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Genome Res. 14:1938-1947, 2004 ©2004 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/04 $5.00 Methods Quantification of Multiple Gene Expression in Individual CellsINSERM U591, Institut Necker, Paris, 75015 France
Quantitative gene expression analysis aims to define the gene expression patterns determining cell behavior. So far, these assessments can only be performed at the population level. Therefore, they determine the average gene expression within a population, overlooking possible cell-to-cell heterogeneity that could lead to different cell behaviors/cell fates. Understanding individual cell behavior requires multiple gene expression analyses of single cells, and may be fundamental for the understanding of all types of biological events and/or differentiation processes. We here describe a new reverse transcription-polymerase chain reaction (RT-PCR) approach allowing the simultaneous quantification of the expression of 20 genes in the same single cell. This method has broad application, in different species and any type of gene combination. RT efficiency is evaluated. Uniform and maximized amplification conditions for all genes are provided. Abundance relationships are maintained, allowing the precise quantification of the absolute number of mRNA molecules per cell, ranging from 2 to 1.28x109 for each individual gene. We evaluated the impact of this approach on functional genetic read-outs by studying an apparently homogeneous population (monoclonal T cells recovered 4 d after antigen stimulation), using either this method or conventional real-time RT-PCR. Single-cell studies revealed considerable cell-to-cell variation: All T cells did not express all individual genes. Gene coexpression patterns were very heterogeneous. mRNA copy numbers varied between different transcripts and in different cells. As a consequence, this single-cell assay introduces new and fundamental information regarding functional genomic read-outs. By comparison, we also show that conventional quantitative assays determining population averages supply insufficient information, and may even be highly misleading.
Functional genomic analysis is fundamental for understanding how genomic expression profiles influence cell fate. Such studies are usually performed by using either micro-arrays or a real-time quantitative reverse transcription polymerase chain reaction (RT-PCR). These methodologies can determine multiple gene expression, but have a major limitation. They only allow studies at the population level and thus only determine average gene expression. They cannot evaluate variations of gene expression between individual cells. However, in many types of biological events, individual cells within apparently homogeneous populations have different fates. It is likely that these different fates are conditioned by different patterns of gene expression. Because the events occurring in each individual cell are unknown, current methods may fail to identify the gene expression balance that ultimately determines cell behavior. This latter information requires multiple gene expression analysis of single cells, which may be a fundamental step for the understanding of all types of biological events and/or differentiation processes.
Multiple analysis of gene expression at the single-cell level requires major technological advances. Most techniques are qualitative and only allow studies of the expression of a few genes (Phillips and Lipski 2000
In principle, there are no sensitivity limitations for single-cell gene expression analysis. Single-cell methods can detect genomic DNA, that is, two gene copies, when two successive PCR amplifications of the same gene are performed (Loffert et al. 1996
Further difficulties are involved in attempting to quantify gene expression in single cells. Such quantification would require the demonstration of the maintenance of abundance relationships between multiple genes and throughout multiple reactions: from mRNA to cDNA, and throughout two successive PCR amplifications. The template switching required by two-step amplifications may introduce potential bias (Phillips and Lipski 2000 Here we describe a new method in which all previous limitations have been overcome since the expression of 20 different genes can be quantified simultaneously in each cell. We further demonstrate that this powerful technique imparts fundamental new information on cell behavior. In contrast, we also show that gene expression studies performed at the population level do not impart sufficient information and may even be highly misleading.
General Aspects of Quantitative Single-Cell Multiplex RT-PCR Sorted cells are lysed and the mRNA is retrotranscribed using specific 3' primers. A first PCR follows, where both 3' and 5' primers for all 20 different genes are present in the same reaction (Fig. 1). The products of this first amplification are next split into individual wells where a second seminested real-time PCR amplifies each individual gene separately (Fig. 1). To quantify the number of mRNA copies of different genes, the cycle threshold (CT) value obtained for each different gene product is then compared with a known quantified RNA standard that followed the same rules of retrotranscription and amplification of the tested samples. This comparison allows a precise determination of mRNA copy numbers of different genes from a single individual cell.
The feasibility of this method is strictly dependent on multiple parameters: a precise experimental strategy, which includes the use of specific reverse transcription (see Discussion), the use of precise rules for primer design, and particular amplification conditions (see Methods).
