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Updated: Aug 30, 2026

Methods to Enable Spatial Transcriptomics of Bone Tissues
Published on: May 3, 2024
Preparation of Formalin-Fixed, Paraffin-Embedded Bone Marrow Samples for Spatial Transcriptomics Using EDTA-Based
Lorea Campos-Dopazo1, Miguel Cócera2, Paula Aguirre-Ruiz2
1Hematology and Oncology Program, Center for Applied Medical Research (CIMA), Instituto de Investigaciones Sanitarias de Navarra (IdiSNA), Cancer Center Clínica Universidad de Navarra (CCUN).
Abstract:
Bone marrow (BM) is a complex and dynamic structure in which spatial relationships influence cell behavior, signaling, and function. Because of this, understanding the full dynamics of cellular interactions requires complementary spatial techniques that preserve and map the architecture of cell populations in situ. Despite significant advances in single-cell technologies that have contributed to understanding the transcriptional heterogeneity of healthy and diseased BM tissue, the spatial organization of different cell types, niche-specific regulatory programs, and their interactions still need further study. Recent developments in spatial transcriptomics have enabled unbiased gene expression analysis with spatial context across different tissues, making these technologies complementary to single-cell methods that lack spatial resolution. While spatial transcriptomics has been widely used in soft tissues, its use in mineralized tissues such as BM remains limited because of the challenges associated with processing bone tissue. The protocol presented here has been used to prepare formalin-fixed, paraffin-embedded (FFPE) samples from healthy and diseased mouse and human tissues. A 10-day ethylenediaminetetraacetic acid (EDTA) based decalcification step is included to preserve RNA integrity and support the use of spatial transcriptomics on long bone samples. This protocol describes the preparation of BM samples for spatial transcriptomic analysis, including tissue processing, preservation, and sectioning, as well as essential quality assessment steps, such as evaluating section integrity, tissue morphology, and RNA quality. The workflow also supports cell type identification and integration with single-cell transcriptomic data to characterize cellular composition, define cell-cell interactions, and visualize the spatial distribution of transcriptionally heterogeneous cells in healthy and diseased states. Overall, this workflow prepares fully mineralized healthy and malignant tissues for spatial transcriptomic analysis while preserving BM architecture.
