The methods presented in this study are aimed at the identification of subpopulations (clones) of genetically similar cells within tissue samples through measurement of loci-specific Fluorescence in-situ hybridization (FISH) spot signals for each nucleus and analyzing cell spatial distributions by way of Voronoi tessellation and Delaunay triangulation to robustly define cell neighbourhoods. The motivation for the system is to examine lung cancer patient for subpopulations of Non-Small Cell Lung Cancer (NSCLC) cells with biologically meaningful gene copy-number profiles: patterns of genetic alterations statistically associated with resistance to cis-platinum/vinorelbine doublet chemotherapy treatment. Current technologies for gene-copy number profiling rely on large amount of cellular material, which is not always available and suffers from limited sensitivity to only the most dominant clone in often heterogeneous samples. Thus, through the use of FISH, the detection of gene copy-numbers is possible in unprocessed tissues, allowing identification of specific tumour clones with biologically relevant patterns of genetic aberrations. The tissue-wide characterization of multiplexed loci-specific FISH signals, described herein, is achieved through a fully automated, multicolour fluorescence imaging microscope and object segmentation algorithms to identify cell nuclei and FISH spots within. Related tumour clones are identified through analysis of robustly defined cell neighbourhoods and cell-to-cell connections for regions of cells with homogenous and highly interconnected FISH spot signal characteristics. This study presents experiments which demonstrate the system’s ability to accurately quantify FISH spot signals in various tumour tissues and in up to 5 colours simultaneously or more through multiple rounds of FISH staining. Furthermore, the system’s FISH-based cell classification performance is evaluated at a sensitivity of 84% and specificity 81% and clonal identification algorithm results are determined to be comparable to clone delineation by a human-observer. Additionally, guidelines and procedures to perform anticipated, routine analysis experiments are established.

The methods presented in this study are aimed at the identification of subpopulations (clones) of genetically similar cells within tissue samples through measurement of loci-specific Fluorescence in-situ hybridization (FISH) spot signals for each nucleus and analyzing cell spatial distributions by way of Voronoi tessellation and Delaunay triangulation to robustly define cell neighbourhoods. The motivation for the system is to examine lung cancer patient for subpopulations of Non-Small Cell Lung Cancer (NSCLC) cells with biologically meaningful gene copy-number profiles: patterns of genetic alterations statistically associated with resistance to cis-platinum/vinorelbine doublet chemotherapy treatment. Current technologies for gene-copy number profiling rely on large amount of cellular material, which is not always available and suffers from limited sensitivity to only the most dominant clone in often heterogeneous samples. Thus, through the use of FISH, the detection of gene copy-numbers is possible in unprocessed tissues, allowing identification of specific tumour clones with biologically relevant patterns of genetic aberrations. The tissue-wide characterization of multiplexed loci-specific FISH signals, described herein, is achieved through a fully automated, multicolour fluorescence imaging microscope and object segmentation algorithms to identify cell nuclei and FISH spots within. Related tumour clones are identified through analysis of robustly defined cell neighbourhoods and cell-to-cell connections for regions of cells with homogenous and highly interconnected FISH spot signal characteristics. This study presents experiments which demonstrate the system’s ability to accurately quantify FISH spot signals in various tumour tissues and in up to 5 colours simultaneously or more through multiple rounds of FISH staining. Furthermore, the system’s FISH-based cell classification performance is evaluated at a sensitivity of 84% and specificity 81% and clonal identification algorithm results are determined to be comparable to clone delineation by a human-observer. Additionally, guidelines and procedures to perform anticipated, routine analysis experiments are established.

The methods presented in this study are aimed at the identification of subpopulations (clones) of genetically similar cells within tissue samples through measurement of loci-specific Fluorescence in-situ hybridization (FISH) spot signals for each nucleus and analyzing cell spatial distributions by way of Voronoi tessellation and Delaunay triangulation to robustly define cell neighbourhoods. The motivation for the system is to examine lung cancer patient for subpopulations of Non-Small Cell Lung Cancer (NSCLC) cells with biologically meaningful gene copy-number profiles: patterns of genetic alterations statistically associated with resistance to cis-platinum/vinorelbine doublet chemotherapy treatment. Current technologies for gene-copy number profiling rely on large amount of cellular material, which is not always available and suffers from limited sensitivity to only the most dominant clone in often heterogeneous samples. Thus, through the use of FISH, the detection of gene copy-numbers is possible in unprocessed tissues, allowing identification of specific tumour clones with biologically relevant patterns of genetic aberrations. The tissue-wide characterization of multiplexed loci-specific FISH signals, described herein, is achieved through a fully automated, multicolour fluorescence imaging microscope and object segmentation algorithms to identify cell nuclei and FISH spots within. Related tumour clones are identified through analysis of robustly defined cell neighbourhoods and cell-to-cell connections for regions of cells with homogenous and highly interconnected FISH spot signal characteristics. This study presents experiments which demonstrate the system’s ability to accurately quantify FISH spot signals in various tumour tissues and in up to 5 colours simultaneously or more through multiple rounds of FISH staining. Furthermore, the system’s FISH-based cell classification performance is evaluated at a sensitivity of 84% and specificity 81% and clonal identification algorithm results are determined to be comparable to clone delineation by a human-observer. Additionally, guidelines and procedures to perform anticipated, routine analysis experiments are established. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

