Abstract 2279: Improving cell type and state classification in CITE-seq using probabilistic models and hierarchical cell ID matrix

Abstract

Abstract Accurate classification of cells in a population is important for most immune phenotyping applications. Surface protein marker expression measured with multiplexed platforms like Flow Cytometry and sequential gating strategies are typically employed to classify cells. However, new single-cell RNA sequencing (scRNA-seq) methods that measure RNA expression alone or Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) methods that concurrently measure both surface protein and RNA expression in single cells are providing new data and opportunities to develop methods both to classify cells and to assess their states of activation. Traditional manual gating methods using biaxial plots, where one designates a threshold separating a bimodal distribution through visual approximation, are subjective and can vary widely between individuals. Instead of manual, sequential gating for cell classification, we have implemented a probabilistic Gaussian Mixture Model (GMM) and K-means clustering to separate cell populations based on a library of marker thresholds for a variety of cell types present in human peripheral blood mononuclear cells (PBMCs) using a CITE-seq ADT expression matrix that has been preprocessed with centered-log-ratio (CLR) normalization. Furthermore, we coupled this GMM method with a hierarchical approach for cell typing. We have developed a Cell ID Matrix that outlines over 40 different immune cell types using consensus markers applicable to immune cell types across various studies. In this typing method, major cell types are identified following positive and negative marker expression as described in the Cell ID Matrix. Hierarchical gating of cells can then be used to identify subpopulations in a principled manner. The information in the Cell ID matrix is input as a binary matrix for each hierarchy level and is matched to the binarized expression values for classifying the cells. This approach can also be extended to CITE-seq datasets from disassociated tumor cells (DTCs). We conclude that automated cell gating with CITE-seq data may enhance the classification of numerous cell types in addition to significantly decreasing the amount of human intervention and time allocated for gating cells. Identifying cell states is a work in progress and will supplement this technique in the future. Citation Format: Lila Fakharzadeh, Julien Tessier, Shannon McGrath, Emma Wang, Angelique Biancotto, Alexei Protopopov, Jack Pollard, Joon Sang Lee. Improving cell type and state classification in CITE-seq using probabilistic models and hierarchical cell ID matrix [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2279.