Harmful Microalgae Detection: Comparison
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Please note this is a comparison between Version 2 by Vivi Li and Version 3 by Md. Mahbubur Rahman.

There has been a steady stream of information on the methods and techniques available for detecting harmful algae species. The conventional approaches to identify harmful algal bloom (HAB), such as microscopy and molecular biological methods are mainly laboratory-based and require long assay times, skilled manpower, and pre-enrichment of samples involving various pre-experimental preparations. As an alternative, biosensors with a simple and rapid detection strategy could be an improvement over conventional methods for the detection of toxic algae species. Moreover, recent biosensors that involve the use of nanomaterials to detect HAB are showing further enhanced detection limits with a broader linear range. The improvement is attributed to nanomaterials’ high surface area to volume ratio, excellent biological compatibility with biomolecules, and being capable of amplifying the electrochemical signal. 

  • harmful algae
  • red tide
  • biosensor
  • HAB detection method
  • nanomaterial
  • conventional method

1. Introduction

The ocean and freshwater constitute a global source of life, containing a diverse range of marine and freshwater phytoplankton species, such as algae and cyanobacteria. These organisms serve an important role in the aquatic biological environment by supplying oxygen and food for other species. However, algal bloom events can be triggered due to adequate nutrients discharged from agriculture, domestic, and industry [1][2][3][4][5]. Furthermore, the combined parameters of temperature, coastal development, water column structure, and eutrophication are also likely to cause algal blooms [6][7]. The increasing incidence of algae blooms is fast becoming a global concern as some algae species can produce toxins that could endanger aquatic life and human [8][9]. Hence, the proliferation of microalgae that produces harmful toxins is known as harmful algal blooms (HABs).
The intrinsic factor of freshwater and marine ecosystems are the results of photosynthetic biomass, such as phytoplankton and zooplankton, being in significant quantity. This biomass is regarded as food for a huge population of aquatic inhabitants [10][11]. Among the different types of photosynthetic biomasses, microalgae are one of the major producers due to their high photosynthetic activities and their place in the food chain of aquatic organisms [8][11]. Additionally, among the 5000 species of aquatic algae that have been discovered, over 300 species are known to proliferate or bloom rapidly and subsequently affects the colour of the sea surface [2][8].
HAB poses severe threats to marine ecosystems and humans and its effect is intensifying as a result of increased seafood consumption, growth in the number of coastal inhabitants, and tourism [12][13][14][15]. HABs can produce hazardous toxins, mainly neurotoxins that can be introduced into the human body by the consumption of contaminated fish, seafood products, or water [16][17]. These activities are highly contributing to human exposure to HAB which can affect digestive tract problems, respiratory illness, memory loss, seizures, lesions, and skin irritation [18][19][20]. In some cases, high exposure to HAB can cause mortality. The estimated health cost for mild, moderate, and severe digestive illnesses was found to be USD 86, USD 1015, and USD 12,605, respectively [21]. Whereas treatment for respiratory illness ranging from mild, moderate, and severe cases will cost about USD 86, USD 1235, and USD 14,600, respectively.
Due to the possible impact of HAB, it is vital to have HAB monitoring techniques that are rapid and accurate in identifying and assessing aquatic pathogens, environmental phenomena, seaside dynamics, and processes that can influence ocean ecosystems [22][23][24]. A broad range of methods and techniques that were detailed in the last decade are largely based on the conventional methods of HAB detection. However, conventional methods generally hold several disadvantages, such as being costly, time-consuming, lengthy experiments, and so on.
The detection technology for harmful microalgae is currently mostly using morphological characteristics of algal cells, traditional microscopic examination technique (MET) and various techniques based on high-performance liquid chromatography (HPLC), absorption spectral analysis (ASA), and fluorescence spectral analysis (FSA). As pointed out by Liu et al. [25] in a review published recently, these methods have their strengths and weaknesses. With respect to their weaknesses, for example, HPLC analysis and molecular detection methods, such as fluorescence in situ hybridization (FISH), sandwich hybridization assay (SHA), and quantitative polymerase chain reaction (qPCR) require sophisticated instruments and professional operation, thus not suited for on-site rapid detection. Methods based on ASA and FSA have a poor resolution, which means that they could only differentiate between algal species at the phylum level. Immunological technology, such as immunofluorescence assay (IFA), immunosensing assay (ISA), and enzyme-linked immunosorbent assay (ELISA) have been developed for the detection of harmful microalgae but they have the weakness of troublesome in the preparation of antibodies.
Although the needs for the detection of harmful microalgae species vary largely, the main desirable methods for the detection are the accuracy, reliability, and convenience for operation. Equally important is the portability and the implementation of intelligence and digitization for newly emerging technologies. Thus, rapid, high efficiency, simplicity, and automation will be the ultimate objective of a detection technology for harmful microalgae. All this will contribute to the real-time and rapid detection of harmful microalgae in the field. This is likely to be fulfilled by biosensor technology that is currently developing rapidly. Biosensors represent an attractive alternative to environmental monitoring that allows researchers to circumvent the drawbacks of conventional methods. Biosensors generally show high specificity, wide linearity, low detection limit, portability, and comparatively low cost. Nevertheless, there have been very few advancements in biosensors for the detection of toxic algae species. Additionally, limited reports can be found on detecting harmful algae with biosensors fabricated with nanomaterials that can further improve the detection limit and linear range capacity.

