key: cord-0936420-um9o8elh
authors: Truyols Vives, Joan; Muncunill, Josep; Toledo Pons, Núria; Baldoví, Herme G.; Sala Llinàs, Ernest; Mercader Barceló, Josep
title: SARS‐CoV‐2 detection in bioaerosols using a liquid impinger collector and ddPCR
date: 2022-02-21
journal: Indoor Air
DOI: 10.1111/ina.13002
sha: 349ab1cdbeecf1c0020b19da8426feaa21a64700
doc_id: 936420
cord_uid: um9o8elh

The airborne route is the dominant form of COVID‐19 transmission, and therefore, the development of methodologies to quantify SARS‐CoV‐2 in bioaerosols is needed. We aimed to identify SARS‐CoV‐2 in bioaerosols by using a highly efficient sampler for the collection of 1–3 µm particles, followed by a highly sensitive detection method. 65 bioaerosol samples were collected in hospital rooms in the presence of a COVID‐19 patient using a liquid impinger sampler. The SARS‐CoV‐2 genome was detected by ddPCR using different primer/probe sets. 44.6% of the samples resulted positive for SARS‐CoV‐2 following this protocol. By increasing the sampled air volume from 339 to 650 L, the percentage of positive samples went from 41% to 50%. We detected five times less positives with a commercial one‐step RT‐PCR assay. However, the selection of primer/probe sets might be one of the most determining factor for bioaerosol SARS‐CoV‐2 detection since with the ORF1ab set more than 40% of the samples were positive, compared to <10% with other sets. In conclusion, the use of a liquid impinger collector and ddPCR is an adequate strategy to detect SARS‐CoV‐2 in bioaerosols. However, there are still some methodological aspects that must be adjusted to optimize and standardize a definitive protocol.

of methodologies to detect and quantify the virus in bioaerosols is needed to design preventive measures, manage more efficiently the disinfection of contaminated areas, and to be able to estimate the risk of infection. In this regard, the infection risk of the airborne virus in a public institution could be inferred according to the virus quantification in the air, the known virus airborne dose able to initiate an infection and the exposure time. 7 In the case of SARS-CoV-2, there is an urgent need to develop a reliable methodology to quantify the airborne levels, particularly in bioaerosols.

Since the COVID-19 outbreak, several research groups worldwide have analyzed the presence of airborne SARS-CoV-2 in hospital wards using different procedures, yielding very different results.

Hence, the virus could be detected in a variable number of samples in some studies, 6, [8] [9] [10] [11] [12] [13] while in others it was not detected in any sample. 14 Among the variety of air samplers, the SKC Biosampler ® liquid impinger collector display distinctive features that make it attractive for the collection of RNA virus-containing bioaerosols, such as SARS-CoV-2. This device presents three tangential nozzles designed to gently collect particles onto the collection liquid. This device displayed the highest efficiency to collect inert air particles in between 1 and 3 μm diameter among a total of 29 air samplers 19 and outperformed the gelatin and glass fiber filter samplers in collecting H1N1 influenza A virus. 20 In addition, the SKC Biosampler ® sampler better preserves virus integrity compared to other samplers, 21 which turns an important characteristic for SARS-CoV-2 collection owing to the RNA lability. Therefore, this device has been used for the collection of bioaerosols to detect respiratory viruses in clinical settings. 22 However, on the other hand, the liquid impinger collector is connected to a pump that does not allow the collection of high air volumes as other devices do, which could represent a limitation to sample bioaerosols in large rooms.

