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GEO help: Mouse over screen elements for information. |
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Status |
Public on May 11, 2023 |
Title |
aCD4_Tumor_1 Tag |
Sample type |
SRA |
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Source name |
tumor on day 14 after innoculation of B16F10 with anti-CD4 mAb treatment
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Organism |
Mus musculus |
Characteristics |
strain: C57BL/6 treatment: anti-CD4 mAb cell type: Intravenous (i.v.) staining-negative, lineage-negative (CD11b, B220, NK1.1 and Ter119), TCRbeta+ and CD4- CD8+ T cells age: 8 weeks Sex: female tumour cell line: B16F10 melanoma organ: B16F10 date: 0
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Treatment protocol |
B16F10 cells (5 × 10^5 cells) were inoculated subcutaneously (s.c.) into the right flanks of C57BL/6 mice. Anti-PD-L1 mAb (clone 10F.9G2, BioLegend) or Anti-CD4 mAb (clone GK1.5, BioLegend) was injected intraperitoneally (i.p.) at a dose of 200 μg per mouse on days 5 and 9 after tumor inoculation.
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Extracted molecule |
total RNA |
Extraction protocol |
Intravascular leukocytes were stained by i.v. injection of FITC-conjugated anti-CD45.2 mAb (3 mg/mouse, clone 104, BioLegend) three minutes before sacrifice. Tumors were cut into small fragments and digested for 45 minutes at 37°C with 0.1mg/mL Liberase TM (Roche). The cells were then subjected to 40% Percoll PLUS (Cytiva) and Histopaque-1083 (Sigma-Aldrich) gradient and leukocytes were recovered from the interphase. The cell concentration of the suspensions was determined using Flow-Count fluorospheres (Beckman Coulter) and a Cytoflex flow cytometer (Beckman Coulter). Cells were then stained with a mix of Fc Block (anti-mouse CD16/CD32 mAb; clone 93, BioLegend) and fluorophore-conjugated anti-mouse mAbs. Cells were also stained with Sample Tag oligonucleotide-conjugated antibodies in BD® Single-Cell Multiplexing Kit (BD Biosciences) as summarized in Supplementary Table 1. CD8+ T cells were sorted using FACS Aria II or Aria III (BD Biosciences). Nonviable cells were excluded from the analysis based on forward and side scatter profiles and propidium iodide staining. Intravascular leukocytes were also excluded. Purity of sorted cells was always over 95%. T cells from the spleen of Pmel-1 transgenic mice which stained with Sample Tag oligonucleotide-conjugated antibodies as summarized in Supplementary Table 1 were spiked in after cell sorting. Live cells were stained with Calcein AM (Nacalai) and counted using Flow-Count fluorospheres and a Cytoflex flow cytometer. For single-cell RNA/TCRseq, 18,000-30,000 labeled cells were trapped and reverse-transcribed using the BD RhapsodyTM (BD) according to the manufacturer's instructions. Targeted mRNA, TCRseq and Sample Tag libraries were prepared using according to the manufacturer's instructions (VDJ CDR3 and Sample Tag Library Preparation Protocol) with the following modifications; (1) BD RhapsodyTM Immune Response Panel Mm with supplement panel including Cmpk2, Ifi204, Ifi211, Ifit1, Ifit3, Ifit3b, Ifit3b, Isg15, Mx1, Rsad2, and Usp18 were used for target mRNA amplification. (2) we used original set of primer for TCR amplification as summarized in Supplementary Table 2, and performed additional PCR (3rd PCR, 6 cycles) to introduce unique dual index for sequencing. (3) we used ProNex Size-Selection DNA purification System (Promega) for purification of PCR product. Amplified libraries were quantified using a KAPA SYBR Fast qPCR Kit (KAPA Biosystems) and size distribution was analyzed by Microchip Electrophoresis System MultiNA (Shimadzu). Targeted RNAseq and Sample Tag libraries were sequenced on the Illumina Novaseq 6000 S4 flowcell (67 bp read 1 and 141 bp read 2) (Illumina) to a depth of approximately 20,000-40,000 reads and 5,000-10,000 reads per cell, respectively. TCRseq libraries were sequenced on the Illumina Novaseq 6000 SP flowcell (67 bp read 1 and 459 or 141 bp read 2) (Illumina) to a depth of approximately 5,000-20,000 reads. Bulk TCR sequencing library was prepared according to the previous report (Tsunoda et al., 2021). In brief, to perform reverse transcription and template-switching, mRNA-trapped oligo-dT-immobilized Dynabeads M270-streptavidin (Thermo Fisher Scientific) were suspended in 10 µL of RT mix [1× First Strand buffer (Thermo Fisher Scientific), 1 mM dNTP, 2.5 mM DTT (Thermo Fisher Scientific), 1 M