- Technical Note
- Open Access
Multiplexed measurements of gene signatures in different analytes using the Nanostring nCounter™ Assay System
© Malkov et al; licensee BioMed Central Ltd. 2009
- Received: 03 September 2008
- Accepted: 09 May 2009
- Published: 09 May 2009
We assessed NanoString's nCounter™ Analysis System for its ability to quantify gene expression of forty-eight genes in a single reaction with 100 ng of total RNA or an equivalent amount of tissue lysate. In the nCounter™ System, multiplexed gene expression target levels are directly detected, without enzymatic reactions, via two sequence-specific probes. The individual mRNA is captured with one mRNA target sequence-specific capture probe that is used in a post-hybridization affinity purification procedure. The second mRNA target specific-sequence and fluorescent-labeled colored coded probe is then used in the detection with the 3-component complex separated on a surface via an applied electric field followed by imaging. We evaluated reproducibility, accuracy, concordance with quantitative RT-PCR, linearity, dynamic range, and the ability of the system to assay different inputs (matched samples of total RNA from Flash Frozen (FF) and Formalin Fixed Paraffin Embedded Tissues (FFPET), and crude tissue lysates (CTL)).
The nCounter™ Analysis System provided data equivalent to that produced by Taqman®-based assays for genes expressed within the ranges of the calibration curves (above ~0.5 mRNA copies per human cell based on an assumption of 10 pg of total RNA per cell). System response was linear over more than two orders of magnitude with typical CVs of ~6% for concentrations above 1 fM (105 molecules per mL). Profiling the industry-standard MAQC data set yielded correlation coefficients of >0.83 for intensity values and >0.99 for measured ratios. Ninety percent of nCounter™ ratio measurements were within 1.27–1.33 fold changes of the Taqman® data (0.34–0.41 in log2 scale) for FF total RNA samples.
The nCounter™ Analysis System generated robust data for multi-gene expression signatures across three different sample preparation conditions.
- Xenograft Tissue
- Xenograft Sample
- Flash Freeze
- Multiplex Measurement
- MAQC Data
Analyses of gene expression from microarrays can be used to define a specific set of sequences (signatures) relevant to a particular biological phenomenon or response . These signatures can comprise tens to hundreds of genes, a range that falls between the optimal economic and logistic space for two widely-used tools for measuring gene expression, RT-PCR and microarrays. A solution for follow-up would provide cost-effective, multiplexed measurements of gene expression for tens to hundreds of genes while producing data equivalent to that generated by microarrays and RT-PCR. This solution should also be able to analyze input materials of clinical relevance (e.g., total RNA from formalin-fixed, paraffin embedded tissues (FFPET) and crude tissue lysates (CTL)).
# of Samples
Synthetic spike-ins added?
Total # assayed
MAQC samples (Brain, UHR, and 25%:75% proportional mixes of each)
Flash frozen total RNA (4 xenografts at 4 different dosage conditions)
FFPET total RNA
List of transcripts for the nCounter™ assay
Gene list for nCounter™ probe synthesis.
Representative Transcript/Transcript ID
Taqman probe ID (if used)
Samples (see Table 1) for the assay came from two sources: 1) EBC-1 lung cancer cell line xenograft tissues treated with vehicle or varying amounts of a compound; and 2) MAQC samples obtained from Ambion (Human Brain) and Stratagene (Universal Human Reference). Proportional mixes (25%:75% and 75%:25% UHR to Brain) were created. Crude tissue lysates (CTLs) were made by homogenizing 50–100 mg of FF xenograft tissues in 1 mL of Qiagen buffer RLT http://www.Qiagen.com and snap freezing a 100 μL aliquot (1/10th of total volume). Total RNA was purified from the remaining lysate using a Trizol-based protocol http://www.invitrogen.com. We isolated RNA from FFPE EBC1 xenografts using the Ambion RecoverAll protocol http://www.ambion.com. In all samples, one or the other set of Rosetta spike-ins were added to provide a measure of ratio accuracy. Samples were blinded before assaying at Nanostring Technologies, where the samples were processed to generate raw data (i.e. counts/gene). nCounter™ assay spike-ins control mixes #3 or #4 were added at random to each of the blinded RNA samples on the day the assay was performed.
The nCounter™ System assay
We performed the nCounter™ assay using 100 ng of total RNA or 2 μL of tissue lysate per replicate. Each assay was performed in triplicate to improve precision of the measurements. Details can be found in .
Fourteen genes were assayed via a Taqman® quantitative RT-PCR protocol according to manufacturer's specifications using Applied Biosystem's High Capacity cDNA Reverse Transcription Kit (part # 4374967) and Taqman® Universal PCR Master Mix (part # 4364340). Taqman reporter probes were used (see Table 1 for a list of specific ABI assay identifiers). An aliquot of 400 ng of total RNA was reverse-transcribed and 1/80th of the reaction used for each replicate for each probe. The reaction volume for each replicate was 10 μL, with 0.5 μL of the Taqman 20× gene expression assay, 1 μL of sample, 5 μL of 2× Master Mix and 3.5 μL dH2O. All samples were assayed in quadruplicate for each probe according to Rosetta internal SOPs. Samples were run on an ABI 7900 HT system using the recommended ABI cycling protocol http://www3.appliedbiosystems.com/AB_Home/index.htm. See Table 2 for the specific Taqman® identifiers for the 14 probes used.
