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scProAtlas: an atlas of multiplexed single-cell spatial proteomics imaging in human tissues.
Spatial proteomics can visualize and quantify protein expression profiles within tissues at single-cell resolution. Although spatial proteomics can only detect a limited number of proteins compared to spatial transcriptomics, it provides comprehensive spatial information with single-cell resolution. By studying the spatial distribution of cells, we can clearly obtain the spatial context within tissues at multiple scales. Spatial context includes the spatial composition of cell types, the distribution of functional structures, and the spatial communication between functional regions, all of which are crucial for the patterns of cellular distribution. Here, we constructed a comprehensive spatial proteomics functional annotation knowledgebase, scProAtlas (https://relab.xidian.edu.cn/scProAtlas/#/), which is designed to help users comprehensively understand the spatial context within different tissue types at single-cell resolution and across multiple scales. scProAtlas contains multiple modules, including neighborhood analysis, proximity analysis and neighborhood network, to comprehensively construct spatial cell maps of tissues and multi-modal integration, spatial gene identification, cell-cell interaction and spatial pathway analysis to display spatial variable genes. scProAtlas includes data from eight spatial protein imaging techniques across 15 tissues and provides detailed functional annotation information for 17 468 394 cells from 945 region of interests. The aim of scProAtlas is to offer a new insight into the spatial structure of various tissues and provides detailed spatial functional annotation.
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Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity.
Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals. Unbiased clustering analysis reveals the existence of three distinct epithelial cell populations, one basal and two luminal cell types, which we identify as secretory L1- and hormone-responsive L2-type cells. Pseudotemporal reconstruction of differentiation trajectories produces one continuous lineage hierarchy that closely connects the basal lineage to the two differentiated luminal branches. Our comprehensive cell atlas provides insights into the cellular blueprint of the human breast epithelium and will form the foundation to understand how the system goes awry during breast cancer.
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Identification of Ligand-Receptor Pairs Associated With Tumour Characteristics in Clear Cell Renal Cell Carcinoma.
The tumour microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) comprises multiple cell types, which promote tumour progression and modulate drug resistance and immune cell infiltrations via ligand-receptor (LR) interactions. However, the interactions, expression patterns, and clinical relevance of LR in the TME in ccRCC are insufficiently characterised. This study characterises the complex composition of the TME in ccRCC by analysing the single-cell sequencing (scRNA-seq) data of patients with ccRCC from the Gene expression omnibus database. On analysing the scRNA-seq data combined with the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) dataset, 46 LR-pairs were identified that were significantly correlated and had prognostic values. Furthermore, a new molecular subtyping model was proposed based on these 46 LR-pairs. Molecular subtyping was performed in two ccRCC cohorts, revealing significant differences in prognosis between the subtypes of the two ccRCC cohorts. Different molecular subtypes exhibited different clinicopathological features, mutational, pathway, and immune signatures. Finally, the LR.score model that was constructed using ten essential LR-pairs that were identified based on LASSO Cox regression analysis revealed that the model could accurately predict the prognosis of patients with ccRCC. In addition, the differential expression of ten LR-pairs in tumour and normal cell lines was identified. Further functional experiments showed that CX3CL1 can exert anti-tumorigenic role in ccRCC cell line. Altogether, the effects of immunotherapy were connected to LR.scores, indicating that potential medications targeting these LR-pairs could contribute to the clinical benefit of immunotherapy. Therefore, this study identifies LR-pairs that could be effective biomarkers and predictors for molecular subtyping and immunotherapy effects in ccRCC. Targeting LR-pairs provides a new direction for immunotherapy regimens and prognostic evaluations in ccRCC.
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Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis.
BACKGROUND: Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood. METHODS: Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman's analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype. RESULT: Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein-protein interaction network construction, and Spearman's correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis. CONCLUSION: We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.
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Molecular landscape of tumor-associated tissue-resident memory T cells in tumor microenvironment of hepatocellular carcinoma.
