Robust Single Cell Quantification of Immune Cell Subtypes in Histological Samples

Abstract

Due to the rapid increase in immunotherapies there is an urgent need to develop new tools for robust in situ immune cell-typing and quantification to understand disease mechanisms and therapeutic responses. In this paper, we present a new machine-learning based method for classifying immune cell types in human tissue from highly multiplexed data. The proposed method is based on: i) identifying the most representative cell clusters across multiple slides by performing hierarchical multi-channel and multi-slide clustering; ii) from the clusters of interest, we then learn a biological-phenotypical-taxonomical cell model by solving a multi-class and multi-label classification problem. We have applied this methodology for the simultaneous classification of T and B cells using CD3 and CD20 markers and further sub-classification of T cells (CD3+) into CD4+ and CD8+, and FoxP3+ cells (within CD3+ and CD4+ cells). The method allows estimating statistical measurements used for correlation analysis with clinical data. Our method is generic and can be applied for any cell type classification problem. We obtain an average accuracy of ~95% across six immune cell types/subtypes following simultaneous classification with this approach.