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Dendritic-cell diversity in equine blood revealed by single-cell transcriptomics.
INTRODUCTION: Detailed classification of equine dendritic cells (DC) is necessary to address various research questions, such as the role of DC subsets in immune-mediated diseases of horses. METHODS: We applied single-cell RNA sequencing (scRNA-seq) on DC enriched from the blood of two horses. RESULTS: All main DC subsets were detected by key gene expression, including conventional DC type 1 (cDC1; XCR1) and type 2 (cDC2; FCER1A, CD1E), as well as plasmacytoid DC (pDC; TCF4). In addition, we detected a small cluster of hematopoietic progenitors, as well as transitional DC (tDC; FCER1A, TCF4) and putative DC type 3 (DC3; FLT3, CD163). Our data confirms the previously reported phenotype of equine pDC (Flt3+MHC-IIlowCADM1lowCD172aint), cDC1 (Flt3+MHC-IIhighCADM1highCD172alow-int), and cDC2 (Flt3+MHC-IIhighCADM1intCD172ahigh), while also highlighting considerable CD14 expression for cDC2. Two subclusters of equine cDC2 were found to be enriched in FCER1A or CX3CR1 transcripts (cDC2.1 and cDC2.2, respectively), with suggested enhanced extravasation and T-cell stimulatory capacities of the latter. Conservation of DC subsets across species (horse, pig, human, mouse) was illustrated by enrichment analyses with subset-specific gene signatures and by cross-species data integration with publicly available scRNA-seq datasets. CONCLUSION: Our atlas of equine blood DC is a valuable resource for comparative analyses, and it forms the foundation for understanding the involvement of distinct DC subsets in infections and immune-mediated pathologies.
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Single-cell RNA-Seq reveals the heterogeneity of fibroblasts within the tympanojugular paraganglioma microenvironment.
Tympanojugular paragangliomas (TJP) originate from the parasympathetic ganglia in the lateral base of the skull. Although the cellular composition and oncogenic mechanisms of paragangliomas have been evaluated, a comprehensive transcriptomic atlas specific to TJP remains to be established to facilitate further investigations. In this study, single-cell RNA sequencing and whole-exome sequencing were conducted on six surgically excised TJP samples to determine their cellular composition and intratumoral heterogeneity. Fibroblasts were sub-classified into two distinct groups: myofibroblasts and fibroblasts associated with bone remodeling. Additionally, an elaborate regulatory and cell-cell communication network was determined, highlighting the multifaceted role of fibroblasts, which varies depending on expression transitions. The Kit receptor (KIT) signaling pathway mediated interactions between fibroblasts and mast cells, whereas robust connections with endothelial and Schwann cell-like cells were facilitated through the platelet-derived growth factor signaling pathway. These findings establish a foundation for studying the mechanisms underlying protumor angiogenesis and the specific contributions of fibroblasts within the TJP microenvironment. IL6 signaling pathway of fibroblasts interacting with macrophages and endothelial cells may be involved in tumor regrowth. These results enhance our understanding of fibroblast functionality and provide a resource for future therapeutic targeting of TJP.
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Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data.
Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.
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Distinct T and innate-like lymphocyte reprogramming following lyophilized fecal microbiota transplantation in recurrent C. difficile infection.