2. Conventional Methods for HAB Identification

The main conventional methods in HAB monitoring and detection is based on light microscopy and counting chamber [26]. Light microscopy can be utilized to do species identification among the various types of HAB, but is not accurate. Whereas counting chamber will be used to estimate the quantity of HAB species found in samples collected. Other approaches in HAB identification can be based on mouse bioassay (MBA) or chromatographic technique. Although, these two mentioned approaches focus more on detecting the HAB toxins content and is thereby unsuitable for early warning system of HAB event but only to monitor the toxicity of the water. Alternatively, many species from HAB, in particular, and phytoplankton in general, can be detected via molecular methods, which are fast and accurate methods capable of simultaneous qualitative detection [27]. Molecular methods can also identify and quantify harmful algae species [28][29]. The biomedical research and diagnostic industries have been designing simplified ways to sample preparation and distribution while utilizing molecular probe technologies to interpret results [30]. For instance, sample preparation and analysis systems that are portable for in situ detection have been designed but no major implementation had occurred [31]. Although other techniques used for the same purpose include DNA microarrays with different molecular probe techniques [32][33], when it comes to identifying phytoplankton, molecular approaches are considered to be quicker and more precise than light microscopy [34]. Several types of molecular methods, including fluorescent in situ hybridization (FISH) of whole-cell, polymerase chain reaction (PCR)-based assays, sandwich hybridization assays, enzyme-linked immunosorbent assay (ELISA) colorimetric whole-cell analysis, monoclonal antibody probes, and DNA microarrays are capable of identifying algae species and detecting toxic algae in routine monitoring programs [24][25][28][35].

2.1. Microscopic Analysis

Globally, nations have employed HAB monitoring programs as warning systems. In the last decade, one of the main methods of monitoring and mitigation depends on visually inspecting water discolouration, dead fishes, and laborious cell counts [34]. Samples will be required to be dispatched to a laboratory for testing purposes to examine for the presence of harmful compounds using tried-and-tested procedures, such as light microscopy (LM) [13]. However, heterotrophs and autotrophs with sizes below 5 μm will be extremely difficult to be distinguished under LM [36]. For instance, Pseudo-nitzschia species cannot be identified at the species level via LM [11], whereas the high difficulty is present in species identification among Alexandrium variations. Furthermore, the counting of HAB cells is completed by humans, and this will inevitably result in human error mistakes that could lead to inaccurate results being produced. Thereby, the drawback of LM would be its incapability to discern minor differences between species. Both transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are capable of doing identification of various pico- and nano-sized organisms but will inevitably increase overall expenses and time [27]. Whereas epifluorescence microscopy (EFM) does allow the sizing distinction among heterotrophs and autotrophs. EFM also provides a more accurate enumeration than LM but still holds difficulty in species identification [36]. Optical microscopy with normal light or epifluorescence would be used to identify paralytic shellfish poison (PSP) via Alexandrium (dinoflagellate) and other HABs in marine waters [37]. This approach is highly successful but is restricted by the sheer amount of samples that could take several days to complete analysing high numbers of samples. The examination of harmful algae species from water samples will require a rough estimation of 2 h on average. This will be reflected as an estimation work rate of processing 20 samples per week by one person [11]. Sometimes filtration of water samples is necessary during the sample preparation phase with 4 μm pore size filters and air dry the cells in immersion oil for 2–4 days before being examined under standard LM [36]. These time lags will impede the early warning systems of bloom events, as numerous samples are required from the different sites of the water body to accurately conclude if there will be a possibility of HAB. A simplified diagram is illustrated in Figure 1 to show the flow of microscopy approaches in HAB examination.
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Figure 1. Simplified diagram of microscopy methods in HAB examination.
An alternative method to quantifying HAB content will be based on identifying the chlorophyll concentration in the sample [38]. The quantity can only be estimated if the biomass to chlorophyll ratio is known beforehand. In addition, flow cytometry can be utilized in calculating HAB cell concentrations but the downside is the fact that organism’s sizing is not precise [36]. This approach is only useful for homogenous populations that have distinctive pigment or size and is not successful in organisms with a wide range of sizes and shapes in field samples [39]. Despite all the disadvantages stated, the microscopical method does hold an advantage in allowing excellent biological detail in the whole size spectrum of HAB species and other microbial autotrophs [36]. Although, due to the mentioned drawbacks, improvement in monitoring of HAB for on-site and near-real-time water analyses are desired.

