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1. DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data [version 2; peer review: 2 approved]

Author

Solène Brohard, Carole Escartin, Eric Bonnet, Fernando Perez-Sanz, Jean-François Deleuze, and Miriam Riquelme-Perez

Subjects
Bioinformatics, transcriptomics, RNA sequencing, differential expression analysis, enrichment analysis, visualization, eng, Medicine, Science
Abstract

We are at a time of considerable growth in transcriptomics studies and subsequent in silico analysis. RNA sequencing (RNA-Seq) is the most widely used approach to analyse the transcriptome and is integrated in many studies. The processing of transcriptomic data typically requires a noteworthy number of steps, statistical knowledge, and coding skills, which are not accessible to all scientists. Despite the development of a plethora of software applications over the past few years to address this concern, there is still room for improvement. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. The intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, and statistical comparisons of expression profile levels between groups. Further meta-analysis such as enrichment analysis is also possible, without the need for prior bioinformatics expertise. DEVEA performs a comprehensive analysis from multiple and flexible data sources representing distinct analytical steps. Consequently, it produces dynamic graphs and tables, to explore the expression levels and statistical results from differential expression analysis. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted to enable the scientists to explore results beyond the application. DEVEA is freely accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.

Published
2023
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2. DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data [version 2; peer review: 2 approved]

Author

Miriam Riquelme-Perez, Fernando Perez-Sanz, Jean-François Deleuze, Carole Escartin, Eric Bonnet, and Solène Brohard

Subjects
Software Tool Article, Articles, Bioinformatics, transcriptomics, RNA sequencing, differential expression analysis, enrichment analysis, visualization, R, Shiny, interactive reports.
Abstract

We are at a time of considerable growth in transcriptomics studies and subsequent in silico analysis. RNA sequencing (RNA-Seq) is the most widely used approach to analyse the transcriptome and is integrated in many studies. The processing of transcriptomic data typically requires a noteworthy number of steps, statistical knowledge, and coding skills, which are not accessible to all scientists. Despite the development of a plethora of software applications over the past few years to address this concern, there is still room for improvement. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. The intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, and statistical comparisons of expression profile levels between groups. Further meta-analysis such as enrichment analysis is also possible, without the need for prior bioinformatics expertise. DEVEA performs a comprehensive analysis from multiple and flexible data sources representing distinct analytical steps. Consequently, it produces dynamic graphs and tables, to explore the expression levels and statistical results from differential expression analysis. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted to enable the scientists to explore results beyond the application. DEVEA is freely accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.

Published
2023
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3. DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data [version 1; peer review: 1 approved, 1 approved with reservations]

Author

Miriam Riquelme-Perez, Fernando Perez-Sanz, Jean-François Deleuze, Carole Escartin, Eric Bonnet, and Solène Brohard

Subjects
Software Tool Article, Articles, Bioinformatics, transcriptomics, RNA sequencing, differential expression analysis, enrichment analysis, visualization, R, Shiny, interactive reports.
Abstract

We are at a time of considerable growth in the use and development of transcriptomics studies and subsequent in silico analysis. RNA sequencing is one of the most widely used approaches, now integrated in many studies. The processing of these data may typically require a noteworthy number of steps, statistical knowledge, and coding skills which is not accessible to all scientists. Despite the undeniable development of software applications over the years to address this concern, it is still possible to improve. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. Its intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, statistical comparisons of expression profile levels between groups and further meta-analysis such as enrichment analysis, without bioinformatics expertise. DEVEA performs a thorough analysis from multiple and flexible input data representing distinct analysis stages. From them, it produces dynamic graphs and tables, to explore the expression levels and statistical differential expression analysis results. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted for further result exploration outside the application. DEVEA is accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.

