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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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2
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Smets T, Waelkens E, De Moor B. Prioritization of m/z-Values in Mass Spectrometry Imaging Profiles Obtained Using Uniform Manifold Approximation and Projection for Dimensionality Reduction. Anal Chem 2020; 92:5240-5248. [DOI: 10.1021/acs.analchem.9b05764] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Tina Smets
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
| | - Etienne Waelkens
- Department of Cellular and Molecular Medicine, KU Leuven, 3001 Leuven, Belgium
| | - Bart De Moor
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
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3
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Wüllems K, Kölling J, Bednarz H, Niehaus K, Hans VH, Nattkemper TW. Detection and visualization of communities in mass spectrometry imaging data. BMC Bioinformatics 2019; 20:303. [PMID: 31164082 PMCID: PMC6549267 DOI: 10.1186/s12859-019-2890-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 05/10/2019] [Indexed: 11/10/2022] Open
Abstract
Background The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/z-images to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section). Results For the barley sample data set, our approach is able to reproduce the findings of a previous work that identified groups of molecules with distributions that correlate with anatomical structures of the barley seed. The analysis of glioblastoma section data revealed that some molecular compositions are locally focused, indicating the existence of a meaningful separation in at least two areas. This result is in line with the prior histological knowledge. In addition to confirming prior findings, the resulting graph structures revealed new subcommunities of m/z-images (i.e. metabolites) with more detailed distribution patterns. Another result of our work is the development of an interactive webtool called GRINE (Analysis of GRaph mapped Image Data NEtworks). Conclusions The proposed method was successfully applied to identify molecular communities of laterally co-localized molecules. For both application examples, the detected communities showed inherent substructures that could easily be investigated with the proposed visualization tool. This shows the potential of this approach as a complementary addition to pixel clustering methods. Electronic supplementary material The online version of this article (10.1186/s12859-019-2890-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Karsten Wüllems
- International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany. .,Biodata Mining Group, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany. .,Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany.
| | - Jan Kölling
- International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany.,Biodata Mining Group, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany
| | - Hanna Bednarz
- Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany.,Proteome and Metabolome Research, Faculty of Biology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany
| | - Karsten Niehaus
- Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany.,Proteome and Metabolome Research, Faculty of Biology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany
| | - Volkmar H Hans
- Department of Neuropathology, Institute for Clinical Pathology, Dietrich-Bonhoeffer-Klinikum, Salvador-Allende-Straße 30, Neubrandenburg, 17036, Germany.,Department of Neuropathology, Essen University Hospital (AöR), Hufelandstraße 55, Essen, 45147, Germany
| | - Tim W Nattkemper
- Biodata Mining Group, Faculty of Technology, Bielefeld University, Universitätsstraße 25, Bielefeld, 33613, Germany.,Center for Biotechnology (CeBiTec), Universitätsstraße 25, Bielefeld, 33613, Germany
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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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5
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Shen F, Lee Y. Knowledge Discovery from Biomedical Ontologies in Cross Domains. PLoS One 2016; 11:e0160005. [PMID: 27548262 PMCID: PMC4993478 DOI: 10.1371/journal.pone.0160005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 07/12/2016] [Indexed: 01/19/2023] Open
Abstract
In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.
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Affiliation(s)
- Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Yugyung Lee
- School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, Missouri, United States of America
- * E-mail:
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6
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Shen F, Liu H, Sohn S, Larson DW, Lee Y. Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery. INTELLIGENT INFORMATION MANAGEMENT 2016; 8:66-85. [PMID: 28983419 PMCID: PMC5626454 DOI: 10.4236/iim.2016.83006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
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Affiliation(s)
- Feichen Shen
- CSEE Department, University of Missouri, Kansas City, MO, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Sunghwan Sohn
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - David W Larson
- Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Yugyung Lee
- CSEE Department, University of Missouri, Kansas City, MO, USA
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7
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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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8
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Gorzolka K, Kölling J, Nattkemper TW, Niehaus K. Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging. PLoS One 2016; 11:e0150208. [PMID: 26938880 PMCID: PMC4777520 DOI: 10.1371/journal.pone.0150208] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 02/10/2016] [Indexed: 12/11/2022] Open
Abstract
MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals' germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.
