Characterization of the bone marrow architecture of multiple myeloma using spatial transcriptomics
This comprehensive analysis delves into the spatial transcriptomic characterization of multiple myeloma within the bone marrow, leveraging advanced Visium technology. We unveil novel insights into disease progression, cellular interactions, and potential therapeutic targets.
Executive Impact
Our study utilizes Visium Spatial Gene Expression on FFPE bone marrow sections from both healthy and Multiple Myeloma (MM) mouse models, as well as MM patient samples. We developed a custom analytical framework integrating spatial and single-cell transcriptomic data to map cellular composition and interactions in situ. Key findings include the spatial characterization of heterogeneous malignant plasma cells (MM-PC), identification of spatially distinct gene programs linked to MM pathogenesis (e.g., NETosis and IL-17 signaling), and a transition gradient from effector to exhausted T cell phenotype associated with remoteness from MM-PC. These patterns were confirmed in human MM patient biopsies, demonstrating both the capabilities and limitations of Visium technology in understanding MM pathogenesis.
New Therapeutic Targets Identified
Resolution Improvement (x-fold)
Patient Cohort Insights (%)
Deep Analysis & Enterprise Applications
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This paper leverages advanced biotechnologies, specifically spatial transcriptomics, to unravel complex biological systems at a high resolution. It contributes to the field of omics by integrating spatial context with gene expression data, providing a holistic view of tissue architecture and cellular interactions.
Genes Profiled per Sample
Enterprise Process Flow
FFPE BM Sectioning
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Decalcification & RNA Preservation
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Visium Spatial Gene Expression
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Custom Analytical Framework
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Cell Composition Mapping
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Spatial Gene Program Identification
Focusing on Multiple Myeloma (MM), this research provides critical insights into the disease microenvironment. It employs both mouse models and human patient samples to characterize MM pathogenesis, identifying specific cellular and molecular changes linked to tumor progression.
| Feature | Healthy BM | Multiple Myeloma BM |
|---|---|---|
| Malignant Plasma Cells |
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| NETosis/IL-17 Signaling |
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| T-Cell Phenotype |
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Translating Mouse Model Findings to Human MM
Context: Validation of mouse model observations in human FFPE BM biopsies presents significant technical and biological challenges.
Challenge: Mineralized tissue decalcification can compromise RNA integrity. Heterogeneity and low cellularity in MM samples further complicate analysis. The limited single-cell resolution of Visium requires robust deconvolution strategies.
Solution: Developed a custom analytical framework for FFPE BM, integrating spatial and single-cell transcriptomic data. Applied robust thresholding and clustering to identify relevant spatial patterns despite technical limitations.
Outcome: Successfully identified conserved spatial patterns of MM-PC heterogeneity and microenvironment changes between mouse and human samples, validating key pathological mechanisms and demonstrating the platform's clinical potential.
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Data Acquisition & Preprocessing
Collection and decalcification of FFPE BM samples. Spatial transcriptomics profiling (Visium) and initial quality control (SpaceRanger, Seurat, STutility).
Spatial Deconvolution & Cell Type Mapping
Integration of scRNA-seq data (scBMReference) with spatial data for cell type proportion estimation per spot. Clustering based on cell composition.
Spatial Heterogeneity & Gene Program Identification
Identification of MM-PC rich regions (Hotspot, Border, Remote zones) and characterization of spatially distinct MM-PC groups. Differential expression and pathway analysis (ORA, KEGG) for these regions.
Cross-Validation & Translational Insights
Confirmation of findings using immunohistochemistry (IHC) in both mouse and human samples. Application of analytical framework to human MM biopsies for clinical relevance and conserved features.
Future Therapeutic Strategy Identification
Leveraging spatial insights into MM pathogenesis for novel therapeutic target identification and personalized treatment approaches.
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