Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells.

This study provides an assessment of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. The system combines microfluidic technology and nanoliter-scale reactions. We sequenced 622 cells, allowing identification of 341 islet cells with high-quality gene expression profiles. The cells clustered into populations of α-cells (5%), ß-cells (92%), δ-cells (1%), and pancreatic polypeptide cells (2%). We identified cell-type-specific transcription factors and pathways primarily involved in nutrient sensing and oxidation and cell signaling. Unexpectedly, 281 cells had to be removed from the analysis due to low viability, low sequencing quality, or contamination resulting in the detection of more than one islet hormone. Collectively, we provide a resource for identification of high-quality gene expression datasets to help expand insights into genes and pathways characterizing islet cell types. We reveal limitations in the C1 Fluidigm cell capture process resulting in contaminated cells with altered gene expression patterns. This calls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system.

Complete Transcriptome Analysis by 5'-End Single-Cell RNA-Seq with Random Priming.

Single-cell transcriptome analysis reveals heterogeneous cell types in complex tissues and leads to unexpected biological findings when compared to bulk populations. However most of the methods focus on the 3'-end of polyadenylated transcripts using droplet-based technology. To achieve complete transcriptome, we describe single-cell 5'-end transcriptome protocol with random primed-cDNA harvesting on the Fluidigm C1™ platform which can isolate and process up to 96 cells from a single run with custom library preparation. The method enables detection of Transcription Start Site (TSS) at the single-cell resolution yielding a more comprehensive overview of gene regulatory elements governing in the EpiSC-like cell (EpiLC) including non-polyadenylated RNA and enhancer RNA activities.

Single-Cell RNA Sequencing Analysis Using Fluidigm C1 Platform for Characterization of Heterogeneous Transcriptomes.

Single-cell RNA sequencing (scRNA-seq) is an innovative technology that can be used to characterize transcriptome heterogeneity at the single-cell resolution. The Fluidigm C1 is an automated system for preparing complementary deoxyribonucleic acid (cDNA) samples that have been synthesized and preamplified from single cells for next-generation sequencing (NGS) analysis. Herein, we detail a workflow and protocol for scRNA-seq analysis of 3'-end enriched cDNA libraries and introduce strategies for validation of differential gene expression.

Corrigendum: A Single-Cell Sequencing Guide for Immunologists.

[This corrects the article DOI: 10.3389/fimmu.2018.02425.].

Single-Cell Tagged Reverse Transcription (STRT-Seq).

Single-cell RNA sequencing (scRNA-seq) has become an established approach to profile entire transcriptomes of individual cells from different cell types, tissues, species, and organisms. Single-cell tagged reverse transcription sequencing (STRT-seq) is one of the early single-cell methods which utilize 5' tag counting of transcripts. STRT-seq performed on microfluidics Fluidigm C1 platform (STRT-C1) is a flexible scRNA-seq approach that allows for accurate, sensitive and importantly molecular counting of transcripts at single-cell level. Herein, I describe the STRT-C1 method and the steps involved in capturing 96 cells across C1 microfluidics chip, cDNA synthesis, and preparing single-cell libraries for Illumina short-read sequencing.

The Use of the Fluidigm C1 for RNA Expression Analyses of Single Cells.

Understanding the transcriptional heterogeneity that occurs on the level of a single cell is critical to understanding the gene-regulatory mechanisms underlying development and disease. Population-level whole-transcriptome profiling approaches average gene expression across thousands to millions of cells and are unable to delineate the transcriptional signature of individual cells. Considerable biological heterogeneity between individual cells arises from differences in cell lineage, environment, or response to stimulus. The development of single-cell RNA sequencing (RNA-seq) enabled a high-resolution and unbiased analysis of cell transcriptomes. This unit describes a procedure utilizing an automated microfluidic platform, the Fluidigm C1 system, to simultaneously isolate dozens of single cells in a size- and shape-dependent manner. The microfluidic platform processes cells in individual nanoliter-scale reactions to convert their contents into double-stranded cDNA. This cDNA is used to make dual-indexed libraries using the Illumina Nextera XT library preparation kit for eventual RNA-seq analysis. © 2018 by John Wiley & Sons, Inc.

A Single-Cell Sequencing Guide for Immunologists.

In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods.