A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages

Abstract

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study

“…The virus load in the lung was assessed by RNAscope ® , which had the sensitivity to detect single molecules in a cell. The distribution of virus load was quantified by STRISH, a robust image processing pipeline ( 22 ). The average measurements of SARS-CoV-2 spike mRNA (nCoV2019) per grid (tissue region) of neighbouring cells were visualized by STRISH using a tissue heatmap ( Figure 1A ).…”

A robust Platform for Integrative Spatial Multi-omics Analysis to Map Immune Responses to SARS-CoV-2 infection in Lung Tissues

“…To detect the presence of SARS-CoV-2 mRNA signalling the four core biopsy samples ( Fig S1a ), we first applied RNAScope to target the SARS-CoV-2 spike mRNA, followed by quantification and localisation of the virus by our STRISH pipeline (17). This analysis classified samples as two viral mRNA signal high- and two viral signal low-samples and also defined the spatial location of the virus within lung tissues ( Fig S1b ).…”

Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review

“…MIBI and IMC can be classified under mass cytometry-based imaging techniques as both use metal isotopes bound to antibodies. OPAL mIHC allows for simultaneous assessment of 4-9 antibodies ( 33 , 39 ), while next generation IHC methods such as IMC and MIBI can assess up to 40 antibodies simultaneously. These techniques allow for assessment of both immune cell populations, their function and spatial arrangement in the TME ( 37 , 38 ).…”

“…The primary and secondary antibodies can then be stripped, and the process repeated several times for different proteins of interest ( 33 ). OPAL mIHC allows for simultaneous assessment of up to 9 proteins in formalin-fixed and paraffin-embedded (FFPE) tissue samples ( 39 ).…”