-trait overlap into distinct signals and trait-specific biological hypotheses. This work demonstrates how PGI can extend genetic discovery beyond association mapping by enabling biological evidence to accumulate across studies.

genetics↗

bioRxiv2026

Substrate recognition and ion coupling mechanism of the human VIAAT

Inhibitory neurotransmission, essential for neural circuit homeostasis and proper neurological function, depends on the efficient sequestration of {gamma}-aminobutyric acid (GABA) and glycine into synaptic vesicles. This critical process is mediated exclusively by the vesicular inhibitory amino acid transporter (VIAAT). Despite extensive biochemical and physiological investigation, the molecular mechanism governing VIAAT-mediated transport has remained incompletely understood. Here, we determined structures of human VIAAT in multiple functional states, including apo, GABA-bound, and glycine-bound states, as well as apo state in chloride-free condition. VIAAT adopts a classical LeuT-fold and we elucidated how its large, electronegative binding pocket accommodates both GABA and glycine. Moreover, we resolved two previously unidentified chloride-binding sites. Through integrative molecular dynamics simulations and functional mutagenesis, our results support roles for chloride and the conserved residue E213 in substrate binding and proton-coupled transport. Together, our findings establish a structural framework for VIAAT-mediated inhibitory neurotransmitter transport that illuminates the molecular basis of inhibitory synaptic transmission and provides a foundation for understanding VIAAT dysfunction in epilepsy and related neurodevelopmental disorders.

neuroscience↗

bioRxiv2026

Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section

Spatial transcriptomics often relies on reference-based deconvolution to infer cell types in a tissue context; however, public single-cell datasets can miss patient-specific biology. Here we introduce SIMPlex, a method that generates matched spatial and single-nucleus gene-expression profiles from the same 5 um FFPE section. We demonstrate context-matched profiles across mouse brain, breast cancer and prostate cancer tissues, resolving fine-grained cell-states with distinct spatial signatures. By extracting both spatial and nuclear layers, SIMPlex maximises the information recovered from a single tissue section, an advantage for scarce archival and clinical specimens.

molecular biology↗

bioRxiv2026

Soils from different landscape elements diverge in response to multiple global change factors

Global change factors (GCFs) are known to affect terrestrial ecosystems across a range of land-use types, including farmlands, grasslands, and forests. However, it remains unclear whether different landscape elements within the same region respond differently to the same global change pressures. Here, we investigated this question using four soils collected from co-located farmland, grassland, pine forest, and oak forest, representing distinct landscape elements under the same regional climatic conditions. Each soil was exposed to one of six individual GCFs, warming, drought, nitrogen deposition, salinity, microplastics, and antibiotics, as well as to all six factors combined. We found that landscape elements exhibited strongly divergent responses to the same GCFs. The effects on soil functions also varied among soils under combined stress, with responses diverging from different null-model predictions depending on soils and response variable. Moreover, landscape-element specific response patterns became more pronounced under multiple concurrent stressors. Overall, our findings show that landscape heterogeneity represents a mosaic of different capacities to resist and respond to global change, even under shared climatic and geographic conditions. Global change assessments and ecosystem models should therefore better account for landscape-level heterogeneity, and management strategies aimed at enhancing ecosystem resilience should be tailored to individual landscape elements.

ecology↗

bioRxiv2026

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

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

bioinformatics↗

bioRxiv2026

Chemotherapy-induced multicellularity drives drug-tolerant persistence state in tumor cells