2.2. Fluorescent In Situ Hybridization (FISH)

This approach relies on the principle of oligonucleotide such as DNA or PNA as the probe that is attached with a fluorescent marker and required to penetrate through cells to hybridize with the target sequence. The targeted cells will fluoresce and can be visually detected with epifluorescence microscopy (Figure 2). Studies can be found on applying FISH to detecting certain HAB species, but several disadvantages are present. For instance, before observing fluorescently labelled cells, several necessary purification steps including: (I) sample treatment, (II) centrifugation, (III) pipetting, and (IV) washing may lead to the loss of target cells [40]. This will lead to unreliable quantification results unless a gentle filtration method is applied. In addition, the loss of cells from purification steps, the duration from sample collection to reaching a laboratory may gradually decompose the rRNA in the cells [41]. This can reduce the fluorescent intensity of labelled cells. Furthermore, different types of cell walls and membranes can be found in marine phytoplankton and this causes the difficulty in having a universal FISH protocol in fixing all sorts of microalgae cells [42]. For instance, certain modified saline ethanol can allow high permeability for a probe to enter and access target sequence in some microalgal species but does not work well with Alexandrium species, where auto-fluorescence was observed [43]. The background noises can mask over target fluorescent and make results hard to be interpreted.
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Figure 2. Principle of fluorescence in situ hybridization (FISH) technique. A method that utilizes fluorescent labelled probes to cause target cells to fluoresce.
The FISH approach was investigated to detect A. minutum harmful algae species with ribosomal DNA probes [43]. Their results showed high specificity towards A. minutum and no cross-reactivity with other Alexandrium species was observed. However, their methodology involved multiple centrifugation and washing steps that may cause some loss of microalgae cells. Whereas another developed FISH that used a specific PNA probe can detect Prorocentrum donghaiense [44]. Their findings found that the PNA probe is more sensitive compared to the DNA probe in detecting the harmful algae species as fluorescent is more intensive with the PNA probe. Although, the quantification of harmful algae cells is only as liable to that of the LM approach and is rather time-consuming.

3. Biosensor Methods

Biosensor methods involve a biochemical recognition component that is combined with a signal transducer to detect specific targets. The recognition or bio-receptor component, such as specific probe sequence, antibodies, or enzymes could specifically bind to the target of interest or catalyse a biochemical reaction [45]. The bio-recognition event will then be converted into a quantifiable signal via a transducer (Figure 3). Biosensors are attractive candidates to overcome the limitations of traditional detection quantification methods due to their accuracy, simplicity of use, user-friendly, cost-effective, robustness, low power requirements, as well as their rapid turnaround time, high sensitivity, and versatility [46]. In addition, biosensor has the capacity for miniaturization and in-field application, e.g., portable device, which is suitable to improve monitoring methods by allowing for rapid on-site identification of microbiological pollutants. Biosensor design that could perform multiple targets detection concurrently can be an advantage for reducing the sample required [47].
Figure 3. Schematic diagram of biosensor functionality.
Biosensors can be categorized into piezoelectric biosensor, calorimetric biosensor, electrochemical biosensor, and optical detection, which are all according to different types of transducer. Electrochemical and optical biosensors are the most widely used among other types of biosensors due to the ease of operation, high sensitivity, and enable direct, real-time, and label-free detection [48][49]. The application for electrochemical and optical detection of HAB has been further discussed in the section below.

