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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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; B12 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>Na2EDTA 20 mg/L; Ferric ammonium citrate 120 mg/L; Citric acid&#183;1H2O 120 mg/L; CaCl2&#183;2H2O 700 mg/L, MgSO4&#183;7H2O 1.5 g/L, K2HPO4&#183;3H2O 800 mg/L, NiSO4(NH4)2SO4&#183;6H2O (0.1 mM stock) 5 mL; Na2SeO4 (0.1 mM stock) 2 mL, Nitsch&#39;s Solution 20 mL</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Hermle</td>
			<td>Z36HK</td>
			<td></td>
		</tr>
		<tr>
			<td>CHCl3</td>
			<td>Honeywell</td>
			<td>32211.2.5L</td>
			<td></td>
		</tr>
		<tr>
			<td>H2O</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&#160;</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&#39;s Solution</td>
			<td></td>
			<td></td>
			<td>H3BO3 0.5 g/L 
			MnSO4&#183; H2O 2.28 g/L 
			ZnSO4&#183;7H2O 0.5 g/L 
			CuSO4&#183;5H2O 0.025 g/L 
			COCl2&#183;6H2O 0.135 g/L 
			Na2MoO4&#183;2H2O 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 &#181;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

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

The direct liquid deposition of solution standards onto sorbent-filled thermal desorption tubes is used for the quantitative analysis of trace explosive vapor samples. The direct liquid deposition method yields a higher fidelity between the analysis of vapor samples and the analysis of solution standards than using separate injection methods for vapors and solutions, i.e., samples collected on vapor collection tubes and standards prepared in solution vials. Additionally, the method can account for instrumentation losses, which makes it ideal for minimizing variability and quantitative trace chemical detection. Gas chromatography with an electron capture detector is an instrumentation configuration sensitive to nitro-energetics, such as TNT and RDX, due to their relatively high electron affinity. However, vapor quantitation of these compounds is difficult without viable vapor standards. Thus, we eliminate the requirement for vapor standards by combining the sensitivity of the instrumentation with a direct liquid deposition protocol to analyze trace explosive vapor samples.

Trace explosive vapors of TNT and RDX collected on sorbent-filled thermal desorption tubes were analyzed using a programmed temperature desorption system coupled to GC with an electron capture detector. The instrumental analysis is combined with direct liquid deposition method to reduce sample variability and account for instrumentation drift and losses.

The direct liquid deposition of solution standards onto sorbent-filled thermal desorption tubes is used for the quantitative analysis of trace explosive ...

Synthesis of Indoxyl-glycosides for Detection of Glycosidase Activities

Indoxyl glycosides proved to be valuable and versatile tools for monitoring glycosidase activities. Indoxyls are released by enzymatic hydrolysis and are rapidly oxidized, for example by atmospheric oxygen, to indigo type dyes. This reaction enables fast and easy screening in vivo without isolation or purification of enzymes, as well as rapid tests on agar plates or in solution (e.g., blue-white screening, micro-wells) and is used in biochemistry, histochemistry, bacteriology and molecular biology. Unfortunately the synthesis of such substrates proved to be difficult, due to various side reactions and the low reactivity of the indoxyl hydroxyl function. Especially for glucose type structures low yields were observed. Our novel approach employs indoxylic acid ester as key intermediates. Indoxylic acid esters with varied substitution patterns were prepared on scalable pathways. Phase transfer glycosylations with those acceptors and peracetylated glycosyl halides can be performed under common conditions in high yields. Ester cleavage and subsequent mild silver mediated glycosylation yields the peracetylated indoxyl glycosides in high yields. Finally deprotection is performed according to Zempl&#233;n.

Indoxyl glycosides are well-established and widely used tools for enzyme screening and enzyme activity monitoring. Especially for glucose type structures previous syntheses proved to be challenging and low yielding. Our novel approach employs indoxylic acid esters as precious intermediates to yield a considerable number of indoxyl glycosides in good yields.

Indoxyl glycosides proved to be valuable and versatile tools for monitoring glycosidase activities. Indoxyls are released by enzymatic hydrolysis and are ...

