Automated Three-Dimensional Microbial Sensing and Recognition Using Digital Holography and Statistical Sampling

We overview an approach to providing automated three-dimensional (3D) sensing and recognition of biological micro/nanoorganisms integrating Gabor digital holographic microscopy and statistical sampling methods. For 3D data acquisition of biological specimens, a coherent beam propagates through the specimen and its transversely and longitudinally magnified diffraction pattern observed by the microscope objective is optically recorded with an image sensor array interfaced with a computer. 3D visualization of the biological specimen from the magnified diffraction pattern is accomplished by using the computational Fresnel propagation algorithm. For 3D recognition of the biological specimen, a watershed image segmentation algorithm is applied to automatically remove the unnecessary background parts in the reconstructed holographic image. Statistical estimation and inference algorithms are developed to the automatically segmented holographic image. Overviews of preliminary experimental results illustrate how the holographic image reconstructed from the Gabor digital hologram of biological specimen contains important information for microbial recognition

Quantitative phase study of the dynamic cellular response in femtosecond laser photoporation

We use Digital Holographic Microscopy to study dynamic responses of live cells to femtosecond laser cellular membrane photoporation. Temporal and spatial characteristics of morphological changes as well as dry mass variation are analyzed and compared with conventional fluorescent assays for viability and photoporation efficiency. With the latter, the results provide a new insight into the efficiency and toxicity of this novel optical method of drug delivery. In addition, quantitative phase maps reveal photoporation related sub-cellular dynamics of cytoplasmic vesicles

Enhanced robustness digital holographic microscopy for demanding environment of space biology

We describe an optimized digital holographic microscopy system (DHM) suitable for high-resolution visualization of living cells under conditions of altered macroscopic mechanical forces such as those that arise from changes in gravitational force. Experiments were performed on both a ground-based microgravity simulation platform known as the random positioning machine (RPM) as well as during a parabolic flight campaign (PFC). Under these conditions the DHM system proved to be robust and reliable. In addition, the stability of the system during disturbances in gravitational force was further enhanced by implementing post-processing algorithms that best exploit the intrinsic advantages of DHM for hologram autofocusing and subsequent image registration. Preliminary results obtained in the form of series of phase images point towards sensible changes of cytoarchitecture under states of altered gravity

A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes

Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities. Conclusions/Significance: The developed method enables biomedical researchers to automatically and robustly quantify the pro- or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment. © 2011 Adanja et al.SCOPUS: ar.jinfo:eu-repo/semantics/publishe