Once bioaerosols are collected, the virus can be detected by synthesizing complementary DNA (cDNA) and subsequent amplification of specific sequences of the SARS-CoV-2 genome by polymerase chain reaction (PCR). The researchers must select among different retrotranscriptases, polimerases, and primer sequences for SARS-CoV-2 determination, or available one-step RT-PCR kits specifically designed for the quantification of SARS-CoV-2 in biological samples. 23, 24 The suitability of these elements for the quantification of the virus in environmental samples could be different and should be adapted to this type of samples. Droplet digital PCR (ddPCR) is an alternative PCR method that allows absolute quantification and is more precise than standard PCR, therefore being used for the detection of low amount specimens. Accordingly, ddPCR has been used in SARS-CoV-2 clinical research allowing the identification of falsenegative biological samples obtained by standard RT-PCR. 25 Since virus concentration in air samples is expected to be much lower than in biological samples, the use of ddPCR for the detection of SARS-CoV-2 in bioaerosols might be a better option.

In this observational study, we aimed to identify the presence of SARS-CoV-2 in bioaerosols collected in COVID-19 patient rooms by using the SKC BioSampler ® followed by ddPCR. Moreover, we analyzed the potential influence in the detection of SARS-CoV-2 of several methodological aspects, such as the volume of collected air, the suitability of a one-step RT-PCR kit, and the use of different primer sequences.

This study was approved by the research commission of the Son Espases University Hospital (CI-458-20).

Air samples were collected in individual ward patient rooms and in intensive care units (ICU) at the Son Espases University Hospital (HUSE), in the presence of a diagnosed COVID-19 patient from September 2020 to May 2021. Ventilation was minimal during sampling since the window facing outwards and the exit door were closed during the sampling period ( Figure 1A ). COVID-19 patients were with no oxygen therapy or with nasal cannula, venturi mask, or high flow oxygen therapy during the sampling period. Patients with invasive or non-invasive mechanical ventilation were excluded. Air was collected with a SKC BioSampler ® liquid impinger device of 5 mL of capacity connected to a Biolite pump at 12.5 L/ min flow. The sampler components were autoclaved before each use. The sampler was placed between 1 and 1.5 m distance from the patient's face, and it was accommodated to patient's face

• BioSampler ® is a suitable device to collect SARS-CoV-2laden bioaerosols.

• The high sensitivity of droplet digital PCR allows the detection of SARS-CoV-2 present in bioaerosols.

• Similar results on SARS-CoV-2 detection are obtained with the different air volumes sampled and the collection media used in this work.

• The use of the ORF1ab primers and probe set is necessary to obtain quantifiable results in bioaerosols samples.

height ( Figure 1B ). Bioaerosols were collected onto 5 mL of collection liquid, consisting of deionized sterile water or a virus collection medium, prepared with Dulbecco's modified eagle medium (DMEM) containing 10% fetal bovine serum, 0.5% bovine serum albumin, 1% penicillin-streptomycin solution, and 0.5% antifoam A.

The collection period was 20-30 min when water was used as a collection medium, while it was extended to 45-60 min using the virus collection medium. The collection period varied within the given time ranges to try to obtain a sufficient and constant collection fluid volume.

After bioaerosols collection, the remaining variable volumes of collection liquid were placed on ice and immediately processed for RNA extraction. The procedures to extract RNA are detailed in Appendix 1. In the first 5 samples, RNA was extracted with the MagMAX™ viral/pathogen RNA extraction kit using 250-400 µL of collection liquid, and then, the manufacturer's indications were followed. From sample 6 onward, a phenol protocol was used by mixing 150-350 µL of collection liquid with TRItidy G™ (Panreac AppliChem). The comparison between the two methods was made by referring the amount of extracted RNA to the collection liquid used. Moreover, simultaneous extractions with both methods were performed using equal volumes (250 µL) of the same samples. Total RNA was quantified using Synergy H1 spectrophotometer.

For cDNA synthesis, we used 10 µL of total RNA and M-MuLV TRANSCRIPTME reverse transcriptase (Blirt). The protocol for retrotranscription is detailed in Appendix 1.

To detect the SARS-CoV-2 genome, we selected four primer and probe sequence sets used in previous studies 25-27 that align to two regions of the SARS-CoV-2 nucleocapside (N1 and N) gene, the RNA-dependent RNA polymerase (RdRP) gene and the ORF1ab. 