Multicellularity is a well-documented microbial response to stress, however its role as an adaptive survival strategy in cancer remains unresolved. Here we reveal that drug stress, such as paclitaxel treatment, enable rapidly (within 24-48 hours) and efficiently ([~]20-40%) convert single mouse breast 4T1 cancer cells into clonal multicellular spheroids, ultimately generating multicellular masses. Notably, multicellularity is reversible: upon stress removal, most of them restore a unicellular lifestyle that quickly becomes dominant. This transient multicellular state shields cells from hostile niches, functions as a drug-tolerant persistence (DTP) reservoir, and fuels post-therapy relapse, revealing multicellularity as a facultative evolutionary pivot for fitness gain. Importantly, blocking primordial germ cell (PGC) specification suppresses the multicellularity transition. Our findings reveal that certain cancer cells enable adopt unicellular-multicellular life cycle through phenotypic plasticity, dynamically adapting to microenvironmental shifts to maximize fitness. This discovery reframes cancer evolution and the drug-tolerant persistence (DTP) state, highlighting multicellularity as an adaptive, stress-inducible survival strategy against therapy. HighlightO_LIDrug induced evolution from unicell to clonal multicellularity. C_LIO_LIProviding a novel DTP state. C_LIO_LIRevealing environment-dependent bistable plasticity: multicellular persistence (stress) versus unicellular proliferation (favorable). C_LIO_LIBlocking primordial germ cell (PGC) specification suppresses the multicellularity. C_LI

cancer biology↗

bioRxiv2026

Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics

The spatial organization of tissues emerges from cell interactions across multiple scales, yet current spatial omics analysis tools often emphasize local neighborhoods and may not summarize broader tissue architecture. Here we introduce Cophenetic Spatial Topology Embedding (COSTE), a computational framework that embeds directed nearest-neighbor distance profiles into a hierarchical metric space without requiring the user to define a spatial radius or neighborhood cutoff. COSTE can be applied to cell-level and single-transcript inputs without requiring cell segmentation. It constructs directed distance profiles between cell populations and uses hierarchical clustering to quantify tissue topology. This yields a Spatial Separation Score (SSS), a sample-normalized score from 0 to 1 that summarizes relative spatial separation within an analyzed tissue. We apply COSTE to spatial transcriptomics datasets of pulmonary fibrosis and triple-negative breast cancer (TNBC), where it delineates tissue structures, nominates spatially defined cell states, and highlights disease-or treatment-associated architectural patterns that are not readily captured by local neighborhood-based analyses. Our approach provides an interpretable framework for exploring tissue architecture and cell-cell spatial relationships in spatial omics data.

bioinformatics↗

bioRxiv2026

Immunometabolic Remodeling of Perivascular Adipose Tissue in Murine Lupus: Implications for Lupus Vasculopathy

BackgroundPatients with systemic lupus erythematosus (SLE) face markedly increased cardiovascular disease (CVD) risk driven by mechanisms beyond traditional risk factors. Thoracic aortic perivascular adipose tissue (tPVAT) is dysfunctional in lupus and exacerbates endothelial dysfunction, yet the molecular basis of this dysfunction remains poorly defined. MethodsIntegrated multi-omics profiling, including bulk RNA-seq, untargeted proteomics, lipidomics, and high-dimensional spectral flow cytometry, was performed on tPVAT from 15-week-old MRL/lpr mice (active lupus, n = 4-6) and MRL control mice (n = 5-6). Adipogenic differentiation capacity of tPVAT adipose stromal and progenitor cells (ASPCs) from MRL/lpr was assessed by Oil Red O staining at 5 (pre-dieasea) and 15 weeks (active disease), with subcutaneous ASPCs used as depot controls. ResultsTranscriptomic profiling of tPVAT from MRL/lpr mice identified 2,742 upregulated and 1,494 downregulated genes (adjusted p < 0.001, |log2FC| > 1), with strong activation of interferon, IL6-JAK-STAT3, and TNFA signaling pathways together with suppression of fatty acid metabolism, oxidative phosphorylation, and adipogenic pathways. Proteomic and lipidomic analyses were concordant, revealing broad downregulation of mitochondrial bioenergetic machinery, depletion of cardiolipin and acylcarnitines, and enrichment of ceramide phosphoinositols and lysophosphatidylcholines. Cardiolipin strongly correlated with the mitochondrial/metabolic protein module (r = 0.95) and inversely with the immune/inflammatory protein module (r = -0.92). Spectral flow cytometry confirmed marked CD45+ leukocyte infiltration dominated by T cells, together with a significantly reduced Treg/CD4+ ratio indicating loss of local immunoregulatory balance. ASPCs derived from PVAT of 15-week-old MRL/lpr mice exhibited impaired white and beige adipogenic differentiation, while APCs from PVAT of 5-week-old MRL/lpr mice, and from subcutaneous adipose tissues of 15-week-old MRL/lpr mice, had normal white and beige differentiation, consistent with an acquired, depot-specific, disease-stage-dependent progenitor defect in PVAT of MRL/lpr mice. ConclusionsLupus tPVAT undergoes a concordant cross-platform molecular reprogramming of mitochondrial bioenergetic genes coupled with establishment of an interferon-dominant immune niche and acquired loss of ASPC adipogenic capacity. These findings provide a molecular framework for lupus PVAT dysfunction and identify restoration of mitochondrial function, suppression of interferon-driven inflammation, and renewal of progenitor differentiation as potential therapeutic strategies for lupus vasculopathy.

