Immune-mediated tumor control in the 5TGM1 transfer model of multiple myeloma
Description
Single cell mRNA sequencing was used to investigate alterations in the immune microenvironment during multiple myeloma. In the 5TGM1 murine myeloma model, scRNAseq was performed on bone marrow immune cells sorted from steady state and tumor-bearing C57Bl/6JOlaHsd and C57Bl/KaLwRijHsd mice. Tumor-bearing mice were sacrificed 21 days after injection of 5TGM1 cells. Tumor-bearing C57Bl/6JOlaHsd presented with stable bone marrow myeloma, while C57Bl/KaLwRijHsd had progressive bone marrow myeloma. Next, scRNAseq was performed on bone marrow cells from relapsing myeloma patients and non-tumor controls. Compared to non-tumor controls, we observe an expansion of IFN-responsive NK cells in both tumor-bearing mice and in relapsing myeloma patients.
Files
Steps to reproduce
Murine data: The Cellranger output files are in 5TGM1_BM_immune.zip; this contains subfolders per mice with the filtered_feature_bc_matrix output directories, including all sorted cells (NK, T, B, monocyte/macrophages, and neutrophils). Due to transcriptomic overlap with T cells, NK cells were subsetted in two steps. First, NK and T cells were first subsetted from individual mice using barcodes and a script available on our Github page (https://github.com/MyelomaRotterdam/Kellermayer-et-al-2023), and then merged into one object. NK cells were then subsetted from this NK/T object and then integrated into one NK object (BM_NK.rds). Metadata tags: timepoint (SS vs dis), Strain (b6 vs kalwrij), Mouse (individual mice IDs) Human data: NK cells were identified in silico from a CD38-positive immune dataset of control samples previously generated at Myeloma Research Rotterdam (De Jong et al. Nat Immunol. 2021 Jun;22(6):769-780). This was further supplemented by 6 RRMM patient samples. The CD38pos.zip folder contains 2 subfolders: control and relapse. Within the timepoint subfolders there are subfolders per patient which contain the filtered_feature_bc_matrix output directories for the CD38-positive immune dataset. We first processed each individual patient separately prior to combining data from multiple patients followed by integration of individual datasets in Seurat. Next, NK cells were identified based upon transcription of KLRF1, KLRD1, NKG7 and GNLY and subsetted. Well-defined marker genes were used to identify potential contaminating cell populations (T cells: CD8A, CD8B, CD4; B cells: MS4A1, CD19, VPREB1; plasma cells: SDC1, LAMP5, SLAMF7; and myeloid cells: LYZ, CD14, FCGR3B, ELANE, FCER1A, CD1C). The ctrl_relapse_human_NK.rds file is the integrated object containing the NK cells from the 5 controls and the 6 relapse patients. With any questions, please contact us at t.cupedo@erasmusmc.nl or z.kellermayer@erasmusmc.nl.