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Results for “single cell RNA sequencing”

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PubMed2026

Single-cell transcriptomics reveals that air-liquid interface culture promotes goblet cell differentiation and inhibits glycolysis in organoid cell monolayers.

Faithfully recapitulating the cellular heterogeneity of the intestinal epithelium is essential when using organoid models. Air-liquid interface (ALI) culture has been shown to promote secretory cell differentiation, but its impact on gene expression in each epithelial cell type remains unclear. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize the cellular heterogeneity of rabbit cecum-derived organoid monolayers grown under immerged or ALI conditions. We then compared these organoid cell type-specific gene expression profiles to a scRNA-seq atlas of the rabbit cecal epithelium in vivo. We selected the rabbit model notably because, unlike mice, it possesses BEST4+ epithelial cells, a newly discovered subset of mature absorptive cells. Our analysis revealed a high degree of transcriptomic similarity between in vivo and organoid-derived stem and transit-amplifying cells. ALI culture markedly enhanced the differentiation of the secretory lineage, especially goblet cells, whose transcriptome closely resembled that of in vivo goblet cells. Furthermore, ALI was the only condition allowing the detection of enteroendocrine cells. BEST4+ cells, however, were absent from organoids in immerged or ALI conditions despite their presence in vivo. In addition, ALI culture led to a consistent downregulation of hypoxia and glycolysis-associated genes across all cell types, which suggests a metabolic shift likely driven by increased oxygen availability in ALI conditions. Cell-cell communication analyses further indicated that ALI more closely mirrored in vivo patterns than immerged condition. Altogether, these results demonstrate that ALI culture allows for better recapitulation of the in vivo cellular heterogeneity and molecular signatures of the intestinal epithelium.NEW & NOTEWORTHY Using single-cell RNA sequencing, this study shows that air-liquid interface (ALI) culture enhances secretory lineage differentiation of intestinal organoid cell monolayers and improves transcriptomic similarity to the native epithelium. ALI reduced hypoxia-associated gene expression and better recapitulates in vivo-like cell-cell interactions, supporting its value for modeling intestinal epithelial heterogeneity in organoids.

Animals↗

PubMed2026

Post-Hoc Long-Read Sequencing Links Leukemic Mutation Status to Single-Cell Transcriptomes.

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we systematically evaluated the potential and limitations of repurposing amplified cDNA generated during the 10X Genomics 3' workflow for post hoc genotyping of individual cells. We first established a mixed leukemic cell line system comprising one cell line with KIT point mutations and another with the BCR::ABL1 fusion gene. Targeted long-read PacBio sequencing enabled post hoc assignment of mutation data to transcriptionally profiled cells, but recovery differed between targets. Consistent with ambient RNA in microfluidics-based single-cell workflows, mutation-associated transcripts were detected in cells not expected to carry the corresponding mutations, illustrating how transcript recovery complicates cell-level genotype assignment. Target-specific thresholds mitigated this source of misclassification. In primary chronic myeloid leukemia samples, the post hoc approach detected BCR::ABL1-positive cells at diagnosis, but not during imatinib treatment. Together, we present a framework for adding mutation status to cells already profiled using the 10X Genomics workflow and highlight broader considerations for transcript-based single-cell genotyping.

BCR::ABL1↗

PubMed2026

Oncogenic Mutations and Tumor Microenvironment Alterations in Diffuse Large B-Cell Lymphoma With Bulky Disease.

