March 28-31
Pittsburgh, PA
Booth #220
Breaking barriers in spatial multiomics
Join us and engage directly with our expert scientists to discover spatial multiomics on COMET™. Experience firsthand how you can simultaneously detect RNA and proteins within the same section, all at a subcellular level of detail.
Our protease-free, fully-automated workflow enables multiomics scalability for all stages of research. Are you interested in elevating your spatial biology projects with our multiomics solutions? Visit Lunaphore’s booth or book an in-person meeting with our team.
Program Highlights
Poster Presentations
March 30 | 12:30 – 1:30 PM
12:30 – 1:30 PM
The tumor microenvironment (TME) is a complex and dynamic ecosystem playing a crucial role in tumor development, progression, immune evasion, and therapeutic response. Integrating protein expression with complementary outputs, such as mapping cytokine and chemokine-expressing cells, is crucial to unveiling functional cell states and mechanisms of cancer progression (PMID: 36978735, 37598036). However, accurately visualizing secreted molecules while preserving spatial context remains a significant challenge in spatial biology.
Here, we employed an automated hyperplex multiomics approach to simultaneously detect protein and RNA expression, providing in-depth cellular profiling of the TME across a variety of cancer types.
We analyzed a formalin-fixed paraffin-embedded Tissue Microarray (TMA) comprising specimens of multiple human cancer types, namely prostate cancer, lung cancer, breast cancer, colorectal cancer, melanoma, and lymphoma. A TMA section was stained and imaged on the COMET™ automated platform, integrating RNAscope™ (PMID: 22155210) HiPlex Pro and sequential immunofluorescence (seqIF™, PMID: 37697822). This multiomics approach enabled concomitant detection of up to 12 RNA and over 50 protein targets on the same tissue section. The resulting image was analyzed using the HORIZON™ software to reveal in situ single-cell features and spatially map their distribution.
The high-plex proteomic panel enabled detailed characterization of the TME composition, including tumor cells, a variety of immune cell subsets, cancer-associated fibroblasts, vasculature, and markers of cell state and activation, including cell division and immune checkpoints. The combined detection of transcripts encoding secreted molecules, such as cytokines and chemokines, provided insight into immune activity and cell-cell communication within the tumor and surrounding stromal compartments.
This automated multiomics workflow enables comprehensive analysis of the TME across multiple cancer types, while significantly reducing experimental turnover and sample consumption. Multiomics spatial profiling at single-cell resolution opens new opportunities to explore cellular interactions at the tumor-immune interface and identify functional states relevant to immunotherapy and disease progression.
12:30 – 1:30 PM
Lymphoid malignancies present diagnostic challenges due to overlapping immunophenotypic features between tumor cells and reactive background cells, complicating the distinction between indolent lymphomas and benign lymph node hyperplasia [Nicolae A, Hemato, 2024, DOI:10.3390/hemato5030026]. Some Hodgkin lymphoma (HL) subtypes, such as lymphocyte-rich (LRHL) and nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), and specific variants of diffuse large B-cell lymphoma (DLBCL), exhibit similar phenotypic profiles despite having different therapeutic implications [PMID: 35741318]. This study aims to leverage sequential immunofluorescence (seqIF™, PMID: 37813886) for the spatial profiling of lymphoma to improve differential diagnosis and to uncover spatial patterns unique to each lymphoma subtype, providing insights into potential predictors of immunotherapy response.
We optimized a seqIF™ panel of 21 markers, using antibodies approved for clinical immunohistochemistry (IHC), to phenotype mature B-cell lymphomas on the COMET™ platform. This panel was applied to tissue microarrays prepared from representative cases of DLBCL, HL, indolent lymphomas, and benign lymph node hyperplasia. Each marker of the optimized panel was visually validated by certified pathologists, comparing seqIF™ results against corresponding IHC images. Computation of the spatial matrix successfully identified tumor cells and reactive cells using thresholding of fluorescence intensity and cell diameter, particularly in HL and DLBCL cases that, at first glance, appeared similar. This enabled us to map classified cell events back to the tissue sections, allowing direct spatial correlation with stained images for a comprehensive analysis of cell distribution. Ongoing work aims to refine our lymphoma spatial profiles by integrating spatial transcriptomic readouts from the same sample cohort.
