QuantAS: a comprehensive pipeline to study alternative splicing by absolute quantification of splice isoforms

Abstract

Alternative splicing (AS) is a mechanism by which cells generate abundant protein diversity from a limited number of genes (Baralle & Giudice, 2017). AS plays a crucial role in regulating various life activities such as growth, development, and aging in plants (Zhu et al., 2017; Godoy Herz & Kornblihtt, 2019; Jabre et al., 2019; Chen et al., 2020; Reddy et al., 2020; Zhang et al., 2020), where it greatly influences plant growth, development, and response to biotic and abiotic stresses (Motion et al., 2015; Laloum et al., 2018; Chaudhary et al., 2019; Chen et al., 2021; Ganie & Reddy, 2021; Saini et al., 2021; Zhu et al., 2023; Supporting Information Fig. S1). The traditional method for the identification of AS is semi-quantitative RT-PCR, which is easy to perform (Palusa et al., 2007; Li et al., 2020; Riegler et al., 2021; Han et al., 2022). Quantitative PCR (qPCR) is also widely used in AS research, as it enables real-time monitoring of fluorescence signals and accurate quantification of isoform copy numbers through the use of specific primers (Hefti et al., 2018; Liu et al., 2018; Huang et al., 2021). With the emergence of digital PCR (dPCR), the identification methods of AS have become more diversified, which disperses each single target fragment into separate droplets as much as possible through the calculation of positive droplets (Fig. S2; Gao et al., 2021). Based on the urgent need for the accurate quantification of various isoforms, an AS detection method called QuantAS was established (Fig. 1), which allows us to accurately quantify all isoforms of genes based on absolute quantification technology and specific primer design. The method utilizes isoform-specific primers to overcome the isoform identification difficulty caused by different AS events and is designed by using the functional coding region as the isoform structure classification unit to ensure isoform independence (Fig. 2a). RT-qPCR enables real-time monitoring of changes in the fluorescence signal, quantification of differences between expression levels, and simultaneous detection of multiple in a single reaction. According to the copy number of different isoforms, isoform expression patterns can be identified by combining with absolute quantitative techniques. This method greatly increases the accuracy of identification and reduces the cost of repeated experiments. Furthermore, the absolute quantification of AS isoforms employing the combination of qPCR and dPCR could provide their respective advantages, thus rapidly obtaining all isoform information of the potential functional genes to be investigated. QuantAS consists of three stages: (1) gene structure assembly and specific primer design, including AS event analysis; (2) accurate quantitative analysis of the isoforms in the treated samples using qPCR and dPCR to obtain the copy number of each isoform; and (3) absolute quantification, which involves data analysis to explore the existence and levels of isoforms (the outline of the protocol for QuantAS is shown in detail in Figs 1, S3). After determining the target genes, we obtain and sort out the gene sequence information. Gene sequence data were obtained through databases such as NCBI (https://www.ncbi.nlm.nih.gov/)/Phytozome (https://phytozome-next.jgi.doe.gov)/TAIR (https://www.arabidopsis.org/), including gDNA, cDNA, and coding sequence (CDS) data corresponding to multiple isoforms contained in the database. Through RNA-Seq data processing, new isoforms of cDNA sequence information can be obtained through Expasy (https://web.expasy.org/translate/), which can acquire information on the splicing of multiple open reading frames (ORFs). The CDS of the isoform can be obtained by comparing it with other cDNAs. Due to the variety of database versions and the revisions of transcriptome data, the isoform number may vary. Therefore, all obtained gene sequences were renamed and ordered according to gene names and the number of isoforms and stored in FASTA files according to gDNA cDNA and CDS respectively. Gene structure was assembled by GSDS (http://gsds.gao-lab.org/), and the sequence (FASTA) option was selected in the format of gene features for sequence alignment. Upload the CDS files of all genes at the CDS (FASTA) input data, upload the gDNA sequence files at the genomic sequence (FASTA), choose SVG as the export format, and obtain the corresponding gene structures of all genes after submission. It should be noted that the gene structure at this time only includes the structure of the CDS region but not the untranslation region (UTR), which needs to be modified. All cDNA sequence files were uploaded to CDS (FASTA) input data, and gDNA