Bacterial cell identification in differential interference contrast microscopy images

Abstract

BackgroundMicroscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.ResultsWe have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.ConclusionsResults demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

EzColocalization: An ImageJ plugin for visualizing and measuring colocalization in cells and organisms

“…1B ), and images with an extracellular dye that outlines cells. Differential interference contrast (DIC) images create shadows that make it difficult for automated selection of cells using threshold methods 27 ; therefore for DIC images we recommend that ROIs be created using the “selection tools” in ImageJ to manually outline cell areas, and then adding them to a list by choosing “Add to Manager” (in “Selection” submenu of the “Edit” menu). Once the ROIs for all cells of interest in an image are selected, a binary mask can be created using the “Clear Outside” and “Autothreshold” functions of ImageJ.…”

Multi-Beam Interference Patterns of Living Sarcoma Cells may be Approximated by a Fractal Julia Set and Rieman Spheres or Fatou Components Decoded as a Siegel Discs

“…Different types of cells may be measured and quantitative or semi-quantitative visualized by different methods of interference microscopy Barer and Joseph, 1957;Dunn, Zicha, 1994). Examples include:  calculation of lignin concentration and porosity of cell-wall regions by interference microscopy (Boutelje, 1972;Donaldson, 1985);  dry mass and cell area measurements (Goldacre et al, 1957;Lee at al., 1960);  cell-substrate interactions in amoeboid locomotion (King et al, 1983);  interaction between intracellular vacuoles and the cell surface (Gingell, 1982);  growth cone interactions with cell and substrate adhesion molecules of cells (Drazba et al, 1997);  visualization of red cell membranes (Miller and Dvorak, 1973);  bacterial cell identification in DIC interference contrast microscopy in label-free conditions (Obara et al, 2013);  radiation dose effect analysis (Lee and Richards, 1964);  comparative analysis of epithelial cells (Pappelis et al, 1976);  real time 3D and "4D" imaging of cells (Salmon et al, 1998;Li et al, 2007;, from yeast cells to cancer cels.…”

Application of Microfluidics in Experimental Ecology: The Importance of Being Spatial

“…With all the advantages of the micro-scale (high-throughput, controlled lab experiments vs. field experiments), microfluidics is restricted for use with microscopic organisms. Various microscopy imaging methods and digital image processing techniques may be used to observe and analyze microbes in microfluidic devices (Van Teeffelen et al, 2012 ; Obara et al, 2013 ; Sadanandan et al, 2015 ). Regardless of size restrictions, artificial microfluidic microbial ecosystems have a scientific impact that goes beyond microbial ecology and thus can serve as model systems to test and validate general or multiscale ecological theories and concepts.…”