3.1. Electrochemical Biosensor Methods

The fundamental principle of the electrochemical biosensor is the chemical interactions between immobilized biocomponents and analyte that produces or consume ions or electrons, changing the measurable electrical properties of the solution, such as potential or electric current (Figure 4) [50]. Electrochemical biosensors with increased specificity, stability and sensitivity that are small and easy to fabricate are now accessible [51]. Furthermore, electrochemical biosensors are highly sensitive, portable, relatively inexpensive, and simple to build [52]. Therefore, the electrochemical DNA biosensor can serve as an appropriate HAB monitoring program.
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Figure 4. Basic concept of electrochemical biosensor.
Two detection methods, i.e., ‘Rapid PCR-Detection’ and ‘Hybrid PCR-Detection’ were introduced for targeted detection (Table 1). The ‘Rapid PCR-Detection’ identifies and quantifies PCR products through biotin and fluorescein labelling during PCR without a hybridization step. ‘Hybrid PCR-Detection’, on the other hand, involves the increased specificity via hybridization to a DNA probe. However, this method based on a single-plex reaction was only capable of identifying single-base mutations in nucleic acids isolated from pure culture [53]. An 8-plex assay was designed based on the two detection methods for multi-target detection of microbial contaminations and also dinoflagellate Karenia brevis (K. brevis) in coastal waters [54]. Whereas a handheld DNA biosensor approach, based on sandwich hybridization and molecular DNA probes, was capable of detecting the ribosomal RNA (rRNA) of harmful algae A. ostenfeldii [32][55]. This portable biosensor simplifies the detection of harmful algae, however manual RNA separation and manipulation of the hybridization procedures are required [54][55].
Table 1. Summary of the previously reported biosensors methods for the detection of HAB species.
Reference Target Instruments/Methods Response Time

(h)		 Detection Limit (Cells L−1) Advantages Drawbacks
[53] DNA/RNA of microbial pathogens Rapid PCR-Detect and Hybrid PCR-Detect. 4–6 - Sensitive detection of sample DNA/RNA. Only capable of detecting single-base mutations from pure culture isolate.
[55] rRNA of toxic algae (toxic dinoflagellate A. Ostenfeldii) Molecular DNA probes 7–10 5 × 109 Simplified detection methods Manual RNA isolation and manipulation of the hybridization steps are required at high temperature system.
[54] Microbial pathogens and Karenia brevis 8-plex assay of microbes 3–5 1000 Able to multi-target electrochemical detection of microbial pathogens. Complex steps in DNA extraction.
[23] rRNA of harmful algae species Multi-probe chip and a semi-automated rRNA biosensor ~2 - Allows in situ detection and monitoring of toxic algae. Manual rRNA isolation.
[35] Alexandrium minutum Multi-probe biosensor (ALGADEC) ~2 25,000 Almost fully automated device for in situ analysis. Poor limit detection.
[56] Prymnesium parvum, Gymnodinium catenatum, and Pseudo-nitzschia australis DIG-enzymatic label assay 1–2 - Simple and easy handling amperometric techniques. Very poor response for cyclic voltammetry.
[57] Alexandrium species Surface Plasmon Resonance (SPR) biosensing instrument and peptide nucleic acid probes >3.5 - Cost effective and yield quick result. Require tubing flushing maintenance.
Another monitoring system, a multi-probe chip, and a semi-automated rRNA biosensor were employed in conjunction with a sandwich hybridization assay approach for in situ detection of harmful algae [23]. The target organism is identified by specific capture and signal probes to the harmful algae rRNA. The capture probes are immobilized on the surface of the sensor chip and the signal probe is coupled to digoxigenin, which binds to an antibody-enzyme complex. The enzyme catalyses a redox reaction that can be measured as an electrochemical signal using a potentiostat. The main drawback of this procedure was the isolation of rRNA from the target organism, and most preparatory stages were completed manually in a well-equipped laboratory [23][55]. To address the mentioned limitation, a semi-automated device, known as ALGADEC, was developed for in situ analysis of potentially harmful algae [36]. The semi-automated part highlights the use of lysis protocol instead of manual RNA separation. The detection approach involves a sandwich hybridization with capture probe and signal probe (digoxigenin-labelled), and the resulting electrical current can be measured upon substrate addition. Furthermore, an electrochemical DNA biosensor based on a double DIG-enzymatic label and direct HRP-labelled signal probe could detect three species of HABs [56]. The electrochemical signal was examined using cyclic voltammetry or by simply checking the amperometric current magnitude. This amperometric procedure was then investigated in detecting HABs with mixed self-assembled monolayer and bovine serum albumin as a blocking agent in the electrochemical signal [34]. The biosensor’s performance was improved in terms of greater sensitivity and enhanced detection limit. Molecular recognition in DNA biosensors is accomplished by hybridization of the target sequence with the complementary probe. The ability of a biosensor to directly recognize nucleic acids in complex samples is a significant advantage over other techniques, such as polymerase chain reaction (PCR) or ELISA, which needs extensive purification and amplification procedures [34]. The detection methods are usually carried out by using electrochemical and optical biosensors [58][59]. Both the electrochemical and optical biosensors approaches can directly identify nucleic acids from complex samples without the need for target purification and amplification [60]. Previously established DNA biosensors for the detection of K. brevis had achieved an accurate detection limit of 1000 cells L−1 within 3 h to 5 h [54], while another DNA-based sensor was capable of detecting 58 µg of synthetic Prymnesium parvum [34]. The biosensors used for both studies were based on electrochemical transducers. Immunosensors are analytical devices that detect antigen–antibody interaction via coupling of immunochemical response to the surface of a device. There are two important factors in this context, which include the immobilization of antibodies on the sensor surface and the efficient generation of electrochemical signals [61]. The former involves antibody orientation vis-a-vis antigen binding sites. The literature points out the construction of electrochemical immunosensors based on carbon electrodes. In recent years, immunosensors with precise detection of target antigen have sparked attention as a tool for environmental evaluations, clinical diagnostics, food control, and industrial monitoring [62]. Additionally, immunosensors generally possess high sensitivity and specificity in the diagnosis field [45] and are thereby suitable for HAB detection. Electrochemical immunosensors are capable of detecting analytes in complex biological media with high specificity and sensitivity. For instance, a designed electrochemical immunosensor was capable of detecting as low as 1 cell of A. minutum harmful algae in a millilitre of water sample with minimum cross reaction with non-toxic algae species [63]. In terms of detection time, the older model of biosensor designs [53][55] had a rather slow response time that require more than 4 h, while newer biosensors [54][64] have improved detection time and showed good response time of within 2 h [23][35][54][56]. Thereby, optimization and implementation of suitable biosensor design can possibly further improve the detection time.

