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An automated multicolour fluorescence in situ hybridization workstation for the identification of clonally related cells

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.

  1. http://hdl.handle.net/2429/733
Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU./733
Date05 1900
CreatorsDubrowski, Piotr
PublisherUniversity of British Columbia
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Format16694654 bytes, application/pdf

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