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Bona fide gene expression analysis of samples from the bovine reproductive system by microfluidic platform
Analytical Biochemistry 596 (2020) 113641
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Analytical Biochemistry journal homepage: www.elsevier.com/locate/yabio
Bona fide gene expression analysis of samples from the bovine reproductive system by microfluidic platform
T
Patricia Kubo Fontesa,∗, Anthony César Souza Castilhoa,b, Eduardo Montanari Razzaa, Marcelo Fábio Gouveia Nogueiraa,c a Laboratory of Phytomedicines, Pharmacology and Biotechnology, Department of Pharmacology, Institute of Biosciences, University of São Paulo State (Unesp), Campus of Botucatu, São Paulo, Brazil b University of Western São Paulo (Unoeste), Campus of Presidente Prudente, São Paulo, Brazil c Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (Unesp), Campus of Assis, São Paulo, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Gene expression Microfluids Low RNA concentration Preamplification Reproductive system High-throughput qPCR system
Sample types such as those from reproductive systems often yield scarce material, which limits RT-qPCR analysis to only a few targets. Recently developed high-throughput systems can potentially change this scenario, however, the nanoliter scale of such platforms requires extra processing, e.g., preamplification, which needs to be defined through observation and experience. In order to establish best practices in high-throughput PCR approaches using samples from reproductive systems, we evaluated the Biomark™ HD performance using 11 different sample types from the bovine reproductive system: blastocyst (single/pool), oocyte (pool), cumulus, granulosa, and theca cells, oviduct tissue, fetal ovary, testicle (adult/fetal), and uterine horn. We observed that the preamplification step is not just reliable, but mandatory. Our results indicated that 14-preamplification cycles associated to 5- and 7-fold-dilution is the best approach for those samples. Additionally, the Biomark™ HD system has a high intra and inter reproducibility, therefore its performance in duplicate is unnecessary for the ΔCq analysis, taking in consideration the cutoff value 4 < Cq < 22. In summary, this high-throughput approach is a reliable and excellent tool for studying the bovine reproductive system, especially using quantitatively-limited samples, as a larger number of target genes can be assessed from a very low amount of starting material.
1. Introduction Nowadays, researchers seek to explain molecular mechanisms in which cells play a role or respond to any particular agent. The central dogma of molecular biology postulates that DNA uses RNA as an intermediate molecule to synthesize proteins that will be part of a functional organism [1]. As gene expression studies are based on the detection and quantification of RNAs related to a specific gene, the quantitative Polymerase Chain Reaction (qPCR) approach has been used to investigate gene expression in several species [2]. Unlike most systems in the body, the reproductive system has a quite particular physiology and its mature functionality depends on a highly dynamic and intricate control of processes for cellular proliferation and differentiation. For instance, working with germline cells, the female gamete is a particularly challenging sample type.
Researchers usually struggle to collect significant numbers of cumulusoocyte complexes and where that is not an issue, they are often held back by platforms that do not include assays for specific genes in the oocyte. With such limitations, home-brewed tests are most likely mandatory, hence large-scale approaches become more difficult for reproductive samples than any other [3]. High-throughput systems have been developed in the last decade to overcome the limited number of genes analyzed by the conventional microliters scale RT-qPCR. The high-throughput systems use the same principle of RT-qPCR, however they own a particular sample/assay loading process that enables a reliable nanoliter scale reaction [4]. The Biomark™ HD System is a high-throughput platform from Fluidigm® Company (San Francisco, CA). The complete principle of Biomark™ HD was described by Spurgeon et al. [5]. Briefly, samples and assays are manually pipetted inside individual inlets of the Integrated Fluid Circuit
∗
Corresponding author. Laboratory of Phytomedicines, Pharmacology and Biotechnology, Department of Pharmacology, Institute of Biosciences, University of São Paulo State (Unesp), Rua Prof. Antonio Celso Wagner Zanin, s/n, Zip Code: 18618-689, Botucatu, São Paulo, Brazil. E-mail addresses: [email protected] (P.K. Fontes), [email protected] (A.C.S. Castilho), [email protected] (E.M. Razza), [email protected] (M.F.G. Nogueira). https://doi.org/10.1016/j.ab.2020.113641 Received 21 November 2019; Received in revised form 5 February 2020; Accepted 18 February 2020 Available online 20 February 2020 0003-2697/ © 2020 Elsevier Inc. All rights reserved.
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(IFC), which is a network of fluid lines, valves and chambers. The valves, which are made of an elastomeric material, control the flow of liquids by closing and opening the fluid lines. The loading process of samples and assays is automatically performed by the system, taking the samples and assays from their inlets and distributing them into the individual reaction chambers and mixing the reagents [5]. The nanoliter scale of high-throughput systems requires extra preparation of the samples, for example a preamplification step to allow a homogeneous distribution into the reaction chambers [6,7]. Moreover, some conditions of the protocol, as the preamplification cycle number and the fold-dilution need to be determined empirically (Quick Reference PN 100-5876 B1, Fluidigm®). Therefore, we aimed to investigate the performance of samples from the bovine reproductive system on the Biomark™ HD platform to establish a reliable procedure to accomplish the data collection and processing. Moreover, we would like to spread the message of a new technical approach to guarantee reproducibility and reliability of nanoliter-scale qPCR applied on research about bovine reproductive system.
that, reverse transcription was performed as manufacturer's instructions with specific total RNA amount for each sample (specified at Table 1). 2.2. Preamplification Prior to qPCR thermal cycling, each sample was submitted to sequence-specific preamplification. Specific TaqMan® primers (Applied Biosystems, Foster City, CA; Supplementary Table 1) were pooled at 0.2X final concentration (2 μL of each of 48 primers was diluted in 104 μL of DNA Suspension Buffer - 10 mM Tris, pH 8.0, 0.1 mM EDTA; TEKnova, Hollister, CA). The total volume of preamplification reaction was 5 μL for each sample. The reaction contained 1.25 μL pooled primers (0.2X), 2.5 μL TaqMan PreAmp Master Mix (Applied Biosystems, Foster City, CA), and 1.25 μL cDNA (equivalent to 6.25 or 31.25 ng total RNA – Table 1). Thermal cycling conditions were as follows: initial hold at 95 °C for 10 min followed by 10 or 14 cycles of denaturing at 95 °C for 15 s and annealing/amplification at 60 °C for 4 min. The number of cycles was chosen following the company's instructions (Quick Reference PN 100-5876 B1, Fluidigm®). After the preamplification reaction, the sample must be diluted at least 5-fold (Quick Reference PN 100-5876 B1, Fluidigm®). For technical analysis of Biomark™ HD System (Fluidigm, San Francisco, CA), each sample was diluted 5-, 7-, and 9-folds. Therefore, based on number of preamplification cycles (10 or 14) and fold dilution (5, 7, or 9), six deviation from each sample were produced: 10|1:5, 10|1:7, 10|1:9 14|1:5, 14|1:7, and 14|1:9.