3.2. Optical Biosensors

Optical chemical sensors have also been employed to detect HAB [65]. Optical chemical sensors are based on the reaction between biomolecule compounds and analytes, where the optical characteristics are induced by UV-vis absorption, bioluminescence, chemiluminescene, fluorescence, or reflectance. These biosensors have the benefits of being flexible, compact, and resistant to electrical noise (Figure 5) [66]. Furthermore, thanks to the use of optical fibre, it is feasible to construct very compact sensors, making them suited for measurement [64][67].
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Figure 5. General principle of optical sensor.
Unlike toxins from microalgae, to date there are not many optical biosensors for the determination of microalgae species. So far, Alexandrium species had been identified via an optical technique known as SPIRIT+, which utilizes portable surface plasmon resonance (SPR) biosensing equipment and peptide nucleic acid (PNA) probes [57]. SPR is a label-free technology that measures changes in the refractive index when a target sequence is hybridized on a surface. The resonance unit (RU) was used to quantify changes in the refractive index, where one RU is equal to 1 × 10−6 degrees. Additionally, SPIRIT is claimed to be a cost-effective and user-friendly approach that yields quick results.

References

  1. Laabir, M.; Jauzein, C.; Genovesi, B.; Masseret, E.; Cecchi, P.; Collos, Y. Influence of temperature, salinity and irradiance on the growth and cell yield of the harmful red tide dinoflagellate Alexandrium catenella colonizing Mediterranean waters. J. Plankton. Res. 2011, 33, 1550–1563.
  2. Adam, A.; Mohammad-Noor, N.; Anton, A.; Saleh, E.; Saad, S.; Muhd Shaleh, S.R. Temporal and spatial distribution of harmful algal bloom (HAB) species in coastal waters of Kota Kinabalu, Sabah, Malaysia. Harmful Algae 2011, 10, 495–502.
  3. Dale, B.; Edwards, M.; Reid, P.C. Climate Change and Harmful Algal Blooms. In Ecology of Harmful Algae; Springer: Berlin/Heidelberg, Germany, 2006; pp. 367–378.
  4. Handy, S.M.; Demir, E.; Hutchins, D.A.; Portune, K.J.; Whereat, E.B.; Hare, C.E.; Rose, J.M.; Warner, M.; Farestad, M.; Cary, S.C. Using quantitative real-time PCR to study competition and community dynamics among Delaware Inland Bays harmful algae in field and laboratory studies. Harmful Algae 2008, 7, 599–613.
  5. Bricker, S.B.; Longstaff, B.; Dennison, W.; Jones, A.; Boicourt, K.; Wicks, C.; Woerner, J. Effects of nutrient enrichment in the nation’s estuaries: A decade of change. Harmful Algae 2008, 8, 21–32.
  6. Paerl, H.W.; Gardner, W.S.; Havens, K.E.; Joyner, A.R.; Mccarthy, M.J.; Newell, S.E.; Qin, B.; Scott, J.T. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 2016, 54, 213–222.
  7. O’neil, J.; Davis, T.W.; Burford, M.A.; Gobler, C. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 2012, 14, 313–334.
  8. Branch, G.M.; Bustamante, R.H.; Robinson, T.B. Impacts of a ‘black tide’ harmful algal bloom on rocky-shore intertidal communities on the West Coast of South Africa. Harmful Algae 2013, 24, 54–64.
  9. Zhen, Y.; Mi, T.; Yu, Z. Detection of several harmful algal species by sandwich hybridization integrated with a nuclease protection assay. Harmful Algae 2009, 8, 651–657.
  10. Meneely, J.P.; Campbell, K.; Greef, C.; Lochhead, M.J.; Elliott, C.T. Development and validation of an ultrasensitive fluorescence planar waveguide biosensor for the detection of paralytic shellfish toxins in marine algae. Biosens. Bioelectron. 2013, 41, 691–697.
