Single-Cell Dataset of Combined DBP and/or BaP Exposure-Induced Renal Injury in Mice

This dataset employs single-cell RNA sequencing to profile renal injury in C57BL/6J mice following chronic low-dose combined exposure to DBP and/or BaP, comprising 12 samples: 3 controls, 3 BaP-exposed, 3 DBP-exposed, and 3 DBP+BaP co-exposed. 1. IntroductionThe 10x Genomics Chromium is based on GemCode technology and allows high-throughput single-cell 3' mRNA quantitative analysis based on GemCodeTM technology in which describes intercellular heterogeneity. This protocol is developed for 10x Genomics high-throughput single-cell gene expression profiling library construction.2. Experimental Procedure2.1 Microscope Assessment of the Cell or NucleiThe cellular or nuclei suspension is stained with 0.4% trypan blue to assess cell viability under microscopic observation. Cells with greater than 80% viability are qualified for library construction process. 2.2 GEM Generation and Reverse TranscriptionPrepared single-cell suspension is then partitioned into GEMs (Gel Beads in Emulsions) in the automated Chromium Controller, and then mRNAs are reverse transcribed into cDNAs. 2.3 Breaking GEMs The reaction system is configured for breaking GEMs. After reacting at the suitable temperature for a fixed period of time, the products are purified.2.4 cDNA AmplificationThe reaction system is configured for PCR. After reacting at the suitable temperature for a fixed period of time, the products are purified.2.5 Fragmentation, end Repair & A-tailing The reaction system is configured. A certain amount of cDNA amplified products is taken. After reacting at the suitable temperature for a fixed period of time, the cDNAs are subjected to fragmentation, end repair and addition of “A” base at the 3'-end of each strand. Finally, the products are purified.2.6 Adaptor Ligation The reaction system is configured. After reacting at the suitable temperature for a fixed period of time, the cDNAs are subjected to adaptor ligation. Finally, the products are purified.2.7 PCRThe PCR reaction system is configured. After reacting at the suitable temperature for a fixed period of time, amplification is processed via PCR. The products are then purified.2.8 Library QCThe corresponding library quality control protocol will be selected depending upon product requirements.2.9 CircularizationSingle-stranded PCR products are produced via denaturation. The reaction system and program for circularization are subsequently configured and set up. Single-stranded cyclized products are produced, while uncyclized linear DNA molecules are digested.2.10 SequencingSingle-stranded circle DNA molecules are replicated via rolling cycle amplification, and a DNA nanoball (DNB) which contain multiple copies of DNA is generated. Sufficient quality DNBs are then loaded into patterned nanoarrays using high-intensity DNA nanochip technique and sequenced through combinatorial Probe-Anchor Synthesis (cPAS).3 Data Analysis3.1 Quality control of the single-cell data and determination of the major cell types. The raw gene expression matrix generated from each sample were aggregated using CellRanger(v5.0.1) [1] provided on the 10x genomics website. Downstream analysis was done using the R package Seurat (v 3.2.0) [2]. Quality control was applied to cells based on number of detected genes and proportion of mitochondrial reads per cell. Specifically, cells with less than 200 detected genes or cells with >90% of the proportion of the maximum genes were filtered out. For the mitochondrial metric, sorted the cells in descending order of mitochondrial read ratio, and filtered out the top 15% of cells. Potential doublets were identified and removed by DoubletDetection [3]. Cell cycle analysis was performed by using CellCycleScoring function in Seurat program. The gene expression dataset was normalized, subsequent principal component (n=15) analysis was conducted using only the 2000 highly variable genes in the dataset.U-MAP was then used for two-dimensional visualization of the resulting clusters. For each cluster, the marker genes were identified using the FindAllMarkers function as implemented in the Seurat package (logfc. threshold >0.25, minPct >0.1 and Padj≤0.05). Then, clusters were remarked to a known cell type by SCSA [4] method. Differently expressed gene across different samples were identified using the FindMarkers function in Seurat with parameters ‘logfc. threshold >0.25, minPct>0.1 and Padj≤0.05’. 3.2 Gene Function AnalysisGO(associated three integrated databases: Uniprot http://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz, NCBI’s gene2GO ftp://ftp.ncbi.nih.gov/gene/DATA/gene2go.gz , GO’s official website:ftp://ftp.pir.georgetown.edu/databases/idmapping/idmapping.tb.gz, downloaded in May 2020) analysis and as well as KEGG (V93.0) pathway analysis were performed using phyper, a function of R. Only GO terms or KEGG pathways with FDR < =0.05 were considered to be significantly enriched.3.3 Developmental trajectory inference.The Monocle2[5,6] uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We used Monocle2 to do Pseudo-time analysis.3.4 Cell-cell interactions analysis by CellPhoneDBCellPhoneDB [7] is a public database that stores receptors, ligands, and their interactions. Both ligands and receptors include subunit structures that accurately represent heteromeric complexes. We performed cell-to-cell interaction analysis with CellPhoneDB (v2.1.4), receptor-ligand pairs can be downloaded from https://www.cellphonedb.org/downloads, and significant cell-cell interactions was selected with p-value < 0.05. References1. Zheng G X Y, Lau B T, Schnall-Levin M, et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing[J]. Nature biotechnology, 2016, 34(3): 303.2. Butler, A., Hoffman, P., Smibert, P. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36, 411–420 (2018) doi:10.1038/nbt.4096.3.See Gayoso, Adam, & Shor, Jonathan. (2018, July 17). DoubletDetection (Version v2.4). Zenodo. http://doi.org/10.5281/zenodo.26780424. Cao Y et al (2020)., SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data. Front. Genet. 11:4905. Qiu, X., Hill, A., Packer, J. et al. Single-cell mRNA quantification and differential analysis with Census. Nat Methods 14, 309–315 (2017) doi:10.1038/nmeth.4150.6. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells[J]. Nature biotechnology, 2014, 32(4): 381.7.Efremova M, Vento-Tormo M, Teichmann S A, et al. CellPhoneDB: inferring cell–cell communication from combined expression of multisubunit ligand–receptor complexes[J]. Nature protocols, 2020, 15(4): 1484-1506.