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| Status |
Public on Jul 19, 2024 |
| Title |
Untreated_HTO_Tumor |
| Sample type |
SRA |
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| Source name |
Tumor-draining lymph node
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| Organism |
Mus musculus |
| Characteristics |
tissue: Tumor-draining lymph node
cell type: CD8+ T cell
genotype: C57BL/6
treatment: Untreated
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| Treatment protocol |
To discriminate vascular T cells from T cells in tissues, tumor-bearing mice were injected retro-orbitally with fluorescently-labeled anti-CD45 antibodies (CD45-IV) 3 minutes prior to euthanasia. Spleens and lymph nodes were dissected from mice and physically dissociated through a 70 µm filter to generate single cell suspensions. Splenocyte suspensions were lysed with ACK lysis buffer (Gibco) for 3 minutes to deplete red blood cells. Subcutaneous tumors were dissected from mice, weighed, and collected in 5 mL DMEM (Gibco) containing the human tumor dissociation kit (Miltenyi) enzymes. Tumors were dissociated using a Mixer HC (USA Scientific) set to 37 degrees Celsius, shaking at 500 RPM for 45 – 60 minutes. Following the digestion, tumors were mashed through a 70 μm filter with a 1 mL syringe plunger to generate a single cell suspension. The dissociated cells were washed 2 times with PBS and were layered over Ficoll (GE). Cells were spun over Ficoll at 450 g for 30 minutes with the lowest settings of acceleration and brakes. The layer at the interface of Ficoll and PBS was collected and washed with PBS.
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| Growth protocol |
Mice were injected intraperitoneally with anti-CTLA-4 (clone UC10-4F10-11, Bio X Cell) and anti-PD-L1 (clone 10F.9G2, Bio X Cell) antibodies on days 7, 10, 13, and 16 post-tumor inoculation for tumor outgrowth studies, or on days 7 and 10 post-tumor inoculation for day 14 analyses. Each mouse received 100 mg of each antibody per treatment.
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Prior to staining, cells were washed with FACS staining buffer (chilled PBS containing 1% FBS and 2 mM EDTA). Cells were resuspended in 50 mL of the antibody-containing staining buffer, plus eBioscience Fixable Viability Dye eFluor 780 or eFluor 506 to distinguish live and dead cells and with anti-CD16/CD32 (clone 93, BioLegend) to prevent non-specific antibody binding. Cell surface proteins were stained for 20 min on ice with fluorophore-conjugated antibodies at a 1:200 dilution. Cells were then washed twice and resuspended in eBioscience Fixation/Permeabilization buffer and incubated 30 minutes at room temperature. Cells were then washed twice and resuspended in staining buffer with intracellular antibodies. To obtain absolute counts of cells, Precision Count Beads (BioLegend) were added to samples according to manufacturer’s instructions. Flow cytometry sample acquisition was performed on a LSR Fortessa cytometer (BD), and the collected data was analyzed using FlowJo v10.5.3 software (TreeStar). For cell sorting, the surface staining was performed as described above under sterile conditions, and cells were acquired and sorted into complete medium using a FACSAria III sorter (BD). For CD8+ T cell analysis, cells were pre-gated on FSA and SSC, Live, CD45+, CD45-IV-, CD3+ or TCRbeta+, single cells, CD4-, CD8+. Antibodies used: CD4 clone RM4-5, CD45 clone 30-F11, CD45.1 clone A20, CD45.2 clone 104, CD8a clone 53-6.7, TCRb clone H57-597, CD3 clone 17A2, Thy1.2 clone 53-2.1, CXCR3 clone CXCR3-173, CX3CR1 clone SAO11F11, KLRG1 clone 2F1/KLRG1. SIY-tetramer was obtained from the NIH tetramer core, conjugated to PE, and added at 1:500-1:1,000 to the extracellular staining antibody mix. For scRNA-seq experiments with cell hashing, cells were also stained with Totalseq A anti-mouse hashing antibodies (Biolegend) before FACS.
Sorted cells were then processed for scRNA-seq using the Seq-Well platform with second strand chemistry, as previously described (63, 64). Whole transcriptome libraries were barcoded and amplified using the Nextera XT kit (Illumina) and were sequenced on a Novaseq 6000 (Illumina). Hashtag oligo libraries were amplified as described previously and were sequenced on a Nextseq 550(38).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
SIY-specific CD8+ T cells
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| Data processing |
Raw read processing of scRNA-seq reads was performed as previously described(65). Briefly, reads were aligned to the mm10 reference genome and collapsed by cell barcode and unique molecular identifier (UMI). Then, cells with less than 300 unique genes detected or with greater than 25% mitochondrial gene counts and genes detected in fewer than 5 cells were filtered out. Cell cycle scores for individual cells were computed using CellCycleScoring function in Seurat. Data was then integrated by batch using Seurat v4.1.1(65). The ScaleData function was then used to regress out the number of RNA features in each cell, as well as S and G2/M cell cycle scores and fraction of mitochondrial gene expression. The number of principal components used for visualization was determined by examination of the elbow plot, and two-dimensional embeddings were generated using uniform manifold approximation and projection (UMAP). Clusters were determined using Louvain clustering, as implemented in the FindClusters function in Seurat. DEG analysis was performed for each cluster and between indicated cell populations using the FindMarkers function. Data was iteratively reclustered to remove clusters with gene expression consistent with naïve T cells, monocytes, and NK cells. Label transfer of cluster labels onto proliferating cell populations was performed using the FindTransferAnchors and TransferData functions in Seurat (65).
