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PubMed2026

Integrated multi-omics analyses identify an RAS-SLC11A2-associated molecular framework linking iron metabolism with PCOS-related cardiometabolic risk.

INTRODUCTION: PCOS is a common endocrine disorder with elevated cardiometabolic risk, yet the role of the renin-angiotensin system (RAS)-iron metabolism axis in this comorbidity remains unclear. We explored its underlying mechanisms and evaluated the therapeutic potential of gentiopicroside. METHODS: Integrated multi-omics analyses combining transcriptomics, single-cell RNA sequencing, Mendelian randomization, machine learning, molecular docking, and in vitro functional assays were performed to identify shared molecular pathways and therapeutic targets across PCOS, hypertension, NAFLD, and T2DM. RESULTS: SLC11A2 was consistently dysregulated in PCOS transcriptomic datasets, and associated with iron metabolism, inflammatory response and oxidative stress pathways. Genetic analyses validated RAS-related regulation in hypertension susceptibility and revealed shared genetic architecture between PCOS and cardiometabolic traits. Network and single-cell analyses characterized SLC11A2-associated molecular patterns in disease-relevant cell types; machine learning identified disease-classifying molecular signatures. Gentiopicroside alleviated inflammatory and oxidative stress phenotypes, including reduced IL-6 expression and reactive oxygen species accumulation. CONCLUSION: This study defines an RAS-SLC11A2 molecular framework linking iron metabolism dysregulation to PCOS-related cardiometabolic risk, elucidating the mechanisms connecting ovarian dysfunction, inflammation, oxidative stress and hypertension, and supports gentiopicroside as a promising therapeutic candidate.

Humans↗
PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗
PubMed2026

CCT2 defines a highly cisplatin-resistant and poor-prognosis subtype of lung adenocarcinoma.

Cisplatin-based chemotherapy is a standard treatment for lung adenocarcinoma (LUAD), yet acquired cisplatin resistance remains a marked cause of treatment failure. The molecular mechanisms driving cisplatin resistance in LUAD have not been fully elucidated. The present study integrated bulk transcriptomic data, genomic mutation profiles and single-cell RNA sequencing data to systematically investigate cisplatin resistance in LUAD. Resistance-associated genes were identified through differential expression, survival analysis and database integration. Unsupervised clustering was used to define cisplatin resistance-associated subtypes. Functional characteristics were explored using pathway enrichment, immune infiltration, tumor mutation burden and weighted gene co-expression network analysis. A machine learning framework incorporating 101 algorithms was applied to identify key genes and construct a prognostic model. Single-cell analyses and in vitro experiments were performed to validate the biological role of the core gene. Molecular docking and molecular dynamics simulations were conducted to identify potential therapeutic compounds. A total of two molecular subtypes with distinct cisplatin resistance levels and prognostic outcomes were identified. The high-resistance subtype exhibited enhanced cell cycle activity, DNA repair signaling and immune heterogeneity. Machine learning analysis revealed a five-gene signature, with chaperonin-containing TCP1 subunit 2 (CCT2) emerging as a key regulator of cisplatin resistance. Single-cell analyses showed that CCT2 was predominantly enriched in resistant epithelial cell subpopulations. Functional experiments demonstrated that CCT2 knockdown significantly inhibited cell proliferation and enhanced cisplatin sensitivity in LUAD cell lines. A number of candidate compounds targeting CCT2 exhibited stable binding in silico. The present findings identified CCT2 as a key mediator of cisplatin resistance in LUAD and provided potential therapeutic strategies to overcome chemotherapy resistance.

chaperonin-containing TCP-1 subunit 2↗
PubMed2025

RELA Haploinsufficiency Manifesting as an Atypical Phenotype of Crohn's Disease.

