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Decoding crops one cell at a time: from cell atlases to single-cell genetics.
Understanding the mechanisms underlying key agricultural traits remains a central challenge in crop research, but recent advances in technologies are providing powerful tools to address this issue. Among these, single-cell and spatial transcriptomics have revealed tissue heterogeneity and spatial organization, offering unique insights into cellular gene expression dynamics and the coordinated activity of multiple cell types. These approaches help uncover how specific cell types contribute to agricultural traits and refine candidate loci lists through integration with trait-associated loci. Additionally, single-cell and spatial transcriptomics have the potential to serve as cell-level readout platforms integrating cellular perturbations, enabling high-throughput discovery of causal relationships between genotype and gene expression at the cellular level in plants. Successful implementation will accelerate the identification of key genetic variants for crop improvement. Here we review lessons learned from application of single-cell screening in mammalian cells, highlight major technical and biological barriers to its use in plants, and outline potential strategies to overcome these challenges. Together, the widespread application and integration of single-cell and spatial transcriptomics with other technologies enable not only the descriptive cataloging of cell states but also the causal interrogation of sequence functions and regulatory networks at cell-type resolution, ultimately advancing gene function studies and accelerating crop improvement.
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Integrated analysis of single-cell and bulk transcriptome identifies a signature based on NK cell marker genes to predict prognosis and therapeutic response in clear cell renal cell carcinoma.
BACKGROUND: Accumulating evidence has highlighted the effects of natural killer (NK) cells on shaping anti-tumor immunity. This study aimed to construct an NK cell marker gene signature (NKMS) to predict prognosis and therapeutic response of clear cell renal cell carcinoma (ccRCC) patients. METHODS: Publicly available single-cell and bulk RNA profiles with matched clinical information of ccRCC patients were collected from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), ArrayExpress, and International Cancer Genome Consortium (ICGC) databases. A novel NKMS was constructed, and its prognostic value, associated immunogenomic features and predictive capability to immune checkpoint inhibitors (ICIs) and anti-angiogenic therapies were evaluated in ccRCC patients. RESULTS: We identified 52 NK cell marker genes by single-cell RNA-sequencing (scRNA-seq) analysis in GSE152938 and GSE159115. After least absolute shrinkage and selection operator (LASSO) and Cox regression, the most prognostic 7 genes (CLEC2B, PLAC8, CD7, SH3BGRL3, CALM1, KLRF1, and JAK1) composed NKMS using bulk transcriptome from TCGA. Survival and time-dependent receiver operating characteristic (ROC) analysis exhibited exceptional predictive capability of the signature in the training set and two independent validation cohorts (E-MTAB-1980 and RECA-EU cohorts). The seven-gene signature was able to identify patients within high Fuhrman grade (G3-G4) and American Joint Committee on Cancer (AJCC) stage (III-IV). Multivariate analysis confirmed the independent prognostic value of the signature, and a nomogram was built for clinical utility. The high-risk group was characterized by a higher tumor mutation burden (TMB) and greater infiltration of immunocytes, particularly CD8+ T cells, regulatory T (Treg) cells and follicular helper T (Tfh) cells, in parallel with higher expression of genes negatively regulating anti-tumor immunity. Moreover, high-risk tumors exhibited higher richness and diversity of T-cell receptor (TCR) repertoire. In two therapy cohorts of ccRCC patients (PMID32472114 and E-MTAB-3267), we demonstrated that high-risk group showed greater sensitivity to ICIs, whereas the low-risk group was more likely to benefit from anti-angiogenic therapy. CONCLUSIONS: We identified a novel signature that can be utilized as an independent predictive biomarker and a tool for selecting the individualized treatment for ccRCC patients.
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CellCover Defines Marker Gene Panels Capturing Developmental Progression in Neocortical Neural Stem Cell Identity.
Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. Interrogating binarized expression data, we aim to identify discriminating panels of genes that are specific to, not only enriched in, individual cell types. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing marker gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. Using scRNA-seq data from blood and brain tissue, we show that this new method, CellCover, performs as good or better than DE and other methods in defining cell-type discriminating gene panels, while reducing gene redundancy and capturing cell-class-specific signals that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neocortical neurogenesis, as well as developmental progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex likely appeared in gliogenic precursors of the rodent before the full program emerged in neurogenic cells of the primate lineage. We have assembled the public datasets we use in this report within the NeMO Analytics multi-omic data exploration environment [1], where the expression of individual genes (NeMO: Individual genes in cortex and NeMO: Individual genes in blood) and marker gene panels (NeMO: Telley 3 CellCover Panels, NeMO: Telley 12 CellCover Panels, NeMO: Sorted Brain Cell CellCover Panels, and NeMO: Blood 34 CellCover Panels) can be freely explored without coding expertise. CellCover is available in CellCover R and CellCover Python.
