Multi-Omics 2024

Advances in spatial biology have allowed for a deeper understanding of signaling networks and cellular interactions at a single-cell level. Techniques such as multiplexed immunofluorescence (mIF) and RNA in situ hybridization (ISH) enable the detection of multiple protein and RNA biomarkers. In complex tissues such as tumor microenvironments and neural tissues, precise information on cellular interconnections and signaling activity is crucial in understanding the biological processes involved in development and disease. 

In this study, we present a novel, fully automated spatial multiomics approach that integrates the RNAscope™ HiPlexPro assay [1] and sequential immunofluorescence (seqIF™) protocols [2] on the COMET™ platform for co-detection of RNA and protein targets on the same tissue section. The combined multiplexing capability of up to 12-plex RNA and 24-plex protein targets enabled a comprehensive understanding of complex and fragile tissues. This approach was applied to formalin-fixed paraffin-embedded human solid tumors and frozen mouse brain tissues, demonstrating the versatility and robustness of the technique. 

Through this automated assay, RNAscope™ probes targeting biomarkers relevant to either immuno-oncology or neuroscience were combined with protein markers to profile multiple cell types and their microenvironment. RNA probes targeting key cytokines were combined with cell marker antibodies to map activated T cells and macrophages infiltrating the tumor. On brain samples, RNA probes targeting neurotransmitters, receptors, and other glial cells were combined with selected protein markers for an in-depth profiling of neuronal and non-neuronal subpopulations.  

Overall, our spatial multiomics approach demonstrated the potential and versatility of the combined RNAscope™ and seqIF™ protocols on the COMET™ platform in immuno-oncology and neuroscience and opened doors to new discoveries in studies of biomarker and drug development. 

Spatial biology revolutionizes the research studies of tumor microenvironment (TME). Multiplex immunofluorescence (mIF) methods enable precise profiling of key players within the TME, revealing their spatial distribution and interactions (PMID: 38012408). In situ hybridization (ISH) technologies complement protein profiling by mapping cytokine- and chemokine-expressing cells, crucial for deciphering signaling networks and immune activation. 

Here, we present a novel multiomics approach that integrates RNAscope™ and sequential immunofluorescence (seqIF™) protocols. This workflow achieves the same-section co-detection of RNAs and proteins within the TME. The process is automated using COMET™, an advanced tissue staining and imaging platform. By precisely controlling temperature and reagent distribution, COMET™ ensures maximum assay efficiency and reproducibility. Our integrated multiomics protocol allows up to three RNAscope™ detection cycles combined with twelve seqIF™ cycles, resulting in a final 12-plex RNA and 24-plex protein panel. 

In our study, we harnessed the power of the COMET™ platform to automate the RNAscope™ protocol. By analyzing positive and negative control genes, we validated their sensitivity and specificity. To explore the intricate landscape of TME, we developed a panel of 12 probes targeting crucial RNA biomarkers in tumor-infiltrating lymphocytes and their activation status. Simultaneously, we employed a 24-antibody panel to detect protein biomarkers, enabling single-cell profiling. Applying this approach to human FFPE tumor tissues, we demonstrated that co-detection of RNA and protein biomarkers in the same section enhances our understanding of key cellular components involved in tumor progression and immune response. This integrated approach promises deeper insights into cancer biology. 

Our findings underscore the promise of spatial multiomics technologies in advancing immune cell research and unraveling intricate cellular interactions within TME. By fully automating these technologies on platforms like COMET™, we enhance efficiency, reduce user interventions, and increase robustness. This progress has important implications for creating predictive markers, refining cancer diagnoses, and tailoring personalized therapies.