Near full-automation of COPMAN using a LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a leading indicator

Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 105 copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5–13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.


H I G H L I G H T S G R A P H I C A L A B S T R A C T
• COPMAN is a detection method for viral RNA in 8-10 mL of wastewater. • Near-full automation was achieved by combining COPMAN with a versatile robot. • A linear correlation between detected SARS-CoV-2 and reported cases was observed. • COPMAN-detected SARS-CoV-2 could be a leading indicator of reported cases.

Keywords:
Wastewater-based epidemiology COVID-19 Sewage Environmental surveillance Automation Maholo LabDroid Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 10 5 copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5-13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-

Introduction
Most nationwide wastewater-based epidemiology (WBE) studies have been conducted using manual viral RNA detection processes in many countries, including the United States, United Kingdom and Austria Morvan et al., 2022;Amman et al., 2022). Manual processes require a significant amount of labor, cost, and time, which negates some of the benefits of WBE. What is needed is a robust and automated detection system for large-scale analysis that is applicable to areas with low COVID-19 prevalence (Safford et al., 2022).
As an automatable WBE method for social implementation, we developed COPMAN, the COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater (Katayama et al., 2023), which consists of viral concentration from small volumes (8-10 mL) of wastewater, nucleic acid extraction, reverse transcription, pre-amplification, and qPCR. COPMAN employs a high-basicity polyaluminum chloride (PAC) coagulation and magnetic bead-based RNA purification to minimize the need for centrifugation, which is less compatible with automation. The small amount of wastewater sample (8-10 mL) can be suitable for transportation to the laboratory and compatible with an automated-pipetting device or a simultaneous decapping/recapping system. In our previous study, we proved the sensitivity of COPMAN on a manual basis and demonstrated its high sensitivity in detecting SARS-CoV-2 RNA in wastewater at a low prevalence of 0.6 new cases daily per 100,000 inhabitants in the study area (Katayama et al., 2023). In the present study, we developed a near full-automation line for COPMAN by using the Maholo LabDroid, a multipurpose humanoid with two arms, that can conduct most operations performed by humans in the laboratory (Yachie and Natsume, 2017).
Previous studies have demonstrated that SARS-CoV-2 RNA concentrations in wastewater reflect clinical infection trends in a timely manner , Li et al., 2023, Ahmed et al., 2021, Tiwari et al., 2022. WBE can be a valuable complementary tool for community surveillance. Indeed, WBE is used as an important tool to monitor COVID-19 worldwide (COVID-19 WBE Collaborative, 2023). The increased throughput (576 samples by LabDroid/week) of the automated COPMAN might be a solution for a potential limitation of WBE expansion due to manual operations.
We evaluated the automated COPMAN by detecting SARS-CoV-2 RNA from wastewater sample derived from wastewater treatment plant (WWTP) during the sixth wave of infection in Japan. For comparison, we tested, by manual operation, the same set of wastewater samples using EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported highly sensitive detection method (the lowest detection limit = 21.1 copies/mL when heat-inactivated SARS-CoV-2 was spiked in wastewater). We also tested how well the output data correlated with clinically confirmed cases in these areas.

Wastewater sampling
Between 1 November 2021 and 24 May 2022, 132 influent wastewater samples were collected from two WWTPs on the right (Shinomiya Water Reclamation Center) and left (Yanagishima Water Reclamation Center) banks of the Sagami River in Kanagawa, Japan (Fig. 2, A, WWTP R and L, respectively). Since these areas are part of the greater Tokyo and large number of people are serviced by the WWTPs (1.27 and 0.541 million inhabitants, respectively), we considered that these areas are suitable for monitoring of SARS-CoV-2. Another reason for choosing the sampling sites was that the local government has allowed us to publish the data. Wastewater samples were collected every 2-5 days with the following exceptions: samples were not collected between 28 December 2021 and 4 January 2022, between 31 March and 18 April 2022, and between 27 April and 5 May 2022. The daily reported numbers of newly infected cases ranged from 0 to 130.3 per 100,000 inhabitants during the sampling period. The collected samples were immediately transported to the laboratory on ice and stored at −80°C until use (maximum storage period: 7 months).