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Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma.
Glioblastoma (GBM) is the most malignant tumor in center nervous system. Clinical statistics revealed that senior GBM patients had a worse overall survival (OS) comparing with that of patients in other ages, which is mainly related with tumor microenvironment including tumor-associated immune cells in particular. However, the immune heterogeneity and age-related prognosis in GBM are under studied. Here we developed a machine learning-based method to integrate public large-scale single-cell RNA sequencing (scRNA-seq) datasets to establish a comprehensive atlas of immune cells infiltrating in cross-age GBM. We found that the compositions of the immune cells are remarkably different across ages. Brain-resident microglia constitute the majority of glioblastoma-associated macrophages (GAMs) in patients, whereas dramatic elevation of extracranial monocyte-derived macrophages (MDMs) is observed in GAMs of senior patients, which contributes to the worse prognosis of aged patients. Further analysis suggests that the increased MDMs arisen from excessive recruitment and proliferation of peripheral monocytes not only lead to the T cell function inhibition in GBM, but also stimulate tumor cells proliferation via VEGFA secretion. In summary, our work provides new cues for the correlational relationship between the immune microenvironment of GBM and aging, which might be insightful for precise and effective therapeutic interventions for senior GBM patients.
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Unveiling mitophagy-mediated molecular heterogeneity and development of a risk signature model for colorectal cancer by integrated scRNA-seq and bulk RNA-seq analysis.
BACKGROUND: Accumulating researchers have recognized mitophagy as a key player in tumors, but few studies have investigated its role in the tumor microenvironment (TME). Advances in the technology of single-cell RNA sequencing (scRNA-seq) have allowed unveiling the concealed features of the TME at cellular resolution. This study aimed to elucidate the role of mitophagy within the TME of colorectal cancer (CRC) and to establish a mitophagy-mediated risk model. METHODS: We assessed mitophagy-related pathway activities at both single-cell and tissue levels. Subsequently, an unsupervised clustering algorithm was employed to identify mitophagy-mediated subtypes. Furthermore, we developed a mitophagy-mediated risk signature (MMRS) using least absolute shrinkage and selection operator (LASSO) Cox analysis and constructed a MMRS model incorporating the risk score and clinical variables. Subsequently, we used quantitative reverse transcription polymerase chain reaction analysis to verify the expression of the screened genes. RESULTS: We retrieved and annotated a total of 14,719 cells from eight samples in the scRNA-seq GSE132465 data set. The activities of mitophagy-related pathways were uniformly upregulated in cancer cells. Integrating with bulk RNA-seq data, we identified two mitophagy-mediated clusters (C1 and C2) with distinct characteristics and prognoses. C2 was identified as a mitophagy-high cluster. Then, we developed a five-gene MMRS via LASSO Cox analysis in The Cancer Genome Atlas (TCGA) cohort. We utilized the GSE39582 cohort to validate the efficacy of our model. The expression of CX3CL1 and INHBB was upregulated in CRC tissues. CONCLUSIONS: The present study identified two mitophagy-mediated CRC subtypes with distinct features. Our MMRS may provide potential therapeutic strategies for CRC. The findings of our work offer novel insights into the involvement of mitophagy in CRC.
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Single-Cell Transcriptomic Analysis Reveals Cell Heterogeneity and Altered Signaling Pathways in Jellyfish Sting Patients.
Jellyfish stings induce a range of symptoms, from localized irritation to life-threatening systemic reactions, yet the underlying immune mechanisms remain poorly understood. This study employed single-cell RNA sequencing (scRNA-seq) to analyze peripheral blood mononuclear cells (PBMCs) from a severely affected patient and healthy controls, uncovering the immune landscape at single-cell resolution and identifying the signaling pathways. We identified 11 major immune cell types, with a marked increase in CD14+ monocytes (81.86% of total cells) and significant reductions in T cells, B cells, and CD16+ monocytes in the envenomated patient. Subclustering revealed six monocyte and four neutrophil subsets, each displaying distinct functional profiles. Patient monocytes were enriched for MMP9+ and RETN+ subsets, associated with leukocyte migration and inflammation, whereas healthy controls exhibited CD74+ monocytes linked to oxidative phosphorylation. Neutrophils in the patient were predominantly LTF+ and S100A12+, implicating inflammatory and immune regulatory pathways. These findings provide a detailed single-cell atlas of immune dysregulation post-jellyfish sting, highlighting the pivotal roles of MMP9+ monocytes and S100A12+ neutrophils in driving inflammation. This study offers potential therapeutic targets for mitigating severe immune responses in jellyfish envenomation.