2. Material and methods 2.1. Sample preparation (RNA extraction and reverse transcription) Biological materials were collected post-mortem at a local slaughterhouse, with the permission of the direction of the slaughterhouse and the agreement of local sanitary services. Reproductive tracts were transported on safety conditions for RNA extraction (cold saline solution on ice) within 60 min from the slaughterhouse. All the samples were collected from bovine species and submitted to total RNA extraction and DNAse treatment, following the manufacturer's instructions of each specific protocol (Table 1). All the samples were submitted to total RNA quantification using a spectrophotometer (Nanodrop 2000™; Thermo Fisher Scientific, Waltham, MA), except the Blastocyst (single) sample, due to the one-step protocol, and the Oocyte (pool) sample, due to undetectable low RNA concentration. Moreover, the RNA quality was evaluated with a 2100 Bioanalyzer using the RNA Nano chips (Agilent Technologies, Waldbronn, Germany), except for Blastocyst (single) sample, due to the one-step protocol. All the samples analyzed presented the RNA integrity number (RIN) > 7.0. Following
2.3. High-throughput system: Biomark™ HD The high-throughput RT-qPCR was performed using the Biomark™ HD System (Fluidigm, San Francisco, CA), associated to the IFC Controller HX, which supports the 96.96 Dynamic Array™ IFC (analysis of 96 assays in 96 samples at the same time) and the FLEXsix™ Gene Expression IFC (six independent partitions with the 12.12 format – analysis of 12 assays in 12 samples at the same time or in sequential runs). In this study, the 96.96 Dynamic Array™ IFC was used to analyze 48 assays (Supplementary Table 1) performed in duplicate and 96 cDNA samples [12 samples (Table 1) under eight situations: non-preamplified (NPA), 10|1:5, 10|1:7, 10|1:9 14|1:5, 14|1:7 (in duplicate), and 14|1:9]. The 96.96 IFC accumulators (i.e., the inlet cells) were filled with control line fluid, followed by the IFC priming step in the Controller HX. After, each assay inlet and sample inlet were loaded with 5 μL of assay solution and 5 μL of sample solution, respectively. Each assay solution contained 2.5 μL of 20X TaqMan Gene Expression Assay (Applied Biosystems, Foster City, CA) and 2.5 μL of 2X Assay Loading Reagent (Fluidigm, San Francisco, CA). Each sample solution contained 2.25 μL of cDNA sample, 2.5 μL of TaqMan™ Universal PCR Master Mix (2X, Applied Biosystems, Foster City, CA), and 0.25 μL of 20X GE Sample Loading Reagent (Fluidigm, San Francisco, CA). The 96.96 IFC chip was placed back into the Controller HX to the loading process. The assay and sample solutions were automatically distributed from the inlets into multiple reaction chambers by the Controller HX. After, the IFC was transferred into the Biomark™ HD, where the qPCR thermal cycling was performed. The protocol TaqMan GE 96x96 Standard was used: one stage of Thermal Mix (50 °C for 2 min, 70 °C for 20 min, and 25 °C for 10 min), one Hot Start stage (50 °C for 2 min and 95 °C for 10 min), followed by 40 cycles of denaturation (95 °C for 15 s), and primer annealing and extension (60 °C for 60 s). At the end, the cycle threshold was determined specifically for each assay using the Fluidigm Software and Cq values were exported to excel file.
Table 1 Bovine samples, RNA extraction and reverse transcription details. Sample
RNA extraction
ID
Name
Observation
1
Blastocyst (pool) Blastocyst (single) Cumulus cells
Pool of three IVP-BL One IVP-BL
5 6 7
Granulosa cells Theca cells Fetal Ovary Oocyte (pool)
Collected from one pre-ovulatory follicle Fetus 150-180 days Pool of 20 oocytes
8 9 10 11 12
Oviduct Adult testicle Fetal testicle Uterine horn Fetal liver
Ampulla segment Cortical part Fetus 150-180 days Cranial part Fetus 150-180 days
2 3 4
Pool from 20 COCs
Reverse transcription (total RNAa)
High Capacityc (100) PicoPure RNAb REPLI-g Single Cell RNA Libraryc RNeasy Microc Trizold
High Capacitye (100)
Trizold Trizold RNeasy Microc Trizold Trizold Trizold Trizold Trizold
High Capacitye (500) High Capacitye (500) High Capacitye (n.a.)
High Capacitye (500)
High Capacitye (500) High Capacitye (500) High Capacitye (500) High Capacitye (500) High Capacitye (500)
a
Nanogram Applied Biosystems, Foster City, CA. c Qiagen Inc., Valencia, CA. d Invitrogen, São Paulo, SP, Brazil e Applied Biosystems, Foster City, CA. IVP-BL: in vitro produced - blastocyst, COCs: cumulus-oocyte complexes, n.a.: not available. b
2.4. Conventional 96-well plate: StepOnePlus™ The StepOnePlus™ (Applied Biosystems, Foster City, CA) is a real2
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submitted to preamplification performance analysis. The target gene, ATF4, and the reference gene, GAPDH, were quantified in the conventional qPCR system, and the preamplification uniformity was checked by calculating the ΔCq (ΔCq = CqATF4 – CqGAPDH), and determining the ΔΔCq’ between non-preamplified (NPA) and preamplified (PA) samples (ΔΔCq’ = ΔCqNPA – ΔCqPA). ΔΔCq’ values close to zero indicated preamplification uniformity. Targets that produce ΔΔCq’ values within ± 1.5 were considered uniformly preamplified. The Cq data obtained from the high-throughput system was not submitted to this analysis due to the absence of information of NPA samples, not allowing the comparison of NPA and PA samples.
time PCR instrument designed to analyze reactions in 96-well plate. All the 96 samples analyzed in the Biomark™ HD system was submitted to microliter-scale analysis in the StepOnePlus™ system. The mRNA abundance of two genes was quantified: the reference gene, GAPDH, and the target gene, ATF4 (Supplementary Table 1). Reaction consisted of 10 μL TaqMan™ Universal PCR Master Mix 2X (Applied Biosystems, Foster City, CA), 8 μL of RNAse-free water, 1 μL of 20X TaqMan Gene Expression Assay (Applied Biosystems, Foster City, CA), and 1 μL of cDNA, totalizing 20 μL. All the 96 samples were analyzed in duplicate, divided among two 96-well plates for each gene. The cycling program for both genes consisted of 10 min incubation at 95 °C followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. At the end, the threshold was determined to each gene individually, and Cq values exported to an excel file.
3. Results and discussion Studies in the bovine reproductive system have wide application and purposes. However, the samples studied in this research field can be, in some occasions, quantitatively-limited as a single oocyte, a biopsy, a single pre-ovulatory follicle, and others. Additionally, the samples can also be very rare, e.g. dead animal and genetic-specific animal. Due to limited amount and/or limitation of sample collection, here, we highlighted that a high-throughput system can be a key solution to enlarge the access of studies in these fields.
2.5. Analysis 2.5.1. qPCR data processing The two most commonly used methods to analyze data from qPCR experiments are relative quantification, within single (reference gene) or double normalization (reference gene plus calibration sample) [8]. The single normalization by the reference gene is the main point, for the purpose of controlling for bias in the amount of RNA added to the reverse transcription reactions [8]. The double normalization by the calibration sample is applied to calculate the expression amount in relation to a specific situation (untreated group, time zero, etc.) and is also necessary when the data collection is multi-plates, that is in experiments with sequential runs [8]. For both platforms (Biomark™ HD and StepOnePlus™), the ΔCq value was calculated using GAPDH as the reference gene (ΔCq = Cqtarget gene – CqGADPH) and the ΔΔCq was calculated using the sample “Blastocyst (pool), 14|1:5” as calibrator (ΔΔCq = ΔCqtarget sample – ΔCqblastocyst (pool) 14|1:5). The ΔCq values were used to evaluate the intra-reproducibility of Biomark™ HD system. And the ΔΔCq values were used to evaluate the inter-reproducibility between Biomark™ HD versus StepOnePlus™ platforms. Target transcript's abundance measured on the different platforms was evaluated by comparing the slope of the best fitted line of a least square linear regression of the raw Cq values and the ΔΔCq values between platforms to the ideal slope of 1. All the linear regression analysis were performed in the GraphPad Prism version 6 for Windows (GraphPad Software, La Jolla California USA), using P < 0.05 as statistical significance. The sample “Fetal Liver” was supposed to be the calibrator sample, and it was added in this study to work as a standard sample of the proceeds. However, this sample did not present a good performance in the target assays investigated, maybe due to the specific pattern of expression. Therefore, this sample was not used as calibrator, and no comparison was performed using this sample.