Published
2022
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4. Reactive astrocytes promote proteostasis in Huntington’s disease through the JAK2-STAT3 pathway

Author

Laurene Abjean, Lucile Ben Haim, Miriam Riquelme-Perez, Pauline Gipchtein, Céline Derbois, Marie-Ange Palomares, Fanny Petit, Anne-Sophie Hérard, Marie-Claude Gaillard, Martine Guillermier, Mylène Gaudin-Guérif, Gwenaelle Aurégan, Nisrine Sagar, Cameron Héry, Noëlle Dufour, Noémie Robil, Mehdi Kabani, Ronald Melki, Pierre De la Grange, Alexis P. Bemelmans, Gilles Bonvento, Jean-François Deleuze, Philippe Hantraye, Julien Flament, Eric Bonnet, Solène Brohard, Robert Olaso, Emmanuel Brouillet, Maria-Angeles Carrillo-de Sauvage, Carole Escartin, Laboratoire des Maladies Neurodégénératives - UMR 9199 (LMN), Service MIRCEN (MIRCEN), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Centre National de Recherche en Génomique Humaine (CNRGH), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), GenoSplice [Paris], Fondation maladies rares (GenOmic_2019-0203, Program High throughput sequencing and rare diseases), Association Huntington France, 'Region Ile-de-France' via the 'DIM Cerveau et Pensée', Fondation pour la Recherche Medicale fellowship (ARF201909009244), ANR-10-INBS-04/ FranceBioImagingANR‐ 11‐ IDEX‐003‐02/ Saclay Plant SciencesFrance Génomique national infrastructure, funded by the « Investissements d’Avenir » program managed by the French National Research Agency (ANR-10-INBS-09), ANR-16-TERC-0016,DecodAstro,Decoder la complexité de la réactivité astrocytaire dans les maladies neurodégénératives(2016), ANR-20-CE16-0012,STERO-HD,Signalisation des stérols dans le striatum et effets protecteurs dans la Maladie de Huntington(2020), ANR-17-CE12-0027,EpiHD,Rôle des mécanismes épigénétiques dans la maladie de Huntington(2017), ANR-17-RAR3-0008,TreatPolyQ,Allele-specific lowering of mutant polyQ proteins as treatment for Huntington disease, spinocerebellar ataxia type 3 and spinocerebellar ataxia type 7(2017), ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), and ANR-11-EQPX-0029,MORPHOSCOPE 2,Imagerie et reconstruction multiéchelles de la morphogenèse. (Plateforme d'innovation technologique et méthodologique pour l'imagerie in vivo et la reconstruction des dynamiques multiéchelles de la morphogenèse)(2011)

Subjects
congenital, hereditary, and neonatal diseases and abnormalities, Huntingtin, neuron-astrocyte interactions, viral vectors, Mutant, JAK2-STAT3 signalling, 03 medical and health sciences, 0302 clinical medicine, Huntington's disease, mental disorders, medicine, neurodegenerative diseases, DNAJB1, 030304 developmental biology, 0303 health sciences, Chemistry, Putamen, Neurodegeneration, medicine.disease, Microvesicles, 3. Good health, Cell biology, Proteostasis, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), aggregated protein clearance, 030217 neurology & neurosurgery
Abstract

Huntington’s disease is a fatal neurodegenerative disease characterized by striatal neurodegeneration, aggregation of mutant Huntingtin and the presence of reactive astrocytes. Astrocytes are important partners for neurons and engage in a specific reactive response in Huntington’s disease that involves morphological, molecular and functional changes. How reactive astrocytes contribute to Huntington’s disease is still an open question, especially because their reactive state is poorly reproduced in experimental mouse models. Here, we show that the JAK2-STAT3 pathway, a central cascade controlling astrocyte reactive response, is activated in the putamen of Huntington’s disease patients. Selective activation of this cascade in astrocytes through viral gene transfer reduces the number and size of mutant Huntingtin aggregates in neurons and improves neuronal defects in two complementary mouse models of Huntington’s disease. It also reduces striatal atrophy and increases glutamate levels, two central clinical outcomes measured by non-invasive magnetic resonance imaging. Moreover, astrocyte-specific transcriptomic analysis shows that activation of the JAK2-STAT3 pathway in astrocytes coordinates a transcriptional program that increases their intrinsic proteolytic capacity, through the lysosomal and ubiquitin-proteasome degradation systems. This pathway also enhances their production and exosomal release of the co-chaperone DNAJB1, which contributes to mutant Huntingtin clearance in neurons. Together, our results show that the JAK2-STAT3 pathway controls a beneficial proteostasis response in reactive astrocytes in Huntington’s disease, which involves bi-directional signalling with neurons to reduce mutant Huntingtin aggregation, eventually improving disease outcomes.

