Abstract 5930: Single day workflow for high-quality whole genome analysis of thousands of single cells

DNA sequencing technology provides the capability to tell the story of every cell in a heterogenous sample. ResolveDNA® generates uniform coverage of >95% of a single genome, enabling researchers to generate single-cell genomic data of unparalleled quality. Here, we report an enhancement to the ResolveDNA® workflow that increases the rate of data collection; the ability to reliably generate complete genomic information from up to 1536 cells in parallel from a single experiment. To achieve this, we have coupled a high-throughput automated process to a low volume reaction using the latest ResolveDNA® 2.5-hour chemistry. This establishes the potential for users to go from a single cell, or low-input DNA sample, to ready to sequence libraries in a single day for >3000 samples. As with earlier ResolveDNA® workflows, these reactions generate negligible amounts of contaminating and off target DNA during the reaction. A typical single-cell reaction yields ~ 85 ng of DNA. The single cell’s amplified DNA product is amenable to multiple post amplification library preparation processes. We utilized both BioSkryb ResolveDNA® and Watchmaker Library Preparation kits for Illumina sequencing. We did not identify significant differences in the performance of the two products. Libraries were assessed at a depth of ~ 2million reads and determined to be of high quality, with low rates of chimeric DNA formation and less than 1% mtDNA resulting in the reaction product. A subset of samples was probed at >200 million reads, with >85% of the genome covered at 1x. Consistent with earlier ResolveDNA® workflows, the libraries were characterized with >99% precision and >80% sensitivity. The story told from a single cell’s genome drives discovery and shines a light on the questions surrounding the causes of disease and cellular function. This workflow is designed to empower researchers to probe the genomic landscape of a heterogenous tissue sample.

Citation Format: Jay A. West, Jeff G. Blackinton, Kyle Hukari, Joe Dahl. Single day workflow for high-quality whole genome analysis of thousands of single cells [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 5930.

Microfluidic live tracking and transcriptomics of cancer-immune cell doublets link intercellular proximity and gene regulation

Cell–cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell–cell interactions. We track the temporal variations in natural killer—triple-negative breast cancer cell distances and compare them with terminal cellular transcriptome profiles. The results show the time-bound activities of regulatory modules and allude to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live-cell interactions at doublet resolution. Together, our findings highlight the use of our approach across different cancers and cell types.

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

Fluidic Logic Used in a Systems Approach to Enable Integrated Single-Cell Functional Analysis

The study of single cells has evolved over the past several years to include expression and genomic analysis of an increasing number of single cells. Several studies have demonstrated wide spread variation and heterogeneity within cell populations of similar phenotype. While the characterization of these populations will likely set the foundation for our understanding of genomic- and expression-based diversity, it will not be able to link the functional differences of a single cell to its underlying genomic structure and activity. Currently, it is difficult to perturb single cells in a controlled environment, monitor and measure the response due to perturbation, and link these response measurements to downstream genomic and transcriptomic analysis. In order to address this challenge, we developed a platform to integrate and miniaturize many of the experimental steps required to study single-cell function. The heart of this platform is an elastomer-based integrated fluidic circuit that uses fluidic logic to select and sequester specific single cells based on a phenotypic trait for downstream experimentation. Experiments with sequestered cells that have been performed include on-chip culture, exposure to various stimulants, and post-exposure image-based response analysis, followed by preparation of the mRNA transcriptome for massively parallel sequencing analysis. The flexible system embodies experimental design and execution that enable routine functional studies of single cells.