Introduction
Multi-cellular populations are fundamentally driven by the collective properties of individual cells. However, our understanding of gene expression dynamics derived from cultures or tissues is based on measurements made from the entire population. A growing body of data collected from individual cells has challenged these basic assumptions. The data suggest that properties driving lineage, development and disease emerge from the transcriptional heterogeneity and signaling architectures of distinct sub-populations of cells. To understand this heterogeneity more fully we performed mRNA transcriptome analysis of single cells across a wide sampling of phenotypically distinct populations. Using an automated platform, the C1™ Single-Cell Auto Prep System, for the capture, lysis, imaging, and routine preparation of full-length mRNA-sequencing libraries of single, live cells we have enabled the delineation of transcriptional heterogeneity within and between cell populations at the level of the individual cell. We present data that compares full length amplified transcriptome profiling by both high throughput gene expression using the BioMark HD System as well as downstream NGS sequencing of prepared cDNA libraries. The data presented here was derived from a wide variety of cell types (>15), including cultured cell lines and from primary cell isolations. In addition, data was collected from both mouse and human cell types, and libraries were generated using several sizes of the C1TM Integrated Fluidics Circuit (IFC), for medium and small cell diameters. mRNA-Seq mapping was conducted using the Tophat v2.0/bowtiev2 and gene expression values were derived using Cufflinks v2. Finally, the single-cell gene expression data was analyzed using Fluidigm’s SINGuLAR™ Analysis Toolset v2.0 to perform outlier identification, principal component analysis, and hierarchical clustering on this wide variety of cell types.
Results
Multi-cellular populations are fundamentally driven by the collective properties of individual cells. However, our understanding of gene expression dynamics derived from cultures or tissues is based on measurements made from the entire population. A growing body of data collected from
individual cells has challenged these basic assumptions. The data suggest that properties driving lineage, development and disease emerge from the transcriptional heterogeneity and signaling architectures of distinct sub-populations of cells. To understand this heterogeneity more fully we performed mRNA transcriptome analysis of single cells across a wide sampling of phenotypically distinct populations. Using an automated platform, the C1™ Single-Cell Auto Prep System, for the capture, lysis, imaging, and routine preparation of full-length mRNA-sequencing libraries of single, live cells we have enabled the delineation of transcriptional heterogeneity within and between cell populations at the level of the individual cell. We present data that compares full length amplified transcriptome profiling by both high throughput gene expression using the BioMark HD System as well as downstream NGS sequencing of prepared cDNA libraries. The data presented here was derived from a wide variety of cell types (>15), including cultured cell lines and from primary cell isolations. In addition, data was collected from both mouse and human cell types, and libraries were generated using several sizes of the C1TM Integrated Fluidics Circuit (IFC), for medium and small cell diameters. mRNA-Seq mapping was conducted using the Tophat v2.0/bowtiev2 and gene expression values were derived using Cufflinks v2. Finally, the single-cell gene expression data was analyzed using Fluidigm’s SINGuLAR™ Analysis Toolset v2.0 to perform outlier identification, principal component analysis, and hierarchical clustering on this wide variety of cell types.
Figure 1. A simplified workflow for the C1 Single-Cell mRNA-Seq application. a) C1 Single-Cell mRNA-Seq Workflow. C1 Single-Cell Auto Prep System automates single-cell isolation, washing, staining, lysis, reverse transcription (RT), and PCR to provide high quality cDNA for further analysis. All the cell handling steps are completed in a C1 IFC. The harvested cDNA is subject to tagmentation and library prep using Illumina® Nextera XT DNA Sample Preparation Kit and is then sequenced on an Illumina sequencer. b) IFC Architecture c) Cell capture module. Each cell capture module can perform up to 5 reaction steps including cell capture, live/dead staining, washing, lysis, reverse transcription, and PCR amplification with active mixing between chambers. d) Bioanalyzer trace of cDNA product obtained from K562 cells (DNA high sensitivity chip). The red line corresponds to cDNA produced from a single K562 cell and the blue line corresponds to a reaction chamber with no cell captured. The 800 bp peak corresponds to synthetic RNA controls included in the mRNA-Seq workflow. e) Bioanalyzer trace of one library pool generated from 96 individual cells by Nextera® XT sample prep kit without size selection and two rounds of cleanup by solid-phase reverse immobilization (SPRI).
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