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Results for “single cell RNA sequencing”

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PubMed2026

Mycosis Fungoides-Like Atopic Dermatitis Represents a Th22-Dominant Inflammatory Endotype.

BACKGROUND: Early-stage mycosis fungoides (MF) often presents diagnostic challenges because of its clinical overlap with atopic dermatitis (AD). In clinical practice, we encountered a subset of patients with severe AD who fulfilled the MF diagnostic criteria yet remained clinically indistinguishable from AD and presented refractoriness to advanced therapies. We termed this ambiguous entity "mycosis fungoides-like AD" (mfAD) and sought to determine whether it represents malignant transformation or a distinct inflammatory endotype of AD. METHODS: Skin biopsies were obtained from 7 patients with AD and 11 patients with mfAD. We performed paired single-cell RNA sequencing and single-cell T-cell receptor sequencing analyses. Publicly available MF and AD datasets were integrated for comparative analysis. Spatial transcriptomic profiling was used to contextualize single-cell findings within the tissue architecture. RESULTS: Comparative transcriptomic analysis revealed that T cells in mfAD were aligned with those in AD and lacked genomic instability. High-resolution profiling showed that mfAD was characterized by oligoclonal Th22 expansion rather than a single dominant malignant clone. Notably, all patients with mfAD achieved rapid clinical remission with selective JAK1 inhibition, indicating the therapeutic response characteristics of inflammatory dermatoses. CONCLUSION: Our findings demonstrate that mfAD is not a true malignancy, but rather a Th22-driven inflammatory endotype of AD. These results redefine mfAD as an inflammatory subtype within the AD spectrum, providing a mechanistic explanation for both the "pseudo-monoclonality" that leads to MF misdiagnosis and the failure of dupilumab. This study establishes a rationale for the use of JAK inhibitors in precision medicine for this patient population.

JAK inhibitor↗

PubMed2025

Genetic predisposition to systemic inflammatory proteins is causally associated with inflammatory bowel disease: Insights from multi-omics association study and single-cell RNA-sequencing analysis.

Systemic inflammatory proteins have been reported to be related to inflammatory bowel disease (IBD) in previous observational research. However, their causal links remain obscure. Herein, we performed a Mendelian randomization (MR) analysis to analyze the causality between systemic inflammatory proteins and IBD. Genetic variants related to systemic inflammatory proteins were extracted from a meta-analysis of genome-wide association study (GWAS) data of 8293 European participants. Summary statistics of IBD diverse subtypes were obtained from the international IBD genetic consortium (IIBDGC). We conducted multi-omics method and MR study to detect the causal links through integrating GWAS and protein quantity trait loci (pQTL) data. Inverse variance weighted (IVW) approach was utilized as the dominated analysis method. Moreover, complementary approaches such as MR-Egger intercept test, Cochran Q test and leave-one-out analysis were utilized to validate pleiotropy and heterogeneity. Finally, single-cell RNA-sequencing analysis was performed to detect the expression of significant genes. For IBD, IVW estimates suggested that genetically predicted IL-10 and IL-13 were suggestively associated with an elevated risk of IBD (IL-10: OR: 1.12, 95% CI: 1.00-1.24, P = .04; IL-13: OR: 1.09, 95% CI: 1.01-1.18, P = .023), while CXCL10 was suggestively linked to a lower risk of IBD (CXCL10: OR: 0.90, 95% CI: 0.82-0.99, P = .037). For Crohn disease (CD), the IVW approach provided evidence to sustain that genetically determined IL-13 and CCL3 had a suggestive association with a higher risk of CD (IL-13: OR: 1.13, 95% CI: 1.02-1.26, P = .023; CCL3: OR: 1.22, 95% CI: 1.03-1.45, P = .018). Sensitivity analysis did not explore any heterogeneity and pleiotropy. Our findings supported the causal relationships between 4 specific inflammatory proteins (IL-10, IL-13, CXCL10, and CCL3) and the risk of IBD and CD, thereby providing promising biomarkers of various subtypes stratification and new insights for the prevention and therapeutic target of IBD.

Humans↗

PubMed2025

scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.

MOTIVATION: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data. RESULTS: This article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representations in the embedding space to related peak-gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak-gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: The source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10× Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Gene Expression Regulation↗

PubMed2026

Distinct cochlear cell types associated with genetic susceptibility to sensory and metabolic hearing loss in older adults.