BACKGROUND: Immunotherapy for liver cancer is used to rejuvenate tumor-infiltrating lymphocytes by modulating the immune microenvironment. Thus, early protective functions of T cell subtypes with tissue-specific residency have been studied in the tumor microenvironment (TME). We identified tumor-associated tissue-resident memory T (TA-TRM) cells in hepatocellular carcinoma (HCC) and characterized their molecular signatures. METHODS: We obtained single-cell RNA and single-cell TCR sequencing data from five patients with HCC. The heterogeneous characteristics of TRM cell subsets within the TME were then investigated and validated. Risk scores were calculated for survival analysis using significant core marker genes based on data from The Cancer Genome Atlas and the International Cancer Genome Consortium. The signaling pathways, trajectories, and clonal diversity of TA-TRM cells were investigated. RESULTS: We characterized two TRM clusters (CD69+ and CD103+) that expressed unique signature genes and validated their similar molecular patterns in an independent dataset. Risk scores based on core gene expression in TA-TRM cells were associated with survival in both datasets. Trajectory analysis revealed that the two lineages followed different trajectory paths with distinct marker gene expression across pseudo-time. CD103+ TA-TRM cells showed diverse clonotypes and shared clonotypes with other cell groups. Lower clonal diversity and distinct signaling interactions were observed in the recurrent than in the non-recurrent samples. The CXCL13-CXCR3 interaction between CD103+ TA-TRM and regulatory T cells was observed only in the recurrent samples. CONCLUSIONS: We identified two subtypes of TA-TRM cells in HCC and demonstrated their unique molecular signatures, relevance to survival, and distinct signaling networks according to recurrence. The study findings provide a better understanding of the molecular characteristics of TA-TRM cells in HCC and potential immunotherapeutic strategies.
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Single-cell transcriptomics reveal the heterogeneity and dynamic of cancer stem-like cells during breast tumor progression.
Breast cancer stem-like cells (BCSCs) play vital roles in tumorigenesis and progression. However, the origin and dynamic changes of BCSCs are still to be elucidated. Using the breast cancer mouse model MMTV-PyMT, we constructed a single-cell atlas of 31,778 cells from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma), during which malignant transition occurs. We identified that the precise cell type of ERlow epithelial cell lineage gave rise to the tumors, and the differentiation of ERhigh epithelial cell lineage was blocked. Furthermore, we discovered a specific signature with a continuum of gene expression profiles along the tumor progression and significantly correlated with clinical outcomes, and we also found a stem-like cell cluster existed among ERlow epithelial cells. Further clustering on this stem-like cluster showed several sub-clusters indicating heterogeneity of stem-like epithelial cells. Moreover, we distinguished normal and cancer stem-like cells in this stem-like epithelial cell cluster and profiled the molecular portraits from normal stem-like cell to cancer stem-like cells during the malignant transition. Finally, we found the diverse immune cell infiltration displayed immunosuppressive characteristics along tumor progression. We also found the specific expression pattern of cytokines and their corresponding cytokine receptors in BCSCs and immune cells, suggesting the possible cross-talk between BCSCs and the immune cells. These data provide a useful resource for illuminating BCSC heterogeneity and the immune cell remodeling during breast tumor progression, and shed new light on transcriptomic dynamics during the progression at the single-cell level.
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Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis.
BACKGROUND: Helicases have been classified as a class of enzymes that determine the stability of the cellular genome. There is growing evidence that helicases can help tumor cells resist drug killing by repairing Deoxyribose Nucleic Acid (DNA) or stabilizing transcription, which may contribute to the understanding of drug resistance. Currently, identifying cancer biomarkers among helicases and stratifying patients according to helicase activity will be able to guide treatment well. METHODS: We clustered 371 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) by consensus clustering based on helicase expression profiles to identify potential molecular subtypes. The Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm was used to find core differential gene modules between different molecular subtypes, and single-cell analysis was utlized to explore the potential function of hub gene. Immunohistochemical (IHC) staining was used to verify the diagnostic value of DDX56 and its ability to reflect the proliferation efficiency of cancer cells. RESULTS: We identified two subtypes associated with helicase. High helicase subtype was associated with poor clinical outcome, massive M0 macrophage infiltration, and highly active cell proliferation features. In addition, we identified a new biomarker, DDX56, which has not been reported in HCC, was highly expressed in HCC tissues, associated with poor prognosis, and was also shown to be associated with high cell proliferative activity. CONCLUSION: In conclusion, based on helicase expression profiles, we have developed a new classification system for HCC, which is a proliferation-related system, and has clinical significance in evaluating prognosis and treating HCC patients, including immunotherapy and chemotherapy. In addition, we identified a new biomarker, DDX 56, which is overexpressed in HCC tissues, predicts a poor prognosis and is a validated index of tumor cell proliferation.
