transcriptomic sequencing  (Azenta)

Journal: Clinical & Translational Immunology

Article Title: Spatial omics for profiling the dynamic tumor microenvironment

doi: 10.1002/cti2.70084

Figure Lengend Snippet: Spatial omics driven characterisation of the TME evolution. (a) , Performing spatial multiomics on tumor samples collected from different stages of cancer development (normal, pre‐malignant, and malignant tissues) enables the characterisation of the spatial landscape associated with tumor progression. This will reveal stage‐specific changes in the spatial architecture of the tumor and will aid in the identification of potential targets to prevent the lesion from becoming malignant. (b) Decoding the spatial biology behind therapeutic response by using spatially resolved technologies reveals the region‐specific ligand‐receptor interactions driving therapeutic response and the cellular architecture that correlates with response. A few examples of spatial correlates of therapeutic response are given. Tumors sensitive to ICB are often associated with the presence of TLS, increased TILs, with elevation of T cell populations such as TCF7 + CD8 + T cells and GZMB + CD8 + T cells. An oncofetal niche consisting of POSTN + CAFs, FOLR2 + TAMs and PLVAP + endothelial cells has been identified to be associated with better response to combination immunotherapy in HCC. The spatial analysis of ICB resistant tumors from multiple cancer types has identified CAF subtypes forming physical barriers, which prevent the infiltration of immune cells into the tumor. ICB, immune checkpoint blockade, TLS, Tertiary Lymphoid Structure, TILs, Tumor‐infiltrating lymphocytes.

Article Snippet: Visium and Visium HD (10× Genomics) are sequencing‐based whole‐transcriptomic approaches, with Visium achieving 55 μM resolution covering 8–20 cells, and Visium HD with 2 μm single‐cell resolution., Spatially enhanced resolution omics sequencing (Stereo‐seq), commercialised as STOmics (BGI Group), uses chips with DNA nanoballs (DNBs) containing unique coordinate identity (CID), barcodes (UMI) and poly‐T oligonucleotides.

Techniques: Clinical Proteomics

Journal: Clinical & Translational Immunology

Article Title: Spatial omics for profiling the dynamic tumor microenvironment

doi: 10.1002/cti2.70084

Figure Lengend Snippet: AI‐Enabled Multi‐omics for Cancer Translational and Clinical Research. AI‐driven integration of different modalities, including (i) histology images (H&E/IHC whole‐slide images), (ii) spatial omics (spatial transcriptomics, spatial proteomics), and (iii) clinical metadata (treatment response and survival data) are jointly modelled with integrative analysis (IA), artificial intelligence (AI), machine learning (ML), and deep learning (DL). Central models learn representations across modalities to support downstream spatial analyses, including reconstruction of cellular landscapes, inference of spatially defined ligand–receptor interactions, cellular neighbourhood (CN) profiling, and trajectory/pseudotime analysis. Insights generalise to biological and clinical applications such as cancer detection, biomarker prediction, survival analysis, cell‐type clustering, tumor‐microenvironment (TME) studies, and personalised treatment selection. The bottom timeline shows the evolution of deep learning approaches.

Article Snippet: Visium and Visium HD (10× Genomics) are sequencing‐based whole‐transcriptomic approaches, with Visium achieving 55 μM resolution covering 8–20 cells, and Visium HD with 2 μm single‐cell resolution., Spatially enhanced resolution omics sequencing (Stereo‐seq), commercialised as STOmics (BGI Group), uses chips with DNA nanoballs (DNBs) containing unique coordinate identity (CID), barcodes (UMI) and poly‐T oligonucleotides.

Techniques: Biomarker Discovery, Spatial Transcriptomics, Spatial Proteomics, Selection