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A single-cell transcriptome atlas reveals paradoxical monoallelic expression of RNA editing and heterozygous SNPs.
Both genomic mutations and RNA editing contribute to functional complexity and drive adaptive evolution. Single-cell profiling offers deep insight into the cis-regulatory mechanisms underlying these variations. Using 13 025 single-cell Smart-Seq libraries from whole-body Drosophila melanogaster, we unexpectedly found that 94.0% of adenosine-to-inosine RNA editing sites and 92.8% of heterozygous single nucleotide polymorphismss (SNPs) with sufficient "unique fragment support" exhibit binary expression (0 or 1) in a single cell. The genotypes of representative heterozygous SNPs were validated by Sanger sequencing. Meanwhile, binary RNA editing itself is logically questionable due to elusive mechanism, compromised condition specificity, and untenable heterozygote advantage. This fact that for most cases in Smart-Seq, only a single allele (out of the various haplotypes) is finally maintained per cell, raises the following concern. Regardless of the biological or technical explanations like monoallelic transcriptional burst, dropout, or amplification bias that might account for this binary expression pattern, our findings conservatively indicate that Smart-Seq may not be good at analyzing molecular diversity and that the results need to be interpreted with caution.
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Mosaic: Single-Cell Atlas of Stress.
Stress has been prevalent and has become an epidemic health burden, loaded with chronic disorders. The stress response is an adaptive mechanism that prepares an individual to respond to threats or other stressors in a fight-or-flight situation. The stress response involves the induction of neurological and hormonal networks and is usually resolved when stress subsides; however, persistent stress leads to permanent and detrimental impacts on health. With the rise of advanced single-cell analysis technologies, a wave of basic and translational research aimed at elucidating stress has shed light on the underlying mechanisms. Among 80 studies in this review, stressors are classified into acute/chronic physical, physiological, and psychological groups, whereas some studies have more than one stress source. Single-cell RNA-seq was the dominant technology utilized in these studies. This advanced technique systematically reveals cellular heterogeneity in gene expression patterns and the differential transcriptomic landscape of stress response in a wide array of tissues and organ systems, e.g., the nervous system, the endocrine system, the immune system, and others. Bioinformatics identified a single-cell atlas of stress-specific cell subtypes, cell-to-cell interactions, and enriched pathways, showing promise for stress syndrome biomarkers, attenuation, and targeted therapy. The limits of these stress studies were mainly focused on transcriptomics, so future studies using multi-omics approaches across multiple organ systems will yield insights into stress disorders and novel therapeutic strategies.
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Single-cell RNA-seq reveals the transcriptional program underlying tumor progression and metastasis in neuroblastoma.
Neuroblastoma (NB) is one of the most common childhood malignancies. Sixty percent of patients present with widely disseminated clinical signs at diagnosis and exhibit poor outcomes. However, the molecular mechanisms triggering NB metastasis remain largely uncharacterized. In this study, we generated a transcriptomic atlas of 15 447 NB cells from eight NB samples, including paired samples of primary tumors and bone marrow metastases. We used time-resolved analysis to chart the evolutionary trajectory of NB cells from the primary tumor to the metastases in the same patient and identified a common 'starter' subpopulation that initiates tumor development and metastasis. The 'starter' population exhibited high expression levels of multiple cell cycle-related genes, indicating the important role of cell cycle upregulation in NB tumor progression. In addition, our evolutionary trajectory analysis demonstrated the involvement of partial epithelial-to-mesenchymal transition (p-EMT) along the metastatic route from the primary site to the bone marrow. Our study provides insights into the program driving NB metastasis and presents a signature of metastasis-initiating cells as an independent prognostic indicator and potential therapeutic target to inhibit the initiation of NB metastasis.
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Single-cell RNA-Seq reveals the earliest lineage specification and X chromosome dosage compensation in bovine preimplantation embryos.
