使用自动化的微流控芯片系统在单细胞中检测MicroRNA的异质性
编辑推荐：
Fluidigm 公司开发了一种在C1TM单细胞自动制备系统及BiomarkTM HD系统上检测单细胞miRNA表达谱的实验方案。此方案可以在小于24小时的时间内，平行处理96个单细胞，在一张GE 96.96芯片对每个单细胞分别检测多达96种miRNA。配套的SINGuLAR™ 2.0分析软件可以对数据进行非监督式聚类分析及PCA分析，有效的根据miRNA表达类型在单细胞水平揭示细胞群体的异质性，为研究miRNA调控提供更多数据。
使用自动化的微流控芯片系统在单细胞中检测MicroRNA的异质性
Leyrat Anne, Shuga Joe, Li Nianzhen, Szpankowski Lukasz, Unger Marc & West Jay
（ISSCR 2013 poster: F-3201）
介绍
MicroRNA (miRNAs)是一类短小（18-24个核苷酸）的非编码RNA，它们可以通过破坏信使RNA（mRNA）的稳定性和抑制mRNA翻译来调控基因表达。细胞群体中miRNA的表达通常认为可以驱动下游基因表达和蛋白功能。我们的目的是使用一种微流控系统在单细胞水平确定miRNA表达的变化，这种系统可以自动化的将单细胞捕获及miRNA预扩增以便进行后续表达分析。我们开发了一种简单、模块化的流程，可以将对细胞群体的分析轻松降至单细胞水平（图1）。此流程包括两个核心部件：C1TM单细胞自动制备系统（图1a：样本制备，包括细胞分离和从miRNA制备cDNA）和动态芯片（Dynamic Array™ IFC）及BiomarkTM HD系统（图1b：读出，高度平行的表达分析）。对C1TM芯片捕获的每个单细胞进行的目标特异性扩增（Specific Target Amplification, STA），借用了Single Cell-to-Ct™试剂盒(Life Technologies)完成裂解及预扩增步骤，以及TaqMan® MicroRNA Reverse Transcription 试剂盒(Life Technologies)完成逆转录步骤（图2）。
使用动态芯片及BiomarkTM HD系统，可以使用96对microRNA TaqMan表达引物，平行分析从96个单细胞预扩增所得的96个cDNA样本。使用Fluidigm SINGuLAR™ 2.0分析软件对数据进行主成分分析（Principal Component Analysis，PCA），揭示了从单一表型所得的一群单细胞中miRNA表达的显著变化（图3,4,5）。比较不同表型的细胞群体（人类胚胎成纤维细胞，人类诱导多能干细胞（iPS），从iPS所得人类神经祖细胞（NPC），以及完全分化的人类神经元（HN）），除同一类细胞之间表达的异质性外，展示了(不同类型细胞间)更巨大的差异。
结果
图1. 在单细胞进行miRNA分析的整合流程
C1单细胞自动制备系统使用Life Technologies 开发的试剂和实验方法（“Single-cell MicroRNA expression analysis”），对单细胞中的miRNA转录本进行目标特异性扩增（STA）。从将细胞悬液加至C1芯片到完成数据分析的整个流程，可以在不到24小时内完成。
图2. C1 MicroRNA STA实验流程
单细胞悬液（200-1000个细胞）被加入C1 IFC芯片的细胞进样孔。使用来自Single Cell-to-Ct™试剂盒(Life Technologies)的裂解和预扩增试剂，以及来自TaqMan® MicroRNA Reverse Transcription 试剂盒(Life Technologies)的逆转录试剂，进行逆转录（使用MegaplexTM RT pool）和预扩增（使用MegaplexTM PreAmp pool)，以便可以检测每个单细胞中多达380种miRNA的表达。C1 IFC捕获单细胞，然后冲洗，裂解，在反应仓平行对每个单细胞的miRNA进行逆转录和预扩增。96个预扩增过的样本然后被导出，使用Biomark HD系统在一块96.96动态芯片运行，研究每个样本中多达96种miRNA的表达。
图3. 分析：单个iPS细胞及其NPC后裔
(A) 对数据进行非监督式聚类分析可以清楚的区别开iPS细胞及使用小分子从iPS获得的NPC后裔1. 同时揭示了每种细胞中的亚群。
(B) PCA清楚的揭示了这两种表型不同的细胞群之间的差异。
(C) Violin Plot显示了不同亚群中miRNA 的差异性表达，以及主成分1和2的主要贡献者（左上至右下的顺序）。iPS和NPC之间5种miRNA的表达变化，和使用microassay从胚胎干细胞（ES）及其NPC后裔得到的趋势相同（未发表数据，Yao Shuyuan友情提供）。
图4. 人类神经元，iPS和NPC细胞
(A) 对从iPS，NPC和成熟神经元（HN）获得的数据进行非监督式聚类分析，可以清楚的将HN细胞从iPS和NPC细胞区分开，同时也揭示了每类细胞中存在的亚群。miR-9更频繁且更高水平的在成熟神经元（HN）中表达。
(B) 根据miRNA表达，PCA可以清楚的区分这三类细胞。miR-20a，19b，17，和106a在HN中表达水平较低，符合基于神经分化和衰老数据的预期2,3。
图5. 不同传代数的胚胎成纤维细胞
(A) 对从两个不同传代数（P13和P24）获得的BJ胚胎成纤维细胞得到的数据进行非监督式聚类分析，虽然可以揭示不同细胞间miRNA表达类型的不同，却无法区分这两种群体。
