Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems
Abstract
:1. Introduction
2. Methods for the Disruption and Lysis of Algal Bloom Cyanobacteria
2.1. Chemical Cell Lysis
2.2. Ultrasonic Cell Lysis
Lysis Method | Lysis Efficiency | Advantages | Limitations | |
---|---|---|---|---|
Chemical | Detergent | 37% [43] | High efficiency, high yield, low DNA degradation | Deposit contaminants that interfere with downstream assays |
Detergent-enzyme cocktail | 100% [43] | |||
Ultrasonic | Bath sonication | 73% [42] | Avoids chemical contaminants, increases purity of extracted biomolecule | Long processing time, incomplete lysis, DNA shearing |
Probe sonication | 80% [56] | |||
Mechanical | Bead beating | 50–99% [42,43] | Avoids chemical contaminants, increases purity of extracted biomolecule | DNA shearing (requires optimal bead beating parameters), inconsistent lysis efficiency depending on cell morphology |
Cryogenic | Freeze-thaw | 19–100% [42,56] | Avoids chemical contaminants, increases purity of extracted biomolecule | Inconsistent lysis efficiency |
Lyophilization | 92–98% [42] | High efficiency, avoids chemical contaminants, increases purity of extracted biomolecule | Long waiting times can limit the use of rapid detection methods |
2.3. Physical Cell Lysis
2.4. Combinatorial Cell Lysis Methodologies
3. Methods and Technologies for Cyanobacterial and Cyanotoxin Monitoring
3.1. Conventional Methods and Techniques
3.2. Molecular Methods and Techniques
3.2.1. Polymerase Chain Reaction (PCR) and DNA Sequencing
3.2.2. Microfluidic and DNA Capture Devices
Method | Sensitivity | Advantages | Limitations |
---|---|---|---|
Enzyme-linked immunosorbent assay (ELISA) | 0.02–0.30 ng/mL [31,76] | Rapid, high sensitivity, the limit of detection within Health Canada guidelines | Low specificity, congener-independent, cross-reaction with cyanotoxin metabolites lead to false positives/overestimation |
Liquid chromatography-mass spectrometry (LC/MS) | 0.000004–0.02 ng/mL [85,86] | High sensitivity, the limit of detection within Health Canada guidelines, congener-specific | Few standards are commercially available, require highly trained personnel, high recurrent cost |
Quantitative polymerase chain reaction (qPCR) targeting mcyE | 3–63 gene copies per reaction [108] | Rapid, allows both qualitative and quantitative analysis, allows assessment of cyanotoxin potential | Environmental contaminants can inhibit amplification, species with sequence variations may go undetected, detection of non-viable cells |
Microfluidic device | 0.16 ng/mL [120,121] | Rapid, portable, and suitable for the field use, requires minimal sample volume | Reduced sensitivity, interference from sample background |
DNA capture targeting mcyE | 1–5 fmol of DNA [126] | Rapid, convenient, potential for field use, requires minimal sample volume | Reduced sensitivity, interference from sample background, limited testing on complex environmental samples. |
4. Future Directions
4.1. Remote Sensing/Satellite Imaging
4.2. Artificial Intelligence and Machine-Learning-Based Prediction Tools
Method | Sensitivity | Advantages | Limitations |
---|---|---|---|
Microscopic enumeration | Not applicable | Simplicity, identification up to the genus and species levels | Time-consuming, requires trained personnel, and accuracy is dependent on the skill of the analyst |
Quantitative polymerase chain reaction (qPCR) targeting 16 s rRNA | 25 gene copies per reaction [98] | Rapid, allows both qualitative and quantitative analysis | Non-specificity of a target gene, environmental contaminants inhibit amplification, detection of unviable cells |
Antibody microarray chip (CYANOCHIP) | 100 cells [122,123] | Rapid, convenient, potential for field use, requires minimal sample volume | Reduced sensitivity, interference from sample background |
Biosensor assay | 50–500 cells/mL [125] | Rapid, convenient, potential for field use, requires minimal sample volume | Reduced sensitivity, interference from sample background |
DNA capture device targeting 16 s rRNA | 1–5 fmol of DNA [126] | Rapid, convenient, potential for field use, requires minimal sample volume | Reduced sensitivity, interference from sample background |
Remote sensing | 1 Cyanobacteria Index (CI) value [132] | Extensive coverage of the geographical area, useful for inland bodies of water | Variability in the correlation between surrogate pigment and toxicity, inaccessible for smaller areas, and differing cyanobacterial composition leads to misinterpretation |
Artificial intelligence (convolutional neural network) | 50 cells [141] | Capable of complex analysis, reducing the need for onsite expertise | Need to integrate multiple algorithms for high reliability, requires extensive and diverse datasets, not applicable across geospatial differences |
5. Conclusions
- Cyanobacterial cells resist disruption methods making quantitative recovery of potential diagnostic molecules difficult. However, optimizing protocol parameters and combining multiple lysis methods can lead to complete disruption
- ELISA-based toxin detection methods (including microfluidic devices) alone cannot resolve or quantify all microcystin congeners. Chromatography–mass spectrometry methods can unambiguously identify microcystins and other cyanotoxins. However, the obtained information is difficult to incorporate into a public health response due to the lack of commercially available standards.
- DNA diagnostic methods (PCR, DNA capture devices) targeting the 16 s rRNA gene, while useful for other bacteria, is of limited value for cyanobacteria due to insufficient specificity. This obstacle can be countered by metabarcoding or targeting a toxin gene such as the Mcy (microcystin synthetase) gene cluster.
- Bloom monitoring may be aided by novel technologies, particularly in quickly establishing spatiotemporal characteristics of specific events. These technologies can augment traditional characterization methods in producing a public health response. However, while ongoing, standardization of common tools for this ancillary information still needs to be completed.
- Newer/modern technologies, including satellite imaging, biosensors, and machine learning/artificial intelligence methods, can be integrated with the conventional/standard molecular methods to overcome the problems associated with cyanobacterial detection in recreational water ecosystems, including the Great Lakes.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Saleem, F.; Jiang, J.L.; Atrache, R.; Paschos, A.; Edge, T.A.; Schellhorn, H.E. Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems. Microorganisms 2023, 11, 851. https://doi.org/10.3390/microorganisms11040851
Saleem F, Jiang JL, Atrache R, Paschos A, Edge TA, Schellhorn HE. Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems. Microorganisms. 2023; 11(4):851. https://doi.org/10.3390/microorganisms11040851
Chicago/Turabian StyleSaleem, Faizan, Jennifer L. Jiang, Rachelle Atrache, Athanasios Paschos, Thomas A. Edge, and Herb E. Schellhorn. 2023. "Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems" Microorganisms 11, no. 4: 851. https://doi.org/10.3390/microorganisms11040851