BACKGROUND: Mutations in RELA, a key component of NF-κB signaling, are associated with dysregulated immune responses and inflammatory disorders. While immunodeficiency phenotypes associated with RELA haploinsufficiency have been reported, gastrointestinal manifestations remain poorly described. This study aimed to characterize the clinical, genomic, and immunological features of a patient presenting with an atypical Crohn's-like phenotype driven by RELA haploinsufficiency. METHODS: Whole-exome sequencing was performed, and results were confirmed by Sanger sequencing. Protein modeling, Western blotting, immunofluorescence, and nuclear extract-based NF-κB activation assays were conducted to assess the functional impact of the identified variant. Immune profiling was performed using mass cytometry time of flight (CyTOF) and single-cell RNA sequencing (scRNA-seq) and compared to controls. RESULTS: We studied a 17-year-old male diagnosed with pan-enteric Crohn's disease (CD), perianal fistulas, chronic mucocutaneous candidiasis, and chronic lymphopenia. Sequencing identified a heterozygous missense variant in RELA (c.587T>C, p.V196A) that potentially impairs RelA (p65) protein stability, confirmed by reduced activity and diminished protein expression. CyTOF analysis revealed decreased circulating T regulatory cells (Tregs), absence of mucosal Tregs, high apoptotic rates, and elevated IFN-γ induced levels, while scRNA-seq demonstrated a robust type I/II interferon signature in multiple immune subsets. Dysregulated mucosal-associated invariant T (MAIT) and cytotoxic CD4+ T cells exhibited upregulation of IL23R and ADAM12, further linking RELA dysfunction to enhanced pro-inflammatory T cell response and tissue inflammation. CONCLUSION: This study links RELA haploinsufficiency with CD-like features, Th1/Th17 polarization, and interferon-driven inflammation, emphasizing the importance of genetic evaluation in patients with atypical or refractory IBD.

Humans↗
PubMed2026

Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.

BACKGROUND: Cell type annotation is essential for gaining biological insight from single-cell RNA sequencing data, yet manual labeling remains time-consuming and difficult to reproduce. Various computational approaches have been developed to automate this process, and recent studies suggest that large language models can infer cell types with promising accuracy in single-cell analysis. However, most workflows still rely on cluster-specific markers derived from gene expression alone or manual curation. As a result, marker selection can be sensitive to statistical criteria and dataset-dependent bias, which may lead to the selection of less informative genes or missing important markers, while providing limited biological context. RESULTS: To address this limitation, we introduce CELLIA, an LLM-based workflow for automated and robust cell type annotation. CELLIA employs an integrative evidence-knowledge marker selection strategy that combines statistical differential expression criteria with curated tissue-specific marker resources to identify informative marker genes. In benchmarking analyses of 102 cell types, this approach improved agreement with manual annotations. In addition, CELLIA achieved higher agreement in subtype-level analyses of closely related immune populations and was further evaluated in a non-immune stromal subtype setting, covering 25 cell types in total. CONCLUSION: By integrating evidence-knowledge from gene expression with curated biological prior knowledge, CELLIA provides a more stable marker selection and improves the reliability of LLM-cell type annotation.

Cell type annotation↗
PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗
PubMed2026

Exploring the transcriptional crosstalk between adipose tissue and locoregional recurrence in breast cancer using independent component analysis.

Locoregional recurrence (LRR) poses a persistent clinical challenge in breast cancer, with emerging evidence implicating the tumor-associated adipose tissue in modulating recurrence risk. This study investigates shared transcriptional programs between adipose tissue and breast tumors and examines their association with disease-free survival (DFS), particularly in the context of reconstructive surgery where adipose tissue from different body compartments are commonly used. We analyzed bulk gene expression data from 5,691 breast tumors and 978 human adipose tissue samples from different body compartments using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs). Gene set enrichment analysis (GSEA) and copy number alteration profiling were used for biological annotation. Associations between TCs and DFS were evaluated through univariate Cox regression. Key findings were validated using spatial transcriptomic and single-cell RNA sequencing datasets. Among the 411 TCs identified, 332 showed biological enrichment, and 35 were significantly associated with DFS. Four DFS-associated TCs (TC257, TC350, TC371, TC400) were enriched for adipogenesis-related genes and exhibited heightened activity in high-grade, triple-negative tumors and in patients with elevated BMI. Notably, TC350 was highly active in adipose tissue from common reconstructive donor sites (abdomen, omentum, subcutis) but not in native breast adipose tissue. Spatial transcriptomic and single-cell analyses confirmed the increased activity of these adipogenesis-related TCs in tumor regions and adipose cells. TC350 included FABP4, a gene previously linked to poor prognosis in breast cancer and considered as a potential new therapeutic target. Adipose tissue-derived transcriptional programs influence breast cancer prognosis and this seems to differ by tissue origin. These findings generate a hypothesis that donor site selection for adipose tissue in reconstructive surgery may impact LRR risk through adipogenesis-associated mechanisms. Further research is warranted to elucidate the biological and clinical implications of adipose-tumor transcriptional interactions.

