This research was funded by &#34;Centro di Riferimento Regionale per la Sicurezza Sanitaria del Pescato (CRiSSaP)&#34; in the frame of the project &#34;Attivit&#224; pilota di Monitoraggio di Cianobatteri nella fascia costiera della regione Campania&#34;, and performed in cooperation with the Campania Region Environmental Protection Agency, Italy (ARPAC), &#34;Istituto Zooprofilattico Sperimentale del Mezzogiorno/Osservatorio Regionale per la Sicurezza Alimentare&#34; (IZSM/ORSA), University of Naples &#34;Federico II&#34; - Department of Veterinary Medicine and Animal Production, ref. prof. A. Anastasio).

1. Remote and proximal sensing: data acquisition and analysis
NOTE: In this case, remote/proximal sensing data are used for a first macro-area survey and to select specific spots of coastal areas to be sampled. In the MuM3 framework17 scheme the logic flow is based on a hierarchical monitoring model that includes several levels named information layers. The information of each level is based on data acquired using one or more sensors carried onboard platforms located at different altitudes. Each level defines a spatial scale depending on the altitude of the measurement19. There is the potential for multiple sensors at each level. Some examples are: visible near infrared (VNIR) and thermal infrared (TIR) imaging20 on satellites, aircraft, helicopters, UAV21 and at the surface; physical, chemical, and biological analyses, etc.22 at the surface and in fast response using the mobile lab. The data acquired by each sensor is processed and combined to calculate multispectral indexes (e.g., normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Chlorophyll Index, etc.), so the raw data is converted into more useful parameters and formats (e.g., thematic map).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61889/61889fig2v3.jpg" /> 
Figure 2: Remote/proximal sensing analyses for sampling (steps 1-2).&#160;Multi-level and multi-sensor approach for the detection of cyanobacterial bloom. Data acquisition is performed by satellite (A), aircraft (B), and/or drone (C).&#160;<a href="https://www.jove.com/files/ftp_upload/61889/61889fig2v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Remote and proximal sensing data retrieval
	<ol>
		<li>Retrieve data from the various public and private remote sensing datasets, produced especially for content-based retrieval and scene classification. Typical dataset sources used in this step include: Landsat products provided by U.S. Geological Survey23, Sentinel-2,3 products provided by NASA24, and Copernicus Open Access Hub25, MODIS-Aqua26.</li>
		<li>Identify the products available for the specific area, dates range and consider the limit derived by the cloud cover. Define: A) the region of interest using graphical user interface and polygon selection; B) the dates range and the cloud coverage using the text query filter fields. 
		NOTE: Even if many scientific reports cite that an acceptable cloud cover value is &#60;20%, in this type of research, it is important to pay attention to the real cloud cover only on the water surface, so an effectiveness value for this region is &#60;5%.</li>
		<li>Choose products derived from the platforms best fitting the specific needs of the mission. From each combination of satellite and payload technical specifications, it is possible to obtain several collections of products. For example, the NASA EOSDIS24 data products are processed at various levels ranging from Level 0 to Level 4: the Level 0 products are raw data at full instrument resolution while at higher levels the data is converted into more useful parameters and formats. Usually, the Levels 0-1 are available, while the products in Levels 2-4 need specific processing related to specific research goals, so their availability is limited in time and the region covered.</li>
		<li>Download the chosen dataset of satellite observations. In detail, selecting among the list of the results from the previous action, download a complete dataset product (e.g., a group of geo-tiff files, one for each band, plus infos, and other metadata) or only a specific single band. 
		NOTE: For the specific description of each basic action included in the procedure of step 1, please also refer to Huan-Huan Chen et al. 202027.</li>
	</ol>
	</li>
	<li>Data pre-processing and processing for the analysis and transformation into more useful parameters and formats to permit a first screening of the macro-area. 
	&#8203;NOTE: The following actions (step 1.2) are dedicated to data processing finalized to transform raw data coming from lower levels into information and then into useful information (higher levels). This step consists of processing of the raw data from each sensor (e.g., product Levels 0-1) and transformation into higher-level products (e.g., Levels 2-4) and, subsequently, in information layers to generate more useful parameters and formats (e.g., thematic map).
	<ol>
		<li>Pre-process raw data involving geometric and radiometric calibration. This action could be performed using specific software that, in an automatic or semi-automatic way (unsupervised or supervised), provides a set of connected tools for raster processing in order to perform an easy classification respecting a correct workflow. 
		NOTE: In this research, two free open source tools are used: Q-GIS 3.1428 combined with Semi-Automatic Classification Plugin (SCP). The SCP plugin29 allows the semi-automatic classification of remote sensing images, providing a complete tool set for the download of free images, the pre-processing, the post-processing, and the raster calculation.</li>
		<li>Process calibrated data calculating multispectral indexes. Typically, this action is performed using a raster calculator tool. The calculation of a multispectral index defines a correlation between different bands/layers that as a result generates a new layer that could be managed on a GIS platform. For example, starting from Sentinel and Landsat operational land imager sensor (OLI), it is possible to calculate multispectral indexes such asNormalized Difference Vegetation Index (NDVI). Vegetation index is then used to estimate chlorophyll content30,31.</li>
