Dataset View [GSE73121]

TitleSingle-cell transcriptome profiling for metastatic renal cell carcinoma patient-derived cells [RNA-seq]
CountrySouth Korea
ArticlePark WY,Joo KM,Kirsch DG,Jeong BC,Nam DH,Shin Y,Kim H,Shin S,Jeong da E,Song HJ,Lee HO,Lee HW,Kim KT.Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma.Genome biology.2016 Apr 29
Bio ProjectSubSeries of: GSE73122
SraBioProject: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA296119
Overall DesginIn order to identify successful clonal propagation from patient to PDX samples and understand pathogenesis from primary to metastatic RCC, we performed whole-exome sequencing (WES, n=4) and matched aCGH (n=4) on bulk tumor samples. And we utilized single-cell RNA sequencing (scRNA-seq) to model and dissect functional heterogeneity acroass primary and metastatic RCC tumors. We checked whether of capturing live one cell, not more cells, in microfluidics by fluorescent microscopic observation. To construct RNA sequencing libraries, we performed further quality controls including adequate quantities and qualities of amplified transcriptomes respectively from single cells. Tumor cells from the parental mRCC (n=34), PDX-mRCC (n=36) and PDX-pRCC (n=46) were finally analyzed in this study after filtering out poor quality cells.
SummaryClear cell renal cell carcinoma (ccRCC) initiated from the renal epithelium is the most prevalent histological type of adult kidney cancers. Dissecting intratumoral heterogeneity (ITH) of ccRCC has leveraged to extend our knowledge on how primary tumors harboring driver mutations evolve and spread to other sites. The cellular fractions within and across the primary (pRCC) and metastatic RCC (mRCC) are heterogeneous in both their genetic and biological features determining the variability in clinical aggressiveness and sensitivity to the therapy. To achieve sustainable therapeutic benefit with targeted agents in mRCC, the effective target should focus on signaling pathways that are related to driver mutations occurred early in the clonal evolution of the disease and thus should be common to primary tumor and metastatic sites. Considering that extensive genetic heterogeneity may result in drug response variability among patients and treatment resistance, the tailored strategies for metastatic RCC is urgently needed. Here, we analyze single-cell RNA-seq (scRNA-seq) data from a matched primary RCC (pRCC) and lung metastasis (mRCC) to dissect ITH at the highest resolution to date with the objective of discovering the better therapeutic regimen.
Experimental ProtocolIn order to isolate single-cells and amplify initial RNA content enough to transcriptome sequencing, we adopted the C1TM Single-Cell Auto Prep System (Fluidigm, CA, USA) with the SMARTer kit (Clontech, CA, USA). Cells were captured on the C1 chip (17-25 μm) and determined as a live single cell by fluorescence microscopic observation. Quantity and quality of amplified cDNAs from individual single cells were checked by Qubit® 2.0 Fluorometer (Life Technologies, CA, USA) and 2100 Bioanalyzer (Agilent Inc., CA, USA). RNAs from bulk cell samples were also amplified using a SMARTer kit with 10 ng of starting material. For WES, gDNAs were prepared using QIAamp® DNA Mini kit (QIAGEN, CA, USA). Exome sequencing was carried using the SureSelect XT Human All Exon V5 kit (Agilent Inc., CA, USA), according to the manufacturer’s standard protocol.; Libraries were prepared using the Nextera XT DNA Sample Prep Kit (Illumina, CA, USA) following the manufacturer’s instruction, assayed the quantity and quality, pooled, and then sequenced on the HiSeq 2500 (Illumina) using the 100bp paired-end mode of the TruSeq Rapid PE Cluster kit and TruSeq Rapid SBS kit (Illumina) at the Samsung Genome Institute (Seoul, Korea).  Sequencing of the exome library was carried out on the HiSeq 2500 (Illumina, CA, USA) using the 100bp paired-end mode of the TruSeq Rapid PE Cluster kit and TruSeq Rapid SBS kit (Illumina) at the Samsung Genome Institute (Seoul, Korea). 
Data processingFor PDX samples, RNA-seq reads only mapped to the mouse genome reference (mm10) were removed. Then, sequencing reads were aligned to the human genome reference (hg19) together with splice junction information of each sample using the 2-pass mode of STAR_2.4.0d (Dobin et al., Bioinformatics 2012). Transcripts Per Million (TPM) was quantified by implementing RSEM v1.2.18 (Li et al., BMC Bioinformatics 2011) in default mode with Genecode v.19 annotation.; Genome_build: hg19; Supplementary_files_format_and_content: Each row of the tab-delimited text file includes ENSEMBL gene ID,ENSEMBL transcript ID, transcript length, effective_length, expected_count, TPM, FPKM for samples.
Public OnPublic on Sep 18 2015

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