  11. Medlin, L. Molecular tools for monitoring harmful algal blooms. Environ. Sci. Pollut. Res. 2013, 20, 6683–6685.
  12. Gas, F.; Baus, B.; Pinto, L.; Compere, C.; TanchoU, V.; Quéméneur, E. One step immunochromatographic assay for the rapid detection of Alexandrium minutum. Biosens. Bioelectron. 2010, 25, 1235–1239.
  13. Dittami, S.M.; Hostyeva, V.; Egge, E.S.; Kegel, J.U.; Eikrem, W.; Edvardsen, B. Seasonal dynamics of harmful algae in outer Oslofjorden monitored by microarray, qPCR, and microscopy. Environ. Sci. Pollut. R. 2013, 20, 1–14.
  14. Orozco, J.; Medlin, L.K. Review: Advances in electrochemical genosensors-based methods for monitoring blooms of toxic algae. Environ. Sci. Pollut. R. 2013, 20, 6838–6850.
  15. Turner, J.T. Planktonic marine copepods and harmful algae. Harmful Algae 2014, 32, 81–93.
  16. Berdalet, E.; Fleming, L.E.; Gowen, R.; Davidson, K.; Hess, P.; Backer, L.C.; Moore, S.K.; Hoagland, P.; Enevoldsen, H. Marine harmful algal blooms, human health and wellbeing: Challenges and opportunities in the 21st century. J. Mar. Biol. Assoc. UK 2015, 96, 61–91.
  17. Hallegraeff, G.M. A review of harmful algal blooms and their apparent global increase. Phycologia 1993, 32, 79–99.
  18. Ajani, P.; Harwoood, D.T.; Murray, S.A. Recent trends in marine phycotoxins from Australian coastal waters. Mar. Drugs 2017, 15, 33.
  19. Lucas, B.; Dahlmann, J.; Erler, K.; Gerdts, G.; Wasmund, N.; Hummert, C. Overview of key phytoplankton toxins and their recent occurrence in the North and Baltic Sea. Environ. Toxicol. 2005, 20, 1–17.
  20. Watkins, S.M.; Reich, A.; Fleming, L.E.; Hammond, R. Neurotoxic Shellfish Poisoning. Mar. Drugs 2008, 6, 431–455.
  21. Kouakou, C.R.C.; Poder, T.G. Economic impact of harmful algal blooms on human health: A systematic review. J. Water Health 2019, 17, 499–516.
  22. Teen, L.P.; Gires, U.; Pin, L.C. Harmful Algal Blooms in Malaysian Waters. Sains Malays. 2012, 41, 1509–1515.
  23. Diercks, S.; Metfies, K.; Medlin, L.K. Development and adaptation of a multiprobe biosensor for the use in a semi-automated device for the detection of toxic algae. Biosens. Bioelectron. 2008, 23, 1527–1533.
  24. Cox, A.M.; Goodwin, K.D. Sample preparation methods for quantitative detection of DNA by molecular assays and marine biosensors. Mar. Pollut. Bull. 2013, 73, 47–56.
  25. Liu, F.; Zhang, C.; Wang, Y.; Chen, G. A review of the current and emerging detection methods of marine harmful microalgae. Sci. Total Environ. 2022, 815, 152913.
  26. Jipanin, S.J.; Shaleh, S.R.M.; Lim, P.T.; Leaw, C.P.; Mustapha, S. The Monitoring of Harmful Algae Blooms in Sabah, Malaysia. J. Phys. Conf. Ser. 2019, 1358, 1–9.
  27. Scorzetti, G.; Brand, L.; Hitchcock, G.; Rein, K.; Sinigalliano, C.; Fell, J. Multiple simultaneous detection of Harmful Algal Blooms (HABs) through a high throughput bead array technology, with potential use in phytoplankton community analysis. Harmful Algae 2009, 8, 196–211.
  28. Antonella, P.; Luca, G. The quantitative real-time PCR applications in the monitoring of marine harmful algal bloom (HAB) species. Environ. Sci. Pollut. R. 2012, 20, 1–12.
  29. Scholin, C. The development and application of molecular probes and novel instrumentation for detection of harmful algae. In Proceedings of the Ocean Development & International Law, Nice, France, 1 October 1998; pp. 367–370.