sequence files were uploaded to genomic sequence (FASTA) in the same way. At this point, the gene structure does not distinguish between CDS and UTR, but all the gene structure is mapped. By comparing the two images generated on the website, the specific structure in the UTR was modified and the complete gene structure was finally obtained (Fig. S3). The NCBI blast function was used to compare gene sequences. By comparing the gDNA sequence with the CDS and cDNA sequence, the gDNA position corresponding to each exon fragment (including UTR and CDS) was obtained (Fig. S3). According to the gene structure of isoforms, the different areas between each isoform were found. Drawing the structure of pre-mRNA and marking the splicing site for AS event analysis. NCBI blast results can also quickly find the junction site and the sequence of the surrounding region, which is conducive to the subsequent design and selection of specific primers. Based on these results, we find the location of each AS event and choose a suitable primer design method according to the different AS events. To comprehensively identify each type of AS event by PCR, we introduce a QuantAS-based primer design method here by designing isoform-specific primers at exon–exon junction, alternatively spliced exons or retained introns as shown in Fig. 1. In a scenario where a gene has two isoforms AS1/AS2 and intron retention occurred in AS2, a reverse primer encompassing exon 2/3 splice junctions was used to detect AS1, and another primer encompassing 3′ end of intron and 5′ end of exon 3 was used to detect AS2. These two reverse primers correspond exclusively to AS1 and AS2, respectively. It is worth noting that in cases such as AS2, it is recommended to design primers on retained introns, and the proportion of primer sequences on both sides of the splice junction should be taken into account when designing primers on the splice junction to prevent the possibility of primer mismatch to AS1. The three primers used in this paper aim to reduce the cost and experimental complexity, but the experimental design can be changed according to the specific situation of the sequence in the experiment. More specific primer pairs can increase the accuracy of the results (primers were synthesized by Tsingke Biotechnology Co., Ltd, Beijing, China). Different AS events, the design of specific primers, and their corresponding isoforms are presented in Fig. 2(a). The areas that can be selected by primers are marked with magenta lines so that users can intuitively see where the primers can be designed. The plasmid standard was constructed through gene synthesis (Sangon Biotech (Shanghai) Co., Ltd, Shanghai, China), and the complete fragment including specific primers is part of the plasmid. The plasmid was required to be of high purity and devoid of any contaminants, preferably A260/280 = 1.8. The plasmid was dissolved in ddH2O, and the concentration was measured by Thermo ScientificTM Qubit Fluorometer or Thermo ScientificTM Nanodrop Fluorospectrometer (Thermo Fisher Scientific, Shanghai, China), and the unit is ng μl−1. For the standard curve, 10-fold serial dilutions were made starting from a plasmid concentration. The generation of identical duplicates at high CT values was significantly improved by diluting the last concentration solution. The dynamic range of the standard curve spanned at least five orders of magnitude. Droplets from the tube wall may be briefly shaken and centrifuged for collection, which is repeated several times while scaling up the dilution volume so that the standard can be adequately diluted. It is important to note that the accuracy of standard dilution is closely related to the amplification efficiency, so it is important to ensure volume differences with each pipette. Tips: for accurate calculation of copy number, users need to construct the standard curve in each experiment to ensure that the reaction conditions of the sample and the standard are consistent for splicing isoform identification. Since the expression of each isoform in the organism is not fixed, there are often some isoforms with low expression. Therefore, it is recommended to increase the dilution level when diluting the standard sample and ensure that the CT value of the sample will eventually fall into the range of the drawn standard curve utilizing a preliminary experiment. It is important to note that the standard curve may only be used for interpolation and cannot extrapolate the quantity of the unknown sample because the analysis may not be linear beyond the scope covered by the standards tested. The mortar and pestle were soaked in 0.1% DEPC solution, completely wrapped with aluminum foil, and baked at 300°C for > 2 h. EP tubes and other consumables were soaked in 0.1% DEPC solution