Validation of Primer Design Strategy
Competition A key feature of our method is the first PCR reaction, where all 40 primers amplifying different cDNAs are present and the 20 cDNA types are amplified simultaneously. This is required for assessment of multiple gene expressions in the same cell. However, this multiple amplification may result in PCR inhibition and/or reduced PCR efficiency that may invalidate the data (Phillips and Lipski 2000  ; Walter et al. 2000). To evaluate competition, the same amount of IEL cDNA was amplified on the first PCR round: either separately for each individual gene, or in combination with all other genes. Next, the PCR products generated in these two conditions were amplified on a second quantitative PCR. As shown in Figure 2C,D, the relative levels of expression of each gene were the same when the gene was amplified separately or in combination.
We conclude that our method provides a variety of PCR amplifications with similar efficiencies. Moreover, the 20 primer combinations of the first PCR can be associated in the same reaction, showing that our methodology prevents competition between different amplifications, a major handicap of previous methods for multiple simultaneous amplifications (Phillips and Lipski 2000
Broad Spectra Application
We conclude that by using the primer/amplicon selection strategy and the amplification conditions described in the Methods section, this methodology can be applied to the simultaneous quantification of any 20 mRNA combinations. This validates the basic principles of the method for multiparameter analysis and provides proof of its broad spectrum of applications.
From Population Studies to Single-Cell Studies
We tested for bias introduced by the template switching between the first and second PCRs using several approaches. First, we studied the amplification of a synthesized double-stranded template, corresponding to the mouse Gzma sequence we amplified in our PCRs (Fig. 4A). Because the molecular weight of this template was known, we could calculate the absolute number of DNA molecules that was present in each reaction. In this way, we could study possible artifacts of two-step reactions at both high and low copy numbers of starting material. Decreasing concentrations of this template, by a factor of 16, from 1.28x109 to four molecules, were amplified in single or double-step amplification (Fig. 4A). Upon a single amplification, the linear regression curve of this standard had a high correlation coefficient (r2 = 0.999). These results show that we can assess a vast range of template copy numbers while maintaining linearity. Next, we studied the amplification of the same double-stranded template using a two-step amplification. The number of amplification cycles in the first PCR ranged from five to 30. When the first PCR was 15 cycles, high correlation coefficients (r2 = 0.999) were maintained in the second PCR. Furthermore, using this 15-cycle preamplification, linearity was maintained at both high and low template concentrations (Fig. 4A). We tested these same parameters for other synthesized cDNA sequences, namely mouse Gzmb and Prf-1 and human CD3-
These results show that when a 15-cycle amplification is used in the first PCR, we can exclude the existence of bias or randomness introduced by template switching at template copy numbers from 1.28 x 109 to 4 molecules. It must be noted that at higher or lower amplification cycles on the first PCR, the second PCR does not follow the same rules of linearity (data not shown). When the first PCR has less than 15 cycles, low template concentrations were not amplified efficiently in the second PCR, as tube-to-tube variability was observed. Conversely, when the first PCR had more than 15 cycles we observed saturation when high template concentrations were used. We next investigated whether preamplification was biasing quantitative estimates in cDNA extracted from normal cells. We first analyzed bias on highly expressed genes by studying mouse 28S (Mrp-S21) mRNA. We used serial dilutions of cDNA that was amplified in a double-round PCR (the first PCR was 15 cycles). We show that the correlation coefficient was high even when this highly expressed gene was tested, and high amounts of total cDNA were used (Fig. 4C). These results demonstrate that our double-round PCR conditions maintained linearity even when high template amounts were amplified.
We next analyzed possible bias in the amplification of rare messages. We studied tube-to-tube variability in mouse Hprt-1 expression, considered a low-expressed gene (Pannetier et al. 1993 We next investigated whether we could reproducibly amplify two gene copies, by amplifying genomic DNA from individual cells. For that purpose, single cells were sorted, lysed, and processed for DNA extraction. In these conditions, mRNA is degraded and only DNA can be amplified. We used primer combinations spanning a small intron. In these conditions, amplicons generated after DNA amplification were only slightly longer than those generated when cDNA was amplified, ensuring that DNA and cDNA amplification had similar efficiency. We show that even with a two-copy template in the first PCR round, all samples amplified in the second PCR had the same CT (Fig. 4E), excluding quantitative randomness effects and demonstrating the high sensitivity of our amplification procedure. Finally, we tested the relative contribution of nonspecific signaling to our PCR read-outs. Indeed, SYBR Green incorporation in primers dimers could induce some type of background amplification. To evaluate this possibility, we studied several expresser and nonexpresser single cells for a particular gene product. As expected, some SYBR Green accumulation was sometimes detected in negative cells, but exponential accumulation (as found in positive cells) was never observed (Fig. 5A). Because CT values are evaluated in the linear phase of SYBR Green accumulation, CT determination in negative samples was not significantly different from samples where template was not included (Fig. 5B). These results demonstrate that negative cells did not originate significant background in our analysis.
