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Transcriptional and imprinting complexity in Arabidopsis seeds at single-nucleus resolution.
Seeds are a key life cycle stage for many plants. Seeds are also the basis of agriculture and the primary source of calories consumed by humans1. Here, we employ single-nucleus RNA-sequencing to generate a transcriptional atlas of developing Arabidopsis thaliana seeds, with a focus on endosperm. Endosperm, the primary site of gene imprinting in flowering plants, mediates the relationship between the maternal parent and the embryo2. We identify transcriptionally uncharacterized nuclei types in the chalazal endosperm, which interfaces with maternal tissue for nutrient unloading3,4. We demonstrate that the extent of parental bias of maternally expressed imprinted genes varies with cell-cycle phase, and that imprinting of paternally expressed imprinted genes is strongest in chalazal endosperm. Thus, imprinting is spatially and temporally heterogeneous. Increased paternal expression in the chalazal region suggests that parental conflict, which is proposed to drive imprinting evolution, is fiercest at the boundary between filial and maternal tissues.
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Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals diagnostic and prognostic signatures and immunoinfiltration in gastric cancer.
BACKGROUND: Early diagnosis and prognostic predication of gastric cancer (GC) pose significant challenges in current clinical practice of GC treatments. Therefore, our aim was to explore relevant gene signatures that can predict the prognosis of GC patients. METHODS: Here, we established a single-cell transcriptional atlas of GC, focusing on the expression of T-cell-related genes for cell-cell communication analysis, trajectory analysis, and transcription factor regulatory network analysis. Additionally, we conducted validation and prediction of immune-related prognostic gene signatures in GC patients using TCGA and GEO data. Based on these prognostic gene signatures, we predicted the immune infiltration status of GC patients by grouping the patient samples into high or low-risk groups. RESULTS: Based on 10 tumor samples and corresponding normal samples from GC patients, we selected 18,416 cells for subsequent analysis using single-cell sequencing. From these, we identified 3,284 T-cells and obtained 641 differentially expressed genes related to T-cells from 5 different T-cell subtypes. By integrating bulk RNA sequencing data, we identified prognostic signatures associated with T-cells. Stratifying patients based on these prognostic signatures into high-risk or low-risk groups allowed us to effectively predict their survival rates and the immunoinfiltration status of the tumor microenvironment. CONCLUSION: This study explored prognostic gene signatures associated with T-cells in GC patients, providing insights into predicting patients' survival rates and immunoinfiltration levels.
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Comprehensive pan-cancer analysis of FSCN1 as a marker for prognosis and immunity.
BACKGROUND: Fascin actin-bundling protein 1 (FSCN1), a member of actin cytoskeletal protein family genes, plays critical roles in cell migration, motility, adhesion, and other cellular interactions. It has shown its ascending importance in tumors of multiple systems, including the nervous system, respiratory system, digestive system and urinary system. Nevertheless, no systematic pan-cancer analysis has been carried out to investigate its function in diagnosis, prognosis, and immunological prediction. METHODS: We used UCSC Xena, The Cancer Genome Atlas (TCGA), Genotype Tissue Expression Project (GTEx), Human Protein Atlas (HPA), cBioPortal, STRING, Cancer single-cell state Atlas (CancerSEA), Genomics of Drug Sensitivity in Cancer (GDSC) and Xiantao Academic analysis tool websites and databases for the extraction of pan-cancer data on FSCN1. We adopted bioinformatics methods to explore the potential role of FSCN1 in pan-cancer, including analysis of the correlation between FSCN1 and clinicopathologic features, prognosis, genetic alteration, immune, DNA methylation, single cell function and drug sensitivity. Furthermore, a protein–protein interaction network was drawn, and gene set enrichment analysis was conducted to explore the functions of FSCN1. Finally, we validated the effects of FSCN1 on cell proliferation and apoptosis through experiments. RESULTS: The findings of the comprehensive pan-cancer analysis revealed that FSCN1 was highly expressed in most cancers, except in acute myeloid leukemia, prostate adenocarcinoma and thyroid carcinoma, and it has a certain diagnostic and prognosis value in many cancers. Furthermore, FSCN1 expression was found to correlate with immune cell infiltration, immune checkpoint (ICP) genes expression, DNA methylation and drug sensitivity in a variety of cancers. In addition, we discovered that FSCN1 participated in the proliferation and DNA repair at the single-cell level. Besides, Gene function enrichment analyses revealed that genes that were co-expressed with FSCN1 were enriched within the actin filament regulation pathways. CCK-8 assay and flow cytometry apoptosis assay results indicated that FSCN1 knockdown inhibited proliferation and induced apoptosis in mesothelioma (MESO). CONCLUSION: FSCN1 is a prospective marker for the diagnosis, prognosis, immunology, chemotherapy, and small molecular drugs targeted for pan-cancer treatment.