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Resolving inter- and intra-patient heterogeneity in NPM1-mutated AML at single-cell resolution.
NPM1-mutated AML is one of the largest entities in international classification systems of myeloid neoplasms, which are based on integrating morphologic and clinical data with genomic data. Previous research, however, indicates that bulk transcriptomics-based subtyping may improve prognostication and therapy guidance. Here, we characterized the heterogeneity in NPM1-mutated AML by performing single-cell RNA-sequencing and spectral flow cytometry on 16 AML belonging to three distinct subtypes previously identified by bulk transcriptomics. Using single-cell expression profiling we generated a comprehensive atlas of NPM1-mutated AML, collectively reconstituting complete myelopoiesis. The three NPM1-mutated transcriptional subtypes showed consistent differences in the proportions of myeloid cell clusters with distinct patterns in lineage commitment and maturational arrest. In all samples, leukemic cells were detected across different myeloid cell clusters, indicating that NPM1-mutated AML are heavily skewed but not fully arrested in myelopoiesis. Same-sample multi-color spectral flow cytometry recapitulated these skewing patterns, indicating that the three NPM1-mutated subtypes can be consistently identified across platforms. Moreover, our analyses highlighted differences in the abundance of rare hematopoietic stem cells suggesting that skewing occurs early in myelopoiesis. To conclude, by harnessing single-cell RNA-sequencing and spectral flow cytometry, we provide a detailed description of three distinct and reproducible patterns in lineage skewing in NPM1-mutated AML that may have potential relevance for prognosis and treatment of patients with NPM1-mutated AML.
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Multiomics analysis of COL12A1 as a promising prognostic biomarker for immune-related treatment of gastric cancer.
COL12A1, a collagen type XII alpha 1 chain, has an instrumental role in the extracellular matrix (ECM), but its effect on gastric cancer (GC) and clinical significance remain unclear. In this study, co-expressed differentially expressed genes (co-DEGs) were identified using Venn diagrams based on datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) for gastric cancer RNA sequencing. Intersections between co-DEGs and TCGA prognosis-related genes were used to pinpoint genes that were differentially associated with prognosis. COL12A1 emerged as a key candidate gene. We analyzed COL12A1 expression in human GC tissues and explored its association with various clinicopathological parameters. Survival analysis, including overall survival (OS) and disease-free survival (DFS), was conducted using TCGA data, and a prognostic nomogram was constructed. The relative protein-encoding gene expression was assessed via immunohistochemistry (IHC) analysis of four downloaded datasets, along with PCR experiments. COL12A1-related DEGs were further identified using the LinkedOmics database, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Multiple databases were analysed for the relationship between COL12A1 and methylation. Immune cell infiltration was evaluated through CIBERSORT and TIMER databases, revealing a strong correlation between COL12A1 expression and immune cell presence in the tumor microenvironment. Single-cell expression analysis using TISCH provided further insights into the cellular context of COL12A1 expression. TISCH was used to analyse the single-cell expression of COL12A1. Finally, this work examined the correlation of COL12A1 with drug sensitivity via the RNAactDrug database. COL12A1 overexpression in gastric cancer was detected and foundto be closely related to clinicopathological features. GO/KEGG functional enrichment analysis revealed that COL12A1 was associated with pathways such as extracellular structure organization, extracellular matrix organization and the collagen-containing extracellular matrix. Analysis of several databases revealed that COL12A1 was associated with gene mutation and methylation. Additionally, as revealed by CIBERSORT profiling and TIMER database analysis, COL12A1 expression is related to immune cell infiltration. Correlation analysis via the RNAactDrug website revealed that COL12A1 is associated with drug sensitivity. In conclusion, COL12A1 is significantly overexpressed in gastric cancer and is associated with tumor progression, immune infiltration, and drug sensitivity. These findings suggest that COL12A1 may serve as a potential diagnostic and therapeutic biomarker for gastric cancer.
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Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.
BACKGROUND: This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. METHODS: We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. RESULTS: We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. CONCLUSION: This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.