Fecal microbiota transplantation (FMT) is highly effective in preventing recurrent Clostridioides difficile infection (rCDI), yet its immunological mechanisms remain poorly defined. While bacterial engraftment and recovery of microbial diversity are central to FMT efficacy, accumulating evidence suggests that host immune reprogramming is involved. In murine models, regulatory CD4⁺ T cells are indispensable for clearing C. difficile. To address this mechanistic gap, we examined systemic immune reprogramming following FMT by performing flow cytometry and single-cell RNA sequencing (scRNA-seq) on a subset of successfully treated participants from a clinical trial comparing lyophilized FMT (LFMT) with lyophilized sterile fecal filtrate (LSFF, no live bacteria) for preventing rCDI. Flow cytometry was performed on peripheral mononuclear cells from 19 LFMT recipients and 18 LSFF recipients, and scRNA-seq analysis was performed on two LFMT recipients. Although flow cytometry results did not show significant changes in the assessed markers after rCDI resolution in either treatment group, exploratory scRNA-seq in the two LFMT recipients revealed distinct LFMT-associated transcriptional signatures across adaptive and innate-like lymphocyte populations. LFMT was associated with upregulated activation and regulatory genes (CD69, STAT1, TOX, RORA, FOXP3) in CD4⁺ and CD8⁺ T cells, suggesting enhanced immune regulation with reduced cytotoxic gene expression (GZMB, PRF1, GNLY). Innate-like lymphocytes displayed broad activation, with natural killer cells showing increased KLRD1, PRF1, and IL2RB and mucosal-associated invariant T cells (MAIT cells) upregulating STAT1, JUN, and RORA while downregulating KLRB1 and STAT3. These transcriptional programs are consistent with recalibration of T cell homeostasis and innate-like lymphocyte activation, potentially driven by microbial restoration. Collectively, this exploratory study provides the first single-cell immune atlas of LFMT in rCDI, identifying coordinated activation of regulatory, effector, and innate immune pathways. Given the small sample size, these findings should be considered hypothesis-generating, requiring validation in larger cohorts.
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An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age.
Aging profoundly affects immune-system function, promoting susceptibility to pathogens, cancers and chronic inflammation. We previously identified a population of IL-10-producing, T follicular helper-like cells (" Tfh10 "), linked to suppressed vaccine responses in aged mice. Here, we integrate single-cell ( sc )RNA-seq, scATAC-seq and genome-scale modeling to characterize Tfh10 - and the full CD4 + memory T cell ( CD4 + TM ) compartment - in young and old mice. We identified 13 CD4 + TM populations, which we validated through cross-comparison to prior scRNA-seq studies. We built gene regulatory networks ( GRNs ) that predict transcription-factor control of gene expression in each T-cell population and how these circuits change with age. Through integration with pan-cell aging atlases, we identified intercellular-signaling networks driving age-dependent changes in CD4 + TM. Our atlas of finely resolved CD4 + TM subsets, GRNs and cell-cell communication networks is a comprehensive resource of predicted regulatory mechanisms operative in memory T cells, presenting new opportunities to improve immune responses in the elderly.
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Leveraging Integrated RNA Sequencing to Decipher Adrenomedullin's Protective Mechanisms in Experimental Bronchopulmonary Dysplasia.
Bronchopulmonary dysplasia (BPD) is a chronic lung disease commonly affecting premature infants, with limited therapeutic options and increased long-term consequences. Adrenomedullin (Adm), a proangiogenic peptide hormone, has been found to protect rodents against experimental BPD. This study aims to elucidate the molecular and cellular mechanisms through which Adm influences BPD pathogenesis using a lipopolysaccharide (LPS)-induced model of experimental BPD in mice. Bulk RNA sequencing of Adm-sufficient (wild-type or Adm+/+) and Adm-haplodeficient (Adm+/-) mice lungs, integrated with single-cell RNA sequencing data, revealed distinct gene expression patterns and cell type alterations associated with Adm deficiency and LPS exposure. Notably, computational integration with cell atlas data revealed that Adm-haplodeficient mouse lungs exhibited gene expression signatures characteristic of increased inflammation, natural killer (NK) cell frequency, and decreased endothelial cell and type II pneumocyte frequency. Furthermore, in silico human BPD patient data analysis supported our cell type frequency finding, highlighting elevated NK cells in BPD infants. These results underscore the protective role of Adm in experimental BPD and emphasize that it is a potential therapeutic target for BPD infants with an inflammatory phenotype.
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Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets.
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.
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Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.
