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Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding.

ABSTRACT: Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell-cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell-cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.

SUBMITTER: Wang L

PROVIDER: S-EPMC8287331 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Study questionHow can we study the full transcriptome of endometrial stromal and epithelial cells at the single-cell level?Summary answerBy compiling and developing novel analytical tools for biopsy, tissue cryopreservation and disaggregation, single-cell sorting, library preparation, RNA sequencing (RNA-seq) and statistical data analysis.What is known alreadyAlthough single-cell transcriptome analyses from various biopsied tissues have been published recently, corresponding protocols for human endometrium have not been described.Study design, size, durationThe frozen-thawed endometrial biopsies were fluorescence-activated cell sorted (FACS) to distinguish CD13-positive stromal and CD9-positive epithelial cells and single-cell transcriptome analysis performed from biopsied tissues without culturing the cells. We studied gene transcription, applying a modern and efficient RNA-seq protocol. In parallel, endometrial stromal cells were cultured and global expression profiles were compared with uncultured cells.Participants/materials, setting, methodsFor method validation, we used two endometrial biopsies, one from mid-secretory phase (Day 21, LH+8) and another from late-secretory phase (Day 25). The samples underwent single-cell FACS sorting, single-cell RNA-seq library preparation and Illumina sequencing.Main results and the role of chanceHere we present a complete pipeline for single-cell gene-expression studies, from clinical sampling to statistical data analysis. Tissue manipulation, starting from disaggregation and cell-type-specific labelling and ending with single-cell automated sorting, is managed within 90 min at low temperature to minimize changes in the gene expression profile. The single living stromal and epithelial cells were sorted using CD13- and CD9-specific antibodies, respectively. Of the 8622 detected genes, 2661 were more active in cultured stromal cells than in biopsy cells. In the comparison of biopsy versus cultured cells, 5603 commonly expressed genes were detected, with 241 significantly differentially expressed genes. Of these, 231 genes were up- and 10 down-regulated in cultured cells, respectively. In addition, we performed a gene ontology analysis of the differentially expressed genes and found that these genes are mainly related to cell cycle, translational processes and metabolism.Limitations, reasons for cautionAlthough CD9-positive single epithelial cells sorting was successfully established in our laboratory, the amount of transcriptome data per individual epithelial cell was low, complicating further analysis. This step most likely failed due to the high dose of RNases that are released by the cells' natural processes, or due to rapid turnaround time or the apoptotic conditions in freezing- or single-cell solutions. Since only the cells from the late-secretory phase were subject to more focused analysis, further studies including larger sample size from the different time-points of the natural menstrual cycle are needed. The methodology also needs further optimization to examine different cell types at high quality.Wider implications of the findingsThe symbiosis between clinical biopsy and the sophisticated laboratory and bioinformatic protocols described here brings together clinical diagnostic needs and modern laboratory and bioinformatic solutions, enabling us to implement a precise analytical toolbox for studying the endometrial tissue even at the single-cell level.

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Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding.

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