Benchmarking the Multiplex Circulating and Cellular mirna Assays using Abcam s Firefly particle technology

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1 Benchmarking the Multiplex Circulating and Cellular mirna Assays using Abcam s Firefly particle technology Technical note Abstract The full utility of mirna profiling has yet to be realized largely due to the limited throughput of existing validation technologies, errors introduced during sample purification and insufficient methods used for analytical normalization and quality control. We introduce Abcam s Firefly particle technology with assays that enable researchers to rapidly and cost-effectively evaluate mirna biomarker signatures on standard flow cytometers. In order to accommodate studies in solid tissues and circulating biofluids, we developed the Multiplex Cellular mirna Assay and Multiplex Circulating mirna Assay, respectively. We demonstrate that these assays provide mirna profiles that correlate well with currently available mirna profiling technology and show excellent reproducibility even with diminishing RNA input. We have integrated bioinformatics tools into our assays to effectively interpret experimental data, help identify candidate signatures and produce publication-quality figures. Background Although single-marker diagnostic indicators provide the most straightforward assessment of disease, they are rarely able to provide a reliable and complete biological snapshot necessary to characterize diseases (Pepe et al., 2001). Multiplexed approaches that test for many markers simultaneously have been shown to be more sensitive and accurate in detecting and staging complex diseases like cancer, even surpassing the performance of today s current practice (Cao et al., 2011; Shimizu et al., 2012). While most multiplexed biomarker signatures today are based on genes or proteins, there is a significant push to use micrornas (mirnas) as independent or supplemental biomarkers (Iorio & Croce, 2012). These regulatory molecules have been implicated in diabetes, Alzheimer s disease, heart disease, and cancers including leukemia, brain, colon, liver and lung cancers (Calin et al., 2005, Cummins & Velculescu, 2006; Esquela-Kerscher & Slack, 2006; Jay et al., 2007; Volinia et al., 2006; Wu et al., 2007). Biomarker signatures based on mirnas can be derived from solid tissues or circulating biofluids. Tissue-based samples yield an abundance of RNA (typically 1 10 μg), which is suitable for most mirna profiling platforms. Conversely, RNA obtained from biofluids is quite scarce, requiring platforms with enhanced sensitivity. Circulating mirnas represent ideal biomarkers for several reasons: (1) mirnas are stable in the bloodstream and in collected samples (Chen et al., 2008; Grasedieck et al., 2012), (2) blood is easily accessible, (3) circulating tissue-specific mirnas can be measured to assess tissue damage (Laterza et al., 2009) and (4) there is significant evidence that various cancers have distinct mirna profiles (Mitchell et al., 2008). Most multiplexing technologies for mirna detection are designed for either an in-depth analysis of a few samples (e.g. sequencing) or a few targets over many samples (e.g. manual qrt-pcr). However, because the number of samples required to make statisticallysound observations in validation studies increases dramatically with biomarker number (Olkin & Finn, 1995), appropriately designed validation studies require profiling of mirnas across hundreds or even thousands of samples. Several experimental design and pre-analytical factors can lead to error when performing mirna studies, including sample collection, RNA purification, data normalization and identification of confounding factors