Iridium(III) Luminescent Probe for Detection of the Malarial Protein Biomarker Histidine Rich Protein-II

This work outlines the synthesis of a non-emissive, cyclometalated Ir(III) complex, Ir(ppy)2(H2O)2+ (Ir1), which elicits a rapid, long-lived phosphorescent signal when coordinated to a histidine-containing protein immobilized on the surface of a magnetic particle. Synthesis of Ir1, in high yields,is complete O/N&#160;and involves splitting of the parent cyclometalated Ir(III) chloro-bridged dimer into two equivalents of the solvated complex. To confirm specificity, several amino acids were probed for coordination activity when added to the synthesized probe, and only histidine elicited a signal response. Using BNT-II, a branched peptide mimic of the malarial biomarker Histidine Rich Protein II (pfHRP-II), the iridium probe was validated as a tool for HRP-II detection. Quenching effects were noted in the BNT-II/Ir1 titration when compared to L-Histidine/Ir1, but these were attributed to steric hindrance and triplet state quenching. Biolayer interferometry was used to determine real-time kinetics of interaction of Ir1 with BNT-II. Once the system was optimized, the limit of detection of rcHRP-II using the probe was found to be 12.8 nM in solution. When this protein was immobilized on the surface of a 50 &#181;m magnetic agarose particle, the limit of detection was 14.5 nM. The robust signal response of this inorganic probe, as well as its flexibility of use in solution or immobilized on a surface, can lend itself toward a variety of applications, from diagnostic use to imaging.

IridiumIII Luminescent Probe for Detection of the Malarial Protein Biomarker Histidine Rich Protein-II

Robust detection reagents are of increasing necessity for developing new malaria diagnostic tools. An iridium(III) probe was designed that emits long-lasting luminescent signal in the presence of a histidine-rich malarial protein biomarker. Detection of the protein either in solution or immobilized on a magnetic particle affords flexibility in application.

This work outlines the synthesis of a non-emissive, cyclometalated Ir(III) complex, Ir(ppy)2(H2O)2+ (Ir1), which elicits a rapid, long-lived ...

Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection

Active flow technology (AFT) is new form of column technology that was designed to overcome flow heterogeneity to increase separation performance in terms of efficiency and sensitivity and to enable multiplexed detection. This form of AFT uses a parallel segmented flow (PSF) column. A PSF column outlet end-fitting consists of 2 or 4 ports, which can be multiplexed to connect up to 4 detectors. The PSF column not only allows a platform for multiplexed detection but also the combination of both destructive and non-destructive detectors, without additional dead volume tubing, simultaneously. The amount of flow through each port can also be adjusted through pressure management to suit the requirements of a specific detector(s). To achieve multiplexed detection using a PSF column there are a number of parameters which can be controlled to ensure optimal separation performance and quality of results; that is tube dimensions for each port, choice of port for each type of detector and flow adjustment. This protocol is intended to show how to use and tune a PSF column functioning in a multiplexed mode of detection.

Here, we present a protocol for the operation and tuning of parallel segmented flow chromatography columns to enable multiplexed detection.

Active flow technology (AFT) is new form of column technology that was designed to overcome flow heterogeneity to increase separation performance in terms ...

A Quantitative Glycomics and Proteomics Combined Purification Strategy

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define pathways, improve treatment, understand disease, and aid biomarker discovery. N-linked glycosylation is one of the most important and robust post-translational modifications on proteins and regulates critical cell functions such as signaling, adhesion, and enzymatic function. Analytical techniques to purify and analyze N-glycans have remained relatively static over the last decade. While accurate and effective, they commonly require significant expertise and resources. Though some high-throughput purification schemes have been developed, they have yet to find widespread adoption and often rely on the enrichment of glycopeptides. One promising method, developed by Thomas-Oates et al., filter aided N-glycan separation (FANGS), was qualitatively demonstrated on tissues. Herein, we adapted FANGS to plasma and coupled it to the individuality normalization when labeling with glycan hydrazide tags strategy in order to achieve accurate relative quantification by liquid chromatography mass spectrometry and enhanced electrospray ionization. Furthermore, we designed new functionality to the protocol by achieving tandem, shotgun proteomics and glycosylation site analysis on hen plasma. We showed that N-glycans purified on filter and derivatized by hydrophobic hydrazide tags were comparable in terms of abundance and class to those by solid phase extraction (SPE); the latter is considered a gold standard in the field. Importantly, the variability in the two protocols was not statistically different. Proteomic data that was collected in-line with glycomic data had the same depth compared to a standard trypsin digest. Peptide deamidation is minimized in the protocol, limiting non-specific deamidation detected at glycosylation motifs. This allowed for direct glycosylation site analysis, though the protocol can accommodate 18O site labeling as well. Overall, we demonstrated a new in-line high-throughput, unbiased, filter based protocol for quantitative glycomics and proteomics analysis.

A high-throughput protocol was developed for combined proteomics and glycomics purification and LC-MS/MS quantification in plasma. Deamidation analysis of N-linked glycosylation motifs was specific to deglycosylated sites. Accurate quantitation of N-glycans was achieved by coupling filter aided N-glycan separation to the individuality normalization when labeling with glycan hydrazide tags strategy.

