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Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling.
BACKGROUND: The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it is usually necessary to generate further specific datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare-cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. RESULTS: We demonstrate the effectiveness of our method for three independent use cases, each consisting of already published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8635). This use case was designed to take a larger imbalance ratio (~1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (~1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single-cell capture procedures and the impact of "less" rare-cell types. The third dataset refers to the murine data of the Allen Brain Atlas, including more than 1 million cells. For validation purposes only, all datasets have also been analyzed traditionally using common data analysis approaches, such as the Seurat workflow. CONCLUSIONS: In comparison to baseline testing without oversampling, our approach identifies rare-cells with a robust precision-recall balance, including a high accuracy and low false positive detection rate. A practical benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis in R and Python is publicly available at FairdomHub, as well as GitHub, and can easily be transferred to identify other rare-cell types.
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Dissecting order amidst chaos of programmed cell deaths: construction of a diagnostic model for KIRC using transcriptomic information in blood-derived exosomes and single-cell multi-omics data in tumor microenvironment.
BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most frequently diagnosed subtype of renal cell carcinoma (RCC); however, the pathogenesis and diagnostic approaches for KIRC remain elusive. Using single-cell transcriptomic information of KIRC, we constructed a diagnostic model depicting the landscape of programmed cell death (PCD)-associated genes, namely cell death-related genes (CDRGs). METHODS: In this study, six CDRG categories, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA sequencing (RNA-seq) data of blood-derived exosomes from the exoRBase database, RNA-seq data of tissues from The Cancer Genome Atlas (TCGA) combined with control samples from the GTEx databases, and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were downloaded. Next, we intersected the differentially expressed genes (DEGs) of the KIRC cohort from exoRBase and the TCGA databases with CDRGs and DEGs obtained from single-cell datasets, further screening out the candidate biomarker genes using clinical indicators and machine learning methods and thus constructing a diagnostic model for KIRC. Finally, we investigated the underlying mechanisms of key genes and their roles in the tumor microenvironment using scRNA-seq, single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq), and the spatial transcriptomics sequencing (stRNA-seq) data of KIRC provided by the GEO database. RESULT: We obtained 1,428 samples and 216,155 single cells. After the rational screening, we constructed a 13-gene diagnostic model for KIRC, which had high diagnostic efficacy in the exoRBase KIRC cohort (training set: AUC = 1; testing set: AUC = 0.965) and TCGA KIRC cohort (training set: AUC = 1; testing set: AUC = 0.982), with an additional validation cohort from GEO databases presenting an AUC value of 0.914. The results of a subsequent analysis revealed a specific tumor epithelial cell of TRIB3high subset. Moreover, the results of a mechanical analysis showed the relatively elevated chromatin accessibility of TRIB3 in tumor epithelial cells in the scATAC data, while stRNA-seq verified that TRIB3 was predominantly expressed in cancer tissues. CONCLUSIONS: The 13-gene diagnostic model yielded high accuracy in KIRC screening, and TRIB3high tumor epithelial cells could be a promising therapeutic target for KIRC.
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Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing.
Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer's brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.
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Identification of leukemia-enriched signature through the development of a comprehensive pediatric single-cell atlas.
Single-cell transcriptome profiling enables unparalleled characterization of the heterogeneous microenvironment of pediatric leukemias. To facilitate comparative analyses and generate pediatric leukemia signatures, we collect, process, and annotate single-cell data comprising over 540,000 cells from 159 different pediatric acute leukemia (myeloid, lymphoid, mixed phenotype lineages) and healthy bone marrow (BM) samples, profiled in our lab and curated from publicly available studies. The analysis identifies a leukemia-enriched signature of nine genes with over-expression in leukemic blast compared to healthy BM cells. This signature is also consistently over-expressed in leukemia samples compared to normal BM in bulk RNA-seq datasets (over 2000 samples). Outcome-based analysis on diagnosis samples using measurable residual disease (MRD) status depicts a significant association of oncogene-induced senescence and g-protein activation pathways with MRD positivity. MRD positivity across pediatric leukemias is also correlated with significant depletion of CD8+ and CD4+ naïve T-cells and M1-macrophages at diagnosis. To enable easy access to this comprehensive pediatric leukemia single-cell atlas, we develop the Pediatric Single-cell Cancer Atlas (PedSCAtlas, https://bhasinlab.bmi.emory.edu/PediatricSCAtlas/ ). The atlas allows for quick exploration of single-cell data based on genes, cell type composition, and clinical outcomes to understand the cellular landscape of pediatric leukemias.
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Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer.
BACKGROUND: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA). METHODS: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response. RESULTS: Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis. CONCLUSIONS: Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients.
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Development and validation of a gene model predicting lymph node metastasis and prognosis of oral squamous cell carcinoma based on single-cell and bulk RNA-seq analysis.