(B) 对从P13和P24细胞得到的miRNA表达数据进行PCA分析，进一步确认无法根据miRNA表达区分这两群细胞。
(C) 当传代数相距更远（P7 对P24），对miRNA数据（热图未显示）进行PCA分析可以区别他们。
结论
· 我们在C1TM单细胞自动制备系统开发了一种简洁的实验方案，能以最少的手工操作，在不到24小时内，平行处理高达96个单细胞，对其miRNA表达谱进行分析。
· C1 miRNA STA实验方案使用了Life Technologies为miRNA优化过的试剂。特别的，Megaplex™ RT及PreAmp pool可以从C1 IFC上每个单细胞获得多达380种不同miRNA的cDNA。使用Biomark系统，在96.96 GE动态芯片可以读出表达类型。
· 对来自不同类型细胞的数据进行非监督式聚类分析及PCA分析，揭示了不同类型细胞之间、或者同一类型细胞中miRNA表达类型的差异（被microarray或者文献所证实）。
参考文献
1. Chambers SM, et al. (2009) Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat Biotechnol. 27(3), 275-280.
2. Trompeter H-I, Abbad H, Iwaniuk KM, Hafner M, Renwick N, et al. (2011) MicroRNAs MiR-17, MiR-20a, and MiR-106b Act in Concert to Modulate E2F Activity on Cell Cycle Arrest during Neuronal Lineage Differentiation of USSC. PLoS ONE 6(1): e16138. doi:10.1371/journal.pone.0016138
3. Hackl M., Brunner S., Fortschegger M., Laschober G.T., Micutkova L., et al. (2010), miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell, 9(2), 291-296.
Detection of MicroRNA Heterogeneity in Single Cells Using an Automated
Introduction
Introduction
MicroRNA (miRNAs) are short (18–24 nucleotides), non-coding RNAs that regulate gene expression by both disrupting messenger RNA (mRNA) stability and inhibiting mRNA translation. The expression of miRNA species in cellular populations is thought to drive downstream gene expression and protein functionality. Our goal was to determine the variability in miRNA expression at the single cell level using a microfluidic system which automates single cell capture and miRNA pre-amplification for downstream expression analysis. We have developed a simple, modular workflow for streamlined analysis of cell populations down to the single-cell level (Figure 1). The workflow is centered on two key components: the C1TM Single Cell Auto Prep System (Figure 1a: Sample Prep, including cell isolation and cDNA preparation from miRNA species) and the Dynamic Array™ IFC and BiomarkTM HD System (Figure 1b: Read out, for highly parallel expression analysis). The Specific Target Amplification (STA) chemistry performed on each individual cell captured on the C1TM IFC borrows components from the Single Cell-to-Ct™ kit (Life Technologies) for the lysis and preamplification steps and components from the TaqMan® MicroRNA Reverse Transcription Kit (Life Technologies) for the Reverse Transcription step (Figure 2).
