1
|
Manrique-Castano D, Bhaskar D, ElAli A. Dissecting glial scar formation by spatial point pattern and topological data analysis. Sci Rep 2024; 14:19035. [PMID: 39152163 PMCID: PMC11329771 DOI: 10.1038/s41598-024-69426-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Glial scar formation represents a fundamental response to central nervous system (CNS) injuries. It is mainly characterized by a well-defined spatial rearrangement of reactive astrocytes and microglia. The mechanisms underlying glial scar formation have been extensively studied, yet quantitative descriptors of the spatial arrangement of reactive glial cells remain limited. Here, we present a novel approach using point pattern analysis (PPA) and topological data analysis (TDA) to quantify spatial patterns of reactive glial cells after experimental ischemic stroke in mice. We provide open and reproducible tools using R and Julia to quantify spatial intensity, cell covariance and conditional distribution, cell-to-cell interactions, and short/long-scale arrangement, which collectively disentangle the arrangement patterns of the glial scar. This approach unravels a substantial divergence in the distribution of GFAP+ and IBA1+ cells after injury that conventional analysis methods cannot fully characterize. PPA and TDA are valuable tools for studying the complex spatial arrangement of reactive glia and other nervous cells following CNS injuries and have potential applications for evaluating glial-targeted restorative therapies.
Collapse
Affiliation(s)
- Daniel Manrique-Castano
- Neuroscience Axis, Research Center of CHU de Québec-Université Laval, Quebec City, QC, Canada.
- Department of Psychiatry and Neuroscience, Faculty of Medicine, Université Laval, Quebec City, QC, Canada.
| | | | - Ayman ElAli
- Neuroscience Axis, Research Center of CHU de Québec-Université Laval, Quebec City, QC, Canada.
- Department of Psychiatry and Neuroscience, Faculty of Medicine, Université Laval, Quebec City, QC, Canada.
| |
Collapse
|
2
|
Benjamin K, Bhandari A, Kepple JD, Qi R, Shang Z, Xing Y, An Y, Zhang N, Hou Y, Crockford TL, McCallion O, Issa F, Hester J, Tillmann U, Harrington HA, Bull KR. Multiscale topology classifies cells in subcellular spatial transcriptomics. Nature 2024; 630:943-949. [PMID: 38898271 PMCID: PMC11208150 DOI: 10.1038/s41586-024-07563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 06/21/2024]
Abstract
Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.
Collapse
Affiliation(s)
| | - Aneesha Bhandari
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jessica D Kepple
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rui Qi
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Zhouchun Shang
- BGI Research, Riga, Latvia
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yanan Xing
- BGI Research, Riga, Latvia
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | | | | | | | - Tanya L Crockford
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Oliver McCallion
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Fadi Issa
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Joanna Hester
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ulrike Tillmann
- Mathematical Institute, University of Oxford, Oxford, UK
- Isaac Newton Institute for Mathematical Sciences, University of Cambridge, Cambridge, UK
| | - Heather A Harrington
- Mathematical Institute, University of Oxford, Oxford, UK.
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
- Centre for Systems Biology, Dresden, Dresden, Germany.
- Faculty of Mathematics, Technische Universität Dresden, Dresden, Germany.
| | - Katherine R Bull
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK.
| |
Collapse
|
3
|
Seal S, Neelon B, Angel PM, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta AS, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA. J Proteome Res 2024; 23:1131-1143. [PMID: 38417823 PMCID: PMC11002919 DOI: 10.1021/acs.jproteome.3c00462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 03/01/2024]
Abstract
Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.
Collapse
Affiliation(s)
- Souvik Seal
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Brian Neelon
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Peggi M. Angel
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth C. O’Quinn
- Translational
Science Laboratory, Hollings Cancer Center, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth Hill
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Thao Vu
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Debashis Ghosh
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Anand S. Mehta
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Kristin Wallace
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Alexander V. Alekseyenko
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| |
Collapse
|
4
|
Bull JA, Mulholland EJ, Leedham SJ, Byrne HM. Extended correlation functions for spatial analysis of multiplex imaging data. BIOLOGICAL IMAGING 2024; 4:e2. [PMID: 38516631 PMCID: PMC10951806 DOI: 10.1017/s2633903x24000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 01/11/2024] [Accepted: 01/28/2024] [Indexed: 03/23/2024]
Abstract
Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.
Collapse
Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
| | - Eoghan J. Mulholland
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
| | - Simon J. Leedham
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7DQ, UK
| |
Collapse
|
5
|
Wang H, Huang G, Zhao Z, Cheng L, Juncker-Jensen A, Nagy ML, Lu X, Zhang X, Chen DZ. CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3179-3193. [PMID: 37027573 DOI: 10.1109/tmi.2023.3249343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing geometric and hierarchically structured cell distribution topology, oncologists can identify densely-packed and cancer-relevant cell communities (CCs) for making decisions. Compared to commonly-used pixel-level Convolution Neural Network (CNN) features and cell-instance-level Graph Neural Network (GNN) features, CC topology features are at a higher level of granularity and geometry. However, topological features have not been well exploited by recent deep learning (DL) methods for pathology image classification due to lack of effective topological descriptors for cell distribution and gathering patterns. In this paper, inspired by clinical practice, we analyze and classify pathology images by comprehensively learning cell appearance, microenvironment, and topology in a fine-to-coarse manner. To describe and exploit topology, we design Cell Community Forest (CCF), a novel graph that represents the hierarchical formulation process of big-sparse CCs from small-dense CCs. Using CCF as a new geometric topological descriptor of tumor cells in pathology images, we propose CCF-GNN, a GNN model that successively aggregates heterogeneous features (e.g., appearance, microenvironment) from cell-instance-level, cell-community-level, into image-level for pathology image classification. Extensive cross-validation experiments show that our method significantly outperforms alternative methods on H&E-stained and immunofluorescence images for disease grading tasks with multiple cancer types. Our proposed CCF-GNN establishes a new topological data analysis (TDA) based method, which facilitates integrating multi-level heterogeneous features of point clouds (e.g., for cells) into a unified DL framework.
Collapse
|
6
|
Chulián S, Stolz BJ, Martínez-Rubio Á, Blázquez Goñi C, Rodríguez Gutiérrez JF, Caballero Velázquez T, Molinos Quintana Á, Ramírez Orellana M, Castillo Robleda A, Fuster Soler JL, Minguela Puras A, Martínez Sánchez MV, Rosa M, Pérez-García VM, Byrne HM. The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia. PLoS Comput Biol 2023; 19:e1011329. [PMID: 37578973 PMCID: PMC10468039 DOI: 10.1371/journal.pcbi.1011329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 08/30/2023] [Accepted: 07/05/2023] [Indexed: 08/16/2023] Open
Abstract
Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.