		<li>Analyze the results obtained from the estimation of chlorophyll content index represented as in false color thematic maps and define critical area with high chlorophyll concentration. In this study, chlorophyll-a map is generated using dataset of MODIS sensor32, mounted on Terra and Aqua satellite platforms. The sampling spots are localized where abnormal values are registered. 
		NOTE: The algal bloom flag (that define each specific spot) is raised in the remote sensing domain when the Chl-a concentration exceeds 20 mg/L33.</li>
	</ol>
	</li>
</ol>
2. Guided samplings
NOTE: The choice of sampling spots is driven by remote/proximal sensing layer analysis that allows to select spots in large coastal areas. Taking account that sampling could be dangerous for operators due to microcystins&#39; toxicity, safety sampling procedures are needed. Particularly, it is needed to protect operators from aerosol inhalation and skin contact. Then, perform sampling following the procedure detailed below.
<ol>
	<li>Wear goggles for eye protection, FFP2 mask to prevent aerosol inhalation, and safety gloves to prevent skin contact.</li>
	<li>Collect 0.5 L of water in triplicate in each site.</li>
	<li>Measure the salinity for each sample using a refractometer. Put a drop of the sample on the refractometer and read the salinity value in terms of parts per thousand (ppt - &#8240;).</li>
	<li>At the end of sampling, first wash the hands; then in turn remove the gloves, mask, and goggles taking care not to touch the external surface of the personal protection equipment.</li>
	<li>Carry the sample to the lab at room temperature.</li>
</ol>
3. Identification of cyanobacteria species by microscopic observations and taxonomic identification
<ol>
	<li>Centrifuge each sample (&#8776;0.5 L) at 11,200 x g for 5 min.</li>
	<li>Extract supernatants as follows: pour 500 mL of butanol in each sample and using a funnel, transfer the mixture to be extracted into the separatory funnel. Place the separatory funnel upright in the ring clamp to allow both the layers to separate. Let the aqueous phase to drain in an Erlenmeyer flask. Repeat this step three times. Then, concentrate the organic phases under vacuum and weigh them.</li>
	<li>Collect pellets from step 3.1 and analyze them at 400x and 1,000x magnification with an optical microscope equipped with an 18 MP digital camera for microscope. Look for the presence of cyanobacteria on the basis of their morphological features: blue-green color, cell shape, and size allow to recognize cyanobacteria among other microorganisms.</li>
	<li>Identify the species through taxonomic analysis by microscopic observation, according to the procedure described in Komar&#233;k et al. 201434. 
	&#8203;NOTE: Complementary 16S metagenomic analysis can be performed in order to identify cyanobacterial taxa3.</li>
	<li>Dilute an aliquot of the pellets with 10 mL of seawater/freshwater BG11 media, according to its salinity, for cultivation.</li>
</ol>
4. Identification of cyanotoxins
<ol>
	<li>Extraction of samples with organic solvents
	<ol>
		<li>Put each sample of pellet in a flask and sonicate for 5 min using an ice-bath. Then, add 50 mL of fresh MeOH and gently shake. Filter the solution using a paper filter and collect the filtrate in a round bottom flask. Repeat this step twice. Then, extract the pellets adding 50 mL mixture of MeOH/DCM twice (1:1, 50 mL), and twice using 100% DCM (x2, 50 mL)35. Label each round bottom flask respectively, as &#34;MeOH extract&#34;, &#34;MeOH/DCM extract&#34;, and &#34;DCM extract&#34;, and concentrate under vacuum.</li>
		<li>Analyze each organic extract (MeOH, MeOH/DCM, DCM) by LC-HRMS/MS.</li>
	</ol>
	</li>
	<li>LC-HRMS/MS analysis
	<ol>
		<li>LC-HRMS and LC-HRMS/MS samples preparation: dissolve each sample using MeOH &#8805; 99.9% to get a final concentration of 10 mg/mL.</li>
		<li>Analyze each sample using a high-resolution ESI mass spectrometer coupled with an HPLC system (LC-HRMS/MS system). Work on the HPLC with a 5-&#181;m C18 column (100 x 2.10 mm), at room temperature. Use a gradient elution with H2O (supplemented with 0.1% HCOOH) and MeOHat 200 &#181;L min-1. The gradient program is: 10% MeOH for 3 min, 10% to 100% MeOH for 30 min, 100% MeOH for 7 min. Use MeOH as the control.</li>
		<li>Data acquisition. Collect data in the data-dependent acquisition mode (DDA): the 10 most intensive ions of a full-scan mass spectrum have to be subjected to high-resolution tandem mass spectrometry (HRMS/MS) analysis. Acquire HRMS/MS scans for selected ions with CID fragmentation, an isolation width of 2.0, normalized collision energy of 35, Activation Q of 0.250, and an activation time of 30 ms.</li>
	</ol>
	</li>
	<li>Bioinformatic analyses and molecular networking
	<ol>
		<li>Analyze fragmentation patterns for each intense ion using MS specific software.</li>
		<li>Use MS-Cluster to cluster the data (parent mass tolerance of 1.0 Da and MS/MS fragment ion tolerance of 0.5 Da). Consensus spectra containing less than 2 spectra are eliminated.</li>
		<li>Analyze MS/MS data by GNPS (Global Natural Products Social36) for molecular networking.</li>
		<li>Pairwise compare consensus spectra and also with those reported in the GNPS-Mass-Ive libraries.</li>
		<li>Visualize the obtained network37. 
		NOTE: For molecular networking please refer to Sigrist et al. 202038.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61889/61889fig3v3.jpg" /> 
Figure 3: FDS in-lab steps (3-4).&#160;Visual representation of the main activities carried out in laboratory after sampling (steps 3 and 4).&#160;<a href="https://www.jove.com/files/ftp_upload/61889/61889fig3v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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S., Angolini, C. F. F., De Oliveira, L. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry-guided+genome+mining+as+a+tool+to+uncover+novel+natural+products.">Mass spectrometry-guided genome mining as a tool to uncover novel natural products.</a> Journal of Visualized Experiments: JoVE. (157), (2020).</li></ol>

