Recently developed technologies for digital imaging and highly multiplexed immunohistochemistry (mIHC) advancing the field of histology into a quantitative era, allowing for more complex descriptions of tissue architecture.
Imaging cytometry by time of flight (CyTOF), multiplexed ion beam imaging, and co-detection by indexing (CODEX) can be used to simultaneously profile the expression of dozens of proteins in a tissue section with single-cell resolution. However, annotating cell populations or states that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging.
This webinar will present a computational approach that was developed to overcome this obstacle. Spatially resolved Transcriptomics via Epitope Anchoring (STvEA) enriches mIHC images with single-cell RNA-seq data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements such as CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing).
Pablo G. Camara of the Perelman School of Medicine at the University of Pennsylvania will share details of STvEA, which performs transcriptome-guided annotation of highly multiplexed cytometry datasets.
Dr. Camara will demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published CODEX and CyTOF datasets, and a CITE-seq atlas of the murine spleen that his team has generated.
In this talk attendees will:
- Learn about current challenges in the analysis of mIHC images
- Understand the concept of STvEA and the algorithmic steps involved
- Get familiarized with the computational analyses enabled by STvEA
- See several examples of the application of STvEA
About the Series: Spatial Multiomics: Analysis Strategies for Enriching Single-Cell Phenotyping Data
In this multi-part webinar series, our expert speakers will review analytical frameworks and algorithms to integrate imaging-based single-cell spatial phenotyping data with complementary transcriptomic and genomic datasets.
High-plex cell phenotyping methods like single-cell RNA-seq capture the deep cellular heterogeneity of samples, but cell behavior is a function of all that surrounds it. Imaging-based spatial phenotyping platforms enable researchers to visualize and analyze cell diversity, interactive networks, and cellular behavior within the spatial context of whole tissue sections. Both types of data have complementary features, which give researchers the ability to merge information about a cell’s proteome and transcriptome with its single-cell, spatial context.
This webinar series will highlight the latest advances driving integrative multiomics analysis.