International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 293-304 http://medscipublisher.com/index.php/ijmms 297 (FFPE) tissue processing are commonly used, allowing for the simultaneous assessment of gene and protein expression (Wang et al., 2021). Additionally, the use of high-resolution imaging and staining methods, such as multiplex immunohistochemistry, ensures accurate identification and localization of various cell types within the TME (Trigos et al., 2020). Figure 2 Spatial comparison of periphery and center of tumor transcriptomes (Adopted from Berglund et al., 2018) Imagine caption: a~d Tissue sample 1.2; e~h Tissue sample 3.3; a, e Area comprising spots taken for normalization of ST counts, within this area spots are chosen as periphery and center. Choice of spots is based on the pathologist’s annotation and the activity of the factors “cancer” and “reactive stroma”; b, f Volcano plot of significantly differentially expressed genes between periphery and center; c, g Box plots showing expression levels of noteworthy genes significantly upregulated in either periphery or the cancer center; d, h Enriched pathways for significantly (p < 0.05) differentially expressed genes in center and periphery. P-values per gene were calculated with a two-sample t-test (Adopted from Berglund et al., 2018) 5.2 Data acquisition and processing Data acquisition in spatial transcriptomics involves capturing spatially resolved gene expression data from tissue sections. Technologies like 10X Genomics Visium and GeoMx Digital Spatial Profiler (DSP) are widely used for this purpose. These platforms enable high-plex RNA and protein profiling while maintaining spatial information (Wang et al., 2021). The integration of imaging techniques, such as MERFISH and Slide-seq, further enhances the resolution and depth of spatial transcriptomic data (Wang et al., 2021; Price et al., 2022). Accurate data processing, including image alignment and spot-level expression quantification, is crucial for downstream analyses.
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