molecular biology↗

bioRxiv2026

Gradient-specified optimization based on muscle surface mesh and moment arm as an effect-oriented approach of automated musculotendon path modeling

There is more to musculotendon path modeling than aligning a cable to reflect the geometric features of a musculotendon unit. From the perspective of simulation accuracy, the key is to replicate the length- and moment arm-joint angle relations of the target muscle. In this study, we propose an effect-oriented approach of automated path modeling, via hybrid calibration based on muscle surface mesh and moment arm. The task is formulated as a least-squares optimization problem with a threefold objective for the path to: (1) pass through multiple ellipses representing muscle cross-sections, (2) yield moment arms that match experimental measurements, and (3) yield moment arms in the expected functional directions. We demonstrate the performance of our optimization framework with the musculoskeletal surface mesh from the Visible Human Male and moment arm datasets from literature--producing 42 paths that are anatomically realistic and biomechanically accurate within 20.1 minutes. Our optimization framework is gradient-specified, which is faster and more accurate than using the default numerical gradient, making it applicable for large-scale subject-specific uses.

bioengineering↗

bioRxiv2026

Targeted sequencing of mutations via RNA-templated gap filling of oligonucleotides for single-cell RNA-seq

We describe a method for targeted sequencing of expressed mutations in single cells. The approach exploits the reverse transcriptase and nick translation activities of the Bst DNA polymerase (Full Length) from Bacillus stearothermophilus (BstFL) to perform an RNA-templated gap filling and ligation between flanking probes. Integrated with probe-based single cell RNAseq workflows, it enables simultaneous whole transcriptomic and multiplexed mutation profiling from the same cell, overcoming key limitations of mutation detection in existing scRNA-seq approaches.

molecular biology↗

bioRxiv2026

gSV: a general structural variant detector using the third-generation sequencing data

