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Single-Cell Immune Signature and Response to Neoadjuvant Chemotherapy in BRCA1/2-Mutated Breast Cancer.
PURPOSE: The atlas of immune microenvironment at single-cell level in BRCA1/2-mutated breast cancer is largely unknown and whether an immune signature on the basis of single-cell atlas is associated with response to neoadjuvant chemotherapy remains to be investigated. MATERIALS AND METHODS: The immune microenvironment between BRCA1/2-mutated and BRCA wild-type breast tumors was explored using single-cell RNA sequencing (scRNA-seq) assay and was validated in an independent cohort of 40 BRCA1/2 carriers and 56 noncarriers via immunohistochemistry assay (IHC). Bulk RNA-seq was performed using RNA extracted from fresh-frozen pretreatment core-needle tumor tissues in 80 BRCA1/2 carriers with operable primary human epidermal growth factor receptor 2-negative tumors who received neoadjuvant chemotherapy, and the associations between immune cell subtypes defined by scRNA-seq and pathologic complete response (pCR) were investigated. RESULTS: BRCA1/2-mutated tumors exhibited an enriched immune microenvironment compared with the wild-type counterparts at single-cell level, particularly regulatory T cells and exhausted T cells, which were validated in the IHC cohort. Among the neoadjuvant chemotherapy cohort of 80 BRCA1/2 carriers, 36.2% achieved a pCR. We established an immune signature on the basis of the single-cell and bulk RNA-seq data, named BRCA-IM. The BRCA-IM model exhibited an excellent prediction of pCR in the neoadjuvant chemotherapy cohort, with AUC values of 0.81(95% CI, 0.69 to 0.92) in the training set and 0.91(95% CI, 0.79 to 1.00) in the test set, respectively; and the BRCA-IM model remained as an independent predictor for pCR after adjusting for other factors. Moreover, higher BRCA-IM scores were significantly associated with more favorable survival in the neoadjuvant chemotherapy cohort. CONCLUSION: BRCA1/2-mutated breast cancer shows an enriched tumor immune microenvironment, and the BRCA-IM model exhibits a good performance in prediction of pCR in BRCA1/2-mutated tumors.
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Scaling concepts in 'omics: Nuclear lamin-B scales with tumor growth and often predicts poor prognosis, unlike fibrosis.
Physicochemical principles such as stoichiometry and fractal assembly can give rise to characteristic scaling between components that potentially include coexpressed transcripts. For key structural factors within the nucleus and extracellular matrix, we discover specific gene-gene scaling exponents across many of the 32 tumor types in The Cancer Genome Atlas, and we demonstrate utility in predicting patient survival as well as scaling-informed machine learning (SIML). All tumors with adjacent tissue data show cancer-elevated proliferation genes, with some genes scaling with the nuclear filament LMNB1, including the transcription factor FOXM1 that we show directly regulates LMNB1 SIML shows that such regulated cancers cluster together with longer overall survival than dysregulated cancers, but high LMNB1 and FOXM1 in half of regulated cancers surprisingly predict poor survival, including for liver cancer. COL1A1 is also studied because it too increases in tumors, and a pan-cancer set of fibrosis genes shows substoichiometric scaling with COL1A1 but predicts patient outcome only for liver cancer-unexpectedly being prosurvival. Single-cell RNA-seq data show nontrivial scaling consistent with power laws from bulk RNA and protein analyses, and SIML segregates synthetic from contractile cancer fibroblasts. Our scaling approach thus yields fundamentals-based power laws relatable to survival, gene function, and experiments.
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Landscape of cell heterogeneity and evolutionary trajectory in ulcerative colitis-associated colon cancer revealed by single-cell RNA sequencing.
OBJECTIVE: The goal of this study was to get preliminary insight on the intra-tumor heterogeneity in colitis-associated cancer (CAC) and to reveal a potential evolutionary trajectory from ulcerative colitis (UC) to CAC at the single-cell level. METHODS: Fresh samples of tumor tissues and adjacent UC tissues from a CAC patient with pT3N1M0 stage cancer were examined by single-cell RNA sequencing (scRNA-seq). Data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas were used to confirm the different expression levels in normal and tumor tissues and to determine their relationships with patient prognosis. RESULTS: Ultimately, 4,777 single-cell transcriptomes (1,220 genes per cell) were examined, of which 2,250 (47%) and 2,527 (53%) originated from tumor and adjacent UC tissues, respectively. We defined the composition of cancer-associated stromal cells and identified six cell clusters, including myeloid, T and B cells, fibroblasts, endothelial and epithelial cells. Notable pathways and transcription factors involved in these cell clusters were analyzed and described. Moreover, the precise cellular composition and developmental trajectory from UC to UC-associated colon cancer were graphed, and it was predicted that CD74, CLCA1, and DPEP1 played a potential role in disease progression. CONCLUSIONS: scRNA-seq technology revealed intra-tumor cell heterogeneity in UC-associated colon cancer, and might provide a promising direction to identify novel potential therapeutic targets in the evolution from UC to CAC.
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The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.
Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.
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Integrated analysis of single-cell and bulk RNA-seq establishes a novel signature for prediction in gastric cancer.