Hearing loss is a heterogeneous condition that can be classified into different subtypes with diverse genetic and cellular components. To investigate the cochlear cell types underlying the genetic basis of sensory and metabolic components of age-related hearing loss (ARHL), we integrated human genome-wide association study data with mouse cochlear single-cell RNA sequencing data using the single-cell disease relevance score tool. These analyses revealed that genes associated with the sensory component of ARHL were most highly expressed in hair cells, while genes associated with the metabolic component of ARHL were most highly expressed in spiral ganglion neurons. We also investigated whether ARHL-associated gene expression patterns differed across subpopulations of the same cell type. Sensory hearing loss-associated genes showed differential expression across supporting cell subpopulations in younger mice, whereas metabolic hearing loss-associated genes exhibited differences across intermediate cell subpopulations of the stria vascularis in older mice. These findings provide evidence for the role of distinct genetic and cellular risk profiles for different ARHL subtypes, suggesting that prevention and therapeutic strategies may require targeting specific cell populations at different life stages.

ARHL↗

PubMed2026

Parallel single-cell host immune profiling and pathogen genomic characterization in Klebsiella-associated sepsis: a pilot study.

OBJECTIVES: Sepsis is a life-threatening syndrome characterized by profound immune dysregulation and substantial biological heterogeneity. Here, we conducted a pilot study to explore host immune remodeling in Klebsiella-associated sepsis by combining single-cell RNA sequencing of peripheral blood mononuclear cells with whole-genome sequencing of the corresponding bloodstream isolates. METHODS: In this prospective observational pilot study, we analyzed peripheral blood mononuclear cells (PBMCs) from two patients with Klebsiella-associated sepsis and two healthy controls (HC) using single-cell RNA sequencing. PBMC composition, differential transcriptional responses, and pathway analysis were assessed across immune subsets. The corresponding bloodstream isolates were characterized by phenotypic antimicrobial susceptibility testing and whole-genome sequencing. RESULTS: Compared to HC, septic patients showed expansion of the myeloid compartment and contraction of the NK/T compartment. High-resolution analysis suggested shifts within lymphoid populations. At the transcriptional level, sepsis was associated with compartment-specific enrichment of interferon-related and host-defence pathways, as well as oxidative phosphorylation, ATP synthesis, and mitochondrial electron transport signatures across multiple PBMC subsets. Classical monocytes exhibited a coordinated decrease in MHC class II-related transcripts. The sepsis-associated isolates were identified as Klebsiella pneumoniae and Klebsiella variicola and were notable for overall antimicrobial susceptibility, limited resistomes, and absence of canonical hypervirulence determinants. CONCLUSION: Our data provide a preliminary description of immune remodeling during Klebsiella-associated sepsis and suggest that severe clinical disease may be associated with isolates lacking classical multidrug-resistance or hypervirulence features. These findings should be interpreted as preliminary and hypothesis-generating and require validation in larger cohorts with detailed clinical severity assessment.

Female↗

PubMed2025

Analysis of ferroptosis-related genes in cerebral ischemic stroke via immune infiltration and single-cell RNA-sequencing.

Ischemic stroke (IS) represents a harmful neurological disorder with limited treatment options. Ferroptosis accounts for the iron-dependent, nonapoptotic cell death pattern, which shows the feature of fatal lipid ROS accumulation. Nonetheless, ferroptosis-related biomarkers for identifying IS early are currently lacking. The present study focused on investigating the possible ferroptosis-related biomarkers for IS and analyzing their effects on immune infiltration. Altogether five hub differentially expressed ferroptosis-related genes (DEFRGs) were identified from the relevant databases. Additionally, single-cell RNA-sequencing (seq) analysis was conducted for the comprehensive mapping of cell populations based on the IS database. These five hub DEFRGs were analyzed using gene set enrichment analysis, miRNA prediction, and single-cell RNA-seq analysis. A transient middle cerebral artery occlusion mouse model was constructed. We also adopted bioinformatics methods combined with western blot, changes to mitochondria, hematoxylin & eosin staining, Nissl staining, ROS fluorescence staining, immunohistochemistry, and quantitative real-time polymerase chain reaction (qRT-PCR) to show the involvement of ferroptosis in IS progression. The results revealed that nuclear factor erythroid-derived 2-like 2 (Nfe2l2) was the potential candidate biomarker for IS diagnosis, and ferroptosis may be suppressed via the Nfe2l2/HO-1 pathway. Thus, drug targeting Nfe2l2 can shed novel lights on IS treatment.

Ferroptosis↗

PubMed2025

CryoSCAPE: Scalable immune profiling using cryopreserved whole blood for multi-omic single cell and functional assays.

