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| Status |
Public on May 15, 2024 |
| Title |
APRIL-BB-1XX_IL-18_HTO |
| Sample type |
SRA |
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| Source name |
bone marrow
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| Organism |
Mus musculus |
| Characteristics |
tissue: bone marrow
cell type: CD45+ bone marrow
genotype: BALB/c
treatment: APRIL-BB-1XX_IL-18 CAR T-cells
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| Treatment protocol |
Myeloma bearing mice were injected with 4E5 MOPC315.BM (TACI KO) on day 0, followed by 2E6 APRIL-BB-1XX or APRIL-BB-1XX/IL-18 T-cells on day 3.
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| Extracted molecule |
other |
| Extraction protocol |
CAR T-cell treated mice were harvested for bone marrow on day 7 of experiment (4 days after CAR T-cells). Cells were erythrocyte-lysed and stained with Fc-block. Cells were then labeled with fluorescent antibodies and DAPI for FACS sorting and hashtag oligonucleotide (HTO) antibodies (Biolegend) to barcode each mouse for identification post-sequencing. Cells were FACS-sorted for CD45+/CD3+ T-cells and CD45+/CD3- non-T-cells. Cells were re-pooled at 1:2 T- cells:non-T-cells and stained with FcX (Biolegend) followed by Biolegend TotalSeqA mouse universal cocktail (for CITE-seq). ~100,000 cells from each biological group were submitted for single-cell sequencing (n=3 mice per group, ~30,000 cells /mouse).
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 above 80%, 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) and specific extracellular protein epitope characterized (using Antibody Derived Tag, Biolegend TotalSeqA) 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. HTO and CITE were sequenced at ~88 million reads per sample.
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| Library strategy |
OTHER |
| Library source |
other |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
20230508_MM_IL-18_singlecell.h5ad
T_cells.h5ad
Mac_mono.h5ad
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| Data processing |
FASTQ files were processed using the 10x Cell Ranger package (v6.1.2). 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.
For CITE-seq, filtered_feature_bc_matrix.h5 files were again loaded and concatenated in the same fashion as RNA files. Total raw counts were obtained for each epitope/antibody read, and data were plotted on the UMAP projection that was generated with scRNAseq data. Dotplot were generated using transformed and normalized expression.
Patient data for myeloma-associated antigen expression was obtained from the Nature Communications publication by Tirier et al. (2021) and accessed on GEO (GSE161801). Sequencing samples denoted as “CD138+ tumor” were taken and concatenated with Scanpy. Upon clustering data with Leiden and by patient ID, we removed clusters that were not homogenously categorized as a single patient or did not have expression of the markers SDC1, SLAMF7, CD38 (described method by Tirier et al.)12. RNA expression for genes of interest was imputed with MAGIC13 and then plotted as UMAP projection of heatmap.
Assembly: mm10-20-a
Supplementary files format and content: 20230508_MM_IL-18_singlecell.h5ad: H5AD file with raw and normalized read counts and cell annotation for all cells (after bad quality cell filtering)
Supplementary files format and content: T_cells.h5ad: H5AD file with raw and normalized read counts and cell annotation of T-cells only (after bad quality cell filtering)
Supplementary files format and content: Mac_mono.h5ad: H5AD file with raw and normalized read counts and cell annotation of macrophages/monocytes only (after bad quality cell filtering)
Supplementary files format and content: adata_Tirier_Nat_comm_2021_filtered.h5ad: H5AD of reanalyzed data from GEO accession #GSE161801 after MAGIC imputation
Supplementary files format and content: adata_Tirier_Nat_comm_2021_filtered_magic.h5ad: H5AD of reanalyzed data from GEO accession #GSE161802
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
Library strategy: CITE-seq
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| Submission date |
Oct 31, 2023 |
| Last update date |
May 15, 2024 |
| Contact name |
Anastasia I Kousa |
| E-mail(s) |
akousa@coh.org
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| Organization name |
City of Hope
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| Department |
Hematology / HCT
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| Lab |
The Marcel van den Brink Lab
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| Street address |
1500 E Duarte Rd
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| City |
Duarte |
| State/province |
CA |
| ZIP/Postal code |
91010 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (1) |
| GSE246682 |
IL-18-secreting multi-antigen targeting CAR T-cells eliminate antigen-low myeloma in an immunocompetent mouse model [in vivo] |
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| Relations |
| BioSample |
SAMN38053179 |
| SRA |
SRX22323133 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7874581_BN-2099_group2_HTO.h5ad.gz |
649.3 Kb |
(ftp)(http) |
H5AD |
SRA Run Selector |
| Raw data are available in SRA |
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