Automated COPMAN
A schematic of the automated COPMAN is shown in Fig. 1. To develop COPMAN with the Maholo LabDroid, we optimized its protocol as follows. First, we used 10 mL jacket tubes with screw caps that were compatible with a simultaneous decapping/recapping system and centrifuge. The input volume of wastewater was set at 8 mL. We found that 96 deep well plates were suitable for PCI extraction using an automated-pipetting device (VS8; Biotech, Tokyo, Japan). RNA extraction using magnetic beads was performed using the Maholo LabDroid.
The virus concentration and RNA extraction process employed in the automated COPMAN are described in the instructions for the COPMAN viral RNA kit (AdvanSentinel, Osaka, Japan) with slight modifications. Briefly, after thawing the wastewater samples, 1 μL of polyaluminum chloride (PAC) was added to 8 mL of wastewater in a 10-mL jacket tube using an automated-pipetting device. The samples were then vigorously shaken 30 times and mixed 100 times with a rotator. The formulated flocs were collected by centrifugation at 3000 ×g for 10 min, suspended in 250 μL SDS-based lysis buffer, and digested with 14.25 μL proteinase K solution for 15 min in an incubator set at 60°C. Next, 550 μL phenol/chloroform/ isoamyl alcohol (25:24:1, pH 5.2) was added to the samples with the automated pipetting device followed by mixing on a plate shaker at 2400 rpm for 2 min. The samples were then transferred to 1-mL deep well plates and centrifuged at 3000 ×g for 15 min to obtain 85-μL crude extracts, which were subsequently transferred to 96-well plates using an automatedpipetting device. Magnetic bead-based extraction of nucleic acids was performed using the Maholo LabDroid to obtain a final RNA extract volume of 25 μL. The nucleic acid concentrations and ratios of absorbance at 260 nm and 280 nm were measured using NanoDrop 2000 (Thermo Fisher Scientific) ( Fig. S1A and B).
The RT-preamp master mix and the qPCR master mix were prepared by the Maholo LabDroid with the commercialized products recommended in the COPMAN viral RNA kit, as described in a previous study (Katayama et al., 2023) with slight modifications. Briefly, reverse transcription was performed using a Reliance Select cDNA synthesis kit (Bio-Rad laboratories, Hercules. CA. USA) as follows: 4 μL RNA extract, 2.0 pmol 2019-nCoV_N1-R (Lu et al., 2020), and 2.0 pmol PMMoV reverse primer (Haramoto et al., 2013) in a total 20-μL reaction mix with the temperature setting of 50°C for 60 min, and 95°C for 1 min. Next, 20-μL RT products were amplified using Biotaq HS (Bioline Reagents Ltd., London, UK) in a 30-μL reaction mix, containing 3 μL ImmoBuffer, 0.6 μL dNTP mix, 1.2 μL MgCl 2 , 0.7 μL Biotaq HS Pol, 9 pmol 2019-nCoV_N1-F, 9 pmol 2019-nCoV_N1-R, and 4.05 μL nuclease free water, at 95°C for 10 min, and 10 cycles of 95°C for 15 s, 55°C for 15 s, and 72°C for 30 s. The use of PMMoV reverse primer alone in this reaction terminates with reverse transcription for PMMoV RNA and prevents it from deviating from the quantitative range of qPCR for PMMoV, which is abundantly present in wastewater. qPCR was then performed with the TaqMan Environmental Master Mix 2.0 (Thermo Fisher Scientific) at 95°C for 10 min, followed by 45 cycles of 95°C for 15 s and 60°C for 30 s, in a 20-μL reaction mix containing 10 pmol reverse and forward primers and 7.5 pmol TaqMan probe. Although the operation was technically possible, the preamp reactions and transfer of the preamp products to qPCR plates were not performed by the Maholo LabDroid to prevent contamination with the preamp products due to institutional restrictions. Before we analyzed the qPCR results, slopes (average = −3.374, amplification efficiency > 97.6 %) and R 2 values (average = 0.9952) of the standard curves were quantified for quality control.
PMMoV (pepper mild mottle virus), a widely used human fecal strength indicator (Kitajima et al., 2014(Kitajima et al., , 2018, was constantly detected at high concentrations (over 1.3 × 10 6 copies/L in all wastewater samples by both methods, data not shown). Normalization with such an indicator might be effective for cancelling the side effects, for example, dilution of wastewater with stormwater. However, in the present study, normalization with PMMoV did not necessarily improve the correlation when we divided the copy number of SARS-CoV-2 RNA by that of PMMoV RNA in the same samples (Fig. S2).

