Abstract 3395: A scalable microfluidic platform for determining cellular heterogeneity by copy number detection
Authors: Andrew Price, Jon Sorenson, Kamila Belhocine, Claudia Catalanotti, Zeljko Dzakula, Susana Jett, Viajy Kumar, Bill Lin, Tony Makarewicz, Alaina Puleo, Mohammad Rahimi, Sanjam Sawhney, Joe Shuga, Maengseok Song, Katie Sullivan-Bibee, Tobias Wheeler, Yifeng Yin, Michael Schnall-Levin, Rajiv Bharadwaj
Journal: Cancer Research (2018)

Abstract The ability to sequence entire genomes at the level of single cells (SCs) is an essential tool for mapping heterogeneity in cancer. Knowledge of this variability provides insight into cancer dynamics and the efficacy of therapeutics. Copy number variant (CNV) analysis requires only sparse coverage creating an affordable means for measuring cellular differences genome-wide. However, current approaches to SC DNA sequencing suffer from low throughput, cumbersome workflows and remain accessible to only a few select laboratories. We have developed a microfluidic, partitioning-based solution that allows sensitive CNV detection of thousands of SCs concomitantly. Isolated cells or nuclei are treated to gain complete accessibility to genomic DNA then co-partitioned along with gel beads supporting oligonucleotide barcodes. Barcoded fragments, representing genomic abundance, are generated and converted into Illumina-compatible libraries. The workflow supports various sample types including primary cells, cultured cells, tissue-dissociated cells and nuclei extracted from flash-frozen tissues. Accompanying this platform we have also developed a scalable bioinformatics pipeline for genome alignment, normalization, copy-number detection, and clustering of SC profiles. Using clusters for CNV analysis we can reliably detect 100 kb scale events and, in large clones, identify CNV events down to tens of kilobases with high confidence. Analysis of our SC whole-genome sequencing data shows excellent coverage uniformity, comparable to leading plate-based SC amplification techniques. To validate the accuracy of CNV detection, calls are measured against orthogonal data from well-characterized cell lines including RPMI8226, HCC1954, and HCC1143 and high concordance is observed. Furthermore, we are able to observe multiple clones including CNVs in normal cell lines GM12878 and BJ suggesting absolute cell line validation requires whole-genome SC sequencing. Evaluating nuclei extracted from stage 1 breast carcinoma and stage 1 renal cell carcinoma we find clones exhibiting common somatic mutations. Furthermore, many cells exhibit extensive genomic heterogeneity, effects we attribute to DNA replication, with the frequency of such events elevated in fast growing samples such as tumors or fresh cell cultures. In conclusion, we demonstrate a novel droplet-based system that permits scalable CNV calling at the SC level enabling high resolution characterization of intra-tumor heterogeneity. It is our intention that a reliable and cost-effective system will encourage widespread adoption of SC analysis as a means of characterizing the progression of cancer, lead to more effective treatments and unveil genomic diversity in other genetically heterogenous systems such as neuronal cells, high-passage cell lines and samples from aging populations. Citation Format: Andrew Price, Jon Sorenson, Kamila Belhocine, Claudia Catalanotti, Zeljko Dzakula, Susana Jett, Viajy Kumar, Bill Lin, Tony Makarewicz, Alaina Puleo, Mohammad Rahimi, Sanjam Sawhney, Joe Shuga, Maengseok Song, Katie Sullivan-Bibee, Tobias Wheeler, Yifeng Yin, Michael Schnall-Levin, Rajiv Bharadwaj. A scalable microfluidic platform for determining cellular heterogeneity by copy number detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3395.