Reconstitution of the multiple myeloma microenvironment following lymphodepletion with BCMA CAR-T therapy
Data files
May 21, 2024 version files 3.68 GB
Abstract
BCMA-targeted CAR-T therapy has shown potent treatment outcomes in treating multiple myeloma (MM), a disease characterized by malignant bone marrow (BM) plasma cells. However, the remodeling of MM microenvironment after CAR-T therapy remains poorly understood. Here, we report the reconstitution of MM microenvironment by obtaining single-cell transcriptomes for paired BM specimens (n = 14) from 7 MM patients before (i.e., baseline, ''day −4'') and after (i.e., ''day 28'') post-lymphodepleted BCMA CAR-T therapy. Our analysis revealed heterogeneity in driver gene expression among MM cells, even those harboring the same cytogenetic abnormalities. The best overall responses of patients over the 15-month follow-up are positively correlated with the abundance and targeted cytotoxic activity of CD8+ effector CAR-T cells on day 28 after CAR-T cell infusion. Additionally, favorable responses are associated with attenuated immunosuppression mediated by regulatory T cells (Tregs), enhanced CD8+ effector T cell cytotoxic activity, and elevated type 1 conventional dendritic cell (cDC1) antigen presentation ability. DC re-clustering inferred intramedullary-originated cDC3s with extramedullary migration. Cell-cell communication network analysis indicated BCMA CAR-T therapy mitigates BAFF/GALECTIN/MK pathway-mediated immunosuppression and activates MIF pathway-mediated anti-MM immunity. Our study sheds light on MM microenvironment dynamics after BCMA CAR-T therapy, offering clues for predicting treatment responsivity.
README: Reconstitution of the Multiple Myeloma Microenvironment Following Lymphodepletion with BCMA CAR-T Therapy
https://doi.org/10.5061/dryad.44j0zpcn7
The collected paired BM specimens from MM patients before and after BCMA CAR-T therapy were isolated into single cell suspensions, and 3'-scRNA-seq (Chromium Single Cell 3' v3 Libraries) analysis was performed on each sample.
Description of the data and file structure
The dataset includes '.mtx' files, each of which is for creating a Seurat object in R.
The naming convention of the '.mtx' files was following the sampling time points of the MM patients receiving BCMA CAR-T therapy. Specifically, the 14 BM specimens that performed scRNA-seq were collected from 7 relapsed or refractory MM patients (i.e. 'P1, P2, P3, P4, P5, P6, and P7') before and after BCMA CAR-T therapy. For these specimens, the baseline (i.e. 'B') specimens (P1_B, P2_B, P3_B, P4_B, P5_B, P6_B, and P7_B) were collected from P1-P7 on day -4 before BCMA CAR-T therapy, and the remission stage (i.e. 'R') specimens (P1_R, P2_R, P3_R, P4_R, P5_R, P6_R, and P7_R) were collected from P1-P7 on day 28 after BCMA CAR-T cell infusion.
The '.mtx' files contained gene-barcode matrix that showed the barcoded cells and gene expression counts. In these matrices, each row represents a barcoded cell name, and each column represents a gene name. Thus, the analysis of the expression level of each gene in each cell was available.
Sharing/Access information
Human transcriptome reference used for our analysis is available at 10x Genomics website (https://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-GRCh38-3.0.0.tar.gz); the code used for scRNA-seq analysis is available at 10x Genomics website (https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-gex-count and at GitHub website (https://github.com/satijalab/seurat/releases/tag/v4.0.2).
Code/Software
The single-cell RNA sequencing data was processed using cellranger (v.6.1.2) and analyzed with the R package Seurat (v.4.0.2).
To analyze the data, firslty running the packages that provided in the code under the R Studio operating environment. Then, inputting all of the '.mtx' files for creating the Seurat object, for which the custom storage path of the downloaded data needing to be changed in the provided script to input the data. The downstream analyses can fully followed the provided code.
Methods
The collected paired BM specimens from MM patients before and after BCMA CAR-T therapy were isolated into single cell suspensions, and 3'-scRNA-seq (Chromium Single Cell 3′ v3 Libraries) analysis was performed on each sample.
The analysis included 7 MM patients (P1-P7). We collected baseline BM aspirate specimens (P1_B, P2_B, P3_B, P4_B, P5_B, P6_B, and P7_B) from each patient before (i.e., baseline, ''day −4'') BCMA CAR-T cell infusion (i.e., ''day 0''), and the BM aspirate specimens (P1_R, P2_R, P3_R, P4_R, P5_R, P6_R, and P7_R) after BCMA CAR-T therapy were collected on day 28. These patients had received 2 or 3 previous lines of therapies, including two patients (P2 and P6) with extramedullary disease; the other patients did not experience extramedullary progression. All patients received cyclophosphamide-mediated lymphodepletion on day −3 with the aim of potentiating the expansion of CAR-T cells. Efficacy assessments based on the International Myeloma Working Group (IMWG) criteria displayed that the patients had achieved varying degrees of remission by day 28: P3 achieved stringent complete remission (sCR); P1 and P4 achieved very good partial remission (VGPR); the other patients achieved partial remission (PR).
The BM aspirate samples were collected during the diagnostic procedure. Bone marrow mononuclear cells (BMMCs) were isolated using Ficoll Paque Plus (Cytiva) and washed three times with Hanks Balanced Salt Solution (HBSS, Solarbio). Following centrifugation at 1,000 rpm for 5 min, the supernatant was discarded, and the sediment was resuspended in 1 ml phosphate-buffered saline (PBS, Solarbio). To eliminate red blood cells, 1-2 ml red blood cell lysis buffer (BD) was added at 4°C for 10 min. After centrifugation at 500 g for 5 min, the cells were suspended in PBS. The cell viability was assessed using trypan blue (Sigma) and examined under a microscope.
Utilizing the 10x Genomics Chromium Single Cell 3′ v3 Library Kit and Chromium instrument, approximately 17,500 cells were partitioned into nanoliter droplets to achieve single-cell resolution for a maximum of 10,000 individual cells per sample. The resulting cDNA was tagged with a common 16 nt cell barcode and 10 nt Unique Molecular Identifier during the RT reaction. Full-length cDNA from poly-A mRNA transcripts was enzymatically fragmented and size selected to optimize the cDNA amplicon size (approximately 400 bp) for library construction (10x Genomics). The concentration of the 10x single-cell library was accurately determined through qPCR (Kapa Biosystems) to produce cluster counts appropriate for the HiSeq4000 or NovaSeq6000 platform (Illumina). In all, 26 × 98 bp (3′ v3 libraries) sequence data were generated targeting between 25 and 50K read pairs/cell, which provided digital gene expression profiles for each individual cell.
For single-cell RNA-seq analysis, we used CellRanger (10x Genomics, v.6.1.2) to pre-process the single-cell RNA-seq data after obtaining the paired-end raw reads. Cell barcodes and unique molecular identifiers (UMIs) of the library were extracted from read 1. Then, the reads were split according to their cell (barcode) IDs, and the UMI sequences from read 2 were simultaneously recorded for each cell. Quality control on these raw readings was subsequently performed to eliminate adapter contamination, duplicates, and low-quality bases. After filtering barcodes and low-quality readings that were not related to cells, we mapped the cleaned readings to the human genome (GRCh38) and retained the uniquely mapped readings for UMIs counts. Next, we estimated the accurate molecular counts and generated a UMI count matrix for each cell by counting UMIs for each sample. Finally, we generated a gene-barcode matrix that showed the barcoded cells and gene expression counts.