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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.
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Benchmarking strategies for cross-species integration of single-cell RNA sequencing data.
The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.
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Single-Cell Transcriptomic Atlas of Gingival Mucosa in Type 2 Diabetes.
The oral gingival barrier is a constantly stimulated and dynamic environment where homeostasis is often disrupted, resulting in inflammatory periodontal diseases. Type 2 diabetes (T2D) has been reported to be associated with gingival barrier dysfunction, but the effect and underlying mechanism are inconclusive. Herein, we performed single-cell RNA sequencing (scRNA-seq) of gingiva from leptin receptor-deficient mice (db/db) to examine the gingival heterogeneity in the context of T2D. Periodontal health of control mice is characterized by populations of Krt14+-expressing epithelial cells and Col1a1+-fibroblasts mediating immune homeostasis primarily through the enrichment of innate lymphoid cells. The db/db gingiva exhibited decreased epithelial/stromal ratio and dysfunctional barrier. We further observed stromal, particularly fibroblast immune hyperresponsiveness, linked to the recruitment of myeloid-derived cells at the db/db gingiva. Both scRNA-seq and histological analysis suggested the inflammatory signaling between fibroblasts and neutrophils as a potential driver of diabetes-induced periodontal damage. Notably, the "immune-like" stromal cells were wired toward the induction of gingival IL-17A hyperresponsiveness in db/db mice. Our work reveals that the "immune-like" fibroblasts with transcriptional diversity are involved in the innate immune homeostasis at the diabetic gingiva. It highlights a potentially significant role of these cell types in its pathogenesis.
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A multi-dataset single-cell meta-analysis of human keloid skin across Asian, Black and White populations reveals endothelial and mesenchymal programs linked to ethnic disparities.
INTRODUCTION: Keloid is a fibroproliferative scar with marked ethnic disparities, disproportionately affecting Asian and Black populations. Single-cell RNA sequencing (scRNA-seq) studies have mapped keloid pathology, but cross-study differences complicate generalization. We performed a meta-analysis to identify composition and transcriptional programs associated with keloid across ethnicities. METHODS: We aggregated human skin scRNA-seq datasets containing keloid and healthy samples with documented ethnicity (Asian, Black, White). After quality control and batch integration (scVI/scANVI), we annotated cell types and subtypes. Differential abundance was estimated with scCODA, Milo, and propeller. Pseudobulk differential expression was meta-analyzed using random-effects models. Ligand-receptor signaling was inferred with CellChat/NicheNet. RESULTS: The harmonized atlas comprised 112 donors, 147 samples, and 487,293 cells from 8 studies. Keloid demonstrated higher proportions of vascular endothelial cells and fibroblasts across all methods. Ethnicity-stratified analysis revealed endothelial expansion in Asian and Black relative to White populations, while fibroblast expansion was greater in White keloids. Endothelial subtyping showed increased post-capillary venules and arteriolar states. Fibroblast analysis revealed expansion of mesenchymal/ADAM12+ and pro-inflammatory populations. Meta-analysis implicated TGF-β/SMAD, integrin/FAK, and YAP/TAZ mechanotransduction pathways as candidate pathogenic programs warranting functional validation. CONCLUSION: Cross-study synthesis identifies shared and ethnicity-stratified endothelial and fibroblast programs in keloid. Expansion of post-capillary venules and ADAM12+ fibroblasts provides candidate therapeutic targets pending functional validation and a framework for understanding ethnic disparities in keloid formation.
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An atlas of transcriptional, chromatin accessibility, and surface marker changes in human mesoderm development.
Mesoderm is the developmental precursor to myriad human tissues including bone, heart, and skeletal muscle. Unravelling the molecular events through which these lineages become diversified from one another is integral to developmental biology and understanding changes in cellular fate. To this end, we developed an in vitro system to differentiate human pluripotent stem cells through primitive streak intermediates into paraxial mesoderm and its derivatives (somites, sclerotome, dermomyotome) and separately, into lateral mesoderm and its derivatives (cardiac mesoderm). Whole-population and single-cell analyses of these purified populations of human mesoderm lineages through RNA-seq, ATAC-seq, and high-throughput surface marker screens illustrated how transcriptional changes co-occur with changes in open chromatin and surface marker landscapes throughout human mesoderm development. This molecular atlas will facilitate study of human mesoderm development (which cannot be interrogated in vivo due to restrictions on human embryo studies) and provides a broad resource for the study of gene regulation in development at the single-cell level, knowledge that might one day be exploited for regenerative medicine.
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Spatial, transcriptomic and epigenomic analyses link dorsal horn neurons to chronic pain genetic predisposition.
Key mechanisms underlying chronic pain occur within the neural circuitry of the dorsal horn. Recent genome-wide association studies (GWAS) have identified genetic variants associated with the predisposition to chronic pain. However, most of these variants lie in regulatory non-coding regions that have so far not been linked to spinal cord function. Here, we take a multi-species approach to determine whether chronic pain variants impact regulatory elements of dorsal horn neurons. We first built a more comprehensive single cell atlas; filling gaps by generating a high-quality Rhesus macaque atlas and integrating it with human and mouse. With cellular-resolution spatial transcriptomics, we mapped the laminar distributions of the resulting species-conserved neuron subtypes, uncovering an unexpected organization. Lastly, we generated a mouse single-nucleus open chromatin atlas to partition the heritability of chronic pain traits. From this, we identified strong, selective associations between specific, conserved neuron subtypes and major forms of chronic pain.
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Fibroblasts in the tumor microenvironment: heterogeneity and dynamic interactions in tumor progression revealed by spatial transcriptomics.
