Automated image analysis to improve bead ingestion toxicity test counts in the protozoan Tetrahymena pyriformis

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.

Eukaryotic inhibitors or activators elicit responses to chemosensory compounds by ruminal isotrichid and entodiniomorphid protozoa

Our objectives were to evaluate potential signaling pathways regulating rumen protozoal chemotaxis using eukaryotic inhibitors potentially coordinated with phagocytosis as assessed by fluorescent bead uptake kinetics. Wortmannin (inhibitor of phosphoinositide 3-kinase), insulin, genistein (purported inhibitor of a receptor tyrosine kinase), U73122 (inhibitor of phospholipase C), and sodium nitroprusside (Snp, nitric oxide generator, activating protein kinase G) were preincubated with mixed ruminal protozoa for 3h before assessing uptake of fluorescent beads and chemosensory behavior to glucose, peptides, and their combination; peptides were also combined with guanosine triphosphate (GTP; a chemorepellent). Entodiniomorphids were chemoattracted to both glucose and peptides, but chemoattraction to glucose was increased by Snp and wortmannin without effect on chemoattraction to peptides. Rate of fluorescent bead uptake by an Entodinium caudatum culture decreased when beads were added simultaneously with feeding and incubated with wortmannin (statistical interaction). Wortmannin also decreased the proportion of mixed entodiniomorphids consuming beads. Isotrichid protozoa exhibited greater chemotaxis to glucose but, compared with entodiniomorphids, were chemorepelled to peptides. Wortmannin increased chemotaxis by entodiniomorphids but decreased chemotaxis to glucose by isotrichids. Motility assays documented that Snp and wortmannin decreased net swimming speed (distance among 2 points per second) but not total swimming speed (including turns) by entodiniomorphids. Wortmannin decreased both net and total swimming behavior in isotrichids. Results mechanistically explain the isotrichid migratory ecology to rapidly take up newly ingested sugars and subsequent sedimentation back to the ventral reticulorumen. In contrast, entodiniomorphids apparently integrate cellular motility with feeding behavior to consume small particulates and thereby stay associated and pass with the degradable fraction of rumen particulates. These results extend findings from aerobic ciliate models to explain how rumen protozoa have adapted physiology for their specific ecological niches.