and sterilized at high temperature and high pressure. All the items were dried before use. The samples were ground into powder in liquid nitrogen and stored at −80°C. One hundred milligram of sample powder was added to the EP tube, and 1 ml TRIzon Reagent (CW0580S; CoWin Biotech, Beijing, China) was added. The mixture was shaken mixed and allowed to stand for 5 min. After adding 200 μl chloroform, the liquid was quickly shaken and mixed, incubated for 10 min, and centrifuged at 13 500 g at 4°C for 15 min. After centrifugation, the liquid was divided into three layers and RNA was concentrated in the supernatant. The supernatant was collected (being careful not to suck into the middle layer), a new EP tube was added, and isopropyl alcohol was added, fully absorbed, and mixed. The samples were incubated for 10 min and then centrifuged at 4°C for 10 min. The supernatant was adsorbed and discarded, freshly prepared 75% ethanol (RNase-free water was used for preparation) was added to the tube, and the precipitate was gently tapped to ensure that the alcohol fully contacted the precipitate. Then, the supernatant was adsorbed and discarded after centrifugation at 4°C for 5 min. After a short centrifugation, the liquid was removed as much as possible, and the EP tube was dried on a super-clean platform for 10 min. Care was taken not to over-dry the RNA sample, otherwise, the precipitate would be difficult to dissolve. Add 50 μl RNase-free water and mix gently. The RNA concentration was determined by UV spectrophotometry, and RNA integrity was examined by agarose gel electrophoresis. Finally, cDNA was obtained by reverse transcription with Evo M-MLV RT Premix for qPCR (AG11706; Accurate Biotechnology (Hunan)Co., Ltd, ChangSha, China). Experiments were conducted in Thermo ScientificTM StepOnePlus Real-Time PCR System (Thermo Fisher Scientific) according to the manufacturer's instructions. All reactions were performed in 20 μl reaction volumes in 8-Strip PCR tubes with domed lids. The 2× SYBR Green Pro Taq HS Premix (AG11701; Accurate Biotechnology (Hunan)Co., Ltd) and 20 μM ROX passive reference dye were used. After full mixing, they were evenly divided into each well (Table S1). The cDNA template was mixed with water to reduce error and then added to the point sample well. After capping the tube or sealing the film, the bubbles are removed by centrifugation at high speed. Thermal cycling consisted of an initial denaturation at 95°C for 30 s followed by 40 cycles of denaturation at 95°C for 5 s, and annealing and extension at 60°C for 30 s. Melting curve analysis was carried out from 60°C to 95°C with 0.3°C increments. Threshold cycle (CT) values were determined by automated threshold analysis. PCR efficiencies were determined from dilutions of DNA and calculated from the slopes of the standard curves according to the equation. The standard curve mode was selected for equipment options, the standard curve was set, and the two-step method was used for the experiment. (If the amplification efficiency consistently fails to meet the expected standard, a three-step method may be considered. Thermal cycling consisted of an initial denaturation at 95°C for 30 s followed by 40 cycles of denaturation at 95°C for 5 s, annealing at 60°C for 30 s, and extension at 72°C for 30 s). After completing the qPCR program, the original data were exported for subsequent and analysis. curves corresponding to standard curves should have consistent and The standard curve high > and the amplification efficiency was between and The curve required that the curve of the same gene should be the curve should be and the fluorescence should be at the same level as shown in Fig. are often such as primer primer amplification and RNA Due to the of different and equipment the standard curve amplification efficiency and PCR reaction may be to different analysis and to experimental are shown in Experiments were conducted in Thermo ScientificTM StepOnePlus (Thermo Fisher Scientific) according to the manufacturer's instructions. All reactions were performed in 20 μl reaction volumes in 8-Strip PCR tubes with domed lids. The 2× Pro Taq HS Premix Accurate Biotechnology (Hunan)Co., Ltd) and 20 μM passive reference dye were used (Table After mixing, they were evenly divided into each well. It is worth noting that in primers and their corresponding need to be added in reaction. To ensure that different of signals can be by the the of should be determined according to the equipment Thermal cycling consisted of an initial denaturation at 95°C for 30 s followed by 40 cycles of denaturation at 95°C for 5 s, and annealing and extension at 60°C for 30 s. Threshold cycle (CT) values were determined by automated threshold analysis. 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