Precursor states of multiple myeloma (MM) and its native tumor microenvironment need in-depth molecular characterization to better stratify and treat patients at risk. Using single-cell RNA sequencing of bone marrow cells from precursor stages, monoclonal gammopathy of unknown significance and smoldering MM, to full-blown MM alongside healthy donors, we demonstrate early immune changes during patient progression. We find that natural killer cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. As early as smoldering MM, we show loss of granzyme K+ memory cytotoxic T cells and show their critical role in MM immunosurveillance in mouse models. Finally, we report major histocompatibility complex class II dysregulation in CD14+ monocytes, which results in T-cell suppression in vitro. These results provide a comprehensive map of immune changes at play over the evolution of premalignant MM, which will help develop strategies for immune-based patient stratification.
Bone marrow (BM) plasma cell malignancy MM remains an incurable disease1. Despite the readily detectable monoclonal gammopathy of unknown significance (MGUS) and smoldering MM (SMM) precursor states1,2,3, treatment is not administered until patients progress to MM. Since early premalignancy not always leads to progression, treatment cannot be justified solely on this basis. Low- and high-risk SMM exhibit 5-year progression rates of 15 and 70%, respectively, compared to a 20-year rate of 2 and 27% for low- and high-risk MGUS4,5. Therefore, further molecular characterization is required to accurately identify and thwart high-risk premalignant MM.
Genomic studies have shown that MGUS/SMM clones may already harbor chromosomal alterations that define MM (translocations involving IgH or hyperdiploidy2,6) and that progression can be driven by the acquisition of events like MYC translocations, 1q gains and TP53 mutations7,8. However, not all patients with MGUS/SMM with a similar genetic makeup eventually progress to MM, implying that further nongenomic alterations may be required for disease progression4,9,10,11. Indeed, it is becoming more recognized that tumors represent complex ecosystems12. Not only can tumor behavior be regulated by the extracellular milieu13,14,15, but emerging evidence documents that compositional and expression changes of individual immune and stromal components correlate with disease subtypes and prognostic and therapeutic outcomes in breast, colorectal and other solid cancers16,17,18,19,20.
Prior studies have confirmed that the BM microenvironment in MM exhibits dysregulation in receptor signaling21, cytokine expression21,22 and numerical alterations in T, natural killer (NK) and dendritic cells23,24,25,26. MM cells induce immunosuppression, which comprises expansion of regulatory T (Treg) cells27,28, myeloid-derived suppressor cells (MDSCs)29,30, tumor-associated macrophages31,32 and dysfunction of NK cells33. Alterations to the microenvironment have been linked to reduced antitumor responses, induction of angiogenesis, chemotherapy resistance and progression27,31,34,35.
In this study, we used single-cell transcriptomics to dissect the immune microenvironment in the BM in people with MGUS, SMM and overt MM compared to healthy donors. We observe that the tumor microenvironment exhibits substantial alterations beginning at the MGUS stage, with increased populations of NK cells, T cells and nonclassical monocytes. Among T cells, we observe an early accumulation of regulatory and gamma delta T cells, followed by loss of CD8+ memory populations and elevation of IFN signaling at the SMM stage. We demonstrate the critical importance of memory T cells for MM immunosurveillance. In CD14+ monocytes, we find dysregulated expression of major histocompatibility complex (MHC) type II genes. We show that MM cells lead to loss of antigen presentation, inducing a T-cell suppressive phenotype in monocytes. Together, our results characterize transcriptional and compositional changes occurring in the BM compartment during MM progression. They hint at mechanisms of antitumor immune response and immune evasion. Importantly, the immune patterns we observed are often heterogeneous across patients and thus may prove important biomarkers for risk assessment and therapeutic strategies for the prevention of progression in MM.
To better understand the changes occurring in the tumor microenvironment during MM progression, we performed single-cell RNA sequencing (RNA-seq; 10x Genomics) on BM aspirates from patients at varying stages of progression. We sequenced approximately 19,000 CD45+/CD138− cells from the microenvironment (approximately 15,000 from MM stages) from patients with MGUS (n = 5 patients), low-risk SMM (SMM-low; n = 3), high-risk SMM (SMM-high; n = 8) and newly diagnosed MM (n = 7), as well as nine healthy donors (normal BM (NBM), n = 9; Supplementary Tables 1 and 2). One patient contributed sequential samples (SMM-low-1 and MM-8). Patients with SMM were stratified by risk of progression into low (SMMl) and high (SMMh) based on the Mayo clinic established criteria10,36.
The CD45+CD138− subset was isolated using magnetic bead sorting of the BM samples and further in silico cell filtering based on gene expression (Experimental procedures). By clustering cells based on expression profile, we isolated 21 subpopulations. Using (1) the expression of known marker genes (Extended Data Fig. 1) and (2) finding the top differentially expressed genes for each cluster (Supplementary Table 3), we classified our clusters with ten broad cell types, ranging from hematopoietic progenitor cells and pre-B cells to mature populations engaged in the immune response (Fig. 1a).
Compositional alterations in the microenvironment occur early in MM progression
To investigate the relative contribution of different cell types to the microenvironmental repertoire across different stages of disease, we searched for populations that were enriched on average in the disease microenvironment compared to normal BM. Consistent with previous data23,24,25,26, we observed a significant enrichment in NK, T and CD16+ cells in the diseased BM, as well as a relative decrease in plasmacytoid dendritic cells, immature neutrophils, CD14+ monocytes and other progenitor cells (Fig. 1b,c). Several of these alterations could already be observed as early as the MGUS stage. Interestingly, the degree of this compositional shift was not uniform across patients. While BM samples from healthy donors universally had <2% CD16+ macrophages and <10% NK cells, patient samples occupied a spectrum from unaltered compositions to approximately 12% CD16+ macrophages and approximately 40% NK cells. These changes were observed independent of disease stage or tumor burden, suggesting they were mediated by the properties of the tumor itself or the immune response of the individual patients (Fig. 1c). In particular, NK-cell fractions correlated with the proportion of nonclassical monocytes and this relationship did not correspond to the cytogenetic characteristics of the tumor, disease stage or tumor burden (Fig. 1d).
NK levels were most elevated in patients with immunoglobulin G–secreting tumors, which given their interaction with immunoglobulin G via the Fc receptor III in normal physiology37, could indicate a reason for their presence or be a factor in their failure to clear the disease (Extended Data Fig. 2a). Furthermore, samples with overall increases in NK cells were accompanied by a shift in their phenotype. Subclustering of NK cells revealed three populations: a CXCR4+, a CX3CR1+/CCL3+ and a less frequent IL7R+CD62L+ population (markers of the classical immature CD56bright subset38) (Fig. 1e). Notably, we observed a correlation between the overall fraction of NK cells in a given sample and the representation of the CXCR4+ subset (Spearman ρ = 0.60, 95% bootstrapped confidence interval (0.18,0.86), n = 23, samples with ≥5% NK cells; Fig. 1f,g and Extended Data Fig. 2b). Samples with enrichment (greater than the median) for NK cell frequency also had significant enrichment for the CXCR4+ subset (Fisher exact test, P = 0.012, n = 23), while samples with lower NK-cell frequencies displayed a shift toward the CX3CR1+ subset (Extended Data Fig. 2c). The current understanding is that CXCR4 is required for NK homing toward CXCL12-producing cells in the BM and signifies less mature NK cells, while CX3CR1 expression causes migration toward BM sinusoids, leading to egress from the BM39. This suggests chemotactic relocalization could be the basis for the observed heterogeneity in NK-cell frequency.