The authors declare no conflicts of interest.

During the last years, our team tested and validated several different approaches that allowed unraveling the presence of cyanobacteria and cyanotoxins in water bodies and bivalves. The new developed strategy represents the result of these studies. The optimal techniques and technologies that fit the scope of fast detection, are gathered under the hat of a unique procedure that maximize the effectiveness of each single step. The target area, the bloom extension, and growing stage are the driving force to the choice of suitable methods and technologies to use.
When cyanobacteria and cyanotoxins fast detection is the priority, the strategy is streamlined reducing the total number to four main steps: (1) Remote and Proximal sensing and data analysis for a first survey, localization of sites and definition of bloom pattern and extension; (2) Guided sampling; (3) Microscopic observation and taxonomic analysis; (4) Chemical analysis and molecular networking of LC-MS data for dereplication of the water samples and fast detection of cyanotoxins.
Regarding the first step, even if the availability of data acquired by a complete chain of platforms that cover all the layers of hierarchical monitoring approach would be the best solution to restitute a complete vision of the analyzed scenario, often just one information layer can drive the area survey action and effectively focus on the hot spots to perform in-situ sampling actions. According to the reported experiences in which data was acquired using satellites, aircrafts, helicopters, UAVs, the solution that totally matches the needs required by the fast detection strategy is the use of the only satellite products.
In addition, the information layers that derive from missions performed by platforms that fly at lower altitudes than satellites (e.g., aircrafts, helicopters, UAVs) restitute information with great resolution but these are very expensive and also require more time to complete the full acquisition process that also includes flight plan defining and approval.
Once the spots to be samples have been selected (step 2), analytical/bioinformatics analyses (Molecular networking of LC-MS data) is the tool for fast dereplication of the water samples and fast detection of cyanotoxins (steps 3 and 4). 16S metagenomic analysis takes at least 2 weeks of work. Moreover, even when cyanobacterial species that are generically toxic are identified, their toxin production is not demonstrated. For the same reason, microscopic observation is not itself sufficient to reveal the presence of toxic cyanobacteria. Of course, MS analysis and molecular networking have some limitations; they are quite effective if compounds of interest (e.g., toxins) are well ionized in the applied conditions, if they are in sufficient amount to be detected. For the purpose of the known cyanobacterial toxin detection and monitoring, MS-based molecular networking actually represents one of the more robust and reliable technologies.
Therefore, this approach proves to be quite useful when a fast detection of cyanobacteria and related cyanotoxins is needed; moreover quantification of both cyanobacterial bloom and toxin over space and time is also possible by this strategy to prevent health communities&#39; problems that could arise by large cyanobacterial toxic blooms.