The procedure we followed to prepare mix solutions, run ddPCR reactions and for the quantification of the results can be found in Appendix 1.

The limit of detection (LoD) of N, N1, and ORF1ab targets was experimentally calculated by using known genomic copies of different plasmids (TibMolbiol) containing the target sequences. Serial dilutions of each plasmid solution were made close to the expected LoD and ten replicates were run. The percent of detected replicates was plotted against the number of target copies per reaction to calculate the LoD. Probit analysis was also performed to define the LoD of each target.

SARS-CoV-2 genome detection in bioaerosols was also analyzed by using a commercial one-step RT-PCR kit intended for the qualitative detection of the virus in nasopharyngeal and oropharyngeal swab specimens, the GenomeCoV19 Detection Kit (MyBioSource).

Reactions were prepared following the manufacturer's instructions, and the details of the protocol can be found in Appendix 1.

A descriptive analysis of the methodological variables was performed. Continuous variables were described as the median range in normal distributed variables or as the mean and standard deviation in non-normal distributed variables. Normality was assessed with Shapiro-Wilks test. The categorical variables were described as frequency and percentages.

Associations between categorical variables were tested by chisquare or Fisher's exact test when necessary. Continuous variables were tested by The Wilcoxon-Mann-Whitney U or t-test (nonnormal or normal distributed data). Receiver-operating characteristics (ROC) curves were generated to assess the capacity of the RNA concentration to discern the positive cases. The level of significance for all statistical tests was 0.05. Statistical analysis was performed using R software version 3.4.0.

Since there is no standardized protocol to collect and isolate virus from bioaerosols, we preliminary compared the efficiency of two liquid impinger systems and two methods to isolate RNA. The SKC BioSampler ® displays a higher collection efficiency of inert particles compared the other liquid impinger systems due to lower particle re-aerosolization. 19 We questioned whether the SKC BioSampler ® also displays a higher collection efficiency of virus-laden bioaerosols than the AGI-30 all-glass impinger measured as the amount of isolated RNA. To check it, simultaneous air samplings using both samplers were performed in the same patient room. Samplers were placed at the same height and distance from patient and 12.5 L/min flow rate was set for both. The amount of extracted RNA per mL of collection media was 2.1-fold higher using the SKC BioSampler ® , and therefore, we used only this system for the whole study as initially proposed.

We selected the MagMAX ™ viral/pathogen RNA extraction kit protocol that is indicated for biofluids and transport media, which was compared to the Tritidy ™ protocol at the beginning of the study. Simultaneous RNA extractions from equal sample volumes were performed in two samples, and the mean RNA yield, as per ng RNA/mL of collection liquid used, was 198.3 ± 25.9 for the kit and 6786.9 ± 1506.7 for the phenol protocol. We obtained a mean of 220.7 ng/mL in all the samples that were processed with the kit, while with the phenol method we obtained 3804.3 ng/mL. The amount of the total extracted RNA per mL of collection media resulted more than 10 times higher using the phenol protocol, which was the only method used from that point forward.

Sixty-five air samples were collected from 52 individual rooms and 1 double room (Table 1) . Bioaerosols were collected in seven rooms occupied by an asymptomatic patient, while the remaining rooms were occupied by a symptomatic patient, whose symptoms had been initiated between 1 and 44 days before sample collection ( Table 1) .

The rooms were sampled once, except for patients 1, 2, 5, 6, 11, 22, and 25 that were sampled twice or more. In the latter cases, samples were collected in the same day (1 and 25) or in different days (2, 5, 6, 11, and 22) . The retrotranscribed RNA was tested by ddPCR. Positive and negative control samples were included in each ddPCR run. In the positive controls, the mean number of genomic copies per reaction obtained in different runs using the same RNA quantity (50 ng) was 234 ± 14, 241 ± 25, and 72 ± 11, for the N, N1, and ORF1ab targets, respectively. The number of genomic copies per reaction in the negative controls for each target was 0 in all the runs (n = 15 for each target). The limit of blank (LoB), defined as the highest apparent number of positive droplets expected to be found when replicates of a sample containing no target are tested, was considered as 0 for the three targets.

