Fluorescence-assisted image analysis of freshwater microalgae

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Water monitoring: automated and real time identification and classification of algae using digital microscopy

Microalgae are unicellular photoautotrophs that grow in any habitat from fresh and saline water bodies, to hot springs and ice. Microalgae can be used as indicators to monitor water ecosystem conditions. These organisms react quickly and predictably to a broad range of environmental stressors, thus providing early signals of a changing environment. When grown extensively, microalgae may produce harmful effects on marine or freshwater ecology and fishery resources. Rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a methodology for automatic and real time identification and enumeration of microalgae by means of image analysis is presented. The methodology is based on segmentation, shape feature extraction, pigment signature determination and neural network grouping; it attained 98.6% accuracy from a set of 53,869 images of 23 different microalgae representing the major algal phyla. In our opinion this methodology partly overcomes the lack of automated identification systems and is on the forefront of developing a computer-based image processing technique to automatically detect, recognize, identify and enumerate microalgae genera and species from all the divisions. This methodology could be useful for an appropriate and effective water resource management.

Potential of fluorescence imaging techniques to monitor mutagenic PAH uptake by microalga

Benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon (PAH), is one of the major environmental pollutants that causes mutagenesis and cancer. BaP has been shown to accumulate in phytoplankton and zooplankton. We have studied the localization and aggregation of BaP in Chlorella sp., a microalga that is one of the primary producers in the food chain, using fluorescence confocal microscopy and fluorescence lifetime imaging microscopy with the phasor approach to characterize the location and the aggregation of BaP in the cell. Our results show that BaP accumulates in the lipid bodies of Chlorella sp. and that there is Förster resonance energy transfer between BaP and photosystems of Chlorella sp., indicating the close proximity of the two molecular systems. The lifetime of BaP fluorescence was measured to be 14 ns in N,N-dimethylformamide, an average of 7 ns in Bold's basal medium, and 8 ns in Chlorella cells. Number and brightness analysis suggests that BaP does not aggregate inside Chlorella sp. (average brightness = 5.330), while it aggregates in the supernatant. In Chlorella grown in sediments spiked with BaP, in 12 h the BaP uptake could be visualized using fluorescence microscopy.

Changing microspatial patterns of sulfate-reducing microorganisms (SRM) during cycling of marine stromatolite mats

Microspatial arrangements of sulfate-reducing microorganisms (SRM) in surface microbial mats (~1.5 mm) forming open marine stromatolites were investigated. Previous research revealed three different mat types associated with these stromatolites, each with a unique petrographic signature. Here we focused on comparing "non-lithifying" (Type-1) and "lithifying" (Type-2) mats. Our results revealed three major trends: (1) Molecular typing using the dsrA probe revealed a shift in the SRM community composition between Type-1 and Type-2 mats. Fluorescence in-situ hybridization (FISH) coupled to confocal scanning-laser microscopy (CSLM)-based image analyses, and 35SO4(2-)-silver foil patterns showed that SRM were present in surfaces of both mat types, but in significantly (p < 0.05) higher abundances in Type-2 mats. Over 85% of SRM cells in the top 0.5 mm of Type-2 mats were contained in a dense 130 µm thick horizontal layer comprised of clusters of varying sizes; (2) Microspatial mapping revealed that locations of SRM and CaCO3 precipitation were significantly correlated (p < 0.05); (3) Extracts from Type-2 mats contained acylhomoserine-lactones (C4- ,C6- ,oxo-C6,C7- ,C8- ,C10- ,C12- , C14-AHLs) involved in cell-cell communication. Similar AHLs were produced by SRM mat-isolates. These trends suggest that development of a microspatially-organized SRM community is closely-associated with the hallmark transition of stromatolite surface mats from a non-lithifying to a lithifying state.

Automatic identification of algal community from microscopic images

A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.

Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation

An automatic microscope image acquisition, evaluation, and recognition system was developed for the analysis of Utermöhl plankton chambers in terms of taxonomic algae recognition. The system called PLASA (Plankton Structure Analysis) comprises (1) fully automatic archiving (optical fixation) of aqueous specimens as digital bright field and fluorescence images, (2) phytoplankton analysis and recognition, and (3) training facilities for new taxa. It enables characterization of aqueous specimens by their populations. The system is described in detail with emphasis on image analytical aspects. Plankton chambers are scanned by sizable grids, divers objective(s), and up to four fluorescence spectral bands. Acquisition positions are focused and digitized by a TV camera and archived on disk. The image data sets are evaluated by a large set of quantitative features. Automatic classifications for a number of organisms are developed and embedded in the program. Interactive programs for the design of training sets were additionally implemented. A long-term sampling period of 23 weeks from two ponds at two different locations each was performed to generate a reliable data set for training and testing purposes. These data were used to present this system's results for phytoplankton structure characterization. PLASA represents an automatic system, comprising all steps from specimen processing to algae identification up to species level and quantification.

Three-dimensional differentiation of photo-autotrophic biofilm constituents by multi-channel laser scanning microscopy (single-photon and two-photon excitation)

A simple microscopic method to three-dimensionally differentiate between various members in photo-autotrophic biofilm systems is described. By dual-channel single-photon (confocal) and two-photon laser scanning microscopy, the signals in the red and far red channels as well as their combination can be simultaneously recorded. The method takes advantage of the autofluorescent signal of cyanobacteria-recorded in the red and far red channel and the autofluorescent signal of the green algae-recorded in the far red channel only. The differentiation is based on the specific pigment composition of cyanobacteria and green algae in combination with the appropriate filter settings for detection of the autofluorescent emission signals. The method allows the non-destructive, three-dimensional examination of fully hydrated interfacial microbial communities at high resolution as well as the clear separation between autofluorescent signals of cyanobacteria and green algae. Furthermore, there is a third option to record additional signals simultaneously such as nucleic acid stained bacteria, bacteria labeled with phylogenetic probes or glycoconjugates stained by using lectins. With state of the art laser scanning microscopes, even a fourth channel is available for recording yet another parameter, e.g. in the reflection (single-photon only) or fluorescence (single- and two-photon) mode. Thus the approach represents a convenient tool to study multiple parameters of complex photo-autotrophic biofilm systems.