EPISENS-S
The EPISENS-S method was performed as previously described (Ando et al., 2022). Briefly, 40-mL wastewater samples were centrifuged at 3000 ×g for 10 min to form pellets, and total RNA was extracted from the pellets using the RNeasy Power Microbiome kit (Qiagen, Hilden, Germany) to obtain 50-μL RNA extracts. Nucleic acid concentrations and ratios of absorbance at 260 nm and 280 nm were measured using NanoDrop 2000 (Fig. S1A and B). Subsequently, 13.5 μL of the RNA extract was subjected to one-step RT-preamp followed by qPCR using the iScript™ Explore One-Step RT and PreAmp Kit (Bio-Rad Laboratories, Hercules, CA, USA) and QuantiTect Probe PCR Kit (Qiagen, Hilden, Germany), which was performed using the CDC N1 and PMMoV primers and probe. For the detection of PMMoV RNA, only the reverse primer was included in the one-step RT-preamp step. The reaction mixture for the one-step RT-preamp (30 μL) contained 15.0 μL SsoAdvanced preamp supermix, 0.6 μL iScript explore reaction booster, 0.6 μL iScript advanced reverse transcriptase, 1.0 pmol 2019-nCoV_N1-F, 1.0 pmol 2019-nCoV_N1-R, 1.0 pmol PMMoV reverse primer, and 13.5 μL RNA template. The thermal cycling conditions for RT-preamp were as follows: 25°C for 5 min, 45°C for 60 min, and 95°C for 3 min followed by 10 cycles of 95°C for 15 s and 55°C for 4 min. The qPCR reaction was performed in a 20-μL reaction mix containing 12.5 μL QuantiTect Probe PCR Mix, 10 pmol forward primer, 10 pmol reverse primer, 10 pmol TaqMan probe, 7.25 μL RNase-free water, and 2.5 μL template. The thermal cycling conditions for qPCR were as follows: 50°C for 2 min and 95°C for 10 min followed by 45 cycles of 95°C for 3 s and 55°C for 32 s. Before we analyzed the qPCR results, slopes (average = −3.39, amplification efficiency >97.2 %) and R 2 values (average = 0.9979) of the standard curves were quantified for quality control.

Collection of reported numbers of newly infected case
Newly reported cases were retrieved from daily reports on the official website of Kanagawa prefecture (https://www.pref.kanagawa.jp/docs/ ga4/covid19/index.html). In the Sagami River basin, the total service populations of the left-and right-bank WWTPs were 1.27 and 0.541 million, respectively. To calculate the reported COVID-19 cases in the area, we used data from major cities which are serviced by WWTPs of the left and right banks, with 1.29 (consisting of Chigasaki, Ebina, Sagamihara, Samukawa, and Zama areas) and 0.552 (consisting of Hiratsuka, Aikawa, Atsugi, and Oiso areas) million inhabitants, respectively.

Statistical analysis
The concentrations of viral RNA (copies/L) were theoretically calculated based on the amount of input wastewater in the final qPCR tube. All statistical analyses including Pearson's and Spearman's correlation tests, paired Student's t-test, and autocorrelation were performed using R version 4.2.1 (R Core Team, 2019). Automated and high-throughput SARS-CoV-2 detection procedure using 8 mL raw wastewater samples.

Trends of wastewater SARS-CoV-2 concentrations determined by automated COPMAN and EPISENS-S
To evaluate the performance of the automated COPMAN, we quantified the concentrations of SARS-CoV-2 RNA in wastewater from the Sagami River basin in Japan ( Fig. 2A) using automated COPMAN and EPISENS-S. We began sampling in November 2021, before the sixth infection wave had started in the area. The daily reported numbers of newly infected cases ranged from 0 to 130.3 per 100,000 inhabitants during the sampling period. As a result of RNA extraction, the yields of nucleic acid per 1 mL of wastewater tended to be higher when using the automated COPMAN (average = 1.29 μg/mL) compared with EPISENS-S (average = 0.87 μg/mL, p < 0.01, paired t-test). On the other hand, the absorbance ratio 260/280 was higher with EPISENS-S (average = 2.19) than COPMAN (average = 1.99). The automated COPMAN and EPISENS-S began to detect SARS-CoV-2 RNA on 15 and 10 December 2021, respectively, before 5 January 2022 when the daily reported COVID-19 cases reached 1 per 100,000 inhabitants. Both methods detected SARS-CoV-2 stably after the reported cases reached 10 per 100,000 inhabitants on 14 January 2022 (Fig. 2B). However, the detection patterns of the two methods differed. Regarding the number of detections, automated COPMAN and the EPISENS-S detected SARS-CoV-2 RNA from 81 and 85 out of 132 WWTP samples throughout the period, respectively (Fig. S1C). However, automated COPMAN detected higher copy numbers of SARS-CoV-2 RNA compared with EPISENS-S (Fig. S1D, p = 2.62 × 10 −7 , paired Student's t-test). The detected RNA concentrations were not strongly correlated between the two methods (r = 0.13; Pearson's correlation).