Eleven positive control nCounter™ spike-ins (spanning from 0.27 fM to 55 fM) were used to create the calibration curve for each nCounter™ array. Nine negative control spike-ins were used to assess the level of background (typically on the order of 10 counts). Mean of the negative controls was deducted from all other transcripts in the same assay prior to logarithmic transformation (log base 2). We used a standard linear regression model to find the least square fit of logarithm-transformed concentration on the logarithm-transformed number of molecules above background to generate the equation for the rest of the transcripts in the same assay. Each nCounter™ assay result was converted to an equivalent concentration using the assay standard curve. Use of the standard curve allows absolute measurements to be assigned to nCounter™ counts as needed.
To deduce the precision of the nCounter™ assay itself, we mean centered the data in log2 scale, resulting in a correction of approximately 1.08 fold. To achieve specified precision, NanoString recommends running each sample (by experiment) in triplicate. To mimic a typical experiment, therefore, we averaged triplicate assays for Rosetta spike-ins as well. Standard deviations of resulting mean values were used to calculate CVs.
To generate across-multiple-samples, gene-by-gene equivalency plots, both Taqman® and nCounter™ data were normalized to CUGBP1 as a reference gene for the xenograft samples. Originally four reference genes were identified from previous experiments as not varying significantly across our experimental conditions and were planned to be used as references in aggregate. However, three of the four did not reliably give signals above the lower limit of our standard curve and so were not used. This led us to deviate from generally accepted practice in which more than one reference gene is used to normalize data.
In the MAQC analysis, although the published Taqman® data were normalized to POLR2A, the nCounter™ normalization did not utilize any reference genes. This is because POLR2A was not one of the genes present in our genelist and so was unavailable as a common control. As a result of this approach, Taqman® data were normalized to mRNA amount, while nCounter™ normalization relied on the same amount of total RNA (100 ng) in each sample. This distinction is important because the MAQC study showed that UHR has 1.5-fold higher mRNA content than Brain (3% vs. 2%). To compensate for different mRNA content, 0.585 Ct, 0.46 Ct and 0.17 Ct were deducted from all genes of 0% Brain/100% UHR; 25% Brain/75% UHR; and 75% Brian/25% UHR samples, respectively.
For performance evaluations, a comparison was done for each possible pair of samples because we did not wish to artificially bias our data by arbitrarily assigning one sample as the "standard" to which other replicates would be compared. In these cases, we normalized to the mean of log intensity of the subset of genes in the corresponding sample for which measurements were above 0.27 fM in both samples of the pair. The same subset of genes was used to normalize Taqman®, using their mean Ct.
Expected and back-calculated (observed) concentrations in fmoles of Nanostring spike-in mixes 3 and 4, including %CV and %Bias.
Nanostring spike-in Mix #3
Nanostring spike-in Mix #4
Expected and observed concentrations for Rosetta spike-ins 11 and 12, including %CV and %Bias.
Rosetta spike-in #11
Intended conc, AU
Best Fit Expected
Rosetta spike-in #12
Analysis of Xenograft-derived Samples
The sample set comprised four treatment conditions with four mouse xenografts per condition for a total of 16 samples. The 16 tissue samples were split and preserved by three methods (FF, FFPET, and CTL in Qiagen buffer RLT); total RNA was isolated from FF and FFPET for RT-PCR analysis. Fourteen genes were chosen for Taqman® comparison using samples that were either vehicle treated or treated with the highest level of compound. Ten genes were expected to change either up or down, and four reference genes were expected to remain constant. The genes chosen for Taqman® and Nanostring comparisons were picked based on internal Merck criteria. The differential expression in a previous microarray study of these samples showed relatively modest fold changes (~2 fold) at the highest compound treatment level used for this study (data for other, intermediate treatment levels is not shown).
As with the MAQC data, these sample sets show generally good agreement between the two platforms. The one outlier is NARG1 (top right graph in each Figure), which was consistently discordant in all Taqman® to nCounter™ comparisons. Since the region of NARG1 assayed by the Taqman® probe is at the junction between exons 1 and 2 and the region selected for the nCounter™ probe is close to the 3' end of the transcript, the two systems may be capturing valid but different transcript behavior of this gene. It should be noted that variability of measurements in the CTL samples (as represented by the error bars) was higher than for the other two sample types.
The FF and lysate data have a high degree of correlation, suggesting minor loss of data quality by using CTLs rather than purified total RNA. Taken together, the data in Figures 1, 2 and 3 suggest that the nCounter™ assay can be used to generate data from clinical samples with degraded RNA (FFPET, see Additional File 6 for representative quality) or from lysate preparations. It should be pointed out, however, that the FFPET model used (xenograft tissue) is not a perfect match for typical clinical samples and that not all degrees of degraded RNA will be amenable to this system
Our impetus to assess the nCounter™ Analysis System was driven by its relative simplicity (that is, no need for amplification steps), its multiplexed format, and its potential to measure gene expression in samples from pre-clinical and clinical settings (e.g, fine-needle biopsies in lysate buffers, and FFPET materials). Our results confirmed the system has potential for pre-clinical and clinical measurements of multiple gene signatures in settings where the initial tissue collection would be conducive to FFPET or CTL preparations.
This platform could be used to fill an important and growing gap in drug development research. Microarray experiments routinely are used in basic research but often identify too many genes to allow higher-throughput downstream use of those signatures for screening or readouts. By allowing the cost effective and accurate measurement of expression of tens of genes from clinical samples, the nCounter™ system could facilitate translation of multi-gene expression based biomarkers into the clinic.
The authors would like to acknowledge the following individuals: Dr. Daniel Holder, Dr. Matthew Marton, Dr. Sergey Lezhnin, Dr. Jeffrey Sachs, Mr. Mark Parrish, Mr. Andrew Shoesmith, Ms. Sally Dow. These people helped in the preparation of materials, in providing useful advice and in reading the manuscript.
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