BACKGROUND: Bulky disease represents a clinically aggressive subset of diffuse large B-cell lymphoma (DLBCL) associated with adverse clinical outcomes. The aim of this study was to investigate the influence of oncogenic mutations and tumor microenvironment alterations on bulky disease in DLBCL. METHODS: We analyzed a cohort of 939 patients with newly diagnosed DLBCL. Using DNA (n = 934) and RNA (n = 524) sequencing, we compared oncogenic mutations and tumor microenvironment (TME) alterations based on tumor diameter, with cutoff values at 5.0 cm and 10.0 cm. Further stratification by mutations in key genes (CD58, STAT6, EBF1) correlated with tumor diameter revealed distinct transcriptomic and immunologic profiles. Subsequent single-cell RNA sequencing, guided by these mutational signatures, resolved the cellular heterogeneity within the TME. RESULTS: Integrative analysis revealed that tumor diameter correlated with increased incidence of mutations in CD58, STAT6, and EBF1; adverse genetic subtypes such as EZB-like MYC+ and TP53Mut; activation of oncogenic pathways (JAK/STAT, BCR, PI3K, and MYC); and an immunosuppressive tumor microenvironment. Notably, immune checkpoint molecules varied across the bulky stages, with CTLA-4, TIGIT, ICOS, and CD28 expression inversely correlated with tumor diameter, while CD70 and 4-1BBL expression positively correlated. Single-cell RNA sequencing further revealed mutation-specific tumor microenvironment insights. CD58-mutated tumor exhibited a profoundly immune-deserted microenvironment dominated by malignant B cells with minimal immune infiltration, whereas STAT6-mutated tumor was associated with increased fibroblasts and CD4 + T cells, particularly regulatory T cells (Treg) and Th1-like cells; EBF1-mutated tumor was characterized by increased proportions of malignant B cells. CONCLUSIONS: Collectively, our findings highlight the biological complexity of bulky disease, identifying candidate molecular targets and providing a biological framework for future therapeutic hypothesis generation in this clinically aggressive subset of DLBCL.

Humans↗

PubMed2025

scPlantLLM: A Foundation Model for Exploring Single-cell Expression Atlases in Plants.

Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into plant cellular diversity by enabling high-resolution analyses of gene expression at the single-cell level. However, the complexity of scRNA-seq data, including challenges in batch integration, cell type annotation, and gene regulatory network (GRN) inference, demands advanced computational approaches. To address these challenges, we developed scPlantLLM, a Transformer model trained on millions of plant single-cell data points. Using a sequential pretraining strategy incorporating masked language modeling and cell type annotation tasks, scPlantLLM generates robust and interpretable single-cell data embeddings. When applied to Arabidopsis thaliana datasets, scPlantLLM excels in clustering, cell type annotation, and batch integration, achieving an accuracy of up to 0.91 in zero-shot learning scenarios. Furthermore, the model demonstrates an ability to identify biologically meaningful GRNs and subtle cellular subtypes, showcasing its potential to advance plant biology research. Compared to traditional methods, scPlantLLM outperforms in key metrics such as adjusted rand index (ARI), normalized mutual information (NMI), and silhouette score (SIL), highlighting its superior clustering accuracy and biological relevance. scPlantLLM represents a foundation model for exploring plant single-cell expression atlases, offering unprecedented capabilities to resolve cellular heterogeneity and regulatory dynamics across diverse plant systems. The code used in this study is available at https://github.com/compbioNJU/scPlantLLM.

Single-Cell Analysis↗

PubMed2025

Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer.

BACKGROUND: Occult lymph node metastasis (LNM) is a key prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). This study was to establish radiomics models derived from intratumoral, peritumoral, and habitat regions for identifying occult LNM in HNSCC. METHODS: Patients with pathologically confirmed HNSCC from three medical Centers (from March 2014 to April 2024) and The Cancer Genome Atlas (TCGA) were enrolled. Center 1 was split into training (n = 330) and internal test sets (n = 154), while Center 2 and Center 3 served as the external test set (n = 183). Genomic set (n = 50) from TCGA and single-cell RNA sequencing set (n = 6) from Center 1 were used for biological analysis. We used the intratumoral, peritumoral, and habitat volumes of interest (VOIs) to extract radiomics features, respectively. Based on Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers, nine radiomics models were built to confirm the optimal predictive performance. The best-performing model, along with clinical-radiologic data, was combined to develop a hybrid model. The log-rank test was used to evaluate the model's prognostic performance. Additionally, bulk and single-cell RNA sequencing were applied for investigating the biological mechanisms underlying the optimal model. RESULTS: The RF-habitat radiomics model showed the best performance, achieving AUCs of 0.835-0.919 across all datasets. Survival analysis further confirmed the prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. CONCLUSIONS: The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.

