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Pajanoja C, Kerosuo L. ShapeMetrics: A 3D Cell Segmentation Pipeline for Single-Cell Spatial Morphometric Analysis. Methods Mol Biol 2024; 2767:263-273. [PMID: 37219813 DOI: 10.1007/7651_2023_489] [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] [Indexed: 05/24/2023]
Abstract
There is a growing need for single-cell level data analysis in correlation with the advancements of microscopy techniques. Morphology-based statistics gathered from individual cells are essential for detection and quantification of even subtle changes within the complex tissues, yet the information available from high-resolution imaging is oftentimes sub-optimally utilized due to the lack of proper computational analysis software. Here we present ShapeMetrics, a 3D cell segmentation pipeline that we have developed to identify, analyze, and quantify single cells in an image. This MATLAB-based script enables users to extract morphological parameters, such as ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. We have specifically invested in creating a user-friendly pipeline, aimed for biologists with a limited computational background. Our pipeline is presented with detailed stepwise instructions, starting from the establishment of machine learning-based prediction files of immuno-labeled cell membranes followed by the application of 3D cell segmentation and parameter extraction script, leading to the morphometric analysis and spatial visualization of cell clusters defined by their morphometric features.
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Affiliation(s)
- Ceren Pajanoja
- Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, Intramural Research Program, Neural Crest Development and Disease Unit, National Institutes of Health, Bethesda, ML, USA
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Laura Kerosuo
- Neural Crest Development and Disease Unit, National Institute of Dental and Craniofacial Research, Intramural Research Program, Neural Crest Development and Disease Unit, National Institutes of Health, Bethesda, ML, USA
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2
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Diegmiller R, Imran Alsous J, Li D, Yamashita YM, Shvartsman SY. Fusome topology and inheritance during insect gametogenesis. PLoS Comput Biol 2023; 19:e1010875. [PMID: 36821548 PMCID: PMC9949678 DOI: 10.1371/journal.pcbi.1010875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/16/2023] [Indexed: 02/24/2023] Open
Abstract
From insects to mammals, oocytes and sperm develop within germline cysts comprising cells connected by intercellular bridges (ICBs). In numerous insects, formation of the cyst is accompanied by growth of the fusome-a membranous organelle that permeates the cyst. Fusome composition and function are best understood in Drosophila melanogaster: during oogenesis, the fusome dictates cyst topology and size and facilitates oocyte selection, while during spermatogenesis, the fusome synchronizes the cyst's response to DNA damage. Despite its distinct and sex-specific roles during insect gametogenesis, elucidating fusome growth and inheritance in females and its structure and connectivity in males has remained challenging. Here, we take advantage of advances in three-dimensional (3D) confocal microscopy and computational image processing tools to reconstruct the topology, growth, and distribution of the fusome in both sexes. In females, our experimental findings inform a theoretical model for fusome assembly and inheritance and suggest that oocyte selection proceeds through an 'equivalency with a bias' mechanism. In males, we find that cell divisions can deviate from the maximally branched pattern observed in females, leading to greater topological variability. Our work consolidates existing disjointed experimental observations and contributes a readily generalizable computational approach for quantitative studies of gametogenesis within and across species.
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Affiliation(s)
- Rocky Diegmiller
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Jasmin Imran Alsous
- Flatiron Institute, Simons Foundation, New York, New York, United States of America
| | - Duojia Li
- Whitehead Institute for Biomedical Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Yukiko M. Yamashita
- Whitehead Institute for Biomedical Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Stanislav Y. Shvartsman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Flatiron Institute, Simons Foundation, New York, New York, United States of America
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
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3
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Guignet M, Schmuck M, Harvey DJ, Nguyen D, Bruun D, Echeverri A, Gurkoff G, Lein PJ. Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease. Heliyon 2023; 9:e13449. [PMID: 36873154 PMCID: PMC9975095 DOI: 10.1016/j.heliyon.2023.e13449] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 12/18/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.