In the samples in which the SARS-CoV-2 genome was detected, only one or two positive droplets were produced, that is equivalent to a range between 1.2 and 10.8 virus genomic copies per reaction (Table 2) . These values are above the LoB and therefore are unlikely to be false positives. They are close to the LoD of ddPCR, which is 1 TA B L E 1 Characteristics of the bioaerosols samplings in hospital wards and method used for RNA extraction 

copy per reaction. The LoD of each target was experimentally calculated to check whether these results are only detectable or can be considered as quantifiable. The LoD was calculated by extracting the copies per reaction at 95% probability. As illustrated in Figure 2A ,

the LoD for N1, N, and ORF1ab targets was, respectively, 34.5, 33, and 1.1 genomic copies per reaction. Similar LoD values were obtained by using the probit model ( Figure 2B -D). We concluded that both N1 and N targets are detectable, but not quantifiable. On the other hand, the ORF1ab target is quantifiable because the number of genomic copies obtained is above both the theoretical and experimental LoD.

As illustrated in Figure 3A , there were only two samples in which all targets were amplified, while in seven samples two targets were amplified and in twenty samples there was only one target detected.

The number of samples in which at least one out of the three targets was detected was 29 (Table 2) . We considered as SARS-CoV-2-positive samples, those with at least one target detected under these conditions, and according to this criterion, 44.6% samples of the study were defined as SARS-CoV-2 positive.

Since the detection of the ORF1ab target allows the quantification of the results, the number of SARS-CoV-2 genomic copies present in the room air processed could be estimated according the results obtained with this target (Table 2) . For this estimation, we took into account the volume of processed air and the fraction of the volumes of the RNA and cDNA solutions used. According to this calculation, the estimated range resulted to be between 11 and 96 genomic copies per m3 of air. This concentration range is similar to that calculated in other studies. 11,12,28

We wondered whether the volume of collected air with the SKC BioSampler ® is indeed a critical factor for virus detection in bioaerosols, and therefore, we aimed to increase the collected air volume to analyze the influence of such factor. When sterile water was used as collection liquid, medium was quickly evaporated during air collection thus impeding that sampling length was no longer than 30 min.

Because of this, the volume of collected air was limited, on average, 

We speculated whether the amount of the total extracted RNA obtained could be used as an indirect indicator of the SARS-CoV-2 presence, since the air was sampled in a room in which the only bioaerosols emitter during the air collection was expected to be the COVID-19 patient. Therefore, we compared the amount of extracted RNA between SARS-CoV-2-positive and -negative samples.

As it is shown in Table 6 Table 6 , the AUC values of the ROC curves indicated that no value could discriminate positive from negative SARS-CoV-2 samples, suggesting that SARS-CoV-2 represents only a small fraction of the RNA specimens collected.

Since the pandemic outbreak several one-step RT-PCR kits have been developed. Compared with two-step assays, one-step systems can be more sensitive for quantification of certain targets, 29 in part because they include specific primers that are more efficient at synthetizing cDNA than the random oligomers and oligo-dT used in the two-step reactions. Further considering that these kits have been previously optimized, they are presented as an interesting option to analyze the presence of SARS-CoV-2 genome in specimens with low target concentrations, as it was expected to occur in our environmental samples. Therefore, the detection of the SARS-CoV-2 genome was also analyzed by using a commercial one-step RT-PCR kit in those samples that were still available (72%). This kit includes two pair of primers to amplify targets within the N and RdRP sequences.

With the one-step assay, the percentage of samples with at least one amplified target was 10% (Table 7) , being the N target amplified in 4 samples (8%), while the RdRP target was detected in 1 sample (2%). In any sample, both targets were simultaneously amplified.