Cancer-associated fibroblasts (CAFs) constitute the most abundant and functionally versatile stromal component of the tumor microenvironment (TME), with their phenotype and spatial location jointly governing tumor growth, immune evasion, metastasis, and therapeutic resistance. While traditional single-cell RNA sequencing has unveiled CAF transcriptional heterogeneity, it forfeits crucial tissue-contextual information; the advent of high-resolution spatial transcriptomics (ST)—encompassing platforms such as 10xVisium, Slide-seq, Stereo-seq, and MERFISH—now overcomes this limitation by preserving native tissue architecture while simultaneously capturing whole-transcriptome data and single-cell spatial coordinates, enabling comprehensive mapping of CAF “spatial atlases.” Recent literature has consolidated CAFs into five functional subtypes: myofibroblastic CAFs (myCAFs), inflammatory CAFs (iCAFs), antigen-presenting CAFs (apCAFs), matrix-remodeling CAFs (matCAFs), and proliferative CAFs (pCAFs), each exhibiting spatial preferences and dynamic plasticity within tumor cores, hypoxic niches, invasive fronts, and tertiary lymphoid structures. Distinct subpopulations form sub-micron-scale interaction networks with SPP1+ macrophages, CXCL13+ CD8+ T cells, natural killer cells, or endothelial cells to orchestrate either immune exclusion or activation. Multi-cancer investigations in colorectal, pancreatic, hepatic, and lung malignancies demonstrate that peri-tumoral enrichment of POSTN+ myCAFs predicts immune exclusion and shortened survival, whereas therapeutic targeting of CAF-immune or CAF-cancer signaling axes—such as IL-34/CSF1R, TGF-β/LOXL2, and JAG1/NOTCH1—can reverse immunotherapy resistance. Looking forward, integrative multi-omics, subcellular-resolution in-vivo tracking, and AI-driven spatial interaction modeling will further decode CAF spatial phenotypes and expedite their incorporation into precision oncology frameworks.
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Cascading effects of hypobaric hypoxia on the testis: insights from a single-cell RNA sequencing analysis.
Most mammals tolerate exposure to hypobaric hypoxia poorly as it may affect multiple regulatory mechanisms and inhibit cell proliferation, promote apoptosis, limit tissue vascularization, and disrupt the acid-base equilibrium. Here, we quantified the functional state of germ cell development and demonstrated the interaction between the germ and somatic cells via single-cell RNA sequencing (scRNA-seq). The present study elucidated the regulatory effects of hypobaric hypoxia exposure on germ cell formation and sperm differentiation by applying enrichment analysis to genomic regions. Hypobaric hypoxia downregulates the genes controlling granule secretion and organic matter biosynthesis, upregulates tektin 1 (TEKT1) and kinesin family member 2C (KIF2C), and downregulates 60S ribosomal protein 11 (RPL11) and cilia- and flagella-associated protein 206 (CFAP206). Our research indicated that prosaposin-G protein-coupled receptor 37 (PSAP-GPR37) ligands mediate the damage to supporting cells caused by hypobaric hypoxic exposure. The present work revealed that hypoxia injures peritubular myoid (PTM) cells and spermatocytes in the S phase. It also showed that elongating spermatids promote maturation toward the G2 phase and increase their functional reserve for sperm-egg binding. The results of this study provide a theoretical basis for future investigations on prophylactic and therapeutic approaches toward protecting the reproductive system against the harmful effects of hypobaric hypoxic exposure.
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Single-Cell RNA Sequencing Reveals Cellular Heterogeneity and Microenvironmental Remodeling in Human Ureteral Scar Stricture Tissue.
PURPOSE: This study aimed to construct a comprehensive single-cell transcriptomic atlas of human ureteral scar stricture tissue using single-cell RNA sequencing (scRNA-seq), to uncover cellular heterogeneity, subpopulation dynamics, and intercellular communication networks. METHODS: Ureteral tissues were collected from three normal controls (CTR) and three patients with ureteral scar stricture (US). Single-cell suspensions were prepared using the MobiNova-100 platform and sequenced on the Illumina NovaSeq 6000 system. Data were analyzed using Seurat, Harmony, Monocle2 (for pseudotime trajectory analysis), CellChat (for cell-cell communication), and SCP (for GO/KEGG enrichment). Key findings were validated by multiplex immunofluorescence (IF) and immunohistochemistry (IHC). RESULTS: Eleven major cell types were identified, including epithelial, stromal, endothelial, and immune cells, each comprising distinct subpopulations. Compared to CTR tissues, US tissues exhibited an increased proportion of S100A8+ and MT1E+ basal epithelial cells with pro-inflammatory characteristics. Fibroblasts displayed substantial heterogeneity, with expansion of inflammatory fibroblasts and smooth muscle cell subsets. Endothelial cells (ECs) showed upregulated inflammatory and antigen presentation pathways. Macrophages exhibited mixed M1/M2 polarization, with enrichment of APOE+ and APOBEC3A+ subsets. Additionally, Th17, Treg, and CD8+ T cell populations were elevated. Cell-cell communication analysis revealed enhanced signaling among fibroblasts, ECs, and immune subsets, particularly via PERIOSTIN, collagen, and laminin pathways. CONCLUSION: This study presents the first high-resolution single-cell atlas of ureteral scar stricture tissue, revealing profound cellular heterogeneity and remodeling of the immune-stromal-epithelial landscape. The findings also highlight intensified intercellular communication within the fibrotic microenvironment, offering novel insights into disease pathogenesis and potential therapeutic targets.
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Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients.
BACKGROUND: Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC. OBJECTIVE: To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients. DESIGN, SETTING, AND PARTICIPANTS: We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples. INTERVENTION: Radical or partial nephrectomy on untreated ccRCC patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters. RESULTS AND LIMITATIONS: We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients. CONCLUSIONS: We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management. PATIENT SUMMARY: We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.