Cyanobacterial blooms have emerged as an environmental problem all over the world in the last 15 years1,2. Cyanobacterial blooms are due to the overgrowth of microorganisms named cyanobacteria. They are a conspicuous group of photosynthetic microorganisms that have adapted themselves to live in a large array of environments, including tropical areas and extremely cold waters. They are known for producing large blooms covering water surfaces, especially in response to a massive enrichment of nutrients, the so-called eutrophication process3.
Therefore, cyanobacteria are excellent bioindicators of water pollution4,5,6. They can also produce a wide array of natural compounds with interesting pharmacological properties7,8. The environmental problem related to cyanobacteria are the blooms themselves. Blooms can block sunlight to underwater grasses, consume oxygen in the water leading to fish kills, produce surface scum and odors, and interfere with the filter feeding of organisms9.
In addition, and even more seriously, in a specific combination of factors such as temperature, nutrients (phosphorus and nitrogen), sunlight (for the photosynthesis), and pH of the water, cyanobacterial blooms trigger toxin production; therefore, they become harmful to humans and animals. The most studied class of cyanotoxins is produced by the genera Microcystis. These are cyclic peptides known under the general name of microcystins (MCs): microcystin-LR being the most studied as being able to produce severe hepatoxicity10. Animals and humans may be exposed to MCs by ingestion of contaminated drinking water or food. The World Health Organization (WHO) suggested a total microcystin-LR value of 0.001 mg/L as a guideline11. However, this is related only to one variant (i.e., MC-LR) out of more than 100 microcystins that have been isolated so far.
Combined methods previously reported, such as remote sensing with MALDI-TOF MS analysis12,13,14,15, have focused on the concentration detection of MCs. The most recent methods use low-resolution sensors that are effective in detecting only wide bloom expanses; they are also capable of revealing only toxins for which standards are available. Moreover, most of these procedures are time-consuming, and time is a dramatic factor for early detection of the bloom to prevent or minimize safety problems. The multidisciplinary strategy proposed here provides rapid detection of cyanobacteria bloom and cyanotoxins, after only 24 h16.
In the frame of the program called MuM3, &#34;Multi-disciplinary, Multi- scale and Multi-parametric Monitoring in the three-dimensional (3D) physical space&#34;17,18, a Fast Detection Strategy (FDS) combines the advantages of several techniques: 1) remote sensing to detect the bloom; 2) microscopic observation to detect cyanobacteria species; and 3) analytical/bioinformatics analyses, namely, LC-HRMS-based molecular networking, to detect cyanotoxins. Results are obtained within 24 h.
The new approach is useful to monitor wide coastal areas in a short time, avoiding numerous sampling and analyses, and reducing detection-time and costs. This strategy is the result of the study and application of different approaches to the monitoring of cyanobacteria and their toxins and combines the advantages of each of them. Specifically, the analysis of the results, coming from the use of different platforms (satellite, aircraft, drones) and sensors (MODIS, thermal infrared) for remote sensing analysis, such as of diverse methodological approaches for the identification of cyanobacterial species (microscope, UV-Vis spectroscopy, 16S analysis) and toxins (LC-MS analysis, molecular networking), allowed the selection of the most appropriate method both for the specific and general purposes. The new methodology was experimented and validated in subsequent monitoring campaigns on Campania coasts (Italy), in the frame of Campania environmental protection agency monitoring program.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61889/61889fig1v3.jpg" /> 
Figure 1: FDS strategy.&#160;An overview of Fast Detection Strategy for cyanobacteria and cyanotoxins.&#160;<a href="https://www.jove.com/files/ftp_upload/61889/61889fig1v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>10X Vitamin mix</td><td></td><td></td><td>Nicotinic acid 100 mg/100 mL; PABA 10 mg/100 mL; Biotin 1 mg/100 mL; Thiamine 200 mg/100 mL; B&lt;sub&gt;12&lt;/sub&gt; 1 mg/100 mL; Folic Acid 1 mg/100 mL; i-inositol 1 mg/100 mL; Ca-pantothenate 100 mg/100 mL</td></tr><tr><td>1-BuOH</td><td>Sigma-Aldrich</td><td>33065.2.5L-R</td><td></td></tr><tr><td>BG11 stock solution</td><td></td><td></td><td>Na&lt;sub&gt;2&lt;/sub&gt;EDTA 20 mg/L; Ferric ammonium citrate 120 mg/L; Citric acid&amp;middot;1H&lt;sub&gt;2&lt;/sub&gt;O 120 mg/L; CaCl&lt;sub&gt;2&lt;/sub&gt;&amp;middot;2H&lt;sub&gt;2&lt;/sub&gt;O 700 mg/L, MgSO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;7H&lt;sub&gt;2&lt;/sub&gt;O 1.5 g/L, K&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;3H&lt;sub&gt;2&lt;/sub&gt;O 800 mg/L, NiSO&lt;sub&gt;4&lt;/sub&gt;(NH&lt;sub&gt;4&lt;/sub&gt;)2SO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;6H&lt;sub&gt;2&lt;/sub&gt;O (0.1 mM stock) 5 mL; Na&lt;sub&gt;2&lt;/sub&gt;SeO&lt;sub&gt;4&lt;/sub&gt; (0.1 mM stock) 2 mL, Nitsch&#039;s Solution 20 mL</td></tr><tr><td>Centrifuge</td><td>Hermle</td><td>Z36HK</td><td></td></tr><tr><td>CHCl&lt;sub&gt;3&lt;/sub&gt;</td><td>Honeywell</td><td>32211.2.5L</td><td></td></tr><tr><td>H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Sigma-Aldrich</td><td>34877.2.5L</td><td></td></tr><tr><td>Kinetex C18 cloumn</td><td>Phenomenex</td><td></td><td></td></tr><tr><td>LTQ Orbitrap XL high-resolution ESI mass spectrometer coupled to a U3000 HPLC system</td><td>Thermo</td><td></td><td></td></tr><tr><td>MeOH</td><td>Honeywell</td><td>32213.2.5L</td><td></td></tr><tr><td>Microscope equipped with an OMAX 18 MP CMOS camera&amp;nbsp;</td><td>Optech</td><td>Biostar B3</td><td></td></tr><tr><td>Multiband camera</td><td>Intergraph DMC</td><td></td><td></td></tr><tr><td>Nitsch&#039;s Solution</td><td></td><td></td><td>H&lt;sub&gt;3&lt;/sub&gt;BO&lt;sub&gt;3 &lt;/sub&gt;0.5 g/L&lt;br/&gt; MnSO&lt;sub&gt;4&lt;/sub&gt;&amp;middot; H&lt;sub&gt;2&lt;/sub&gt;O 2.28 g/L&lt;br/&gt; ZnSO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;7H&lt;sub&gt;2&lt;/sub&gt;O 0.5 g/L&lt;br/&gt; CuSO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;5H&lt;sub&gt;2&lt;/sub&gt;O 0.025 g/L&lt;br/&gt; COCl&lt;sub&gt;2&lt;/sub&gt;&amp;middot;6H&lt;sub&gt;2&lt;/sub&gt;O 0.135 g/L&lt;br/&gt; Na&lt;sub&gt;2&lt;/sub&gt;MoO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;2H&lt;sub&gt;2&lt;/sub&gt;O 0.025 g/L</td></tr><tr><td>Refractomer mr 100 ATC</td><td>AQL</td><td></td><td></td></tr><tr><td>SWBG11 medium</td><td></td><td></td><td>BG11 stock solution 50 mL/L; Instant Ocean 33 g/L; Water 950 mL/L 10X; Vitamin mix 100 &amp;micro;L/L</td></tr></tbody></table>