To further validate the compositional changes observed in our sequencing data, we performed simultaneous deep phenotyping of viably frozen BM aspirates of patients with MGUS, SMM and MM (n = 13) and healthy BM controls (n = 4) using mass cytometry by time-of-flight (CyTOF). Overall, CyTOF data correlated with the RNA-seq results (Extended Data Fig. 3a–c), with the exception that we observed substantially lower neutrophil output from the 10x Genomics assay compared to the CyTOF data. While in CyTOF neutrophils represented 8–50% of live cells in the BM (Extended Data Fig. 3d), the 10x Genomics cell distribution favored T cells and monocytes. Due to low messenger RNA content in mature neutrophils, they may not be distinguishable as cells in single-cell RNA data from empty droplets with ambient RNA. However, this type of bias decreases neutrophil counts in both healthy-donor and MM samples, and thus should not alter the results for comparisons of a given cell type across samples.
Composition of noncytotoxic T cells shows increased Treg numbers and patient-specific heterogeneity
To delve deeper into the T-cell subtypes and states, we used two complementary approaches. First, we performed additional subclustering at higher resolution using genes variably expressed among T cells, resulting in 12 subclusters (Fig. 2). Then, because activation of continuous gene expression modules may not be captured by discrete clustering, we used a Bayesian variant of nonnegative matrix factorization to extract seven expression signatures (Fig. 3). First, we examined the noncytotoxic subsets marked by high expression of signature T-sig1 (Fig. 3a). T-cell clusters distinguished by T-sig1 included three CCR7-expressing naive CD4 subsets, two populations of helper CD4 cells and a population of Treg cells (Fig. 2a–c). An additional cluster of helper CD4 cells showed upregulated interferon (IFN) type I-activated genes; indeed, in our signature analysis we found a corresponding signature of IFN response that was highly upregulated in five late-stage samples (T-sig2, Fig. 4a, 1 SMM-high and four MM). In addition, as described previously27,28, we saw a significant upregulation of Treg cells in the diseased microenvironment (Fig. 2d,e, q = 0.0005, d.f. = 26.1, Benjamini–Hochberg-corrected t-test of NBM versus disease samples). Interestingly, transcriptional profiles of Treg cells demonstrate high expression of MHC type II proteins, which have been reported to inhibit T-cell proliferation and cytokine production via an early contact-dependent mechanism40. Additionally, our patient with sequential samples had CD4+ subsets that express corticotropin-releasing hormone, which promotes inflammation41.
Cytotoxic T-cell populations shift toward effector phenotype during disease progression
Our cytotoxic T cells were broadly separated by two signatures of (T-sig4 and T-sig7; Fig. 3a), most notably corresponding to the expression of either granzymes B (GZMB, GrB) and H (GZMH, GrH; T-sig4) or granzyme K (GZMK, GrK; T-sig 7; Fig. 3b), present exclusively in CD8+ cells. The GrK-to-GrB transition has been associated with the differentiation of cytotoxic T cells from memory to effector state42. The T cells marked by the GrB expression signature included clusters of CD8+ cytotoxic effector and gamma delta T cells, in addition to a subpopulation of CD4+ cytotoxic cells and NK T cells predominantly derived from one patient (Fig. 2d).
Among cells expressing one of two cytotoxic signatures (considered as >100 and >50 activity for signatures T-sig4 and T-sig7 based on their distribution), T-sig7 was more prevalent in healthy individuals and patients with MGUS (P = 0.0013, d.f. = 21.5, n = 30, t-test on patient mean signature activity across these cytotoxic cells between healthy/MGUS and SMM/MM), whereas the SMM and MM stages presented a striking shift to T-sig4 (P = 3.1 × 10−4, d.f. = 27.9, n = 30, Fig. 3c), suggesting a depletion of memory CD8+ T cells later during disease progression. In support of this result, we observed in our CyTOF data a twofold decrease in memory CD8+ cells in the BM aspirates of patients with SMM (central memory and effector memory T cells, GrA+GrB−; Fig. 3d and Extended Data Fig. 4a).
We next examined whether these populations displayed the characteristics of T-cell exhaustion. Of four key markers of exhaustion (PDCD1, HAVCR2, LAG3 and TIGIT), we found that only LAG3 and TIGIT were expressed at detectable levels and were relatively specific to the cytotoxic subsets (with the exception of TIGIT expression on Treg cells; Extended Data Fig. 4b). Across cytotoxic T cells, we did not see a difference between healthy individuals and patients in the expression of these genes (Extended Data Fig. 4c). However, since healthy donor samples primarily were composed of GrK+ cytotoxic subsets, we cannot reliably compare the expression of specifically GrB+ subsets, which probably would be the relevant subsets to display classical exhaustion.
Of note, in our CyTOF data, we observed significantly higher expression of the checkpoint receptor programmed cell death protein 1 (PD-1) on memory cells in healthy individuals than in patients with SMM (Extended Data Fig. 4d,e, P = 0.047). Recent publications have connected the expression of PD-1 at the early stages of T-cell differentiation with a mechanism for T-cell activation to balance effector and memory responses43,44; however, its role in MM remains to be elucidated.
Notably, the degree of the shift from memory to effector was variable, and in several samples, such as MM-7, it was completely absent. We did not observe any correlation between plasma cell cytogenetics and types of cells composing the cytotoxic T-cell cluster.
To address the role of GrK-expressing cytotoxic memory cells in MM progression, we performed in vivo assays using syngeneic transplantable myeloma mice with different maturation stages of the memory cell pool. For this, 10-week-old KaLwRiJ mice with impaired memory cell function and fully immunocompetent older animals (1.5 years old) were injected with 5TGM1 MM cells. Young mice with impaired memory cells showed significantly shorter survival than older animals (Fig. 3e). In a similar assay with C57BL/6J mice injected with Vk*MYC MM cells, we observed early accumulation of plasma cells in the spleen and faster abundance of monoclonal proteins in the blood of younger animals (Fig. 3f,g). However, no difference in survival of myeloma-injected mice was obtained when animal groups with fully established memory cell pool were compared (6-month-old versus 1.5-year-old KaLwRiJ mice; Fig. 3e, right panel), providing compelling evidence for a previously undetermined critical role of memory cells for immunosurveillance against MM. Future studies in age-matched mice are necessary to further eliminate potential age-associated confounding factors.