betaine (Sigma-Aldrich), 9 mM MgCl2 (NIPPON GENE), 1 U/µL RNaseIn Plus RNase Inhibitor (Promega), 10 U/µL Superscript II (Thermo Fisher Scientific), and 1 µM of i5-TSO], and incubated for 60 min at 42ºC and immediately cooled on ice. To amplify the TCR cDNA containing complementarity determining region 3 (CDR3), nested PCR of the TCR locus was performed following the purification of PCR product by an Agencort AM Pure XP kit (Beckman Coulter) at a 0.7:1 ratio of beads to sample and eluted with 20 µL of 10 mM Tris-HCl (pH 8.0). To amplify TCR libraries and add adaptor sequences for the next-generation sequencer, the third PCR was performed. The third-PCR products were purified as second PCR. The products were pooled and then purified and subjected to dual size selection using ProNex size-selective purification system (Promega) and eluted with 25 μL of 10 mM Tris-HCl (pH 8.5). Final TCR libraries, whose lengths were about 600 base pairs were sequenced using an Illumina Novaseq 6000 S4 flowcell (67 bp read 1 and 141 bp read 2) (Illumina) to a depth of approximately 20,000-40,000 reads and 5,000-10,000 reads per cell, respectively. TCRseq libraries were sequenced on the Illumina Novaseq 6000 SP flowcell (67 bp read 1 and 459 or 141 bp read 2) (Illumina) to a depth of approximately 5,000-20,000 reads.(67 bp read 1 and 140 bp read 2) (Illumina). Only read2 contained the sequence regarding the definition of T cell clones. single-cell targeted RNA-seq / TCR-seq Bulk TCR-seq
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Library strategy |
RNA-Seq |
Library source |
transcriptomic |
Library selection |
cDNA |
Instrument model |
Illumina NovaSeq 6000 |
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Data processing |
Data processing of scRNAseq was performed as described in Shichino et al. (Shichino et al., 2021). For target sequencing data, filtered cell barcode reads were annotated by Python script provided by BD Biosciences with minor modification for compatibility to Python 3.7. Associated cDNA reads were mapped to reference RNA using bowtie2-2.4.2 (Langmead et al., 2012) by the following parameters: -p 2 --very-sensitive-local -N 1 -norc -seed 656565 -reorder. Then, cell barcode information of each read was added to the bowtie2-mapped BAM files by the python script and pysam 0.15.4 (https://github.com/pysam-developers/pysam), and read counts of each gene in each cell barcode were counted using mawk. Resultant count data was converted to a single-cell gene-expression matrix file by using R-3.6.3. The inflection point of the knee-plot (total read count versus the rank of the read count) was detected using DropletUtils package (Lun et al., 2019) in R-3.6.3. Cells of which total read count was over inflection point were considered as valid cells. For sample tag data, adapter trimming of sequencing data was performed by using cutadapt 3.4. Filtered reads were chunked to 64 parts for parallel processing by using Seqkit 0.15.0. Filtered cell barcode reads were annotated by Python script provided by BD with minor modification for compatible to Python3.8. Associated sample tag reads were mapped to known barcode fasta by using bowtie2-2.4.2 by the following parameters: -p 2 -D 50 -R 20 -N 0 -L 14 -i S,1,0.75 –norc –seed 656565 –reorder –trim-to 3:40 –score-min L,-9,0 –mp 3,3 –np 3 –rdg 3,3. Then, cell barcode information of each read were added to the bowtie2-mapped BAM files, and read counts of each Tag in each cell barcode were counted by using mawk. Resulted count data was converted to Tags x cells matrix file by using data.table package in R 3.6.3, and top 1M cell barcodes were extracted. For assignment of each tags to each cell barcodes, read counts of each tag in each valid cell barcode, which defined by the cDNA matrix, were extracted from tag/cell barcode expression matrix. Unassigned cell barcodes were labeled as “not-detected” cells. Then, sum of the total read counts of each tags were normalized to the minimum sum count of each tags, and log2 fold-change between first most tag counts and second most tag counts within each cell barcode. Each cell barcode were ranked by the fold-change ascending order, and top N cells were identified as doublets (N was calculated as theoretically detectable doublets calculated by the Poisson’s distribution based on the number of loaded cells, total Rhapsody well number, and number of tags used). Correspondence table between sample tag and sample sources is summarized in Supplementary Table 1. Raw data of single-cell