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.

Single-Cell Dosing and mRNA Sequencing of Suspension and Adherent Cells Using the PolarisTM System.

Single-cell functional analysis provides a natural next step in the now widely adopted single-cell mRNA sequencing studies. Functional studies can be designed to study cellular context by using single-cell culture, perturbation, manipulation, or treatment. Here we present a method for a functional study of 48 single cells by single-cell isolation, dosing, and mRNA sequencing with an integrated fluidic circuit (IFC) on the Fluidigm® Polaris™ system. The major procedures required to execute this protocol are (1) cell preparation and staining; (2) priming, single-cell selection, cell dosing, cell staining, and cDNA generation on the Polaris IFC; and (3) preparation and sequencing of single-cell mRNA-seq libraries. The cell preparation and staining steps employ the use of a universal tracking dye to trace all cells that enter the IFC, while additional fluorescence dyes chosen by the user can be used to differentiate cell types in the overall mix. The steps on the Polaris IFC follow standard protocols, which are also described in the Fluidigm user documentation. The library preparation step adds Illumina® Nextera® XT indexes to the cDNA generated on the Polaris IFC. The resulting sequencing libraries can be sequenced on any Illumina sequencing platform.

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

[This corrects the article on p. 70 in vol. 4, PMID: 27709111.].

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.