IFN response is seen across immune cell types in late-stage disease
The T-sig2 signature was highly enriched for IFN-responsive genes (for example, STAT1, IFI44L and ISG15) and uniquely present in a subset of late-stage patients (Fig. 4a). Likewise, signature analysis in CD14+ monocytes isolated an expression program (M-sig1) with highly overlapping marker genes. This signature was present in precisely the same samples, suggesting a common response to the external signals. Indeed, when we visualized expression of the top genes, we observed IFN response in subpopulations of different cell types, including NK, nonclassical monocytes and small numbers of B cells (Fig. 4b). While this signature was strongly present in 4 out of 23 samples, which means we are underpowered to directly link it to myeloma progression (Fisher exact test, P = 0.054, n = 27), we have shown in previous work that IFN signaling is significantly upregulated in malignant plasma cells27. Interrogating a publicly available dataset from CD138-enriched plasma cells at different stages of the MM disease (n = 147) and healthy donors (n = 15, GSE6477) for the expression of ISG15 and MX1, which scored highly in gene signature analyses in our single-cell data, we found a significantly increased expression of these genes throughout MM progression (Extended Data Fig. 5). Thus, our data from the immune microenvironment provide evidence that signaling in this context occurs not just in myeloma cells, but across immune populations.
CD14+ monocytes in the MM microenvironment show defective antigen presentation due to intracellular accumulation of human leukocyte antigen DR isotype (HLA-DR)
In addition to the IFN signature found in CD14+ monocytes, we discovered that the M-sig2 signature was significantly upregulated in all malignant cases (t-test, n = 30, d.f. = 19.7, q = 1.3 × 10−6). The majority of genes highly contributing to this signature were encoding the MHC class II cell surface receptor (HLA-DRA, HLA-DPB1, HLA-DRB1 and HLA-DPA1; q ≤ 2 × 10−4; Fig. 5a and Extended Data Fig. 6a). Expression of MHC class II encoding genes individually was significantly upregulated in CD14+ monocytes/macrophages across all disease stages, including MGUS (Fig. 5a and Extended Data Fig. 6a). The mRNA levels of MHC class II receptors together with the expression of IFN-activated genes predominantly determined the organization of CD14+ monocytic subclusters (Extended Data Fig. 6b). Additional subclusters were built from individual patients with high expression of the mannose receptor C-type 1 (MRC1), CCL3/4 and CCAAT/enhancer binding protein signaling (Extended Data Fig. 6b).
However, study of MHC class II expression using CyTOF revealed that despite elevated mRNA levels, CD14+ cells from patients with SMM and MM exhibited significantly lower HLA-DR surface expression compared to those from healthy donors (Fig. 5b), suggesting pathway dysregulation yet resulting in compromised antigen presentation in these cells. To elucidate whether this change in expression represented a shift in cell subpopulations (for example, MDSCs are known to have low levels of MHC class II surface presentation) or a phenotypic change specifically induced by the myeloma cells, we cocultured CD14+ cells isolated from the peripheral blood of healthy donors with MM.1S or RPMI 8226 myeloma cells and analyzed HLA-DRA mRNA and HLA-DR and HLA-DP surface expression levels. Indeed, while quantitative reverse-transcription PCR showed a significant increase of HLA-DRA mRNA in monocytes cocultured with MM cells, as compared to coculture with CD19+ control cells (Extended Data Fig. 7a), a dramatic drop in the surface expression of both HLA-DR and HLA-DP molecules was observed (Fig. 5c, left panel). However, the proportion of CD14+ cells expressing HLA-DR and HLA-DP intracellularly remained similar or slightly lower compared to that in controls (Fig. 5c, right panel). We observed intracellular accumulation of HLA-DR in CD14-expressing cells in tissue microarray (TMA) slides of patients with MM compared to membrane-bound HLA-DR in CD14 monocytes from healthy donors (Fig. 5d and Extended Data Fig. 7b). Taken together, our results suggest that MM cells induce internalization of MHC class II in CD14+ cells, reducing their potential for antigen presentation.
Looking for the alterations in the expression of genes responsible for the posttranslational control of MHC class II, we found the gene encoding the E3 ubiquitin protein ligase MARCHF1/MARCH1 to be progressively upregulated in patients compared to healthy individuals (P = 0.008) and found decreased levels of the gene encoding ER-resident protein VAMP associated protein A (VAPA), which plays a role in the inward transport of MHC class II (P = 0.01, Fig. 5e). By treating CD14+ cells cocultured with MM cell lines with anti-MARCHF1/MARCH1 small interfering RNAs (siRNAs), we rescued HLA-DR surface presentation (Extended Data Fig. 8). Thus, our results suggest that the upregulation of MARCHF1/MARCH1 observed in our single-cell RNA dataset could indeed lead to internalization of MHC class II in monocytes in the MM microenvironment.
CD14+ monocytes in the MM microenvironment can promote proliferation of myeloma cells and suppress T-cell activation
Since CD14+CD11b+HLA-DRlow cells have been previously defined as one of the subsets of monocytic MDSCs (mMDSC), we wanted to further characterize the CD14+HLA-DRlow cell population from the MM environment and understand its role in myeloma progression and immune suppression. According to our sequencing results, the population of CD14+ cells expressed CD11b at levels similar to those in healthy cells. Likewise, it expressed various markers that define mature macrophages, such as CD86, CD163 and CD68, with highly elevated CD206 in one patient (Extended Data Fig. 9a). According to suggested guidelines for the characterization of MDSCs45, we did not observe increased expression of MDSC-associated genes in patients relative to healthy donors (Extended Data Fig. 9b). Furthermore, CD14+ cells from healthy donors after coculture with MM cells expressed inducible nitric oxide synthase, CD206 and CD163 and did not polarize toward the Lin−CD11b+CD33+ mMDSC phenotype (Extended Data Fig. 9c). Thus, while this cell population may overlap with CD14+CD11b+HLA-DRlow populations termed mMDSCs described in other studies, our results agree with the observation of others that additional markers are needed to isolate myeloid cells with a specific mMDSC phenotype.
Interestingly, after coculture of myeloma cell lines (MM1.S, RPMI 8226, OPM-2, KMS-18) with CD14+HLA-DRlow cells, we observed significant changes in the cell cycle of MM1.S cells resulting in a significantly increased fraction of cells in the S (P = 0.006) and G2M (P = 0.0012) phases of the cell cycle, as compared to that in control cells, indicating that CD14+HLA-DRlow cells can accelerate the proliferation of MM cells (Fig. 6a,b).
Then, we tested the ability of CD14+ cells from the MM environment to alter T-cell activity. We assessed the expression of the CD44 activation marker in T cells after coculture with CD14+ cells that were preconditioned with MM cells (Fig. 6c). In contrast to B-cell controls, CD8+ cells after coculture with CD14+ monocytes from the MM environment showed reduced expression of CD44 (Fig. 6d), indicating suppression of T-cell activation. In a similar experiment with peripheral blood mononuclear cells (PBMCs) cocultured with either CD14+ cells or CD19+ B cells isolated from the BM aspirates of patients with SMM (Fig. 6e), we observed downregulation of CD44 in CD4+ T cells cocultured with CD14+ cells (Fig. 6f).