Lineage specification and X chromosome dosage compensation are two crucial biological processes that occur during preimplantation embryonic development. Although extensively studied in mice, the timing and regulation of these processes remain elusive in other species, including humans. Previous studies have suggested conserved principles of human and bovine early development. This study aims to provide fundamental insights into these programs and the regulation using a bovine embryo model by employing single-cell transcriptomics and genome editing approaches. The study analyzes the transcriptomes of 286 individual cells and reveals that bovine trophectoderm/inner cell mass transcriptomes diverge at the early blastocyst stage, after cavitation but before blastocyst expansion. The study also identifies transcriptomic markers and provides the timing of lineage specification events in the bovine embryo. Importantly, we find that SOX2 is required for the first cell decision program in bovine embryos. Moreover, the study shows the occurrence of X chromosome dosage compensation from morula to late blastocyst and reveals that this compensation results from downregulation of X-linked genes in female embryonic cells. The transcriptional atlas generated by this study is expected to be widely useful in improving our understanding of mammalian early embryo development.
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Deep single-cell decoding of human pancreatic islets reveals T2D β-cell gene expression defects.
Pancreatic islets maintain glucose homeostasis through coordinated action of endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single-cell transcriptome profiling of 245,878 human islet cells from 48 donors spanning non-diabetic, pre-diabetic, and T2D states, and we identify 14 distinct cell types detected in every donor. Cell-cluster analysis reveals ~25-30% β-cell reductions consisting of β-cell loss and proportional increases in a senescent β-cell subpopulation in T2D donors, consistent with previous reports. Further, comparative data integration identifies 511 differentially expressed genes (DEGs) in T2D β-cells, including T2D-associated vitamin A metabolism genes, which are linked to impaired β-cell viability by multimodal functional validation. Integration with T2D genetic, proteomic, and mouse model metabolic phenotypes nominates 58 candidate causal T2D genes, including PDZK1 and GRAMD2B, which preserve β-cell mass. Together, this genomic resource provides an enhanced type 2 diabetes expression-atlas for data exploration, analysis, and hypothesis testing, as well as a novel genomic resource for insights into T2D pathophysiology and human islet dysfunction.
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Beneficial mechanisms of dimethyl fumarate in autoimmune uveitis: insights from single-cell RNA sequencing.
BACKGROUND: Dimethyl fumarate (DMF) is a fumaric acid ester that exhibits immunoregulatory and anti-inflammatory properties. However, the function of DMF in autoimmune uveitis (AU) is incompletely understood, and studies comprehensively exploring the impact of DMF on immune cells are still lacking. METHODS: To explore the function of DMF in uveitis and its underlying mechanisms, we conducted single-cell RNA sequencing (scRNA-seq) on the cervical draining lymph node (CDLN) cells of normal, experimental autoimmune uveitis (EAU), and DMF-treated EAU mice. Additionally, we integrated scRNA-seq data of the retina and CDLNs to identify the potential impact of DMF on ocular immune cell infiltration. Flow cytometry was conducted to verify the potential target molecules of DMF. RESULTS: Our study showed that DMF treatment effectively ameliorated EAU symptoms. The proportional and transcriptional alterations in each immune cell type during EAU were reversed by DMF treatment. Bioinformatics analysis in our study indicated that the enhanced expression of Pim1 and Cxcr4 in EAU was reversed by DMF treatment. Further experiments demonstrated that DMF restored the balance between effector T (Teff) /regulatory T (Treg) cells through inhibiting the pathway of PIM1-protein kinase B (AKT)-Forkhead box O1 (FOXO1). By incorporating the scRNA-seq data of the retina from EAU mice into analysis, our study identified that T cells highly expressing Pim1 and Cxcr4 were enriched in the retina. DMF repressed the ocular infiltration of Teff cells, and this effect might depend on its inhibition of PIM1 and CXCR4 expression. Additionally, our study indicated that DMF might reduce the proportion of plasma cells by inhibiting PIM1 expression in B cells. CONCLUSIONS: DMF effectively attenuated EAU symptoms. During EAU, DMF reversed the Teff/Treg cell imbalance and suppressed the ocular infiltration of Teff cells by inhibiting PIM1 and CXCR4 expression. Thus, DMF may act as a new drug option for the treatment of AU.