In the field of regenerative medicine, there is a need to develop technologies that can increase the overall efficiency of imaging and expanding cells in culture and in complex heterogeneous arrangements necessary for tissue construction. Long-term live cell imaging has the potential to significantly enhance our understanding of intercellular signaling pathways and the dependence of phenotype on cell arrangement. A transdisciplinary approach has been taken to bridge the fields of cell biology, robotics, and photonics to create a long-term live cell imaging system capable of single cell handling as well as the acquisition of multiple types of data needed for data mining and a general informatics approach to cell culture. A Bio-Assembly Mosaic Builder and Informatics (BAMBI) system was designed and developed using custom software to control a 3-axis stage manufactured by Galil Inc, and custom 1-axis micromanipulator for robotic operations. The software also employs a Sony charged-coupled device sensor for real-time image feedback and data acquisition. The system is mounted on a Carl Zeiss Axiovert 200 inverted microscope. Custom-built environmental controls are used to maintain the temperature, humidity, and gas conditions for extended live cell work. The software was designed using Visual C++ for the Windows PC platform using an object orientated and modular design methodology to allow the BAMBI software to continue to grow with new tasks and demands as needed. The modular approach keeps functional groups of code within context boundaries allowing for easy removal, addition, or changes of functions without compromising the usability of the whole system. BAMBI has been used to image cells within a novel cell culture chamber that constricts cell growth to a true monolayer for high-resolution imaging. In one specific application, BAMBI was also used to characterize and track the development of individual Colony Forming Units (CFU) over the five-day culture period in 5-day CFU-Hill colony assays. The integrated system successfully enabled the tracking and identification of cell types responsible for the formation of the CFU-Hill colonies (a putative endothelial stem cell). BAMBI has been used to isolate single hematopoietic stem cell (HSC) candidate cells, accumulate long-term live cell images, and then return these cells back to the in-vivo environment for further characterization. From these results, further data mining and lineage informatics suggested a novel way to isolate and purify HSCs. Studies such as these are the fundamental next step in developing new therapies for regenerative medicine in the future.

In the field of regenerative medicine, there is a need to develop technologies that can increase the overall efficiency of imaging and expanding cells in culture and in complex heterogeneous arrangements necessary for tissue construction. Long-term live cell imaging has the potential to significantly enhance our understanding of intercellular signaling pathways and the dependence of phenotype on cell arrangement. A transdisciplinary approach has been taken to bridge the fields of cell biology, robotics, and photonics to create a long-term live cell imaging system capable of single cell handling as well as the acquisition of multiple types of data needed for data mining and a general informatics approach to cell culture. A Bio-Assembly Mosaic Builder and Informatics (BAMBI) system was designed and developed using custom software to control a 3-axis stage manufactured by Galil Inc, and custom 1-axis micromanipulator for robotic operations. The software also employs a Sony charged-coupled device sensor for real-time image feedback and data acquisition. The system is mounted on a Carl Zeiss Axiovert 200 inverted microscope. Custom-built environmental controls are used to maintain the temperature, humidity, and gas conditions for extended live cell work. The software was designed using Visual C++ for the Windows PC platform using an object orientated and modular design methodology to allow the BAMBI software to continue to grow with new tasks and demands as needed. The modular approach keeps functional groups of code within context boundaries allowing for easy removal, addition, or changes of functions without compromising the usability of the whole system. BAMBI has been used to image cells within a novel cell culture chamber that constricts cell growth to a true monolayer for high-resolution imaging. In one specific application, BAMBI was also used to characterize and track the development of individual Colony Forming Units (CFU) over the five-day culture period in 5-day CFU-Hill colony assays. The integrated system successfully enabled the tracking and identification of cell types responsible for the formation of the CFU-Hill colonies (a putative endothelial stem cell). BAMBI has been used to isolate single hematopoietic stem cell (HSC) candidate cells, accumulate long-term live cell images, and then return these cells back to the in-vivo environment for further characterization. From these results, further data mining and lineage informatics suggested a novel way to isolate and purify HSCs. Studies such as these are the fundamental next step in developing new therapies for regenerative medicine in the future.