Research paper
Highly multiplexed quantitation of gene expression on single cells

https://doi.org/10.1016/j.jim.2013.03.002Get rights and content

Highlights

  • Primer qualification method for highly multiplexed, single-cell gene expression

  • Nonhuman primate cross-reactivity of primers

  • Conditions for single copy detection; established quantitative scale for system

  • Limited value of endogenous controls in single-cell gene expression systems

  • Comparison of bulk (multi-cell, “pooled-cell array”) approach to single-cell assay

Abstract

Highly multiplexed, single-cell technologies reveal important heterogeneity within cell populations. Recently, technologies to simultaneously measure expression of 96 (or more) genes from a single cell have been developed for immunologic monitoring. Here, we report a rigorous, optimized, quantitative methodology for using this technology. Specifically: we describe a unique primer/probe qualification method necessary for quantitative results; we show that primers do not compete in highly multiplexed amplifications; we define the limit of detection for this assay as a single mRNA transcript; and, we show that the technical reproducibility of the system is very high. We illustrate two disparate applications of the platform: a “bulk” approach that measures expression patterns from 100 cells at a time in high throughput to define gene signatures, and a single-cell approach to define the coordinate expression patterns of multiple genes and reveal unique subsets of cells.

Introduction

Cellular processes require the intricate coordination of expression of many gene products. Increasingly, our understanding of complex biologic systems depends on the ability to distinguish the genes critical in a cellular process from the myriad of other expressed transcripts. Determining the coordinate expression patterns for multiple genes uniquely defines cellular (and pathophysiological) states than cannot be achieved by measuring a single marker (Seder et al., 2008, Bedognetti et al., 2010). For these reasons, there is a need for methods that simultaneously measure many gene transcripts from a single cell.

Since its introduction over ten years ago, microarray technology has been used in a wide variety of settings (Shaffer et al., 2001, Caskey et al., 2011, Reddy et al., 2012) to identify gene signatures that describe cellular, disease, or vaccine processes. The power of microarrays lies in the high degree of multiplexing: more than 40,000 gene transcripts are analyzed from a single sample. However, typical methods require large numbers of cells (on the order of 106) or substantial nonlinear pre-amplification, limiting the sensitivity and utility of this technology. Importantly, information is lost about coordinate regulation of genes within a cell.

A system that combines the sensitivity and utility of single-cell qPCR with the multiplexed capabilities of microarray analyses is therefore valuable. The Fluidigm Dynamic Array (or BioMark™ system) for single-cell gene expression was recently developed to address this need; the assay is performed on 96 samples simultaneously, and can measure 96 (or more) genes on each sample. It has been used recently in disparate biological settings (Flatz et al., 2011, Narsinh et al., 2011b, Citri et al., 2012); however, methodologic details for optimal and quantitative application of this technology have not been detailed.

Here, we describe methods for qualifying the TaqMan™ primers and probes used in the BioMark™ technology. We determined the optimal methodological parameters ensuring quantitative results, and defined quantitative aspects of assay performance to show, for example, that the limit of detection is a single mRNA transcript. We also investigated whether “endogenous controls” provide useful information in this setting.

We illustrate applications of this technology to immunologic assessments. We show that the measurement of small “bulk” (100-cell) samples, which we term “pooled-cell array,” provides remarkable sensitivity for identifying gene signatures. Finally, we demonstrate the unique power of this technology by applying it to single-cell samples. In particular, we identify genes that are modulated following T-cell activation, and illustrate the heterogeneity of gene expression patterns by activated T-cells.

Section snippets

Biological samples

PBMC were used from human and nonhuman primate specimens. Human specimens were obtained from fully anonymized donors and used under IRB (NIAID, NIH) exemption. NHP specimens were obtained from our cryo-repository of NHP specimens; all specimens were collected as part of IACUC (VRC, NIH)-approved studies.

Primer selection

Assay targets were selected based on their relevance to T-cell immunity, and included genes for T-cell homing, cytokines, cytolytic enzymes, apoptosis, signal transduction, and transcription

Primer qualification

Although TaqMan™ primer/probe sets are nominally tested by the manufacturer for standard qPCR assays, reaction volumes and conditions in the BioMark™ system are significantly different. Therefore, it is important to qualify all primers for this system. Our initial tests showed that primers targeting highly abundant transcripts qualified with highly diluted bulk mRNA; the Et saturated with increasing concentrations of template RNA (e.g., CD7 and MAPK3, Fig. 2A). However, transcripts with low

Discussion

Over the past five years, highly multiplexed, single-cell gene expression has been used increasingly to interrogate complex biological systems. These studies demonstrate that significant heterogeneity underlies cell populations previously thought to be relatively uniform, including early embryonic stages (Guo et al., 2010), neuronal populations (Citri et al., 2012), pluripotent stem cells (Narsinh et al., 2011a), and tumors (Powell et al., 2012). Comparatively little work, however, has been

Conclusions

Quantitative, high throughput qPCR is a powerful technology for identifying gene signatures and unique cell subsets associated with immunological states. As we show here, rigorous optimization and qualification of the technology can provide a wealth of information at either the many-cell level (pooled-cell array) or the single-cell level with similar sensitivity and accuracy.

The following are the supplementary data related to this article.

Acknowledgments

The authors would like to acknowledge the following individuals and contributions: Margaret Beddall, Stephen P. Perfetto, David Ambrozak, and Richard Nguyen (for cell processing, flow cytometry, and cell sorting); Ken Livak, Alain Mir, Andy May, Candida Brown, John Lynch, and Gajus Worthington (for discussion and advice on gene expression assays); the Nonhuman Primate Core of the Vaccine Research Center (for obtaining and preparing Rhesus macaque samples); Christopher Fletez-Brant for

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