Using the Dynamic Array IFCs and the Biomark HD System, up to 96 cDNA samples preamplified from the 96 single cells are each analyzed in parallel with up to 96 microRNA TaqMan expression assays. Principal Component Analysis (PCA) of the data using Fluidigm’s SINGuLAR™ Analysis Toolset v2.0 reveals significant variations in the expression of discrete miRNA species in a population of single cells from a single phenotype (Figure 3, 4, and 5). Comparison of phenotypically distinct populations (human embryonic fibroblasts, human induced Pluripotent Stem Cells (iPS), human Neural Progenitor Cells (NPC) derived from the iPS, and fully differentiated human neurons (HN)) demonstrate more dramatic differences in addition to the heterogeneity of expression within each group.
Results
Figure 1: Integrated workflow for miRNA analysis in single cells
The C1 Single-Cell Auto Prep System performs Specific Target Amplification (STA) of miRNA transcripts from single cells using the reagents and a protocol developed for this purpose by Life Technologies (protocol “Single-cell MicroRNA expression analysis”). The whole process, from loading the cell suspension on the C1 Integrated Fluidic Circuit (IFC) to full data analysis of the data can be accomplished in less than 24 hours.
Figure 2. C1 MicroRNA STA experimental workflow
Figure 3. Analysis: Single iPS cells and their NPC progeny
A) Unsupervised clustering of the data clearly distinguishes iPS cells from their NPC progeny obtained using small molecules1. Subpopulations are also revealed within each group of cells. B) PCA shows a clear difference between the two phenotypically distinct cell populations. C) Violin plots show differential expression of miRNAs in different subpopulations and reveal the main contributors to Principal Components 1 and 2 (in order from top left to right). The variations in expression of a set of five miRNAs between iPS and NPC shows the same trends as microarray measurements obtained with Embryonic Stem cells (ES) and their NPC progeny (unpublished data, courtesy of Yao Shuyuan).
Figure 4. Human Neurons, iPS and NPC cells
A) Unsupervised clustering of the data obtained with iPS, NPC and mature neurons (HN) clearly distinguishes HN cells from iPS and NPC cells and also reveals subpopulations within each cell type. miR-9 is more frequently and more highly expressed in mature neurons (HN). B) PCA clearly distinguishes between the three cell types based on miRNA expression.The expression of miR-20a, 19b, 17 & 106a is lower in HN, as expected based on neural differentiation and aging data2,3 .
Figure 5. Embryonic fibroblasts at different passage number
A) Unsupervised clustering of the data from two different cultures of BJ embryonic fibroblasts obtained at difference passage numbers (P13 and P24) is not able to distinguish the populations from one another, even though it can reveal different miRNA expression patterns between individual cells. B) PCA analysis of the miRNA expression data from P13 and P24 cells confirms that the two cell populations are undistinguishable based on miRNA expression. C) When the passage numbers are more distant (P7 vs. P24), PCA analysis of the miRNA data (heatmap not shown) distinguishes passage number P7 from P24.
Conclusion
•We have developed a streamlined protocol on the C1TSingle-Cell Auto Prep System to analyze the expression patterns of miRNA species in up to 96 individual cells processed in parallel with minimum hands-on time, in less than 24 hours.
A) Unsupervised clustering of the data from two different cultures of BJ embryonic fibroblasts obtained at difference passage numbers (P13 and P24) is not able to distinguish the populations from one another, even though it can reveal different miRNA expression patterns between individual cells. B) PCA analysis of the miRNA expression data from P13 and P24 cells confirms that the two cell populations are undistinguishable based on miRNA expression. C) When the passage numbers are more distant (P7 vs. P24), PCA analysis of the miRNA data (heatmap not shown) distinguishes passage number P7 from P24.
Conclusion
•We have developed a streamlined protocol on the C1TSingle-Cell Auto Prep System to analyze the expression patterns of miRNA species in up to 96 individual cells processed in parallel with minimum hands-on time, in less than 24 hours.
•The C1 miRNA STA protocol uses reagents optimized by Life Technologies for miRNA analysis. In particular, the Megaplex™ pools of RT and PreAmp primers allow to produce cDNA from up to 380 different miRNA species in each cell processed in the C1 IFC. The expression patterns are read out using the Biomark HD System on 96.96 GE Dynamic Array IFCs.
•Unsupervised clustering analysis and PCA of the miRNA data from different cell types reveal different patterns of miRNA expression between the different cell types (confirmed by microarray data or the literature) and also within each cell type.