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Spatial multi-omics reveals the putative biosynthetic sites and distribution patterns of key flavor-contributing metabolites in tea leaves.
Tea (Camellia sinensis) quality is determined by key metabolites including catechins, caffeine, theanine, and volatiles, yet their biosynthesis and spatial distribution at the cellular level remain poorly understood. Here, we constructed a cell-type-resolved spatial multi-omics atlas of tea leaves from C. sinensis var. sinensis 'Jinxuan' and C. sinensis var. assamica 'Yinghong No. 9' by integrating single-nucleus RNA sequencing (snRNA-seq), physically isolated cell-type multi-omics, and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Multi-omics analyses identified mesophyll cells as the major site of catechin biosynthesis, particularly for terminal esterification steps. Theanine biosynthesis was inferred to be predominantly associated with mesophyll cells but accumulated in both mesophyll and vascular bundle cells, consistent with the distribution of its precursors, glutamate and glutamine. Caffeine biosynthesis and accumulation were mainly associated with mesophyll cells, whereas other alkaloids showed vascular-associated accumulation patterns. Benzenoid and fatty acid-derived volatiles showed broader cellular distributions, whereas monoterpene biosynthesis was mainly associated with mesophyll and epidermal cells and their glycosylated derivatives preferentially accumulated in vascular tissues. Further comparison of the two cultivars showed that linalool and geraniol displayed distinct cultivar-dependent accumulation patterns associated with cell density, synthase expression, and enzyme catalytic properties. Overall, key flavor-related metabolites are mainly synthesized in mesophyll cells, whereas vascular bundle cells may contribute to their accumulation and redistribution. These findings reveal the cellular organization of tea flavor metabolism and provide a cellular and molecular framework for future evaluation of tea germplasm with desirable flavor traits.
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The developing leaf of the wild grass Brachypodium distachyon at single-cell resolution.
Leaves are the plant's main photosynthetic organs and drive Earth's primary production. Grasses form longitudinal leaves with parallel venation and graminoid stomata. Yet, how distinct leaf tissues coordinately develop to build functional grass leaf anatomy is not well understood. Here, we decoded the developing grass leaf from vegetative meristems to mature tissues using single-cell RNA-sequencing in the wild grass Brachypodium distachyon. In-depth analysis of epidermal clusters and multiplexed whole-mount RNA-fluorescence in situ hybridization resolved most epidermal lineages and confirmed them in planta. Gene regulatory network analysis distinguished the targetomes of the 2 co-expressed, yet functionally divergent stomatal transcription factors BdMUTE and BdFAMA. Finally, we used our dataset to identify and functionally describe the role of the transcription factor gene BdGRAS32 in inhibiting cell division and a stomata-specific role for the putative cell wall-modifying enzyme BdPME53-like. Our single-cell grass leaf atlas enables the dissection of developmental processes that make the grass leaf, which sustains global food production.
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Dynamics of Cell Fate Decisions during Chemically Induced Multi-Lineage Trans-Differentiation at Single-Cell Level.
Cell trans-differentiation offers a powerful means to manipulate cell identities. By exposing cells to a combination of small molecules (SMs), cell trans-differentiation can be induced in a simple and cost-effective manner. However, a comprehensive atlas detailing chemical-induced cell trans-differentiation across multiple cell fates has yet to be established. In this study, the underlying mechanisms of trans-differentiation is investigated and constructed an in-depth single-cell atlas of this process. The time-course trajectory is demonstrated for trans-differentiation of mouse embryonic fibroblasts (MEFs) into multiple cell lineages including epithelial, neural, extraembryonic endoderm like (XEN-like) cells, and endothelial cells, when induced by SMs cocktail 6TCF (E616452, tranylcypromine, CHIR99021, and forskolin). These trans-differentiated cells closely resemble various somatic cell types in the fetus. It is found that trans-differentiation is marked by dynamic shifts in entropy and the cell cycle during cell fate transitions. A common intermediate feature is revealed characterized by high ribosomal gene expression. This study combines high-resolution landscape with comparative analyses of trans-differentiation dynamics, providing new insights into the complex mechanisms driving cell fate determination in vitro. Future study shall explore the applicability of the model in human cell trans-differentiation.