Humans↗

PubMed2024

A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we present a deep learning framework, called the dynamic batching adversarial autoencoder (DB-AAE), for denoising scRNA-seq datasets. First, we describe steps to set up the computing environment, training, and tuning. Then, we depict the visualization of the denoising results. For complete details on the use and execution of this protocol, please refer to Ko et al.1.

Deep Learning↗

PubMed2025

Predicting and comparing transcription start sites in single cell populations.

The advent of 5' single-cell RNA sequencing (scRNA-seq) technologies offers unique opportunities to identify and analyze transcription start sites (TSSs) at a single-cell resolution. These technologies have the potential to uncover the complexities of transcription initiation and alternative TSS usage across different cell types and conditions. Despite the emergence of computational methods designed to analyze 5' RNA sequencing data, current methods often lack comparative evaluations in single-cell contexts and are predominantly tailored for paired-end data, neglecting the potential of single-end data. This study introduces scTSS, a computational pipeline developed to bridge this gap by accommodating both paired-end and single-end 5' scRNA-seq data. scTSS enables joint analysis of multiple single-cell samples, starting with TSS cluster prediction and quantification, followed by differential TSS usage analysis. It employs a Binomial generalized linear mixed model to accurately and efficiently detect differential TSS usage. We demonstrate the utility of scTSS through its application in analyzing transcriptional initiation from single-cell data of two distinct diseases. The results illustrate scTSS's ability to discern alternative TSS usage between different cell types or biological conditions and to identify cell subpopulations characterized by unique TSS-level expression profiles.

Transcription Initiation Site↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS. METHODS: We conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for 7 cancers (>290 000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types. RESULTS: At a false discovery rate of 5%, we identified 106 (Bonferroni 5%: 13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue single-cell RNA sequencing data with 113 individuals validated 18 of 32 (56.3%) statistically significant genes for lung cancer. Across cancers, 139 statistically significant genes were shared by at least 2 cancer types and were primarily enriched in specific immune cell types. CONCLUSION: Cell type-specific TWAS improve the identification of novel cancer susceptibility loci and provide insights into the immune landscape of cancer etiology.

Humans↗

PubMed2026

Single-cell transcriptomics on FFPE placenta: A novel method for comprehensive exploration of an entire placental section.

INTRODUCTION: The placenta's complex cellular diversity challenges traditional transcriptomic analyses. Single-cell RNA sequencing (scRNA-seq) offers breakthrough capabilities by enabling transcriptome profiling at the single-cell level. However, traditional scRNA-seq relies on fresh or frozen samples, which present practical storage and quality challenges. Applying scRNA-seq to Formalin-Fixed, Paraffin-Embedded (FFPE) placentas could harness archived samples for clinical insights. METHODS: We used 10x Genomics Flex technology to analyze 8 non-pathological placentas ranging from 21 + 6 weeks of gestation (WoG) to 39 + 4 WoG. RESULTS: Our approach identifies diverse cell populations and allows us to discern maternal from fetal cells. Despite sample size limitations, the method yields comparable data to prior fresh/frozen tissue studies and we complete these data by integrating new molecular markers. The potential to correlate single-cell results with histopathology enables us to conduct an in-depth analysis across entire placental sections by concurrently addressing both fetal and maternal cells. We could thus confirm molecular markers like KRT5/6 using immunohistochemistry by revisiting the slide. DISCUSSION: This innovation could aid in understanding focal anomalies observed on standard histology slides, thereby enhancing traditional histopathological assessments. Given its practicality, integrating our method into routine practice is both feasible and promising.

Differentially expressed genes (DEG)↗

PubMed2026

Pan-cancer analysis identifies APOC1 as a TAM-derived modulator of adaptive immune resistance and predictor of therapeutic response.