3.1. Intra-reproducibility: technical replicates In the Biomark™ HD system, each assay and each sample are separately pipetted by the user into the assay inlets and sample inlets, respectively. The loading process to mix assays and samples into the individual reaction chambers is automatically performed by a controller. Due to this loading system, technical replicates are dispensable according to the company's instruction. To determine the reproducibility of this high-throughput system, all assays were twice pipetted in different assay inlets, indicated as “technical replicates (assays)” (Supplementary Fig. S1A). Moreover, all the 12 samples submitted to 14-preamplification cycles followed by 7-fold dilution (14|1:7) were pipetted in duplicate, indicated as “technical replicates (samples)” (Supplementary Fig. S1B). As an ordinary indicator of reproducibility, a standard deviation value of ≤0.3 between raw Cq values is considered uniformly detected. In this study, when we performed the technical replicates (assays), 88% of the detected expressions were reported within standard deviation equal or below 0.3. Moreover, for Cq value of 22 or less, the average standard deviation was below 0.25 (Supplementary Fig. S1A). A high uniform detection was also observed when the same sample was applied in two different inlets during technical replicates (samples), 92% of the raw Cq values were reported within standard deviation equal or below 0.3. Moreover, for Cq value of 23 or less, the average standard deviation was below 0.27 (Supplementary Fig. S1B). As mentioned in previous research [9], high Cq values are not reproducible within high-throughput system, as nanoliter PCR reactions result in high Poisson variability due to lower initial template concentrations. To solve that, the authors applied a cutoff value of Cq > 22 to guarantee the reproducibility of the reports [9]. Corroborating with this study, our study also observed a safe detection when Cq values are below 22 (average standard deviation below 0.25, Supplementary Fig. S1). Besides the evaluation using the raw Cq value, the Biomark™ HD system ensure a high reproducibility result by the analysis of ΔCq value. During PCR analysis, appropriate normalization strategies are required to control experimental bias [8,10]; the use of reference genes is essential for this normalization. The Biomark™ HD loading system enables the detection of all assays from the same sample input, including targets and references genes, it ensures that ΔCq value is more precise and reproducible within this system. Indeed, a low standard variation of ΔCq values were observed in this study when the same sample was pipetted in two different inlets, presenting a great result of 98% of the
2.5.2. Assay performance: amplification efficiency and linearity PCR amplification efficiency and linearity for each primer pair was determined via the differential final concentration of samples submitted to preamplification processes and fold-dilutions (10|1:5, 10|1:7, 10|1:9, 14|1:5, 14|1:7, and 14|1:9). The slope of the Cq value vs. input concentration was determined for each assay, and efficiency was calculated as follows: Efficiency = 10^(−1/slope) – 1. Linearity for each assay was calculated as the correlation coefficient (R2) between the Cq value and input concentration. The optimum value for the primer efficiency would be 1.00 (100%) if the template is doubled within every PCR cycle. The 90-110% efficiency was considered a narrow acceptable range, and 85-115% efficiency was considered a mild acceptable range (Quick Reference PN 101-7005 A1, Fluidigm®). For the assay linearity, the acceptable value was considered above 0.98. All the analysis was performed at the Microsoft Excel version 2010 (Windows). 2.5.3. Preamplification performance The Cq data obtained from conventional qPCR (96-well plate) was 3
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very strong for GAPDH (R2 = 0.995, p < 0.0001, Fig. 2A) and AFT4 (R2 = 0.995, p < 0.0001, Fig. 2B). Due to differences on absolute signals of gene expression values generated by different platforms, the normalized RT-qPCR data using ΔΔCq [8] were calculated to exclude the bias of a direct comparison of raw Cq values. The ΔΔCq method is based on a double normalization system by the reference gene and a calibrator sample [8], necessary when the data is collected from different plates (the condition of the 96well plate data collection). The comparison by linear regression of the ΔΔCq values also confirmed a strong reproducibility between the platforms (R2 = 0.935, p < 0.0001, Fig. 2C). Therefore, the highthroughput system is essentially identical in quality to the conventional gold-standard system. Fig. 1. Intra reproducibility (technical replicates). Standard deviation (arbitrary unit) of ΔCq values (ΔCq = Cqtarget – CqGAPDH) of samples pipetted in duplicate in the high-throughput system. Showing the percentage (%) of the data that presented standard deviation values lower than 0.6.
3.3. Assay performance on high-throughput system The qPCR measurements require a reliable validation and standardization process. A serial dilution to build a standard curve is a good practice on assessing qPCR parameters. The amplification efficiency and linearity are essential parameters mainly related to the assay performance. However, other factors also alter the PCR efficiency: the sample matrix, reagent quality and concentrations, protocols, and even the platform [11]. In the current study, the amplification efficiencies of 79% of the assays were within the narrow acceptable range of 90–110%, and 92% of the assays were within a mild acceptable range (85-115%, Fig. 3). Linearity is also important to be determined during the performance of qPCR analysis. For 94% of the assays, the linearity was equal or greater than 0.98 (Fig. 3). Overall, the Biomark™ HD system allows testing the assays performance, presenting a precise detection in samples related to the bovine reproductive system. The outliers data [out of amplification range (85115%), and linearity lower than 0.98] seems be a specific characteristic of different sample type, as specified in the Supplementary Table 2 (amplification and linearity values per assay in each sample type).
reports with a standard variation ≤0.6 (Fig. 1). Even though 2% of the data is still out of the acceptable range (> 0.6), most of this data presents a high ΔCq value (which comes from Cq values higher than 22). 3.2. Inter-reproducibility: correlation between platforms For all the following analyses, a cutoff value of Cq > 22 was applied, since Cq values above 23 were not reproducible in the highthroughput system. Consequently, the comparison between platforms (Biomark™ HD and StepOnePlus™) was not possible to be performed using non-preamplified (NPA) samples, as such data was not reliable in the high-throughput system. Conventional microliter RT-qPCR is considered a gold-standard system for gene expression analysis. To evaluate the inter-reproducibility between the high-throughput system and the conventional system, all the samples were both submitted to mRNA quantification of a reference gene (GAPDH) and a target gene (ATF4) at the 96-well plate platform. The analysis by linear regression comparison confirms a high reproducibility between systems. The correlation of raw Cq values is
3.4. Preamplification performance The nanoliter reaction scale of the high-throughput system requires Fig. 2. Inter reproducibility (Biomark™ HD vs. StepOnePlus™). Comparison by linear regression (R2) between the two platforms, A) Comparison of raw Cq values of GAPDH, B) Comparison of raw Cq values of ATF4, C) Comparison of ΔΔCq values (ΔΔCq = ΔCqtarget sample – ΔCqblastocyst (pool) 14|1:5) between the two platforms. P < 0.05 indicates the presence of correlation between the platforms.
4
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Fig. 3. Amplification efficiency and linearity. Histogram showing the distribution of frequency of amplification efficiency and linearity of the assays (48 assays) in all the samples type (12 samples). The percentage (%) indicates the assays included in efficiency between 85 and 115% and linearity ≥0.98.