INTRODUCTION: Skin cutaneous melanoma (SKCM) is a highly aggressive form of cancer with poor prognosis, characterized by significant molecular and immune heterogeneity. The activation of KRAS signaling pathways is implicated in melanoma progression, yet its role in shaping the tumor microenvironment, particularly in macrophage infiltration, remains poorly understood. METHODS: A comprehensive multi-platform approach was employed, analyzing gene expression data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene set enrichment analysis (GSEA) was utilized to characterize the molecular pathways associated with KRAS signaling. Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. A KRAS-Macrophage Prognostic Associated Gene (KMPAG) signature was developed by integrating these findings, followed by validation using a least absolute shrinkage and selection operator (LASSO) regression model. The prognostic value of the KMPAG signature was assessed through its correlation with clinical outcomes, immune cell infiltration patterns, response to therapy, drug sensitivity, and miRNA-gene regulatory interactions. Cell-cell communication within the SKCM microenvironment was explored using the "CellChat" tool. Experimental validation of gene expression was performed via immunohistochemistry (IHC) and functional assays in gene-modified melanoma cell lines. RESULTS: Twenty-two genes involved in KRAS signaling were identified as critical for patient survival. Single-cell analysis revealed nine distinct cell populations within the SKCM microenvironment, leading to the construction of the KMPAG risk model, which incorporated three key genes-CLEC4A, CXCL10, and LAT2. This signature effectively reclassified macrophage subsets, offering improved diagnostic and prognostic capabilities. Furthermore, the KMPAG signature correlated with a range of clinical parameters, including immune infiltration levels, tumor stage, and therapy response. The model also provided insights into the immune landscape of SKCM, facilitating the prediction of responses to immunotherapy. Functional assays demonstrated that downregulation of CLEC4A significantly promoted melanoma cell proliferation, migration, and invasion. CONCLUSION: This study highlights the importance of KRAS signaling and macrophage infiltration in melanoma prognosis. The KMPAG gene signature presents a novel prognostic tool, offering insights into personalized treatment strategies and predictive biomarkers for immunotherapy in SKCM. Further exploration of CLEC4A's role in melanoma progression may provide new therapeutic avenues for targeted intervention.
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A Single-Cell Transcriptomic Atlas of the Ovine Rumen Microbiome Characterizes Lineage-Specific Metabolic Shifts Associated with Host Heat Tolerance.
The adaptation of complex, host-associated microbiomes to environmental perturbations is a critical determinant of ecosystem stability and resilience to climate change, as exemplified in ruminants. While single-microbe RNA sequencing advances community interrogation, complex microbial cell walls severely constrain unbiased single-cell transcriptomic profiling in the rumen. In this study, we developed an optimized 25 min time-resolved enzymatic lysis strategy using smRandom-seq to map the sheep rumen microbiome at single-cell resolution. By profiling 60 748 cells across 21 samples, we captured previously intractable lineages, resolving the transcriptional states of 213 genera and 662 species, achieving a physiologically relevant 0.303% recovery of methanogenic archaea. Unsupervised clustering partitioned the ecosystem into seven cross-species functional clusters, uncovering a spatial coupling between microbial lifestyle and metabolic specialization. Applying this framework to a model of host thermal adaptation demonstrated that host resilience was associated with rapid transcriptional activation of key energy-metabolism clusters. Notably, a lineage-specific metabolic shift toward a glycolytic phenotype in Anaerovibrio lipolyticus contributes to a compensatory "nutritional sparing" effect associated with host resilience. This dataset provides a foundational resource for rumen microbial ecology and establishes a technical framework for dissecting phenotypic plasticity within complex microbiomes.
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Single-cell multiome and enhancer connectome of human retinal pigment epithelium and choroid nominate pathogenic variants in age-related macular degeneration.
Age-related macular degeneration (AMD) is a leading cause of vision loss worldwide. Genome-wide association studies (GWAS) of AMD have identified dozens of risk loci that may house disease targets. However, variants at these loci are largely noncoding, making it difficult to assess their function and whether they are causal. Here, we present a single-cell gene expression and chromatin accessibility atlas of human retinal pigment epithelium (RPE) and choroid to systematically analyze both coding and noncoding variants implicated in AMD. We employ HiChIP and Activity-by-Contact modeling to map enhancers in these tissues and predict cell and gene targets of risk variants. We further perform allele-specific self-transcribing active regulatory region sequencing (STARR-seq) to functionally test variant activity in RPE cells, including in the context of complement activation. Our work nominates new pathogenic variants and mechanisms in AMD and offers a rich and accessible resource for studying diseases of the RPE and choroid.