Humans↗
PubMed2025

From genetic causality to druggable targets: A multiomics framework identifies ZSCAN16 in gout pathogenesis.

ObjectiveGout is a prevalent form of inflammatory arthritis in which many patients respond suboptimally to current therapies. Drug development is hampered by a lack of genetically validated targets, leading to high clinical trial attrition. This study aimed to systematically identify and prioritize novel, druggable targets for gout via a multilayered genetic and functional genomics approach.MethodsWe performed two-sample Mendelian randomization (MR) using cis-expression quantitative trait locus (cis-eQTL) data and dual independent gout genome-wide association study (GWAS) cohorts (openGWAS and FinnGen). The candidate genes were subjected to a rigorous validation pipeline including Bayesian colocalization, phenome-wide association studies (PheWASs) to assess pleiotropy and on-target safety, and single-cell RNA sequencing (scRNA-seq) to delineate the cellular context. Molecular docking was used to evaluate the structural druggability of prioritized targets.ResultsMR analysis revealed 15 genes causally associated with gout. Colocalization analysis (PPH4 > 0.8) prioritized two targets: ZSCAN16 (risk-increasing, OR = 1.04, 95% CI [1.02-1.06]) and TRIM10 (protective, OR = 0.96, 95% CI [0.94-0.98]). Crucially, PheWAS revealed that ZSCAN16 is highly specific to gout, whereas TRIM10 exhibited extensive pleiotropy with hematological and cardiometabolic traits, indicating significant safety risks. Single-cell analysis provided orthogonal validation, demonstrating flare-specific upregulation of ZSCAN16 in cytotoxic T/NK cells. Molecular docking confirmed ZSCAN16 as a structurally druggable target, showing high-affinity binding with known compounds (e.g. digoxin, binding energy = -9.6 kcal/mol).ConclusionsOur study identifies ZSCAN16 as a high-potential, druggable therapeutic target for gout, highlighting its genetic influence on specific immune cell activities during acute flares. Conversely, TRIM10 was deprioritized owing to substantial pleiotropic liabilities and poor chemical tractability. These findings suggest that ZSCAN16 could play a crucial role in the pathogenesis of gout and may provide a valuable lead for future drug discovery efforts.

Humans↗
PubMed2026

BCL11B enhancer hijacking by t(14;16)(q32;q24) translocation defines a novel high-risk subtype of T-ALL.

The molecular classification of T-cell acute lymphoblastic leukemia (T-ALL) remains incomplete, limiting risk stratification and the development of targeted therapies. Enhancer hijacking is a critical oncogenic mechanism that deregulates proto-oncogenes by repositioning cisregulatory regions via structural variants. Here, we performed an integrated analysis of pediatric and adult T-ALL and mixed-phenotype acute leukemias (MPALs), using whole-genome and whole-transcriptome sequencing. This analysis identified a group of 14 patients with predominantly T-lineage neoplasms driven by a t(14;16)(q32;q24) translocation, harboring universal GATA3 mutations and CDKN2A/B deletions. Mechanistically, this translocation repositions the ThymoD locus downstream of BCL11B, causing monoallelic, ectopic overexpression of FENDRR and mesenchymal transcription factor genes FOXF1 and FOXC2 and activating epithelial-mesenchymal transition transcription signatures. Immunophenotypic and single-cell RNA sequencing analyses revealed marked lineage ambiguity with myeloid and B-cell differentiation potentials specific to this subtype. Furthermore, functional analyses in CD34+ cord blood cells demonstrated that FOXF1 overexpression promotes myeloid differentiation while suppressing T-cell differentiation, serving as a key factor for lineage specification. Clinically, this subtype was detected in 0.15% to 4.0% of T-ALL/MPAL cases depending on the cohort, showing a median age of 15 years and enrichment in adolescents and young adults. Importantly, patients with t(14;16)(q32;q24) have an extremely poor prognosis, showing a trend toward worse outcomes than high-risk groups such as KMT2A-rearranged early T-cell progenitor-like, SPI1-rearranged, and LMO2 γδ-like T-ALLs. The unique molecular landscape and poor prognosis of patients with the t(14;16)(q32;q24) translocation underscore the need for the development of novel subtype-specific therapeutic approaches.