Estimation of time lags between wastewater surveillance and clinical reporting
Next, we examined whether the SARS-CoV-2 RNA concentration in wastewater reflected daily reported clinical cases. To estimate time lags between the reported clinical cases and the concentration of SARS-CoV-2 RNA in wastewater, we performed a cross-correlation analysis. Considering the day-of-the-week effect due to the business hours of the health clinics and local governments, we used the 7-day moving average of the reported clinical cases. Some public health centers on the left bank reported new cases of infection during weekdays. In fact, a slight temporal autocorrelation was observed in the reported cases over a period of 7 days (Fig. S3).
The SARS-CoV-2 RNA detected with automated COPMAN showed the strongest correlation with the reported clinical cases 5 and 13 days later for the left and right banks, respectively (max r = 0.85 and 0.76, Pearson's correlation, Fig. 3A and C). In contrast, EPISENS-S did not show a strong correlation (max r = 0.48 and 0.37, Lag = −10 and − 20 days, for the left and right banks, respectively; Pearson's correlation, Fig. 3A and C). We also confirmed the time-shifted correlation in the scatter plot (Fig. 3B   Fig. 3. Time lag analysis between SARS-CoV-2 RNA concentrations in wastewater and reported cases. (A, C) Pearson's correlation between SARS-CoV-2 RNA in wastewater and newly reported infections in the area. Cross correlation curve between SARS-CoV-2 RNA and reported cases in the day (gray line) or 7-day moving average (blue and orange lines). The horizontal axis shows time lags of reported cases relative to wastewater sampling date. The positive lag indicates the correlation of wastewater data with clinical data of the following day and vice versa for the negative lag. Error bars represent 95 % confidence interval. Circles indicate T value of Pearson's correlation in each lag. Colored dotted lines represent the lag of cross-correlation peak that means the number of days that the SARS-CoV-2 level in wastewater preceded reported clinical cases. (B, D) Scatter plots from the time lag analysis. Without time lag (center), the max correlation shift (colored), and shift in an opposite direction are displayed. and D). These results indicate that SARS-CoV-2 RNA detected with automated COPMAN reflects cases reported several days later.
As non-linear correlations may be observed in the presence of PCR inhibitors, we also performed Spearman's correlation analysis, which does not assume linearity. SARS-CoV-2 RNA detected with EPISENS-S was strongly correlated with clinical cases using Spearman's method (max rho = 0.91 and 0.85, Lag = −1 and +11 days, for the left and right banks, respectively) (Fig. S4). The concentration of SARS-CoV-2 RNA detected with COPMAN was also strongly correlated with future case reports using Spearman's method (max rho = 0.92 and 0.89, Lag = +6 and +11 days, for the left and right banks, respectively).