early detection of cyanobacterial blooms and associated cyanotoxins using fast detection strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

In a first study3, four anthropogenically-impacted sites along the Campania coast in SW Italy were observed using satellite Landsat 8 and aircraft during summer 2015. Landsat 8 operational land imager sensor (OLI) and the aircraft multispectral camera allowed to create Normalized Difference Water Index (NDWI) images for the areas, therefore, to reveal the presence of cyanobacterial communities. Cyanobacterial community composition was determined through spectrophotometric analyses for the detection of the cyanobacterial pigment phycocyanin (PC). Then, complementary 16S metagenomic analysis allowed to identify cyanobacterial taxa. The simplified multispectral image indexing and classification through satellite/aerial platforms in combination with metagenomic analyses were effective in detecting the presence of cyanobacteria belonging to genera associated with strong eutrophic conditions (such as Leptolyngbya sp., Pseudooscillatoria sp.), at an early stage of blooming.
In a second study14, FDS approach was tested during Spring/Summer 2017. Satellite data were used as the only remote sensing level. In detail, data acquired by MODerate Image Spectroradiometer (MODIS) sensor, mounted on Terra and Aqua satellite platforms, allowed quantification of chlorophyll-a (Chl-a) in the water bodies along Campania coasts and drove the choice of ten sampling spots. Samples were processed in lab by microscopic observation and taxonomic identification, then extracted with organic solvents. Organic extracts were processed by LC-MS-MS analysis. Data obtained by MS-MS were analyzed using a bioinformatic approach, using the GNPS platform to create a network of molecules. The network was analyzed to detect and identify toxins comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allowed to detect known toxins and unknown analogues that will appear related in the same molecular network. Specifically, Lyngbyatoxin A, a lipophilic dermatotoxins, was detected in all water-samples and bivalves&#39; samples; in the Lyngbyatoxin A molecular cluster, nodes not related to any known compounds of lyngbyatoxins family were also present, suggesting the presence of unknown lyngbyatoxin analogues. No microcystins and other toxins were detected in the samples. All the results were obtained within 24 man-hours.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61889/61889fig4v3.jpg" /> 
Figure 4: FDS representative results. An example of application of FDS strategy on Campania coast (Italy).&#160;<a href="https://www.jove.com/files/ftp_upload/61889/61889fig4v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy at JoVE.com

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

early-detection-cyanobacterial-blooms-associated-cyanotoxins-using

Research

JoVE Journal

Chemistry

3.7K Views.  University of Naples Federico II. A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Video: Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

This protocol describes rapid colorimetric detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from large volumes (10 L) of agricultural waters. Here, water is filtered through sterile Modified Moore Swabs (MMS), which consist&#160;of a simple gauze filter enclosed in a plastic cartridge, to concentrate bacteria. Following filtration, non-selective or selective enrichments for the target bacteria are performed in the MMS. For colorimetric detection of the target bacteria, the enrichments are then assayed using paper-based analytical devices (&#181;PADs) embedded with bacteria-indicative substrates. Each substrate reacts with target-indicative bacterial enzymes, generating colored products that can be detected visually (qualitative detection) on the &#181;PAD. Alternatively, digital images of the reacted &#181;PADs can be generated with common scanning or photographic devices and analyzed using ImageJ software, allowing for more objective and standardized interpretation of results. Although the biochemical screening procedures are designed to identify the aforementioned bacterial pathogens, in some cases enzymes produced by background microbiota or the degradation of the colorimetric substrates may produce a false positive. Therefore, confirmation using a more discriminatory diagnostic is needed. Nonetheless, this bacterial concentration and detection platform is inexpensive, sensitive (0.1 CFU/ml detection limit), easy to perform, and rapid (concentration, enrichment, and detection are performed within approximately 24&#160;hr), justifying its use as an initial screening method for the microbiological quality of agricultural water.

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

A protocol involving integrated concentration, enrichment, and end-point colorimetric detection of foodborne pathogens in large volumes of agricultural water is presented here. Water is filtered through Modified Moore Swabs (MMS), enriched with selective or non-selective media, and detection is performed using paper-based analytical devices (&#181;PAD) imbedded with bacterial-indicative colorimetric substrates.

This protocol describes rapid colorimetric detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from large volumes (10 L) of ...

