Abstract 226: Targeted transcriptome sequencing enables exponential scaling of combinatorial barcoding in AML samples

Abstract

Abstract In the thirteen years since its inception, single-cell RNA-sequencing (scRNA-seq) has rapidly spread across multiple fields of research, leading to many new discoveries. As technologies have matured, the number of cells that can be processed in a single experiment has seen exponential growth with workflows now assaying up to one million cells in an individual experiment. While high throughput sequencing methods have facilitated the discovery and characterization of various cell types, sequencing costs can be prohibitively high for routine use. Many applications of scRNA-seq are focused on cell type identification, gene regulatory networks, or biomarker discovery. These applications often do not require surveying the entire transcriptome, but rather require the interrogation of specific sets of well-characterized genes. In these cases, sequencing the entire transcriptome may be adding unnecessary project costs. To increase throughput and minimize sequencing costs, the development of a targeted gene enrichment method is required. Here we extend our whole transcriptome split-pool combinatorial barcoding technology, to enable enrichment of a subset of genes in the final single cell sequencing libraries. To illustrate the power of our technology, we enriched a whole transcriptome library of human bone marrow mononuclear cells (BMMCs) from four acute myeloid leukemia (AML) donors and four control donors. From a library of 27,000 cells, we used our immune gene panel to enrich 1,000 genes representing canonical immune cell markers and pathways. Our method increased the percent of reads on target from as low as 1.6% in the whole transcriptome libraries to 75% in the targeted libraries. Furthermore, despite a nearly ten-fold reduction in sequencing reads between unenriched and enriched libraries, the resulting clustering yielded very high concordance of cell type identities and preserved AML-specific signatures such as FLT3, MKI67, and CD19. Overall, we demonstrate our modular enrichment strategy preserves biological structure of the data and allows for deep characterization of gene signatures in health and disease. We envision our approach will enable researchers to simultaneously reduce sequencing costs while drastically scaling up the number of cells and samples across experiments. Citation Format: Grace Kim, Efthymia Papalexi, Peter Matulich, Ryan Koehler, Sarah Schroeder, Vuong Tran, Charles Roco, Alexander Rosenberg. Targeted transcriptome sequencing enables exponential scaling of combinatorial barcoding in AML samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 226.