  30. Scholin, C.; Doucette, G.; Cembella, A. Prospects for developing automated systems for in situ detection of harmful algae and their toxins. In Real-time Coastal Observing Systems for Marine Ecosystem Dynamics and Harmful Algal Blooms; The United Nations Educational, Scientific and Cultural Organization: Paris, France, 2008; pp. 413–462.
  31. Metfies, K.; Töbe, K.; Scholin, C.; Medlin, L.K. Laboratory and Field Applications of Ribosomal RNA Probes to Aid the Detection and Monitoring of Harmful Algae. Ecol. Harmful Algae 2006, 189, 311–325.
  32. Barlaan, E.A.; Furukawa, S.; Takeuchi, K. Detection of bacteria associated with harmful algal blooms from coastal and microcosm environments using electronic microarrays. Environ. Microbiol. 2007, 9, 690–702.
  33. Diercks, S.; Gescher, C.; Metfies, K.; Medlin, L.K. Evaluation of locked nucleic acids for signal enhancement of oligonucleotide probes for microalgae immobilised on solid surfaces. J. Appl. Phycol. 2009, 21, 657–668.
  34. Orozco, J.; Medlin, L.K. Electrochemical performance of a DNA-based sensor device for detecting toxic algae. Sens. Actuators B-Chem. 2011, 153, 71–77.
  35. Diercks-Horn, S.; Metfies, K.; Jäckel, S.; Medlin, L.K. The ALGADEC device: A semi-automated rRNA biosensor for the detection of toxic algae. Harmful Algae 2011, 10, 395–401.
  36. Booth, B.C. Estimating Cell Concentration and Biomass of Autotrophic Plankton Using Microscopy. In Handbook of Methods in Aquatic Microbial Ecology; CRC Press: Boca Raton, FL, USA, 2018; pp. 199–205.
  37. Usup, G.; Pin, L.C.; Ahmad, A.; Teen, L.P. Alexandrium (Dinophyceae) species in Malaysian waters. Harmful Algae 2002, 1, 265–275.
  38. Lorenzen, C.J. A method for the continuous measurement of in vivo chlorophyll concentration. Deep-Sea Res. 1966, 13, 223.
  39. Legner, M. Phytoplankton quantity assessment by means of flow cytometry. Mar. Microb. Food Webs 1990, 4, 161.
  40. Sako, Y.; Hosoi-Tanabe, S.; Uchida, A. Fluorescence in situ hybridization using rrna-targeted probes for simple and rapid identification of the toxic dinoflagellates Alexandrium tamarense and Alexandrium catenella. J. Phycol. 2004, 40, 598–605.
  41. Miller, P.E.; Scholin, C.A. Identification and enumeration of cultured and wild Pseudo-nitzschia probes and filter-based whole cell hybridization. J. Phycol. 1998, 34, 371–382.
  42. Medlin, L.K.; Orozco, J. Molecular techniques for the detection of organisms in aquatic environments, with emphasis on harmful algal bloom species. Sensors 2017, 17, 1184.
  43. Yek, L.H.; Hii, K.S.; Tan, T.H.; Kon, N.F.; Lim, P.T.; Leaw, C.P. Rapid detection of toxic dinoflagellate, Alexandrium minutum (Dinophyceae) using fluorescence in situ hybridization (FISH). Malays. J. Sci. 2013, 32, 1–12.
  44. Chen, G.; Zhang, C.; Zhang, B.; Wang, G.; Lu, D.; Xu, Z.; Yan, P. Development of a PNA probe for fluorescence in situ hybridization detection of Prorocentrum donghaiense. PLoS ONE 2011, 6, e25527.
  45. Naresh, V.; Lee, N. Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors 2021, 21, 1109.
  46. Andryukov, B.G.; Besednova, N.N.; Romashko, R.V.; Zaporozhets, T.S.; Efimov, T.A. Label-Free Biosensors for Laboratory-Based Diagnostics of Infections: Current Achievements and New Trends. Biosensors 2020, 10, 11.
  47. Pilas, J.; Yazici, Y.; Selmer, T.; Keusgen, M.; Schöning, M.J. Application of a Portable Multi-Analyte Biosensor for Organic Acid Determination in Silage. Sensors 2018, 18, 1470.
  48. Damborský, P.; Švitel, J.; Katrlík, J. Optical biosensors. Essays Biochem. 2016, 60, 91–100.