BACKGROUND: Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease. However, previous single-cell sequencing studies on gastric cancer (GC) have largely focused on immune cells and stromal cells, and further elucidation is required regarding the alterations that occur in gastric epithelial cells during the development of GC. AIM: To create a GC prediction model based on single-cell and bulk RNA sequencing (bulk RNA-seq) data. METHODS: In this study, we conducted a comprehensive analysis by integrating three single-cell RNA sequencing (scRNA-seq) datasets and ten bulk RNA-seq datasets. Our analysis mainly focused on determining cell proportions and identifying differentially expressed genes (DEGs). Specifically, we performed differential expression analysis among epithelial cells in GC tissues and normal gastric tissues (NAGs) and utilized both single-cell and bulk RNA-seq data to establish a prediction model for GC. We further validated the accuracy of the GC prediction model in bulk RNA-seq data. We also used Kaplan-Meier plots to verify the correlation between genes in the prediction model and the prognosis of GC. RESULTS: By analyzing scRNA-seq data from a total of 70707 cells from GC tissue, NAG, and chronic gastric tissue, 10 cell types were identified, and DEGs in GC and normal epithelial cells were screened. After determining the DEGs in GC and normal gastric samples identified by bulk RNA-seq data, a GC predictive classifier was constructed using the Least absolute shrinkage and selection operator (LASSO) and random forest methods. The LASSO classifier showed good performance in both validation and model verification using The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) datasets [area under the curve (AUC)_min = 0.988, AUC_1se = 0.994], and the random forest model also achieved good results with the validation set (AUC = 0.92). Genes TIMP1, PLOD3, CKS2, TYMP, TNFRSF10B, CPNE1, GDF15, BCAP31, and CLDN7 were identified to have high importance values in multiple GC predictive models, and KM-PLOTTER analysis showed their relevance to GC prognosis, suggesting their potential for use in GC diagnosis and treatment. CONCLUSION: A predictive classifier was established based on the analysis of RNA-seq data, and the genes in it are expected to serve as auxiliary markers in the clinical diagnosis of GC.
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Unlocking Skin's Symphony: Single-Cell and Spatial Transcriptomics Reveal the Harmonious Interplay among Diverse Cell Populations.
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Fast searches of large collections of single-cell data using scfind.
Single-cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single-cell data we have developed scfind, a single-cell analysis tool that facilitates fast search of biologically or clinically relevant marker genes in cell atlases. Using transcriptome data from six mouse cell atlases, we show how scfind can be used to evaluate marker genes, perform in silico gating, and identify both cell-type-specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.
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Early transcriptional responses reveal cell type-specific vulnerability and neuroprotective mechanisms in the neonatal ischemic hippocampus.
Neonatal hypoxic-ischemic (H-I) brain injury, a leading cause of neurodevelopmental disabilities, severely affects the metabolically active and neurogenic hippocampus. To investigate its acute effects and identify drug targets for early therapeutic windows, we applied single-nucleus RNA sequencing on postnatal day 8 (P8) mouse hippocampi under sham, hypoxic, and hypoxic-ischemic conditions. We constructed a comprehensive hippocampal cell atlas and developed a machine-learning classifier for precise cell type identification. Our analysis reveals early vulnerabilities in mature neurons and notable resilience in immature DG, GABAergic, and Cajal-Retzius cells following H-I. Gene regulatory network analysis identified key transcription factors associated with neuronal vulnerability, along with upregulated ribosome biogenesis and dysregulated calcium homeostasis pathways. We observed rapid activation of astrocytes and microglia, with Runx1 identified as a potential key transcription factor associated with early microglia immune responses. Endothelial cells displayed complex transcriptional changes and predicted intercellular signaling patterns that may influence vascular repair and recovery. Our study advances the understanding of immediate cellular and transcriptional responses to neonatal H-I injury, providing new insights into hippocampal cell heterogeneity and pathophysiology. The integrated hippocampal atlas, post-H-I atlas, and machine learning classifier are available at https://hippo-seq.org .
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Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data.
BACKGROUND: This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data. METHODS: The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset. RESULTS: We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival. CONCLUSION: The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.
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High-dimensional analysis reveals an immune atlas and novel neutrophil clusters in the lungs of model animals with Actinobacillus pleuropneumoniae-induced pneumonia.
Due to the increase in bacterial resistance, improving the anti-infectious immunity of the host is rapidly becoming a new strategy for the prevention and treatment of bacterial pneumonia. However, the specific lung immune responses and key immune cell subsets involved in bacterial infection are obscure. Actinobacillus pleuropneumoniae (APP) can cause porcine pleuropneumonia, a highly contagious respiratory disease that has caused severe economic losses in the swine industry. Here, using high-dimensional mass cytometry, the major immune cell repertoire in the lungs of mice with APP infection was profiled. Various phenotypically distinct neutrophil subsets and Ly-6C+ inflammatory monocytes/macrophages accumulated post-infection. Moreover, a linear differentiation trajectory from inactivated to activated to apoptotic neutrophils corresponded with the stages of uninfected, onset, and recovery of APP infection. CD14+ neutrophils, which mainly increased in number during the recovery stage of infection, were revealed to have a stronger ability to produce cytokines, especially IL-10 and IL-21, than their CD14- counterparts. Importantly, MHC-II+ neutrophils with antigen-presenting cell features were identified, and their numbers increased in the lung after APP infection. Similar results were further confirmed in the lungs of piglets infected with APP and Klebsiella pneumoniae infection by using a single-cell RNA-seq technique. Additionally, a correlation analysis between cluster composition and the infection process yielded a dynamic and temporally associated immune landscape where key immune clusters, including previously unrecognized ones, marked various stages of infection. Thus, these results reveal the characteristics of key neutrophil clusters and provide a detailed understanding of the immune response to bacterial pneumonia.