Structural variants (SVs) are major contributors to genome diversity and disease susceptibility, particularly in cancer. Although third-generation sequencing technologies have substantially improved SV detection sensitivity, accurate detection of complex SVs remains challenging due to fragmented and heterogeneous alignment signals, as well as the dependence of many existing methods on predefined variant models. In this paper, we propose gSV, a general SV detector that integrates alignment-based and assembly-based approaches with the maximum exact match (MEM) strategy, with particular emphasis on resolving SVs with complex or atypical alignment signatures. Without predefined assumptions about SV types, gSV captures diverse variant signals, enabling the detection of SVs that are usually missed by conventional tools. Benchmarking using both simulated datasets and real long-read sequencing data demonstrates that gSV achieves improved sensitivity and overall detection performance compared with current state-of-the-art SV callers, particularly for simple and complex SV events with complex alignment patterns. Unique SV discoveries in four breast cancer cell lines, particularly in cancer-associated genes, demonstrate the potential biological relevance of gSV-enabled discoveries. Furthermore, analysis of a breast cancer cohort from the Chinese population highlights the utility of gSV for population-scale genomic studies. Collectively, gSV provides a unified framework for comprehensive SV discovery in both research and clinical genomics settings. O_TEXTBOXKey PointsO_LIExisting structural variant (SV) detection tools are limited in resolving SVs with complex alignment patterns due to their reliance on predefined variant models. C_LIO_LIgSV integrates alignment-based and assembly-based evidence using a maximum exact match (MEM) strategy, enabling capture of diverse and complex SV signals. C_LIO_LIBenchmarking on simulated and real long-read sequencing datasets demonstrates that gSV achieves competitive performance on canonical SV classes and improved sensitivity for complex SV patterns. C_LIO_LIApplication of gSV to breast cancer cell lines and a population-scale breast cancer cohort reveals previously unresolved SVs in cancer-associated genes, highlighting its utility in genomic and clinical studies. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2026

Metabolic starvation-induced cell swelling drives solid stress in tumors

Solid stress shapes tumor growth, invasion, and therapeutic response, yet its physical origin and clinical relevance remain unclear. Here, we develop a mechano-electro-osmotic model integrating metabolic gradients, ion transport, and cellular mechanics to explain residual solid stress emergence in tumor spheroids, common models of solid tumors. We show that solid stress arises predominantly from osmotic cell swelling driven by metabolic deprivation and ion accumulation, rather than proliferation. This mechanism generates a characteristic stress architecture: isotropic compression in the hypoxic core balanced by peripheral tangential tension, causing pronounced cell and nuclear deformation. The resulting nuclear strain provides a mechanical basis for DNA damage and genomic instability implicated in disease progression and treatment resistance. We validate these predictions in breast cancer using MDA-MB-231 spheroids and patient-derived ductal carcinoma in situ lesions, and corroborate them across published spheroid models and in vivo and ex vivo tumors spanning additional cancer types. Our findings link tumor metabolism to clinically relevant mechanical stresses, suggesting opportunities to target osmotic and metabolic pathways to mitigate solid stress and improve therapeutic outcomes.

bioengineering↗

bioRxiv2026

Single Platform Solution for Identity and Quantification of Influenza Hemagglutinin (HA) mRNA Constructs and Resulting Expressed Proteins for Application to Influenza mRNA Vaccines

The rapid response to the Coronavirus Disease (COVID-19) pandemic highlighted mRNA as an attractive modality for vaccines and therapeutics to address infectious diseases, cancer, and other diseases. The current analytical methods for identification and quantification of both the mRNA construct as well as the resulting expressed protein(s) relevant to mRNA vaccines are inherently singleplex and usually labor and time intensive, presenting a bottleneck as these vaccines become increasing multivalent. Here, we first present performance data from an approximately 2-hour DNA microarray assay developed specifically to provide identity and quantity measurements for the mRNA constructs present in an influenza virus hemagglutinin (HA)-encoding multivalent mRNA vaccine regardless of the specific strain or codon optimization strategy utilized. The assay functions on both naked and lipid nanoparticle (LNP)-encapsulated mRNAs without the requirement of an upfront mRNA extraction or amplification. Second, we show that a separate [~]2-hour multiplexed immunoassay executed on the same VaxArray Platform can be utilized to measure the expressed proteins produced from these influenza HA mRNAs post-transfection as an in vitro potency assay readout. Both assays provide the necessary specificity for each component present in a multivalent mixture and represent new tools in the analytical toolbox for multiplexed mRNA vaccine analytics.

molecular biology↗

bioRxiv2026

RVPSD: A comprehensive and user-friendly web database for RNA viral protein structures

