Single-cell Sequencing Datasets Research Articles

An Exploration of Osteosarcoma Metastasis Diagnostic Markers Based on Tumor-Associated Neutrophils.

The high rate of the recurrence and metastasis of osteosarcoma (OS) is the major cause of its poor prognosis. There is a strong correlation between tumor-associated neutrophils (TANs) and tumor progression, progression, and metastasis. This study aimed to identify potential markers that could predict OS metastasis based on analysis of TANs in the tissues of OS patients. A single-cell sequencing dataset (GSE152048), containing seven primary OS lesions, two recurrent OS lesions, and two lung metastatic OS lesions was used for TANs subset identification using the R software (version 4.1.0, R Project for Statistical Computing, Vienna, Austria; https://www.r-project.org/). Immune cell infiltration and immune score were analyzed using CIBERSORT algorithm and ESTIMATE database, respectively. The differentially expressed genes (DEGs) of TANs were used for weighted gene co-expression network analysis (WGCNA) to screen key genes associated with OS metastasis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were used to analyze the functions and signaling pathways involved in key genes. The mRNA levels of protein phosphatase 2 regulatory subunit B'gamma (PPP2R5C) were validated in human osteosarcoma cell lines U2-OS and MG63, human normal cervical endometrial cell line HUCEC, and human foreskin fibroblast (HFF-1) cell line by real-time qPCR (RT-qPCR). PPP2R5C-siRNA991 was transfected into U2-OS and MG63 for 48 h, then the expression levels of PPP2R5C, AKT serine/threonine kinase (AKT), and phospho-AKT (p-AKT) were determined by RT-qPCR and Western blotting. Cell proliferation, migration, and apoptosis were measured by cell counting kit-8 (CCK-8), Transwell, and flow cytometry, respectively. We identified TANs subsets in primary, metastatic, and recurrent OS. Immune infiltration analysis showed that TANs were expressed in OS. Compared with non-metastatic OS, metastatic OS had lower stromal score, immune score, ESTIMATE score, and higher tumor purity. WGCNA classified DEGs into five clusters, according to their function and identified PPP2R5C, protein phosphatase 2 regulatory subunit B'epsilon (PPP2R5E), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma (YWHAG) and CREB binding protein (CREBBP), as potential markers that may affect TANs-induced OS metastasis via hypoxia inducible factor 1 (HIF-1), phosphatidylinositol 3-kinases (PI3K)-AKT and Janus kinase (JAK)-signal transducers and activators of transcription (STAT) signaling pathways. In vitro experiments demonstrated that the mRNA and protein expressions of PPP2R5C, PPP2R5E, YWHAG, and CREBBP were highly expressed in U2-OS and MG63 cells (p < 0.01). Furthermore, PPP2R5C reduced proliferation and migration (p < 0.01) and increased apoptosis and p-AKT protein levels in U2-OS and MG6 cells (p < 0.01). PPP2R5C affects OS metastasis via PI3K/AKT pathway, which may be a potential marker for OS metastasis and recurrence.

Transcriptional networks of transient cell states during human prefrontal cortex development.

The human brain is divided into various anatomical regions that control and coordinate unique functions. The prefrontal cortex (PFC) is a large brain region that comprises a range of neuronal and non-neuronal cell types, sharing extensive interconnections with subcortical areas, and plays a critical role in cognition and memory. A timely appearance of distinct cell types through embryonic development is crucial for an anatomically perfect and functional brain. Direct tracing of cell fate development in the human brain is not possible, but single-cell transcriptome sequencing (scRNA-seq) datasets provide the opportunity to dissect cellular heterogeneity and its molecular regulators. Here, using scRNA-seq data of human PFC from fetal stages, we elucidate distinct transient cell states during PFC development and their underlying gene regulatory circuitry. We further identified that distinct intermediate cell states consist of specific gene regulatory modules essential to reach terminal fate using discrete developmental paths. Moreover, using in silico gene knock-out and over-expression analysis, we validated crucial gene regulatory components during the lineage specification of oligodendrocyte progenitor cells. Our study illustrates unique intermediate states and specific gene interaction networks that warrant further investigation for their functional contribution to typical brain development and discusses how this knowledge can be harvested for therapeutic intervention in challenging neurodevelopmental disorders.

Potential biomarkers and immune cell infiltration involved in aortic valve calcification identified through integrated bioinformatics analysis.

Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood. Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify key feature genes as potential biomarkers for Calcific aortic valve disease (CAVD). Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of key genes. The CIBERSORT deconvolution algorithm was used to determine differential immune cell infiltration and the relationship between key genes and immune cell types. Finally, the Expression level and diagnostic ability of the identified biomarkers were further validated in an external dataset (GSE83453), a single-cell sequencing dataset (SRP222100), and immunohistochemical staining of human clinical tissue samples, respectively. Results: In total, 34 identified DEGs included 21 upregulated and 13 downregulated genes. DEGs were mainly involved in immune-related pathways such as leukocyte migration, granulocyte chemotaxis, cytokine activity, and IL-17 signaling. The machine learning algorithm identified SCG2 and CCL19 as key feature genes [area under the ROC curve (AUC) = 0.940 and 0.913, respectively; validation AUC = 0.917 and 0.903, respectively]. CIBERSORT analysis indicated that the proportion of immune cells in Calcific aortic valve disease (CAVD) was different from that in normal aortic valve tissues, specifically M2 and M0 macrophages. Key genes SCG2 and CCL19 were significantly positively correlated with M0 macrophages. Single-cell sequencing analysis and immunohistochemical staining of human aortic valve tissue samples showed that SCG2 and CCL19 were increased in Calcific aortic valve disease (CAVD) valves. Conclusion: SCG2 and CCL19 are potential novel biomarkers of Calcific aortic valve disease (CAVD) and may play important roles in the biological process of Calcific aortic valve disease (CAVD). Our findings advance understanding of the underlying mechanisms of Calcific aortic valve disease (CAVD) pathogenesis and provide valuable information for future research into novel diagnostic and immunotherapeutic targets for Calcific aortic valve disease (CAVD).

Comparison of DNA methylation profiles of human embryos cultured in either uninterrupted or interrupted incubators.

We aimed to compare the DNA methylation profiles of human embryos cultured in uninterrupted or interrupted incubators. This study included 9 women, ≤ 30years old (range: 20-30years), without a history of genetic diseases or smoking, undergoing ICSI treatment, and each woman donated one oocyte. Embryos were randomly assigned to culture in either time-lapse imaging or standard incubators after ICSI. We compared the DNA methylation profiles of human eight-cell embryos cultured in uninterrupted condition using time-lapse imaging (TLI) incubator (EmbryoScope) to those cultured in interrupted culture model using standard incubators (SI, G185 K-System). Nine single-cell whole-genome bisulfite sequencing (WGBS) datasets were analyzed, including four SI-cultured embryos and five TLI-cultured embryos at the eight-cell stage. A total of 581,140,020 and 732,348,182 clean reads were generated from the TLI and SI groups, respectively. TLI-cultured embryos had similar genome-wide methylation patterns to SI-cultured embryos. There were no significant differences in the methylation and transcription levels of transposable elements and imprinted control regions. Although a total of 198 differentially methylated genes (DMGs) were identified, only five DMGs had significantly different transcription levels between the two groups. This is the first study to compare the DNA methylation profiles of embryos cultured in TLI and SI and will provide a foundation for evaluating the safety of TLI application in assisted reproductive technologies. However, further study with a larger cohort of samples was needed for the data validation.

ACAP1 Deficiency Predicts Inferior Immunotherapy Response in Solid Tumors

ACAP1 plays a key role in endocytic recycling, which is essential for the normal function of lymphocytes. However, the expression and function of ACAP1 in lymphocytes have rarely been studied. Large-scale genomic data, including multiple bulk RNA-sequencing datasets, single-cell sequencing datasets, and immunotherapy cohorts, were exploited to comprehensively characterize ACAP1 expression, regulation, and function. Gene set enrichment analysis (GSEA) was used to uncover the pathways associated with ACAP1 expression. Eight algorithms, including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, xCELL, MCPCOUNTER, EPIC, and TIDE, were applied to estimate the infiltrating level of immune cells. Western blotting, qPCR, and ChIP-PCR were used to validate the findings from bioinformatic analyses. A T-cell co-culture killing assay was used to investigate the function of ACAP1 in lymphocytes. ACAP1 was highly expressed in immune-related tissues and cells and minimally in other tissues. Moreover, single-cell sequencing analysis in tumor samples revealed that ACAP1 is expressed primarily in tumor-infiltrating lymphocytes (TILs), including T, B, and NK cells. ACAP1 expression is negatively regulated by promoter DNA methylation, with its promoter hypo-methylated in immune cells but hyper-methylated in other cells. Furthermore, SPI1 binds to the ACAP1 promoter and positively regulates its expression in immune cells. ACAP1 levels positively correlate with the infiltrating levels of TILs, especially CD8+ T cells, across a broad range of solid cancer types. ACAP1 deficiency is associated with poor prognosis and immunotherapeutic response in multiple cancer types treated with checkpoint blockade therapy (ICT). Functionally, the depletion of ACAP1 by RNA interference significantly impairs the T cell-mediated killing of tumor cells. Our study demonstrates that ACAP1 is essential for the normal function of TILs, and its deficiency indicates an immunologically "cold" status of tumors that are resistant to ICT.