These results highlight the potential of multiplex immunofluorescence (mIF) for the diagnostic phenotyping of lymphoma, transferring clinically validated antibodies into a mIF panel. The ability to assess both phenotypic and spatial characteristics of tumor and reactive cells will enhance our understanding of lymphoma and provide insights into predictive markers for immunotherapy responses.
Poster Presentations
March 30 | 12:30 – 1:30 PM
12:30 – 1:30 PM
The tumor microenvironment (TME) is a complex and dynamic ecosystem playing a crucial role in tumor development, progression, immune evasion, and therapeutic response. Integrating protein expression with complementary outputs, such as mapping cytokine and chemokine-expressing cells, is crucial to unveiling functional cell states and mechanisms of cancer progression (PMID: 36978735, 37598036). However, accurately visualizing secreted molecules while preserving spatial context remains a significant challenge in spatial biology.
Here, we employed an automated hyperplex multiomics approach to simultaneously detect protein and RNA expression, providing in-depth cellular profiling of the TME across a variety of cancer types.
We analyzed a formalin-fixed paraffin-embedded Tissue Microarray (TMA) comprising specimens of multiple human cancer types, namely prostate cancer, lung cancer, breast cancer, colorectal cancer, melanoma, and lymphoma. A TMA section was stained and imaged on the COMET™ automated platform, integrating RNAscope™ (PMID: 22155210) HiPlex Pro and sequential immunofluorescence (seqIF™, PMID: 37697822). This multiomics approach enabled concomitant detection of up to 12 RNA and over 50 protein targets on the same tissue section. The resulting image was analyzed using the HORIZON™ software to reveal in situ single-cell features and spatially map their distribution.
The high-plex proteomic panel enabled detailed characterization of the TME composition, including tumor cells, a variety of immune cell subsets, cancer-associated fibroblasts, vasculature, and markers of cell state and activation, including cell division and immune checkpoints. The combined detection of transcripts encoding secreted molecules, such as cytokines and chemokines, provided insight into immune activity and cell-cell communication within the tumor and surrounding stromal compartments.
This automated multiomics workflow enables comprehensive analysis of the TME across multiple cancer types, while significantly reducing experimental turnover and sample consumption. Multiomics spatial profiling at single-cell resolution opens new opportunities to explore cellular interactions at the tumor-immune interface and identify functional states relevant to immunotherapy and disease progression.
12:30 – 1:30 PM
Lymphoid malignancies present diagnostic challenges due to overlapping immunophenotypic features between tumor cells and reactive background cells, complicating the distinction between indolent lymphomas and benign lymph node hyperplasia [Nicolae A, Hemato, 2024, DOI:10.3390/hemato5030026]. Some Hodgkin lymphoma (HL) subtypes, such as lymphocyte-rich (LRHL) and nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), and specific variants of diffuse large B-cell lymphoma (DLBCL), exhibit similar phenotypic profiles despite having different therapeutic implications [PMID: 35741318]. This study aims to leverage sequential immunofluorescence (seqIF™, PMID: 37813886) for the spatial profiling of lymphoma to improve differential diagnosis and to uncover spatial patterns unique to each lymphoma subtype, providing insights into potential predictors of immunotherapy response.
We optimized a seqIF™ panel of 21 markers, using antibodies approved for clinical immunohistochemistry (IHC), to phenotype mature B-cell lymphomas on the COMET™ platform. This panel was applied to tissue microarrays prepared from representative cases of DLBCL, HL, indolent lymphomas, and benign lymph node hyperplasia. Each marker of the optimized panel was visually validated by certified pathologists, comparing seqIF™ results against corresponding IHC images. Computation of the spatial matrix successfully identified tumor cells and reactive cells using thresholding of fluorescence intensity and cell diameter, particularly in HL and DLBCL cases that, at first glance, appeared similar. This enabled us to map classified cell events back to the tissue sections, allowing direct spatial correlation with stained images for a comprehensive analysis of cell distribution. Ongoing work aims to refine our lymphoma spatial profiles by integrating spatial transcriptomic readouts from the same sample cohort.
These results highlight the potential of multiplex immunofluorescence (mIF) for the diagnostic phenotyping of lymphoma, transferring clinically validated antibodies into a mIF panel. The ability to assess both phenotypic and spatial characteristics of tumor and reactive cells will enhance our understanding of lymphoma and provide insights into predictive markers for immunotherapy responses.