Protein structures are central to understanding the diversity and biological function of RNA viruses. However, few experimentally validated structures of RNA viruses are currently available. To address this gap, we established the RNA Viral Protein Structure Database (RVPSD) that comprises diverse RNA viral protein structures predicted using AlphaFold2. RVPSD integrates taxonomic classification, viral nucleotide and protein sequences, functional annotations, and predicted three-dimensional protein structures to support a systematic exploration of the RNA viral proteomes. Currently, RVPSD comprises a total of 154,716 AlphaFold2-predicted RNA viral protein structures from 5,263 RNA virus species, with 84.9% exhibiting high pLDDT scores (>70) and representing a large-scale and comprehensive resource for RNA viral protein structures. RVPSD also provides a user-friendly web interface, with advanced search, sequence- and structure-based retrieval, interactive 3D visualization, and access and downloading structural data, enabling researchers to perform comparative structural analyses and functional annotation of viral "dark matter".

microbiology↗

bioRxiv2025

MLL3 and MLL4 sustain hematopoietic stem cell multipotency by opposing a B-cell default state

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are sustained by networks of transcription factors and epigenetic regulators that prime lineage-specific programs yet maintain multipotency. Two epigenetic regulators, MLL3 and MLL4, play important but opposing roles in maintaining this balance. MLL3 promotes HSC differentiation, whereas MLL4 opposes differentiation. These opposing functions are essential for both normal homeostasis and leukemia suppression, yet it is not clear how MLL3 and MLL4 regulate HSC and MPP gene expression to control HSC/MPP fate decisions. To resolve these mechanisms, we performed an extensive series of single cell genomic studies after conditionally deleting Mll3, Mll4 or both genes together. Mll3 deletion had only limited effects on HSC/MPP enhancer networks at steady state, whereas Mll4 deletion led to precocious activation of myeloid enhancers. Surprisingly, compound Mll3/4 deletion eliminated all myeloid, erythroid and megakaryocytic potential within the hematopoietic hierarchy and caused all progenitors to rapidly default to a B-cell-like identity. These changes were accompanied by widespread inactivation of HSC/MPP enhancers and superenhancers, and ectopic activation of B-cell superenhancers. Disabling MLL3/4 histone methyltransferase activity did not recapitulate the pervasive changes in cell identity that were observed when MLL3 and MLL4 were fully inactivated, indicating that MLL3 and MLL4 activate HSC/MPP enhancers independently from their enzymatic activities. Our findings show that HSC/MPP multipotency requires sustained tension between MLL3/4-dependent enhancers that maintain myeloid, erythroid and megakaryocyte potential, and MLL3/4-independent enhancers that prime B-cell identity. MLL3 and MLL4 therefore serve as critical linchpins of multilineage hematopoiesis. KEY POINTSO_LIMLL3 and MLL4 act redundantly in HSCs to sustain transcription factor and enhancer networks that support multipotency C_LIO_LISimultaneous loss of MLL3 and MLL4 drives hematopoietic progenitors into a uniform B-cell-like default state C_LI

cell biology↗

bioRxiv2025

Electrochemical Lateral Flow Assay with Linked Analytics for the Surveillance of Cassava Brown Streak Disease in East Africa

Cassava brown streak disease (CBSD) severely threatens food security in East Africa and the livelihoods of hundreds of millions globally. Effective control requires large-scale surveillance in resource-limited settings, which is currently lacking. We present ELLA (Electrochemical Lateral Flow Assay with Linked Analytics), a portable, battery-free, low-cost digital diagnostic platform integrating lateral flow assays with near-field communication and electrochemical readouts for cloud-based data storage and analytics. Validated through extensive field trials in East Africa and smaller studies in Brazil, ELLA achieved 89% agreement with RT-qPCR and 95% with ELISA, often surpassing ELISA sensitivity at a material cost below US$1 per test. Leveraging ELLAs molecular results, we trained a deep-learning model (DeepELLA) for rapid, image-based diagnosis of CBSD, enabling scalable surveillance of this and potentially emerging plant pathogens. By combining electrochemical sensing, digital connectivity, and AI-driven analytics, ELLA offers a powerful tool to strengthen plant disease monitoring and food security. Its modular design also allows adaptation to other chemical and biological targets, creating opportunities for novel datasets and new insights into plant and environmental health.

