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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.
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Implicating Gene and Cell Networks Responsible for Differential COVID-19 Host Responses via an Interactive Single Cell Web Portal.
Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a datamine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to probe these via a new interactive web portal (http://toppcell.cchmc.org/COVID-19). As examples, we develop three hypotheses: (1) a multicellular signaling cascade among alternatively differentiated monocyte-derived macrophages whose tasks include T cell recruitment and activation; (2) novel platelet subtypes with drastically modulated expression of genes responsible for adhesion, coagulation and thrombosis; and (3) a multilineage cell activator network able to drive extrafollicular B maturation via an ensemble of genes strongly associated with risk for developing post-viral autoimmunity.
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Spatial transcriptional mapping of the human nephrogenic program.
Congenital abnormalities of the kidney and urinary tract are among the most common birth defects, affecting 3% of newborns. The human kidney forms around a million nephrons from a pool of nephron progenitors over a 30-week period of development. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlage, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single-cell RNA sequencing identified progenitor states, which were spatially mapped to the nephron anatomy, enabling the generation of functional gene networks predicting interactions within and between nephron cell types. Network mining identified known developmental disease genes and predicted targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease and enhance efforts to generate new kidney structures.
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Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis.
Chondrocyte is considered the only cell type in cartilage. However, the cell heterogeneity of chondrocytes in human articular cartilage is still not well defined, which hinders our understanding of the pathogenesis of osteoarthritis (OA). Here, we constructed a single-cell transcriptomic atlas of chondrocytes in healthy cartilage and identified nine chondrocyte subsets including homeostatic chondrocytes, proliferate fibrochondrocytes, and hypertrophic chondrocytes (HTC). Interestingly, we identified two distinct HTC subpopulations, among which HTC-1 specifically expressed genes associated with apoptosis and programmed cell death. We identified two main trajectories of chondrocytes, one of which differentiates into fibrochondrocytes, while the other terminates in apoptosis. Comparison of chondrocyte subsets between healthy and OA cartilage showed that proliferate fibrochondrocytes and HTC-1 expanded in OA patients, whereas homeostatic chondrocytes decreased. Interestingly, we discovered an OA-specific proliferate fibrochondrocyte subset that may contribute to the development of OA via inflammation. In summary, this study significantly enhanced our understanding of cell heterogeneity of chondrocytes in articular cartilage and provides insight into the pathogenesis of OA.
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Integrated Single-Cell and Spatial Transcriptomics Reveal Cell-Type-Specific Immune Regulatory Networks in Maize Responding to Southern Corn Rust.
Southern corn rust (SCR), caused by Puccinia polysora Underw. (P. polysora), poses a significant threat to maize production, yet the cell-type-specific defense mechanisms remain insufficiently characterized. To address this, we integrated single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomic sequencing (stRNA-seq) to elucidate the cell-type-specific transcriptional dynamics in maize leaves during early infection with P. polysora. Analyses at 24 and 48 h post infection (hpi) revealed eight major cell types and highlighted key defense responses, which are primarily initiated in the mesophyll and epidermal cells 24 hpi. Notably, the cell-type-specific activation of RLPs/RLKs and jasmonic acid was observed. Functional defense modules were activated in specific cell types at 24 hpi, with pseudotime and cell-cell communication analyses further uncovering immune-related cellular dynamics. Importantly, multi-omics analysis identified core DEGs across critical cell types and time points. Functional validation through virus-induced gene silencing (VIGS) demonstrated that silencing the ZmXET1 gene significantly reduced disease severity and pathogen biomass, while silencing the positive regulator ZmRBG, increased susceptibility. This study provides a high-resolution spatiotemporal atlas of maize defense against P. polysora, identifying ZmXET1 as a key susceptibility factor and ZmRBG as a resistance component, thereby offering valuable targets for disease resistance breeding.
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Cell type diversity in scallop adductor muscles revealed by single-cell RNA-Seq.
Studies on cell atlas in marine invertebrates provide a better understanding of cell types, stem cell maintenance, and lineages of cell differentiation. To investigate the molecular features of various cell types in molluscan muscles, we performed single-cell RNA sequencing (scRNA-seq) to map cell types in scallop adductor muscles. We uncovered the cell type-specific features of 20 cell clusters defined by the expression of multiple specific molecular markers. These cell clusters are mainly classified into four broad classes, including mesenchymal stem cells, muscle cells, neurons, and haemolymph cells. In particular, we identified a diverse repertoire of neurons in the striated adductor muscle, but not in the smooth muscle. We further reconstructed the cell differentiation events using all the cell clusters by single-cell pseudotemporal trajectories. By integrating dual BrdU-PCNA immunodetection, neuron-specific staining and electron microscopy observation, we showed the spatial distribution of mesenchymal stem cells and neurons in striated adductor muscle of scallops. The present findings will not only be useful to address the cell type-specific gene expression profiles in scallop muscles, but also provide valuable resources for cross-species comparison of marine organisms.