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define ...

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Micro-analytical techniques based on chemical element imaging enable the localization and quantification of chemical composition at the cellular level. They offer new possibilities for the characterization of living systems and are particularly appropriate for detecting, localizing and quantifying the presence of metal oxide nanoparticles both in biological specimens and the environment. Indeed, these techniques all meet relevant requirements in terms of (i) sensitivity (from 1 up to 10 &#181;g.g-1 of dry mass), (ii) micrometer range spatial resolution, and (iii) multi-element detection. Given these characteristics, microbeam chemical element imaging can powerfully complement routine imaging techniques such as optical and fluorescence microscopy. This protocol describes how to perform a nuclear microprobe analysis on cultured cells (U2OS) exposed to titanium dioxide nanoparticles. Cells must grow on and be exposed directly in a specially designed sample holder used on the optical microscope and in the nuclear microprobe analysis stages. Plunge-freeze cryogenic fixation of the samples preserves both the cellular organization and the chemical element distribution. Simultaneous nuclear microprobe analysis (scanning transmission ion microscopy, Rutherford backscattering spectrometry and particle induced X-ray emission) performed on the sample provides information about the cellular density, the local distribution of the chemical elements, as well as the cellular content of nanoparticles. There is a growing need for such analytical tools within biology, especially in the emerging context of Nanotoxicology and Nanomedicine for which our comprehension of the interactions between nanoparticles and biological samples must be deepened. In particular, as nuclear microprobe analysis does not require nanoparticles to be labelled, nanoparticle abundances are quantifiable down to the individual cell level in a cell population, independently of their surface state.

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

We describe a procedure for the detection of chemical elements present in situ in human cells as well as their in vitro quantification. The method is well-suited to any cell type and is particularly useful for quantitative chemical analyses in single cells following in vitro metal oxide nanoparticles exposure.

Micro-analytical techniques based on chemical element imaging enable the localization and quantification of chemical composition at the cellular level. ...

Integration of Miniaturized Solid Phase Extraction and LC-MS/MS Detection of 3-Nitrotyrosine in Human Urine for Clinical Applications

Free 3-nitrotyrosine (3-NT) has been extensively used as a possible biomarker for oxidative stress. Increased levels of 3-NT have been reported in a wide variety of pathological conditions. However, existing methods lack the sufficient sensitivity and/or specificity necessary to measure the low endogenous level of 3-NT reliably and are too cumbersome for clinical applications. Hence, analytical improvement is urgently needed to accurately quantify the levels of 3-NT and verify the role of 3-NT in pathological conditions. This protocol presents the development of a novel liquid chromatography tandem mass spectrometry (LC-MS/MS) detection combined with a miniaturized solid phase extraction (SPE) for the rapid and accurate measurement of 3-NT in human urine as a non-invasive biomarker for oxidative stress. SPE using a 96-well plate markedly simplified the process by combining sample cleanup and analyte enrichment without tedious derivatization and evaporation steps, reducing solvent consumption, waste disposal, risk of contamination and overall processing time. The employment of 25 mM ammonium acetate (NH4OAc) at pH 9 as the SPE elution solution substantially enhanced the selectivity. Mass spectrometry signal response was improved through adjustment of the multiple reaction monitoring (MRM) transitions. Use of 0.01% HCOOH as additive on a pentafluorophenyl (PFP) column (150 mm x 2.1 mm, 3 &#181;m) improved signal response another 2.5-fold and shortened the overall run time to 7 min. A lower limit of quantitation (LLOQ) of 10 pg/mL (0.044 nM) was achieved, representing a significant sensitivity improvement over the reported assays. This simplified, rapid, selective and sensitive method allows two plates of urine samples (n = 192) to be processed in a 24 h time-period. Considering the markedly improved analytical performance, and non-invasive and inexpensive urine sampling, the proposed assay is beneficial for pre-clinical and clinical studies.

A selective and sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) method coupled with an efficient solid phase extraction on a mixed-mode cation-exchange (MCX) 96-well microplate was developed for the measurement of free 3-nitrotyrosine (3-NT) in human urine with high throughput, which is suitable for clinical applications.

Free 3-nitrotyrosine (3-NT) has been extensively used as a possible biomarker for oxidative stress. Increased levels of 3-NT have been reported in a wide ...