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SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine.
Single-cell multi-omics sequencing has greatly accelerated reproductive research in recent years, and the data are continually growing. However, utilizing these data resources is challenging for wet-lab researchers. A comprehensive platform for exploring single-cell multi-omics data related to reproduction is urgently needed. Here, we introduce the single-cell multi-omics atlas of reproduction (SMARTdb), an integrative and user-friendly platform for exploring molecular dynamics of reproductive development, aging, and disease, which covers multi-omics, multi-species, and multi-stage data. We curated and analyzed single-cell transcriptomic and epigenomic data of over 2.0 million cells from 6 species across the entire lifespan. A series of powerful functionalities are provided, such as "Query gene expression", "DIY expression plot", "DNA methylation plot", and "Epigenome browser". With SMARTdb, we found that the male germ cell-specific expression pattern of RPL39L and RPL10L is conserved between human and other model animals. Moreover, DNA hypomethylation and open chromatin may collectively regulate the specific expression pattern of RPL39L in both male and female germ cells. In summary, SMARTdb is a powerful platform for convenient data mining and gaining novel insights into reproductive development, aging, and disease. SMARTdb is publicly available at https://smart-db.cn.
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A Novel Necroptosis-Related Prognostic Signature of Glioblastoma Based on Transcriptomics Analysis and Single Cell Sequencing Analysis.
Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.
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Single-cell transcriptomic profiling reveals cell type heterogeneity between HFpEF and HFrEF.
Heart failure with preserved ejection fraction (HFpEF) as a high heterogeneity clinical syndrome, is commonly associated with diastolic dysfunction, and has no effective therapy, which is obviously distinct from Heart failure with reduced ejection fraction (HFrEF). Currently the differences of cell type heterogeneity between HFpEF and HFrEF remain largely unknown. Here we illustrate an atlas consisting of 21,747 cardiac cells, including both HFpEF and HFrEF. Cell-cell communication analysis reveals cardiomyocytes rather than endothelium or fibroblasts were dominant communication "hub" in HFpEF. The subtypes of cardiomyocytes are highly heterogeneous between HFpEF and HFrEF. Notably, a specific subtype of cardiomyocytes shows significant gene expression associated with the metabolism of fatty acids. Additionally, regulon analysis reveals that Ppargc1a, Atf6, E2f6, and Mitf exhibited specific elevated regulation in the subtype of cardiomyocytes of HFpEF. Furthermore, we have identified 210 HF susceptibility genes from HF-associated GWAS data. After integrating scRNA-seq, GWAS, and eQTL data, the genetic susceptibility underlying HFpEF and HFrEF were discussed. In conclusion, this study not only comprehensively characterizes the differences of cardiomyocytes changes but also provides insights into potential targets for cell type- and subtype-specific molecules between HFpEF and HFrEF.
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Single-cell sequencing reveals the expansion and diversity of T cell subsets in the bone marrow microenvironment of chronic myeloid leukemia.
The immune microenvironment plays an important role in leukemia treatment. However, a specific single-cell profiling of the immune alteration in bone marrow of chronic myeloid leukemia (CML) patients is still lacking. We performed multi-level single-cell sequencing to systematically decipher the bone marrow T cell atlas of CML patients. The results exhibited extensive changes of T cells, including the decreased CD4 T cells and increased CD8 T cells in the CML bone marrow. Subpopulation analysis revealed a significant increase of CD8 terminal effector (TE) cells and a significant decrease of CD4 naïve T cells. T cell receptor sequencing showed that the overall diversity of the T cell receptor repertoire was reduced in CML, with the exception of the CD8 TE cell. In addition, CD8 TE cells were the main source of gene expression differences in CD8 T cells. Intercellular communication analysis revealed the altered interaction between CD8 TE and other non-T cells in CML, including neutrophil subtype, indicating the potential regulation of bone marrow microenvironment cells on CD8 TE dynamics. Collectively, our work characterises the alteration of T cell subsets in CML patients at multiple single-cell levels, providing a valuable resource for understanding the immune microenvironment and developing new immune strategies for CML therapy.