7 research outputs found

    Correlation Between Circulating Tumor Cell DNA Genomic Alterations and Mesenchymal CTCs or CTC-Associated White Blood Cell Clusters in Hepatocellular Carcinoma

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    PurposeLiquid biopsy is attracting attention as a method of real-time monitoring of patients with tumors. It can be used to understand the temporal and spatial heterogeneity of tumors and has good clinical application prospects. We explored a new type of circulating tumor cell (CTC) enrichment technology combined with next-generation sequencing (NGS) to analyze the correlation between genomic alterations in circulating tumor cells of hepatocellular carcinoma and the counts of mesenchymal CTCs and CTC-associated white blood cell (CTC-WBC) clusters.MethodsWe collected peripheral blood samples from 29 patients with hepatocellular carcinoma from January 2016 to December 2019. We then used the CanPatrol™ system to capture and analyze mesenchymal CTCs and CTC-WBC clusters for all the patients. A customized Illumina panel was used for DNA sequencing and the Mann–Whitney U test was used to test the correlation between mesenchymal CTCs, CTC-WBC cluster counts, and specific genomic changes.ResultsAt least one somatic hotspot mutation was detected in each of the 29 sequenced patients. A total of 42 somatic hot spot mutations were detected in tumor tissue DNA, and 39 mutations were detected in CTC-DNA, all of which included common changes in PTEN, MET, EGFR, RET, and FGFR3. The number of mesenchymal CTCs was positively correlated with the somatic genomic alterations in the PTEN and MET genes (PTEN, P = 0.021; MET, P  = 0.008, Mann–Whitney U test) and negatively correlated with the somatic genomic alterations in the EGFR gene (P = 0.006, Mann–Whitney U test). The number of CTC-WBC clusters was positively correlated with the somatic genomic alterations in RET genes (P  = 0.01, Mann–Whitney U test) and negatively correlated with the somatic genomic alterations in FGFR3 (P = 0.039, Mann–Whitney U test).ConclusionsWe report a novel method of a CTC enrichment platform combined with NGS technology to analyze genetic variation, which further demonstrates the potential clinical application of this method for spatiotemporal heterogeneity monitoring of hepatocellular carcinoma. We found that the number of peripheral blood mesenchymal CTCs and CTC-WBC clusters in patients with hepatocellular carcinoma was related to a specific genome profile

    Single-cell profiling reveals distinct immune response landscapes in tuberculous pleural effusion and non-TPE

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    BackgroundTuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) and remains a major health threat worldwide. However, a detailed understanding of the immune cells and inflammatory mediators in Mtb-infected tissues is still lacking. Tuberculous pleural effusion (TPE), which is characterized by an influx of immune cells to the pleural space, is thus a suitable platform for dissecting complex tissue responses to Mtb infection.MethodsWe employed singe-cell RNA sequencing to 10 pleural fluid (PF) samples from 6 patients with TPE and 4 non-TPEs including 2 samples from patients with TSPE (transudative pleural effusion) and 2 samples with MPE (malignant pleural effusion).ResultCompared to TSPE and MPE, TPE displayed obvious difference in the abundance of major cell types (e.g., NK, CD4+T, Macrophages), which showed notable associations with disease type. Further analyses revealed that the CD4 lymphocyte population in TPE favored a Th1 and Th17 response. Tumor necrosis factors (TNF)-, and XIAP related factor 1 (XAF1)-pathways induced T cell apoptosis in patients with TPE. Immune exhaustion in NK cells was an important feature in TPE. Myeloid cells in TPE displayed stronger functional capacity for phagocytosis, antigen presentation and IFN-γ response, than TSPE and MPE. Systemic elevation of inflammatory response genes and pro-inflammatory cytokines were mainly driven by macrophages in patients with TPE.ConclusionWe provide a tissue immune landscape of PF immune cells, and revealed a distinct local immune response in TPE and non-TPE (TSPE and MPE). These findings will improve our understanding of local TB immunopathogenesis and provide potential targets for TB therapy

    HTCA: a database with an in-depth characterization of the single-cell human transcriptome

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    Single-cell RNA-sequencing (scRNA-seq) is one of the most used single-cell omics in recent decades. The exponential growth of single-cell data has immense potential for large-scale integration and in-depth explorations that are more representative of the study population. Efforts have been made to consolidate published data, yet extensive characterization is still lacking. Many focused on raw-data database constructions while others concentrate mainly on gene expression queries. Hereby, we present HTCA (www.htcatlas.org), an interactive database constructed based on ∼2.3 million high-quality cells from ∼3000 scRNA-seq samples and comprised in-depth phenotype profiles of 19 healthy adult and matching fetal tissues. HTCA provides a one-stop interactive query to gene signatures, transcription factor (TF) activities, TF motifs, receptor-ligand interactions, enriched gene ontology (GO) terms, etc. across cell types in adult and fetal tissues. At the same time, HTCA encompasses single-cell splicing variant profiles of 16 adult and fetal tissues, spatial transcriptomics profiles of 11 adult and fetal tissues, and single-cell ATAC-sequencing (scATAC-seq) profiles of 27 adult and fetal tissues. Besides, HTCA provides online analysis tools to perform major steps in a typical scRNA-seq analysis. Altogether, HTCA allows real-time explorations of multi-omics adult and fetal phenotypic profiles and provides tools for a flexible scRNA-seq analysis.Published versionFunding: Karolinska Institute Network Medicine Global Alliance Collaborative Grant [C24401073 to X.L. and L.P.]; National Natural Science Foundation of China [8210100902 to X.Z.]; Nature Science Foundation of Shandong Province [ZR2021MH393 to X.Z.]. Funding for open access charge: Karolinska Institute Network Medicine Global Alliance Collaborative Grant [C24401073 to X.L. and L.P.]. The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Rackham, partially funded by the Swedish Research Council through grant agreement no. 2018-05973
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