Tools for single cell proteomics

Despite recent advances that offer control of single cells, in terms of manipulation and sorting and the ability to measure gene expression, the need to measure protein copy number remains unmet. Measuring protein copy number in single cells and related quantities such as levels of phosphorylation and protein-protein interaction is the basis of single cell proteomics. A technology platform to undertake the analysis of protein copy number from single cells has been developed. The approach described is ‘all-optical’ whereby single cells are manipulated into separate analysis chambers using an optical trap; single cells are lysed by mechanical shearing caused by laser-induced microcavitation; and the protein released from a single cell is measured by total internal reflection microscopy as it is bound to micro-printed antibody spots within the device. The platform was tested using GFP transfected cells and the relative precision of the measurement method was determined to be 88%. Single cell measurements were also made on a breast cancer cell line to measure the relative levels of unlabelled human tumour suppressor protein p53 using a chip incorporating an antibody sandwich assay format. This demonstrates the ability count protein copy number from single cells in a manner which could be applied in principle to any set of proteins and for any cell type without the need for genetic engineering. Metabolism can undergo alteration in diseases such as cancer and heart failure and also as cells differentiate during development. In order to assess how it may inform a proteomic measurement, multidimensional two-photon fluorescence metabolic imaging is conducted on a cultured cancer cell line, primary adult rat cardiomyocytes and human embryonic stem cells. By measuring the parameters of fluorescence such as intensity and lifetime of the autofluorescent metabolic co-factors NADH and FAD, it was found to be possible to contrast cells under various conditions and metabolic stimuli. In particular, human embryonic stem cells were able to be contrasted at 3 stages of development as they underwent differentiation into embryonic stem cell derived cardiomyocytes. Metabolic imaging provides a non-destructive method to monitor cellular metabolic activity with high resolution. This is complimentary to the single cell proteomic platform and the convergence of both techniques holds promise in future investigations into how metabolism influences cell function and the proteome in development and disease

Evaluating single molecule detection methods for microarrays with high dynamic range for quantitative single cell analysis

Single molecule microarrays have been used in quantitative proteomics, in particular, single cell analysis requiring high sensitivity and ultra-low limits of detection. In this paper, several image analysis methods are evaluated for their ability to accurately enumerate single molecules bound to a microarray spot. Crucially, protein abundance in single cells can vary significantly and may span several orders of magnitude. This poses a challenge to single molecule image analysis. In order to quantitatively assess the performance of each method, synthetic image datasets are generated with known ground truth whereby the number of single molecules varies over 5 orders of magnitude with a range of signal to noise ratios. Experiments were performed on synthetic datasets whereby the number of single molecules per spot corresponds to realistic single cell distributions whose ground truth summary statistics are known. The methods of image analysis are assessed in their ability to accurately estimate the distribution parameters. It is shown that super-resolution image analysis methods can significantly improve counting accuracy and better cope with single molecule congestion. The results highlight the challenge posed by quantitative single cell analysis and the implications to performing such analyses using microarray based approaches are discussed

A model for calculating EM field in layered medium with application to biological implants

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Modern wireless telecommunication devices (GSM Mobile system) (cellular telephones and wireless modems on laptop computers) have the potential to interfere with implantable medical devices/prostheses and cause possible malfunction. An implant of resonant dimensions within a homogeneous dielectric lossy sphere can enhance local values of SAR (the specific absorption rate). Also antenna radiation pattern and other characteristics are significantly altered by the presence of the composite dielectric entities such as the human body. Besides, the current safety limits do not take into account the possible effect of hot spots arising from metallic implants resonant at mobile phone frequencies. Although considerable attention has been given to study and measurement of scattering from a dielectric sphere, no rigorous treatment using electromagnetic theory has been given to the implanted dielectric spherical head/cylindrical body. This thesis aims to deal with the scattering of a plane electromagnetic wave from a perfectly conducting or dielectric spherical/cylindrical implant of electrically small radius (of resonant length), embedded eccentrically into a dielectric spherical head model. The method of dyadic Green's function (DGF) for spherical vector wave functions is used. Analytical expressions for the scattered fields of both cylindrical and spherical implants as well as layered spherical head and cylindrical torso models are obtained separately in different chapters. The whole structure is assumed to be uniform along the propagation direction. To further check the accuracy of the proposed solution, the numerical results from the analytical expressions computed for the problem of implanted head/body are compared with the numerical results from the Finite-Difference Time-Domain (FDTD) method using the EMU-FDTD Electromagnetic simulator. Good agreement is observed between the numerical results based on the proposed method and the FDTD numerical technique. This research presents a new approach, away from simulation work, to the study of exact computation of EM fields in biological systems. Its salient characteristics are its simplicity, the saving in memory and CPU computational time and speed.Cochlear UK Limited and EPSR

RPNCH: A method for constructing rooted phylogenetic networks from rooted triplets based on height function

     Phylogenetic networks are a generalization of phylogenetic trees which permit the representation the non-tree-like events. It is NP-hard to construct an optimal rooted phylogenetic network from a given set of rooted triplets. This paper presents a novel algorithm called RPNCH. For a given set of rooted triplets, RPNCH tries to construct a rooted phylogenetic network with the minimum number of reticulation nodes that contains all the given rooted triplets. The performance of RPNCH algorithm on simulated data is reported here