2 (Kroh et al., 2010). Specifically, it is well known that methods currently used for mirna purification can have profound effects on mirna profile, yield and throughput, and all create the potential for user error. The sequencespecific bias introduced during RNA purification has already led to erroneous conclusions in at least one important mirna study (Kim et al., 2012). Ideally, mirnas would be detected directly from clinical samples with no RNA purification. Another critical consideration in the development of a biomarker signature is the use of robust normalization and quality control methods. Lack of attention to normalization can lead to false positives, false negatives and biased assay results. Because single-target normalization has been shown to be problematic (Vandesompele et al., 2002), a normalization method using multiple endogenous features and robust analytical methods could greatly increase concordance of mirna profiling across studies. Overview of Firefly particle technology The Firefly particle technology is designed to meet the needs of mirna biomarker validation, providing a combination of performance, multiplexing, throughput, cost and ease of use not available from existing technologies. The platform offers: Use of hydrogel particles, which provides 75x increase in hybridization sensitivity and two extra logs of dynamic range when compared with the leading bead-based system Ability to detect mirnas directly from crude sample digests Readout on existing instruments without the purchase of new equipment Parallel processing of 96 samples Multiplexed detection of up to 70 targets (68 mirna and 2 internal controls) in every sample Streamlined analysis with a full-service software suite To accommodate the large sample numbers required for appropriately-powered validation studies, Firefly particle technology enables the high-throughput analysis of hundreds of samples over mirna targets (Figure 1A). Furthermore, in order to meet the range of input requirements seen across the biomarker development field, we have developed the Multiplex Cellular mirna Assay to be used when RNA is abundant, and the Multiplex Circulating mirna Assay for highly-sensitive mirna profiling in biofluids or when extracted RNA samples are limiting (Figure 1B). A B # Samples 1, Firefly technology RT- Microarrays Required Input ,000 10, ,000 #mirnas per Sample Total RNA (ng) Figure 1: Position of Firefly particle technology in the mirna profiling landscape. (a) Capability of different mirna profiling technologies at handling multiple mirnas across numerous samples, (b) required input RNA amounts suggested for major platforms.

3 The flexibility of Firefly particle technology allows complete customization and analysis of a broad range of small RNAs, whether annotated in mirbase or not. Importantly, the assays are not susceptible to PCR inhibitors, and have been shown to work well with modified RNAs, including small interfering RNAs (sirnas) and plant mirnas. The Multiplex Circulating mirna Assay is intended for applications where RNA input is extremely scarce, as is the case with circulating biofluids. This assay is optimized for use in crude digests of biofluids including plasma, serum, exosomes and milk. This allows researchers to completely eliminate workflow challenges and target biases seen in RNA purification protocols. The Multiplex Cellular mirna Assay is designed for simplicity and robustness. With only three primary steps, the workflow is extremely streamlined; it is fully automatable and does not require reverse transcription, amplification or other complicated molecular biology steps typical of library preparation for next generation sequencing. This assay is ideal when the starting material is derived from tissue, cell culture or any other medium where RNA is fairly abundant. Cross-platform concordance mirna profiling platforms exhibit platform-specific effects with respect to sensitivity, specificity and sequence-dependent detection efficiency (Mestdagh et al., 2014). While direct comparison of platforms across mirnas in a single sample rarely provides strongly correlating data, it is more important that the differential expression of mirnas across samples is accurately captured. To assess agreement with currently available mirna profiling technology, we compared the mirna profiles obtained using Firefly particle technology with those obtained using sequencing (Illumina MiSeq) and (TaqMan Low Density Array, TLDA, Figure 2A). As mirnas are differentially expressed across tissue types, we used total RNA isolated from three diverse human tissues, profiling mirnas known to vary greatly in expression across them. Commercially available total RNA isolated from brain, lung and liver were used as an input to the assays. Multiplex Circulating and Cellular mirna Assays were performed in-house, while sequencing was performed by Genewiz and analysis was performed by the Shannon McCormack Molecular Diagnostics Laboratory at Dana Farber Cancer Institute. Input amounts for the Multiplex Cellular mirna Assay, Multiplex Circulating mirna Assay, and sequencing assays were 1 µg, 5 ng, 60 ng and 1 µg, respectively. For comparison of mirna profiles, signals were first transformed to linear space. For each platform, signals were geometric-mean normalized across targets, then for each target across the three samples. This approach is used to account for differences in RNA input amounts and overall analytical signal magnitude. Using these normalized data, cross-platform comparison was qualified using Pearson correlation (Figure 2B). Overall, platforms showed good concordance yielding correlations above 88% in all cases, with the Multiplex mirna Assays giving correlations consistently above 90%. Interestingly, while the Multiplex Cellular and Circulating mirna Assays showed the highest correlation (Figure 2C), and sequencing showed the lowest across this target set (Figure 2D). These data suggest that for differential expression analysis, the Multiplex Cellular and Circulating mirna Assays compare very well with both sequencing and -based approaches. Similar results were obtained with microarray-based approaches (data not shown). Additionally, the high level of agreement between the Multiplex Cellular and Circulating mirna Assays demonstrates excellent intra-platform concordance.

