Abstract 246: Method to analyze mutational and phenotypic profiles from single cell for clonal evolution

Abstract Single cell multiomics assays targeting RNA and Protein from the same cell provide a high-resolution view of the heterogeneity of the sample. However both analytes target the phenotype of the cells and unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. To address this gap, we have developed the Tapestri multi-omics workflow to analyze the DNA and Protein information from the same cell. After pre-processing the reads, cell calling is performed using DNA reads. Variant calling and filtering is carried out using DNA reads to identify the high quality genetic variants within each cell and the variant-cell matrix is then analyzed to identify clones. The Protein reads for the valid cells are log normalized. A systemic artefact resulting in random uniform amplification of antibodies is corrected for by learning the scaling factor from the read count distribution. This reduces the number of false positives. Furthermore read depth dependence of expression is corrected by identifying the correlation of the mean expression and background counts with the total reads in the cell. The mean signal and background for each cell is learned using a 2 component gaussian mixture model. Then, z-score normalization is applied to prevent high expressors from skewing further analysis. The scaled values are dimensionally reduced using PCA. A graph structure is created using KNN with weights calculated using Jaccard similarity following which community detection is employed to identify the cell types. We test this method on a model system with two donor PBMC, 2 cell lines mixture titrated at a 47:47:5:1 ratio with a 312 amplicon DNA panel and 45 plex antibody panel. We filter cells using isotype controls from the antibody panel and mixed cell signatures using the DNA panel. We can identify 4 clones using the genetic variants. We can identify multiple cell types including the major populations such as T-cells, B-cells, Monocytes and NK-cells using the phenotypic expression in the PBMC donors. Moreover, we are able to correlate the proportions of each PBMC cell type for the identified clones with that of the individual donors. Due to the corrections applied during the normalization of protein counts we are also able to accurately identify various expected expression profiles in PBMCs such as CD45RA-CD45RO and CD8-CD4 mutual exclusion, and the trimodal expression of CD4. Citation Format: Saurabh Parikh, Manimozhi Manivannan, Jacqueline Marin, Kate Thompson, Daniel Mendoza, Benjamin Schroeder, Saurabh Gulati, Shu Wang, Aik Ooi. Method to analyze mutational and phenotypic profiles from single cell for clonal evolution [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 246.