Cell hashing data was aligned to HTO barcodes using CITE-seq-Count v1.4.2. First, cells receiving fewer than five total HTO counts were classified as negatives. To establish thresholds for positivity for each HTO barcode, we first performed centered log-ratio normalization of the HTO matrix and then performed k-medoids clustering with k=5 (one for each HTO). This produced consistently five clusters, each dominated by one of the 5 barcodes. For each cluster, we first identified the HTO barcode that was dominant in that cluster. We then considered the threshold to be the lowest value for that HTO barcode among the cells classified in that cluster. To account for the scenario in which this value was substantially lower than the rest of the values in the cluster, we used Grubbs’ test to determine whether this threshold was statistically an outlier relative to the rest of the cluster. If the lower bound was determined to be an outlier at p=0.05, it was removed from the cluster, and the next lowest value was used as the new threshold. This procedure was iteratively applied until the lowest value in the cluster was no longer considered an outlier at p=0.05. Cells were then determined to be “positive” or “negative” for each HTO barcode based on these thresholds. Cells that were positive for multiple HTOs or were negative for all HTOs were excluded from downstream analysis. To account for differences in sequencing depth between samples, these steps were performed separately for each Seq-Well array that was processed. Thresholds calculated for each sample were manually inspected and adjusted if necessary. Cells marked as “doublets” or “negatives” by this procedure were excluded from downstream analysis.
Paired TCR sequencing and read alignment was performed as previously described (66). Briefly, whole transcriptome amplification product from each single-cell library was enriched for TCR transcripts using biotinylated Tcrb and Tcra probes and magnetic streptavidin beads. The enrichment product was further amplified using V-region primers and Nextera sequencing handles, and the resulting libraries were sequenced on an Illumina Novaseq 6000 or Nextseq 550. Processing of raw sequencing reads was performed using the Immcantation software suite (67, 68). First, the FilterSeq.py function was used to remove reads with an average quality score less than 25. Then, reads were aggregated by cell barcode and UMI, and UMI with under 10 reads were discarded ClusterSets.py was used to divide sequences for each UMI into sets of similar sequences. Only sets of sequences that comprised greater than 70% of the sequences obtained for that UMI were considered further. Consensus sequences for each UMI were determined using the BuildConsensus.py function. Consensus sequences were then mapped against TCRV and TCRJ IMGT references sequences with IgBlast (69). Sequences for which a CDR3 sequence could not be unambiguously determined were discarded. UMI for consensus sequences were corrected using a directional UMI collapse, as implemented in UMI-tools. TCR sequences were then mapped to single cell transcriptomes by matching cell barcodes. If multiple Tcra or Tcrb sequences were detected for a single cell barcode, then the corresponding sequence with the highest number of UMI and raw reads was retained. To define clonotypes of cells, we first segregated cells by mouse and unique Tcrb CDR3 junction nucleotide sequences. For each unique combination of mouse and CDR3β junction, we determined the most common TCRα sequence in cells with paired TCR recovery. We then imputed missing beta chains from cells with recovery of only alpha chain by matching to these combinations of mouse, beta chain, and alpha chain.
Assembly: mm10
Supplementary files format and content: WT_DGE.txt.gz: cell/gene UMI count matrix from whole-transcriptome sequencing
Supplementary files format and content: HTO_processed.txt.gz: HTO count matrix
Supplementary files format and content: TCR_processed.txt.gz: aligned and assembeled TCR sequences
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| Submission date |
Jun 17, 2024 |
| Last update date |
Jul 19, 2024 |
| Contact name |
Duncan Matthew Morgan |
| E-mail(s) |
dmmorgan@mit.edu
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| Organization name |
MIT
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| Department |
Koch Institute
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| Lab |
Love lab
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| Street address |
500 Main St
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| City |
CAMBRIDGE |
| State/province |
MA |
| ZIP/Postal code |
02139 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (1) |
| GSE270050 |
Expansion of Tumor-Infiltrating CD8+ T Cell Clonotypes Occurs in the Spleen in Response to Immune Checkpoint Blockade |
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| Relations |
| SRA |
SRX24948416 |
| BioSample |
SAMN41874917 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM8333770_Untreated_Tumor_HTO_processed.tsv.gz |
11.8 Kb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Raw data are available in SRA |
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