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Affiliation(s)
- Michelle Guignet
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA
| | - Martin Schmuck
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA
| | - Danielle J. Harvey
- Department of Public Health Sciences, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Danh Nguyen
- Division of General Internal Medicine, Department of Medicine, School of Medicine, University of California-Irvine, 100 Theory, Suite 120, Irvine, CA, 92617, USA
| | - Donald Bruun
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA
| | - Angela Echeverri
- Department of Neurological Surgery, School of Medicine, University of California-Davis, 4800 Y Street, Sacramento, CA, 95817, USA
| | - Gene Gurkoff
- Department of Neurological Surgery, School of Medicine, University of California-Davis, 4800 Y Street, Sacramento, CA, 95817, USA
- Center for Neuroscience, University of California-Davis, 1544 Newton Court, Davis, CA, 95618, USA
| | - Pamela J. Lein
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA
- MIND Institute, School of Medicine, University of California-Davis, 2825 50th Street, Sacramento, CA, 95817, USA
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4
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Kleinberg G, Wang S, Comellas E, Monaghan JR, Shefelbine SJ. Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose. Cells Dev 2022; 172:203806. [PMID: 36029974 DOI: 10.1016/j.cdev.2022.203806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/21/2022] [Accepted: 08/22/2022] [Indexed: 01/25/2023]
Abstract
Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time. For those unfamiliar with machine learning or 3D image analysis, the rapid advancement of the field can make navigating the newest software options confusing. In this paper, two open-source machine learning algorithms, Cellpose and Stardist, are compared in their application on a 3D light sheet dataset counting fluorescently stained proliferative cell nuclei. The effects of image tiling and background subtraction are shown through image analysis pipelines for both algorithms. Based on our analysis, the relative ease of use of Cellpose and the absence of need to train a model leaves it a strong option for 3D cell segmentation despite relatively longer processing times. When Cellpose's pretrained model yields results that are not of sufficient quality, or the analysis of a large dataset is required, Stardist may be more appropriate. Despite the time it takes to train the model, Stardist can create a model specialized to the users' dataset that can be iteratively improved until predictions are satisfactory with far lower processing time relative to other methods.
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Affiliation(s)
- Giona Kleinberg
- Department of Bioengineering, Northeastern University, Boston, USA.
| | - Sophia Wang
- Department of Electrical and Computer Engineering, Northeastern University, Boston, USA.
| | - Ester Comellas
- Serra Húnter Fellow, Department of Physics, Laboratori de Càlcul Numeric (LaCàN), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, USA.
| | - James R Monaghan
- Department of Biology, Northeastern University, Boston, USA; Institute for Chemical Imaging of Living Systems, Northeastern University, Boston, USA.
| | - Sandra J Shefelbine
- Department of Bioengineering, Northeastern University, Boston, USA; Department of Mechanical and Industrial Engineering, Northeastern University, Boston, USA.
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5
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Allenby MC, Woodruff MA. Image analyses for engineering advanced tissue biomanufacturing processes. Biomaterials 2022; 284:121514. [DOI: 10.1016/j.biomaterials.2022.121514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/02/2022]
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Sarkar R, Darby D, Meilhac S, Olivo-Marin JC. 3D cell morphology detection by association for embryo heart morphogenesis. BIOLOGICAL IMAGING 2022; 2:e2. [PMID: 38510433 PMCID: PMC10951799 DOI: 10.1017/s2633903x22000022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/21/2022] [Accepted: 03/04/2022] [Indexed: 03/22/2024]
Abstract
Advances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation.
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Affiliation(s)
- Rituparna Sarkar
- BioImage Analysis Unit, Institut Pasteur, Paris, France
- CNRS UMR 3691, Paris, France
| | - Daniel Darby
- Unit of Heart Morphogenesis, Imagine-Institut Pasteur, Paris, France
- Université de Paris, INSERM UMR 1163, Paris, France
| | - Sigolène Meilhac
- Unit of Heart Morphogenesis, Imagine-Institut Pasteur, Paris, France
- Université de Paris, INSERM UMR 1163, Paris, France
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7
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Wang A, Zhang Q, Han Y, Megason S, Hormoz S, Mosaliganti KR, Lam JCK, Li VOK. A novel deep learning-based 3D cell segmentation framework for future image-based disease detection. Sci Rep 2022; 12:342. [PMID: 35013443 PMCID: PMC8748745 DOI: 10.1038/s41598-021-04048-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 12/09/2021] [Indexed: 11/12/2022] Open
Abstract
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.
