BackgroundThe tumor microenvironment (TME) is composed of highly heterogeneous extracellular structures and cell types such as endothelial cells, immune cells, and fibroblasts that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in cancer development and progression and can significantly affect a tumor’s response to therapy and capacity for multi-drug resistance. High resolution analyses of gene and protein expression with spatial context can provide deeper insights into the interactions between tumor cells and surrounding cells within the TME, where a better understanding of the underlying biology can improve treatment efficacy and patient outcomes. Here, we demonstrated the ability to perform streamlined multi-omic tumor analyses by utilizing the 10X Genomics Visium Spatial Gene Expression Solution for FFPE with multiplex protein enablement. This technique simultaneously assesses gene and protein expression to elucidate the immunological profile and microenvironment of different breast cancer samples in conjunction with standard pathological methods.MethodsSerial (5 µm) sections of FFPE human breast cancer samples were placed on Visium Gene Expression (GEX) slides. The Visium GEX slides incorporate ~5,000 molecularly barcoded, spatially encoded capture spots onto which tissue sections are placed, stained, and imaged. Following incubation with a human whole transcriptome, probe-based RNA panel and an immuno-oncology oligo-tagged antibody panel, developed with Abcam conjugated antibodies, the tissues are permeabilized and the representative probes are captured. Paired GEX and protein libraries are generated for each section and then sequenced on an Illumina NovaSeq at a depth of ~50,000 reads per spot. Resulting reads from both libraries are aligned and overlaid with H&E-stained tissue images, enabling analysis of both mRNA and protein expression. Additional analyses and data visualizations were performed on the Loupe Browser v4.1 desktop software.ConclusionsSpatial transcriptomics technology complements pathological examination by combining histological assessment with the throughput and deep biological insight of highly-multiplexed protein detection and RNA-seq. Taken together, our work demonstrated that Visium Spatial technology provides a spatially-resolved, multi-analyte view of the tumor microenvironment, where a greater understanding of cellular behavior in and around tumors can help drive discovery of new biomarkers and therapeutic targets.
The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) and immunohistochemistry staining have been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome‐wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell‐cell interactions within the TME, analysis of multiple analytes is necessary. Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome‐wide gene expression and highly multiplexed protein expression profiling from the same formalin‐fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E stained tissues were then imaged, followed by incubation with transcriptome‐wide probes and a high‐plex DNA‐barcoded antibody panel containing intra‐ and extracellular markers. Transcriptome probes and antibody‐barcodes were then spatially captured on the slide and converted into sequencing‐ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E morphology, RNA, protein) from the same tissue section. We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune‐oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker‐guided regional selection and differential gene expression analysis on the defined areas. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development.
The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) staining has been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome-wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell-cell interactions within the TME, multiple layers of information are needed and must be studied together. Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome-wide gene expression and highly multiplexed protein expression profiling from the same formalin-fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E or immunofluorescence stained tissues were then imaged, followed by incubation with transcriptome-wide probes and a high-plex DNA-barcoded antibody panel containing intra- and extracellular markers. Transcriptome probes and antibody-barcodes were then spatially captured on the slide and converted into sequencing-ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E/immunofluorescence, RNA, protein) from the same tissue section. We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune-oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker-guided regional selection and differential gene expression analysis on the defined regions. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development. Citation Format: Cedric Uytingco, Jennifer Chew, Naishitha Anaparthy, Jun D. Chiang, Christina Galonska, Karthik Ganapathy, Ryo Hatori, Alexander Hermes, Layla Katiraee, Anna-Maria Katsori, William Nitsch, Patrick Roelli, Joe Shuga, Rapolas Spalinskas, Mesruh Turkekul, Benton Veire, Dan Walker, Neil Weisenfeld, Stephen R. Williams, Zachary Bent, Marlon Stoeckius. Multiomic characterization of the tumor microenvironment in FFPE tissue by simultaneous protein and gene expression profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3814.
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