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Comprehensive analysis of gene signature linking hypoxia and lactylation for predicting prognosis and immunotherapy response in patients with gastric cancer.
BACKGROUND: Gastric cancer (GC) is the fifth most commonly diagnosed cancer worldwide and ranks as the third leading cause of cancer-related deaths. In the tumor microenvironment (TME), malignant cells undergo hypoxic adaptation via metabolic reprogramming, which promotes cellular proliferation and lactate accumulation. However, the significance of hypoxia- and lactylation-related genes (HALRGs) in GC remains unclear. This study aimed to investigate the impact of hypoxia and lactylation on GC and to construct relevant prognostic models. METHODS: We downloaded single-cell RNA sequencing (scRNA-seq), spatial transcriptome (ST) data, and clinical data. The least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a prognostic model, which was validated in an external cohort. Immune cell infiltration was analyzed using the CIBERSORT algorithm, while the Tumor Immune Dysfunction and Exclusion (TIDE) score and ESTIMATE method were combined to evaluate the prognostic potential of immunotherapy response. RESULTS: We analyzed 23,447 genes and 104,150 cells from scRNA-seq, classifying the cells into seven distinct types. The spatial distribution characteristics of these cell types were further examined using ST data. Inference of copy number variations (InferCNV) analysis was performed on epithelial cells to differentiate between malignant and normal cells, revealing that normal epithelial cells could be further categorized into five subtypes. We then developed a prognostic model based on HALRGs using The Cancer Genome Atlas (TCGA) data, which was validated in the Gene Expression Omnibus (GEO) dataset. Patients were divided into high- and low-risk groups according to HALRG scores. Interestingly, the high-risk group exhibited higher TIDE scores, suggesting a poorer response to immunotherapy. CONCLUSIONS: This study uncovered the cellular heterogeneity of GC by integrating scRNA-seq and ST data. The results demonstrated that the HALRG risk model may be effective in predicting the clinical prognosis and has the potential to guide personalized treatment in GC.
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Integrated transcriptomic analysis systematically reveals the heterogeneity and molecular characterization of cancer-associated fibroblasts in osteosarcoma.
BACKGROUND: Osteosarcoma (OS), with a peak incidence during the adolescent growth spurt, is correlated with poor prognosis for its high malignancy. The tumor microenvironment (TME) is highly complicated, with frequent interactions between tumor and stromal cells. The cancer-associated fibroblasts (CAFs) in the TME have been considered to actively involve in the progression, metastasis, and drug resistance of OS. This study aimed to characterize cellular heterogeneity and molecular characterization in CAFs subtypes and explore the potential targeting therapeutic strategies to improve the prognosis of OS patients. METHODS: The single-cell atlas of human OS tumor lesions were constructed from the GEO database. Then significant marker genes and potential biological functions for each CAFs subtype were identified and explored using the Seurat R package. Next, by performing the survival analyses and constructing the risk scores for CAFs subtypes, we aimed to identify and characterize the prognostic values of specific marker genes and different CAFs subtypes. Furthermore, we explored the therapeutic targets and innovative drugs targeting different CAFs subtypes based on the GDSC database. Finally, prognoses related CAFs subtypes were further validated through immunohistochemistry (IHC) on clinical OS specimens. RESULTS: Overall, nine main cell clusters and five subtypes of CAFs were identified. The differentially expressed marker genes for each CAFs clusters were then identified. Moreover, through Gene Ontology (GO) enrichment analysis, we defined the CAFs_2 (upregulated CXCL14 and C3), which was closely related to leukocyte migration and chemotaxis, as inflammatory CAFs (iCAFs). Likewise, we defined the CAFs_4 (upregulated CD74, HLA-DRA and HLA-DRB1), which was closely related to antigen process and presentation, as antigen-presenting CAFs (apCAFs). Furthermore, Kaplan-Meier analyses showed that CAFs_2 and CAFs_4 were correlated with poor clinical prognosis of OS patients. Meanwhile, therapeutic drugs targeting CAFs_2 and CAFs_4, such as 17-AAG/Docetaxel/Bleomycin and PHA-793887/NG-25/KIN001-102, were also explored, respectively. Finally, IHC assay confirmed the abundant CAFs_2 and CAFs_4 subtypes infiltration in the OS microenvironment compared with adjacent tissues. CONCLUSION: Our study revealed the diversity, complexity, and heterogeneity of CAFs in OS, and complemented the single-cell atlas in OS TME.
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Single-cell RNA sequencing reveals different chondrocyte states in femoral cartilage between osteoarthritis and healthy individuals.