4 A Value BRAIN LUNG LIVER Cross-platform correlation vs. B C D vs. Cellular Assay Cellular Assay 100% Circulating Assay 95% 100% Circulating Assay 92% 90% 100% Brain Lung Liver Brain Lung Liver 90% 92% 88% 100% Figure 2. Cross-platform comparison of mirna profiling assays. (a) Heatmap of normalized expressions across Multiplex Cellular mirna Assay, Multiplex Circulating mirna Assay, TaqMan TLDA and Illumina MiSeq assays. (b) Pearson correlations between normalized platform expression profiles, averaged across three sample types. (c) Correlation plots of Multiplex Cellular mirna Assay versus Multiplex Circulating mirna Assay. (d) Correlation plots showing sequencing versus. Assay reproducibility and sensitivity For an assay to be suitable for biomarker validation and potential clinical use, it must be robust and reproducible. Specifically, it is important to understand the detectable fold-change of a target if differential expression is to be used as a decision criterion. To assess assay reproducibility, we performed replicate analysis on a single sample and quantified the number of targets measured within a given fold change. This approach is consistent with that used in the mirna quality control study by Mestdagh et al. (2014). We used 5 ng each of total RNA from brain, lung and liver as input for duplicate analysis with the Multiplex Cellular mirna Assay, Multiplex Circulating mirna Assay and (TLDA). Data were analyzed for expression differences across replicates; the fraction of mirna targets showing smaller deviation than a given fold-difference were plotted against that fold-difference (Figure 3A). Platforms with very good reproducibility yield a cumulative target fraction that quickly approaches a value of 1 (ideal curve shown). Overall, the three platforms show good reproducibility in this study. While 90% of the targets assayed show less than 0.5-fold difference across duplicates for, the same number show less than 0.4-fold for the Multiplex and less than 0.2-fold for the Multiplex. These data suggest that the Multiplex is capable of detecting fairly minute differential expression of most targets. The reproducibility of a platform across input amounts demonstrates assay robustness and indicates dynamic range. Using a 1:1:1 mixture of brain, lung and liver total RNA, we assessed intra-platform reproducibility across a broad range of input amounts. In all cases, we observed Pearson correlations >94% for all input amounts (Figure 3B).

5 This experiment also demonstrates the sensitivity of the Firefly Circulating mirna Assay. While most microarray platforms use micrograms of RNA, and most platforms require nanogram inputs, we show the robust detect of mirnas with as little as 39 picograms. The ability to profile multiple mirnas in a single well without splitting the sample makes Firefly particle technology ideal for low-input assays. A 1 Replicate fold-difference analysis Cumulative Fraction Ideal Curve Relative Expression Difference (log2) B Correlation with input amount 1000 ng 333 ng 111 ng 37 ng 1000 ng 100% 333 ng 99% 100% 111 ng 97% 99% 100% 37 ng 94% 97% 99% 100% 2500 pg 625 pg 156 pg 39 pg 2500 pg 100% 625 pg 100% 100% 156 pg 98% 99% 100% 39 pg 96% 97% 95% 100% Figure 3. Reproducibility of Multiplex Circulating and Cellular mirna Assays. (a) Fold-change analysis for duplicate measurements of brain, lung, liver total RNA mix. (b) Pearson correlation values for the Multiplex Cellular mirna Assay and Multiplex Circulating mirna Assay across total RNA input amounts.