bioengineering↗

bioRxiv2025

Probing the genomic and proteomic basis of encystment in Oxytricha granulifera

Protozoan encystment is a crucial survival strategy for resisting environmental stresses, yet its molecular mechanisms remain poorly understood due to limited genomic resources and lack of integrated multi-omics analyses in model ciliates. In this study, we aimed to elucidate the molecular mechanisms underlying the encystment process in Oxytricha granulifera by sequencing and assembled its genome, and by conducting comprehensive transcriptome, proteome and morphology analyses to reveal gene expression and morphologic changes between vegetative and cyst stages. Morphological observation revealed ciliary dedifferentiation and cyst-wall formation during encystment, events respectively supported by the downregulation of microtubule dynamics related genes and the upregulation of vesicle transport related genes in cyst stage. Expanded gene families for carbohydrate metabolism, autophagy and cellular acidification align with the species mucocyst signature and their observed autophagic clearance, hinting at previously unrecognized mechanisms underlying cyst formation. Elevated expression of the ubiquitin-proteasome system and autophagy pathways facilitates protein turnover, and upregulation of antioxidant enzymes genes helps mitigate oxidative damage, while a rewired post-transcriptional regulation that increases spliceosome activity and alternative splicing frequency--each trend validated at the protein level. Concurrently, the methyltransferases responsible for DNA N6-adenine methylation (6mA), with homologous genes of AMT1 and AMT6/7 significantly downregulated. These results suggesting a multilayered regulation of ciliate encystment and the first integrated evidence that alternative splicing modulates dormancy. Our findings establish the first multi-omics framework for O. granulifera encystment, offering baseline data and tentative clues to the dormancy mechanism that will inform future inquiries into how single-celled eukaryotes endure adverse environments. IMPORTANCEOxytricha species are widely distributed in freshwater and terrestrial ecosystems, playing significant ecological roles in microbial communities. Their ability to undergo encystment provides a powerful model for studying cellular differentiation and stress adaptation in microbial eukaryotes. This study presents the first multi-omics analysis of encystment in Oxytricha granulifera, revealing microbial survival strategies through enhanced protein turnover, autophagy, alternative splicing, and DNA methylation reprogramming. These findings offer fundamental insights into dormancy mechanisms and environmental adaptation in protists, advancing our understanding of microbial resilience, evolutionary innovation, and ecological success in fluctuating environments.

genomics↗

bioRxiv2025

Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance

Most advanced melanomas initially respond to targeted therapy but eventually relapse. Rather than acquiring new mutations, resistance is driven by drug-tolerant persister cells that enter a reversible drug-refractory state. We developed MeRLin, a high-resolution lineage tracing platform integrating cellular barcoding, single-cell transcriptomics, RNA fluorescence in situ hybridization (FISH), and computational analyses to track clonal and transcriptional dynamics in patient-derived melanoma models during prolonged therapy. Clonal dynamics revealed that persister subpopulations first responded to treatment but persisted and expanded during minimal residual disease, ultimately leading to tumor recurrence. Pre-treatment melanoma populations diversified into four conserved persister states characterized by stress-like, lipid metabolism, PI3K signaling, and extracellular matrix remodeling programs associated with adaptive resistance. Spatial transcriptomics showed the organization of these adaptive programs and a complex signaling network of autocrine and paracrine interactions among persister subpopulations. Barcoded RNA-FISH enabled spatial mapping of clonal identity and gene expression, revealing in situ co-localization of a dominant resistant clone with SLC2A1 expression. MeRLin provides a robust framework for dissecting cancer heterogeneity and identifying vulnerabilities in persister populations.

cancer biology↗