The physical manipulation of biological cells is very attractive now in biotechnology (Butler, 1991)) because it opens the possibility of examining and manipulating single molecules. Other methods are based on chemical effects, electrical effects, etc., and they generally do not allow researchers to examine single molecules cell and, thus, to understand their interaction which may encode many useful pieces of information. Such physical manipulation is fully performed by robotic devices. <p> In order to automate the process of physical manipulation, micro machine vision for the fast identification of the objects involved is required. Typical objects that are involved are cells, cell elements, holders and injectors. <p> In the research described in this thesis, which was carried out in the Advanced Engineering Design Laboratory of the Mechanical Engineering Department, University of Saskatchewan, algorithms for the three objects (the cell, holder and injector) were developed, implemented and tested. The results obtained have shown that the fastest identification times for these three objects are respectively 0.12s for the cell oocyte, 6.78s/100 frames for the holder, and 6.72s/100 frames for the injector. These performances are acceptable in the context of the physical manipulation of biological cells.<p> The goal of the research described in this thesis was to develop algorithms that would give a fast recognition of the cell manipulation system. With the aid of the algorithms, an automatic operation of the cell manipulation system would be achieved. Image process and pattern recognition techniques were used in developing the Visual C++ GUI algorithms that would automatically recognize the components of the cell manipulation system for the purpose of manipulating the cells.

The physical manipulation of biological cells is very attractive now in biotechnology (Butler, 1991)) because it opens the possibility of examining and manipulating single molecules. Other methods are based on chemical effects, electrical effects, etc., and they generally do not allow researchers to examine single molecules cell and, thus, to understand their interaction which may encode many useful pieces of information. Such physical manipulation is fully performed by robotic devices. <p> In order to automate the process of physical manipulation, micro machine vision for the fast identification of the objects involved is required. Typical objects that are involved are cells, cell elements, holders and injectors. <p> In the research described in this thesis, which was carried out in the Advanced Engineering Design Laboratory of the Mechanical Engineering Department, University of Saskatchewan, algorithms for the three objects (the cell, holder and injector) were developed, implemented and tested. The results obtained have shown that the fastest identification times for these three objects are respectively 0.12s for the cell oocyte, 6.78s/100 frames for the holder, and 6.72s/100 frames for the injector. These performances are acceptable in the context of the physical manipulation of biological cells.<p> The goal of the research described in this thesis was to develop algorithms that would give a fast recognition of the cell manipulation system. With the aid of the algorithms, an automatic operation of the cell manipulation system would be achieved. Image process and pattern recognition techniques were used in developing the Visual C++ GUI algorithms that would automatically recognize the components of the cell manipulation system for the purpose of manipulating the cells.

Thesis (Ph. D.)--University of Hong Kong, 1980.

This thesis systematically investigates the nano handling and measurement techniques for biological cells in culture and studies the techniques to realize innovative and multi-functional applications in biomedicine. Among them, the technique based on AFM is able to visualize and quantify the dynamics of organic cells in culture on the nano scale. Especially, the cellular shear adhesion force on the various locations of biological cells was firstly accurately measured in the research of the cell-substrate interaction in terms of biophysical perspective. The innovative findings are conductive to study the cell-cell adhesion, the cell-matrix adhesion which is related to the cell morphology structure, function, deformation ability and adhesion of cells and better understand the cellular dynamic behaviors. Herein, a new liquid-AFM probe unit and an increment PID control algorithm were implemented suitable for scanning the cell samples under the air conditions and the liquid environments. The influence between the surface of sample and the probe, and the damage of probe during the sample scanning were reduced. The proposed system is useful for the nano handling and measurement of living cells. Besides, Besides, to overcome the limitations of liquid-AFMs, the multiple optical tweezers were developed to integrate with the liquid-AFM. The technique based on laser interference is able to characterize the optical trap stiffness and the escape velocity, especially to realize the capture and sorting of multiple cells by a polarization-controlled periodic laser interference. It can trap and move hundreds of cells without physical contact, and has broad application prospects in cytology. Herein, a new experimental method integrated with the positioning analysis in the Z direction was used to improve the fluid force method for the calibration and characterize the mechanical forces exerted on optical traps and living cells. Moreover, a sensitive and highly efficient polarization-controlled three-beam interference set-up was developed for the capture and sorting of multiple cells. By controlling the polarization angles of the beams, various intensity distributions and different sizes of dots were obtained. Subsequently, we have experimentally observed multiple optical tweezers and the sorting of cells with different polarization angles, which are in accordance with the theoretical analysis.

Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.