6 Integrated analysis capabilities Proper data interpretation is critical to obtain reliable results. The Firefly Analysis Workbench is a software suite that provides an efficient means of visualization, normalization and data analysis behind a simple point-and-click user interface. Our software includes many advanced features, comparable to packages such as Exiqon Genex, Affymetrix Expression Console or Agilent GeneSpring. In addition to standard visualization features including barplots and scatter plots, the Firefly Analysis Workbench allows for heat map analysis with expression in linear, log or fold-change space, along with hierarchical clustering of samples and/or mirnas (Figure 4). Data preparation for plotting includes blank and negative control signal subtraction (i.e. background signal), as well as normalization either by hand-selected probes or by probes automatically selected according to several algorithms, including GeNorm. Samples can be assigned experimental variables (e.g. disease state, tissue type, etc.) and grouped for subsequent analysis of variance. Probes that are differentially expressed between groups are ranked by their statistical significance and displayed on a volcano plot, which provides a rapid indicator of the mirnas that show sizeable fold-change with high significance (Figure 4, right). Other displays include scatter plots comparing signatures across samples, and box and whisker plots showing relative mirna expression across groups (Figure 4, right). Variable Analysis Plate Preview Heatmap View Sample/Target View Barplot View Sample Correlation Figure 4: Software features included in the Firefly Analysis Workbench. From the main screen (top left), the software allows visualization by heatmap, barplot and sample correlation. Statistical analysis includes ANOVA with p-value calculation and box-and-whisker plots.

7 The Analysis Workbench can be used for the interpretation of one or many experiments. Multiple experimental plates can be loaded simultaneously, and any combination of samples can be mixed and matched into a single experimental dataset, even if they contain dissimilar targets. Overall, the software is well-equipped to handle the experimental scope of large mirna validation studies. Conclusion Firefly particle technology enables the multiplexed, highthroughput validation of mirna biomarkers with readout on standard flow cytometers. The Multiplex Cellular mirna Assay and Multiplex Circulating mirna Assay span the entire range of mirna profiling needs. These assays have strong correlation to other mirna profiling platforms with comparable or improved reproducibility in comparison to. Our bioinformatics tools allow for streamlined data analysis and generation of publicationready figures in minutes. When used together, the Multiplex Circulating and Cellular mirna Assays and Firefly Analysis Workbench provide a fast and easy-touse platform for mirna signature validation. Cited References Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R, Sever NI, Fabbri M, Iuliano R, Palumbo T, Pichiorri F, Roldo C, Garzon R, Sevignani C, Rassenti L, Alder H, Volinia S, Liu C, Kipps TJ, Negrini M, Croce CM (2005). A microrna signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 353, Cao DL, Ye DW, Zhang H-L, Zhu Y, Wang Y-X, Yao X-D (2011). A multiplex model of combining gene-based, protein-based, and metabolite-based with positive and negative markers in urine for the early diagnosis of prostate cancer. Prostate 71, Grasedieck S, Schöler N, Bommer M, Niess JH, Tumani H, Rouhi A, Bloehdorn J, Liebisch P, Mertens D, Döhner H, Buske C, Langer C, Kuchenbauer F (2012). Impact of serum storage conditions on microrna stability. Leukemia 26, Iorio MV, Croce CM (2012). MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4, Jay C, Nemunaitis J, Chen P, Fulgham P, Tong AW (2007). mirna profiling for diagnosis and prognosis of human cancer. DNA Cell Biol 26, Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, Zhang Y, Chen J, Guo X, Li Q, Li X, Wang W, Zhang Y, Wang J, Jiang X, Xiang Y, Xu C, Zheng P, Zhang J, Li R, Zhang H, Shang X, Gong T, Ning G, Wang J, Zen K, Zhang J, Zhang C-Y (2008). Characterization of micrornas in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18, Cummins JM, Velculescu VE (2006). Implications of micro-rna profiling for cancer diagnosis. Oncogene 25, Esquela-Kerscher A, Slack FJ (2006). Oncomirs - micrornas with a role in cancer. Nat Rev Cancer 6, Kim Y-K, Yeo J, Kim B, Ha M, Kim VN (2012). Short structured RNAs with low GC content are selectively lost during extraction from a small number of cells. Mol Cell 46, Kroh EM, Parkin RK, Mitchell PS, Tewari M (2010). Analysis of circulating microrna biomarkers in plasma and serum using quantitative reverse transcription-pcr (qrt-pcr). Methods 50, Laterza OF, Lim L, Garrett-Engele PW, Vlasakova K, Muniappa N, Tanaka WK, Johnson JM, Sina JF, Fare TL, Sistare FD, Glaab WE (2009). Plasma micrornas as sensitive and specific biomarkers of tissue injury. Clin Chem 55,

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