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.
Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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A spatial atlas of the healthy human liver from live donors.
Reconstructing gene expression atlases for human tissues is challenging due to limited access to healthy samples from live individuals. Neurologically deceased donors often show ischaemic changes, and tissues near diseased regions may have altered gene expression1,2. The liver, with its unique regenerative capacity, allows analysis from live healthy donors. Here, using spatial transcriptomics (Visium, Visium HD3, multiplexed error-robust fluorescence in situ hybridization (MERFISH)4 and PhenoCycler imaging5) and single-nucleus RNA sequencing6, we analysed 16 liver samples: 8 from young live healthy donors and 8 from individuals with liver pathology, sampling 'adjacent normal' tissue. Livers from live healthy donors displayed significant gene expression differences compared with the adjacent normal tissues from individuals with liver pathology. Hepatocytes and non-parenchymal cells exhibited marked zonation along the porto-central axis of the liver lobules, with key functions being pericentrally shifted compared to mice and other mammals. Our atlas identified dynamic programmes in early steatotic hepatocytes, including a decline in nuclear-encoded mitochondrial proteins and a compensatory increase in mitochondria-encoded transcripts. This study presents a spatial gene expression reference for the healthy human liver and insights into hepatocyte changes in early steatosis.
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Developmental chronology of mouse embryo from 2-cell stage through birth.
Current single-cell atlases of mouse embryos are limited in temporal resolution and cellular or sequencing depth coverage. Here we report the mouse developmental Cell and Lineage Atlas (mdCLA), which covers 37 time points across the entire embryonic development from the 2-cell stage (E1.5) to birth (E19.0), with two million cells and a median of 4,500 genes detected per cell. The temporal coverage and data quality of mdCLA surpass the existing mouse embryonic atlases, enabling the identification of organ-specific cell type and gene expression. Using mdCLA, we uncovered divergent gene expression profiles between early and late-stage metanephric progenitor populations, marked by the upregulation of epithelial differentiation and immune response pathways in the late-stage population. Moreover, by analysing the newly generated cell types over time, we revealed an epithelium-specific accelerated differentiation process that occurs after E14.5. The transcription factor upregulated during late developmental stages was validated, where specific ablation of the Tub gene in the pituitary epithelium led to a reduction in the populations producing follicle-stimulating hormone. Consequently, this genetic modification resulted in postnatal obesity in mice. Together, mdCLA offers a comprehensive and high-quality reference, providing critical insights into the dynamic processes and mechanisms underlying mammalian embryogenesis.
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Single-cell RNA-seq and bulk RNA-seq explore the prognostic value of exhausted T cells in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC. Therefore, a detailed characterisation of exhausted T cells and their clinical significance warrants further investigation in HCC. Based on the GSE146115, we presented a comprehensive single-cell Atlas in HCC. Pseudo-time analysis revealed that tumour heterogeneity progressively increased, and the exhausted T cells gradually appeared during tumour progression. Functional enrichment analysis revealed that the evolutionary process of exhausted T cells mainly contained the pathway of cadherin binding, proteasome, cell cycle, and T cell receptor regulation of apoptosis. In the International Cancer Genome Consortium database, we divided patients into three clusters with the T cell evolution-associated genes. We found that the exhausted T cells are significantly related to poor outcomes through immunity and survival analysis. In The Cancer Genome Atlas database, the authors enrolled weighted gene co-expression network analysis, univariate Cox analysis, and Lasso Cox analysis, then screened the 19 core genes in T cells evolution and built a robust prognostic model. This study offers a fresh view on evaluating the patients' outcomes from an exhausted T cells perspective and might help clinicians develop therapeutic systems.