Within the samples that could be analyzed with the two systems, the percentage of positive samples using the one-step assay was lower than that obtained with ddPCR (10% vs. 51%). Importantly, the lower efficiency of the one-step assay may not be exclusively attributed to differences in one-step vs. two-step systems themselves, but also to the use of different primer-probe sets in both assays.

To test the RNA samples extracted from bioaerosols by two-step RT-ddPCR, we initially selected two primer-probe sets targeting two regions of the nucleocapside gene to be able to detect both genomic and subgenomic SARS-CoV-2 sequences. Later, we decided to test our environmental samples using also the ORF1ab and RdRP primerprobe sets because it was demonstrated that sensitivity and efficiency of different primer-probe sets were significantly different, 30 as well as their use in testing biological samples produced different results that are probably explained by changes in the virus replicative kinetics throughout the disease progression. 31 Considering the results presented in Table 2 , the primer-probe set with which more positive detections were obtained was the ORF1ab, followed by N and N1, as illustrated in Figure 4A and consistent with the calculated LoD of each target (Figure 2 ). Since the volume of non-diluted cDNA was not enough to run more reactions in such conditions, we tested the RdRP primer-probe set efficiency in a subset of samples TA B L E 6 Analysis of the relation between the amount of the RNA extracted from bioaerosols and SARS-CoV-2 detection

In this work, we present the results of the detection of SARS-CoV-2 in bioaerosols in hospital wards from the HUSE, in Mallorca Island, during the second, third, and fourth waves. We consider that the standardization of a protocol to quantify the airborne SARS-CoV-2 is of great interest, since now the infection through aerosols is being accepted as the most important transmission route. 32 The methodology used in the present study was selected to quantify the virus levels present in a size fraction of bioaerosols that is deposited in different parts of the respiratory system. Since the SARS-CoV-2 concentration is expected to be particularly low in the airborne samples compared to biological samples, a highly sensitive detection method is required. In keeping with these principles, we have selected a protocol that consist in the use of a device that displays a high efficiency for the collection of particles within the range of 1 to 3 μm diameter, followed by the genome detection using ddPCR, which allows the detection of 1 genomic copy per reaction. With this protocol, we could detect the SARS-CoV-2 genome up to 44.6% of the bioaerosols samples.

In parallel to our work, several studies have analyzed the presence of airborne SARS-CoV-2 in hospital wards using different procedures and obtaining different results. Among the influencing factors of the procedure, the type of air sampler used could be a critical point. The selection of the air sampler determines the air volume that can be processed, the size of particles that can be more efficiently collected, and the degree of RNA preservation. In these studies, the most used air samplers are the filter-based ones, and compared to them, we obtained more positive samples than Dumont-Leblond's, 11 Passos', 9 Lane's, 14 Sample  5  6  7  8  9  10  11  12  13  14  16  17  18  21  22  23   ddPCR  -------+  +  +  ------One-step  RT-PCR  assay   N  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  Nd   RdRP  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  nd  Nd   Sample  24  26  27  28  29  30  31  32  33  34  35  36  37  38  39  41 ddPCR and would have hindered to assess the suitability of our procedure.

In conclusion, our results support the use of the BioSampler ® to collect SARS-CoV-2-containing bioaerosols. Remarkably, one-step RT-ddPCR assays have also been developed for SARS-CoV-2 detection and quantification in clinical samples, 38 and it shows a greater sensitivity than that displayed by one-step RT-qPCR assay. 39 Using one-step RT-PCR assay, we detected at least one target in 10% of the tested samples, lower than with the two-step ddPCR workflow. However, the targets selected in this particular kit seem not to be the more appropriate for the airborne SARS-CoV-2 detection according to the differences in their sensitivity we find between the different primer-probe sets, with the ORF1ab set overtaking the N1, N, and RdRP primer-probe sets.