BACKGROUND: Apolipoprotein C1 (APOC1) has been implicated in several malignancies, yet its expression patterns, clinical significance, and immunomodulatory roles across cancer types remain poorly characterized. METHODS: We performed a comprehensive multi-omic analysis of APOC1 across 33 cancer types integrating transcriptomic, proteomic, genomic, epigenomic, and pharmacogenomic data from TCGA, GTEx, CPTAC, and multiple independent external cohorts. Immune infiltration was assessed using seven complementary algorithms. Spatial transcriptomics and single-cell RNA sequencing were employed to determine the cellular source of APOC1 expression. RESULTS: APOC1 upregulation in most cancers was associated with cancer type-specific prognosis. After adjustment for clinical covariates and macrophage infiltration, high APOC1 remained an independent adverse factor in KIRC, LGG, and STAD. APOC1 expression positively correlated with genomic instability hallmarks, including homologous recombination deficiency and aneuploidy, with these associations largely independent of immune infiltration; in contrast, associations with tumor mutational burden were substantially confounded by macrophage abundance. Immune infiltration analysis revealed a pattern consistent with adaptive immune resistance: APOC1 correlated positively with immune-activating signatures (STAT1, MHC-II, TCR signaling) and immunosuppressive M2 macrophages and Tregs, yet negatively with anti-tumor effectors (activated NK cells, dendritic cells). Spatial transcriptomics and single-cell RNA sequencing identified tumor-associated macrophages (TAMs) as the primary cellular source of APOC1, with transcripts co-localizing with CD68 in tissue sections. APOC1 expression correlated with multiple immune checkpoint molecules and was elevated in responders to immune checkpoint blockade, consistent with an inflamed yet regulated tumor microenvironment. Pharmacogenomic analyses revealed that APOC1-high tumors display distinct drug response profiles, characterized by resistance to MAPK pathway inhibitors and potential sensitivity to the HDAC inhibitor Entinostat. CONCLUSION: This pan-cancer analysis establishes APOC1 as a context-dependent biomarker and a TAM-derived modulator of adaptive immune resistance, with prognostic and therapeutic implications across malignancies. APOC1-expressing TAMs represent a potential target for combination immunotherapy strategies.

APOC1↗

PubMed2026

Epigallocatechin gallate is associated with PDGFRB downregulation and altered PI3K-AKT signaling in gastric cancer.

BACKGROUND: Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide. Epigallocatechin gallate (EGCG), a natural polyphenol derived from green tea, exhibits anticancer properties; however, its molecular targets and regulatory mechanisms in GC are not fully elucidated. This study aimed to identify candidate EGCG-associated genes in GC and generate a hypothesis for future mechanistic investigation. METHODS: Differentially expressed genes (DEGs) in GC were identified and intersected with EGCG-associated targets retrieved from The Cancer Genome Atlas (TCGA) and GeneCards public databases. Least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazards analyses were performed to screen prognostically relevant genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Functional enrichment analysis was conducted to explore biological significance. Public single-cell RNA sequencing datasets were analyzed to determine the cellular localization of platelet-derived growth factor receptor beta (PDGFRB), while DepMap transcriptomic data were used to assess its expression across GC cell lines. In vitro assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Transwell migration, and Western blotting, were performed to evaluate the biological effects of EGCG on GC-associated signaling pathways. RESULTS: Thirty-eight EGCG-associated DEGs were identified. Enrichment analysis revealed these genes were involved in cancer-associated pathways. LASSO-Cox modelling identified four candidate genes. Among them, PDGFRB was selected for further investigation based on its prognostic relevance and favorable diagnostic performance. PDGFRB expression was significantly higher in the TCGA genomically stable (GS) subtype than in the other molecular subtypes and was predominantly localized to cancer-associated fibroblasts (CAFs) and pericytes in single-cell RNA sequencing analysis. DepMap data demonstrated heterogeneous PDGFRB expression across GC cell lines. In vitro experiments showed that EGCG inhibited proliferation, migration, and invasion, reduced PDGFRB protein expression, and was associated with apoptosis-related protein changes and altered PI3K-AKT signaling. CONCLUSIONS: Our findings suggest that EGCG treatment was associated with reduced PDGFRB expression and altered PI3K-AKT signaling in GC cells. These findings identify PDGFRB as a candidate EGCG-associated gene and provide a hypothesis for future mechanistic investigation.

