Original ArticleDevelopment of an absolute quantification method for ribosomal RNA gene copy numbers per eukaryotic single cell by digital PCR
Introduction
Harmful Algal Blooms (HABs) can cause acute effects on marine ecosystems due to the production of endogenous toxins and/or enormous biomass, which can also lead to major impacts on local economies and public health (Imai et al., 2006; Dyson and Huppert, 2010; Lewitus et al., 2012; Grattan et al., 2016; Díaz et al., 2019). Frequent coastal monitoring for early detection of HAB species is a basic and yet the most important strategy to prevent and reduce economic and health impacts (Anderson et al., 2001; Andersen et al., 2003; Todd, 2004; Díaz et al., 2019). Direct counting of HAB species by a microscope is a conventional monitoring methodology (Edler and Elbrächter, 2010). However, the species can sometimes be undetected in case of being present in low abundances, morphological similarity to other species, and/or changes in morphology by the addition of fixatives (John et al., 2005; Zingone et al., 2006; Reguera and Pizarro, 2008; Karlson et al., 2010; Rodríguez-Ramos et al., 2014). To overcome those limitations, several molecular methods have been employed, e.g. metabarcoding and high-throughput sequencing (HTS), quantitative PCR (qPCR), and digital PCR (dPCR; Ebenezer et al., 2012; Penna and Galluzzi, 2013; Medlin and Orozco, 2017; Nagai et al., 2017; 2019; Lee et al., 2020).
Metabarcoding and HTS approach is a powerful tool for HAB species detection (Dzhembekova et al., 2017; Elferink et al., 2017; Nagai et al., 2017; Gran-Stadniczeñko et al., 2018; Moreno-Pino et al., 2018; Sildever et al., 2019; Liu et al., 2020). As the information on the whole community composition can be obtained from the same sample, changes in biodiversity can be detected (Lima-Mendez et al., 2015; Sawaya et al., 2019; Djurhuus et al., 2020). Those changes may serve as potential indicators for HAB dynamics (Yang et al., 2015; Hattenrath-Lehmann and Gobler, 2017; Berdjeb et al., 2018; Shin et al., 2018; Hattenrath-Lehmann et al., 2019; Nagai et al., 2019; Liu et al., 2020). At the same time, the approach is not quantitative as the relative sequence abundances do not reflect the cell abundances directly due to the influence of several factors, e.g. sample preservation (Mäki et al., 2017; Majaneva et al., 2018), DNA extraction (van der Loos and Nijland, 2020), choice of polymerase (Nichols et al., 2018), PCR bias (Suzuki and Giovannoni, 1996; Aird et al., 2011; Gonzalez et al., 2012), primer specificity (Elbrecht and Leese, 2015; Piñol et al., 2015) and copy number of the target gene (Not et al., 2009; Kembel et al., 2012; Mäki et al., 2017; Saad et al., 2020). Although all the mentioned factors may influence the resulting relative sequence abundances, the main influence is due to the variation in the rRNA gene copy numbers (Mäki et al., 2017; Saad et al., 2020).
At the same time, qPCR allows identification of target species as well as estimation of cell abundances and gene copy numbers (Bowers et al., 2000; Gray et al., 2003; Dyhrman et al., 2006; Murray et al., 2011; Penna and Galluzzi, 2013; Eckford-Soper and Daugbjerg, 2016; Ruvindy et al., 2018). The method is rapid and suitable for detecting HAB species present in low abundance (Hosoi-Tanabe and Sako, 2005; Kamikawa et al., 2006; Yuan et al., 2012; Zamor et al., 2012; Zhang and Li, 2012). To enumerate the cells, the preparation of standard curves for each target species is necessary (Penna et al., 2013). However, the standard curves generated based on laboratory cultures may be biased compared to DNA extracted from environmental samples (Vaitomaa et al., 2003; Penna and Galluzzi, 2013). This may be due to the presence of PCR inhibitors in natural samples (Flekna et al., 2007; Ellison et al., 2011) influencing amplification efficiency (Coyne et al., 2005) as well as due to the potential difference in the rRNA gene copy number between the laboratory culture and natural cells (Galluzzi et al., 2010; Ruvindy et al., 2018; Meistertzheim et al., 2019; Murray et al., 2019). To reduce the influence of PCR inhibitors and avoid the need for standard curves, an alternative approach, digital PCR (dPCR), can be used for the identification and quantification of target species (Te et al., 2015; Medlin and Orozco, 2017). Due to the partitioning of reaction mix into thousands of small reactions (pico- to nanoliters), the influence of PCR inhibitors is minimized (Basu, 2017; Quan et al., 2018). Also, the partitioning allows estimating the copy number without the standard curve as each droplet or microwell is expected to contain 0 or ≥ 1 copies of the target gene resulting in either positive or negative detection (Medlin and Orozco, 2017; Quan et al., 2018). The number of positive detections is corrected with the Poisson statistic to account for the presence of >1 copies per partition to provide a correct estimate for the target gene copies (Majumdar et al., 2015).
However, the rRNA genes are grouped into one repeat unit consisting of three coding units (18S, 5.8S, 28S), which is separated by the following repeat unit by intergenic spacers (Weider et al., 2005). When several tandemly connected rRNA gene units are contained in a single partition in dPCR, the positive signal is counted as one copy, causing underestimation of copy number measurements. Thus, it is necessary to digest genomic DNAs for sample preparation in dPCR to separate tandem gene copies and ensure proper random partitioning into droplets (Alanio et al., 2016; Joaunin, et al. 2020).