BACKGROUND: The field of single cell technologies has rapidly advanced our comprehension of the human immune system, offering unprecedented insights into cellular heterogeneity and immune function. While cryopreserved peripheral blood mononuclear cell (PBMC) samples enable deep characterization of immune cells, challenges in clinical isolation and preservation limit their application in underserved communities with limited access to research facilities. We present CryoSCAPE (Cryopreservation for Scalable Cellular And Proteomic Exploration), a scalable method for immune studies of human PBMC with multi-omic single cell assays using direct cryopreservation of whole blood. RESULTS: Comparative analyses of matched human PBMC from cryopreserved whole blood and density gradient isolation demonstrate the efficacy of this methodology in capturing cell proportions and molecular features. The method was then optimized and verified for high sample throughput using fixed single cell RNA sequencing and liquid handling automation with a single batch of 60 cryopreserved whole blood samples. Additionally, cryopreserved whole blood was demonstrated to be compatible with functional assays, enabling this sample preservation method for clinical research. CONCLUSIONS: The CryoSCAPE method, optimized for scalability and cost-effectiveness, allows for high-throughput single cell RNA sequencing and functional assays while minimizing sample handling challenges. Utilization of this method in the clinic has the potential to democratize access to single-cell assays and enhance our understanding of immune function across diverse populations.

Humans↗

PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗

PubMed2024

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Algorithms↗

PubMed2026

Translation of scRNA-seq to a clinical blood test for infection diagnostics.

INTRODUCTION: Early and accurate triage of patients with febrile illness is crucial for appropriate treatment. While standard inflammatory biomarkers are often nonspecific, transcriptome analysis of peripheral blood has diagnostic potential. However, bulk gene expression data is often confounded by changes in cell count proportions, a more robust quantification of gene expression in specific single-cell types, such as monocytes, is required to serve as a reliable clinical biomarker. AREAS COVERED: Various methods to obtain single-cell-type gene expression results, including the gold standard of gene expression analysis after cell sorting and single-cell RNA sequencing, which are difficult to implement in the routine settings are discussed. Other method to interrogate gene expression of a single cell-type is needed. Finally, monocyte cell-type specific ratio-based biomarker (RBB, called Direct Leukocyte Single cell-type Transcript Abundance, or DIRECT LS-TA) which can estimate single cell-type (monocyte) specific gene expression without cell sorting is introduced. EXPERT OPINION: Traditional diagnostic test for differentiating infection has several limitations requiring breakthrough including turn-around time and cost. DIRECT LS-TA provides a reliable way to quantify monocyte-specific gene expression that strongly correlates with gold-standard methods. It is more affordable than single-cell RNA sequencing and can be readily implemented in clinical laboratories using widely available quantitative PCR or digital PCR machines.

Humans↗

PubMed2025

Paradoxical Effect of Myosteatosis on the Immune Checkpoint Inhibitor Response in Metastatic Renal Cell Carcinoma.

BACKGROUND: Treatment for metastatic renal cell carcinoma (mRCC) has shifted from tyrosine kinase inhibitor (TKI) therapy to immune checkpoint inhibitor (ICI)-based therapy, improving outcomes but with variable individual responses. This study investigated the prognostic implications of pretreatment low skeletal muscle mass (LSMM) and myosteatosis in patients with mRCC undergoing first-line ICI-based therapies, comparing outcomes between PD-1 inhibitor + CTLA-4 inhibitor and PD-1 inhibitor + TKI, incorporating single-cell RNA sequencing. METHODS: A retrospective analysis was performed on 90 patients with mRCC treated with ICI-based therapies between November 2019 and March 2023. Patients were grouped based on whether they received PD-1 inhibitor + CTLA-4 inhibitor or PD-1 inhibitor + TKI combinations. LSMM was defined as skeletal muscle index below 40.8 cm2/m2 for men and 34.9 cm2/m2 for women. Myosteatosis was defined using skeletal muscle density, with cut-off values < 41 HU for BMI < 25 kg/m2 and < 33 HU for BMI ≥ 25 kg/m2. Progression-free survival (PFS) and overall survival (OS) were compared using Kaplan-Meier curves and multivariable models. Single-cell RNA sequencing was performed on pretreatment samples to compare the immune microenvironment between patients with and without myosteatosis. RESULTS: The study cohort (26.7% female; median age: 60.5 years) included 59 patients (65.6%) treated with PD-1 inhibitor + CTLA-4 inhibitor and 31 patients (34.4%) treated with PD-1 inhibitor + TKI. LSMM was present in 18.9% of patients, and myosteatosis in 41.1%, with comparable proportions across groups. During follow-up, 29 patients (32.2%) died: 16 in the PD-1 inhibitor + CTLA-4 inhibitor group and 13 in the PD-1 inhibitor + TKI group. The overall 1-year mortality rate was 22.2%, and PFS rate was 53.3%. Myosteatosis predicted poor OS (HR, 5.389; p = 0.008) and PFS (HR, 2.930; p = 0.022) in the PD-1 inhibitor + TKI group but was protective for PFS (HR, 0.461; p = 0.049) in the PD-1 inhibitor + CTLA-4 inhibitor group. LSMM did not significantly affect outcomes in either group. Single-cell RNA sequencing revealed higher CTLA-4 expression in regulatory T cells and more effector memory CD8+ T cells in patients with myosteatosis, whereas patients without myosteatosis had more anti-tumoural non-classical monocytes. CONCLUSIONS: Myosteatosis negatively impacts OS and PFS in patients with mRCC treated with PD-1 inhibitor + TKI therapy but is protective for PFS in those treated with PD-1 inhibitor + CTLA-4 inhibitor therapy. Altered checkpoint expression and immune cell composition associated with myosteatosis may contribute to these differential responses.