a process to concentrate the target molecules in order to ensure an adequate distribution of templates into the reaction chamber. To solve this barrier, a preamplification step is recommended [6,7,11]. In a hypothetical situation of a sample that contains a minimum of 15 copies of a target gene per microliter, if a conventional 20 μL qPCR is performed with this sample, a reliable technical reproducibility will be reached; however, if this sample is submitted to a 6.75 nL qPCR reaction (96.96 Biomark™ HD Array), an average of 0.09 copy of the target gene will be present in each reaction chamber. In other words: most of the chambers will be without the template, and it will not be reproducible. Indeed, the non-preamplified samples are not reproducible on high-throughput systems, as observed on Supplementary Fig. S2, where most of the genes were undetectable or detectable on an unreliable Cq value (cutoff value of Cq > 22). For the Blastocyst (pool), Cumulus cells, and Oocyte (pool) samples no gene can be detected on a reliable Cq value (Cq < 22) in non-preamplified samples. For the samples within higher amount of starting material (500 ng of total RNA, Table 1) and the Blastocyst (single) sample, some genes can be detected (Cq < 22) in the samples not submitted to the preamplification step. However, the percentages of genes detected in non-preamplified samples in relation to preamplified samples are very low: 24% (Blastocyst – single), 16% (Granulosa cells), 27% (Theca cells), 22% (Fetal ovary), 24% (Oviduct), 12% (Adult testicle), 18% (Fetal testicle), and 13% (Uterine horn); among the detectable genes in non-preamplified samples, more than 75% are reference genes. Therefore, the preamplification step is necessary for the high-throughput system analysis. The ΔΔCq’ (ΔCqNPA – ΔCqPA) value is a good parameter to evaluate the preamplification process, where ΔΔCq’ close to zero indicates preamplification uniformity, and ΔΔCq’ value within ± 1.5 indicates an acceptable preamplification [6,7]. Due to the low template concentration of non-preamplified samples, it was not possible to evaluate the preamplification uniformity for each individual gene in the highthroughput system. However, when checking the preamplification uniformity of ATF4 performed in 20 μL qPCR reaction, the overall relative gene expression of preamplified samples remained proportional to the non-preamplified samples (Table 2), except in the Blastocyst (single) sample, where the preamplification was not fully successful (ΔΔCq’ > 1.5, Table 2). The Blastocyst (single) is a sample of a very small number of cells processed by the REPLI-g Single Cell RNA Library Kit (Qiagen Inc., Valencia, CA). In this protocol, all the reaction steps are combined in a one-tube reaction from the intact cells until the library construction (REPLI-g® Mini/Midi Handbook, Qiagen®). The kit includes an amplification step in the library preparation that explains the unique result of this sample. The template excess is also a relevant point to be considered for qPCR analysis. The Blastocyst (single) sample, when submitted to 14-preamplification cycles, did not present a uniform
Table 2 Preamplification uniformity evaluation (ΔΔCq’ values). Sample
Blastocyst (pool) Blastocyst (single) Cumulus Cells Granulosa Cells Theca Cells Fetal Ovary Oocyte (pool) Oviduct Adult Testicle Fetal Testicle Uterus horn Fetal Liver
ΔΔCq’ (ΔCqNPA - ΔCqPA) 10|1:5
10|1:7
10|1:9
14|1:5
14|1:7
14|1:9
−0,3 1,1 −0,7 −0,4 −0,6 −0,5 0,0 −0,6 −0,1 −0,8 0,2 0,7
−0,1 1,4 −0,8 0,0 0,3 0,1 0,2 −0,7 0,1 0,4 0,3 0,4
−0,5 0,8 −0,3 −0,6 0,5 0,0 0,4 −0,9 −0,6 0,2 0,0 1,0
−0,7 2,9 −1,2 0,3 −0,1 −0,5 −0,1 −0,8 0,2 0,0 −0,4 0,5
−0,3 2,2 −0,7 0,4 0,2 0,1 0,2 −0,7 0,2 0,4 −0,1 0,5
−0,6 2,2 −0,7 0,4 0,1 0,0 0,0 −0,4 0,1 0,1 −0,3 0,5
NPA: non-preamplified, PA: preamplified.
preamplification (ΔΔCq’ > 1.5, Table 2), whereas the same sample when submitted to 10-preamplification cycles presented an acceptable preamplification process (ΔΔCq’ within ± 1.5, Table 2). The poorquality collection data of samples with template in excess can be even observed on the amplification curve, which incapacitates data analysis because of the premature Cq value (Supplementary Fig. S3). The Biomark™ HD software also identifies this sample as “failed”, due to the premature Cq value (Supplementary Fig. S3). Accordingly, a cutoff value of Cq < 4 was set to exclude the premature amplification, associated to the cutoff value of Cq > 22 that guarantee the exclusion of non-reproducible samples with low template concentration. Therefore, the preamplification process requires an ideal template concentration, limited to avoid a premature amplification, and enough for an adequate template distribution in the chambers. According to the Fluidigm® instructions, generally 10-14 preamplification cycles followed by at least 5-fold dilution is the adequate protocol (Quick Reference PN 100-5876 B1, Fluidigm®). Based on this, the 12 samples evaluated in this study were submitted to 10- or 14-preamplification cycles followed by 5-, 7-, or 9-folds dilution. The number of genes detected by each condition were identified taking in account the cutoff value of 4 < Cq < 22. Out of 48 genes, the number of genes detected in each sample type is variable depending on the template concentration (number of preamplification cycles + fold-dilution; Fig. 4). The highest number of genes was detected in the cumulus cell sample (Fig. 4). Overall, lower numbers of genes were detected when samples were submitted to 10-preamplification cycles compared to 14-preamplification cycles (green line and black lines, Fig. 4); not observed in oocyte sample, where no clear variation was observed between 10- and 14-preamplification cycles (blue line, Fig. 4). Otherwise, in the 5
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Fig. 4. Assay detection per sample type. Number of assay detected per sample type in each sample concentration: 10 or 14 preamplification cycle, 5-, 7-, or 9-folddilution.
(FAPESP, São Paulo, Brazil, grant number #2012/50533–2). The authors acknowledge Joachim Valk for providing English language editing.
blastocyst (single) sample, higher numbers of genes were detected when the sample was submitted to 10-preamplification cycles instead of 14preamplification cycles, due to the excess of start template as discussed before (orange line, Fig. 4). Regarding the fold-dilution, in general, 5and 7- fold-dilution are the best association to 14-preamplification cycles, to all the samples type, except to Blastocyst (single) sample, which the 10|1:9 protocol was the more convenient (Fig. 4).
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ab.2020.113641.
4. Conclusion References We summarize that high-throughput system is a reliable and excellent tool for studying the bovine reproductive system, especially samples with quantitatively-limited, once a high number of target genes can be investigate using a very low amount of sample. Nevertheless, due to the nanoliter scale of RT-qPCR reaction, we reinforced that the preamplification step is indispensable, reliable and we highlighted that 14-preamplification cycles associated to 5- and 7-fold-dilution is the best approach for those samples. Ultimately, the Biomark™ HD system has a high intra- and inter reproducibility, therefore its performance in duplicate is unnecessary for the ΔCq analysis, taking in consideration the cutoff value 4 < Cq < 22.
[1] G.W. Beadle, E.L. Tatum, Genetic control of biochemical reactions in neurospora, Proc. Natl. Acad. Sci. U. S. A. 27 (1941) 499–506, https://doi.org/10.1073/pnas. 27.11.499. [2] R. Higuchi, G. Dollinger, P.S. Walsh, R. Griffith, Simultaneous amplification and detection of specific DNA sequences, Biotechnology 10 (1992) 413–417, https:// doi.org/10.1038/nbt0492-413. [3] T. Hamatani, G. Falco, M.G. Carter, H. Akutsu, C.A. Stagg, A.A. Sharov, D.B. Dudekula, V. VanBuren, M.S. Ko, Age-associated alteration of gene expression patterns in mouse oocytes, Hum. Mol. Genet. 13 (2004) 2263–2278, https://doi. org/10.1093/hmg/ddh241. [4] R.J. Farr, A.S. Januszewski, M.V. Joglekar, H. Liang, A.K. McAulley, A.W. Hewitt, H.E. Thomas, T. Loudovaris, T.W. Kay, A. Jenkins, A.A. Hardikar, A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy, Sci. Rep. 5 (2015) 10375, https://doi.org/10. 1038/srep10375. [5] S.L. Spurgeon, R.C. Jones, R. Ramakrishnan, High throughput gene expression measurement with real time PCR in a microfluidic dynamic array, PloS One 3 (2008) e1662, , https://doi.org/10.1371/journal.pone.0001662. [6] L. Mengual, M. Burset, M. Marín-Aguilera, M.J. Ribal, A. Alcaraz, Multiplex preamplification of specific cDNA targets prior to gene expression analysis by TaqMan Arrays, BMC Res. Notes 1 (2008) 21, https://doi.org/10.1186/1756-0500-1-21. [7] V. Korenková, J. Scott, V. Novosadová, M. Jindřichová, L. Langerová, D. Švec, M. Šídová, R. Sjöback, Pre-amplification in the context of high-throughput qPCR gene expression experiment, BMC Mol. Biol. 16 (2015) 5, https://doi.org/10.1186/ s12867-015-0033-9. [8] K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using realtime quantitative PCR and the 2(-Delta Delta C(T)) method, Methods 25 (2001) 402–408, https://doi.org/10.1006/meth.2001.1262. [9] B.M. Fischer, D. Neumann, A.L. Piberger, S.F. Risnes, B. Köberle, A. Hartwig, Use of high-throughput RT-qPCR to assess modulations of gene expression profiles related to genomic stability and interactions by cadmium, Arch. Toxicol. 90 (2016) 2745–2761, https://doi.org/10.1007/s00204-015-1621-7. [10] M.W. Pfaffl, A new mathematical model for relative quantification in real-time RTPCR, Nucleic Acids Res. 29 (2001) e45. [11] D. Svec, V. Rusnakova, V. Korenkova, M. Kubista, Dye-Based High-Throughput qPCR in Microfluidic Platform BioMark™, CRC Press, 2012, pp. 323–336.