In summary, these results demonstrate that our method of amplification preserves the initial representation of rare genes, simultaneously excluding excessive amplification of highly abundant gene copies and therefore allowing precise quantification assessments of copy numbers ranging from 1.28x109 to 2 molecules.
Maintenance of Abundance Relationships When the three sequences were all mixed at the 1/1 ratio, all PCRs had the same CT (Fig. 6, upper left). When the different sequences were mixed at 1/1, 1/64, or 1/4096 ratios, differences of CT values between different dilutions of different genes reflected initial dilutions, that is, six cycles for a 64-fold difference and 12 cycles for a 4096-fold difference. This occurred for all genes, tested in all types of ratio combinations (Fig. 6). Therefore, despite very different initial template proportions, our PCR procedure provided a measure (test ratios) that was faithful to the original template proportions. These data demonstrate that the maintenance of abundance relationships is guaranteed even on a large dilution range of the target template in a sample.
Reverse Transcription To ensure maximal efficiency in capturing mRNA molecules present in individual cells, we used specific reverse transcription (RT), and the 5' extreme of the amplified gene fragments of the first PCR was designed to be located between 300 and 400 bp from the 3' RT origin (see Discussion). To validate our approach, we assessed the efficiency of the RT in these conditions. RNA fragments from different genes and respective complement cDNA sequences were produced and purified. We compared the direct amplification of a precise number of cDNA molecules with the amplification of the same number of synthesized RNA copies after their reverse transcription. We found that both cDNA and reverse-transcribed RNA template were amplified with similar efficiency (Fig. 7). This occurred when RNA templates coding for different genes were tested (Fig. 7A), and such efficient RT was detected at all RNA concentrations studied (Fig. 7B). These results demonstrate that we maintain copy numbers in the RNA to cDNA transition, and that this RT approach is highly efficient.
Population Versus Single-Cell Studies: The Impact of Single-Cell Analysis in the Evaluation of Functional Genetic Profiles To compare the gene expression profiles obtained by single-cell analysis to those evaluated in bulk populations, we studied the same T-cell population using both methods. Mouse monoclonal CD8 T cells, 4 d after in vivo antigen stimulation (Tanchot et al. 1998 ; Veiga-Fernandes et al. 2000) were sorted either as 20 cells/well for population studies or as 20 single cells. All samples were retrotranscribed and amplified as described above. For simplicity, only four genes are shown.
Real-time PCR at a population level (Fig. 8A) showed a hierarchy of gene expression: Gzmb > Tgf-
Results of single-cell studies revealed a very different scenario (Fig. 8B). First, most CD8 T cells differentiate into Tgf-  -expressing cells (17/20), whereas Ifn- and Gzmb expression was quite rare (4-6/20 cells). Moreover, Gzmb and Prf-1 were usually expressed by different cells. These findings indicate that this CD8 population should be virtually devoid of killer activity, because individual cells do not coexpress the two genes required to kill target cells. We conclude that single-cell multiparameter studies of gene expression reveal fundamental new insights into cell behavior. Conversely, the studies performed in bulk populations may be highly misleading.