Published
2021
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5. Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Author

Marina Carpes-Ruiz, Alejandro Salazar Nicolás, Jesús de la Peña-Moral, María del Carmen Legaz-García, Enrique Martínez-Barba, Miriam Riquelme-Perez, Angel Esteban-Gil, Fernando Perez-Sanz, Carlos Martinez, María Antonia Parreño-González, and Pablo Ramírez

Subjects
Computer science, Feature vector, medicine.medical_treatment, sudan stain, Liver transplantation, Machine learning, computer.software_genre, lcsh:Chemical technology, Biochemistry, Article, computer vision, Analytical Chemistry, Sudan, 03 medical and health sciences, Liver disease, Naive Bayes classifier, chemistry.chemical_compound, 0302 clinical medicine, medicine, Frozen Sections, Humans, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, Pixel, liver transplantation, business.industry, Bayes Theorem, medicine.disease, Atomic and Molecular Physics, and Optics, Statistical classification, machine learning, chemistry, 030220 oncology & carcinogenesis, Sudan stain, macrovesicular steatosis, RGB color model, 030211 gastroenterology & hepatology, Artificial intelligence, business, Algorithm, computer, Algorithms
Abstract

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (&gt, 0.99) with a sensitivity between 0.844 and 1, together with a specificity &gt, 0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