CRediT authorship contribution statement Patricia Kubo Fontes: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing - original draft, Writing - review & editing. Anthony César Souza Castilho: Conceptualization, Investigation, Project administration, Validation, Writing - review & editing. Eduardo Montanari Razza: Conceptualization, Investigation, Validation, Visualization, Writing original draft, Writing - review & editing. Marcelo Fábio Gouveia Nogueira: Conceptualization, Funding acquisition, Project administration, Resources, Validation, Visualization, Writing - review & editing. Declaration of competing interest The authors have declared that no competing interests exist. Acknowledgment This work was supported by São Paulo Research Foundation
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Analytical Biochemistry journal homepage: www.elsevier.com/locate/yabio
Bona fide gene expression analysis of samples from the bovine reproductive system by microfluidic platform
T
Patricia Kubo Fontesa,∗, Anthony César Souza Castilhoa,b, Eduardo Montanari Razzaa, Marcelo Fábio Gouveia Nogueiraa,c a Laboratory of Phytomedicines, Pharmacology and Biotechnology, Department of Pharmacology, Institute of Biosciences, University of São Paulo State (Unesp), Campus of Botucatu, São Paulo, Brazil b University of Western São Paulo (Unoeste), Campus of Presidente Prudente, São Paulo, Brazil c Department of Biological Sciences, School of Sciences and Languages, São Paulo State University (Unesp), Campus of Assis, São Paulo, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Gene expression Microfluids Low RNA concentration Preamplification Reproductive system High-throughput qPCR system
Sample types such as those from reproductive systems often yield scarce material, which limits RT-qPCR analysis to only a few targets. Recently developed high-throughput systems can potentially change this scenario, however, the nanoliter scale of such platforms requires extra processing, e.g., preamplification, which needs to be defined through observation and experience. In order to establish best practices in high-throughput PCR approaches using samples from reproductive systems, we evaluated the Biomark™ HD performance using 11 different sample types from the bovine reproductive system: blastocyst (single/pool), oocyte (pool), cumulus, granulosa, and theca cells, oviduct tissue, fetal ovary, testicle (adult/fetal), and uterine horn. We observed that the preamplification step is not just reliable, but mandatory. Our results indicated that 14-preamplification cycles associated to 5- and 7-fold-dilution is the best approach for those samples. Additionally, the Biomark™ HD system has a high intra and inter reproducibility, therefore its performance in duplicate is unnecessary for the ΔCq analysis, taking in consideration the cutoff value 4 < Cq < 22. In summary, this high-throughput approach is a reliable and excellent tool for studying the bovine reproductive system, especially using quantitatively-limited samples, as a larger number of target genes can be assessed from a very low amount of starting material.
1. Introduction Nowadays, researchers seek to explain molecular mechanisms in which cells play a role or respond to any particular agent. The central dogma of molecular biology postulates that DNA uses RNA as an intermediate molecule to synthesize proteins that will be part of a functional organism [1]. As gene expression studies are based on the detection and quantification of RNAs related to a specific gene, the quantitative Polymerase Chain Reaction (qPCR) approach has been used to investigate gene expression in several species [2]. Unlike most systems in the body, the reproductive system has a quite particular physiology and its mature functionality depends on a highly dynamic and intricate control of processes for cellular proliferation and differentiation. For instance, working with germline cells, the female gamete is a particularly challenging sample type.
Researchers usually struggle to collect significant numbers of cumulusoocyte complexes and where that is not an issue, they are often held back by platforms that do not include assays for specific genes in the oocyte. With such limitations, home-brewed tests are most likely mandatory, hence large-scale approaches become more difficult for reproductive samples than any other [3]. High-throughput systems have been developed in the last decade to overcome the limited number of genes analyzed by the conventional microliters scale RT-qPCR. The high-throughput systems use the same principle of RT-qPCR, however they own a particular sample/assay loading process that enables a reliable nanoliter scale reaction [4]. The Biomark™ HD System is a high-throughput platform from Fluidigm® Company (San Francisco, CA). The complete principle of Biomark™ HD was described by Spurgeon et al. [5]. Briefly, samples and assays are manually pipetted inside individual inlets of the Integrated Fluid Circuit
∗
Corresponding author. Laboratory of Phytomedicines, Pharmacology and Biotechnology, Department of Pharmacology, Institute of Biosciences, University of São Paulo State (Unesp), Rua Prof. Antonio Celso Wagner Zanin, s/n, Zip Code: 18618-689, Botucatu, São Paulo, Brazil. E-mail addresses: [email protected] (P.K. Fontes), [email protected] (A.C.S. Castilho), [email protected] (E.M. Razza), [email protected] (M.F.G. Nogueira). https://doi.org/10.1016/j.ab.2020.113641 Received 21 November 2019; Received in revised form 5 February 2020; Accepted 18 February 2020 Available online 20 February 2020 0003-2697/ © 2020 Elsevier Inc. All rights reserved.
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(IFC), which is a network of fluid lines, valves and chambers. The valves, which are made of an elastomeric material, control the flow of liquids by closing and opening the fluid lines. The loading process of samples and assays is automatically performed by the system, taking the samples and assays from their inlets and distributing them into the individual reaction chambers and mixing the reagents [5]. The nanoliter scale of high-throughput systems requires extra preparation of the samples, for example a preamplification step to allow a homogeneous distribution into the reaction chambers [6,7]. Moreover, some conditions of the protocol, as the preamplification cycle number and the fold-dilution need to be determined empirically (Quick Reference PN 100-5876 B1, Fluidigm®). Therefore, we aimed to investigate the performance of samples from the bovine reproductive system on the Biomark™ HD platform to establish a reliable procedure to accomplish the data collection and processing. Moreover, we would like to spread the message of a new technical approach to guarantee reproducibility and reliability of nanoliter-scale qPCR applied on research about bovine reproductive system.
that, reverse transcription was performed as manufacturer's instructions with specific total RNA amount for each sample (specified at Table 1). 2.2. Preamplification Prior to qPCR thermal cycling, each sample was submitted to sequence-specific preamplification. Specific TaqMan® primers (Applied Biosystems, Foster City, CA; Supplementary Table 1) were pooled at 0.2X final concentration (2 μL of each of 48 primers was diluted in 104 μL of DNA Suspension Buffer - 10 mM Tris, pH 8.0, 0.1 mM EDTA; TEKnova, Hollister, CA). The total volume of preamplification reaction was 5 μL for each sample. The reaction contained 1.25 μL pooled primers (0.2X), 2.5 μL TaqMan PreAmp Master Mix (Applied Biosystems, Foster City, CA), and 1.25 μL cDNA (equivalent to 6.25 or 31.25 ng total RNA – Table 1). Thermal cycling conditions were as follows: initial hold at 95 °C for 10 min followed by 10 or 14 cycles of denaturing at 95 °C for 15 s and annealing/amplification at 60 °C for 4 min. The number of cycles was chosen following the company's instructions (Quick Reference PN 100-5876 B1, Fluidigm®). After the preamplification reaction, the sample must be diluted at least 5-fold (Quick Reference PN 100-5876 B1, Fluidigm®). For technical analysis of Biomark™ HD System (Fluidigm, San Francisco, CA), each sample was diluted 5-, 7-, and 9-folds. Therefore, based on number of preamplification cycles (10 or 14) and fold dilution (5, 7, or 9), six deviation from each sample were produced: 10|1:5, 10|1:7, 10|1:9 14|1:5, 14|1:7, and 14|1:9.
