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Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.ORCID iD: 0000-0002-6194-2195
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
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2014 (English)In: Autophagy, ISSN 1554-8627, E-ISSN 1554-8635, Vol. 10, no 1, 57-69 p.Article in journal (Refereed) Published
Abstract [en]

Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.

Place, publisher, year, edition, pages
2014. Vol. 10, no 1, 57-69 p.
Keyword [en]
phase-contrast microscopy, automated microscopy, vesicle detection, autophagy, image processing
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:uu:diva-216046DOI: 10.4161/auto.26678ISI: 000328812400006OAI: oai:DiVA.org:uu-216046DiVA: diva2:689266
Conference
High Content Anlaysis
Available from: 2014-01-20 Created: 2014-01-17 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Towards High-Throughput Phenotypic and Systemic Profiling of in vitro Growing Cell Populations using Label-Free Microscopy and Spectroscopy: Applications in Cancer Pharmacology
Open this publication in new window or tab >>Towards High-Throughput Phenotypic and Systemic Profiling of in vitro Growing Cell Populations using Label-Free Microscopy and Spectroscopy: Applications in Cancer Pharmacology
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern techniques like automated microscopy and spectroscopy now make it possible to study quantitatively, across multiple phenotypic and molecular parameters, how cell populations are affected by different treatments and/or environmental disturbances. As the technology development at the instrument level often is ahead of the data analytical tools and the scientific questions, there is a large and growing need for computational algorithms enabling desired data analysis. These algorithms must have capacity to extract and process quantitative dynamic information about how the cell population is affected by different stimuli with the final goal to transform this information into development of new powerful therapeutic strategies. In particular, there is a great need for automated systems that can facilitate the analysis of massive data streams for label-free methods such as phase contrast microscopy (PCM) imaging and spectroscopy (NMR). Therefore, in this thesis, algorithms for quantitative high-throughput phenotypic and systemic profiling of in vitro growing cell populations via label-free microscopy and spectroscopy are developed and evaluated. First a two-dimensional filter approach for high-throughput screening for drugs inducing autophagy and apoptosis from phase contrast time-lapse microscopy images is studied. Then new methods and applications are presented for label-free extraction and comparison of time-evolving morphological features in phase-contrast time-lapse microscopy images recorded from in vitro growing cell populations. Finally, the use of dynamic morphology and NMR/MS spectra for implementation of a reference database of drug induced changes, analogous to the outstanding mRNA gene expression based Connectivity Map database, is explored. In conclusion, relatively simple computational methods are useful for extraction of very valuable biological and pharmacological information from time-lapse microscopy images and NMR spectroscopy data offering great potential for biomedical applications in general and cancer pharmacology in particular.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 50 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1045
Keyword
label free vesicle detector, high-throughput, phase contrast microscopy, Library of Pharmacologically Active Compounds, High Content Screening, fluorometric microculture cytotoxicity assay, nuclear magnetic resonance, mass spectrometry
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-234565 (URN)978-91-554-9082-9 (ISBN)
Public defence
2014-11-25, Robergsalen, entrance 40, 4th floor, Akademiska Sjukhuset, Uppsala, 09:30 (English)
Opponent
Supervisors
Available from: 2014-11-04 Created: 2014-10-21 Last updated: 2015-02-03

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Aftab, ObaidFryknäs, MårtenHammerling, UlfLarsson, RolfGustafsson, Mats G.

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