BACKGROUND: Lymph node metastasis can independently predict oral squamous cell carcinoma patients' survival. This study would investigate the genetic and cellular differences between oral squamous cell carcinoma with positive and negative lymph node metastases. METHODS: We gathered single-cell RNA sequencing and bulk gene expression data from the Cancer Genome Atlas and Gene Expression Omnibus databases. Sixty lymph node-metastasis-related genes were discovered with refined single-cell RNA sequencing data analysis, and consensus clustering provided three molecular subtypes of oral squamous cell carcinoma. Least absolute shrinkage and selection operator analyses were then utilized to establish a five-gene risk model. CIBERSORT analysis revealed the immune infiltration profile of different risk subgroups. RESULTS: Oral squamous cell carcinoma patients were classified into three subtypes based on the 60 lymph node-metastasis-related key genes identified by single-cell RNA sequencing data. Patients in Subtype 3 showed a tendency for lymph node metastasis and poorer prognosis. Moreover, five biomarkers were selected from the 60 genes to construct a five-gene risk model evaluating the risk of lymph node metastasis. A lower probability of lymph node metastasis and a better prognosis was observed in the low-risk group. The immune infiltration of three different risk groups was explored with CIBERSORT. Besides, further analysis implied different sensitivities of anticancer drugs, including immunotherapy drugs and targeted compounds, in the three risk groups. CONCLUSION: In view of intratumoral heterogeneity, we found 60 genes associated with lymph node metastasis of oral squamous cell carcinoma. Subsequently, we constructed a five-gene signature that could improve the prediction of lymph node metastasis, clinical outcome, and promote individualized treatment strategies for oral squamous cell carcinoma.
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Bulk RNA-Seq Combined with Single-Cell Transcriptome Sequencing Reveals the Possible Mechanisms by Which HDGFL3 Involves in Prostate Cancer Growth and Metastasis.
BACKGROUND: Based on publicly available transcriptome and single-cell sequencing data, the current study aimed to explore the molecular mechanisms underlying the involvement of hepatocellular carcinoma-derived growth factor-like 3 (HDGFL3) in prostate cancer (PCA) growth and metastasis. METHODS: The Gene Expression Omnibus database was used to download the single cell transcriptome of PCA (GSE193337). Single-cell RNA sequencing (scRNA-seq) data were examined to identify which genes are essential for endothelial cell function. The Cancer Genome Atlas Prostate Adenocarcinoma database provided the RNA sequencing data, and univariate COX regression analysis was introduced to identify the genes that were associated with the prognosis of patients with PCA. Human PCA cell lines PC-3 and DU145 were used in in vitro cellular studies to test the effect of silencing HDGFL3. The results were validated using Transwell® assay, scratch assay, and cell counting kit-8 assay. To support the role of HDGFL3 in PCA, an in vivo animal model of PCA transplantation tumor in nude mice was established. Quantitative reverse transcription polymerase chain reaction was introduced to measure HDGFL3 messenger ribonucleic acid (mRNA) expression levels in tumor tissues from nude mice, and Hematoxylin and Eosin staining was used to identify lung metastasis. Immunohistochemical staining was employed to identify the expression levels of HDGFL3 and hematopoietic progenitor cell antigen CD34+. RESULTS: It was discovered through analysis of the scRNA-seq dataset that HDGFL3, a gene specific to endothelial cells, is linked to a poor prognosis in men with PCA. In addition, HDGFL3 and the expression of genes linked to angiogenesis have a substantial association. Studies on cells in vitro revealed that silencing HDGFL3 prevented PC-3 and DU145 cells from proliferation, migrating and invading. Silencing HDGFL3 decreased the weight of prostate tumors, the number of lung metastases, and the area occupied by CD34+ vascular endothelial cells, according to in vivo investigations. CONCLUSIONS: This study identified HDGFL3 as a key gene in endothelial cells that may stimulate tumor angiogenesis to increase the growth and spread of PCA. These results imply that HDGFL3 may represent a possible target for antiangiogenic therapy in the management of PCA.
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Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning.
Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.
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Lysosome-dependent cell death reveals a prognostic signature in colorectal cancer via integrated analysis of scRNA-seq and bulk RNA-seq data.
BACKGROUND: Colorectal cancer (CRC) is one of the most common vulgar malignancies worldwide. Recent researches have displayed that lysosomes, important intracellular degradation and signal transduction centers, dramatically affect cancer occurrence and development via regulating proliferation, metabolism, programmed death and other key biological processes. Lysosomes significantly influence CRC progression by regulating biological processes such as programmed cell death. This study aims to develop a lysosome-dependent cell death (LDCD)-based prognostic signature to predict survival and immunotherapy efficacy in CRC patients. METHODS: In our work, we innovatively incorporated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data to establish an LDCD activity evaluation system and systematically analyzed the dynamic changes in LDCD activity during the differentiation trajectory of tumor cells and its interaction with tumor microenvironment (TME). RESULTS: We observed that high-LDCD-producing tumor cells were present mainly in the initial stage of CRC differentiation via single-cell trajectory analysis. Further cell communication network analysis revealed significantly different interaction patterns between tumor cell subsets with different LDCD activities and TME components. A prognostic signature composed of key LDCD-pertaining genes was constructed based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) multicenter cohort data. Validation analysis indicated that the prognostic signature effectively stratified patients into different risk groups, and high- and low-risk groups presented huge disparities in overall survival, immune microenvironment characteristics, treatment sensitivity and immunotherapy response. CONCLUSIONS: This study characterizes the spatiotemporal heterogeneity of LDCD activity in CRC from a single-cell perspective. The identified prognostic markers act as independent clinical indicators and furnish a critical theoretical foundation and potential therapeutic targets for advancing personalized CRC management.
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scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.