2. Material and methods 2.1. Sample preparation (RNA extraction and reverse transcription) Biological materials were collected post-mortem at a local slaughterhouse, with the permission of the direction of the slaughterhouse and the agreement of local sanitary services. Reproductive tracts were transported on safety conditions for RNA extraction (cold saline solution on ice) within 60 min from the slaughterhouse. All the samples were collected from bovine species and submitted to total RNA extraction and DNAse treatment, following the manufacturer's instructions of each specific protocol (Table 1). All the samples were submitted to total RNA quantification using a spectrophotometer (Nanodrop 2000™; Thermo Fisher Scientific, Waltham, MA), except the Blastocyst (single) sample, due to the one-step protocol, and the Oocyte (pool) sample, due to undetectable low RNA concentration. Moreover, the RNA quality was evaluated with a 2100 Bioanalyzer using the RNA Nano chips (Agilent Technologies, Waldbronn, Germany), except for Blastocyst (single) sample, due to the one-step protocol. All the samples analyzed presented the RNA integrity number (RIN) > 7.0. Following
2.3. High-throughput system: Biomark™ HD The high-throughput RT-qPCR was performed using the Biomark™ HD System (Fluidigm, San Francisco, CA), associated to the IFC Controller HX, which supports the 96.96 Dynamic Array™ IFC (analysis of 96 assays in 96 samples at the same time) and the FLEXsix™ Gene Expression IFC (six independent partitions with the 12.12 format – analysis of 12 assays in 12 samples at the same time or in sequential runs). In this study, the 96.96 Dynamic Array™ IFC was used to analyze 48 assays (Supplementary Table 1) performed in duplicate and 96 cDNA samples [12 samples (Table 1) under eight situations: non-preamplified (NPA), 10|1:5, 10|1:7, 10|1:9 14|1:5, 14|1:7 (in duplicate), and 14|1:9]. The 96.96 IFC accumulators (i.e., the inlet cells) were filled with control line fluid, followed by the IFC priming step in the Controller HX. After, each assay inlet and sample inlet were loaded with 5 μL of assay solution and 5 μL of sample solution, respectively. Each assay solution contained 2.5 μL of 20X TaqMan Gene Expression Assay (Applied Biosystems, Foster City, CA) and 2.5 μL of 2X Assay Loading Reagent (Fluidigm, San Francisco, CA). Each sample solution contained 2.25 μL of cDNA sample, 2.5 μL of TaqMan™ Universal PCR Master Mix (2X, Applied Biosystems, Foster City, CA), and 0.25 μL of 20X GE Sample Loading Reagent (Fluidigm, San Francisco, CA). The 96.96 IFC chip was placed back into the Controller HX to the loading process. The assay and sample solutions were automatically distributed from the inlets into multiple reaction chambers by the Controller HX. After, the IFC was transferred into the Biomark™ HD, where the qPCR thermal cycling was performed. The protocol TaqMan GE 96x96 Standard was used: one stage of Thermal Mix (50 °C for 2 min, 70 °C for 20 min, and 25 °C for 10 min), one Hot Start stage (50 °C for 2 min and 95 °C for 10 min), followed by 40 cycles of denaturation (95 °C for 15 s), and primer annealing and extension (60 °C for 60 s). At the end, the cycle threshold was determined specifically for each assay using the Fluidigm Software and Cq values were exported to excel file.
Table 1 Bovine samples, RNA extraction and reverse transcription details. Sample
RNA extraction
ID
Name
Observation
1
Blastocyst (pool) Blastocyst (single) Cumulus cells
Pool of three IVP-BL One IVP-BL
5 6 7
Granulosa cells Theca cells Fetal Ovary Oocyte (pool)
Collected from one pre-ovulatory follicle Fetus 150-180 days Pool of 20 oocytes
8 9 10 11 12
Oviduct Adult testicle Fetal testicle Uterine horn Fetal liver
Ampulla segment Cortical part Fetus 150-180 days Cranial part Fetus 150-180 days
2 3 4
Pool from 20 COCs
Reverse transcription (total RNAa)
High Capacityc (100) PicoPure RNAb REPLI-g Single Cell RNA Libraryc RNeasy Microc Trizold
High Capacitye (100)
Trizold Trizold RNeasy Microc Trizold Trizold Trizold Trizold Trizold
High Capacitye (500) High Capacitye (500) High Capacitye (n.a.)
High Capacitye (500)
High Capacitye (500) High Capacitye (500) High Capacitye (500) High Capacitye (500) High Capacitye (500)
a
Nanogram Applied Biosystems, Foster City, CA. c Qiagen Inc., Valencia, CA. d Invitrogen, São Paulo, SP, Brazil e Applied Biosystems, Foster City, CA. IVP-BL: in vitro produced - blastocyst, COCs: cumulus-oocyte complexes, n.a.: not available. b
2.4. Conventional 96-well plate: StepOnePlus™ The StepOnePlus™ (Applied Biosystems, Foster City, CA) is a real2
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submitted to preamplification performance analysis. The target gene, ATF4, and the reference gene, GAPDH, were quantified in the conventional qPCR system, and the preamplification uniformity was checked by calculating the ΔCq (ΔCq = CqATF4 – CqGAPDH), and determining the ΔΔCq’ between non-preamplified (NPA) and preamplified (PA) samples (ΔΔCq’ = ΔCqNPA – ΔCqPA). ΔΔCq’ values close to zero indicated preamplification uniformity. Targets that produce ΔΔCq’ values within ± 1.5 were considered uniformly preamplified. The Cq data obtained from the high-throughput system was not submitted to this analysis due to the absence of information of NPA samples, not allowing the comparison of NPA and PA samples.
time PCR instrument designed to analyze reactions in 96-well plate. All the 96 samples analyzed in the Biomark™ HD system was submitted to microliter-scale analysis in the StepOnePlus™ system. The mRNA abundance of two genes was quantified: the reference gene, GAPDH, and the target gene, ATF4 (Supplementary Table 1). Reaction consisted of 10 μL TaqMan™ Universal PCR Master Mix 2X (Applied Biosystems, Foster City, CA), 8 μL of RNAse-free water, 1 μL of 20X TaqMan Gene Expression Assay (Applied Biosystems, Foster City, CA), and 1 μL of cDNA, totalizing 20 μL. All the 96 samples were analyzed in duplicate, divided among two 96-well plates for each gene. The cycling program for both genes consisted of 10 min incubation at 95 °C followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. At the end, the threshold was determined to each gene individually, and Cq values exported to an excel file.
3. Results and discussion Studies in the bovine reproductive system have wide application and purposes. However, the samples studied in this research field can be, in some occasions, quantitatively-limited as a single oocyte, a biopsy, a single pre-ovulatory follicle, and others. Additionally, the samples can also be very rare, e.g. dead animal and genetic-specific animal. Due to limited amount and/or limitation of sample collection, here, we highlighted that a high-throughput system can be a key solution to enlarge the access of studies in these fields.
