Robust Single-cell Matching and Multi-modalAnalysis Using Shared and Distinct FeaturesReveals Orchestrated Immune Responses

Zhu, B.; Chen, S.; Bai, Y.; Chen, H.; Mukherjee, N.; Vazquez, G.; McIlwain, D. R.; Alexandar, T.; Lee, I.; Matthias, M.; Goltsev, Y.; Ma, Z.; Nolan, G. P.; Jiang, S.

doi:10.1101/2021.12.03.471185

bioRxiv · 10.1101/2021.12.03.471185

Robust Single-cell Matching and Multi-modalAnalysis Using Shared and Distinct FeaturesReveals Orchestrated Immune Responses

Zhu, B.·Chen, S.·Bai, Y.·Chen, H.·Mukherjee, N.·Vazquez, G.·McIlwain, D. R.·Alexandar, T.·Lee, I.·Matthias, M.·Goltsev, Y.·Ma, Z.·Nolan, G. P.·Jiang, S.

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Abstract

The ability to align individual cellular information from multiple experimental sources, techniques and systems is fundamental for a true systems-level understanding of biological processes. While single-cell transcriptomic studies have transformed our appreciation for the complexities and contributions of diverse cell types to disease, they can be limited in their ability to assess protein-level phenotypic information and beyond. Therefore, matching and integrating single-cell datasets which utilize robust protein measurements across multiple modalities is critical for a deeper understanding of cell states, and signaling pathways particularly within their native tissue context. Current available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely upon a large number of shared features across datasets for mutual Nearest Neighbor (mNN) matching. This approach is unsuitable when applied to single-cell proteomic datasets, due to the limited number of parameters simultaneously accessed, and lack of shared markers across these experiments. Here, we introduce a novel cell matching algorithm, Matching with pARtIal Overlap (MARIO), that takes into account both shared and distinct features, while consisting of vital filtering steps to avoid sub-optimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multi-modal methods, including spatial techniques, and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via CODEX imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid via CITE-seq. This cross-platform integrative analysis enabled the identification of unique orchestrated immune responses within the lung of complement-expressing macrophages and their impact on the local tissue microenvironment. MARIO thus provides an analytical framework for unified analysis of single-cell data for a comprehensive understanding of the underlying biological system.

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Zhu, B., Chen, S., Bai, Y., Chen, H., Mukherjee, N., Vazquez, G., McIlwain, D. R., Alexandar, T., Lee, I., Matthias, M., Goltsev, Y., Ma, Z., Nolan, G. P., Jiang, S.. 2021-12-05. Robust Single-cell Matching and Multi-modalAnalysis Using Shared and Distinct FeaturesReveals Orchestrated Immune Responses. https://doi.org/10.1101/2021.12.03.471185

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