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Identification of cell-type specificity, trans- and cis-acting functions of plant lincRNAs from single-cell transcriptomes.
Long noncoding RNAs, including intergenic lncRNAs (lincRNAs), play a key role in various biological processes throughout the plant life cycle, and the advent of single-cell RNA sequencing (scRNA-seq) technology has opened up a valuable avenue for scrutinizing the intricate roles of lincRNAs in cellular processes. Here, we identified a new batch of lincRNAs using scRNA-seq data from diverse tissues of plants (rice, Arabidopsis, tomato, and maize). Based on well-annotated single-cell transcriptome atlases, plant lincRNAs were found to possess the same level of cell-type specificity as mRNAs and to be involved in the differentiation of certain cell types based on pseudo-time analysis. Many lincRNAs were predicted to play a hub role in the cell-type-specific co-expression networks of lincRNAs and mRNAs, suggesting their trans-acting abilities. Besides, plant lincRNAs were revealed to have potential cis-acting properties based on their genomic distances and expression correlations with the neighboring mRNAs. Furthermore, an online platform, PscLncRNA (http://ibi.zju.edu.cn/psclncrna/), was constructed for searching and visualizing all identified plant lincRNAs with annotated potential functions. Our work provides new insights into plant lincRNAs at single-cell resolution and an important resource for understanding and further investigation of plant lincRNAs.
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Myeloid Heterogeneity in Kidney Disease as Revealed through Single-Cell RNA Sequencing.
Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation, and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Although this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promise to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater-resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying etiology to further reveal the patterning of immune cells and nonimmune renal cells. This review summarizes the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies, and published findings using scRNA-seq in the context of myeloid cells and the kidney.
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Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease.
BACKGROUND AND OBJECTIVE: Mitochondria are crucial to the function of renal tubular cells, and their dynamic perturbation in many aspects is an important mechanism of diabetic kidney disease (DKD). Single-nucleus RNA sequencing (snRNA-seq) technology is a high-throughput sequencing analysis technique for RNA at the level of a single cell nucleus. Here, our DKD mouse kidney single-cell RNA sequencing conveys a more comprehensive mitochondrial profile, which helps us further understand the therapeutic response of this unique organelle family to drugs. METHODS: After high fat diet (HFD), mice were intraperitoneally injected with streptozotocin (STZ) to induce DKD, and then divided into three subsets: CON (healthy) subset, DKD (vehicle) subset, and LST (losartan; 25 mg/kg/day) subset. Divide HK-2 cell into LG (low glucose; 5 mM) and HG (high glucose; 30 mM) and HG + LST (losartan; 1 µ M) subsets. snRNA-seq was performed on the renal tissues of LST and DKD subset mice. To reveal the effects of losartan on gene function and pathway changes in renal tubular mitochondria, Gene Ontology (GO) enrichment analysis and GSEA/GSVA scoring were performed to analyze the specific response of proximal tubular (PT) cell mitochondria to losartan treatment, including key events in mitochondrial homeostasis such as mitochondrial morphology, dynamics, mitophagy, autophagic flux, mitochondrial respiratory chain, apoptosis, and ROS generation. Preliminary validation through in vitro and in vivo experiments, including observation of changes in mitochondrial morphology and dynamics using probes such as Mitotracker Red, and evaluation of the effect of losartan on key events of mitochondrial homeostasis perturbation using electron microscopy, laser confocal microscopy, immunofluorescence, and Western blotting. Detection of autophagic flux in cells by transfecting Ad-mCherry-GFP-LC3B dual fluorescence labeled adenovirus. Various fluorescent probes and energy detector are used to detect mitochondrial apoptosis, ROS, and respiration of mitochondrion. RESULTS: Through the single-cell atlas of DKD mouse kidneys, it was found that losartan treatment significantly increased the percentage of PT cells. Gene Ontology (GO) enrichment analysis of differentially expressed genes showed enrichment of autophagy of mitochondrion pathway. Further GSEA analysis and GSVA scoring revealed that mitophagy and other key mitochondrial perturbation events, such as ROS production, apoptosis, membrane potential, adenosine triphosphate (ATP) synthesis, and mitochondrial dynamics, were involved in the protective mechanism of losartan on PT cells, thereby improving mitochondrial homeostasis. Consistent results were also obtained in mice and cellular experiments. In addition, we highlighted a specific renal tubular subpopulation with mitophagy phenotype found in single-cell data, and preliminarily validated it with co-localization and increased expression of Pink1 and Gclc in kidney specimens of DKD patients treated with losartan. CONCLUSIONS: Our research suggests that scRNA-seq can reflect the multifaceted mitochondrial landscape of DKD renal tubular cells after drug treatment, and these findings may provide new targets for DKD therapy at the organelle level.
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Stress relief: emerging methods to mitigate dissociation-induced artefacts.
The rapid progress of single-cell RNA-sequencing (scRNA-seq) at large scales has led to what seemed impossible until recently: the generation of comprehensive transcriptional maps of nearly all cells in multicellular tissues. We pinpoint three key elements as being critical to the production of these maps: scalability, spatial information, and accuracy of the transcriptome of the individual cells. Here, we discuss the ramifications of traditional cell-isolation protocols when capturing the transcriptional signature of cells as they exist in their native tissue context, the methods that have been developed to avoid these distortions, and the biological processes that have unraveled on account of these upgraded methodological approaches.
