Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Introducing two strategies for the reproducible detection of whole membrane and surface proteomes

Membranproteine sind in ihrer Struktur und Funktion höchst unterschiedlich und Veränderungen in ihrem Expressionsmuster gehören zu den ersten Ereignissen in pathologischen Situationen. Sie sind daher sowohl als Biomarker, als auch als therapeutische Ansatzpunkte interessant. Sie im großen Maßstab mittels Proteomik zu analysieren, war jedoch bisher eine schwierige Aufgabe. Mehrere Protokolle, die entweder alle Membranproteine umfassen, die unter definierten Bedingungen und zu einem bestimmten Zeitpunkt in einer Zelle exprimiert werden, oder auf Proteine an der Plasmamembran zugeschnitten sind, wurden entwickelt. Dennoch stehen vergleichende Analysen kaum zur Verfügung. Die vorliegende Arbeit bietet einen systematischen Vergleich verschiedener Plasmamembranisolierungsstrategien zur nachfolgenden Analyse durch eindimensionale gel-freie Flüssigchromatographie-Massenspektrometrie. Darüber hinaus wurde das Sulfo-NHS-SS-Biotinylierungsverfahren, das innerhalb der getesteten Kriterien insgesamt am besten abschnitt, durch eine kompetitive Biotin Eluierungsstrategie vereinfacht, die sich als schnell, kosteneffektiv und robust erwies und dadurch eine routinemäßige Evaluierung von Plasmamembran Proteomen in einem größeren Maßstab ermöglicht. Interessanterweise ergab die computergestützte Auswertung verschiedener Datenbanken und Programme zur Vorhersage zellulärer Lokalisationen, dass insgesamt über 90 % der mit der modifizierten Sulfo-NHS-SS-Biotinylierungsmethode identifizierten Proteine mit der Plasmamembran assoziiert sind, überwiegend als Interaktionspartner. Des Weiteren konnte das im Rahmen dieser Arbeit entwickelte Zelloberflächenproteomikverfahren erfolgreich zur Bestimmung von genetischen oder durch Medikamente bedingten Veränderungen in der zellulären Plasmamembranzusammensetzung angewendet werden. Gleichzeitig wurde Triton X-114 Phasentrennung in Verbindung mit filtergestützter Probenvorbereitung als gleichermaßen reproduzierbares und robustes Protokoll zur ergänzenden Analyse aller Membranproteine etabliert. Insgesamt ermöglichen die hier vorgestellten Ergebnisse nicht nur eine verstärkte routinemäßige Evaluierung von Membran- und Zelloberflächenproteomen, die für den funktionellen Zusammenhang von Transport- und Signalwegen sowie für das Verständnis der Wirkungsweise von Medikamenten relevant sind. Vielmehr erlaubt die Kombination beider Technologien deren gegenseitige Abgrenzung und erhöht so die verfügbare Auflösung dieser Schlüsselproteome.Membrane proteins are structurally and functionally highly diverse and changes in their expression pattern are among the first events taking place in pathological conditions. Thus, they are a rich source of biomarkers as well as therapeutic targets. Their large-scale analysis using proteomic strategies has, however, been challenging. Several protocols comprising either all membrane proteins expressed in a cell at a given condition and time or tailored towards proteins located at the plasma membrane have been developed. Still, comparative analyses are only scarcely available. This thesis provides a systematic comparison of different plasma membrane isolation strategies for subsequent analysis by one-dimensional gel-free liquid chromatography mass spectrometry. Moreover, the sulfo-NHS-SS-biotinylation procedure, which overall performed best for the monitored criteria, was simplified by a competitive biotin elution strategy that proved to be fast, cost-effective and robust empowering the routine evaluation of plasma membrane proteomes on a larger scale. Intriguingly, computational analysis using different databases and prediction tools indicated a total of over 90 % of the proteins purified with the modified sulfo-NHS-SS-biotinylation protocol to be associated with the plasma membrane, mostly as interactors. In addition, the cell surface proteomic procedure developed within this thesis could be successfully employed to determine genetic and drug-induced alterations of the cellular plasma membrane composition. At the same time, Triton X-114 phase separation coupled to filter-aided sample preparation was established as an equally reproducible and robust protocol for the complement of all membrane proteins. In summary, the work presented herein not only enables a more routine evaluation of membrane and surface proteomes relevant to the functional correlation of transport, signaling and drug response properties. The combination of both technologies allows to dissect them, ultimately increasing the resolution available for these key sub proteomes.submitted by Katrin HörmannAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersMedizinische Universität Wien, Dissertation, 2016OeBB(VLID)171436

Validation of automated artificial intelligence segmentation of optical coherence tomography images.

PurposeTo benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.MethodsA convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results.ResultsThe CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison.ConclusionThe proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Evaluation of drug-induced neurotoxicity based on metabolomics, proteomics and electrical activity measurements in the complementary CNS in vitro models

The present study was performed in an attempt to develop an in vitro integrated testing strategy to evaluate neurotoxicity of drugs during development phase. A number of endpoints was analyzed using two complementary brain cell culture models, and an in vitro blood-brain barrier model after acute, sub-chronic, and repeated-dose treatments with a series of selected drugs. The developed in vitro BBB model allowed to detect toxic effects on the BBB and to evaluate drug transport through the BBB for prediction free brain concentrations of studied drugs. The electrical activity of cortical neuronal networks recorded with a micro-electrode array was found to be a good tool to predict the neuroactivity and neurotoxicity of drugs and it is suggested as a first-step high content screening test. The histotypic 3D re-aggregating brain cell cultures, containing all brain cell types, were found well suitable for OMICs analyzes. The obtained data suggest that an in vitro integrated testing strategy (ITS), including toxicity to and transport through BBB, as well as metabolomics, proteomics and neuronal electrical activity, measured in stable rodent brain cell culture systems (in the future human stem cell-derived neuronal models), may considerably improve current drug-induced neurotoxicity evaluation. Robustness of this ITS has to be further evaluated with a larger number of studied drugs.JRC.I.5-Systems Toxicolog

Convergent use of phosphatidic acid for hepatitis C virus and SARS-CoV-2 replication organelle formation.

Double membrane vesicles (DMVs) serve as replication organelles of plus-strand RNA viruses such as hepatitis C virus (HCV) and SARS-CoV-2. Viral DMVs are morphologically analogous to DMVs formed during autophagy, but lipids driving their biogenesis are largely unknown. Here we show that production of the lipid phosphatidic acid (PA) by acylglycerolphosphate acyltransferase (AGPAT) 1 and 2 in the ER is important for DMV biogenesis in viral replication and autophagy. Using DMVs in HCV-replicating cells as model, we found that AGPATs are recruited to and critically contribute to HCV and SARS-CoV-2 replication and proper DMV formation. An intracellular PA sensor accumulated at viral DMV formation sites, consistent with elevated levels of PA in fractions of purified DMVs analyzed by lipidomics. Apart from AGPATs, PA is generated by alternative pathways and their pharmacological inhibition also impaired HCV and SARS-CoV-2 replication as well as formation of autophagosome-like DMVs. These data identify PA as host cell lipid involved in proper replication organelle formation by HCV and SARS-CoV-2, two phylogenetically disparate viruses causing very different diseases, i.e. chronic liver disease and COVID-19, respectively. Host-targeting therapy aiming at PA synthesis pathways might be suitable to attenuate replication of these viruses