Altogether, these results indicate that CD14+HLA-DRlow cells from the myeloma environment can accelerate proliferation of MM cells and exhibit suppressive activity by inhibiting T-cell activation.
Myeloma is an incurable disease that can cause irreversible organ damage. Early intervention represents an attractive alternative for treatment, although we cannot accurately predict which patients with MGUS/SMM will progress. Further understanding of disease progression requires comprehensive molecular characterization, including that of the tumor’s microenvironment and the host’s immune response. In this study, we used single-cell RNA-seq (scRNA-seq) of patient samples across all stages of MM to elucidate the transcriptomic alterations within the immune microenvironment along disease progression.
Through analysis of the cellular composition of the tumor microenvironment, we showed a significant, albeit heterogeneous, enrichment of T cells, CD16+ monocytes and NK cells established at the MGUS stage of the disease, indicating an early launch of an immune response. Indeed, expansion of T and NK cells in the peripheral blood and BM of patients with hematopoietic malignancies, including MM, has been reported previously23,24,25,46,47,48 and has been associated with improved outcomes in MM33,38,49,50. Using scRNA-seq, we characterized the subtypes and expression states of these populations.
Our analysis further revealed that in patients with high NK-cell infiltration, this fraction is predominantly composed of CXCR4-expressing cells, while samples with fewer NK cells have a shift toward CX3CR1 expression. Previous studies have shown that these receptors are responsible for chemotaxis, mediating homing to the BM or facilitating BM egress39. If in certain subsets MM may initiate the egress of NK cells into the circulation51, this may explain the heterogeneous frequency of NK cells observed in our data and could suggest an MM-orchestrated mechanism of immune evasion.
Interrogation of the cytotoxic compartment demonstrates that, starting from SMM, there is a loss of memory cells with considerable skewing of T-cell differentiation toward the GrB/H-expressing effector state. From our in vivo results, we demonstrate the previously unrecognized pivotal role of memory cells in the immune response against tumor cells. It is important to note that the proportion of the effector cell subtypes did not correlate with the cytogenetic characteristics of the tumor. Individual subpopulations of cytotoxic effectors, including NK T cells, gamma delta T cells, CD4+ and CD8+ effector cells are well described and their functionality in MM is under extensive investigation. In MM, these cytotoxic cells often exhibit either suppressed phenotype (as CD8+ T cells52) or anergy (as in gamma delta T cells53) and impaired functionality as reported for NK T cells54, and several approaches have been tested to overcome this dysregulation55,56.
The immunosuppressive role of MDSCs, CD14+HLA-DRlow/neg cells from the monocytic lineage, has been previously established in lymphoma, glioblastoma, prostate cancer, renal cell carcinoma and MM29,57,58,59. Our data demonstrate that in addition to MDSCs, mature CD14+ monocytes/macrophages also undergo a phenotypic shift leading to loss of MHC class II surface representation as early as in the MGUS stage. These CD14+ cells with compromised MHC class II representation acquire immunosuppressive potential and can suppress T-cell activation. We demonstrated that overexpression of MARCHF1/MARCH1 contributes to the internalization of MHC class II proteins. Importantly, this condition can be rescued by downregulating MARCHF1/MARCH1 expression, offering a potential for therapeutic interventions. Overall, this signaling was impaired in the majority of patients already at the early precursor MGUS stage, indicating that monocytes/macrophages are very sensitive to the MM-directed shaping of a permissive tumor microenvironment.
Extensive coregulatory networks have been described in the tumor microenvironment, whereby tumor cells regulate the stromal and immune microenvironment and vice versa, all in support of tumor growth and dissemination16,17. We have previously demonstrated that IFN type I secreted from MM cells promotes immunosuppression, which favors MM growth60. In this study, we found highly upregulated IFN signaling across immune subsets. Thus, our results further implicate IFN signaling in MM and indicate that such a complex regulatory network could already be in place at the high-risk SMM stage of disease, identifying it as a potential target for therapeutic intervention to prevent progression.
We profiled sequential immune alterations occurring during MM progression, beginning at the earliest stage (Extended Data Fig. 10). MM represents a unique setting with a readily detectable premalignant stage; observing early immune alterations in response to a neoplastic environment raises the possibility of similar mechanisms appearing in the precursors of other cancers. It is our hope that understanding this evolution of the myeloma microenvironment will provide new targets for therapeutic approaches, and that leveraging inter-patient heterogeneity will enable stratification of patients by risk of progression and create the opportunity for early intervention.
Patient samples and cell preparation
Primary peripheral blood cells or BM samples from patients with MGUS, SMM or MM, as well as healthy donors were collected at the Dana-Farber Cancer Institute. These studies were approved by the Dana-Farber Cancer Institute Institutional Review Board. Informed consent was obtained from all patients and healthy volunteers in accordance with the Declaration of Helsinki protocol (fifth revision from 2000 with Clarifications of Articles 29, 30 (2002–2004), and the most recent iteration from 2013). CD138− or CD45+ BM cell fractions were isolated using magnetic-activated cell sorting technology (Miltenyi Biotec). Selected cells were either viably cryopreserved in dimethylsulfoxide at a final concentration of 10% or used immediately for scRNA-seq.
Human MM.1S and RPMI 8226 cells were purchased from ATCC, while the KMS-18 myeloma cell line was obtained from the Japanese Collection of Research Bioresources and the OPM-2 cell line was obtained from DSMZ. For the in vitro experiments, these cell lines were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium containing 10% fetal calf serum (Sigma-Aldrich), 2 mM of l-glutamine, 100 U ml−1 penicillin and 100 μg ml−1 streptomycin (Gibco).
Sequencing library construction using the 10x Genomics platform
Frozen BM cells were rapidly thawed, washed, counted and resuspended in PBS and 0.04% bovine serum albumin to a final concentration of 1,000 cells per μl. The Chromium Controller (10x Genomics) was used for parallel sample partitioning and molecular barcoding61. To generate a single-cell Gel Bead in EMulsion, cellular suspensions were loaded on a Single Cell 3′ chip together with the Single Cell 3′ Gel Beads, according to the manufacturer’s instructions (10x Genomics). scRNA-seq libraries were prepared using the Chromium Single Cell 3′ Library Kit v.2 (10x Genomics). Fourteen cycles were used for the total complementary DNA amplification reaction and for the total sample index PCR. Generated libraries were combined according to Illumina specifications and paired-end sequenced on HiSeq 2500/4000 platforms with standard Illumina sequencing primers for both sequencing and index reads; 100 cycles were used to sequence Read1 and Read2.