BACKGROUND: Cartilage injury is the main pathological manifestation of osteoarthritis (OA). Healthy chondrocyte is a prerequisite for cartilage regeneration and repair. Differences between healthy and OA chondrocyte types and the role these types play in cartilage regeneration and OA progression are unclear. METHOD: This study conducted single-cell RNA sequencing (scRNA-seq) on the cartilage from normal distal femur of the knee (NC group) and OA femur (OA group) cartilage, the chondrocyte atlas was constructed, and the differences of cell subtypes between the two groups were compared. Pseudo-time and RNA velocity analysis were both performed to verify the possible differentiation sequence of cell subtypes. GO and KEGG pathway enrichment analysis were used to explore the potential functional characteristics of each cell subtype, and to predict the functional changes during cell differentiation. Differences in transcriptional regulation in subtypes were explored by single-cell regulatory network inference and clustering (SCENIC). The distribution of each cell subtype in cartilage tissue was identified by immunohistochemical staining (IHC). RESULT: A total of 75,104 cells were included, they were divided into 19 clusters and annotated as 11 chondrocyte subtypes, including two new chondrocyte subtypes: METRNL+ and PRG4+ subtype. METRNL+ is in an early stage during chondrocyte differentiation, and RegC-B is in an intermediate state before chondrocyte dedifferentiation. With cell differentiation, cell subtypes shift from genetic expression to extracellular matrix adhesion and collagen remodeling, and signal pathways shift from HIF-1 to Hippo. The 11 subtypes were finally classified as intrinsic chondrocytes, effector chondrocytes, abnormally differentiated chondrocytes and dedifferentiated chondrocytes. IHC was used to verify the presence and distribution of each chondrocyte subtype. CONCLUSION: This study screened two new chondrocyte subtypes, and a novel classification of each subtype was proposed. METRNL+ subtype is in an early stage during chondrocyte differentiation, and its transcriptomic characteristics and specific pathways provide a foundation for cartilage regeneration. EC-B, PRG4+ RegC-B, and FC are typical subtypes in the OA group, and the HippO-Taz pathway enriched by these cell subtypes may play a role in cartilage repair and OA progression. RegC-B is in the intermediate state before chondrocyte dedifferentiation, and its transcriptomic characteristics may provide a theoretical basis for intervening chondrocyte dedifferentiation.
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[Single-cell transcriptome analysis reveals development atlas of mouse molar pulp cells].
Objective: Single-cell RNA sequencing (scRNA-seq) was used to analyze the developing mouse molars, in order to construct a spatiotemporal development atlas of pulp cells, and further to reveal the developmental process and regulatory mechanism of tooth development. Methods: Ten mandibular first molars from C57BL/6 mice in postnatal day (PN) 0 and 3 were respectively dissected and digested to obtain single-cell suspensions. scRNA-seq was performed on 10× Genomics platform. PN 7 mouse molar scRNA-seq data were obtained from our previous study. PN 0, 3, and 7 scRNA-seq data were integrated for following analysis. The initial quality control, mapping and single cell expression matrix construction were performed by Cell Ranger. Quality control, standardization, dimensional reduction and cluster analysis were performed by using Seurat. Monocle was used to generate the pseudotime trajectory. Scillus was used to perform gene ontology analysis. In order to detect the spatiotemporal change of different population of pulp cells, the marker genes of each cluster were demonstrated by RNAscope in situ hybridization. Results: There were twenty-six cell clusters within mouse molars, which were identified as eight different cell types, including dental pulp cells, dental follicle cells, epithelial cells, immune cells, endothelial cells, perivascular cells, glial cells and erythrocytes. We further re-clustered and analyzed dental pulp cells. Cluster 0 were mature pulp cells, which located at the upper portion of crown. The main functions of cluster 0 were osteogenesis and extracellular structure organization. Cluster 1 were apical papilla cells, which located at the apical part of roots, whose main functions were extracellular structure organization and organ development. Cluster 2 were cycling cells, which were actively proliferated, resided in the lower portion of the crown. Cluster 3 and 4 were preodontoblasts and odontoblasts, respectively. Their functions were closely related to biomineralization. The proportion of mature pulp cells increased with the development process, while the proportion of cycling cells and odontoblast lineage decreased. According to the expression pattern of marker genes of each cluster, we constructed a cell atlas of dental pulp. Pseudotime trajectory analysis found there were two development trajectories within dental pulp. They both started from SPARC related modular calcium binding 2 (Smoc2)+ dental papilla cells, then went through DNA topoisomerase Ⅱ alpha (Top2a)+ cycling cells, and finally divided into coxsackie virus and adenovirus receptor (Cxadr)+ mature pulp cells or dentin sialophosphoprotein (Dspp)+ odontoblasts two lineages. Conclusions: scRNA-seq could fully discover the intercellular heterogeneity of cells on transcriptome level, which provides a powerful tool to study the process and regulatory mechanism of organ development.