High multiplex analysis of the immune microenvironment in BM trephine samples using GeoMX™ digital spatial profiling
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Background: To date, studies into the bone marrow (BM) immune microenvironment have been limited due to reliance on the analysis of BM aspirates in which the microenvironmental context is lost. GeoMX™ digital spatial profiling (DSP) is a new technique developed for the analysis of formalin-fixed paraffin-embedded tissue samples which allows high multiplex analysis of protein expression in multiple user-defined regions within a tissue section. We examined the applicability of this technique to the analysis of protein expression in diagnostic BM trephine samples. Materials and methods: Archival BM trephines were obtained from patient groups (normal, myelodysplasia and aplastic anaemia). Regions of interest in each section were identified by dual CD3+/CD45+ immunohistochemistry staining to identify immune infiltrates, and DSP was applied. Results: Due to variability in cell number within regions of interest and differing cellular composition of the BM trephines, raw protein expression counts were normalised by internal controls and nuclei count to determine the expression level of each protein within each region of interest. In heat map analysis using Spearman's rank correlation, aplastic anaemia samples clustered away from both normal and myelodysplasia samples, demonstrating significant differences in their BM immunology. Conclusions: GeoMX™ DSP is an innovative new technique that, for the first time, allows the analysis of archival BM trephines at an unprecedented level of detail. It will allow investigations in large cohorts of patients with haematological malignancies to identify new biomarkers, new mechanisms of disease pathogenesis and new drug targets. Keywords: GeoMX; bone marrow; digital spatial profiling; immune microenvironment; immunohistochemistry; trephine.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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