In molecular studies rRNA genes are commonly targeted (John et al., 2005; Stoeck et al., 2010; Elferink et al., 2017; Grzebyk et al., 2017; Engesmo et al., 2018; Ruvindy et al., 2018), although other genes have also been used (Koskenniemi et al., 2007; Delaney et al., 2011; Murray et al., 2011; Churro et al., 2012; Savela et al., 2016). Ribosomal rRNA genes are present in many copies per genome that facilitate their amplification, have variable rates of evolution among the different genes and gene regions that support the identification of various species and genera, as well as contain conserved regions that allow designing universal primers (Hillis and Dixon, 1991). Ribosomal rRNA gene copy number is variable among (Zhu et al., 2005; Godhe et al., 2008; Penna et al., 2013; Mäki et al., 2017; Gong and Marchetti, 2019) and within the species (Lee et al., 2009; Galluzzi et al., 2010). To reliably quantify the cells, correction for the copy number is necessary when using quantitative qPCR or metabarcoding and HTS (Galluzzi and Penna, 2010; Medinger et al., 2010; Kembel et al., 2012; Mäki et al., 2017; Gong and Marchetti, 2019; Saad et al., 2020). However, for many aquatic eukaryotes, except for several phytoplankton species, the information on the rRNA gene copy number is currently lacking (Galluzzi et al., 2010; Penna et al., 2013; Lee et al., 2017, 2020; Banerji et al., 2018; Gong and Marchetti, 2019).
To provide information on the rRNA gene copy number in eukaryotes with the focus on HAB species, a universal primer set accompanied by a labeled probe with a fluorophore and a double-quencher was developed. Using dPCR, the number of rRNA gene copies per cell was determined for 16 cultured species, the majority of those HAB species. Influence of fragment sizes in template DNAs on the copy number measurement was also examined. The results of this study facilitate the determination of copy numbers for various HAB species and support the precise quantification of HAB species through various molecular methods.
Section snippets
Materials and methods
Algal maintenance: A total of 31 clonal strains derived from 16 species from Japan or Chile were maintained in the laboratory cultures (Table 1). To focus on the HAB species, toxin-producing species (Moestrup et al., 2021) were mainly investigated (n = 14). All algal cultures were maintained in sterile plastic flasks with a ventilation sponge containing L1 or F/2 media for dinoflagellates and diatoms, respectively, and exposed to 100 μmol photons m−2 s−1 on a 12hL: 12hD light and dark cycle at
Results
Primer and probe specificity: Based on the in silico PCR, > 90% nucleotide homology was detected from 21/26 bp, 22/23 bp, 20/20 bp in the forward and reverse primers and the probe, respectively (Table 3). Percentages of gaps were 0.5 ± 0.6 (%, average ± SD), 0.2 ± 0.3, 0.4 ± 1.0, respectively. Entropy was 0.31 ± 0.28, 0.11 ± 0.10, 0.14 ± 0.20, respectively, and higher in the forward primer region than in the two other primer and probe regions, confirming the robustness of the newly-developed
Discussion
A set of universal primer-probe was developed to quantify the target 18S rRNA gene of various eukaryotic species, including HAB causing phytoplankton, without the use of a standard curve. To the best of our knowledge, this study demonstrated the first comparison of 18S rRNA gene copy numbers among various phytoplankton species simultaneously measured by dPCR. The newly developed primer-probe set can also be used for qPCR, although standard curves need to be developed in that case. Chelex –
Conclusion
The newly designed universal primer-probe set targeting the 18S rRNA gene for dPCR reported in this study facilitates quantification in gene copy numbers of rRNA gene from 31 strains of 16 phytoplankton species without the need for species-specific primers and standard curves. Among the investigated species, the highest copy numbers were measured for dinoflagellates with up to 2 million copies per cell in Gambierdiscus sp. The newly designed universal primer-probe set is also useful for
Funding source
This study was supported by grants from the study on "Establishing a network of environment and fisheries information", Ministry of Agriculture, Forestry and Fisheries, Japan [SN]; "Technological developments for characterization of harmful plankton in the seawater", Ministry of Agriculture, Forestry and Fisheries, Japan (16808839) [SN]; JST/JICA, Science and Technology Research Partnership for Sustainable Development (JPMJSA1705) [SN]; a Grant-in-Aid for Scientific Research (Kiban-B) by the
Authors’ contribution and declaration
The content represents original and valid work and has not been published or not considered for other publications. All authors reviewed and approved the final article, and each has participated sufficiently in the work to take the following responsibilities; SN contributed to building a strategy of the project, leading the laboratory studies, conducting in silico PCR, data analysis and data review, and supervising the manuscript writing. KY contributed to data reviewing and manuscript writing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We thank Y. Tsushima and K. Hagiya for providing technical support for the JN Medsys Clarity™ Digital PCR system operation. Dr. P. Laas is thanked for the language check of the manuscript.
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2022, Harmful AlgaeCitation Excerpt :Average copy number per cell of this rRNA gene across all taxa examined was from 44 in Pseudochatonella verruculosa to 1,800,000 in Gambierdiscus sp., with Dinophysis fortii having on average ∼16,000–29,000 copies cell−1 (2 cells from each of two strains of D. fortii were examined). It is well understood that most ribosomal genes are in multiple copies within a dinoflagellate genome (Murray et al., 2019), yet dinoflagellates such as Alexandrium can show up to a > 10-fold variation even within a single species ((Brosnahan et al., 2010; Erdner et al., 2010; Galluzzi et al., 2004; Hou et al., 2019; Ruvindy et al., 2018; Savela et al., 2016; Yarimizu et al., 2021). For example, genomic copies per cell−1 for Alexandrium australiense strains have been found to vary between 70,000–150,000, and between 500,000–100,000 for A. catenella (Murray et al., 2011).