The analysis of heterogeneity within cellular populations has a major impact on cell biology. The final aim of this approach is to reveal the gene expression patterns that ultimately characterize and define the fate of individual cells. Different cell fates likely rely on both qualitative and quantitative differences of gene expression that affect multiple genes simultaneously, but tests allowing the assessment of these features in individual cells are lacking. Here we describe a single-cell multiplex RT-PCR that allows simultaneous quantitative analysis and comparison of the expression of 20 genes in each individual cell. We demonstrate that this method substantially improves functional genomic read-outs. Conversely, quantitative studies performed at the population level may be very misleading. It is not surprising that single-cell and population studies do not overlap. Quantitative studies at the population level only determine average rates of gene expression. They do not evaluate the frequency of expressing cells. The same mRNA amount can correspond to rare cells expressing high mRNA levels or to a much higher cell number expressing lower mRNA levels. These two situations may have very different biological meanings. The impact of this potential bias has probably been underestimated, as the range of identical mRNA molecules each cell could express was not known. Here, for the first time, we were able to quantify messages at the single-cell level and found that expression of a single gene in individual cells could vary by 10,000 fold (data not shown). This extensive variation seriously undermines the interpretation of any quantitative studies that are not accompanied by frequency determinations. Indeed, in studies performed at the population level, very rare events (even at 10-4 frequencies) may score similarly to frequent events. Therefore, in population readouts, events that are not representative of a global population behavior may appear as very significant events. This bias is evident in the study we include, where Gzmb expression appears to be a dominant function in the studied population, whereas studies at the single-cell level reveal that only a few cells expressed this mRNA. Another major potential impact of single-cell studies is the possibility to determine gene coexpression. We were surprised to verify that different genes (Prf-1 and Gzmb), which need to be coexpressed for CD8 cytotoxicity, could segregate into different individual cells. This finding emphasizes that studies at the population level are not sufficient to identify cell properties. Rather, coexpression studies at the single-cell level are fundamental for the interpretation of functional read-outs. Concerning the present methodology, quantification of multiple gene expression in the same cell is only possible if several rules are followed simultaneously: the same efficiency of PCRs; the absence of primer and amplicon competition during the first PCR round, and an efficient RT to PCR transition requiring the use of specific RT. Moreover, all of these requirements are strictly interdependent. Comparison of the expression of different genes between themselves requires that all individual PCRs have the same efficiency. This aim alone is not difficult to achieve. Primer combinations claiming to amplify multiple genes with similar efficiency are beginning to be available commercially. However, in these commercial kits (where controls for many important parameters are lacking), each individual gene must be studied separately, using one independent sample for each PCR. The ability to compare the expression of different genes between themselves and attribute coexpression of 20 genes to the same cell requires that all 20 different PCRs reactions be performed in the same tube and in the same PCR round. This imposes the requirement that, besides similar efficiency, the 40 primers and 20 amplicons of the first PCR also do not compete with one another.
It was claimed that analyses of more than five genes in one cell would necessarily lead to nonspecific inhibitions of amplification, which affect amplifications randomly (Walter et al. 2000 The constraints on primer selection impose another strategy: the use of specific reverse transcription which targets the mRNA sequence that is retrotranscribed and subsequently amplified. This is achieved by designing the 5' extreme of the amplified gene fragments to be located between 300 and 400 bp from the 3' RT origin. This strategy is both necessary and optimal for the maintenance of abundance relationships in the mRNA to cDNA transition.
The use of specific RT rather than poly-AAA reverse transcription is necessary to prevent 3' bias that would modify abundance relationships in the transition from mRNA to cDNA. It is well known that poly-AAA reverse transcription preferentially transcribes mRNA fragments localized in the 3' termini. This bias should be a major problem in our type of approach. Indeed, to ensure similar efficiency of amplification and prevent competition (essential aspects of our methodology), primers must be selected throughout the gene and not at the 3' end only. Our strategy thus improves on previous methods used to achieve readings of gene expression in small samples such as modified poly-AAA reverse transcription methods (Dixon et al. 1998
Because all efforts to achieve readings of gene expression in small samples were directed to increase cDNA yields, and thus are incompatible with gene coexpression studies, we used an alternative approach to measure the minute mRNAs recovered from one cell. Instead of generating very high amounts of cDNA, we exponentially amplified the low cDNA yields we obtained from each individual cell, and used this exponential amplification to quantify transcripts. It is usually assumed that this approach can bias the information content of the sample, as theoretical mathematic analysis showed that hybridization kinetics during thermal cycling could cause both sequence- and copy number-dependent bias (Peccoud and Jacob 1996 This technique brings new perspectives to the understanding of biological processes. Most differentiation events have been studied on the basis of a population phenotype which does not necessarily reflect heterogeneity among the population. Conversely, single-cell analysis will allow further dissecting of cell decisions that ultimately influence a population phenotype. This technical approach also has a broader interest for diagnosis of minute samples. Indeed, in several pathologies and infections, only very small tissue samples can be obtained for diagnosis or continuous follow-up of disease progression. This method overcomes all restrictions in sample size by allowing the quantitative assessment of multiple different parameters from just a few cells. Indeed, we are presently using this method to characterize HIV-specific CD8 T cells that were divided into eight subtypes by cell surface markers, each subtype representing less than 0.1% of Peripheral Blood Lymphocyte (PBL). This approach allows the determination of 20 cell functions simultaneously, even in such small sample sizes. Preliminary evidence suggests that we will be able to quantify the expression of up to 40 genes/cell. In conclusion, we here describe a method of quantitative multiplex PCR that can be applied to an extended number of genes expressed in a single cell. We also show that the ability to quantify multiple gene usage by individual cells provides fundamental insights into cell physiology and functional genomics.