  49. Wu, Q.; Zhang, Y.; Yang, Q.; Yuan, N.; Zhang, W. Review of Electrochemical DNA Biosensors for Detecting Food Borne Pathogens. Sensors 2019, 19, 4916.
  50. Hosu, O.; Selvolini, G.; Cristea, C.; Marrazza, G. Electrochemical Immunosensors for Disease Detection and Diagnosis. Curr. Med. Chem. 2018, 25, 4119–4137.
  51. Sokolov, A.N.; Roberts, M.E.; Bao, Z. Fabrication Of Low-Cost Electronic Biosensors. Mater. Today 2009, 12, 12–20.
  52. da Silva, T.S.G.; Souto, E.P.; Barragan, T.C.; Giarola, D.F.; de Moraes, A.C.M.; Kubota, L.T. Electrochemical Biosensors in Point-of-Care Devices: Recent Advances and Future Trends. Chemelectrochem 2017, 4, 778–794.
  53. Wojciechowski, M.F.; Sanderson, M.J.; Hu, J.M. Evidence on the monophyly of Astragalus (Fabaceae) and its major subgroups based on nuclear ribosomal DNA ITS and chloroplast DNA trnL intron data. Syst. Bot. 1999, 24, 409–437.
  54. Lagier, M.J.; Fell, J.W.; Goodwin, K.D. Electrochemical detection of harmful algae and other microbial contaminants in coastal waters using hand-held biosensors. Mar. Pollut. Bull. 2007, 54, 757–770.
  55. Metfies, K.; Huljic, S.; Lange, M.; Medlin, L.K. Electrochemical detection of the toxic dinoflagellate Alexandrium ostenfeldii with a DNA-biosensor. Biosens. Bioelectron. 2005, 20, 1349–1357.
  56. Orozco, J.; Baudart, J.; Medlin, L.K. Evaluation of probe orientation and effect of the digoxigenin-enzymatic label in a sandwich hybridization format to develop toxic algae biosensors. Harmful Algae 2011, 10, 489–494.
  57. Bratcher, A.R.; Connell, L.B. The Use of Peptide Nucleic Acids in Surface Plasmon Resonance for Detection of Red Tide Algae. In Molecular Biological Technologies for Ocean Sensing; Humana Press: Totova, NJ, USA, 2012.
  58. Lucarelli, F.; Tombelli, S.; Minunni, M.; Marrazza, G.; Mascini, M. Electrochemical and piezoelectric DNA biosensors for hybridisation detection. Anal. Chim. Acta 2008, 609, 139–159.
  59. Mannelli, I.; Lecerf, L.; Guerrouache, M.; Goossens, M.; Millot, M.C.; Canva, M. DNA immobilisation procedures for surface plasmon resonance imaging (SPRI) based microarray systems. Biosens. Bioelectron. 2007, 22, 803–809.
  60. Liao, J.; Mastali, M.; Li, Y.; Gau, V. Development of an Advanced Electrochemical DNA Biosensor for Bacterial Pathogen Detection. J. Mol. Diagn. 2007, 9, 158–168.
  61. Fowler, J.M.; Stuart, M.C.; Wong, D.K. Self-assembled layer of thiolated protein G as an immunosensor scaffold. Anal. Chem. 2007, 79, 350–354.
  62. Aydin, M.; Aydin, E.B.; Sezginturk, M.K. Advances in immunosensor technology. Adv. Clin. Chem. 2020, 102, 1–62.
  63. Oloketuyi, S.; Mazzega, E.; Zavasnik, J.; Pungjunun, K.; Kalcher, K.; Marco, A.D.; Mehmeti, E. Electrochemical immunosensor functionalized with nanobodies for the detection of the toxic microalgae Alexandrium minutum using glassy carbon electrode modified with gold nanoparticles. Biosens. Bioelectron. 2020, 154, 112052.
  64. Morgan, C.L.; Newman, D.J.; Price, C. Immunosensors: Technology and opportunities in laboratory medicine. Clin. Chem. 1996, 42, 193–209.
  65. Stauffer, B.A.; Bowers, H.; Buckley, E.; Davis, T.W. Considerations in Harmful Algal Bloom Research and Monitoring: Perspectives From a Consensus-Building Workshop and Technology Testing. Front. Mar. Sci. 2019, 6, 1–12.
  66. Lei, Y.; Chen, W.; Mulchandani, A. Microbial biosensors. Anal. Chim. Acta 2006, 568, 200–210.
  67. Leiner, M.J.; Hartmann, P. Theory and practice in optical pH sensing. Sens. Actuators B-Chem. 1993, 11, 281–289.
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