Our results are in accordance with Dumont-Leblond et al's study, 11 who also detected higher virus genome levels using the ORF1ab than the N target. Then, the inclusion of the ORF1ab target could be a better option when the airborne SARS-CoV-2 is analyzed, a conclusion that is reinforced by the lower airborne virus detection observed in several studies in which the ORF1ab target was not analyzed. 9, 10, 14 Our objective has been to set up a protocol suitable for the detection of SARS-CoV-2 in bioaerosols and try to understand some methodological aspects that influence on such detection. Interestingly, our results suggest that SARS-CoV-2 represents only a small fraction of the total RNA collected. This finding invites to characterize the indoor air microbiome of public spaces using metagenomics, in order to contribute to define hygiene and safety standards.

In conclusion, the use of a liquid impinger collector and ddPCR may be an adequate strategy to detect SARS-CoV-2 in bioaerosols.

However, there are still some methodological aspects that must be adjusted to optimize and standardize a definitive protocol.

The authors thank to the HUSE health staff for their collaboration in the air samplings and the Microbiology Service for providing positive control samples.

The authors have no conflict of interest to declare. 

The The amplified target sequences were then automatically read in the FAM/HEX channels using the QX200 Droplet Reader (Bio-Rad).

Analysis of the ddPCR data was performed with QuantaSoft analysis software (Bio-Rad) to calculate the concentration of the target sequences, along with their Poisson-based 95% confidence intervals.

The positive samples for each primer/probe sets were identified using positive and negative controls. The limit of detection of our ddPCR platform is 1 genomic copy per reaction, which was used as a cut-off to discriminate positive from negative samples.

The The LoD of this assay is 5 copies per reaction.

Modes of transmission of severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2) and factors influencing on the airborne transmission: a review

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Identifying airborne transmission as the dominant route for the spread of COVID-19

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Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1

Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients

Airborne influenza A virus exposure in an elementary school

Presence of SARS-CoV-2 in the air of public places and transportation

Exploratory assessment of the occurrence of SARS-CoV-2 in aerosols in hospital facilities and public spaces of a metropolitan center in Brazil

Characterization of hospital airborne SARS-CoV-2

Low incidence of airborne SARS-CoV-2 in acute care hospital rooms with optimized ventilation

Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals

Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus

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Investigation of hospital indoor air quality for the presence of SARS-Cov-2

Investigation of SARS-CoV-2 in hospital indoor air of COVID-19 patients' ward with impinger method

SARS-CoV-2 concentrations and virus-laden aerosol size distributions in outdoor air in north and south of Italy

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Comparing the performance of 3 bioaerosol samplers for influenza virus

Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler

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Brief comparative evaluation of six open one-step RT-qPCR mastermixes for the detection of SARS-CoV-2 RNA using a Taqman probe

Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia

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Analytical comparisons of SARS-COV-2 detection by qRT-PCR and ddPCR with multiple primer/probe sets

Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

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Analysis of one-step and two-step real-time RT-PCR using SuperScript III

Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT-qPCR primer-probe sets

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Environmental contamination in the isolation rooms of COVID-19 patients with severe pneumonia requiring mechanical ventilation or high-flow oxygen therapy

Comparison of analytical sensitivity of SARS-CoV-2 molecular detection kits

Development of an efficient one-step real-time reverse transcription polymerase chain reaction method for severe acute respiratory syndrome-coronavirus-2 detection

Validation of a one-step reverse transcription-droplet digital PCR (RT-ddPCR) approach to detect and quantify SARS-CoV-2 RNA in nasopharyngeal swabs

Assessing sensitivity and reproducibility of RT-ddPCR and RT-qPCR for the quantification of SARS-CoV-2 in wastewater

Impact of outdoor and indoor meteorological conditions on the COVID-19 transmission in the western region of Saudi Arabia

Temporal dynamics in viral shedding and transmissibility of COVID-19