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Affiliation(s)
- Andong Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Qi Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Yang Han
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Sean Megason
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | | | - Jacqueline C K Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
| | - Victor O K Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
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Mendonca T, Jones AA, Pozo JM, Baxendale S, Whitfield TT, Frangi AF. Origami: Single-cell 3D shape dynamics oriented along the apico-basal axis of folding epithelia from fluorescence microscopy data. PLoS Comput Biol 2021; 17:e1009063. [PMID: 34723957 PMCID: PMC8584784 DOI: 10.1371/journal.pcbi.1009063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 11/11/2021] [Accepted: 10/13/2021] [Indexed: 11/18/2022] Open
Abstract
A common feature of morphogenesis is the formation of three-dimensional structures from the folding of two-dimensional epithelial sheets, aided by cell shape changes at the cellular-level. Changes in cell shape must be studied in the context of cell-polarised biomechanical processes within the epithelial sheet. In epithelia with highly curved surfaces, finding single-cell alignment along a biological axis can be difficult to automate in silico. We present 'Origami', a MATLAB-based image analysis pipeline to compute direction-variant cell shape features along the epithelial apico-basal axis. Our automated method accurately computed direction vectors denoting the apico-basal axis in regions with opposing curvature in synthetic epithelia and fluorescence images of zebrafish embryos. As proof of concept, we identified different cell shape signatures in the developing zebrafish inner ear, where the epithelium deforms in opposite orientations to form different structures. Origami is designed to be user-friendly and is generally applicable to fluorescence images of curved epithelia.
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Affiliation(s)
- Tania Mendonca
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom
- Department of Biomedical Science, Bateson Centre and Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
- * E-mail: (TM); (AFF)
| | - Ana A. Jones
- Department of Biomedical Science, Bateson Centre and Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Jose M. Pozo
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Sarah Baxendale
- Department of Biomedical Science, Bateson Centre and Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Tanya T. Whitfield
- Department of Biomedical Science, Bateson Centre and Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Alejandro F. Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, United Kingdom
- Medical Imaging Research Center (MIRC), University Hospital Gasthuisberg, Cardiovascular Sciences and Electrical Engineering Departments, KU Leuven, Belgium
- * E-mail: (TM); (AFF)
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Simionato G, Hinkelmann K, Chachanidze R, Bianchi P, Fermo E, van Wijk R, Leonetti M, Wagner C, Kaestner L, Quint S. Red blood cell phenotyping from 3D confocal images using artificial neural networks. PLoS Comput Biol 2021; 17:e1008934. [PMID: 33983926 PMCID: PMC8118337 DOI: 10.1371/journal.pcbi.1008934] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 04/01/2021] [Indexed: 12/15/2022] Open
Abstract
The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.
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Affiliation(s)
- Greta Simionato
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
- Institute for Clinical and Experimental Surgery, Saarland University, Campus University Hospital, Homburg, Germany
| | - Konrad Hinkelmann
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
| | - Revaz Chachanidze
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
- CNRS, University Grenoble Alpes, Grenoble INP, LRP, Grenoble, France
| | - Paola Bianchi
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Elisa Fermo
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Richard van Wijk
- Department of Clinical Chemistry & Haematology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marc Leonetti
- CNRS, University Grenoble Alpes, Grenoble INP, LRP, Grenoble, France
| | - Christian Wagner
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
- Physics and Materials Science Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
| | - Lars Kaestner
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Campus University Hospital, Homburg, Germany
| | - Stephan Quint
- Department of Experimental Physics, Saarland University, Campus E2.6, Saarbrücken, Germany
- Cysmic GmbH, Saarland University, Saarbrücken, Germany
- * E-mail:
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