Characterization of the bone marrow architecture of multiple myeloma using spatial transcriptomics

Abstract

The bone marrow (BM) is a complex and compartmentalized tissue where spatial context plays a critical role in regulating cell behavior, signaling, and disease progression. To capture these dynamics, we apply spatial transcriptomics using the Visium Spatial Gene Expression platform on formalin-fixed paraffin-embedded (FFPE) BM sections from both healthy and Multiple Myeloma (MM) mouse models, as well as MM patient samples. Overcoming the technical challenges of working with mineralized long bone tissue, we develop a custom analytical framework integrating spatial and single-cell transcriptomic data to map cellular composition and interactions in situ. This approach enables the spatial characterization of transcriptionally heterogeneous malignant plasma cells (MM-PC) and their surrounding microenvironments. We identify spatially distinct gene programs linked to MM pathogenesis, including signatures of NETosis and IL-17 signalling, which are reduced in MM-PC-rich regions. Additionally, a transition gradient from effector to exhausted T cell phenotype is associated with increased remoteness from MM-PC. These spatial patterns are identified in FFPE BM biopsies from MM patients with varying tumor burdens. In summary, our study demonstrates both the capabilities and limitations of Visium technology in characterizing spatially regulated mechanisms underlying MM pathogenesis.

Fig. 1. Spatial resolution of healthy bone marrow resident cell types.

a Schematic representation of healthy control mouse (YFPcγ1), experimental workflow for bone tissue isolation and spatial transcriptomics analysis (created in BioRender, https://BioRender.com/u8jwp6c). b Quality control of the mice femur tissues, Features, and Counts in two samples from a control mouse model (YFP1 and YFP2). c Cell type proportions per spot derived from a deconvolution analysis using scRNA-seq data as a reference. d Average of the most abundant cell types in the four identified clusters (CN1-CN4). e Correlation analysis of cell types based on spot distribution. f Spatial distribution of the four identified clusters. g Pie charts illustrating each cell type’s proportion to each spot’s transcriptomic signature in a control mouse model (YFP1). h Retnlg and Tfrc mouse gene expression, canonical markers of neutrophils and erythroblasts, respectively in a control mice femur sample (YFP1). i Immunohistochemistry (IHC) of Ly-6G/Ly-6C (Gr-1), a neutrophil marker, in control mice femur sample (YFP). Scales of 500 μm (b, f, and h), scales of 1000 μm (upper panel i) and 50 μm (bottom panels i).

Fig. 2. Unraveling the transcriptional spatial profiling of multiple myeloma bone marrow.

a Schematic representation of MM mouse model (MIcγ1), experimental workflow for isolation bone tissue and conducting spatial transcriptomics (created in BioRender. https://BioRender.com/u8jwp6c). b Estimating malignant plasma cell (MM-PC) percentage per spot by deconvolution analysis using the scBMReference data set. c Enrichment score for malignant PC. d Green Fluorescent Protein (GFP) IHC of healthy YFPcγ1 and pathological MIcγ1 mice (MM3) femur tissues. e Average of the most abundant cell types in each of the seven identified malignant clusters (CM1-CM7). f Spot-based cell type correlation analysis between the different cell types. g Spatial distribution of the clusters identified in malignant samples. h Pie charts representing each cell type’s contribution to each spot’s transcriptomic signature. Scales of 500 μm (b, c, and g) and scales of 1000 μm (left panels d) and 100 μm (right panels d).

Fig. 3. Molecular characterization of plasma cell spatial heterogeneity.

a Spatial gene expression of pathological PC canonical markers. b Anatomical position of the identified areas (Remote zone, Border zone and Hotspot) based on their malignant PC signature. c Spatial distribution of the twelve identified MM-PC groups (Pg1-Pg12). d Heatmap of the top 1% most highly variable genes between the Pg groups shown in (c). e Violin plots of selected genes from (d). f Selected Gene Ontology (GO) terms derived from the over-representation analysis (ORA) of the most variable genes identified in (d) across Pg groups (the entire list is in Supplementary Data 2). Scales of 500 μm (a and b).

Fig. 4. Spatial identification of transcriptional programs associated with multiple myeloma pathogenesis.

a Spatial distribution of the T cell effector (upper panels) and T cell exhaustion (bottom panels) scores in the pathological samples. b Box plots illustrating the T cell effector and T cell exhaustion scores across different identified areas, with their corresponding p-values. c Correlation between the T cell effector and T cell exhaustion profiles in the three identified areas. d Venn diagram displaying the differentially expressed genes (DEG) between the different areas (Remote zone, Border zone, and Hotspot). e Bar plot indicating upregulated KEGG pathways from the 231 commonly upregulated genes identified in (d). f Box plots of selected KEGG pathways from (e). g Spatial distribution and h violin plots of Padi4, Pik3cd, and Mmp9 genes expression level. Scales of 500 μm (a and g).

Fig. 5. Spatial transcriptomic analysis of human bone marrow samples.

a Experimental workflow for isolation and spatial transcriptomics analysis of healthy and MM human bone tissue samples (created in BioRender. https://BioRender.com/u8jwp6c). b Hematoxylin-Eosin staining (H&E) staining. c Spatial representation of the malignant PC enrichment score. d Spatial gene expression of human PC canonical markers. e CD138 (healthy) and CD38 (human malignant bone tissue (hMM)) IHC, of healthy (upper panels) and human malignant bone tissues (bottom panels). f Anatomical position of the identified areas (Remote zone, Border zone, and Hotspot) in two diseases samples, hMM2 and hMM3. g Box plots per area (mean of all the diseased samples) and spatial distribution of the T cell effector and T cell exhaustion scores. h, i Box plots per area (left panel) (mean of all the diseased samples) and spatial distribution of the scores associated with Neutrophil extracellular trap formation (NETs) (h) and with IL-17 signaling pathways (i) in two human malignant tissues (hMM2 and hMM3) (middle and right panel). Scales of 500 μm (b–i).

Fig. 6. Summary figure.

Created in BioRender, https://BioRender.com/u8jwp6c.