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Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects.
Finding the right balance of quality and quantity can be important, and it is essential that project quality does not drop below the level where important main conclusions are missed or misstated. We use knock-out and over-expression studies as a simplification to test recovery of a known causal gene in RNA-Seq cell line experiments. When single-end RNA-Seq reads are aligned with STAR and quantified with htseq-count, we found potential value in testing the use of the Generalized Linear Model (GLM) implementation of edgeR with robust dispersion estimation more frequently for either single-variate or multi-variate 2-group comparisons (with the possibility of defining criteria less stringent than |fold-change| > 1.5 and FDR < 0.05). When considering a limited number of patient sample comparisons with larger sample size, there might be some decreased variability between methods (except for DESeq1). However, at the same time, the ranking of the gene identified using immunohistochemistry (for ER/PR/HER2 in breast cancer samples from The Cancer Genome Atlas) showed as possible shift in performance compared to the cell line comparisons, potentially highlighting utility for standard statistical tests and/or limma-based analysis with larger sample sizes. If this continues to be true in additional studies and comparisons, then that could be consistent with the possibility that it may be important to allocate time for potential methods troubleshooting for genomics projects. Analysis of public data presented in this study does not consider all experimental designs, and presentation of downstream analysis is limited. So, any estimate from this simplification would be an underestimation of the true need for some methods testing for every project. Additionally, this set of independent cell line experiments has a limitation in being able to determine the frequency of missing a highly important gene if the problem is rare (such as 10% or lower). For example, if there was an assumption that only one method can be tested for "initial" analysis, then it is not completely clear to the extent that using edgeR-robust might perform better than DESeq2 in the cell line experiments. Importantly, we do not wish to cause undue concern, and we believe that it should often be possible to define a gene expression differential expression workflow that is suitable for some purposes for many samples. Nevertheless, at the same time, we provide a variety of measures that we believe emphasize the need to critically assess every individual project and maximize confidence in published results.
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A 4D single-cell protein atlas of transcription factors delineates spatiotemporal patterning during embryogenesis.
Complex biological processes such as embryogenesis require precise coordination of cell differentiation programs across both space and time. Using protein-fusion fluorescent reporters and four-dimensional live imaging, we present a protein expression atlas of transcription factors (TFs) mapped onto developmental cell lineages during Caenorhabditis elegans embryogenesis, at single-cell resolution. This atlas reveals a spatiotemporal combinatorial code of TF expression, and a cascade of lineage-specific, tissue-specific and time-specific TFs that specify developmental states. The atlas uncovers regulators of embryogenesis, including an unexpected role of a skin specifier in neurogenesis and the critical function of an uncharacterized TF in convergent muscle differentiation. At the systems level, the atlas provides an opportunity to model cell state-fate relationships, revealing a lineage-dependent state diversity within functionally related cells and a winding trajectory of developmental state progression. Collectively, this single-cell protein atlas represents a valuable resource for elucidating metazoan embryogenesis at the molecular and systems levels.
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C5aR1 blockade reshapes immunosuppressive tumor microenvironment and synergizes with immune checkpoint blockade therapy in high-grade serous ovarian cancer.
High-grade serous ovarian cancer (HGSC), with a modest response to immune checkpoint blockade (ICB) targeting PD-1/PD-L1 monotherapy, is densely infiltrated by M2-polarized tumor-associated macrophages (TAMs) and regulatory T (Treg) cells. The complement C5a/C5aR1 axis contributes to the programming of the immunosuppressive phenotype of TAMs in solid tumors and represents a promising immunomodulatory target for treating HGSCs. Here, we aimed to identify the relevance of C5aR1 in prognosis, immune microenvironment, and immunotherapy response in HGSCs. The expression and relationship of C5aR1 with tumor-infiltrating immune cells were assessed by immunohistochemistry and flow cytometry in the training cohort (n = 120) and fresh HGSC tissues (n = 36). Transcriptomic analyses of the xenografts delineated the mechanisms driving the immunomodulatory activity of PMX53, an orally bioavailable C5aR1 inhibitor. Therapeutic relevance was confirmed in ex vivo tumor cultures and The Cancer Genome Atlas (TCGA) datasets. C5aR1 expression independently predicted dismal prognosis and was linked to the immunoevasive subtype of HGSC, characterized by increased infiltration of pro-tumor cells (Treg cells, M2-polarized macrophages, and neutrophils) and impaired CD8+T functions. PMX53 antagonized subcutaneous tumor growth, modulated immunosuppressive mechanisms and synergized with aPD-1 in several tumor types. Single-cell RNA-seq analysis revealed predominant C5aR1 expression in TAMs, with an immunosuppressive-related expression signature in C5aR1+TAMs. Furthermore, the combination of C5aR1 and PD-L1 was associated with specific molecular characteristics and matched clinical response annotations. Therefore, the abundance of C5aR1 could predict an inferior prognosis in HGSCs, and incorporating PD-L1 may serve as a novel predictive biomarker to guide therapeutic options.
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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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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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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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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.