Humans↗
PubMed2026

Methylation-Associated Differentiation Features Define Biological and Prognostic Heterogeneity in CMS4 Colorectal Cancer.

Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell-cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC.

Humans↗
PubMed1995

Molecular single-cell analysis of Hodgkin and Reed-Sternberg cells.

In Hodgkin's disease, the malignant Hodgkin and Reed-Sternberg (HRS) cells are present in very small numbers in the diseased tissue, thus making molecular analysis of these cells very difficult. Using micromanipulation and single-cell polymerase chain reaction (PCR), rearranged immunoglobulin genes can be amplified and sequenced from single HRS cells. Oligonucleotides chosen from the variable (V)-gene sequences identified in the HRS cells can be used as specific markers for the tumour clone. This technique will allow one to search for members of the tumour clone in various compartments of the patient's body, to follow disease progression during therapy, and to analyse stem-cell populations for contamination by tumour cells before autologous bone-marrow transplantation.

Animals↗
PubMed2026

Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.

Fusion transcripts are hybrid RNA molecules generated through genomic rearrangements or RNA-level fusion mechanisms. They represent important molecular features of many cancers and can function as oncogenic drivers, diagnostic biomarkers, prognostic indicators, and therapeutic targets. Since the discovery of the BCR::ABL1 fusion in chronic myeloid leukemia, numerous cancer-associated fusion transcripts have been identified across hematologic malignancies and solid tumors. These fusion events encompass diverse biological mechanisms, including constitutively active kinases, aberrant transcription factors, epigenetic regulators, and non-coding fusion RNAs. This review summarizes current knowledge of the mechanisms underlying fusion transcript formation, including genomic rearrangement-dependent and rearrangement-independent processes, as well as fusion circular RNAs. The functional roles of fusion transcripts in cancer biology and their clinical relevance as diagnostic, prognostic, and predictive biomarkers are discussed. In addition, recent advances in fusion transcript detection and characterization are reviewed, including next-generation sequencing, long-read sequencing, single-cell approaches, artificial intelligence-assisted computational methods, and CRISPR/Cas9-mediated strategies for functional modeling and functional validation of fusion transcripts. Despite the rapid expansion of fusion transcript catalogs, the biological and clinical significance of most identified fusion events remains incompletely understood. Future progress will depend on integrating advanced sequencing technologies, artificial intelligence-assisted computational prioritization, and systematic functional validation to distinguish clinically actionable fusion transcripts from biologically neutral events. Such multidisciplinary approaches will be essential for translating fusion transcript research into precision oncology and improving cancer diagnosis, patient stratification, and targeted therapy.

Humans↗
PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗
PubMed2025

Founder Homozygous Nonsense CREB3 Variant and Variable-Onset Retinal Degeneration.