2.5. Analysis 2.5.1. qPCR data processing The two most commonly used methods to analyze data from qPCR experiments are relative quantification, within single (reference gene) or double normalization (reference gene plus calibration sample) [8]. The single normalization by the reference gene is the main point, for the purpose of controlling for bias in the amount of RNA added to the reverse transcription reactions [8]. The double normalization by the calibration sample is applied to calculate the expression amount in relation to a specific situation (untreated group, time zero, etc.) and is also necessary when the data collection is multi-plates, that is in experiments with sequential runs [8]. For both platforms (Biomark™ HD and StepOnePlus™), the ΔCq value was calculated using GAPDH as the reference gene (ΔCq = Cqtarget gene – CqGADPH) and the ΔΔCq was calculated using the sample “Blastocyst (pool), 14|1:5” as calibrator (ΔΔCq = ΔCqtarget sample – ΔCqblastocyst (pool) 14|1:5). The ΔCq values were used to evaluate the intra-reproducibility of Biomark™ HD system. And the ΔΔCq values were used to evaluate the inter-reproducibility between Biomark™ HD versus StepOnePlus™ platforms. Target transcript's abundance measured on the different platforms was evaluated by comparing the slope of the best fitted line of a least square linear regression of the raw Cq values and the ΔΔCq values between platforms to the ideal slope of 1. All the linear regression analysis were performed in the GraphPad Prism version 6 for Windows (GraphPad Software, La Jolla California USA), using P < 0.05 as statistical significance. The sample “Fetal Liver” was supposed to be the calibrator sample, and it was added in this study to work as a standard sample of the proceeds. However, this sample did not present a good performance in the target assays investigated, maybe due to the specific pattern of expression. Therefore, this sample was not used as calibrator, and no comparison was performed using this sample.
3.1. Intra-reproducibility: technical replicates In the Biomark™ HD system, each assay and each sample are separately pipetted by the user into the assay inlets and sample inlets, respectively. The loading process to mix assays and samples into the individual reaction chambers is automatically performed by a controller. Due to this loading system, technical replicates are dispensable according to the company's instruction. To determine the reproducibility of this high-throughput system, all assays were twice pipetted in different assay inlets, indicated as “technical replicates (assays)” (Supplementary Fig. S1A). Moreover, all the 12 samples submitted to 14-preamplification cycles followed by 7-fold dilution (14|1:7) were pipetted in duplicate, indicated as “technical replicates (samples)” (Supplementary Fig. S1B). As an ordinary indicator of reproducibility, a standard deviation value of ≤0.3 between raw Cq values is considered uniformly detected. In this study, when we performed the technical replicates (assays), 88% of the detected expressions were reported within standard deviation equal or below 0.3. Moreover, for Cq value of 22 or less, the average standard deviation was below 0.25 (Supplementary Fig. S1A). A high uniform detection was also observed when the same sample was applied in two different inlets during technical replicates (samples), 92% of the raw Cq values were reported within standard deviation equal or below 0.3. Moreover, for Cq value of 23 or less, the average standard deviation was below 0.27 (Supplementary Fig. S1B). As mentioned in previous research [9], high Cq values are not reproducible within high-throughput system, as nanoliter PCR reactions result in high Poisson variability due to lower initial template concentrations. To solve that, the authors applied a cutoff value of Cq > 22 to guarantee the reproducibility of the reports [9]. Corroborating with this study, our study also observed a safe detection when Cq values are below 22 (average standard deviation below 0.25, Supplementary Fig. S1). Besides the evaluation using the raw Cq value, the Biomark™ HD system ensure a high reproducibility result by the analysis of ΔCq value. During PCR analysis, appropriate normalization strategies are required to control experimental bias [8,10]; the use of reference genes is essential for this normalization. The Biomark™ HD loading system enables the detection of all assays from the same sample input, including targets and references genes, it ensures that ΔCq value is more precise and reproducible within this system. Indeed, a low standard variation of ΔCq values were observed in this study when the same sample was pipetted in two different inlets, presenting a great result of 98% of the
2.5.2. Assay performance: amplification efficiency and linearity PCR amplification efficiency and linearity for each primer pair was determined via the differential final concentration of samples submitted to preamplification processes and fold-dilutions (10|1:5, 10|1:7, 10|1:9, 14|1:5, 14|1:7, and 14|1:9). The slope of the Cq value vs. input concentration was determined for each assay, and efficiency was calculated as follows: Efficiency = 10^(−1/slope) – 1. Linearity for each assay was calculated as the correlation coefficient (R2) between the Cq value and input concentration. The optimum value for the primer efficiency would be 1.00 (100%) if the template is doubled within every PCR cycle. The 90-110% efficiency was considered a narrow acceptable range, and 85-115% efficiency was considered a mild acceptable range (Quick Reference PN 101-7005 A1, Fluidigm®). For the assay linearity, the acceptable value was considered above 0.98. All the analysis was performed at the Microsoft Excel version 2010 (Windows). 2.5.3. Preamplification performance The Cq data obtained from conventional qPCR (96-well plate) was 3
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very strong for GAPDH (R2 = 0.995, p < 0.0001, Fig. 2A) and AFT4 (R2 = 0.995, p < 0.0001, Fig. 2B). Due to differences on absolute signals of gene expression values generated by different platforms, the normalized RT-qPCR data using ΔΔCq [8] were calculated to exclude the bias of a direct comparison of raw Cq values. The ΔΔCq method is based on a double normalization system by the reference gene and a calibrator sample [8], necessary when the data is collected from different plates (the condition of the 96well plate data collection). The comparison by linear regression of the ΔΔCq values also confirmed a strong reproducibility between the platforms (R2 = 0.935, p < 0.0001, Fig. 2C). Therefore, the highthroughput system is essentially identical in quality to the conventional gold-standard system. Fig. 1. Intra reproducibility (technical replicates). Standard deviation (arbitrary unit) of ΔCq values (ΔCq = Cqtarget – CqGAPDH) of samples pipetted in duplicate in the high-throughput system. Showing the percentage (%) of the data that presented standard deviation values lower than 0.6.
3.3. Assay performance on high-throughput system The qPCR measurements require a reliable validation and standardization process. A serial dilution to build a standard curve is a good practice on assessing qPCR parameters. The amplification efficiency and linearity are essential parameters mainly related to the assay performance. However, other factors also alter the PCR efficiency: the sample matrix, reagent quality and concentrations, protocols, and even the platform [11]. In the current study, the amplification efficiencies of 79% of the assays were within the narrow acceptable range of 90–110%, and 92% of the assays were within a mild acceptable range (85-115%, Fig. 3). Linearity is also important to be determined during the performance of qPCR analysis. For 94% of the assays, the linearity was equal or greater than 0.98 (Fig. 3). Overall, the Biomark™ HD system allows testing the assays performance, presenting a precise detection in samples related to the bovine reproductive system. The outliers data [out of amplification range (85115%), and linearity lower than 0.98] seems be a specific characteristic of different sample type, as specified in the Supplementary Table 2 (amplification and linearity values per assay in each sample type).
reports with a standard variation ≤0.6 (Fig. 1). Even though 2% of the data is still out of the acceptable range (> 0.6), most of this data presents a high ΔCq value (which comes from Cq values higher than 22). 3.2. Inter-reproducibility: correlation between platforms For all the following analyses, a cutoff value of Cq > 22 was applied, since Cq values above 23 were not reproducible in the highthroughput system. Consequently, the comparison between platforms (Biomark™ HD and StepOnePlus™) was not possible to be performed using non-preamplified (NPA) samples, as such data was not reliable in the high-throughput system. Conventional microliter RT-qPCR is considered a gold-standard system for gene expression analysis. To evaluate the inter-reproducibility between the high-throughput system and the conventional system, all the samples were both submitted to mRNA quantification of a reference gene (GAPDH) and a target gene (ATF4) at the 96-well plate platform. The analysis by linear regression comparison confirms a high reproducibility between systems. The correlation of raw Cq values is
3.4. Preamplification performance The nanoliter reaction scale of the high-throughput system requires Fig. 2. Inter reproducibility (Biomark™ HD vs. StepOnePlus™). Comparison by linear regression (R2) between the two platforms, A) Comparison of raw Cq values of GAPDH, B) Comparison of raw Cq values of ATF4, C) Comparison of ΔΔCq values (ΔΔCq = ΔCqtarget sample – ΔCqblastocyst (pool) 14|1:5) between the two platforms. P < 0.05 indicates the presence of correlation between the platforms.