Discussion
Automation can be a key factor in large-scale WBE for the timely detection of pathogenic viruses. As a result of establishment of automated COPMAN, it became possible to analyze up to 576 wastewater samples per week without laborious manipulations or the risk of exposure to pathogens, making the method more feasible for largescale WBE.
In the present study, SARS-CoV-2 RNA detected by COPMAN demonstrated strong correlation with reported cases by both Pearson's (max r = 0.85 and 0.76) and Spearman's correlations (max r = 0.92 and 0.89), while EPISENS-S showed correlation by Spearman's correlation (max r = 0.91 and 0.85) but not by Pearson's correlation (max r = 0.48 and 0.37) (Fig. 3A to D, Fig. S4). These results confirmed the robustness of the automated COPMAN, which was able to detect the copy number of RNA linearly from wastewater as compared to EPISENS-S. One possible explanation for the linear detection of SARS-CoV-2 by COPMAN would be PCR inhibitors, which in wastewater often affect enzymatic reactions and disrupt the linear detection of nucleic acids (Ahmed et al., 2022). Since COPMAN adopted PCR inhibitor-resistant enzymes for the RT-qPCR assay, the method may have detected RNA more accurately even in the presence of inhibitors, resulting in the higher Pearson's correlation. In this study, automated COPMAN tended to detect higher numbers of SARS-CoV-2 RNA than EPISENS-S (Fig. S1D), which supports the resistance of COPMAN to PCR inhibitors in wastewater. The higher copy numbers of SARS-CoV-2 in COPMAN can also be explained by the higher concentration of nucleic acids in the extracts (Fig. S1A), which might be due to the flocculation of small particles in wastewater using PAC.
In terms of detection rates, EPISENS-S detected SARS-CoV-2 RNA from more wastewater samples than COPMAN (85 vs. 81 out of 132 samples) (Fig. S1C). This difference may be attributed to the volume of wastewater analyzed. While EPISENS-S theoretically analyzes 10.8 mL of the original wastewater in the final qPCR reaction, COPMAN analyzes 356 μL, which makes it difficult to detect very small amounts of SARS-CoV-2 RNA during the initial stage of the infection surge. To analyze a larger volume of wastewater, more extracts could be added for the RT-preamp-qPCR reaction. However, in our preliminary experiments with COPMAN, excessive amounts of extracts affected the enzymatic reactions (data not shown), probably because of the PCR inhibitors in wastewater. Therefore, when starting this research, we decided to adopt a 4-μL input to prioritize the linearity of qPCR.
Another feature of the automated COPMAN is that the detected SARS-CoV-2 RNA correlated with future reported cases of COVID-19 ( Fig. 3A to D). The maximum correlation coefficients were observed at 5-13 days (Pearson's correlation) and 6-11 days (Spearman's correlation) of the 7day moving average for future reported cases. Earlier detection of SARS-CoV-2 RNA in wastewater was also demonstrated by Pearson's correlation in the previous studies: 4-10 days , up to 5 days (Wang et al., 2021), and 2-4 days (Nemudryi et al., 2020). Considering that the clinical report delay was usually more than a few days, wastewater analysis may accurately reflect infectious trends as they are occurring. However, it remains unclear why regional differences were observed. This may be because of differences in the inspection period until result acquisition or of reporting delays; further investigation is needed.
Inflow into a WWTP substantially increases on rainy days due to intrusion of stormwater. To normalize the dilution of the viral RNA in wastewater, the amount of PMMoV has been used as an indicator of fecal strength in wastewater (Dhakar and Geetanjali, 2022;Kitajima et al., 2018). However, in the present study, normalization using PMMoV did not improve Pearson's correlation in COPMAN (Fig. S2). Although this result may be due to the analysis of only two regions in the present study, fecal indicators for normalization still need to be carefully evaluated when considering the previous studies have also reported that normalization using PMMoV or crAssphage reduced the correlation between detected SARS-CoV-2 and infectious cases Feng et al., 2021;Greenwald et al., 2021;Li et al., 2023).
In the post-COVID-19 pandemic period, WBE may play a more important role because notifiable disease surveillance is being replaced by sentinel surveillance to reduce the burden on medical clinics and communities. In this context, WBE with automated COPMAN can serve as a powerful tool because it allows timely monitoring of SARS-CoV-2 in a sufficient number of wastewater samples.

Conclusions
COPMAN, a method for the detection of viral RNA in wastewater, was automated. As the result of half-year surveillance, a high correlation between the detected SARS-CoV-2 in wastewater and newly reported cases was observed with Pearson's correlation for the automated COPMAN. SARS-CoV-2 detection in wastewater was observed 5-13 days earlier than the daily case reports in the Sagami River basin. Automated COPMAN should enable timely detection of SARS-CoV-2 in large-scale WBE implementation. Future research should assess the extensibility of COPMAN to other targets, such as DNA viruses or bacteria genomes in wastewater.

Funding
This study was funded by Shionogi & Co., Ltd. and AdvanSentinel Inc. The employees of Shionogi & Co., Ltd. and AdvanSentinel Inc. involved in the study design, data collection, analysis and interpretation, and the writing of the report made the decision to serve as authors.

Data availability
The authors do not have permission to share data.

Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Masaaki Kitajima reports financial support was provided by Shionogi and Co Ltd. Masaaki Kitajima reports was provided by AdvanSentinel Inc. Shin Hayase reports a relationship with Shionogi and Co Ltd. that includes: employment. Ryo Iwamoto reports a relationship with AdvanSentinel Inc. that includes: employment. Tomohiro Kuroita reports a relationship with AdvanSentinel Inc. that includes: employment. Yuka Adachi Katayama, reports a relationship with Shionogi and Co Ltd. that includes: employment. Yoshinori Ando reports a relationship with Shionogi and Co Ltd. that includes: employment. Tomohiko Okuda reports a relationship with Shionogi and Co Ltd. that includes: employment. Yusaku Masago reports a relationship with Shionogi and Co Ltd. that includes: employment. Shin Hayase has patent pending to Nucleic acid detection and quantification methods from environmental samples pending to Shionogi and Co Ltd., Hokkaido University. Yuka Adachi Katayama has patent pending to Nucleic acid detection and quantification methods from environmental samples pending to Shionogi and Co Ltd., Hokkaido University. Yusaku Masago has patent pending to Nucleic acid detection and quantification methods from environmental samples pending to Shionogi and Co Ltd., Hokkaido University.