Published
2021

6. Reactive astrocyte nomenclature, definitions, and future directions

Author

Arthur M. Butt, Elena Galea, Richard M. Ransohoff, Raymond A. Swanson, Eduardo R. Zimmer, Christopher M. Norris, Gilles Bonvento, Andras Lakatos, Philip G. Haydon, Wei Ting Chen, Christine R. Rose, Alexey Semyanov, Nicola J. Allen, Anna V. Molofsky, Javier Vitorica, Seiji Okada, Alfonso Araque, Antonia Gutierrez, Masamitsu Iino, Alberto Serrano-Pozo, Vittorio Gallo, C. Justin Lee, Magdalena Götz, Baljit S. Khakh, Christian Steinhäuser, Bart De Strooper, Stéphane H. R. Oliet, Andrea Volterra, Swetlana Sirko, Kira E. Poskanzer, Michael V. Sofroniew, Giorgio Carmignoto, Elly M. Hol, Frank W. Pfrieger, Beatriz G. Pérez-Nievas, Ksenia V. Kastanenka, Keith K. Murai, James P. O'Callaghan, Ina B. Wanner, David H. Rowitch, Pierre J. Magistretti, Cinthia Farina, Marcela Pekna, Albee Messing, Jia Qian Wu, Shane A. Liddelow, Dieter Henrik Heiland, Anusha Mishra, Robert Zorec, Levi B. Wood, Marc R. Freeman, Matthew Holt, Luis Barbeito, Gabor C. Petzold, Luc Pellerin, Jeffrey D. Rothstein, Nathalie Rouach, Carole Escartin, Schuichi Koizumi, Gertrudis Perea, Dwight E. Bergles, Miriam Riquelme-Perez, Martine Cohen-Salmon, Stefanie Robel, James E. Goldman, Binhai Zheng, João Filipe Oliveira, Ari Barzilai, Steven A. Goldman, Brian A. MacVicar, Aude Panatier, Helmut Kettenmann, Alexei Verkhratsky, Amit Agarwal, Colm Cunningham, Blanca Diaz-Castro, Francisco J. Quintana, Milos Pekny, Harald Sontheimer, Benjamin Deneen, Vladimir Parpura, Service MIRCEN (MIRCEN), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Universitat Autònoma de Barcelona (UAB), ICREA Infection Biology Laboratory (Department of Experimental and Health Sciences), Universitat Pompeu Fabra [Barcelona] (UPF), University of Cambridge [UK] (CAM), National Institute for Occupational Safety and Health [Morgantown, WV, USA] (NIOSH), German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), University Hospital Bonn, Massachusetts General Hospital [Charlestown, MA, États-Unis], University of Bonn, University of Lausanne (UNIL), Italian National Research Council [Padova, Italy], University of Padua [Italy], Heidelberg University, The Salk Institute for Biological Studies, University of Minnesota [MN, USA], Institut Pasteur de Montevideo, Réseau International des Instituts Pasteur (RIIP), Tel Aviv University [Tel Aviv], Johns Hopkins University School of Medicine [Baltimore], University of Portsmouth, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Trinity College Dublin, Baylor College of Medicine (BCM), Baylor University, University College of London [London] (UCL), University of Edinburgh, San Raffaele Scientific Institute, Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Vollum Institute, Vollum Institute OHSU, National Children's Hospital, Columbia University [New York], University of Rochester Medical Center (URMC), University of Copenhagen = Københavns Universitet (KU), Ludwig Maximilians Universitaet, SYNERGY, Excellence Cluster of Systems Neurology, Ludwig-Maximilians-Universität München (LMU), Universidad de Málaga [Málaga] = University of Málaga [Málaga], Centro de Investigacion Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III [Madrid] (ISC), Tufts University School of Medicine [Boston], University of Freiburg [Freiburg], Utrecht University [Utrecht], VIB-KU Leuven Center for Brain & Disease Research [Leuven, Belgium], Nihon University School of Medicine [Tokyo, Japan], Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, David Geffen School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California-University of California, Yamanashi University, Institute for Basic Science [Daejeon] (IBS), New York University School of Medicine (NYU), New York University School of Medicine, NYU System (NYU)-NYU System (NYU), University of British Columbia (UBC), King Abdullah University of Science and Technology, University of Wisconsin-Madison, Oregon Health and Science University [Portland] (OHSU), University of California [San Francisco] (UCSF), University of California, McGill University Health Center [Montreal] (MUHC), University of Kentucky, Kyushu University [Fukuoka], Physiopathologie de la Plasticité Neuronale (Neurocentre Magendie - U1215 Inserm), Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Minho [Braga], ICVS/3B's—PT Government Associate Laboratory [Braga, Portugal], Polytechnic Institute of Cávado and Ave, University of Alabama at Birmingham [ Birmingham] (UAB), University of Gothenburg (GU), Ischémie Reperfusion en Transplantation d’Organes Mécanismes et Innovations Thérapeutiques ( IRTOMIT), Université de Poitiers-Institut National de la Santé et de la Recherche Médicale (INSERM), Cajal Institute, King‘s College London, Institut des Neurosciences Cellulaires et Intégratives (INCI), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), Third Rock Ventures, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry (IBCh RAS), Russian Academy of Sciences [Moscow] (RAS), Sechenov First Moscow State Medical University, Ludwig Maximilian University [Munich] (LMU), Institute for Stem Cell Research [Neuherberg], Helmholtz-Zentrum München (HZM), Universidad de Sevilla, Georgia Institute of Technology [Atlanta], The University of Texas Health Science Center at Houston (UTHealth), UC San Diego School of Medicine, Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), University of Ljubljana, University of Manchester [Manchester], Ikerbasque - Basque Foundation for Science, Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), European Project: 722053,EU-GliaPhD - H2020-EU.1.3. H2020-EU.1.3.1., Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie François JACOB (JACOB), Universität Bonn = University of Bonn, Université de Lausanne = University of Lausanne (UNIL), Escartin, Carole [0000-0003-3613-4118], Galea, Elena [0000-0003-4537-9897], Lakatos, András [0000-0002-1301-2292], Petzold, Gabor C [0000-0002-0145-8641], Serrano-Pozo, Alberto [0000-0003-0899-7530], Volterra, Andrea [0000-0003-1069-1602], Araque, Alfonso [0000-0003-3840-1144], Barbeito, Luis [0000-0002-3047-232X], Bonvento, Gilles [0000-0002-2886-4228], Butt, Arthur M [0000-0001-7579-0746], Deneen, Benjamin [0000-0002-6335-1081], Farina, Cinthia [0000-0002-4466-9676], Gallo, Vittorio [0000-0002-2429-0845], Goldman, Steven A [0000-0002-5498-4303], Götz, Magdalena [0000-0003-1551-9203], Gutiérrez, Antonia [0000-0002-6264-6152], Haydon, Philip G [0000-0001-5698-6698], Heiland, Dieter H [0000-0002-9258-3033], Hol, Elly M [0000-0001-5604-2603], Holt, Matthew G [0000-0002-8958-4027], Koizumi, Schuichi [0000-0001-6184-3106], MacVicar, Brian A [0000-0003-4596-4623], Mishra, Anusha [0000-0002-3642-5049], Molofsky, Anna V [0000-0002-4709-2411], Okada, Seiji [0000-0002-5107-8209], Oliveira, João F [0000-0002-1005-2328], Panatier, Aude [0000-0002-6107-4463], Pekna, Marcela [0000-0003-2734-8237], Perea, Gertrudis [0000-0001-5924-9175], Pfrieger, Frank W [0000-0001-7085-1431], Poskanzer, Kira E [0000-0003-4830-8891], Rose, Christine R [0000-0002-9684-3592], Rothstein, Jeffrey D [0000-0003-2001-8470], Rouach, Nathalie [0000-0002-5574-888X], Rowitch, David H [0000-0002-0079-0060], Semyanov, Alexey [0000-0002-6800-0942], Sirko, Swetlana [0000-0001-5950-616X], Sontheimer, Harald [0000-0002-5843-9871], Swanson, Raymond A [0000-0002-3664-5359], Zorec, Robert [0000-0002-7478-3875], Sofroniew, Michael V [0000-0001-6075-0178], Verkhratsky, Alexei [0000-0003-2592-9898], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Aging, [SDV]Life Sciences [q-bio], Disease, pathology [Spinal Cord], Article, 03 medical and health sciences, 0302 clinical medicine, pathology [Aging], Response to injury, pathology [Brain], medicine, Psychology, Animals, Humans, ddc:610, Reactive Astrocyte, ComputingMilieux_MISCELLANEOUS, Spinal Cord Injuries, pathology [Astrocytes], Brain Diseases, Neurology & Neurosurgery, Extramural, business.industry, General Neuroscience, pathology [Brain Injuries], Neurosciences, pathology [Brain Diseases], Brain, 3. Good health, Brain Disorders, 030104 developmental biology, medicine.anatomical_structure, Spinal Cord, Astrocytes, Brain Injuries, Neurological, Cognitive Sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], pathology [Spinal Cord Injuries], business, Neuroscience, 030217 neurology & neurosurgery, Astrocyte, Spinal cord pathology
Abstract