FACS Sorting Cells were sorted using a FACS Vantage equipped with an automatic cell deposition unit (Becton Dickinson). Cells were collected in individual PCR tubes containing 5 µL of PBS-DEPC 0.1%, and stored at -80°C.
Reverse Transcription
Primer Design Our primers were manually designed in order to avoid genomic amplification, by choosing 3' and 5' primers that hybridize with different exons. To achieve similar amplification efficiencies, we designed primers of 20 bp size targeting nonrepetitive sequences, with similar melting temperatures (Tm) calculated according to the formula (Tm = 64.9°C + 41°C x (number of G's and C's in the primer - 16.4)/number of bp of the primer) and amplifying fragments of a similar size. The composition of amplified fragments (50.61% ± 5.01% of GC content) was similar, which is required to obtain uniform amplification efficiency for all different mRNAs. To prevent nonspecific amplification, all individual primer sequences were used in a BLAST search (http://www.ncbi.nlm.nih.gov/genome/seq/MmBlast.html) of the mouse genome in order to check potential nonspecific hybridization of primers against other genes besides the targeted gene of interest. No significant hybridization was found with other genes.
To prevent primer competition, we selected primers and potential amplicons that did not cross-hybridize. Primer compatibility and size of the amplified fragments were assessed using the freely available software Amplify 1.2 (Engels 1993
First PCR Amplification
Real-Time Quantitative PCR
Synthesis of Double-Strand DNA Sequences
Molecular Cloning and In Vitro Transcription
We thank O. Bernard for molecular cloning, C. Cordier and G. Megret for cell sorting, B. Schaeffer and A. Le Campion for statistics, and J. Lauber, A. Freitas, A. Eaton, F. Lambolez, E. Treiner, U. Walter, O. Azogui, and P. Vieira for helpful discussions. Supported by Association pour la Recherche sur le Cancer, Ligue pour la Recherche sur le Cancer, Fondation pour la Recherche Medical (H.V.-F.) and Science Technology Foundation (Portugal) (A.P., M.M., and H.V.-F.). This method is covered by a patent deposed by the Institut Necker (patent number 0208593).
1 Corresponding author. E-MAIL rocha{at}necker.fr; FAX 33-1-4061-5580.  [Supplemental material is available online at www.genome.org.] Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2890204.
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http://www.ensembl.org; Ensembl Project homepage. http://engels.genetics.wisc.edu/amplify; Amplify 1.2 Software homepage. http://www.ncbi.nlm.nih.gov/genome/seq/MmBlast.html; Mouse Genome BLAST homepage.
Received July 12, 2004; accepted in revised format July 27, 2004.  CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati What's this?
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; IL15r, genes amplified in multiplex (solid line) or alone (dashed line). (D) Comparison of threshold cycle mean values (CT) obtained for each different gene amplified in multiplex (black bars) or separately (gray bars). No significant differences were observed between the two amplification conditions for each different gene (t-test, P > 0.1).
) or by a two-step PCR of 15 preamplification cycles (
). Decreasing template concentrations by a factor of 16 were used. Means ± SD of quadruplicates are included, although SDs frequently overlap with the symbols as we found little or no variation. Linear regression curves of these amplified standards are indicated and have similar high correlation coefficients (r2 = 0.999). Similar results were obtained with three different types of synthesized cDNA. (B) Four molecules of a double-stranded synthesized Gzma sequence were amplified in triplicate using 15 cycles on the first PCR amplification followed by a second real-time PCR. PCR accumulation curves are shown. (C) A bulk cDNA population (7 x 105 to 680 fg) was used to amplify 28S (Mrp-S21) gene in quadruplicate by a two-step PCR of 15 preamplification cycles. Decreasing template concentrations by a factor of 4 were used. Mean values and standard deviations of quadruplicates are included, but SDs overlap with the symbols, as we found little or no variation. The linear regression curve of this amplified standard is indicated (r2 = 0.9978). (D) Single cells were retrotranscribed and preamplified (15 cycles) for Hprt-1. A second quantitative PCR was used for the evaluation of the relative expression level (CT) of Hprt-1 on each individual cell. Quadruplicates of amplification are shown. (E) Single individual cells were sorted, and treated for genomic DNA amplification (see Methods). The Gzma gene was next amplified in a seminested PCR (15 cycles of preamplification). PCR accumulation curves are shown for four individual cells. Data are from one of three independent experiments.