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Affiliation(s)
- Karin Gorzolka
- Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
| | - Jan Kölling
- Biodata Mining, Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld, Germany.,International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld University, Bielefeld, Germany
| | - Tim W Nattkemper
- Biodata Mining, Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
| | - Karsten Niehaus
- Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld, Germany
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9
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Yang C, Niedieker D, Grosserüschkamp F, Horn M, Tannapfel A, Kallenbach-Thieltges A, Gerwert K, Mosig A. Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections. BMC Bioinformatics 2015; 16:396. [PMID: 26607812 PMCID: PMC4659215 DOI: 10.1186/s12859-015-0804-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 10/29/2015] [Indexed: 01/01/2023] Open
Abstract
Background In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. Results We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. Conclusions Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0804-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chen Yang
- Department of Biophysics, CAS-MPG Partner Institute and Key Laboratory for Computational Biology, 320 Yueyang Road, Shanghai, 200031, China.
| | - Daniel Niedieker
- Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany.
| | - Frederik Grosserüschkamp
- Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany.
| | - Melanie Horn
- Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany.
| | - Andrea Tannapfel
- Institute of Pathology, Ruhr-University Bochum, Bochum, Germany, Bürkle-de-la-Camp-Platz 1, Bochum, 44789, Germany.
| | | | - Klaus Gerwert
- Department of Biophysics, CAS-MPG Partner Institute and Key Laboratory for Computational Biology, 320 Yueyang Road, Shanghai, 200031, China. .,Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany.
| | - Axel Mosig
- Department of Biophysics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, 44780, Germany.
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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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Krauß SD, Petersen D, Niedieker D, Fricke I, Freier E, El-Mashtoly SF, Gerwert K, Mosig A. Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study. Analyst 2015; 140:2360-8. [DOI: 10.1039/c4an02153c] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
This paper introduces algorithms for identifying overlapping observations between Raman and fluorescence microscopic images of one and the same sample.
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Affiliation(s)
- Sascha D. Krauß
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | - Dennis Petersen
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | - Daniel Niedieker
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | - Inka Fricke
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | - Erik Freier
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | | | - Klaus Gerwert
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
| | - Axel Mosig
- Department of Biophysics
- Ruhr-University Bochum
- 44780 Bochum
- Germany
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13
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Holzinger A, Jurisica I. Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. INTERACTIVE KNOWLEDGE DISCOVERY AND DATA MINING IN BIOMEDICAL INFORMATICS 2014. [DOI: 10.1007/978-3-662-43968-5_1] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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14
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Kovacheva VN, Khan AM, Khan M, Epstein DBA, Rajpoot NM. DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data. ACTA ACUST UNITED AC 2013; 30:420-7. [PMID: 24273247 DOI: 10.1093/bioinformatics/btt676] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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. AVAILABILITY AND IMPLEMENTATION http://www2.warwick.ac.uk/fac/sci/dcs/research/combi/research/bic/diswop.
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Affiliation(s)
- Violeta N Kovacheva
- Department of Systems Biology, Department of Computer Science, School of Life Science, Mathematics Institute, The University of Warwick, Coventry CV4 7AL, UK and Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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15
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Affiliation(s)
- Karen A. Antonio
- University of Notre Dame, Department of
Chemistry and Biochemistry, Notre
Dame, Indiana 46556, United States
| | - Zachary D. Schultz
- University of Notre Dame, Department of
Chemistry and Biochemistry, Notre
Dame, Indiana 46556, United States
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16
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Toponome imaging system: multiplex biomarkers in oncology. Trends Mol Med 2012; 18:723-31. [DOI: 10.1016/j.molmed.2012.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Revised: 10/03/2012] [Accepted: 10/09/2012] [Indexed: 12/30/2022]
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Kothari S, Phan JH, Osunkoya AO, Wang MD. Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2012; 2012:218-225. [PMID: 29568817 PMCID: PMC5859578 DOI: 10.1145/2382936.2382964] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.
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Khan AM, Humayun A, Raza SEA, Khan M, Rajpoot NM. A Novel Paradigm for Mining Cell Phenotypes in Multi-tag Bioimages Using a Locality Preserving Nonlinear Embedding. NEURAL INFORMATION PROCESSING 2012. [DOI: 10.1007/978-3-642-34478-7_70] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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