Automated quantification and sizing of unbranched filamentous cyanobacteria by model-based object-oriented image analysis

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

QUANTIFYING ACTIN FILAMENTS IN MICROSCOPIC IMAGES USING KEYPOINT DETECTION TECHNIQUES AND A FAST MARCHING ALGORITHM

The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental. In this paper, we adopt a Convolutional Neural Network (CNN) to segment actin filaments first, and then we utilize a modified Resnet to detect junctions and endpoints of filaments. With binary segmentation and detected keypoints, we apply a fast marching algorithm to obtain the number and length of each actin filament in microscopic images. We have also collected a dataset of 10 microscopic images of actin filaments to test our method. Our experiments show that our approach outperforms other existing approaches tackling this problem regarding both accuracy and inference time.

Intersection To Overpass: Instance Segmentation On Filamentous Structures With An Orientation-Aware Neural Network And Terminus Pairing Algorithm

Filamentous structures play an important role in biological systems. Extracting individual filaments is fundamental for analyzing and quantifying related biological processes. However, segmenting filamentous structures at an instance level is hampered by their complex architecture, uniform appearance, and image quality. In this paper, we introduce an orientation-aware neural network, which contains six orientation-associated branches. Each branch detects filaments with specific range of orientations, thus separating them at junctions, and turning intersections to overpasses. A terminus pairing algorithm is also proposed to regroup filaments from different branches, and achieve individual filaments extraction. We create a synthetic dataset to train our network, and annotate real full resolution microscopy images of microtubules to test our approach. Our experiments have shown that our proposed method outperforms most existing approaches for filaments extraction. We also show that our approach works on other similar structures with a road network dataset.

Grazing of Nuclearia thermophila and Nuclearia delicatula (Nucleariidae, Opisthokonta) on the toxic cyanobacterium Planktothrix rubescens

During the last decades, the planktonic cyanobacterium Planktothrix rubescens became a dominant primary producer in many deep pre-alpine lakes. While altered physiochemical conditions due to lake warming seem to favour this cyanobacterial species, its dominance is partly attributed to factors conferring grazing resistance. The rigid structure of the cyanobacterial filaments and toxic secondary metabolites (e.g. microcystins) protect against diverse grazers. Nonetheless, species of the protistan genus Nuclearia (Nucleariidae, Opisthokonta) are able to overcome this grazing protection. Time lapse video documentation served as tool to record slow feeding processes of N. thermophila and N. delicatula. Three different feeding strategies could be distinguished: (i) Phagocytosis of small fragments, (ii) serial break-ups of cyanobacterial cells and (iii) bending and breaking of filaments. While observations revealed mechanical manipulation to be important for the efficient breakdown of P. rubescens filaments, the toxin microcystin had no pronounced negative effects on nucleariid cells. Growth experiments with N. thermophila/N. delicatula and different accompanying bacterial assemblages pointed to a pivotal role of distinct prokaryotic species for toxin degradation and for the growth success of the protists. Thus, the synergistic effect of nucleariids and specific bacteria favours an efficient degradation of P. rubescens along with its toxin.

ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.

Mechanistic studies of intracellular delivery of proteins by cell-penetrating peptides in cyanobacteria

Background

The plasma membrane plays an essential role in selective permeability, compartmentalization, osmotic balance, and cellular uptake. The characteristics and functions of cyanobacterial membranes have been extensively investigated in recent years. Cell-penetrating peptides (CPPs) are special nanocarriers that can overcome the plasma membrane barrier and enter cells directly, either alone or with associated cargoes. However, the cellular entry mechanisms of CPPs in cyanobacteria have not been studied.

Results

In the present study, we determine CPP-mediated transduction efficiency and internalization mechanisms in cyanobacteria using a combination of biological and biophysical methods. We demonstrate that both Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942 strains of cyanobacteria possess red autofluorescence. Green fluorescent protein (GFP), either alone or noncovalently associated with a CPP comprised of nine arginine residues (R9/GFP complexes), entered cyanobacteria. The ATP-depleting inhibitor of classical endocytosis, N-ethylmaleimide (NEM), could block the spontaneous internalization of GFP, but not the transduction of R9/GFP complexes. Three specific inhibitors of macropinocytosis, cytochalasin D (CytD), 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), and wortmannin, reduced the efficiency of R9/GFP complex transduction, indicating that entry of R9/GFP complexes involves macropinocytosis. Both the 1-(4,5-dimethylthiazol-2-yl)-3,5-diphenylformazan (MTT) and membrane leakage analyses confirmed that R9/GFP complexes were not toxic to the cyanobacteria, nor were the endocytic and macropinocytic inhibitors used in these studies.

Conclusions

In summary, we have demonstrated that cyanobacteria use classical endocytosis and macropinocytosis to internalize exogenous GFP and CPP/GFP proteins, respectively. Moreover, the CPP-mediated delivery system is not toxic to cyanobacteria, and can be used to investigate biological processes at the cellular level in this species. These results suggest that both endocytic and macropinocytic pathways can be used for efficient internalization of regular protein and CPP-mediated protein delivery in cyanobacteria, respectively.