Collapse
Affiliation(s)
- Salvador Chulián
- Department of Mathematics, Universidad de Cádiz, Puerto Real (Cádiz), Spain
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, Spain
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Bernadette J. Stolz
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Laboratory for Topology and Neuroscience, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Álvaro Martínez-Rubio
- Department of Mathematics, Universidad de Cádiz, Puerto Real (Cádiz), Spain
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, Spain
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Cristina Blázquez Goñi
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, Spain
- Department of Paediatric Haematology and Oncology, Hospital Universitario de Jerez, Jerez de la Frontera (Cádiz), Spain
- Department of Haematology, Hospital Universitario Vírgen del Rocío, Instituto de Biomedicina de Sevilla (IBIS), Sevilla, Spain
| | - Juan F. Rodríguez Gutiérrez
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, Spain
- Department of Paediatric Haematology and Oncology, Hospital Universitario de Jerez, Jerez de la Frontera (Cádiz), Spain
| | - Teresa Caballero Velázquez
- Department of Haematology, Hospital Universitario Vírgen del Rocío, Instituto de Biomedicina de Sevilla (IBIS), Sevilla, Spain
- CSIC, University of Sevilla, Sevilla, Spain
| | - Águeda Molinos Quintana
- Department of Haematology, Hospital Universitario Vírgen del Rocío, Instituto de Biomedicina de Sevilla (IBIS), Sevilla, Spain
- CSIC, University of Sevilla, Sevilla, Spain
| | - Manuel Ramírez Orellana
- Department of Paediatric Haematology and Oncology, Hospital Infantil Universitario Niño Jesús - Instituto Investigación Sanitaria La Princesa, Madrid, Spain
| | - Ana Castillo Robleda
- Department of Paediatric Haematology and Oncology, Hospital Infantil Universitario Niño Jesús - Instituto Investigación Sanitaria La Princesa, Madrid, Spain
| | - José Luis Fuster Soler
- Department of Paediatric Haematology and Oncology, Hospital Clínico Universitario Virgen de la Arrixaca - Instituto Murciano de Investigación Biosanitaria (IMIB), Murcia, Spain
| | - Alfredo Minguela Puras
- Immunology Service, Hospital Clínico Universitario Virgen de la Arrixaca - Instituto Murciano de Investigación Biosanitaria (IMIB), Murcia, Spain
| | - María V. Martínez Sánchez
- Immunology Service, Hospital Clínico Universitario Virgen de la Arrixaca - Instituto Murciano de Investigación Biosanitaria (IMIB), Murcia, Spain
| | - María Rosa
- Department of Mathematics, Universidad de Cádiz, Puerto Real (Cádiz), Spain
- Biomedical Research and Innovation Institute of Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, Spain
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Víctor M. Pérez-García
- Department of Mathematics, Mathematical Oncology Laboratory (MOLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
- Instituto de Matemática Aplicada a la Ciencia y la Ingeniería (IMACI), Universidad de Castilla-La Mancha, Ciudad Real, Spain
- ETSI Industriales, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - Helen M. Byrne
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
7
|
Seal S, Neelon B, Angel P, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta A, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.548034. [PMID: 37461579 PMCID: PMC10350074 DOI: 10.1101/2023.07.06.548034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/31/2023]
Abstract
Motivation Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. Results We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. Availability The associated R package can be found here, https://github.com/sealx017/SpaceANOVA.
Collapse
Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Peggi Angel
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina
| | - Elizabeth C. O’Quinn
- Translational Science Laboratory, Hollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina
| | - Elizabeth Hill
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Thao Vu
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, Colorado
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, Colorado
| | - Anand Mehta
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina
| | - Kristin Wallace
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Alexander V. Alekseyenko
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| |
Collapse
|
8
|
Benjamin K, Mukta L, Moryoussef G, Uren C, Harrington HA, Tillmann U, Barbensi A. Homology of homologous knotted proteins. J R Soc Interface 2023; 20:20220727. [PMID: 37122282 PMCID: PMC10130707 DOI: 10.1098/rsif.2022.0727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023] Open
Abstract
Quantification and classification of protein structures, such as knotted proteins, often requires noise-free and complete data. Here, we develop a mathematical pipeline that systematically analyses protein structures. We showcase this geometric framework on proteins forming open-ended trefoil knots, and we demonstrate that the mathematical tool, persistent homology, faithfully represents their structural homology. This topological pipeline identifies important geometric features of protein entanglement and clusters the space of trefoil proteins according to their depth. Persistence landscapes quantify the topological difference between a family of knotted and unknotted proteins in the same structural homology class. This difference is localized and interpreted geometrically with recent advancements in systematic computation of homology generators. The topological and geometric quantification we find is robust to noisy input data, which demonstrates the potential of this approach in contexts where standard knot theoretic tools fail.