IMPORTANCE: Uncovering the genetic basis of inherited retinal diseases (IRDs) can enhance both diagnostic accuracy and the development of targeted treatment strategies. OBJECTIVE: To evaluate the association between a homozygous nonsense variant in CREB3 with IRDs. DESIGN, SETTING, AND PARTICIPANTS: Thirteen patients with a clinical diagnosis of retinitis pigmentosa or cone-rod degeneration were analyzed by whole-genome sequencing (WGS) and whole-exome sequencing (WES). Clinically, patients presented with 2 main phenotypes, rod-cone and cone-rod dystrophies, demonstrating variable electrophysiological and fundoscopic findings. Expression analysis was performed on patient-derived skin fibroblasts using the reverse transcription-polymerase chain reaction and Western blot analysis, and by interrogating previously published retinal single-cell RNA sequence data. Immunohistochemistry staining was performed on wild-type mouse retinal sections using an anti-CREB3 antibody. Patients with variable phenotypes of IRDs were recruited from 3 medical centers in Israel and Italy. Ophthalmologists clinically diagnosed patients at the relevant medical centers and referred them for genetic screening. WES and WGS were performed at different national and international centers, and the findings of the previously unreported gene were shared between investigators. EXPOSURES: CREB3 and IRDs. MAIN OUTCOMES AND MEASURES: The main outcome was evidence supporting an association between CREB3 and IRD. Measures included WES, WGS, and immunohistochemistry staining. RESULTS: A founder homozygous nonsense variant in CREB3 (c.881G>A, p.Trp294*) was identified in 13 patients from 4 unrelated families; 12 descendent from North-African Jewish origins and 1 from Italian origins. All patients manifested retinal degeneration with varying ages at onset. In patient-derived fibroblasts, the variant mRNA transcript generated a truncated CREB3 protein. Expression analysis and immunohistochemistry staining revealed CREB3 RNA and protein expression in various retinal cell types, indicating its vital role in photoreceptor function. CONCLUSIONS AND RELEVANCE: This study found an association between CREB3 and IRDs. CREB3 was previously shown to be upregulated following ultraviolet radiation. This might contribute to the extensive clinical variability observed in this relatively large cohort of homozygous patients with the same truncated variant.

Humans↗
PubMed2026

Single-cell capture of on-ART SIV transcription reveals TGF-β-mediated metabolic control of viral latency.

We previously demonstrated that blocking TGF-β with galunisertib, a safe, orally available small drug, reactivated latent SIV in vivo by shifting T cells toward a transitional effector phenotype. Here, we investigated the mechanisms underlying this effect using single-cell RNA sequencing, metabolic profiling, and high-dimensional spectral flow cytometry of samples from SIV-infected, antiretroviral therapy-treated (ART-treated) macaques before and after galunisertib. To characterize virus-transcribing, infected cells during ART, we developed a novel, sensitive SIV Transcripts Capture Assay (SCAP) that detected 127 SIV-expressing cells within lymph node single-cell transcriptome libraries. Galunisertib drove broad metabolic reprogramming in CD4+ T cells, with transcriptional upregulation of inflammatory and mitochondrial biosynthesis pathways, confirmed by Seahorse profiling. Metabolomics revealed increased energy metabolites and amino acids and enhanced metabolic flux without proliferation. SIV transcript-positive cells before galunisertib were metabolically quiescent compared with cells without detectable viral transcripts. After galunisertib, virus-expressing cells showed a dramatic metabolic activation, with upregulation of glycolysis, fatty acid metabolism, and TNF-α signaling. High-dimensional flow cytometry demonstrated effects beyond CD4+ T cells, including fewer tissue-resident memory T cells, but more inflammatory macrophages. In conclusion, SCAP represents a specific tool for characterizing rare SIV-infected cells transcribing virus during ART, and it reveals TGF-β as a key mediator of viral latency in vivo through metabolic suppression.

Virus Latency↗
PubMed2025

Scalable single-cell total RNA-seq reveals non-coding programs in immunity, infection, and brain development.