Preprocessing of scRNA-seq data
Sample demultiplexing, barcode processing, alignment to the human genome (hg38) and single-cell 3′ gene counting was performed using the Cell Ranger Single-Cell Software Suite v.2.0.1. Cells called by Cell Ranger were further filtered to those with <10% mitochondrial expression, >200 genes covered and >600 total unique molecular identifiers. Expression values were calculated as:
for a cell c with Nc total unique molecular identifiers, ng,c of which map to gene g.
Separation of plasma and immune cell populations
To remove cells incorrectly sorted during bead selection (that is, plasma cells in the CD138− fractions), we first performed coarse clustering of the data including all cells. We selected 2,744 genes with high expression and dispersion, excluding genes on the sex chromosomes, and z-normalized the data after regressing out variation due to total number of unique molecular identifiers or percentage mitochondrial RNA in each cell. We then performed Seurat k-nearest neighbors clustering with resolution 0.6 on the first 20 principal components of the resulting data. A plasma cell score was calculated for each cluster as the mean expression of CD138 (SDC1), CD319 (SLAMF7) and BCMA (TNFRSF17); clusters with a score of >1 were identified as plasma cell clusters. Clusters with >4 mean HBA1 expression were deemed to be erythroid clusters, and cells in these clusters were excluded from future analyses. Cells originating from the CD138− fraction in nonplasma, nonerythroid clusters formed our immune cell dataset.
When selecting genes for downstream analysis, we excluded genes with high potential to be influenced by cross-cell contamination effects. 10x technology exhibits lows rates of mRNAs associated with the barcodes of cells from which they did not originate, possibly from sources such as extracellular mRNAs captured in the droplet. While in the vast majority of cases this is undetectable, for ultra-highly expressed genes such as immunoglobulins, the contributions can become noticeable. Thus, we excluded genes that were drastically higher in expression for the excluded compartments (plasma cells and erythroid cells).
In addition, we filtered genes found to have a strong sex bias in our data. For the purposes of clustering analyses, we removed all genes from the sex chromosomes to avoid any cumulative effects that could separate samples based on sex. For downstream gene- or signature-level analyses, so as not to lose informative genes on the X chromosome, we only excluded genes found to have a statistically significant sex bias (two-sample t-test on mean gene expression at a false discovery rate threshold of 10%).
Analysis of immune cell populations
A total of 2,100 variably expressed genes were identified using the filtering procedure described earlier and the same expression and dispersion criteria as in the analysis of principal components. Clusters were identified using Seurat k-nearest neighbors clustering with resolution 1.4 on the first 20 principal components of the z-normalized data. Cell types were then identified for each cluster by examining the expression of marker genes, as well as the top differentially expressed genes, in each cluster. Cells were visualized by t-distributed stochastic neighbor embedding (t-SNE) on the same 20 principal components.
Nonnegative matrix factorization (NMF)-derived gene expression signatures
We defined gene expression signatures for T cells and CD14+ monocytes using our SignatureAnalyzer-GPU62 tool (https://github.com/broadinstitute/SignatureAnalyzer-GPU), which implements a previously described Bayesian NMF algorithm63. This method approximates the expression profile of each cell (represented as a column in the input matrix) as an additive combination of latent expression programs (each column in the W-matrix output), each with an associated weight or ‘activity’ in each cell given by the H-matrix. This Bayesian variant of NMF encourages sparse interpretable solutions by imposing an exponential prior distribution on the weights of the W- and H-matrices and allows automatic discovery of the number of signatures required to explain the data.
After signature discovery, the columns of W were normalized to a sum of 1 and all the weight was shifted into the H-matrix:
for gene g (out of G total genes), signature k and patient p. Signatures significantly altered between disease states were identified by calculating the mean signature activity for each patient and performing a two-sample t-test, reporting results below a 10% Benjamini–Hochberg false discovery rate level. Marker genes for each signature were identified by sorting genes according to their relative contribution to each signature:
and taking the top 10 genes where the gene contributed at least 1% of the total signature activity.
Analysis of cells by CyTOF
Sample staining for mass cytometry (CyTOF) was performed according to the manufacturer’s guidelines (Fluidigm). Briefly, the CD138− or CD45+ populations of BM samples from patients with MM or healthy donors were thawed, washed and barcoded using the Cell-ID 20-Plex Pd Barcoding Kit (Fluidigm). After barcoding, cells were incubated with human Fc Receptor Blocking Reagent (Miltenyi Biotec) and human surface marker antibodies as a single multiplexed sample (Maxpar Human Immune Monitoring Panel Kit, 29-marker panel; Fluidigm). After staining, cells were washed in Maxpar Cell Staining Buffer (Fluidigm), resuspended at the concentration of 1 × 106 cells ml−1 and acquired on a CyTOF Helios mass cytometer (Fluidigm). Downstream analysis of the individual component samples was performed after running the debarcoding application. An automated analysis GemStone MC software (Verity, provided by Maxpar, Fluidigm) was used to clean the data files by automatically identifying and removing cell doublets, debris and dead cells, as well as identifying major immune cell subsets. Expression levels of HLA-DR molecules on the cell surface were analyzed in Cytobank (Cytobank).
To phenotype the Gr-expressing T-cell populations, CyTOF was performed on CD138− cells from the BM aspirates of patients with SMM and healthy donors using TIGIT-143Nd, PD-1-175Lu, TIM3-153Eu (Maxpar) and the Maxpar Human T-Cell Phenotyping Panel Kit, 16 Marker. T-cell subsets were defined as suggested by the manufacturer.
Flow cytometry analysis
Antihuman antibodies against CD138 (clone MI15), CD14 (clone 63D3), HLA-DR (clone L243) were obtained from BioLegend. HLA-DP (clone DP11.1) was obtained from Santa Cruz Biotechnology. iNOS (catalog no. ab115819) was obtained from Abcam. CD33 (catalog no. 303413), CD11b (catalog no. 301341), CD206 (catalog no. 321113), CD163 (catalog no. 33362) and FITC antihuman Lineage Cocktail (catalog no. 348801) were obtained from BioLegend. Antibodies were used for fluorescence-activated cell sorting (FACS) analyses. The LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (Thermo Fisher Scientific) was used for the discrimination of dead cells. Data from the stained samples was acquired using a FACS Canto II Flow Cytometer (BD Biosciences). The results were analyzed in Cytobank (Cytobank).
