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Compaction of rolling circle amplification products increases signal integrity and signal–to–noise ratio
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
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2015 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, 12317:1-10 p., 12317Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2015. Vol. 5, 12317:1-10 p., 12317
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-260286DOI: 10.1038/srep12317ISI: 000358358900001PubMedID: 26202090OAI: oai:DiVA.org:uu-260286DiVA: diva2:847762
Funder
EU, FP7, Seventh Framework Programme, 278568EU, FP7, Seventh Framework Programme, 259796Swedish Research Council
Available from: 2015-07-23 Created: 2015-08-18 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Analysis of signaling pathway activity in single cells using the in situ Proximity Ligation Assay
Open this publication in new window or tab >>Analysis of signaling pathway activity in single cells using the in situ Proximity Ligation Assay
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A cell that senses signals from its environment uses proteins for signal transduction via post translational modifications (PTMs) and protein- protein interactions (PPIs) from cell membrane into the nucleus where genes controlling cell proliferation, differentiation and apoptosis can be turned on or off, i.e. changing the phenotype or fate of the cell. Aberrations within such proteins are prone to cause diseases, such as cancer. Therefore, it is important so study aberrant signaling to be able to understand and treat diseases.

In this thesis, signaling aberrations of PTMs and PPIs were analyzed with the use of the in situ proximity ligation assay (in situ PLA), and the thesis also contain method development of rolling circle amplification (RCA), which is the method used for signal amplification of in situ PLA reaction products.

Paper I considers the integrity of RCA products. Here, the aim was to generate a smaller and more compact RCA product, for more accurate either visual or automated analysis. This was achieved with the use of an additional so called compaction oligonucleotide that during RCA was able to bind and pull segments of RCA products closer together. The compaction oligonucleotide served to increase the signal to noise ratio and decrease the number of false positive signals.

The crosstalk between the Hippo and TGFβ signaling pathways were studied in paper II. Activity of the Hippo signaling pathway is regulated by cell density sensing and tissue control. We found differences in amounts and localization of interactions between the effector proteins of the two pathways depending on cell density and TGFβ stimulation.

In paper III the NF-кB signaling pathway constitutively activated in chronic lymphocytic leukemia (CLL) was studied. A 4 base-pair frameshift deletion within the NFKBIE gene, which encodes the negative regulator IкBε, was found among 13 of a total 315 cases by the use of targeted deep sequencing. We found reduced levels of IкBε protein, decreased p65 inhibition, and increased phosphorylation, along with increased nuclear localization of p65 in NFKBIE deleted cases compared to healthy cases.

Crosstalk between the Hippo and Wnt signaling pathway are studied within paper IV. Here, we found differences in cellular localization of TAZ/β-catenin interactions depending on colon cancer tumor stage and by further investigate Hippo/WNT crosstalk in cell line model systems we found an increase of complex formations involved in the crosstalk in sparse growing HEK293 cells compared to dense growing cells. Also, active WNT3a signaling was affected by cell density. Since cell density showed to have a big effect on Hippo/WNT crosstalk we continued to investigated the effect of E-cadherin, which has a function in cell junctions and maintenance of epithelial integrity on Hippo/WNT crosstalk. Interestingly, we found that E-cadherin is likely to regulate Hippo/WNT crosstalk.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 45 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1202
Keyword
cell signaling, Wnt, Hippo, TGFB
National Category
Medical and Health Sciences
Research subject
Molecular Medicine
Identifiers
urn:nbn:se:uu:diva-281716 (URN)978-91-554-9529-9 (ISBN)
Public defence
2016-05-20, BMC, B41, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2016-04-28 Created: 2016-03-29 Last updated: 2016-05-12
2. Image Analysis and Deep Learning for Applications in Microscopy
Open this publication in new window or tab >>Image Analysis and Deep Learning for Applications in Microscopy
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. Consequently, methods and algorithms for automated quantitative analysis of these images have become increasingly important. These methods range from traditional image analysis techniques to use of deep learning architectures.

Many biomedical microscopy assays result in fluorescent spots. Robust detection and precise localization of these spots are two important, albeit sometimes overlapping, areas for application of quantitative image analysis. We demonstrate the use of popular deep learning architectures for spot detection and compare them against more traditional parametric model-based approaches. Moreover, we quantify the effect of pre-training and change in the size of training sets on detection performance. Thereafter, we determine the potential of training deep networks on synthetic and semi-synthetic datasets and their comparison with networks trained on manually annotated real data. In addition, we present a two-alternative forced-choice based tool for assisting in manual annotation of real image data. On a spot localization track, we parallelize a popular compressed sensing based localization method and evaluate its performance in conjunction with different optimizers, noise conditions and spot densities. We investigate its sensitivity to different point spread function estimates.

Zebrafish is an important model organism, attractive for whole-organism image-based assays for drug discovery campaigns. The effect of drug-induced neuronal damage may be expressed in the form of zebrafish shape deformation. First, we present an automated method for accurate quantification of tail deformations in multi-fish micro-plate wells using image analysis techniques such as illumination correction, segmentation, generation of branch-free skeletons of partial tail-segments and their fusion to generate complete tails. Later, we demonstrate the use of a deep learning-based pipeline for classifying micro-plate wells as either drug-affected or negative controls, resulting in competitive performance, and compare the performance from deep learning against that from traditional image analysis approaches. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 76 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1371
Keyword
Machine learning, Deep learning, Image analysis, Quantitative microscopy, Bioimaging
National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-283846 (URN)978-91-554-9567-1 (ISBN)
Public defence
2016-06-09, 2446, ITC, Lägerhyddsvägen 2, Hus 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2016-05-18 Created: 2016-04-14 Last updated: 2016-06-01Bibliographically approved

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Arngården, LindaIshaq, OmerKlaesson, AxelKühnemund, MalteGrannas, KarinRanefall, PetterWählby, CarolinaSöderberg, Ola

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