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Graf JF, Zavodszky MI. Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures. PLoS One 2017; 12:e0188878. [PMID: 29190747 PMCID: PMC5708750 DOI: 10.1371/journal.pone.0188878] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Accepted: 11/14/2017] [Indexed: 11/18/2022] Open
Abstract
Background Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view. Results The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence. Conclusions MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).
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Affiliation(s)
- John F. Graf
- GE Global Research, Niskayuna, New York, United States of America
- * E-mail: (JFG); (MIZ)
| | - Maria I. Zavodszky
- GE Global Research, Niskayuna, New York, United States of America
- * E-mail: (JFG); (MIZ)
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Kovacheva VN, Rajpoot NM. Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images. BMC Bioinformatics 2016; 17:430. [PMID: 27770786 PMCID: PMC5075203 DOI: 10.1186/s12859-016-1243-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 09/08/2016] [Indexed: 11/12/2022] Open
Abstract
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.
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Affiliation(s)
- Violeta N Kovacheva
- Department of Systems Biology, University of Warwick, Coventry, CV4 7AL, UK. .,Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK. .,Centre for Molecular Pathology, Institute of Cancer Research, London, SM2 5NG, UK. .,Centre for Evolution and Cancer, Institute of Cancer Research, London, SM2 5NG, UK. .,Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.
| | - Nasir M Rajpoot
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.,Department of Computer Science and Engineering, Qatar University, Doha, Qatar.,Department of Pathology, University Hospitals Coventry & Warwickshire NHS Trust, Coventry, CV2 2DX, UK
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Ahmed Raza SE, Langenkämper D, Sirinukunwattana K, Epstein D, Nattkemper TW, Rajpoot NM. Robust normalization protocols for multiplexed fluorescence bioimage analysis. BioData Min 2016; 9:11. [PMID: 26949415 PMCID: PMC4779207 DOI: 10.1186/s13040-016-0088-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 02/02/2016] [Indexed: 12/18/2022] Open
Abstract
study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270–8, 2006; Proc Natl Acad Sci 110:11982–7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples.
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Affiliation(s)
- Shan E Ahmed Raza
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL UK
| | | | | | - David Epstein
- Mathematics Institute, University of Warwick, Coventry, CV4 7AL UK
| | | | - Nasir M Rajpoot
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL UK ; Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016; 9:234-63. [PMID: 26742143 PMCID: PMC5233461 DOI: 10.1109/rbme.2016.2515127] [Citation(s) in RCA: 212] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling. PLoS One 2015; 10:e0128975. [PMID: 26176839 PMCID: PMC4503351 DOI: 10.1371/journal.pone.0128975] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Accepted: 05/01/2015] [Indexed: 01/02/2023] Open
Abstract
Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.
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Schubert W. Advances in toponomics drug discovery: Imaging cycler microscopy correctly predicts a therapy method of amyotrophic lateral sclerosis. Cytometry A 2015; 87:696-703. [PMID: 25869332 PMCID: PMC4676937 DOI: 10.1002/cyto.a.22671] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
An imaging cycler microscope (ICM) is a fully automated (epi)fluorescence microscope which overcomes the spectral resolution limit resulting in parameter- and dimension-unlimited fluorescence imaging. This enables the spatial resolution of large molecular systems with their emergent topological properties (toponome) in morphologically intact cells and tissues displaying thousands of multi protein assemblies at a time. The resulting combinatorial geometry of these systems has been shown to be key for in-vivo/in-situ detection of lead proteins controlling protein network topology and (dys)function: If lead proteins are blocked or downregulated the corresponding disease protein network disassembles. Here, correct therapeutic predictions are exemplified for ALS. ICM drug target studies have discovered an 18-dimensional cell surface molecular system in ALS-PBMC with a lead drug target protein, whose therapeutic downregulation is now reported to show statistically significant effect with stop of disease progression in one third of the ALS patients. Together, this clinical and the earlier experimental validations of the ICM approach indicate that ICM readily discovers in vivo robustness nodes of disease with lead proteins controlling them. Breaking in vivo robustness nodes using drugs against their lead proteins is likely to overcome current high drug attrition rates. © 2015 The Author. Published by Wiley Periodicals, Inc, on behalf of ISAC.
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Affiliation(s)
- Walter Schubert
- Department of Medicine, Molecular Pattern Recognition Research Group, Otto Von Guericke University, Magdeburg, Germany
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Zhang Z, Sun S, Yi M, Wu X, Ding Y. MIC as an appropriate method to construct the brain functional network. BIOMED RESEARCH INTERNATIONAL 2015; 2015:825136. [PMID: 25710031 PMCID: PMC4331313 DOI: 10.1155/2015/825136] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 10/12/2014] [Accepted: 10/14/2014] [Indexed: 12/15/2022]
Abstract
Using an effective method to measure the brain functional connectivity is an important step to study the brain functional network. The main methods for constructing an undirected brain functional network include correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH). In this paper we demonstrate that the maximal information coefficient (MIC) proposed by Reshef et al. is relevant to constructing a brain functional network because it performs best in the comprehensive comparisons in consistency and robustness. Our work can be used to validate the possible new functional connection measures.
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Affiliation(s)
- Ziqing Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shu Sun
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Ming Yi
- Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
- State Key Laboratories of Transducer Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Yiming Ding
- Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
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