Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images

Background New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have highlighted the need to better understand the interplay between proteins in terms of their colocalisation. Results We recently proposed a cellular-level model of the healthy and cancerous colonic crypt microenvironments. Here, we extend the model to include detailed models of protein expression to generate synthetic multiplex fluorescence data. As a first step, we present models for various cell organelles learned from real immunofluorescence data from the Human Protein Atlas. Comparison between the distribution of various features obtained from the real and synthetic organelles has shown very good agreement. This has included both features that have been used as part of the model input and ones that have not been explicitly considered. We then develop models for six proteins which are important colorectal cancer biomarkers and are associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6, P53 and PTEN. The protein models include their complex expression patterns and which cell phenotypes express them. The models have been validated by comparing distributions of real and synthesised parameters and by application of frameworks for analysing multiplex immunofluorescence image data. Conclusions The six proteins have been chosen as a case study to illustrate how the model can be used to generate synthetic multiplex immunofluorescence data. Further proteins could be included within the model in a similar manner to enable the study of a larger set of proteins of interest and their interactions. To the best of our knowledge, this is the first model for expression of multiple proteins in anatomically intact tissue, rather than within cells in culture.QNRF grant NPRP 5-1345-1-228. BBSRC and University of Warwick Institute of Advanced Study

Modelling and analysis of the tumour microenvironment of colorectal cancer

New bioimaging techniques have recently been proposed to visualise the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyse the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens in order to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level. In the firrst part of the thesis, we propose an approach to analyses cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System (TIS) microscope. It involves segmenting the DAPI labelled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. These were analysed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. This approach enables one to easily identify protein pairs which have significantly higher/lower co-dependence levels in cancerous tissue samples when compared to normal colon tissue. The proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation. Due to the lack of ground truth data for bioimages, the objective evaluation of the methods developed for its analysis can be very challenging. To that end, in the second part of the thesis we propose a model of the healthy and cancerous colonic crypt microenvironments. Our model is designed to generate realistic synthetic fluorescence and histology image data with parameters that allow control over differentiation grade of cancer, crypt morphology, cellularity, cell overlap ratio, image resolution, and objective level. The model learns some of its parameters from real histology image data stained with standard Hematoxylin and Eosin (H&E) dyes in order to generate realistic chromatin texture, nuclei morphology, and crypt architecture. To the best of our knowledge, ours is the first model to simulate image data at subcellular level for healthy and cancerous colon tissue, where the cells are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. The simulated data could be used to validate techniques such as image restoration, cell segmentation, cell phenotyping, crypt segmentation, and differentiation grading, only to name a few. In addition, developing a detailed model of the tumour microenvironment can aid the understanding of the underpinning laws of tumour heterogeneity. In the third part of the thesis, we extend the model to include detailed models of protein expression to generate synthetic multi-tag fluorescence data. As a first step, we have developed models for various cell organelles that have been learned from real immunofluorescence data. We then develop models for five proteins associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6 and p53. The protein models include subcellular location, which cells express the protein and under what conditions

A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

Background There have been great advancements in the field of digital pathology. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating and comparing these methods. In addition, developing a spatial model of the tumour microenvironment can aid our understanding of the underpinning laws of tumour heterogeneity. Results We propose a model of the healthy and cancerous colonic crypt microenvironment. Our model is designed to generate synthetic histology image data with parameters that allow control over cancer grade, cellularity, cell overlap ratio, image resolution, and objective level. Conclusions To the best of our knowledge, ours is the first model to simulate histology image data at sub-cellular level for healthy and cancerous colon tissue, where the cells have different compartments and are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. Qualitative and quantitative validation has been performed on the model results demonstrating good similarity to the real data. The simulated data could be used to validate techniques such as image restoration, cell and crypt segmentation, and cancer grading.BBSRC and University of Warwick Institute of Advanced Study. QNRF grant NPRP 5-1345-1-228

DiSWOP : a novel measure for cell-level protein network analysis in localized proteomics image data

Motivation: New bioimaging techniques have recently been proposed to visualize the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyze the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level. Results: The proposed approach analyzes cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System microscope. It involves segmenting the 4',6-diamidino-2-phenylindole-labelled image into cells and determining the cell phenotypes according to their protein–protein dependence profile. These were analyzed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. These novel quantities were extracted using 11 Toponome Imaging System image stacks from either cancerous or normal human colorectal specimens. This approach enables one to easily identify protein pairs that have significantly higher/lower co-expression levels in cancerous tissue samples when compared with normal colon tissue