Humans↗

PubMed2025

Transcript-guided targeted cell enrichment for scalable single-nucleus RNA sequencing.

Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

Animals↗

PubMed2026

Myeloid landscape of BRAF-mutant papillary thyroid cancer and thyroiditis.

Papillary thyroid cancer (PTC) is less aggressive when associated with lymphocytic thyroiditis (LT), even in the presence of oncogenic BRAF, including smaller tumours, less lymph node involvement and reduced extrathyroidal extension. To investigate possible immune mechanisms underlying this association, we compared the tumour microenvironment of PTC-BRAF with LT and that without LT using single-cell RNA sequencing (scRNA-seq). Single-cell libraries were generated from fresh and fixed tumour samples with post-dissociation viability >70% using the 10x Genomics Chromium Platform and sequenced on an Illumina NovaSeq 6000. We analysed scRNA-seq data from 11 PTC-BRAF tumours: four with LT (one publicly available sample) and seven without LT. Downstream analyses included quality control, batch correction, dimensionality reduction, and differential gene expression analysis. We found that neutrophils were the predominant myeloid cell type in PTCs without LT. Thyrocytes without LT showed significant expression of the neutrophil recruitment chemokine ECRG4. In the absence of LT, neutrophils expressed oncogenic genes with poor clinical outcomes. In contrast, thyrocytes from tumours with LT showed increased expression of MHC-II antigen presentation, consistent with effective immune surveillance. Thyrocytes and macrophages in the presence of LT showed enrichment of interferon gamma response pathways. Our data suggest that LT in thyroid cancer is associated with enhanced antigen presentation and fewer features of pro-tumourigenic innate immune activity. These results identify previously under-recognised innate immune cell population and associated transcriptomic features, which suggest new mechanisms to target immune treatments in PTC refractory to other therapies.

Humans↗

PubMed2025

Droplet-Based Single-Cell 3' mRNA Sequencing of Marburg Virus-Infected Samples.

Single-cell technologies are continually evolving with emerging methods that are gradually uncovering the central DNA-RNA-protein dogma. Single-cell RNA sequencing is one arm of a multi-omic approach that achieves an astounding level of granularity to reveal the complexity of virus-host interactions at the transcriptomic level. Cell tropism, virus replication, pathogenesis, and gene expression changes mediated by the virus and the host's immune response to infection are just some areas of study that are gaining better clarity due to the high-resolution analysis afforded by the technology.We describe a single-cell sequencing protocol for Marburg virus infection in vivo using nonhuman primate blood and the 10× Chromium Next GEM single-cell genomics methodology. Working with pathogens of high consequence is logistically complicated, requiring containment in biosafety level (BSL)-4 laboratories and harsh inactivation procedures before samples can safely be removed to lower biosafety conditions. We provide procedural insight into sample isolation and processing conducted in BSL-4 and describe the requirements for safe sample removal without jeopardizing quality for down-stream sequencing and analysis in BSL-2 conditions. Characterization of complicated biological processes mediated by high-containment pathogens, typically restricted to analogous model systems, e.g., minigenome, can be achieved using live virus.

