Joseph Paul Robinson

A novel and simple cell-based detection system with a collagen-encapsulated B-lymphocyte cell line as a biosensor for rapid detection of pathogens and toxins

Cell-based biosensors (CBBs) are becoming important tools for biosecurity applications and rapid diagnostics in food microbiology for their unique capability of detecting physiologically hazardous materials. A multi-well plate-based biosensor containing B-cell hybridoma, Ped-2E9, encapsulated in type I collagen matrix, was developed for rapid detection of viable cells of pathogenic Listeria, the toxin listeriolysin O, and the enterotoxin from Bacillus species. This sensor measures the alkaline phosphatase release from infected Ped-2E9 cells colorimetrically. Pathogenic L. monocytogenes cells and toxin preparations from L. monocytogenes or B. cereus showed cytotoxicity ranging from 24 to 98% at 3–6 h postinfection. In contrast, nonpathogenic L. innocua (F4247) and B. subtilis induced minimal cytotoxicity, ranging only 0.4–7.6%. Laser scanning cytometry and cryo-nano scanning electron microscopy confirmed the live or dead status of the infected Ped-2E9 cells in gel matrix. This paper presents the first example of a cell-based sensing system using collagen-encapsulated mammalian cells for rapid detection of pathogenic bacteria or toxin, and demonstrates a potential for onsite use as a portable detection system.

Automated quantification and reconstruction of collagen matrix from 3D confocal datasets

The geometrical structure of fibrous extracellular matrix (ECM) impacts on its biological function. In this report, we demonstrate a new algorithm designed to extract quantitative structural information about individual collagen fibres (orientation, length and diameter) from 3D backscattered‐light confocal images of collagen gels. The computed quantitative data allowed us to create surface‐rendered 3D images of the investigated sample.

Multispectral Flow Cytometry: Next Generation Tools for Automated Classification

Flow cytometry has moved from a relatively simple technology 30 years ago, to a very sophisticated and high-speed detection technology today. However, the number of simultaneous fluorescence dyes that can be separated is limited by the difficulty in overlapping spectra and the complexity of resolving this spectral overlap problem. High-speed multianode PMTs may change this situation. The system we propose utilizes such a technology to allow full spectral analysis of cells and particles as they flow past the light source. Making these measurements is very complex and the necessity for advanced spectral overlap calculations creates a number of difficult problems to solve in a very short period of time. Next-generation instruments can either increase the number of detectors or modify the principles of collection. If the detector system were simplified, the overall cost and complexity of single-cell analytical systems might be reduced. This requires changes in both hardware and software that allow for the analysis of 30 or more spectral signals. Analysis of complex data sets requires some completely new analytical approaches, particularly in the area of multispectral analysis. This presentation discusses a next-generation instrument, which can collect simultaneously 32 bands of fluorescence from a particle in less than 5 microseconds. This opens new opportunities for analysis of bioparticles in a very fast and high content fashion.

Modeling ECM fiber formation: structure information extracted by analysis of 2D and 3D image sets

Recent evidence supports the notion that biological functions of extracellular matrix (ECM) are highly correlated to its structure. Understanding this fibrous structure is very crucial in tissue engineering to develop the next generation of biomaterials for restoration of tissues and organs. In this paper, we integrate confocal microscopy imaging and image-processing techniques to analyze the structural properties of ECM. We describe a 2D fiber middle-line tracing algorithm and apply it via Euclidean distance maps (EDM) to extract accurate fibrous structure information, such as fiber diameter, length, orientation, and density, from single slices. Based on a 2D tracing algorithm, we extend our analysis to 3D tracing via Euclidean distance maps to extract 3D fibrous structure information. We use computer simulation to construct the 3D fibrous structure which is subsequently used to test our tracing algorithms. After further image processing, these models are then applied to a variety of ECM constructions from which results of 2D and 3D traces are statistically analyzed.