Environment

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

Global warming and eutrophication make some aquatic ecosystems behave as true bioreactors that trigger rapid and massive cyanobacterial growth; this has relevant health and economic consequences. Many cyanobacterial strains are toxin producers, and only a few cells are necessary to induce irreparable damage to the environment. Therefore, water-body authorities and administrations require rapid and efficient early-warning systems providing reliable data to support their preventive or curative decisions. This manuscript reports an experimental protocol for the in-field detection of toxin-producing cyanobacterial strains by using an antibody microarray chip with 17 antibodies (Abs) with taxonomic resolution (CYANOCHIP). Here, a multiplex fluorescent sandwich microarray immunoassay (FSMI) for the simultaneous monitoring of 17 cyanobacterial strains frequently found blooming in freshwater ecosystems, some of them toxin producers, is described. A microarray with multiple identical replicates (up to 24) of the CYANOCHIP was printed onto a single microscope slide to simultaneously test a similar number of samples. Liquid samples can be tested either by direct incubation with the antibodies (Abs) or after cell concentration by filtration through a 1- to 3-&#956;m filter. Solid samples, such as sediments and ground rocks, are first homogenized and dispersed by a hand-held ultrasonicator in an incubation buffer. They are then filtered (5 - 20 &#956;m) to remove the coarse material, and the filtrate is incubated with Abs. Immunoreactions are revealed by a final incubation with a mixture of the 17 fluorescence-labeled Abs and are read by a portable fluorescence detector. The whole process takes around 3 h, most of it corresponding to two 1-h periods of incubation. The output is an image, where bright spots correspond to the positive detection of cyanobacterial markers.

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

The presence of cyanobacterial toxins in fresh water reservoirs for human consumption is a major concern for water management authorities. To evaluate the risk of water contamination, this article describes an protocol for the in-field detection of cyanobacterial strains in liquid and solid samples by using an antibody microarray chip.

Global warming and eutrophication make some aquatic ecosystems behave as true bioreactors that trigger rapid and massive cyanobacterial growth; this has ...

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

We demonstrate a method to fabricate highly sensitive surface-enhanced Raman spectroscopic (SERS) substrates using a filter syringe system that can be applied to the detection of various chemical contaminants. Silver nanoparticles (Ag NPs) are synthesized via reduction of silver nitrate by sodium citrate. Then the NPs are aggregated by sodium chloride to form nanoclusters that could be trapped in the pores of the filter membrane. A syringe is connected to the filter holder, with a filter membrane inside. By loading the nanoclusters into the syringe and passing through the membrane, the liquid goes through the membrane but not the nanoclusters, forming a SERS-active membrane. When testing the analyte, the liquid sample is loaded into the syringe and flowed through the Ag NPs coated membrane. The analyte binds and concentrates on the Ag NPs coated membrane. Then the membrane is detached from the filter holder, air dried and measured by a Raman instrument. Here we present the study of the volume effect of Ag NPs and sample on the detection sensitivity as well as the detection of 10 ppb ferbam and 1 ppm ampicillin using the developed assay.

A procedure for fabrication and performing the filter-based surface enhanced Raman spectroscopic (SERS) assay for the detection of chemical contaminants (i.e., pesticide ferbam and antibiotic ampicillin) is presented.

We demonstrate a method to fabricate highly sensitive surface-enhanced Raman spectroscopic (SERS) substrates using a filter syringe system that can be ...

Optical Detection of E. coli Bacteria by Mesoporous Silicon Biosensors

A label-free optical biosensor based on a nanostructured porous Si is designed for rapid capture and detection of Escherichia coli K12 bacteria, as a model microorganism. The biosensor relies on direct binding of the target bacteria cells onto its surface, while no pretreatment (e.g.&#160;by cell lysis) of the studied sample is required. A mesoporous Si thin film is used as the optical transducer element of the biosensor. Under white light illumination, the porous layer displays well-resolved Fabry-P&#233;rot fringe patterns in its reflectivity spectrum. Applying a fast Fourier transform (FFT) to reflectivity data results in a single peak. Changes in the intensity of the FFT peak are monitored. Thus, target bacteria capture onto the biosensor surface, through antibody-antigen interactions, induces measurable changes in the intensity of the FFT peaks, allowing for a &#39;real time&#39; observation of bacteria attachment.
The mesoporous Si film, fabricated by an electrochemical anodization process, is conjugated with monoclonal antibodies, specific to the target bacteria. The immobilization, immunoactivity and specificity of the antibodies are confirmed by fluorescent labeling experiments. Once the biosensor is exposed to the target bacteria, the cells are directly captured onto the antibody-modified porous Si surface. These specific capturing events result in intensity changes in the thin-film optical interference spectrum of the biosensor. We demonstrate that these biosensors can detect relatively low bacteria concentrations (detection limit of 104 cells/ml) in less than an hour.

Optical Detection of E. coli Bacteria by Mesoporous Silicon Biosensors

A label-free optical biosensor for rapid bacteria detection is introduced. The biosensor is based on a nanostructured porous Si, which is designed to directly capture the target bacteria cells onto its surface. We use monoclonal antibodies, immobilized onto the porous transducer, as the capture probes. Our studies demonstrate the applicability of these biosensors for the detection of low bacterial concentrations within minutes with no prior sample processing (such as cell lysis).

A label-free optical biosensor based on a nanostructured porous Si is designed for rapid capture and detection of Escherichia coli K12 bacteria, as a ...