Gastric cancer (GC)↗

PubMed2026

ER proteostasis failure in HYOU1 deficiency alters B cells, neutrophils, and interferon signalling.

Hypoxia upregulated 1 (HYOU1) is a stress-inducible ER chaperone. We investigated 2 unrelated patients carrying biallelic HYOU1 variants and presenting with primary immunodeficiency. Patient 1, homozygous for p.Pro444His, displayed failure to thrive, hypoglycemia, B cell lymphopenia, and neutropenia. Patient 2, compound heterozygous for p.Arg262Gln and p.Pro757_Glu758insAla, exhibited recurrent infections, enteropathy, and hypogammaglobulinemia. In Patient 1, while HYOU1 transcription was preserved, the protein was severely reduced. Tunicamycin treatment of dermal fibroblasts showed a blunted unfolded protein response and defective induction of ER stress-responsive genes. Immunophenotyping showed near-absence of circulating B cells, and single-cell RNA sequencing of bone marrow identified an arrest at the pro-B cell stage. Neutrophils displayed hypogranulation and dysregulated IFN- and apoptosis-associated transcriptional signatures, unresponsive to G-CSF. HYOU1 deficiency hence results in ER stress-induced proteostasis failure that simultaneously impairs adaptive immunity through B cell developmental arrest and innate immunity through neutrophil dysfunction and IFN pathway imbalance. This work expands the spectrum of HYOU1 deficiency and further identifies ER proteostasis as a central determinant of immune homeostasis.

Journal Article↗

PubMed2026

Single-cell sequencing reveals synovial fluid γδ T-cell expansion in equine experimental osteoarthritis.

OBJECTIVE: Define temporal cellular changes following joint injury using single-cell RNA sequencing in experimental equine posttraumatic osteoarthritis (PTOA). METHODS: PTOA was induced in 4 Quarter Horses (3 to 5 years) via carpal osteochondral fragmentation and high-speed treadmill exercise. Synovial fluid (SF) cells and synovium were sampled over 18 weeks (November 2023 to April 2024). Single-cell suspensions were processed (10x Genomics Chromium iX), then aligned to the equine genome (Cell Ranger). Downstream analysis was completed in the R Seurat package. Differential gene expression (log2[fold change] > 1; P < .05) and differential abundance analyses were performed (P < .1). RESULTS: Cartilage injury had a modest impact on gene expression changes and cell abundance shifts in SF. Integrated analysis of 90,323 SF cells across 4 time points revealed 9 distinct cell types, primarily T cells (73 ± 19%) followed by myeloid cells (20 ± 13%). Subcluster analysis of T cells revealed 9 transcriptomically distinct subtypes (3 CD8, 2 CD4, 3 γδ, and 1 cycling). Differential abundance analyses of temporal changes identified increased γδ T and decreased CD4+ T-cell subsets in joints over time. Expanded populations of IL-23 receptor-positive γδ T cells exhibited increased T-helper 17 signatures. CONCLUSIONS: IL-23 receptor-positive γδ T-cell expansion, associated with joint inflammation, occurred in PTOA. Limitations include small sample size and individual heterogeneity; further investigation over extended timeframe is necessary to confirm whether later stages of the experimental model reflect natural chronic OA. CLINICAL RELEVANCE: Cellular immunotherapy targeting γδ T cells and IL-23/IL-17 blockade may warrant investigation to mitigate equine OA progression.

equine↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed2025

Refined and benchmarked homemade media for cost-effective, weekend-free human pluripotent stem cell culture.