Myeloma cell coculture assay
Human CD14+ monocytes or CD19+ B cells were obtained from fresh peripheral blood cells using CD14 and CD19 MicroBeads (Miltenyi Biotec). Briefly, red blood cells were lysed (RBC Lysis Buffer; BioLegend), washed and CD14+ and CD19+ cell populations were isolated according to the manufacturer’s instructions. CD14+ monocytes at 2 × 106 were cocultured with 1 × 106 myeloma cell lines. CD19+ B cells were used as a negative control. Transwell-permeable inserts with a pore size of 0.4 μm were used to prevent cell–cell interactions (Corning). After 3–4 d of coculture, cells were collected and analyzed for HLA-DR expression using flow cytometry or quantitative PCR.
siRNA knockdown of MARCHF1/MARCH1
MARCHF1/MARCH1 knockdown in CD14+ cells was performed using anti-MARCHF1/MARCH1 siRNAs (catalog nos. 127048 and 127049; Thermo Fisher Scientific) and Lipofectamine RNAiMAX Transfection Reagent (Thermo Fisher Scientific) according to the manufacturer’s instructions. Briefly, CD14+ monocytes were isolated from the peripheral blood of healthy donors using CD14 MicroBeads (Miltenyi Biotec) and plated into 24-well plates at 0.5 × 106 in 500 μl; 5 pmol of siRNA was used per well. Silencer Negative Control No. 1 siRNA (catalog no. AM4611; Thermo Fisher Scientific) was used as a negative control. Twelve hours posttransfection, 1 × 106 MM cells were added and cultured for the next 24 h. Cells were collected and analyzed for the HLA-DR and MARCHF1/MARCH1 expression using flow cytometry or immunofluorescence microscopy.
RNA isolation and quantitative PCR with reverse-transcription (RT–qPCR)
mRNA was isolated with the miRNeasy Mini Kit (catalog no. 217004; QIAGEN). Purified RNA was measured by NanoDrop (Thermo Fisher Scientific) and cDNA was synthesized from 500 ng of total RNA using a High-Capacity cDNA Reverse Transcription Kit (catalog no. 4368814; Thermo Fisher Scientific). Expression levels of HLA-DRA were obtained using a TaqMan qPCR assay (Hs.PT.58.14332844) and an actin assay as the internal control (Hs.PT.39a.22214847) from Integrated DNA Technologies. To measure the expression of different MARCHF1/MARCH1 transcripts, two primer pairs were designed and MARCHF1/MARCH1 RT–qPCR reactions were prepared using the PowerTrack SYBR Green Master Mix (catalog no. 4367659; Thermo Fisher Scientific). TaqMan and SYBR green reactions were carried out on Quant Studio 7 (Thermo Fisher Scientific). The assays were run in triplicate using CD14+ cells isolated from three independent donors. The ΔΔCt method was used for normalization on the actin and GAPDH housekeeping genes. The primer sequences of MARCHF1/MARCH1 and GAPDH were: MARCHF1/MARCH1 pair 2 forward, 5′-CAGGAGCCAGTCAAGGTTGT-3′; MARCHF1/MARCH1 pair 2 reverse, 5′-AGAGCTCACAGCAGCGTGTA-3′; MARCHF1/MARCH1 pair 3 forward, 5′-TAATCGCGATCACCTGTGTG-3′; MARCHF1/MARCH1 pair 3 reverse, 5′-GCGCCACAACTGAACATAGA-3′; GAPDH forward, 5′-AGCACATCGCTCAGACAC-3′ and GAPDH reverse, 5′-GCCCAATACGACCAAATCC-3′.
5-bromo-2′-deoxyuridine (BrdU) proliferation assay
MM cells were cocultured with CD14+ monocytes isolated from the PBMCs of healthy donors for 48 h. MM cells cocultured with CD19+ B cells were used as the control. After coculture, cells were labeled with 1 mM of BrdU for 2 h and then stained with antibodies for surface expression of CD138 (clone MI15) and CD14 (clone 63D3). After this, cells were fixed and permeabilized with Cytofix/Cytoperm Buffer according to the manufacturer’s protocol (catalog no. 552598; BD Biosciences) before staining with allophycocyanin (APC)-conjugated anti-BrdU for 20 min. Total DNA was stained using a 7-AAD solution (catalog no. 555816, BD Biosciences). The cell cycle of CD138+ MM cells was analyzed using flow cytometry.
Mixed lymphocyte reaction
The mixed lymphocyte reaction was performed in duplicate in 96-well, round-bottom plates (catalog no. 7007; Corning). PBMCs were cultured in the RPMI 1640 medium (Gibco, 11875) supplemented with 100 U ml−1 of penicillin-streptomycin (catalog no. 15140122; Gibco), 10% (vol/vol) fetal calf serum (catalog no. A31604; Gibco) and 50 μM of 2-mercaptoethanol (catalog no. M6250; Sigma-Aldrich), 2 mM of l-glutamine (catalog no. 25030081; Gibco) for 3 d at 37 °C in 5% CO2 in air. Cells were stimulated with 4 μg ml−1 of antihuman CD3 antibody (clone OKT3, catalog no. 317302; BioLegend), 4 μg ml–1 antihuman CD28 antibody (clone CD28.2, catalog no. 302902; BioLegend) and 200 ng of recombinant human IL-2 (catalog no. 200-02; PeproTech).
CD14+ cells were isolated from healthy PBMCs and cocultured with either MM cell lines (OPM-2, MM1.S) or healthy CD19+ B cells for 24 h in a 1:2 ratio. After 24 h, CD14+ cells were isolated and added as modulators to the freshly isolated PBMCs from healthy donors. The cell number of responder versus modulator cells was 1 × 105:8 × 103 in the final volume of 200 μl per well. PBMCs without modulator cells served as a reaction system control. In the second experimental setup, CD14+ or CD19+ cells isolated from fresh BM aspirate of a patient with SMM were used as modulators and PBMCs from the same patient were used as responders. Cells were collected at days 3–5 for flow cytometry (FACSCanto II; BD Biosciences). The LIVE/DEAD Fixable Green Dead Cell Stain Kit (catalog no. L23101; Thermo Fisher Scientific) was used to gate out the dead cells. The antibodies used in this experiment were (all BioLegend): PerCP/Cy5.5 antihuman CD4 (clone OKT4; catalog no. 317428); Brilliant Violet 510 anti-mouse/human CD44 (clone IM7, catalog no.103044); APC/Cy7 antihuman CD8a (clone HIT8a; catalog no. 300926).
Immunohistochemistry and immunofluorescence
Formalin-fixed paraffin-embedded tissue sections were baked in an Isotemp Oven at 60 °C for 30 min to melt excess paraffin. Tumor sections were stained with BOND RX Autostainer (Leica Biosystems) using the BOND Polymer Refine Detection Kit (catalog no. DS9800; Leica Biosystems). Antigen retrieval was performed with the BOND Epitope Retrieval Solution 1 (citrate, pH = 6.0; Leica Biosystems) for 30 min. Dual-immunohistochemistry staining was performed using anti-CD14 (1:100, clone D7A2T; Cell Signaling Technology), which was detected by the Alexa Fluor 594 Tyramide Reagent (catalog no. B40957; Thermo Fisher Scientific) and anti-HLA-DR (1:500, clone TAL.1B5; Dako) detected by Alexa Fluor 488 Tyramide Reagent (catalog no. B40953; Thermo Fisher Scientific). NucBlue Fixed Cell ReadyProbes Reagent (DAPI; catalog no. R37606; Thermo Fisher Scientific) was used as the background stain.