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Comprehensive Integration of Single-Cell Data.
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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Single-cell RNA sequencing to understand host-virus interactions.
Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.
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Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.
Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.
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Heterogeneity of T cells in periapical lesions and in vitro validation of the proangiogenic effect of GZMA on HUVECs.
AIM: T cells are key immunomodulatory cells in periapical lesions. This study aimed to explore the roles of T cells in chronic apical periodontitis (CAP) using single-cell RNA sequencing and to further investigate Granzyme A (GZMA) in angiogenesis regulation. METHODOLOGY: A total of five CAP samples were collected for single-cell RNA sequencing. We performed subcluster and lineage-tracing analyses for T cells. According to differential gene expression, distinct biological functions enriched in T cells of CAP were presented by gene set enrichment analysis (GSEA) and compared with healthy gingiva (data obtained from the GEO database). CellChat was used to explore potential ligand-receptor interactions between T cells and endothelial cells in CAP. The coculture of primary human umbilical vein endothelial cells (HUVECs) and Jurkat T cells, as well as the addition of GZMA recombinant protein, was used to validate the predicted pair of GZMA and coagulation factor II thrombin receptor (F2R) by RT-PCR, angiogenesis and migration assays. RESULTS: A transcriptomic atlas of 44 746 individual cells was constructed from the periapical lesions of five patients with CAP by single-cell RNA-seq, and eight cell types were identified. We identified nine subsets of T cells and deciphered the cellular heterogeneity of T cells in CAP at the functional level by subclustering and GSEA. Lineage tracing revealed a distinct lineage of T cells in CAP and predicted the transition of the T cellular state upon CAP. GSEA revealed multiple biological processes and relevant angiogenesis genes upregulated in CAP T cells. GZMA-F2R pairs were predicted by cell-cell interactions in CAP. High expression of GZMA and F2R was observed in the coculture of HUVECs and Jurkat T cells, and the proangiogenic capacity of the GZMA recombinant protein was emphasized by in vitro experiments. CONCLUSIONS: Our study provides novel insights into the heterogeneity of T cells in periapical lesions and reveals the potential role of GZMA in T cells in regulating angiogenesis in HUVECs.
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Integrative single-cell and machine learning analysis identifies a tumor doubling time-related prognostic signature and therapeutic targets in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) exhibits marked molecular heterogeneity and diverse clinical outcomes, partly influenced by HPV status and the tumor immune microenvironment. Although tumor doubling time (TDT) reflects tumor growth kinetics and has prognostic relevance, its molecular basis in HNSCC remains unclear. METHODS: Single-cell RNA-seq data from GSE164690 were integrated with bulk transcriptomic datasets from TCGA-HNSC, GSE41613, GSE65858, and ICGC. We identified 21 tumor doubling time-related genes (TDTRGs), evaluated their cell type-specific enrichment using UCell, and defined molecular subtypes by consensus clustering. Immune infiltration was assessed by ssGSEA and CIBERSORT. A prognostic model was developed using 117 machine-learning algorithm combinations in the Mime1 package and further evaluated by single-cell visualization, cell cycle scoring, pseudotime analysis, drug sensitivity prediction, and molecular docking. RESULTS: A single-cell atlas comprising 17 cell populations was constructed. TDTRGs were mainly enriched in cycling T cells and epithelial cells and showed HPV-related expression differences. Two molecular subtypes were identified; C2 was associated with poorer progression-free interval (P = 0.001) and enrichment of MET and integrin signaling. A seven-gene signature-CCBE1, MAD2L1, ECT2, BIRC5, ITGA6, ESM1, and NUF2-showed robust prognostic performance across independent cohorts and outperformed 73 published signatures. Single-cell and pseudotime analyses confirmed cell type-specific and dynamic regulation. Venetoclax was predicted as a potential therapeutic candidate, with docking energies below -9 kcal·mol⁻¹. CONCLUSION: This study defines TDTRG-related heterogeneity in HNSCC and establishes a robust seven-gene prognostic signature, providing potential biomarkers and therapeutic insights for personalized management.
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Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.
The selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. Most current approaches to marker gene selection operate in a label-based framework, which is inherently limited by its dependency on predefined cell type labels or clustering results. In contrast, existing label-free methods often struggle to identify genes that characterize rare cell types or subtle spatial patterns, and they frequently fail to scale efficiently with large data sets. Here, we introduce geneCover, a label-free combinatorial method that selects an optimal panel of minimally redundant marker genes based on gene-gene correlations. Our method demonstrates excellent scalability to large data sets and identifies marker gene panels that capture distinct correlation structures across the transcriptome. This allows geneCover to distinguish cell states in various tissues of living organisms effectively, including those associated with rare or otherwise difficult-to-identify cell types. We evaluate the performance of geneCover across various scRNA-seq and spatial transcriptomics data sets, comparing it to other label-free algorithms to highlight its utility and potential in diverse biological contexts.