BACKGROUND: Cost-effective, practical, and reproducible culture of human pluripotent stem cells (hPSCs) is required for basic and translational research. Basal 8 (B8) has emerged as a cost-effective solution for weekend-free and chemically-defined hPSC culture. However, the requirement to home-produce some recombinant growth factors for B8 can hinder access and reproducibility. Moreover, we found the published B8 formulation suboptimal in widely-used normoxic hPSC culture. Lastly, the performance of B8 in functional applications such as genome editing or organoid differentiation required systematic evaluation. METHODS: We formulated B8 with commercially available, growth factors and adjusted its composition to support normoxic culture of WTC11 human induced pluripotent stem cell line. We compared this formulation (B8+) with commercial Essential 8 (cE8) and a home-made, weekend-free E8 formulation (hE8). We measured pluripotency marker expression and cell cycle by flow cytometry, and investigated the transcriptional profiles by bulk and single-cell RNA sequencing. We further assessed genomic stability, genome editing efficiency, single-cell cloning, and differentiation in both monolayer and organoids. Finally, we validated key findings using male (H1) and female (H9) human embryonic stem cells. RESULTS: hE8 performed comparably to cE8 across most functional assays and cell lines. In contrast, cells in B8+ displayed higher NANOG expression and improved genome editing efficiency. At the same time, B8+ led to gene expression changes indicative of marked lineage priming, reflected in altered morphology and differential response to some differentiation protocols. Both weekend-free media resulted in a modest transcriptional shift towards a less metabolically active state, consistent with intermittent media starvation. CONCLUSIONS: Homemade weekend-free media can provide a cost-effective alternative to commercial formulations. hE8, integrating some features of B8 while resembling cE8, emerges as a robust and practical option with limited compromises. B8+, though advantageous in some contexts, warrants caution due to lineage priming effects that may impact differentiation outcomes.

hiPSC; pluripotency; culture media; thermostable F↗

PubMed2025

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing.

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

Humans↗

PubMed2026

Synovial short-lived plasma cells mediate adalimumab resistance in rheumatoid arthritis via MIF-CD74 axis-driven, partially TNF-α-independent inflammation.

OBJECTIVE: Synovial plasma cell infiltration predicts inadequate response to adalimumab in patients with rheumatoid arthritis (RA), yet the cellular and molecular mechanisms underlying this association remain unclear. This study aimed to dissect the functional heterogeneity of synovial plasma cells between adalimumab responders and non-responders at single-cell resolution, and to identify the molecular pathways driving treatment resistance. METHODS: This study was based on a prospective clinical cohort of 101 RA patients receiving adalimumab, from which synovial tissues of 8 patients (4 ACR20 responders and 4 non-responders) were profiled by 10x Genomics single-cell RNA sequencing (66,539 high-quality cells). A systematic ligand-receptor screening was performed to identify candidate signaling axes. Core findings were validated at four levels: an independent single-cell validation cohort (n = 4), external bulk RNA-seq cohorts (GSE15602, GSE47726), multiplex immunofluorescence on synovial tissues (n = 9 per group), and in vitro functional experiments using patient-derived peripheral blood monocyte-derived macrophages stimulated with recombinant human MIF under pharmacological intervention with adalimumab, the MIF inhibitor ISO-1, and an anti-CD74 neutralizing antibody. RESULTS: Plasma cells were significantly enriched in non-responder synovium, with a heterogeneous pattern characterized by quantitative accumulation of long-lived plasma cells (LLPCs) and functional dominance of short-lived plasma cells (SLPCs): SLPCs contributed 58.15% of total ribosomal module activity and preferentially overexpressed MIF. Systematic screening of 145 candidate ligand-receptor pairs identified MIF-CD74 as the only axis satisfying all four independent evidence layers. Tissue-level immunofluorescence confirmed that approximately 95% of synovial CD138+ plasma cells in non-responders co-expressed MIF, compared with approximately 45% in responders. In vitro, rh-MIF upregulated macrophage activation markers (CD74, CD80, CD86, HLA-DR) and induced IL-6 and TNF-α secretion. Adalimumab neutralized supernatant TNF-α but failed to suppress MIF-driven IL-6 and IL-1β activation, whereas ISO-1 and anti-CD74 effectively blocked MIF-induced effects at all levels examined. These findings were replicated in patient-derived PBMC macrophages. CONCLUSION: In adalimumab-resistant RA, a functionally active SLPC subset drives partially TNF-α-independent macrophage inflammation through the MIF-CD74 axis, representing a resistance pathway not fully addressed by anti-TNF therapy. Targeting MIF or CD74 blocked this axis in vitro, supporting MIF-CD74-directed precision intervention.

Adalimumab↗