For cytospins, CD14+ cells transfected with siRNA and cocultured with MM cells for 24 h were purified using flow cytometry. Cells were fixed with 4% paraformaldehyde, washed with PBS and diluted to no more than 0.5 × 106 cells ml−1. Then, 100 μl of cell suspension was loaded into a cuvette with the slides mounted with the paper pad. The cuvette was placed into a metal holder and centrifuged at 800 r.p.m. for 5 min. After centrifugation, cells were immediately fixed, permeabilized with Cytofix/Cytoperm buffer and incubated with primary antibodies for MARCHF1/MARCH1 (1:100, catalog no. PA5-53199; Thermo Fisher Scientific) and HLA-DR (1:100, catalog no. 307602; BioLegend) for 1 h. Slides were washed for 15 min in PBS and incubated with corresponding secondary antibodies (Abcam) for 1 h. Post-incubation, slides were washed twice and mounted using DAPI mounting solution (Dako).
In vivo assays
C57BL/6 mice were purchased from The Jackson Laboratory. C57BL/KalwRiJ mice were purchased from Harlan Laboratories. All mice were treated, monitored and euthanized in accordance with the approved protocol of the Dana-Farber Cancer Institute Animal Care and Use Committee; 4 × 106 5TGM1 cells were injected intravenously into C57BL/KalwRiJ mice for survival studies, while 4 × 106 Vk*MYC cells were injected intravenously into C57BL/6 mice for monoclonal protein secretion (M spike) and plasma cell infiltration analyses. At 3 weeks postinjection, BM cells and peripheral blood were collected. BM cells were obtained through flushing of femurs with 1× PBS. The proportion of CD138+ plasma cells was determined after cell staining with the APC anti-mouse CD138 antibody (catalog no. 142505; BioLegend) by flow cytometry. An M spike analysis was performed with 1 μl of blood serum using QuickGel electrophoresis (Helena Laboratories).
Statistical analysis of in vivo and in vitro studies
One-way analysis of variance (ANOVA) and Tukey multiple comparison test were used when three or more independent groups were compared. An unpaired Student’s t-test was used to compare two independent groups. All tests were two-tailed. The log-rank test was used to analyze survival data. P < 0.05 was considered statistically significant.
Statistics and reproducibility
No statistical method was used to predetermine sample size. Samples with insufficient numbers of cells or library complexity were excluded from the analyses. Investigators were not blinded to the study of human sequencing data.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
The scRNA-seq data that support the findings of this study have been deposited with the Gene Expression Omnibus under accession code GSE124310. Raw sequencing data have been deposited with the database of Genotypes and Phenotypes (phs001323.v1.p1). Previously published microarray data that were reanalyzed in this study are available under accession code GSE6477. Source data for Figs. 1–6 and Extended Data Figs. 2–4 and 6–9 have been provided in corresponding Source Data tables. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.
The single-cell RNA data was processed using cellranger v.2.0.1 (https://www.10xgenomics.com/) and analyzed with the R package Seurat v.2.3.1 (https://satijalab.org/seurat/). Gene expression signatures were extracted using our SignatureAnalyzer algorithm available on github (https://github.com/broadinstitute/getzlab-SignatureAnalyzer).
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We thank the Dana-Farber/Harvard Cancer Center in Boston for the use of the Specialized Histopathology Core for histology and immunohistochemistry service. The Dana-Farber/Harvard Cancer Center is supported in part by a National Cancer Institute Cancer Center Support Grant no. NIH 5 P30 CA06516. G.G. was partially funded by the Paul C. Zamecnik Chair in Oncology, Massachusetts General Hospital Cancer Center. N.J.H. was partially funded by G.G. startup funds at Massachusetts General Hospital, R01 (principal investigator I.M.G.) and the Harvard Graduate Program in Biophysics, Harvard University. Molecular Biophysics Training Grant NIH/NIGMS T32 GM008313 (N.J.H.; principal investigtor J. M. Hogle). This study was partially funded by two National Institutes of Health grants (no. NIH R01 CA 205954 and R01CA181683), the Leukemia and Lymphoma Society, the Multiple Myeloma Research Foundation and Stand Up to Cancer.
G.G. receives research funds from IBM and Pharmacyclics and is an inventor on patent applications related to MuTect, ABSOLUTE, MutSig, MSMuTect and POLYSOLVER. I.M.G. has a consulting/advisory role with GSK, AbbVie and Bristol Myers Squibb. I.M.G has a consulting role with Sanofi, Janssen Pharmaceuticals, Takeda, Celgene, Karyopharm, GNS, Cellectar Biosciences, Medscape, Genetech, Adaptive Biotechnologies, Aptitude, Curio Science, Magenta Therapeutics and Oncopeptides. I.M.G. received research funding/ honoraria from Celgene, Takeda, Bristol Myers Squibb, Janssen Pharmaceuticals and Amgen.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
tSNE representation of CD45 cell populations. In each subplot, cells are colored by log-normalized expression values for a given cell type specific marker gene.
Extended Data Fig. 2 Single-cell RNA sequencing results indicate changes in NK cell compartment as compared to healthy donors.
a, A significant increase in the fraction of NK cells in patients with malignant cells expressing IgG heavy chain. Fractions of NK cells are plotted for patients grouped by immunoglobulin heavy chain. Violin plots show minimum, median, and maximum values. A two-tailed t-test was performed on n=19 patient samples with df=12. b, Spearman correlation between NK cell fraction and CXCR4+ subset frequency calculated on 10,0000 samples with replacement of data points. 95% confidence interval is shown. c, Grouping of samples above and below median values for NK cell frequencies and CXCR4+ subset fractions. Points on the median were assigned in the conservative direction (that is to obtain a less significant p-value). A two-sided fisher-exact test was performed on n=23 patient samples. Source data
Extended Data Fig. 3 Compositional alterations in immune populations of diseased patients as compared to healthy donors.
CyTOF profiling of 4 healthy donors and 13 MGUS-MM patients show a, significantly increased numbers of CD3+CD4+ T cells in BM aspirates of the patients as compared to healthy BM with mean values of 2.3 and 11.12 for CD3+CD4+, 1.6 and 3.7 for NK, and 1.5 and 0.5 for dendritic cells (DC) in healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. b, Plasmacytoid DC (pDC) are significantly enriched in healthy BM as compared to MM patients with mean values of 1.2 and 0.26 respectively. Mean values for monocytic DC (mDC) are 3.7 and 3.2 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent average deviation. c, Classical monocytes (c Monoc) are enriched in BM aspirates of healthy donors compared to MM patients with respective mean values of 67.7 and 26.5. Mean values for non-classical monocytes (nc Monoc) were 13.1 and 44.4 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent average deviation. d, Proportion of CD66b+ cells in BM aspirates of SMM patients and healthy donors. Mean values are 23.8 and 20.6 for healthy donors and MM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. Source data
Extended Data Fig. 4 Transcriptional alterations in cytotoxic T cell populations of diseased patients as compared to healthy donors.