Exocytosis is a fundamental cellular process where membrane-bound secretory granules from within the cell fuse with the plasma membrane to form fusion pore openings through which they expel their contents. This mechanism occurs constitutively in all eukaryotic cells and is responsible for the regulation of numerous bodily functions. Despite intensive study on exocytosis the fusion pore is poorly understood. In this research micro-fabrication techniques were integrated with biology to facilitate the study of fusion pores from cells in the anterior pituitary using the atomic force microscope (AFM). In one method cells were chemically fixed to reveal a diverse range of pore morphologies, which were characterised according to generic descriptions and compared to those in literature. The various pore topographies potentially illustrates different fusion mechanisms or artifacts caused from the impact of chemicals and solvents in distorting dynamic cellular events. Studies were performed to investigate changes in fusion pores in response to stimuli along with techniques designed to image membrane topography with nanometre resolution. To circumvent some deficiencies in traditional chemical fixation methodologies, a Bioimprint replication process was designed to create molecular imprints of cells using imprinting and soft moulding techniques with photo and thermal activated elastomers. Motivation for the transfer of cellular ultrastructure was to enable the non-destructive analysis of cells using the AFM while avoiding the need for chemical fixation. Cell replicas produced accurate images of membrane topology and contained certain fusion pore types similar to those in chemically fixed cells. However, replicas were often dehydrated and overall experiments testing stimuli responses were inconclusive. In a preliminary investigation, a soft replication moulding technique using a PDMS-elastomer was tested on human endometrial cancer cells with the aim of highlighting malignant mutations. Finally, a Biochip comprised of a series of interdigitated microelectrodes was used to position single-cells within an array of cavities using positive and negative dielectrophoresis (DEP). Selective sites either between or on the electrode were exposed as cavities designed to trap and incubate pituitary and cancer cells for analysis by atomic force microscopy (AFMy). Results achieved trapping of pituitary and cancer cells within cavities and demonstrated that positive DEP could be used as a force to effectively position living cells. AFM images of replicas created from cells trapped within cavities illustrated the advantage of integrating the Biochip with Bioimprint for cellular analysis.

No description available.

Infrared (IR) spectroscopy has shown potential to quickly and non-destructively measure the chemical signatures of biomedical samples such as single biological cells, and tissue from biopsy. The size of a single cell (diameter ~10-50 µm) are of a similar magnitude to the mid-IR wavelengths of light (~1-10 µm) giving rise to Mie-type scattering. The result of this scattering is that chemical information is significantly distorted in the IR spectrum.Distortions in biomedical IR spectra are often observed as a broad oscillating baseline on which the absorbance spectrum is superimposed. A spectral feature commonly observed is the sharp decrease in intensity at approximately 1700 cm-1, next to the Amide I band (~1655 cm-1), which pre-2009 was called the 'dispersion artefact'. The first contributing factor towards the 'dispersion artefact' investigated was the reflection signal arising from the air to sample interface entering the collection optics during transflection experiments. This was theoretically modelled, and then experimentally verified. It was shown that IR mapping could be done using reflection mode, yielding information from the optically dense nucleus which previously caused extinction of light in transmission mode.The most important contribution to the spectral distortions was due to resonant Mie scattering (RMieS) which occurs when the scattering particle is strongly absorbing such as biomedical samples. RMieS was shown to explain both the baselines in IR spectra, and the 'dispersion artefact' and was validated using a model system of poly(methyl methacrylate) (PMMA) of varying sizes from 5 to 15 µm. Theoretical simulations and experimental data had an excellent match thus proving the theory proposed. With an understanding of the physics/mathematics of the spectral distortions, a correction algorithm was written, the RMieS extended multiplicative signal correction (RMieS-EMSC). This algorithm modelled the measured spectrum as superposition of a first guess (the reference spectrum) which was of a similar biochemical composition to the pure absorbance spectrum of the sample, and a scattering curve. The scattering curve was estimated as the linear combination of a database of a large number of scattering curves covering a range of feasible physical parameters. Simulated and measured data verified that the RMieS-EMSC increased IR spectral quality.

Yes / When modeling objects that are small compared with the wavelength, e.g., biological cells at radio frequencies, the standard finite-difference time-domain (FDTD) method requires extremely small time-step sizes, which may lead to excessive computation times. The problem can be overcome by implementing a quasi-static approximate version of FDTD based on transferring the working frequency to a higher frequency and scaling back to the frequency of interest after the field has been computed. An approach to modeling and analysis of biological cells, incorporating a generic lumped-element membrane model, is presented here. Since the external medium of the biological cell is lossy material, a modified Berenger absorbing boundary condition is used to truncate the computation grid. Linear assemblages of cells are investigated and then Floquet periodic boundary conditions are imposed to imitate the effect of periodic replication of the assemblages. Thus, the analysis of a large structure of cells is made more computationally efficient than the modeling of the entire structure. The total fields of the simulated structures are shown to give reasonable and stable results at 900,1800, and 2450 MHz. This method will facilitate deeper investigation of the phenomena in the interaction between electromagnetic fields and biological systems.