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Dataset View [GSE72056]

SeriesGSE72056
TitleSingle cell RNA-seq analysis of melanoma
Year2016
CountryUSA
ArticleGarraway LA,Regev A,Shalek AK,Rozenblatt-Rosen O,Yoon CH,Jané-Valbuena J,Frederick DT,Flaherty KT,Sullivan RJ,Sorger PK,Bertagnolli M,Van Allen EM,Andreev AY,Johannessen CM,Villani AC,Kolb KE,Gaillard A,Kazer SW,Ziegler CG,Hughes TK,Genshaft AS,Lu D,Shah P,Cohen O,Lin JR,Dutton-Regester K,Fallahi-Sichani M,Murphy G,Lian C,Rodman C,Rotem A,Trombetta JJ,Treacy D,Wadsworth MH 2nd,Prakadan SM,Izar B,Tirosh I.Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.Science (New York, N.Y.).2016 Apr 8
PMID27124452
Bio ProjectBioProject: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA292832
SraNA
Overall DesginTumors were disaggregated, sorted into single cells, and profiled by Smart-seq2.
SummaryTo understand the diversity of expression states within melanoma tumors, we obtained freshly resected samples, dissagregated the samples, sorted into single cells and profiled them by single-cell RNA-seq.
Experimental ProtocolRNA and DNA were isolated using the Qiagen minikit following the manufacturers recommendations. Whole Transcriptome amplification (WTA) was performed with a modified SMART-Seq2 protocol, with Maxima Reverse Transcriptase (Life Technologies) used in place of superscript II.; WTA products were cleaned with Agencourt XP DNA beads and 70% ethanol (Beckman Coulter) and Illumina sequencing libraries were prepared using Nextera XT (Illumina), as previously described (15). The 96 samples of a multiwall plate were pooled together, and cleaned with two 0.8x DNA SPRIs (Beckman Coulter). Library quality was assessed with a high sensitivity DNA chip (Agilent) and quantified with a high sensitivity dsDNA Quant Kit (Life Technologies). Samples were sequenced on an Illumina NextSeq 500 instrument using 30bp paired-end reads.
Data processingFollowing sequencing on the NextSeq, BAM files were converted to merged, demultiplexed FASTQs. Paired-end reads were then mapped to the UCSC hg19 human transcriptome using Bowtie with parameters -q --phred33-quals -n 1 -e 99999999 -l 25 -I 1 -X 2000 -a -m 15 -S -p 6, which allows alignment of sequences with single base changes such as due to point mutations. Expression levels of genes were quantified as Ei,j=log2(TPMi,j/10+1), where TPMi,j refers to transcript-per-million (TPM) for gene i in sample j, as calculated by RSEM v1.2.3 in paired-end mode. TPM values were divided by 10 since we estimate the complexity of our single cell libraries to be on the order of 100,000 transcripts and would like to avoid counting each transcript ~10 times, as would be the case with TPM, which may inflate the difference between the expression level of a gene in cells in which the gene is detected and those in which it is not detected. For each cell, we quantified the number of genes for which at least one read was mapped, and the average expression level of a curated list of housekeeping genes. We then excluded all cells with either fewer than 1,700 detected genes or an average housekeeping expression (E, as defined above) below 3. Only genes detected with E>2 in at least 5 cells were retained in the processed data file.; Genome_build: hg19; Supplementary_files_format_and_content: Tab-delimited text file containing the normalized expression levels (E) for all genes with E>2 in at least 5 cells, across 3,249 cells that passed QC. the tumor-of-origin is indicated in the name of each cell (CY##). The first row denotes the classification (based on inferred CNVs) to malignant (2), non-malignant (1) or unresolved (0) cells. The second row denotes the inferred cell types for non-malignant cells, including T-cells (1), B-cells (2), Macrophages (3), Endothelial cells (4) and CAFs (5). All other rows correspond to genes.
Following sequencing on the NextSeq, BAM files were converted to merged, demultiplexed FASTQs. Paired-end reads were then mapped to the UCSC hg19 human transcriptome using Bowtie with parameters -q --phred33-quals -n 1 -e 99999999 -l 25 -I 1 -X 2000 -a -m 15 -S -p 6, which allows alignment of sequences with single base changes such as due to point mutations. Expression levels of genes were quantified as Ei,j=log2(TPMi,j/10+1), where TPMi,j refers to transcript-per-million (TPM) for gene i in sample j, as calculated by RSEM v1.2.3 in paired-end mode. TPM values were divided by 10 since we estimate the complexity of our single cell libraries to be on the order of 100,000 transcripts and would like to avoid counting each transcript ~10 times, as would be the case with TPM, which may inflate the difference between the expression level of a gene in cells in which the gene is detected and those in which it is not detected. For each cell, we quantified the number of genes for which at least one read was mapped, and the average expression level of a curated list of housekeeping genes. We then excluded all cells with either fewer than 1,700 detected genes or an average housekeeping expression (E, as defined above) below 3. Only genes detected with E>2 in at least 5 cells were retained in the processed data file.; Genome_build: hg19; Supplementary_files_format_and_content: Tab-delimited text file containing the normalized expression levels (E) for all genes across all cells that passed QC. The first row denotes the tumor-of-origin, the second denotes classification (based on inferred CNVs) to malignant (2), non-malignant (1) or unresolved (0) cells. The third row denotes the inferred cell types for non-malignant cells, including T-cells (1), B-cells (2), Macrophages (3), Endothelial cells (4) and CAFs (5) and NK cells(6). All other rows correspond to genes, as indicated by the gene Symbols at the first column.
PlatformGPL18573
Public OnPublic on Apr 05 2016

Cell Groups

Differential Expression Gene List

KEGG GO Others   

Gene SymbolEnsembl IDFDR
CXCR4ENSG000001219661.91450854935662e-59
STK17BENSG000000813202.01077220189659e-58
CD74ENSG000000195821.04061673481105e-57
CD52ENSG000001694424.7976105670852e-56
PTPRCENSG000000812371.01180320331665e-54
IRF8ENSG000001409689.39499309663133e-49
SYKENSG000001650251.20053374605489e-46
CD79BENSG000000073121.20053374605489e-46
HLA-DRAENSG000002042875.19173403533817e-45
CD53ENSG000001431195.19173403533817e-45
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