TCR sequencing were processed by in-house pipelin. Briefly, sequencing reads were first separated by cell barcode, then adapter trimming and quality filtering of sequencing data were performed by using Cutadapt (Martin et al., 2011). PCR and sequencing error was corrected by lighter (Song et al., 2014). Error-corrected sequence reads were processed by MiXCR (Bolotin et al., 2015) to generate the list of TCRa or TCRb sequences for each cell barcode, using reference mouse TCR V/D/J sequences registered in the international ImMunoGeneTics (IMGT) information system with the parameters summarized in Supplementary Table 3. Quality filtering of cell barcodes was performed with the following criteria: (1) more than 32 TCR reads were detected. (2) the proportion of the most frequent TCR sequence in cell barcode was over 0.6. (3) the cell barcode was annotated to the T cell cluster in scRNAseq analysis. Finally, the most frequent sequence of TCRa and TCRb was adopted and paired in each cell barcode. T-cell clones were determined as cells with the same TCRβ, defined by V segment, J segment, and CDR3 nucleotide sequence. Frequency of each clone was calculated as the proportion of cells belonging to the clone in all T cells whose TCRβ was assigned. Data processing of Bulk TCRseq was performed as described in Aoki et al. (Aoki et al., 2021). Adapter trimming and quality filtering of sequencing data were performed by using Cutadapt-3.2 (Martin et al., 2011) and PRINSEQ-0.20.4 (Schmieder et al., 2011). Sequencing data were processed by MiXCR-3.0.5 (Bolotin et al., 2015). In MiXCR, filtered reads were aligned to reference mouse TCR V/D/J sequences registered in the international IMGT information system: -vParameters.geneFeatureToAlign = VTranscript -vjAlignmentOrder = JThenV, then identical sequences were assembled and grouped in clones with PCR and sequencing error correlation with the following parameters: -badQualityThreshold=10, –separateByV=true, --only-productive=true, –region-of-interest=CDR3. The Variable (V) and Joining (J) segment of TCRs were represented in IMGT gene nomenclature. List of final clones were analyzed by VDJtools-1.2.1 (Shugay et al., 2015). Then, the sequencing reads of sample was normalized to ten-times of the cell count in each sample by “DownSample” command of VDJtools. T-cell clones were determined as TCR reads with the same TCR V segment, J segment and CDR3 nucleotide sequence. Assembly: GRCm38 Assembly: IMGT reference sequences of mouse TRA and TRB Supplementary files format and content: (scRNAseq) tab-delimited text files include raw tag-count values for each sample Supplementary files format and content: (scTCRseq) correspondence table between cell barcodes and TCR sequences Supplementary files format and content: (Bulk TCRseq) text files in VDJtools format include read count and frequency, complementarity determining region 3 (CDR3) nucleotide and amino acid sequences, Variable(V)/ Diversity(D)/ Joining(J) segment names, and V, D and J segment boundaries within CDR3 nucleotide sequence of individual clones
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Submission date |
Mar 09, 2022 |
Last update date |
May 11, 2023 |
Contact name |
Hiroyasu Aoki |
E-mail(s) |
haoki-tky@rs.tus.ac.jp
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Phone |
08013746493
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Organization name |
Tokyo University of Science
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Department |
Research Institute for Biomedical Sciences
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Lab |
Division of Molecular Regulation of Inflammatory and Immune Diseases
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Street address |
2669 Yamazaki
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City |
Noda |
State/province |
Chiba |
ZIP/Postal code |
278-0022 |
Country |
Japan |
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Platform ID |
GPL24247 |
Series (2) |
GSE198209 |
Single-cell analysis of the dLN-tumor OL clones following anti-CD4 mAb treatment |
GSE198211 |
Clonal spreading of tumor-infiltrating T cells underlies the durable antitumor effects of PD-1 blockade and anti-CD4 monoclonal antibody |
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Relations |
BioSample |
SAMN26540076 |
SRA |
SRX14416122 |
Supplementary data files not provided |
SRA Run Selector |
Raw data are available in SRA |
Processed data provided as supplementary file |
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