Collapse
Affiliation(s)
| | - Lamisah Mukta
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | | | - Christopher Uren
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - Heather A Harrington
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Ulrike Tillmann
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
- Isaac Newton Institute for Mathematical Sciences, University of Cambridge, Cambridge CB3 0EH, UK
| | - Agnese Barbensi
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| |
Collapse
|
9
|
Bull JA, Byrne HM. Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLoS Comput Biol 2023; 19:e1010994. [PMID: 36972297 PMCID: PMC10079237 DOI: 10.1371/journal.pcbi.1010994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 04/06/2023] [Accepted: 03/04/2023] [Indexed: 03/29/2023] Open
Abstract
We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.
Collapse
Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
10
|
Panconi L, Makarova M, Lambert ER, May RC, Owen DM. Topology-based fluorescence image analysis for automated cell identification and segmentation. JOURNAL OF BIOPHOTONICS 2023; 16:e202200199. [PMID: 36349740 DOI: 10.1002/jbio.202200199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 06/16/2023]
Abstract
Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.
Collapse
Affiliation(s)
- Luca Panconi
- Institute of Immunology and Immunotherapy, School of Mathematics and Centre of Membrane Proteins and Receptors, University of Birmingham, Birmingham, UK
| | - Maria Makarova
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Eleanor R Lambert
- Institute of Immunology and Immunotherapy, School of Mathematics and Centre of Membrane Proteins and Receptors, University of Birmingham, Birmingham, UK
| | - Robin C May
- School of Biosciences and Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Dylan M Owen
- Institute of Immunology and Immunotherapy, School of Mathematics and Centre of Membrane Proteins and Receptors, University of Birmingham, Birmingham, UK
| |
Collapse
|
11
|
Ryou H, Sirinukunwattana K, Aberdeen A, Grindstaff G, Stolz BJ, Byrne H, Harrington HA, Sousos N, Godfrey AL, Harrison CN, Psaila B, Mead AJ, Rees G, Turner GDH, Rittscher J, Royston D. Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients. Leukemia 2023; 37:348-358. [PMID: 36470992 PMCID: PMC9898027 DOI: 10.1038/s41375-022-01773-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/17/2022] [Accepted: 11/21/2022] [Indexed: 12/09/2022]
Abstract
The grading of fibrosis in myeloproliferative neoplasms (MPN) is an important component of disease classification, prognostication and monitoring. However, current fibrosis grading systems are only semi-quantitative and fail to fully capture sample heterogeneity. To improve the quantitation of reticulin fibrosis, we developed a machine learning approach using bone marrow trephine (BMT) samples (n = 107) from patients diagnosed with MPN or a reactive marrow. The resulting Continuous Indexing of Fibrosis (CIF) enhances the detection and monitoring of fibrosis within BMTs, and aids MPN subtyping. When combined with megakaryocyte feature analysis, CIF discriminates between the frequently challenging differential diagnosis of essential thrombocythemia (ET) and pre-fibrotic myelofibrosis with high predictive accuracy [area under the curve = 0.94]. CIF also shows promise in the identification of MPN patients at risk of disease progression; analysis of samples from 35 patients diagnosed with ET and enrolled in the Primary Thrombocythemia-1 trial identified features predictive of post-ET myelofibrosis (area under the curve = 0.77). In addition to these clinical applications, automated analysis of fibrosis has clear potential to further refine disease classification boundaries and inform future studies of the micro-environmental factors driving disease initiation and progression in MPN and other stem cell disorders.