Bioengineering

A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used microscopic and toxin analyses for decades for this purpose. Molecular biological methods, such as high-throughput DNA sequencing and bacterial assemblage-based approaches, are just beginning to be introduced in Chilean HAB monitoring, and the procedures have not yet been standardized. Here, 16S rRNA and 18S rRNA metabarcoding analyses for monitoring Chilean HABs are introduced stepwise. According to a recent hypothesis, algal-bacterial mutualistic association plays a critical synergetic or antagonistic relationship accounting for bloom initiation, maintenance, and regression. Thus, monitoring HAB from algal-bacterial perspectives may provide a broader understanding of HAB mechanisms and the basis for early warning. Metabarcoding analysis is one of the best suited molecular-based tools for this purpose because it can detect massive algal-bacterial taxonomic information in a sample. The visual procedures of sampling to metabarcoding analysis herein provide specific instructions, aiming to reduce errors and collection of reliable data.

This protocol introduces steps of metabarcoding analysis, targeting 16S rRNA and 18S rRNA genes, for monitoring harmful algal blooms and their associated microbiome in seawater samples. It is a powerful molecular-based tool but requires several procedures, which are visually explained here step-by-step.

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used ...

Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural features, many compounds within the same class undergo similar MS/MS fragmentation and have several identical product ions and/or neutral losses. The purpose of diagnostic fragmentation filtering (DFF) is to efficiently detect all compounds of a given class in a complex extract by screening non-targeted LC-MS/MS datasets for MS/MS spectra that contain class specific product ions and/or neutral losses. This method is based on a DFF module implemented within the open-source MZmine platform that requires sample extracts be analyzed by data-dependent acquisition on a high-resolution mass spectrometer such as quadrupole Orbitrap or quadrupole time-of-flight mass analyzers. The main limitation of this approach is the analyst must first define which product ions and/or neutral losses are specific for the targeted class of natural products. DFF allows for the subsequent discovery of all related natural products within a complex sample, including new compounds. In this work, we demonstrate the effectiveness of DFF by screening extracts of Microcystis aeruginosa, a prominent harmful algal bloom causing cyanobacteria, for the production of microcystins.

Diagnostic fragmentation filtering, implemented into MZmine, is an elegant, post-acquisition approach to screen LC-MS/MS datasets for entire classes of both known and unknown natural products. This tool searches MS/MS spectra for product ions and/or neutral losses that the analyst has defined as being diagnostic for the entire class of compounds.

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural ...

Development of an Electrochemical DNA Biosensor to Detect a Foodborne Pathogen

Vibrio parahaemolyticus (V. parahaemolyticus) is a common foodborne pathogen that contributes to a large proportion of public health problems globally, significantly affecting the rate of human mortality and morbidity. Conventional methods for the detection of V. parahaemolyticus such as culture-based methods, immunological assays, and molecular-based methods require complicated sample handling and are time-consuming, tedious, and costly. Recently, biosensors have proven to be a promising and comprehensive detection method with the advantages of fast detection, cost-effectiveness, and practicality. This research focuses on developing a rapid method of detecting V. parahaemolyticus with high selectivity and sensitivity using the principles of DNA hybridization. In the work, characterization of synthesized polylactic acid-stabilized gold nanoparticles (PLA-AuNPs) was achieved using X-ray Diffraction (XRD), Ultraviolet-visible Spectroscopy (UV-Vis), Transmission Electron Microscopy (TEM), Field-emission Scanning Electron Microscopy (FESEM), and Cyclic Voltammetry (CV). We also carried out further testing of stability, sensitivity, and reproducibility of the PLA-AuNPs. We found that the PLA-AuNPs formed a sound structure of stabilized nanoparticles in aqueous solution. We also observed that the sensitivity improved as a result of the smaller charge transfer resistance (Rct) value and an increase of active surface area (0.41 cm2). The development of our DNA biosensor was based on modification of a screen-printed carbon electrode (SPCE) with PLA-AuNPs and using methylene blue (MB) as the redox indicator. We assessed the immobilization and hybridization events by differential pulse voltammetry (DPV). We found that complementary, non-complementary, and mismatched oligonucleotides were specifically distinguished by the fabricated biosensor. It also showed reliably sensitive detection in cross-reactivity studies against various food-borne pathogens and in the identification of V. parahaemolyticus in fresh cockles.

A protocol for the development of an electrochemical DNA biosensor comprising a polylactic acid-stabilized, gold nanoparticles-modified, screen-printed carbon electrode to detect Vibrio parahaemolyticus is presented.

Vibrio parahaemolyticus (V. parahaemolyticus) is a common foodborne pathogen that contributes to a large proportion of public health problems globally, ...

