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Sample GSM8153073

Query DataSets for GSM8153073

Status

Public on May 15, 2024

Title

APRIL CAR T cells +/- IL-18 replicate 2, HTO

Sample type

SRA

Source name

splenocytes

Organism

Mus musculus

Characteristics

tissue: splenocytes
cell type: T cells
genotype: BALB/c
treatment: co-culture of T cells and MOPC315.BM myeloma

Treatment protocol

1.2x10^6 CAR T cells were stimulated in culture for 24 hours with 2.4x10^6 MOPC315.BM myeloma cells (1:2 T cell-to-MOPC315.BM ratio). Cells were cultured in RPMI complete media lacking IL-2.

Extracted molecule

polyA RNA

Extraction protocol

Cells were then labeled with HTO barcodes and sorted for CD4+ or CD8+ T-cells with CD138+ MOPC315.BM excluded. Post-sort CD4 and CD8 T-cells were then re-pooled at a 1:1 ratio. n=2 biological replicates per group, ~30,000 cells per biological replicate were submitted for sequencing.
Single-cell RNA sequencing of FACS-sorted cell suspensions was performed on the Chromium instrument (10X genomics) following the user guide manual for 3′ v3.1. In brief, FACS- sorted cells were washed once with PBS containing 1% bovine serum albumin (BSA) and resuspended in PBS containing 1% BSA to a final concentration of 1,700–2,000 cells per μl. The viability of cells was ~70%, as confirmed with 0.2% (w/v) Trypan Blue staining (Countess II). Cells were captured indroplets. Following reverse transcription and cell barcoding in droplets, emulsions were broken, and cDNA was purified using Dynabeads MyOne SILANE followed by PCR amplification as per manual instruction. About 30,000 cells were targeted for each sample. Samples were multiplexed together on one lane of 10x Chromium (using HTO) following a previously published protocol3. Final libraries were sequenced on Illumina NovaSeq S4 platform (R1 – 28 cycles, i7 – 8 cycles, R2 – 90 cycles) at a depth of ~575 million reads per sample.

Library strategy

RNA-Seq

Library source

transcriptomic single cell

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

Data processing

FASTQ files were processed using the 10x Cell Ranger package (v7.0.0). The Cell Ranger generated filtered_feature_bc_matrix.h5 files were processed using the pipeline available on the shunPykeR4 GitHub repository which implements Python and R code for streamlined analysis with Scanpy5 and other python implemented tools. Genes that were not expressed in any cell, in addition to ribosomal and hemoglobin genes, were removed prior to downstream analysis. Each cell was then normalized to a total library size of 10,000 reads and gene counts were log- transformed using the log(X+1) formula, in which log denotes the natural logarithm. Principal component analysis was applied to reduce noise prior to data clustering. To select the optimal number of principal components to retain for each dataset, the knee point (eigenvalues smaller radius of curvature) was used. Leiden clustering6 was used to identify clusters within the PCA- reduced data.
Quality of the single cells was computationally assessed based on total counts, number of genes, mitochondrial and ribosomal fraction per cell, with low total counts, low number of genes (≤1000) and high mitochondrial content (≥0.2) as negative indicators of cell quality. Cells characterized by more than one negative indicator were considered as “bad” quality cells. To remove bad quality cells and contaminants in an unbiased way, we assessed them in a cluster basis rather than individually. Leiden clusters with a “bad” quality profile and/or a high number of contaminating cells were removed. Scrublet7 was used to filter out doublets.
Assembly: mm10-2020-a
Supplementary files format and content: *.h5ad: H5AD file with raw and normalized read counts and cell annotation (after bad quality cell filtering)
Supplementary files format and content: filtered_feature_bc_matrix files (barcodes, features, matrix): contain only detected cell-associated barcodes in MEX format; each element of the matrix is the number of UMIs associated with a feature (row) and a barcode (column)
Supplementary files format and content: filtered_feature_bc_matrix.h5: same information as filtered_feature_bc_matrix in HDF5 format
Supplementary files format and content: raw_feature_bc_matrix files (barcodes, features, matrix): contain all detected barcodes in MEX format; each element of the matrix is the number of UMIs associated with a feature (row) and a barcode (column)
Supplementary files format and content: raw_feature_bc_matrix.h5: same information as raw_feature_bc_matrix in HDF5 format

Submission date

Mar 18, 2024

Last update date

May 15, 2024

Contact name

Brandon Ng

E-mail(s)

ngb@mskcc.org

Organization name

Memorial Sloan Kettering Cancer Center

Department

Immunology