Animals↗

PubMed2026

scFANCL: Dual contrastive learning with false-negative correction at cell level for single-cell RNA-seq clustering.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables cellular characterization at single-cell resolution. However, its high dimensionality, sparsity, and noise make clustering challenging. Approaches utilizing contrastive learning and data augmentation have been introduced to improve representation quality for scRNA-seq clustering. In particular, dual contrastive frameworks combining instance- and cluster-level objectives can capture both cell-cell similarities and inter-cluster variations. However, existing dual contrastive frameworks focus primarily on discrete cluster boundaries, neglecting the biological continuity inherent in scRNA-seq data. METHODS: We propose scFANCL, a dual contrastive framework designed to capture biological continuity in scRNA data. Rather than treating all non-augmented samples as negatives, scFANCL applies a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, preserving continuous transcriptional relationships among them while maintaining inter-cluster separation. RESULTS: Extensive experiments across seven publicly available scRNA-seq datasets demonstrated that scFANCL achieves competitive clustering performance compared with existing baseline methods, consistently yielding high ARI and NMI scores across datasets of varying size and complexity. Ablation studies further confirmed the contribution of the false negative filtering component, showing measurable improvements over variants without filtering. Downstream analyses further suggest that the learned embeddings may reflect biologically meaningful transcriptional transitions, including continuous differentiation trajectories within related cell types. The source code is available at https://github.com/mjuailab/scFANCL . CONCLUSIONS: scFANCL addresses a key limitation of conventional contrastive learning by applying a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, thereby preserving biological continuity within cell types while maintaining inter-cluster separation. Evaluations across seven benchmark scRNA-seq datasets demonstrate competitive clustering performance, with learned embeddings capturing biologically meaningful transcriptional structure and characteristics of rare cell populations.

Clustering Algorithms↗

PubMed2024

Genomic and the tumor microenvironment heterogeneity in multifocal hepatocellular carcinoma.

BACKGROUND AND AIMS: Ambiguous understanding of tumors and tumor microenvironments (TMEs) hinders accurate diagnosis and available treatment for multifocal hepatocellular carcinoma (HCC) covering intrahepatic metastasis (IM) and multicentric occurrence (MO). Here, we characterized the diverse TMEs of IM and MO identified by whole-exome sequencing at single-cell resolution. APPROACH AND RESULTS: We performed parallel whole-exome sequencing and scRNA-seq on 23 samples from 7 patients to profile their TMEs when major results were validated by immunohistochemistry in the additional cohort. Integrative analysis of whole-exome sequencing and single-cell RNA sequencing found that malignant cells in IM showed higher intratumor heterogeneity, stemness, and more activated metabolism than those in MO. Tumors from IM shared similar TMEs while distinct TMEs were noticed in those from MO. Furthermore, CD20+ B cells, plasma cells, and conventional type II dendritic cells (cDC2s) were decreased in IM relative to MO while T cells in IM exhibited a more terminally exhausted capacity with a higher proportion of proliferative/exhausted T cells than that in MO. Both CD20 and CD1C correlated with better prognosis in multifocal HCC. Additionally, MMP9+ tumor-associated macrophages were enriched across IM and MO, which formed cellular niches with regulatory T cells and proliferative/exhausted T cells. CONCLUSIONS: Our findings deeply decipher the heterogeneous TMEs between IM and MO, which provide a comprehensive landscape of multifocal HCC.

Humans↗

PubMed2026

Advancing insect research through cell line transcriptomics.

This review emphasizes the significance of insect cell lines in transcriptomic research, highlighting their role as vital tools for uncovering cellular and molecular mechanisms of insect physiology, immune responses, and adaptation to environmental stressors. Cell lines derived from tissues such as the midgut, fat body, nervous system, and reproductive organs enable researchers to examine gene expression changes in a controlled setting, making discoveries that are difficult to achieve through whole-organism studies. High-throughput sequencing and single-cell RNA sequencing (scRNA-seq) have identified genes linked to detoxification, stress response, development, and immune defense, offering valuable insights for future applications in agriculture, pest control, and biotechnology. To organize this information clearly, we have summarized key findings in a table, providing an accessible overview of each cell line's important roles in transcriptomic research. This method not only highlights the adaptability of insect cell lines in functional genomics but also underscores their usefulness as model systems in pest management, virology, and bioengineering. Through utilizing transcriptomics, insect cell lines continue to advance our understanding of insect biology and foster the development of innovative strategies for sustainable crop protection and biotechnological use.

Animals↗

PubMed2025

Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.

The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.

Animals↗