Universal Sample Multiplexing for Single Cell Analysis

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ABSTRACT Cancer progression and resistance to therapy are strongly influenced by tumor heterogeneity. Single-cell RNA sequencing (scRNAseq) is a valuable tool for cancer research because it reveals the molecular details of tumor and microenvironmental heterogeneity at single-cell resolution. However, a mechanistic understanding of how heterogeneity contributes to tumor progression or response to therapy is lacking because such studies require analysis of multiple replicates, time points, and experimental conditions. These experimental designs are currently prohibitively expensive and fraught with artifacts like doublets and batch effects when using the best and most widely-used scRNAseq pipelines. Moreover, similar limitations exist for complementary and powerful single-cell epigenetic analysis methods such as single-nucleus assay for transposase accessible chromatin (snATACseq) and single-nucleus cleavage under targets and transposition (snCUT&Tag). To surmount these barriers and to enable mechanistic studies using single-cell analysis requires simple, robust, and inexpensive methods for quantitatively comparing samples using multiplexing. The goal of this proposal is to advance and further develop MULTIseq: a rapid, simple, inexpensive, scalable, and universal sample multiplexing tool for single-cell RNA and epigenetic analysis. MULTIseq integrates seamlessly with the most popular and best-performing technologies. MULTIseq improves single-cell analysis experiments in an end-to-end fashion by reducing the costs of multiplexed experiments by 5 to 100-fold, increasing the number of cells that can be analyzed in a single run by 3 to 10-fold, allowing removal of artifacts such as doublets and batch effects, avoiding cell-type sampling bias against cells with low RNA content, and enabling the design of new classes of experiments that are currently impossible using scRNAseq workflows. However, MULTIseq has tremendous untapped potential in cancer research and we propose to implement several significant improvements to the technology. In Aim 1 we will develop new workflows enabling sample multiplexing for epigenomic analyses (snATACseq and snCUT&Tag). When deployed together, these methods will provide a comprehensive molecular portrait of chromatin accessibility and multiple histone modifications with reduced batch effects. In Aim 2 we develop a scalable strategy to convert cells into barcoded hydrogel reaction capsules that will significantly extend the scalability of MULTIseq, enable powerful future workflows, facilitate comparison of a more diverse sets of sample types, and ultimately untether MULTIseq from commercial library preparation platforms. We will validate and benchmark the proposed methods on three classes of specimens used routinely by cancer researchers: tumor cell lines, flash frozen human primary and metastatic tumors, and organoids. Successful completion of this proposal will have a broad and sustained impact on cancer research by making comparisons between multiple samples and specimens using single-cell transcriptomic and epigenomic analysis a routine and inexpensive practice available to any basic or clinical oncology research lab.