Krebs von den lungen-6 as a clinical marker for hypersensitivity pneumonitis: A meta-analysis and bioinformatics analysis.

Hypersensitivity pneumonitis (HP), also referred to as exogenous allergic alveolitis, is one of the most common interstitial lung diseases (ILDs). A potential immune biomarker, Krebs von den lgen-6 (KL-6) characterizes the progression and severity of HP. The meta-analysis in this study was conducted to elucidate the variations in the concentrations of KL-6 in different types of HP. A systematic search of various databases such as EMBASE, Pubmed, CNKI, VIP, Web of Science, and WanFang was carried out to find relevant published articles between January 1980 and August 2022 that explored the relationship between KL-6 and allergic pneumonia. Standardized mean difference (SMD) and 95% confidence interval (CI) were used as effect sizes for comparison among different groups. The GSE47460 and GSE150910 datasets were downloaded to extract and validate the differences in KL-6 mRNA expression between HP lung tissue and healthy controls. Furthermore, the single-cell sequencing dataset GSE135893 was downloaded to extract KL-6 mRNA expression in type II alveolar epithelial cells to validate the differences between HP and healthy controls. Two researchers evaluated the quality of the included studies by employing Newcastle-Ottawa Scale. All the qualified studies were subjected to statistical analyses carried out utilizing RevMan 5.2, Stata 11.0, and R software 4.1.3. Twenty studies aligned perfectly with the inclusion criteria of the meta. The concentrations of KL-6 were substantially higher in the blood of HP patients as compared to the control group. Subgroup analyses were carried out in accordance with the allergen source and the results revealed that patients with different allergens had higher blood KL-6 concentrations than healthy controls. Additionally, different subgroups of subjects were created for meta-analysis as per the fibrosis status, race, measurement method, and sample type. The concentration of KL-6 in blood was much higher in all HP subgroups than in healthy control groups. Moreover, the bioinformatics analysis revealed that KL-6 mRNA expression was higher in HP lung tissue and type II alveolar epithelial cells as compared to healthy controls. The present meta-analysis and bioinformatics analysis suggested that the concentration levels of KL-6 varied between HP patients and healthy individuals, and the KL-6 concentrations may be higher in the blood samples of HP patients. https://www.crd.york.ac.uk/prospero/, CRD42022355334.

TNFα/TNFR1 signal induces excessive senescence of decidua stromal cells in recurrent pregnancy loss

Defects in decidual response are associated with adverse pregnancy outcomes which includes recurrent pregnancy loss (RPL). It is reported that cellular senescence happens during decidualization and pro-senescent decidual response in the luteal phase endometrium is related to RPL. However, the underlying mechanisms of how excessive decidual senescence takes place in RPL decidua cells remain largely unexplored. The senescent phenotype of RPL decidua and tumor necrosis factor receptor 1(TNFR1) expression were analyzed by using our previously published single-cell sequencing dataset of decidua cells from 6 RPL and 5 matched normal decidua, which were further verified by PCR and WB in decidual tissues. Effects of TNFα on the decidual stromal cells (DSCs) senescence and underlying molecular pathways were analyzed using the in vitro decidualization model of human endometrial stromal cells (HESCs). We showed that decidual stroma cells from RPL patients exhibited transcriptomic features of cellular senescence by analysis of single-cell datasets. The TNFα level and TNFR1 expression were increased in RPL decidua tissues. Furthermore, in vitro cell model demonstrated that increased TNFα induced excessive senescence during decidualization and TNFR1/p53/p16 pathway mediates TNFα-induced stromal senescence. In addition, we also found that the expression of IGFBP1 was regulated by TNFα-TNFR1 interaction during decidualization. Taken together, the present findings suggest that the increased secretion of TNFα induced stromal cell excessive senescence in RPL decidua, which is mediated via TNFR1, and thus provide a possible therapeutic target for the treatment of RPL.