Non-coding RNAs represent a widespread and diverse layer of post-transcriptional regulation across cell types and states, yet much of their diversity remains uncharted at single-cell resolution. This gap stems from the limitations of widely used single-cell RNA-sequencing protocols, which focus on polyadenylated transcripts and miss many short or non-polyadenylated RNAs. Here, we adapted single-cell RNA-sequencing on the 10x Genomics platform to capture a broad complement of coding and non-coding RNAs-including miRNAs, tRNAs, lncRNAs, histone RNAs, and non-adenylated viral transcripts. This approach enabled the discovery of rich, dynamic non-coding RNA programs across immune cells, virally infected hepatocytes, and the developing human brain. In dengue virus-infected hepatocytes, we detect non-adenylated viral transcripts and distinguish active from transcriptionally quiescent infected states, each with distinct host regulatory signatures. In brain tissue, we identify biotype-specific, cell-type-restricted non-coding RNAs, including miRNAs whose expression anticorrelates with predicted targets, consistent with post-transcriptional regulatory relationships. We show that MIR137, one of the strongest GWAS loci associated with schizophrenia and intellectual disability, is expressed specifically in Cajal-Retzius cells, an early-born but transient population that guides subsequent cortical neuron migration. These findings demonstrate the importance of non-coding RNAs in defining cell identity and state, and show how expanded transcriptome coverage can reveal additional layers of gene control-now accessible through practical and scalable single-cell profiling.

Journal Article↗
PubMed2026

Genomics-informed approach identifies which cell types regulate the metabolome.

MOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most extensive single-cell RNA sequencing resources, Tabula Sapiens. This integration allows us to identify cell types that regulate metabolites body-wide. We find hepatocytes are the primary regulatory cell type for most metabolites, associating with 385/410 (94%) metabolites for whom an association is found. Additionally, our multi-gene approach reveals more metabolite associations with beta cells compared to those identified using a single-gene approach. For example, we identify novel metabolite-cell type associations, such as the association between phenylpropanoic acid and beta cells, this metabolite that was previously thought to be regulated by the microbiome. AVAILABILITY: Code used in this work is available via Github at https://github.com/haimkru/Metabolite-Cell-Type-Associations.

Metabolome↗
PubMed2026

Ascites reprograms innate lymphoid immune cells in ovarian cancer by promoting ILC2 enrichment and dysfunctional NK-cell states.

BACKGROUND: High-grade serous ovarian cancer (HGSOC) is commonly accompanied by malignant ascites, a clinically relevant tumor niche that promotes immune evasion, metastasis, and treatment resistance. Although natural killer (NK)-cell dysfunction has been described in ovarian cancer, the broader innate lymphoid landscape of ascites and the mechanisms linking ascites-derived signals to innate immune suppression remain insufficiently resolved. METHODS: We performed single-cell RNA sequencing of NK/innate lymphoid cells from ovarian cancer ascites to define cellular heterogeneity and differentiation states. Functional assays assessed NK-cell cytotoxicity, degranulation, and receptor expression following exposure to patient-derived ascites, with or without transforming growth factor-β (TGF-β) receptor inhibition. Proteomic profiling was used to characterize the soluble ascites milieu, and clinical associations were examined for innate lymphoid subsets. RESULTS: Single-cell analysis identified eight transcriptionally distinct NK/innate lymphoid states, including cytotoxic, precursor, early-like, tolerant/immunoregulatory, regulatory, proinflammatory, and innate lymphoid populations. Ovarian cancer ascites was characterized by depletion of cytotoxic and precursor NK-cell states together with enrichment of early-like, tolerant, regulatory, pro-inflammatory, and innate lymphoid cell (ILC) populations. Trajectory analysis indicated impaired maturation toward terminally differentiated cytotoxic NK cells. Notably, ascites contained an expanded population of programmed cell death protein 1 (PD-1)+ ILC2s, which were more abundant in patients with shorter progression-free survival. In functional assays, short-term exposure of healthy donor NK cells to ascites suppressed degranulation and tumor-cell killing, reduced expression of activating receptors including NKp30 and DNAM-1, increased inhibitory receptor expression, and shifted NK cells toward a CD56highCD16low phenotype. Proteomic profiling supported a soluble milieu consistent with type 2 immune skewing and NK-cell suppression. Importantly, TGF-β receptor inhibition partially restored NK-cell activation and function in the presence of ascites. CONCLUSIONS: HGSOC ascites establishes a type 2-skewed immunoregulatory niche that coordinately drives NK cell dysfunction and PD-1+ ILC2 accumulation. The findings identify TGF-β-linked suppression and ascites-associated immune regulators as candidate immunotherapeutic vulnerabilities for restoring antitumor immunity in ovarian cancer.

Humans↗