a, Distribution of the Granzyme expression in T-cell cluster. GZMA+GZMB− corresponds to the Granzyme K expressing cells. b, Normalized expression values for four exhaustion-related genes across different T cell subsets, pooling cells across patients. c, Normalized expression values for four exhaustion-related genes across different donors and patients, pooling cells across T subsets. d, Heatmap of immune checkpoint molecules expression levels on different subsets of bone marrow T cells in SMM patients (n=8) as compared to healthy donors (n=4). Individual patients show increased levels of PD-1 and TIGIT in GrB expressing effector cells. Colored scale represents transformed ratio of protein expression. Barplots show variable expression of TIGIT (with mean values of 0.01 vs 0.12), PD-1 (0.52 vs. 0.55) and TIM-3 (0.39 vs. 0.51) in GrB-expressing T effectors from healthy BM and SMM patients respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. e, Healthy memory cells show significantly higher expression of PD-1 compared to those of SMM patients with mean values of 19.5 and 4.7 respectively. Significant difference between groups was tested using a two-sided t-test. Bars represent SD. Source data
Expression of ISG-15 a, and MX1 b, increases during disease progression. Significant difference is observed between MGUS and MM stage, indicating an increase of IFN signals at later stages in the disease progression (GSE6477). Box plot displays the first quartile, median and third quartile for the gene expression levels, bars indicate the minimum and maximum value. Significant difference between groups was tested using one-way ANOVA and Tukey multiple comparison tests for healthy donors (ND, n=15), MGUS (n=21), SMM (n=23) and newly diagnosed MM (n=75).
a, Mean expression of MHC II encoding genes. Violin plots show minimum, median, and maximum values. A BH-corrected two-tailed t-test was performed on n=32 patient samples. b, Heatmap of expression values for the top 10 genes with enriched expression in all monocytes discovered by k-nearest neighbors’ subclustering. Expression values are centered and normalized for each gene. Source data
Extended Data Fig. 7 Dysregulation of HLA-DR surface representation in monocytes from diseased environment.
a, qPCR data demonstrate significant increase of HLA-DR expression in CD14+ monocytes after co-culture with MM cell lines. CD14+ cells alone and those co-cultured with B cells were used as a control. Median ratios were 1.54 for B cells, 2.07 for MM1.S cells, 3.28 for KMS-18, 8.34 for RPMI and 3.12 for OPM2 cells as compared to CD14+ control (1.0). Bars represent SD. Experiment was performed twice with independent donors in triplicates. Representative data from one experiment is shown. b, Immunofluorescence staining of tissue microarrays from MGUS, SMM and MM patients (n=45, TMA performed in triplicates, total of 135 BM sections analysed) demonstrates prevalent intracellular accumulation of HLA-DR (green) in CD14-expressing monocytes (red) in disease settings. Membrane-bound localization of HLA-DR was observed in healthy bone marrow monocytes. (yellow arrows point on cells with HLA-DR localized to the cell membrane, white arrows point on cells with HLA-DR accumulated in the cytoplasm. Source data
Extended Data Fig. 8 MARCH-1 dependent internalization of HLA-DR in CD14+ monocytes in myeloma environment.
a, Gating strategy for HLA-DR on CD14+ cells. b, Knockdown of MARCH-1 by siRNA rescues presentation of HLA-DR molecules on the surface of CD14+ monocytes co-cultured with MM cells. Representative FACS profiles show higher numbers of HLA-DR+ cells after MARCH-1 knockdown with siRNA (experiment performed 3 times with 3 different donors using 2 different cell lines/2 different siRNA for MARCH-1, non-targeting si-RNA is used as a control). c, Mean fluorescence intensity demonstrates the two to 4.5-fold increase in the levels of HLA-DR protein expression on the cell surface of CD14+ cells. d, qPCR data for relative expression of MARCH-1 in CD14+ cells after siRNA knockdown as compared to the si-RNA control. The assay was performed twice with independent donors/2 cell lines/2 siRNAs, performed in triplicates. Representative data from one experiment is shown. In MM1.S cells, siRNA knockdown leads to reduction of MARCH-1 expression to 0.46 and 0.32 (median value, amplified with primer pair 2) and to 0.40 and 0.26 (median value, amplified with primer pair 3) as compared to control siRNA (median value, 1.0). In OPM2 cells, siRNA knockdown leads to reduction of MARCH-1 expression to 0.65 and 0.7 (median value, amplified with primer pair 2) and to 0.29 and 0.36 (median value, amplified with primer pair 3) as compared to control siRNA (median value, 1.0). Bars represent STDEVP. e, CD14+ cells with lower levels of MARCH-1 have increased HLA-DR protein on their cell surface. The experiment was performed twice with two independent donors/2 cell lines/2 siRNAs. f, Stronger correlation of DAPI and HLA-DR localization in MM1.S and OPM2 cells in control cells as compared to those after MARCH-1-siRNA transfection. The experiment was performed twice with two independent donors/2 cell lines/2 siRNAs. Source data
Extended Data Fig. 9 CD14+ monocytes do not show the M-MDSC phenotype after co-culture with MM cells.
Expression of different markers for a, macrophages/monocytes and b, MDSCs on CD14+ cells in patients and healthy donors. c, CD14+ cells from healthy donors were co-cultured with MM cells. FACS analysis was performed on day 3 after co-culture. Representative results from one out of two independent experiments performed with two healthy donors/2 different cell lines. Due to restricted cell numbers, no replicates could be used. All donors have similar distribution of cells as compared to controls. Source data
Extended Data Fig. 10 Alterations in tumor microenvironment start from the precursor stages of the MM and exhibit heterogeneous changes in the immune cell repertoire.
Illustration of immune alterations observed during progression. Bars begin at the stage in which they are first observed in our dataset.
Supplementary Table 1. Patient data and clinical information. List of clinical measurements for samples used for single-cell RNA sequencing. Supplementary Table 2. Sample information. Bead selection and quality metrics for single-cell RNA samples. Supplementary Table 3. Marker genes demonstrating cell type identity of immune cell clusters. List of top 10 differentially expressed genes with average log fold change (mean expression in within-cluster cells compared to mean expression of all out-of-cluster cells) and FDR-corrected rank-sum P values in immune cell clusters discovered by k-nearest neighbors clustering on cells from n = 32 patient samples.
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experimens/repeats reported in the manuscript are provided
Data source. Primary data generated in experimens/repeats reported in the manuscript are provided
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments
Data source. Primary data generated in experiments/repeats reported in the manuscript are provided
Data source. Primary data generated in experiments
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Zavidij, O., Haradhvala, N.J., Mouhieddine, T.H. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat Cancer 1, 493–506 (2020). https://doi.org/10.1038/s43018-020-0053-3
Journal of Clinical Medicine (2020)
Leukemia & Lymphoma (2020)
Journal of Clinical Investigation (2020)