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. Determining the real time state of a microbial cell goes beyond live or dead categories, as microbes can exist in a dormant state, whereby cell division and metabolic activities are reduced. Given the need for detection and quantification of microbes, flow cytometry (FCM) with molecular probes provides a rapid and accurate method to help determine overall population viability. By using SYTOX Green and SYTOX Orange in the model cyanobacteria Microcystis aeruginosa to detect membrane integrity, we develop a transferable method for rapid indication of single cell mortality. The molecular probes used within this journal will be referred to as green or orange nucleic acid probes respectively (although there are other products with similar excitation and emission wavelengths that have a comparable modes of action, we specifically refer to the fore mentioned probes). Protocols using molecular probes vary between species, differing principally in concentration and incubation times. Following this protocol set out on M.aeruginosa the green nucleic acid probe was optimized at concentrations of 0.5 &#181;M after 30 min of incubation and the orange nucleic acid probe at 1 &#181;M after 10 min. In both probes concentrations less than the stated optimal led to an under reporting of cells with membrane damage. Conversely, 5 &#181;M concentrations and higher in both probes exhibited a type of non-specific staining, whereby &#39;live&#39; cells produced a target fluorescence, leading to an over representation of &#39;non-viable&#39; cell numbers. The positive controls (heat-killed) provided testable dead biomass, although the appropriateness of control generation remains subject to debate. By demonstrating a logical sequence of steps for optimizing the green and orange nucleic acid probes we demonstrate how to create a protocol that can be used to analyse cyanobacterial physiological state effectively.

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry

Microbial populations contain substantial cell heterogeneity, which can dictate overall behavior. Molecular probe analysis through flow cytometry can determine physiological states of cells, however its application varies between species. This study provides a protocol to accurately determine cell mortality within a cyanobacterium population, without underestimating or recording false positive results.

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. ...

Medicine

Foodborne Pathogen Screening Using Magneto-fluorescent Nanosensor: Rapid Detection of E. Coli O157:H7

Enterohemorrhagic Escherichia coli O157:H7 has been linked to both waterborne and foodborne illnesses, and remains a threat despite the food- and water-screening methods used currently. While conventional bacterial detection methods, such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA) can specifically detect pathogenic contaminants, they require extensive sample preparation and lengthy waiting periods. In addition, these practices demand sophisticated laboratory instruments and settings, and must be executed by trained professionals. Herein, a protocol is proposed for a simpler diagnostic technique that features the unique combination of magnetic and fluorescent parameters in a nanoparticle-based platform. The proposed multiparametric magneto-fluorescent nanosensors (MFnS) can detect E. coli O157:H7 contamination with as little as 1 colony-forming unit present in solution within less than 1 h. Furthermore, the ability of MFnS to remain highly functional in complex media such as milk and lake water has been verified. Additional specificity assays were also used to demonstrate the ability of MFnS to only detect the specific target bacteria, even in the presence of similar bacterial species. The pairing of magnetic and fluorescent modalities allows for the detection and quantification of pathogen contamination in a wide range of concentrations, exhibiting its high performance in both early- and late-stage contamination detection. The effectiveness, affordability, and portability of the MFnS make them an ideal candidate for point-of-care screening for bacterial contaminants in a wide range of settings, from aquatic reservoirs to commercially packaged foods.

Foodborne Pathogen Screening Using Magneto-fluorescent Nanosensor: Rapid Detection of E. Coli O157:H7

The overall goal of this protocol is to synthesize functional nanosensors for the portable, cost-effective, and rapid detection of specifically targeted pathogenic bacteria through a combination of magnetic relaxation and fluorescence emission modalities.

Enterohemorrhagic Escherichia coli O157:H7 has been linked to both waterborne and foodborne illnesses, and remains a threat despite the food- and ...

Immunology and Infection

Immunocytochemical Visualization of Proteins from Cyanobacterial Cells with High Autofluorescence of Phycoerythrin and Phycourobilin

Presented is a protocol for visualizing and quantifying a specific protein in cells at the cellular level for the marine cyanobacterium Crocosphaera watsonii, a crucial primary producer and nitrogen fixer in oligotrophic oceans. One of the challenges for marine autotrophic N2 fixers (diazotrophs) is distinguishing probe-derived fluorescence signals from autofluorescence. C. watsonii was selected to represent chlorophyll-, phycoerythrin- and phycourobilin-containing cyanobacteria. The protocol allows for simple and semi-quantitative visualization of proteins in C. watsonii at a single-cell level, enabling investigation of protein production under different environmental conditions to evaluate the metabolic activities of the target cyanobacteria. Furthermore, the fixation and permeabilization methods are optimized to enhance the fluorescence signals from target proteins to distinguish them from autofluorescence, especially from phycoerythrin and phycourobilin. The enhanced signal can be visualized using confocal or widefield fluorescence microscopy. Additionally, fluorescence intensity was semi-quantified using Fiji software. This single-cell analysis workflow allows the evaluation of cell-to-cell variations of specific protein content. The protocol can be performed in any life science laboratory as it requires only standard equipment and can also be easily adapted to other phycoerythrin-containing cyanobacterial cells.

This protocol outlines the visualization and quantification of a particular protein within cells at the cellular level for the phycoerythrin-containing cyanobacterium, Crocosphaera watsonii.

Presented is a protocol for visualizing and quantifying a specific protein in cells at the cellular level for the marine cyanobacterium Crocosphaera ...

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Summary

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Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

Foodborne Pathogen Screening Using Magneto-fluorescent Nanosensor: Rapid Detection of E. Coli O157:H7

Immunocytochemical Visualization of Proteins from Cyanobacterial Cells with High Autofluorescence of Phycoerythrin and Phycourobilin