A novel cuproptosis-related gene signature of prognosis and immune microenvironment in head and neck squamous cell carcinoma cancer.

Cuproptosis is a novel form of cell death that is highly related to mitochondrial metabolism and mediated by protein lipoacetylation. This study systematically assessed the differential expression and genetic alterations of cuproptosis-related genes (CRGs) in head and neck squamous-cell carcinoma (HNSCC) and constructed CRG risk models to predict survival in patients with HNSCC. We investigated the expression of 19 CRGs in HNSCC and noncancerous tissues, and the relationship between mutation load, immune infiltration, and clinical features was examined based on data from public databases. CRG risk models were constructed by univariate Cox analysis and lasso regression and validated by independent datasets for their accuracy in predicting survival outcomes in patients with HNSCC. The expression distribution of CRGs in HNSCC cells was further explored in the HNSCC single-cell sequencing dataset. NFE2L2, ATP7A, FDX1, LIAS, DLD, DLAT, PDHB, MTF1 and DBT were highly expressed in noncancerous samples, while GLS, CDKN2A and DLST were highly expressed in HNSCC samples (p < 0.05). Gene copy number variation frequency (CNV) revealed CDKN2A, FDX1 and DLAT copy number deletions and LIPT2 and NFE2L2 copy number increases. Ten CRGs were used to construct a risk model to predict overall survival (OS) in HNSCC, yielding reduced OS in the high-risk group compared to the low-risk group, training group (p = 9.733e - 05), and testing group (p = 0.040). The CRG risk model was significantly correlated with various immune cells, regulatory T cells (Tregs) and memory B cells were significantly negatively correlated (p = 0.027, p = 0.00084), and resting CD4 memory T cells was significantly positively correlated (p = 9e - 04). Most CRGs significantly affected the clinical characteristics of HNSCC. NFE2L2, SLC31A1, PDHA1, CDKN2A and DBT were highly expressed in epithelial cells of HNSCC, while SLC31A1, DBT and NFE2L2 were highly expressed in T cells, and SLC31A1 in B cells. In monocytes, NFE2L2, SLC31A1 and PDHA1 were highly expressed. The CRG risk model can be used as a potential prognostic factor for HNSCC patients and may provide new insights into cancer treatment from the perspective of copper metabolism.

Scoring model based on the signature of non-m6A-related neoantigen-coding lncRNAs assists in immune microenvironment analysis and TCR-neoantigen pair selection in gliomas

BackgroundSmall peptides encoded by long non-coding RNAs (lncRNAs) have attracted attention for their various functions. Recent studies indicate that these small peptides participate in immune responses and antigen presentation. However, the significance of RNA modifications remains unclear.MethodsThirteen non-m6A-related neoantigen-coding lncRNAs were selected for analysis from the TransLnc database. Next, a neoantigen activation score (NAS) model was established based on the characteristics of the lncRNAs. Machine learning was employed to expand the model to two additional RNA-seq and two single-cell sequencing datasets for further validation. The DLpTCR algorithm was used to predict T cell receptor (TCR)-peptide binding probability.ResultsThe non-m6A-related NAS model predicted patients’ overall survival outcomes more precisely than the m6A-related NAS model. Furthermore, the non-m6A-related NAS was positively correlated with tumor cells’ evolutionary level, immune infiltration, and antigen presentation. However, high NAS gliomas also showed more PD-L1 expression and high mutation frequencies of T-cell positive regulators. Interestingly, results of intercellular communication analysis suggest that T cell-high neoplastic cell interaction is weaker in both of the NAS groups which might arise from decreased IFNGR1 expression. Moreover, we identified unique TCR-peptide pairs present in all glioma samples based on peptides encoded by the 13 selected lncRNAs. And increased levels of neoantigen-active TCR patterns were found in high NAS gliomas.ConclusionsOur work suggests that non-m6A-related neoantigen-coding lncRNAs play an essential role in glioma progression and that screened TCR clonotypes might provide potential avenues for chimeric antigen receptor T cell (CAR-T) therapy for gliomas.

SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.

It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch','sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.

Novel IKZF3 transcriptomic signature correlates with positive outcomes of skin cutaneous melanoma: A pan-cancer analysis.