4
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Fig. 3. Amplification efficiency and linearity. Histogram showing the distribution of frequency of amplification efficiency and linearity of the assays (48 assays) in all the samples type (12 samples). The percentage (%) indicates the assays included in efficiency between 85 and 115% and linearity ≥0.98.
a process to concentrate the target molecules in order to ensure an adequate distribution of templates into the reaction chamber. To solve this barrier, a preamplification step is recommended [6,7,11]. In a hypothetical situation of a sample that contains a minimum of 15 copies of a target gene per microliter, if a conventional 20 μL qPCR is performed with this sample, a reliable technical reproducibility will be reached; however, if this sample is submitted to a 6.75 nL qPCR reaction (96.96 Biomark™ HD Array), an average of 0.09 copy of the target gene will be present in each reaction chamber. In other words: most of the chambers will be without the template, and it will not be reproducible. Indeed, the non-preamplified samples are not reproducible on high-throughput systems, as observed on Supplementary Fig. S2, where most of the genes were undetectable or detectable on an unreliable Cq value (cutoff value of Cq > 22). For the Blastocyst (pool), Cumulus cells, and Oocyte (pool) samples no gene can be detected on a reliable Cq value (Cq < 22) in non-preamplified samples. For the samples within higher amount of starting material (500 ng of total RNA, Table 1) and the Blastocyst (single) sample, some genes can be detected (Cq < 22) in the samples not submitted to the preamplification step. However, the percentages of genes detected in non-preamplified samples in relation to preamplified samples are very low: 24% (Blastocyst – single), 16% (Granulosa cells), 27% (Theca cells), 22% (Fetal ovary), 24% (Oviduct), 12% (Adult testicle), 18% (Fetal testicle), and 13% (Uterine horn); among the detectable genes in non-preamplified samples, more than 75% are reference genes. Therefore, the preamplification step is necessary for the high-throughput system analysis. The ΔΔCq’ (ΔCqNPA – ΔCqPA) value is a good parameter to evaluate the preamplification process, where ΔΔCq’ close to zero indicates preamplification uniformity, and ΔΔCq’ value within ± 1.5 indicates an acceptable preamplification [6,7]. Due to the low template concentration of non-preamplified samples, it was not possible to evaluate the preamplification uniformity for each individual gene in the highthroughput system. However, when checking the preamplification uniformity of ATF4 performed in 20 μL qPCR reaction, the overall relative gene expression of preamplified samples remained proportional to the non-preamplified samples (Table 2), except in the Blastocyst (single) sample, where the preamplification was not fully successful (ΔΔCq’ > 1.5, Table 2). The Blastocyst (single) is a sample of a very small number of cells processed by the REPLI-g Single Cell RNA Library Kit (Qiagen Inc., Valencia, CA). In this protocol, all the reaction steps are combined in a one-tube reaction from the intact cells until the library construction (REPLI-g® Mini/Midi Handbook, Qiagen®). The kit includes an amplification step in the library preparation that explains the unique result of this sample. The template excess is also a relevant point to be considered for qPCR analysis. The Blastocyst (single) sample, when submitted to 14-preamplification cycles, did not present a uniform
Table 2 Preamplification uniformity evaluation (ΔΔCq’ values). Sample
Blastocyst (pool) Blastocyst (single) Cumulus Cells Granulosa Cells Theca Cells Fetal Ovary Oocyte (pool) Oviduct Adult Testicle Fetal Testicle Uterus horn Fetal Liver
ΔΔCq’ (ΔCqNPA - ΔCqPA) 10|1:5
10|1:7
10|1:9
14|1:5
14|1:7
14|1:9
−0,3 1,1 −0,7 −0,4 −0,6 −0,5 0,0 −0,6 −0,1 −0,8 0,2 0,7
−0,1 1,4 −0,8 0,0 0,3 0,1 0,2 −0,7 0,1 0,4 0,3 0,4
−0,5 0,8 −0,3 −0,6 0,5 0,0 0,4 −0,9 −0,6 0,2 0,0 1,0
−0,7 2,9 −1,2 0,3 −0,1 −0,5 −0,1 −0,8 0,2 0,0 −0,4 0,5
−0,3 2,2 −0,7 0,4 0,2 0,1 0,2 −0,7 0,2 0,4 −0,1 0,5
−0,6 2,2 −0,7 0,4 0,1 0,0 0,0 −0,4 0,1 0,1 −0,3 0,5
NPA: non-preamplified, PA: preamplified.
preamplification (ΔΔCq’ > 1.5, Table 2), whereas the same sample when submitted to 10-preamplification cycles presented an acceptable preamplification process (ΔΔCq’ within ± 1.5, Table 2). The poorquality collection data of samples with template in excess can be even observed on the amplification curve, which incapacitates data analysis because of the premature Cq value (Supplementary Fig. S3). The Biomark™ HD software also identifies this sample as “failed”, due to the premature Cq value (Supplementary Fig. S3). Accordingly, a cutoff value of Cq < 4 was set to exclude the premature amplification, associated to the cutoff value of Cq > 22 that guarantee the exclusion of non-reproducible samples with low template concentration. Therefore, the preamplification process requires an ideal template concentration, limited to avoid a premature amplification, and enough for an adequate template distribution in the chambers. According to the Fluidigm® instructions, generally 10-14 preamplification cycles followed by at least 5-fold dilution is the adequate protocol (Quick Reference PN 100-5876 B1, Fluidigm®). Based on this, the 12 samples evaluated in this study were submitted to 10- or 14-preamplification cycles followed by 5-, 7-, or 9-folds dilution. The number of genes detected by each condition were identified taking in account the cutoff value of 4 < Cq < 22. Out of 48 genes, the number of genes detected in each sample type is variable depending on the template concentration (number of preamplification cycles + fold-dilution; Fig. 4). The highest number of genes was detected in the cumulus cell sample (Fig. 4). Overall, lower numbers of genes were detected when samples were submitted to 10-preamplification cycles compared to 14-preamplification cycles (green line and black lines, Fig. 4); not observed in oocyte sample, where no clear variation was observed between 10- and 14-preamplification cycles (blue line, Fig. 4). Otherwise, in the 5
Analytical Biochemistry 596 (2020) 113641
P.K. Fontes, et al.
Fig. 4. Assay detection per sample type. Number of assay detected per sample type in each sample concentration: 10 or 14 preamplification cycle, 5-, 7-, or 9-folddilution.
(FAPESP, São Paulo, Brazil, grant number #2012/50533–2). The authors acknowledge Joachim Valk for providing English language editing.
blastocyst (single) sample, higher numbers of genes were detected when the sample was submitted to 10-preamplification cycles instead of 14preamplification cycles, due to the excess of start template as discussed before (orange line, Fig. 4). Regarding the fold-dilution, in general, 5and 7- fold-dilution are the best association to 14-preamplification cycles, to all the samples type, except to Blastocyst (single) sample, which the 10|1:9 protocol was the more convenient (Fig. 4).
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ab.2020.113641.
4. Conclusion References We summarize that high-throughput system is a reliable and excellent tool for studying the bovine reproductive system, especially samples with quantitatively-limited, once a high number of target genes can be investigate using a very low amount of sample. Nevertheless, due to the nanoliter scale of RT-qPCR reaction, we reinforced that the preamplification step is indispensable, reliable and we highlighted that 14-preamplification cycles associated to 5- and 7-fold-dilution is the best approach for those samples. Ultimately, the Biomark™ HD system has a high intra- and inter reproducibility, therefore its performance in duplicate is unnecessary for the ΔCq analysis, taking in consideration the cutoff value 4 < Cq < 22.
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CRediT authorship contribution statement Patricia Kubo Fontes: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing - original draft, Writing - review & editing. Anthony César Souza Castilho: Conceptualization, Investigation, Project administration, Validation, Writing - review & editing. Eduardo Montanari Razza: Conceptualization, Investigation, Validation, Visualization, Writing original draft, Writing - review & editing. Marcelo Fábio Gouveia Nogueira: Conceptualization, Funding acquisition, Project administration, Resources, Validation, Visualization, Writing - review & editing. Declaration of competing interest The authors have declared that no competing interests exist. Acknowledgment This work was supported by São Paulo Research Foundation
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