Total Internal Reflection Absorption Spectroscopy (TIRAS) for the Detection of Solvated Electrons at a Plasma-liquid Interface

The total internal reflection absorption spectroscopy (TIRAS) method presented in this article uses an inexpensive diode laser to detect solvated electrons produced by a low-temperature plasma in contact with an aqueous solution. Solvated electrons are powerful reducing agents, and it has been postulated that they play an important role in the interfacial chemistry between a gaseous plasma or discharge and a conductive liquid. However, due to the high local concentrations of reactive species at the interface, they have a short average lifetime (~1 &#181;s), which makes them extremely difficult to detect. The TIRAS technique uses a unique total internal reflection geometry combined with amplitude-modulated lock-in amplification to distinguish solvated electrons&#39; absorbance signal from other spurious noise sources. This enables the in situ detection of short-lived intermediates in the interfacial region, as opposed to the bulk measurement of stable products in the solution. This approach is especially attractive for the field of plasma electrochemistry, where much of the important chemistry is driven by short-lived free radicals. This experimental method has been used to analyze the reduction of nitrite (NO2-(aq)), nitrate (NO3-(aq)), hydrogen peroxide (H2O2(aq)), and dissolved carbon dioxide (CO2(aq)) by plasma-solvated electrons and deduce effective rate constants. Limitations of the method may arise in the presence of unintended parallel reactions, such as air contamination in the plasma, and absorbance measurements may also be hindered by the precipitation of reduced electrochemical products. Overall, the TIRAS method can be a powerful tool for studying the plasma-liquid interface, but its effectiveness depends on the particular system and reaction chemistry under study.

Total Internal Reflection Absorption Spectroscopy TIRAS for the Detection of Solvated Electrons at a Plasma-liquid Interface

This article presents a total internal reflection absorption spectroscopy (TIRAS) method for measuring short-lived free radicals at a plasma-liquid interface. In particular, TIRAS is used to identify solvated electrons based on their optical absorbance of red light near 700 nm.

The total internal reflection absorption spectroscopy (TIRAS) method presented in this article uses an inexpensive diode laser to detect solvated ...

Color Spot Test As a Presumptive Tool for the Rapid Detection of Synthetic Cathinones

Synthetic cathinones are a large class of new psychoactive substances (NPS) that are increasingly prevalent in drug seizures made by law enforcement and other border protection agencies globally. Color testing is a presumptive identification technique indicating the presence or absence of a particular drug class using rapid and uncomplicated chemical methods. Owing to their relatively recent emergence, a color test for the specific identification of synthetic cathinones is not currently available. In this study, we introduce a protocol for the presumptive identification of synthetic cathinones, employing three aqueous reagent solutions: copper(II) nitrate, 2,9-dimethyl-1,10-phenanthroline (neocuproine) and sodium acetate. Small pin-head sized amounts (approximately 0.1-0.2 mg) of the suspected drugs are added to the wells of a porcelain spot plate, and each reagent is then added dropwise sequentially before heating on a hotplate. A color change from very light blue to yellow-orange after 10 min indicates the likely presence of synthetic cathinones. The highly stable and specific test reagent has the potential for use in the presumptive screening of unknown samples for synthetic cathinones in a forensic laboratory. However, the nuisance of an added heating step for the color change result limits the test to laboratory application and decreases the likelihood of an easy translation to field testing.

Here we present a simple, inexpensive, and selective chemical spot test protocol for the detection of synthetic cathinones, a class of new psychoactive substances. The protocol is suitable for use in various areas of law enforcement that encounter illicit material.

Synthetic cathinones are a large class of new psychoactive substances (NPS) that are increasingly prevalent in drug seizures made by law enforcement and ...

Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue

MicRoboCop is a biosensor that has been designed for a unique application in forensic chemistry. MicRoboCop is a system made up of three devices that, when used together, can indicate the presence of gunshot residue (GSR) by producing a fluorescence signal in the presence of three key analytes (antimony, lead, and organic components of GSR). The protocol describes the synthesis of the biosensors using Escherichia coli (E. coli), and the analytical chemistry methods used to evaluate the selectivity and sensitivity of the sensors. The functioning of the system is demonstrated by using GSR collected from the inside of a spent cartridge casing. Once prepared, the biosensors can be stored until needed and can be used as a test for these key analytes. A positive response from all three analytes provides a presumptive positive test for GSR, while each individual device has applications for detecting the analytes in other samples (e.g., a detector for lead contamination in drinking water). The main limitation of the system is the time required for a positive signal; future work may involve studying different organisms to optimize the response time.

A protocol is presented using synthetic biology techniques to synthesize a set of bacterial biosensors for the analysis of gunshot residue, and to test the functioning of the devices for their intended use using fluorescence spectroscopy.

MicRoboCop is a biosensor that has been designed for a unique application in forensic chemistry. MicRoboCop is a system made up of three devices that, ...