Collapse
Affiliation(s)
- Hosuk Ryou
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Korsuk Sirinukunwattana
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute/Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Ground Truth Labs, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Gillian Grindstaff
- Department of Mathematics, University of California, Los Angeles, CA, USA
| | - Bernadette J Stolz
- Mathematical Institute, University of Oxford, Oxford, UK
- Laboratory for Topology and Neuroscience, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
| | - Heather A Harrington
- Mathematical Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Nikolaos Sousos
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Anna L Godfrey
- Haematopathology & Oncology Diagnostics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Claire N Harrison
- Department of Haematology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Bethan Psaila
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Adam J Mead
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Gabrielle Rees
- Department of Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Gareth D H Turner
- Department of Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jens Rittscher
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute/Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Ground Truth Labs, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK
| | - Daniel Royston
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Department of Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| |
Collapse
|
12
|
Dłotko P, Gurnari D. Euler characteristic curves and profiles: a stable shape invariant for big data problems. Gigascience 2022; 12:giad094. [PMID: 37966428 PMCID: PMC10646871 DOI: 10.1093/gigascience/giad094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/25/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023] Open
Abstract
Tools of topological data analysis provide stable summaries encapsulating the shape of the considered data. Persistent homology, the most standard and well-studied data summary, suffers a number of limitations; its computations are hard to distribute, and it is hard to generalize to multifiltrations and is computationally prohibitive for big datasets. In this article, we study the concept of Euler characteristics curves for 1-parameter filtrations and Euler characteristic profiles for multiparameter filtrations. While being a weaker invariant in one dimension, we show that Euler characteristic-based approaches do not possess some handicaps of persistent homology; we show efficient algorithms to compute them in a distributed way, their generalization to multifiltrations, and practical applicability for big data problems. In addition, we show that the Euler curves and profiles enjoy a certain type of stability, which makes them robust tools for data analysis. Lastly, to show their practical applicability, multiple use cases are considered.
Collapse
Affiliation(s)
- Paweł Dłotko
- Dioscuri Centre in Topological Data Analysis, Mathematical Institute, Polish Academy of Sciences, Warsaw, 00-656, Poland
| | - Davide Gurnari
- Dioscuri Centre in Topological Data Analysis, Mathematical Institute, Polish Academy of Sciences, Warsaw, 00-656, Poland
| |
Collapse
|
13
|
Guo G, Zhao Y, Liu C, Fu Y, Xi X, Jin L, Shi D, Wang L, Duan Y, Huang J, Tan S, Yin G. Method for persistent topological features extraction of schizophrenia patients' electroencephalography signal based on persistent homology. Front Comput Neurosci 2022; 16:1024205. [PMID: 36277610 PMCID: PMC9579369 DOI: 10.3389/fncom.2022.1024205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris-Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.
Collapse
Affiliation(s)
- Guangxing Guo
- College of Geography Science, Taiyuan Normal University, Jinzhong, China
- Institute of Big Data Analysis Technology and Application, Taiyuan Normal University, Jinzhong, China
- College of Resource and Environment, Shanxi Agricultural University, Taigu, China
| | - Yanli Zhao
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Chenxu Liu
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Yongcan Fu
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Xinhua Xi
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Lizhong Jin
- College of Applied Science, Taiyuan University of Science and Technology, Taiyuan, China
| | - Dongli Shi
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Lin Wang
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| | - Yonghong Duan
- College of Resource and Environment, Shanxi Agricultural University, Taigu, China
| | - Jie Huang
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Guimei Yin
- Laboratory of Data Mining and Machine Learning, College of Computer Science and Technology, Taiyuan Normal University, Jinzhong, China
| |
Collapse
|
14
|
Stolz BJ, Kaeppler J, Markelc B, Braun F, Lipsmeier F, Muschel RJ, Byrne HM, Harrington HA. Multiscale topology characterizes dynamic tumor vascular networks. SCIENCE ADVANCES 2022. [PMID: 35687679 DOI: 10.48550/arxiv.2008.08667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the "shape" of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.
Collapse
Affiliation(s)
| | - Jakob Kaeppler
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Bostjan Markelc
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Franziska Braun
- Data Science, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany
| | - Florian Lipsmeier
- Digital Biomarkers, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Ruth J Muschel
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Helen M Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Heather A Harrington
- Mathematical Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| |
Collapse
|
15
|
Stolz BJ, Kaeppler J, Markelc B, Braun F, Lipsmeier F, Muschel RJ, Byrne HM, Harrington HA. Multiscale topology characterizes dynamic tumor vascular networks. SCIENCE ADVANCES 2022; 8:eabm2456. [PMID: 35687679 PMCID: PMC9187234 DOI: 10.1126/sciadv.abm2456] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/27/2022] [Indexed: 06/15/2023]
Abstract
Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the "shape" of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.
