Ethics statement.The present study was approved by the Ethics Committee at Hainan Hospital. All samples were obtained from patients who were diagnosed with RCC between January 2019 and January 2020 at Hainan Hospital. All study subjects provided written informed consent.
Human samples.Samples were collected from four patients who were initially diagnosed with RCC. Blood was collected from the cubital vein before drug treatment or other antineoplastic therapy. PBMC was isolated from human peripheral blood using Ficoll–Paque density gradient centrifugation. First, whole blood was diluted with the same amount of phosphate buffered saline (PBS), and the diluted blood solution was placed onto the Ficoll–Paque gradient medium (GE Healthcare Bio-Sciences), centrifuged at room temperature at 1000 ×g for 20 min, and the PBMCs were carefully collected from the interface layer between plasma and Ficoll solution. The collected PBMCs were washed using PBS, then centrifuged at 500 ×g for 20 min, and the cell concentration was adjusted to 1×107 PBMCs/mL. A single cell RNA library was then constructed.
Single-cell cDNA amplification and RNA-sequencing. According to the manufacturer’s instructions, PBMCs were labeled with BD Human single Cell Multiplexing Kit(BD Biosciences,Cat No.:633793,San Jose, CA, USA). The cell survival rate for all samples was greater than 80%. The labeled samples were evenly mixed in cold BD sample buffer, and each sample had about 30,000–40,000 cells. Single cells were isolated by single cell capture and gene synthesis using the BD Rhapsody Express single cell analysis system, and the library was constructed using a BD Rhapsody DNA full transcriptome analysis and amplification kit.(BD Biosciences, Cat No.:633781,San Jose, CA,USA). The final library was quantified using a Qubit fluorometer with Qubit dsDNA HS Kit (Thermo Fisher, Cat No.:Q32851,Wilmington, Delaware USA). Libraries were sequenced in the paired-end mode on a NovaSeq 6000 by Novogene Biotech Co., Ltd (Beijing, China).
Processing and analysis of single-cell RNA-seq data. The Seurat Package (version:3.1.2) was used for gene expression data analysis. Cell demultiplexing was realized using the HTODemUX function in the Seurat Package. After single cell identification, cells with mitochondrial readings of more than 30%, fewer than 200 genes, or more than 5000 genes are excluded from the analysis. Downstream analysis only considered those genes that exist in more than five cells, and standardization, scaling, and dimensionality reduction steps were performed for each subset of PMBC data. Uniform Manifold Approximation and Projection (UMAP) was then used for two-dimensional representation of the data structure. After clustering, the “findmarkers” and “findallmarkers” functions from the Seurat software package were used to search the clustering biomarkers of each group, and the clustering marker genes were determined by the expression differences. Tags that identify a single cluster were identified and compared with all other cells.
Identification of single cell subpopulation identification. There are many automatic tools for single cell subgroup identification, and they are mainly divided into two categories, as follows: automatic recognition and semi-supervised. The more common automatic recognition is Singler, which has built-in cell data from humans and mice. The basic principle is to determine the cell type by calculating the correlation between a single cell and the built-in database. The advantage of this tool is that a person does not need to provide their own cell types and corresponding marker genes, but its disadvantage is that it can only recognize cell types that already exist in the database, and it cannot recognize particularly fine cell subsets. The cell subpopulation can also be identified based on traditional classical marker genes, and cell subsets can be identified based on marker genes of known cell types. Generally, subgroup identification is not a single gene, but may require multiple genes. The traditional classical marker gene collection generally uses the following two commonly used databases: CellMarker(http://biocc.hrbmu.edu.cn/CellMarker/) and Magi Panglaodb (https://panglaodb.se/index.html). In this study, we used the traditional classical marker gene, combined with automatic identification tool Singler to identify the cell population.