{
  "abstract": "Background Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy, demanding comprehensive multi-omic understanding of its spatial architecture to improve therapeutic strategies. Spatial transcriptomics with the Xenium™ platform enables mapping of hundreds to thousands of RNA targets in tissue sections. However, identification and validation of drug targets requires proteomic assessment to directly reveal the functional mechanisms underlying disease biology. Imaging Mass Cytometry™ (IMC™) is a spatial proteomic technology that utilizes cytometry by time-of-flight (on which CyTOF™ systems are based) to generate high-dimensional, spatially resolved protein expression data at subcellular resolution in tissue sections. Unlike traditional immunohistochemistry or immunofluorescence, IMC platforms use metal-tagged antibodies and laser ablation to simultaneously detect over 40 protein markers without spectral overlap or autofluorescence interference. Here we demonstrate the performance and insights gained from using IMC technology on tissue samples previously processed with the Xenium platform.Methods We profiled HCC FFPE tissue sections using a custom transcriptomic panel with Xenium v1 assay and then performed IMC using a 43-marker immuno-oncology-focused antibody panel on the same tissue section ( figure 1). We used IMC technology in parallel on non-Xenium processed control serial sections and compared performance. To combine the transcriptomic and proteomic datasets, we utilized Xenium Explorer software to co-register the datasets and observe overlay of transcriptomic and proteomic biomarkers.Results IMC technology alone and post-Xenium IMC generated data of similar quality, demonstrating highly consistent tumor and immune cell phenotyping capabilities in HCC ( figure 2). IMC technology alone and post-Xenium IMC similarly detect localization of macrophages (M1 and M2), neutrophils (CD66b), B cells (CD20), cytotoxic T cells (CD8) and T helper cells (CD4) in specific locations around the tissue. Xenium Explorer software permitted import of IMC data through a user-friendly co-registration algorithm that aligned stained nuclei from both datasets. Combining the datasets revealed the presence of subcategories of immune cells (T cells, B cells, macrophages) and their activation states. Additionally, detection of transcript and protein of multiple markers showcased discrepancies in spatial localization, highlighting the importance of validating transcriptomic data with proteomic assessment.Conclusions Here, we highlight the synergistic use of the Xenium platform for transcript detection and IMC technology for protein profiling on the same tissue section, facilitating an integrated understanding of tissue biology. This study provides a multi-omic view of HCC heterogeneity, offering insights into mechanisms of disease and development of potential therapeutic strategies.For Research Use Only. Not for use in diagnostic procedures.Abstract 106 Figure 1Combined workflow for spatial imaging of both RNA and protein on the same slide. The IMC staining protocol can be added onto the end of Xenium acquisition with an additional wash step prior to IMC staining. Slides can be imaged immediately or stored for later acquisitionAbstract 106 Figure 2Low and high abundance proteins were detected by IMC regardless of processing approach. IMC alone and post-Xenium IMC generate the same quality image, demonstrating highly consistent and reproducible data. For post-Xenium slides, the Xenium assay used was a custom panel using the Xenium v1 workflow",
  "authors": [
    {
      "affiliations": [
        "Garvan Institute of Medical Research, Sydney, NSW, Australia",
        "University of New South Wales, Sydney, NSW, Australia"
      ],
      "name": "Atefeh Khakpoor"
    },
    {
      "affiliations": [
        "Standard BioTools, Markham, ON, Canada"
      ],
      "name": "Qanber Raza"
    },
    {
      "affiliations": [
        "Garvan Institute of Medical Research, Sydney, NSW, Australia",
        "University of New South Wales, Sydney, NSW, Australia"
      ],
      "name": "Merrin Mary Eapen"
    },
    {
      "affiliations": [
        "Garvan Institute of Medical Research, Sydney, NSW, Australia"
      ],
      "name": "Dina Kazemi"
    },
    {
      "affiliations": [
        "University of Sydney, School of Medical Sciences, Faculty of Medicine and Health, Sydney, NSW, Australia"
      ],
      "name": "Erin Coll"
    },
    {
      "affiliations": [
        "Standard BioTools, Markham, ON, Canada"
      ],
      "name": "Liang Lim"
    },
    {
      "affiliations": [
        "Standard BioTools, South San Francisco, CA, USA"
      ],
      "name": "Christina Loh"
    },
    {
      "affiliations": [
        "Standard BioTools, Markham, Canada"
      ],
      "name": "Nick Zabinyakov"
    },
    {
      "affiliations": [
        "University of Sydney, Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, Sydney, NSW, Australia"
      ],
      "name": "Liang Qiao"
    },
    {
      "affiliations": [
        "University of Sydney, Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, Sydney, NSW, Australia"
      ],
      "name": "Anna Di Bartolomeo"
    },
    {
      "affiliations": [
        "University of Sydney, Sydney, NSW, Australia"
      ],
      "name": "Helen McGuire"
    },
    {
      "affiliations": [
        "University of Sydney, Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, Sydney, NSW, Australia"
      ],
      "name": "Jacob George"
    },
    {
      "affiliations": [
        "Garvan Institute of Medical Research, Sydney, NSW, Australia"
      ],
      "name": "Ankur Sharma"
    }
  ],
  "title": "106 Spatial multi-omic characterization of tumor microenvironment heterogeneity in hepatocellular carcinoma",
  "uid": "2423d1da-90ba-5eed-8b16-eaa7fe57aa80"
}