Collapse
Affiliation(s)
| | - Jakob Kaeppler
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Bostjan Markelc
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Franziska Braun
- Data Science, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany
| | - Florian Lipsmeier
- Digital Biomarkers, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Ruth J. Muschel
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Helen M. Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Heather A. Harrington
- Mathematical Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| |
Collapse
|
16
|
A topological data analytic approach for discovering biophysical signatures in protein dynamics. PLoS Comput Biol 2022; 18:e1010045. [PMID: 35500014 PMCID: PMC9098046 DOI: 10.1371/journal.pcbi.1010045] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 05/12/2022] [Accepted: 03/22/2022] [Indexed: 12/02/2022] Open
Abstract
Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Structural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.
Collapse
|
17
|
Klowss JJ, Browning AP, Murphy RJ, Carr EJ, Plank MJ, Gunasingh G, Haass NK, Simpson MJ. A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling. J R Soc Interface 2022; 19:20210903. [PMID: 35382573 PMCID: PMC8984298 DOI: 10.1098/rsif.2021.0903] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
In vitro tumour spheroids have been used to study avascular tumour growth and drug design for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing spheroid, giving rise to the notion of a four-dimensional (4D) tumour spheroid. We develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data compares well with experimental data using a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is difficult to measure these data experimentally.
Collapse
Affiliation(s)
- Jonah J Klowss
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
| | - Alexander P Browning
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
| | - Ryan J Murphy
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
| | - Elliot J Carr
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.,Te Pūnaha Matatini, New Zealand Centre of Research Excellence in Complex Systems and Data Analytics, New Zealand
| | - Gency Gunasingh
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - Nikolas K Haass
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Australia
| | - Matthew J Simpson
- School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia
| |
Collapse
|
18
|
Mertins SD. Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning. Front Oncol 2022; 11:805592. [PMID: 35127516 PMCID: PMC8813744 DOI: 10.3389/fonc.2021.805592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/31/2021] [Indexed: 12/02/2022] Open
Abstract
Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.
Collapse
Affiliation(s)
- Susan D. Mertins
- Department of Science, Mount St. Mary’s University, Emmitsburg, MD, United States
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Limited Liability Company (LLC), Frederick, MD, United States
- BioSystems Strategies, Limited Liability Company (LLC), Frederick, MD, United States
- *Correspondence: Susan D. Mertins,
| |
Collapse
|
19
|
Thorne T, Kirk PDW, Harrington HA. OUP accepted manuscript. Bioinformatics 2022; 38:2529-2535. [PMID: 35191485 PMCID: PMC9048691 DOI: 10.1093/bioinformatics/btac118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/07/2022] [Accepted: 02/18/2022] [Indexed: 12/03/2022] Open
Abstract
Motivation Inferring the parameters of models describing biological systems is an important problem in the reverse engineering of the mechanisms underlying these systems. Much work has focused on parameter inference of stochastic and ordinary differential equation models using Approximate Bayesian Computation (ABC). While there is some recent work on inference in spatial models, this remains an open problem. Simultaneously, advances in topological data analysis (TDA), a field of computational mathematics, have enabled spatial patterns in data to be characterized. Results Here, we focus on recent work using TDA to study different regimes of parameter space for a well-studied model of angiogenesis. We propose a method for combining TDA with ABC to infer parameters in the Anderson–Chaplain model of angiogenesis. We demonstrate that this topological approach outperforms ABC approaches that use simpler statistics based on spatial features of the data. This is a first step toward a general framework of spatial parameter inference for biological systems, for which there may be a variety of filtrations, vectorizations and summary statistics to be considered. Availability and implementation All code used to produce our results is available as a Snakemake workflow from github.com/tt104/tabc_angio.
Collapse
Affiliation(s)
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge CB2 0AW, UK
- Cancer Research UK Cambridge Centre, Ovarian Cancer Programme, Cambridge CB2 0RE, UK
| | | |
Collapse
|