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A cis-regulatory atlas in maize at single-cell resolution.
cis-regulatory elements (CREs) encode the genomic blueprints of spatiotemporal gene expression programs enabling highly specialized cell functions. Using single-cell genomics in six maize organs, we determined the cis- and trans-regulatory factors defining diverse cell identities and coordinating chromatin organization by profiling transcription factor (TF) combinatorics, identifying TFs with non-cell-autonomous activity, and uncovering TFs underlying higher-order chromatin interactions. Cell-type-specific CREs were enriched for enhancer activity and within unmethylated long terminal repeat retrotransposons. Moreover, we found cell-type-specific CREs are hotspots for phenotype-associated genetic variants and were targeted by selection during modern maize breeding, highlighting the biological implications of this CRE atlas. Through comparison of maize and Arabidopsis thaliana developmental trajectories, we identified TFs and CREs with conserved and divergent chromatin dynamics, showcasing extensive evolution of gene regulatory networks. In addition to this rich dataset, we developed single-cell analysis software, Socrates, which can be used to understand cis-regulatory variation in any species.
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Restricted TCR Diversity and Expansion of CD177+CD8+ and TLR4+CD4+T Cell Subsets in Septic Shock-Associated Immunosuppression: A Single-Cell Multi-Omics Study.
Sepsis-induced immunosuppression contributes to poor outcomes in sepsis patients, yet the mechanisms underlying T cell dysfunction remain incompletely understood. We integrated single-cell RNA sequencing (scRNA-seq), paired single-cell T cell receptor sequencing (scTCR-seq), bulk RNA sequencing (RNA-seq), and flow cytometry to characterize CD8+ and CD4+ T cells in septic shock-associated immunosuppression. Patients showed reduced T cell receptor (TCR) clonotype diversity in CD4+ T cells but not CD8+ T cells, with restricted clonality correlating with disease severity and lymphopenia. Integrative analyses revealed the expansion of two disease-associated subsets: (1) CD177+CD8+ T cells, enriched for interleukin-17 (IL-17) signaling with hyperexpanded clonotypes, and (2) TLR4+CD4+ T cells, enriched for phagocytosis- and lysosome-related programs with restricted clonality. Both subsets expressed high levels of S100A8, S100A9, and S100A12, correlating positively with severity scores and systemic inflammation. These findings provided single-cell resolution evidence that clonal restriction of CD4+ T cells is a hallmark of septic immunosuppression. Our study depicted a single-cell atlas linking CD4+ clonal restriction and the expansion of pathogenic T cell subsets to outcome, highlighting TCR clonality as a biomarker of immune competence and CD177+CD8+ and TLR4+CD4+ T cells as candidate therapeutic targets in septic shock-associated immunosuppression.
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Single-cell analysis reveal cell-type-specific RNA methylation patterns in breast cancer.
ObjectiveThis study aimed to investigate cell-type-specific RNA methylation patterns in primary breast cancers and metastatic lymph nodes and explore their roles in cancer progression and immune regulation.MethodsUsing single-cell RNA sequencing data from five breast cancer patients (primary cancers and matched metastatic lymph nodes), we calculated RNA methylation scores per cell using the AUCell method. Cell types were annotated using Seurat. Differentially expressed gene analysis, pseudotime trajectory assessment, copy number variation analysis, and cell-cell communication analyses were performed between high- and low-methylation groups. Validation was conducted using bulk RNA sequencing data from The Cancer Genome Atlas.ResultsMetastatic lymph nodes showed significantly lower RNA methylation scores than primary cancers, particularly in T cells and B cells. Low methylation was correlated with enhanced cell differentiation, increased malignancy in epithelial cells, and reduced immune cell communication. Monocytes exhibited opposite trends. Differentially expressed genes from methylation groups stratified The Cancer Genome Atlas patients into two clusters with distinct survival and immune infiltration profiles.ConclusionsRNA methylation is suppressed during breast cancer metastasis, affecting cell differentiation, immune communication, and patient outcomes.
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An entropy-based metric for assessing the purity of single cell populations.
Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas.
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m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.
N 6-Methyladenosine (m6A) is one of the most abundant internal chemical modifications on eukaryote mRNA and is involved in numerous essential molecular functions and biological processes. To facilitate the study of this important post-transcriptional modification, we present here m6A-Atlas v2.0, an updated version of m6A-Atlas. It was expanded to include a total of 797 091 reliable m6A sites from 13 high-resolution technologies and two single-cell m6A profiles. Additionally, three methods (exomePeaks2, MACS2 and TRESS) were used to identify >16 million m6A enrichment peaks from 2712 MeRIP-seq experiments covering 651 conditions in 42 species. Quality control results of MeRIP-seq samples were also provided to help users to select reliable peaks. We also estimated the condition-specific quantitative m6A profiles (i.e. differential methylation) under 172 experimental conditions for 19 species. Further, to provide insights into potential functional circuitry, the m6A epitranscriptomics were annotated with various genomic features, interactions with RNA-binding proteins and microRNA, potentially linked splicing events and single nucleotide polymorphisms. The collected m6A sites and their functional annotations can be freely queried and downloaded via a user-friendly graphical interface at: http://rnamd.org/m6a.
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Xoublet: An Extreme-Scale Doublet Detection Algorithm for Cardiac Single-Cell Transcriptomics.
Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of cardiac cell composition and its underlying pathogenesis. In scRNA-seq data analysis, doublet detection plays a pivotal role in quality control. However, these methods often face challenges related to high computational demands and extended runtimes, particularly in large-scale datasets and rare cell identification. To address these issues, this study introduces the eXtreme-scale Doublet Analyzer (Xoublet) algorithm, which projects single-cell transcriptomic data into principal component analysis space and simulates doublets. Xoublet constructs a hierarchical tree to facilitate rapid screening of similar data, expediting neighborhood searches. Xoublet was compared with Scrublet and DoubletFinder on human-mouse and Cell Hashing datasets with experimentally derived labels, while method-specific calls were examined in an annotated cardiac atlas without independent doublet ground truth. Computational scalability was evaluated separately on HeartMap. Accuracy varied across datasets: Xoublet performed similarly to Scrublet in the cell-line Cell Hashing benchmark but was less discriminative in the human-mouse and peripheral blood mononuclear cell benchmarks. On 2,479,674 HeartMap profiles, Xoublet completed scoring in 574.8 seconds with 4.14 GiB peak resident memory, compared with 1060.6 seconds and 22.81 GiB for the Scrublet core. These findings indicate that Xoublet provides computational advantages for large-scale scoring, although its detection accuracy and sensitivity to specific cell states remain data-dependent.
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De Novo Gene Expression Reconstruction in Space.
The biological function of a gene often depends on spatial context, and an atlas of transcriptional regulation could be instrumental in defining functional elements across the genome. Despite recent advances in single-cell RNA sequencing and in situ RNA imaging, fundamental barriers limit the speed, genome-wide coverage, and resolution of de novo transcriptome assembly in space. Here, I discuss potential next-generation approaches for the de novo assembly of the transcriptome in space, and propose more efficient methods of detecting long-range spatial variations in gene expression. Finally, I discuss future in situ sequencing chemistries for visualizing biological pathways and processes in tissues so that genomics technologies might be more easily applied to conditions of human health and disease.
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HMGA2 Expression Predicts Subtype, Survival, and Treatment Outcome in Pancreatic Ductal Adenocarcinoma.
PURPOSE: The purpose of this study was to establish HMGA2 as a marker of basal-like disease in pancreatic ductal adenocarcinoma (PDAC) and explore its use as a biomarker for prognosis and treatment resistance. EXPERIMENTAL DESIGN: We identified high-mobility group A2 (HMGA2) protein expression in basal PDAC cells in a single-cell RNA sequencing (RNA-seq) atlas of 172 patient samples. We then analyzed HMGA2 expression, along with expression of the classic marker GATA-binding factor 6 (GATA6), in a cohort of 580 PDAC samples with multiplex IHC. We further supplemented these data with an additional 30 diverse patient samples and multiple independent single-cell RNA-seq databases. RESULTS: We found that expression of HMGA2, but not previously described basal markers cytokeratins 5 or 17, predicted overall survival in our cohort. Combining HMGA2 and GATA6 statuses allowed for the identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, low tumor-infiltrating CD8+ T cells, increased FAP+ fibroblasts, and poorer response to gemcitabine-based chemotherapies (n = 94, median survival = 11.2 months after surgery) and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T-cell infiltrate, decreased FAP+ fibroblasts, and improved survival with gemcitabine-based chemotherapy (n = 198, median survival = 21.7 months after surgery). HMGA2 was also prognostic for overall survival in RNA-seq from an independent cohort. CONCLUSIONS: IHC stratification of primary tumors by HMGA2 and GATA6 statuses in pancreatic cancer is associated with differential outcomes, survival following chemotherapy, and tumor microenvironments. As a nuclear marker for basal disease, HMGA2 complements GATA6 to identify disease subtypes in PDAC.
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Single-cell, single-nucleus and xenium-based spatial transcriptomics analyses reveal inflammatory activation and altered cell interactions in the hippocampus in mice with temporal lobe epilepsy.
BACKGROUND: Temporal lobe epilepsy (TLE) is among the most common types of epilepsy and often leads to cognitive, emotional, and psychiatric issues due to the frequent seizures. A notable pathological change related to TLE is hippocampal sclerosis (HS), which is characterized by neuronal loss, gliosis, and an increased neuron fibre density. The mechanisms underlying TLE-HS development remain unclear, but the reactive transcriptomic changes in glial cells and neurons of the hippocampus post-epileptogenesis may provide insights. METHODS: To induce TLE, 200 nl of kainic acid (KA) was stereotactically injected into the hippocampal CA1 region of mice, followed by a 7-day postinjection period. Single-cell RNA sequencing (ScRNA-seq), single-nucleus RNA sequencing (SnRNA-seq), and Xenium-based spatial transcriptomics analyses were employed to evaluate the changes in mRNA expression in glial cells and neurons. RESULTS: From the ScRNA-seq and SnRNA-seq data, 31,390 glial cells and 48,221 neuronal nuclei were identified. Analysis of the differentially expressed genes (DEGs) revealed significant transcriptomic alterations in the hippocampal cells of mice with TLE, affecting hundreds to thousands of mRNAs and their signalling pathways. Enrichment analysis indicated notable activation of stress and inflammatory pathways in the TLE hippocampus, while pathways related to axonal development and neural support were suppressed. Xenium analysis demonstrated the expression of all 247 genes across mouse brain sections, revealing the spatial distributions of their expression in 27 cell types. Integrated analysis of the DEGs identified via the three sequencing techniques revealed that Spp1, Trem2, and Cd68 were upregulated in all glial cell types and in the Xenium data; Penk, Sorcs3, and Plekha2 were upregulated in all neuronal cell types and in the Xenium data; and Tle4 and Sipa1l3 were downregulated in all glial cell types and in the Xenium data. CONCLUSION: In this study, a high-resolution single-cell transcriptomic atlas of the hippocampus in mice with TLE was established, revealing potential intrinsic mechanisms driving TLE-associated inflammatory activation and altered cell interactions. These findings provide valuable insights for further exploration of HS development and epileptogenesis.
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Embryo-scale, single-cell spatial transcriptomics.
Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.