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Naas J, Nies G, Li H, Stoldt S, Schmitzer B, Jakobs S, Munk A. MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy. Commun Biol 2024; 7:1139. [PMID: 39271907 PMCID: PMC11399439 DOI: 10.1038/s42003-024-06772-8] [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: 02/28/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.
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Affiliation(s)
- Julia Naas
- Center for Integrative Bioinformatics Vienna (CIBIV), Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria
- Vienna Biocenter PhD Program, a Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Giacomo Nies
- Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
| | - Housen Li
- Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
| | - Stefan Stoldt
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
- Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Bernhard Schmitzer
- Institute for Computer Science, University of Göttingen, Göttingen, Germany
| | - Stefan Jakobs
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany
- Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Translational Neuroinflammation and Automated Microscopy TNM, Göttingen, Germany
| | - Axel Munk
- Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany.
- Cluster of Excellence 'Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells' (MBExC), University of Göttingen, Göttingen, Germany.
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De Santis I, Lorenzini L, Moretti M, Martella E, Lucarelli E, Calzà L, Bevilacqua A. Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis. SENSORS (BASEL, SWITZERLAND) 2021; 21:6385. [PMID: 34640704 PMCID: PMC8513075 DOI: 10.3390/s21196385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/19/2021] [Accepted: 09/21/2021] [Indexed: 01/19/2023]
Abstract
Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.
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Affiliation(s)
- Ilaria De Santis
- Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum—University of Bologna, I-40138 Bologna, Italy;
- Interdepartmental Center Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), Alma Mater Studiorum—University of Bologna, I-40126 Bologna, Italy
| | - Luca Lorenzini
- Department of Veterinary Medical Sciences (DIMEVET), Alma Mater Studiorum—University of Bologna, I-40064 Ozzano Emilia, Italy;
| | - Marzia Moretti
- Iret Foundation, I-40064 Ozzano Emilia, Italy; (M.M.); (L.C.)
| | - Elisa Martella
- Institute of Organic Synthesis and Photoreactivity (ISOF), National Research Council (CNR), I-40129 Bologna, Italy;
| | - Enrico Lucarelli
- Regenerative Therapies in Oncology, IRCCS Istituto Ortopedico Rizzoli, I-40136 Bologna, Italy;
| | - Laura Calzà
- Iret Foundation, I-40064 Ozzano Emilia, Italy; (M.M.); (L.C.)
- Department of Pharmacy and BioTechnology (FaBiT), Alma Mater Studiorum—University of Bologna, I-40127 Bologna, Italy
| | - Alessandro Bevilacqua
- Advanced Research Center on Electronic Systems (ARCES) for Information and Communication Technologies “E. De Castro”, Alma Mater Studiorum—University of Bologna, I-40125 Bologna, Italy
- Department of Computer Science and Engineering (DISI), Alma Mater Studiorum—University of Bologna, I-40136 Bologna, Italy
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Wang S, Yuan M. Revisiting colocalization via optimal transport. NATURE COMPUTATIONAL SCIENCE 2021; 1:177-178. [PMID: 38183198 DOI: 10.1038/s43588-021-00046-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Affiliation(s)
- Shulei Wang
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Ming Yuan
- Department of Statistics, Columbia University, New York, NY, USA
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Zhang D, Liu SG, Fu Z, He Y, Gao W, Shi X. The method for integrating dual-color fluorescence colocalization and single molecule photobleaching technology on the theophylline sensing platform. MethodsX 2020; 7:101155. [PMID: 33304835 PMCID: PMC7708945 DOI: 10.1016/j.mex.2020.101155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/13/2020] [Indexed: 11/17/2022] Open
Abstract
• Smart usage of single molecule photobleaching technology and dual-color fluorescence colocalization is of critical importance for exploiting the sensing platform. Here, we provide the detailed protocols related to the article “A split aptamer sensing platform for highly sensitive detection of theophylline based on dual-color fluorescence colocalization and single molecule photobleaching” (published online by Biosensors and Bioelectronics) (Liu et al., 2020). The protocols contain: (1) how to clean the slides; (2) how to prepare the probe and detection sample; (3) Single molecule imaging; 4) Data processing by using the Image J. Finally, we used a simple model to confirm the feasibility of the method for integrating dual-color fluorescence colocalization and single molecule photobleaching technology on the theophylline sensing platform. • A simple, ultrasensitive method for the detection of theophylline. • The method is easily comprehensible. • Both strategy formulation and data processing are simple, learnability, and highly reproducible.
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Affiliation(s)
- Dong Zhang
- Laboratory of Micro and Nano Biosensing Technology in Food Safety, Hunan Provincial Key Laboratory of Food Science and Biotechnology, College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Shi Gang Liu
- Laboratory of Micro and Nano Biosensing Technology in Food Safety, Hunan Provincial Key Laboratory of Food Science and Biotechnology, College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Zhaodi Fu
- Analytical Testing Laboratory, Changsha Research Institute of Mining and Metallurgy CO., LTD., Changsha 410012, China
| | - Yu He
- Laboratory of Micro and Nano Biosensing Technology in Food Safety, Hunan Provincial Key Laboratory of Food Science and Biotechnology, College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Wenli Gao
- Laboratory of Micro and Nano Biosensing Technology in Food Safety, Hunan Provincial Key Laboratory of Food Science and Biotechnology, College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
| | - Xingbo Shi
- Laboratory of Micro and Nano Biosensing Technology in Food Safety, Hunan Provincial Key Laboratory of Food Science and Biotechnology, College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
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Xu D, Wang C, Kiryluk K, Buxbaum JD, Ionita-Laza I. Co-localization between Sequence Constraint and Epigenomic Information Improves Interpretation of Whole-Genome Sequencing Data. Am J Hum Genet 2020; 106:513-524. [PMID: 32243819 PMCID: PMC7118583 DOI: 10.1016/j.ajhg.2020.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/09/2020] [Indexed: 10/24/2022] Open
Abstract
The identification of functional regions in the noncoding human genome is difficult but critical in order to gain understanding of the role noncoding variation plays in gene regulation in human health and disease. We describe here a co-localization approach that aims to identify constrained sequences that co-localize with tissue- or cell-type-specific regulatory regions, and we show that the resulting score is particularly well suited for the identification of rare regulatory variants. For 127 tissues and cell types in the ENCODE/Roadmap Epigenomics Project, we provide catalogs of putative tissue- or cell-type-specific regulatory regions under sequence constraint. We use the newly developed co-localization score for brain tissues to score de novo mutations in whole genomes from 1,902 individuals affected with autism spectrum disorder (ASD) and their unaffected siblings in the Simons Simplex Collection. We show that noncoding de novo mutations near genes co-expressed in midfetal brain with high confidence ASD risk genes, and near FMRP gene targets are more likely to be in co-localized regions if they occur in ASD probands versus in their unaffected siblings. We also observed a similar enrichment for mutations near lincRNAs, previously shown to co-express with ASD risk genes. Additionally, we provide strong evidence that prioritized de novo mutations in autism probands point to a small set of well-known ASD genes, the disruption of which produces relevant mouse phenotypes such as abnormal social investigation and abnormal discrimination/associative learning, unlike the de novo mutations in unaffected siblings. The genome-wide co-localization results are available online.
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Affiliation(s)
- Danqing Xu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY 10032, USA; Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Joseph D Buxbaum
- Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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