To investigate the potential relationship between Ikaros family genes and skin cutaneous melanoma (SKCM), we undertook a pan-cancer analysis of the transcriptional signature and clinical data of melanoma through multiple databases. First, 10,327 transcriptomic samples from different cancers were included to determine the overall characteristics and clinical prognoses associated with Ikaros gene expression across cancer types. Second, differentially expressed genes analysis, prognostic evaluation, and gene set enrichment analysis were employed to investigate the role of Ikaros (IKZF) genes in SKCM. Third, we evaluated the relationship between Ikaros family genes and SKCM immune infiltrates and verified the findings using the GEO single-cell sequencing dataset. The results show that Ikaros genes were widely expressed among different cancer types with independently similar patterns as follows: 1. IKZF1 and IKZF3, and 2. IKZF2 and IKZF4-5. IKZF2 and IKZF5 were downregulated in the primary tumor, and IKZF1-3 expression decreased significantly as the T-stage or metastasis increased in SKCM. Moreover, high IKZF1-3 expression was associated with better overall survival, disease-specific survival, and progression-free interval. IKZF3 is an independent prognostic factor of SKCM. Among Ikaros genes, the expression of IKZF1 and IKZF3 positively correlated with the infiltration level of CD4+ T cells and CD8+ T cells, B cells, and Tregs in SKCM and negatively correlated with the infiltration level of M0 and M1 macrophages. Moreover, single-cell sequencing data analysis revealed that IKZF1 and IKZF3 were mainly expressed by immune cells. Correlation analysis shows the immune factors and drug responses associated with IKZF3 expression. In conclusion, the present study is the first, to our knowledge, to identify a pan-cancer genomic signature of the Ikaros gene family among different cancers. Expression of these family members, particularly high levels of IKZF3, indicate positive immunological status and beneficial clinical outcomes of SKCM. IKZF3 may therefore serve as potential targets for immunotherapy of melanoma.

Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set

Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis. A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as "collagen catabolic process", "positive regulation of pathway-restricted SMAD protein phosphorylation", "osteoblast differentiation", "regulation of release of cytochrome c from mitochondria" and "interleukin-1 production". The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells. Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS.

The human myocardium harbors a population of naive B-cells with a distinctive gene expression signature conserved across species.

IntroductionCardiac immunology studies in murine models have identified a sizeable population of myocardial B-cells and have shown that its modulation represents a promising strategy to develop novel therapies for heart failure. However, scarce data on B-cells in the human heart leaves unclear whether findings in rodents are relevant to human biology.MethodsWe performed immunohistochemical stains to characterize the amount and distribution of B-cells in human hearts, analyzing both fresh and post-mortem tissue. To gain insight into the biology of human myocardial B-cells we analyzed publicly-available spatial transcriptomics and single-cell sequencing datasets of myocardial and peripheral blood mononuclear cells (PBMCs). We validated these findings on primary B-cells sorted from the heart and peripheral blood of left ventricular assistive device recipients. To identify biological pathways upregulated in myocardial B-cells across species, we compared differential gene expression in myocardial vs peripheral blood B-cells across the studied human datasets and published rodent datasets.ResultsIn healthy human heart samples, we found B-cells at a ratio of 1:8 compared to T-cells (2.41 ± 0.45 vs 19.36 ± 4.43, p-value <0.001). Myocardial B-cells were more abundant in the interstitium compared with the intravascular space (p-value=0.011), and also more abundant in the myocardium vs. epicardium (p-value=0.048). Single-cell gene expression analysis showed that the human myocardium harbored mostly naive B-cells with a gene expression profile distinct from that of PBMC B-cells. Cross-comparison of differentially-expressed genes in myocardial vs. PBMC B-cells across human and rodent datasets identified 703 genes with consistent differential gene expression across species (binomial p-value=2.9e-48). KEGG pathway analysis highlighted “B-cell receptor signaling pathway,” “Antigen processing and presentation,” and “Cytokine-cytokine receptor interaction” among the top pathways upregulated in cardiac B-cells (FDR <0.001) conserved between species.ConclusionsLike the murine heart, the human heart harbors naive B-cells that are both intravascular and extravascular. Human myocardial B-cells are fewer and more evenly distributed between these two compartments than rodent myocardial B-cells. However, analysis of single-gene expression data indicates that the biological function of myocardial B-cells is conserved across species.

Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories.

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver.

Fast intratumor heterogeneity inference from single-cell sequencing data.

We introduce HUNTRESS, a computational method for mutational intratumor heterogeneity inference from noisy genotype matrices derived from single-cell sequencing data, the running time of which is linear with the number of cells and quadratic with the number of mutations. We prove that, under reasonable conditions, HUNTRESS computes the true progression history of a tumor with high probability. On simulated and real tumor sequencing data, HUNTRESS is demonstrated to be faster than available alternatives with comparable or better accuracy. Additionally, the progression histories of tumors inferred by HUNTRESS on real single-cell sequencing datasets agree with the best known evolution scenarios for the associated tumors.

Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity

Adaptive immune repertoires are composed by the ensemble of B and T-cell receptors within an individual, reflecting both past and current immune responses. Recent advances in single-cell sequencing enable recovery of the complete adaptive immune receptor sequences in addition to transcriptional information. Here, we recovered transcriptome and immune repertoire information for polyclonal T follicular helper cells following lymphocytic choriomeningitis virus (LCMV) infection, CD8+ T cells with binding specificity restricted to two distinct LCMV peptides, and B and T cells isolated from the nervous system in the context of experimental autoimmune encephalomyelitis. We could relate clonal expansion, germline gene usage, and clonal convergence to cell phenotypes spanning activation, memory, naive, antibody secretion, T-cell inflation, and regulation. Together, this dataset provides a resource for immunologists that can be integrated with future single-cell immune repertoire and transcriptome sequencing datasets.

Construction and verification of a hypoxia-related nine-gene prognostic model in uveal melanoma based on integrated single-cell and bulk RNA sequencing analyses

Uveal melanoma (UM) is the most common primary intraocular tumor with high metastasis and poor prognosis among adults. Hypoxia participates in the metastasis process in various types of cancers. It is reported that the increased expression of hypoxia inducible factor 1 alpha subunit (HIF1A), a hypoxia-related molecule, is associated with worse prognoses of UM patients. Based on the integrated analysis of single-cell sequencing (scRNA-seq) dataset from Gene Expression Omnibus (GEO) and bulk RNA-seq dataset from the Cancer Genome Atlas (TCGA), we found hypoxia was the key feature in UM progression and identified 47 common hypoxia-related differentially expressed genes (DEGs) for the following research. Univariate cox analysis and LASSO-Cox regression analysis were performed to establish a nine-gene prognostic model. According to this model, UM patients could be divided into high- and low-risk groups, with a significant difference in overall survival and progression free survival between the two groups (P < 0.001). The accuracy of the predictive model was also verified on two other independent datasets. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hypoxia-related DEGs were enriched in immune and cancer related pathways. The proportion of immune infiltration and the expression of immune biomarkers were different between high- and low-risk UM patients, providing potential targets for UM immunotherapy. Hence, our hypoxia-related nine-gene model could efficiently predict the prognosis and guide personalized therapies for UM patients.

SatuRn:Scalable analysis of differential transcript usagefor bulk and single-cell RNA-sequencing applications.

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seqapplications.

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing.

MotivationSeveral recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing.ResultsWe developed Single Cell Data Tumor Clusterer (SECEDO, lat. ‘to separate’), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of 2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of 0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of 0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants.Availability and implementationSECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955.Supplementary information Supplementary data are available at Bioinformatics online.

D3K: The Dissimilarity-Density-Dynamic Radius K-means Clustering Algorithm for scRNA-Seq Data.

A single-cell sequencing data set has always been a challenge for clustering because of its high dimension and multi-noise points. The traditional K-means algorithm is not suitable for this type of data. Therefore, this study proposes a Dissimilarity-Density-Dynamic Radius-K-means clustering algorithm. The algorithm adds the dynamic radius parameter to the calculation. It flexibly adjusts the active radius according to the data characteristics, which can eliminate the influence of noise points and optimize the clustering results. At the same time, the algorithm calculates the weight through the dissimilarity density of the data set, the average contrast of candidate clusters, and the dissimilarity of candidate clusters. It obtains a set of high-quality initial center points, which solves the randomness of the K-means algorithm in selecting the center points. Finally, compared with similar algorithms, this algorithm shows a better clustering effect on single-cell data. Each clustering index is higher than other single-cell clustering algorithms, which overcomes the shortcomings of the traditional K-means algorithm.