Reactive astrocytes are astrocytes undergoing morphological, molecular, and functional remodeling in response to injury, disease, or infection of the CNS. Although this remodeling was first described over a century ago, uncertainties and controversies remain regarding the contribution of reactive astrocytes to CNS diseases, repair, and aging. It is also unclear whether fixed categories of reactive astrocytes exist and, if so, how to identify them. We point out the shortcomings of binary divisions of reactive astrocytes into good-vs-bad, neurotoxic-vs-neuroprotective or A1-vs-A2. We advocate, instead, that research on reactive astrocytes include assessment of multiple molecular and functional parameters—preferably in vivo—plus multivariate statistics and determination of impact on pathological hallmarks in relevant models. These guidelines may spur the discovery of astrocyte-based biomarkers as well as astrocyte-targeting therapies that abrogate detrimental actions of reactive astrocytes, potentiate their neuro- and glioprotective actions, and restore or augment their homeostatic, modulatory, and defensive functions., Funding: CNRS, CEA, ANR, and France Alzheimer to CE.; MCINN (PID2019-107633RB-I00) and Generalitat de Catalunya (2017-SGR547, Grup de demències Sant Pau) to E.G. US Centers for Disease Control and Prevention to J. P.O. Alzheimer’s Association (AACF-17-524184) and NIH-NIA (K08AG064039) to A.S.-P. DFG (SPP1757, STE 552/5, STE 552/4), EU (H2020-MSCA-ITN project 722053 EU-GliaPhD) and BMBF (16GW0182 CONNEXIN) to C.S. Swiss National Science Foundation grant 31003A 173124/1; SNSF NCCR ‘Transcure’ (51NF40-160620); Synapsis Foundation Heidi Seiler-Stiftung 2018-PI01 to A.Volterra. NIH-NINDS (NS084030), Dr. Miriam and Sheldon G. Adelson Medical Foundation and Wings for Life to M.V.S. The authors thank T. Yohannan of Alpha Language Services, Barcelona, for expert copy editing.

Published
2021
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7. Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Author

Fernando Pérez-Sanz, Miriam Riquelme-Pérez, Enrique Martínez-Barba, Jesús de la Peña-Moral, Alejandro Salazar Nicolás, Marina Carpes-Ruiz, Angel Esteban-Gil, María Del Carmen Legaz-García, María Antonia Parreño-González, Pablo Ramírez, and Carlos M. Martínez

Subjects
liver transplantation, sudan stain, machine learning, computer vision, macrovesicular steatosis, Chemical technology, TP1-1185
Abstract

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

Published
2021
Full Text
View/download PDF

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