Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue

Herold, Julia; Zhou, Luxian; Abouna, Sylvie; Pelengaris, Stella; Epstein, D. B. A.; Khan, Michael; Nattkemper, Tim Wilhelm

doi:10.1016/j.compmedimag.2009.10.004

Cited by 10 publications

(14 citation statements)

References 13 publications

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“…The counting was performed manually and also by the application of new machine based learning software co-developed and validated with our collaborators [30]. The data was largely consistent with that obtained for beta cell cross sectional area and mass.…”

Section: Resultssupporting

confidence: 68%

“…Beta cell number was counted in up to 200 islets from five pancreas levels per mouse (n = 3) by software co-developed with our collaborators [30]. Results showed in MIG mice beta cell number increased by 60% at 4 days Myc deactivation, whereas in MIGKO mice the beta cell mass was barely changed.…”

Section: Resultsmentioning

confidence: 99%

“…IGF-II KO mice were a generous gift from Professor Doug Hanahan, UCSF [22]. Since igf2 is an imprinted gene with the maternal allele silenced [11], IGF-II KO mice are first identified by small size and the genotype confirmed as previously described [30]. IGF-KO strain is crossed with pIns-c-MycER TAM mice to generate pIns-c-MycER TAM /IGF-II +/+ (MIG) and pIns-c-MycER TAM /IGF-II +/− (MIGKO) mice.…”

Section: Methodsmentioning

confidence: 99%

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Re-Expression of IGF-II Is Important for Beta Cell Regeneration in Adult Mice

et al. 2012

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BackgroundThe key factors which support re-expansion of beta cell numbers after injury are largely unknown. Insulin-like growth factor II (IGF-II) plays a critical role in supporting cell division and differentiation during ontogeny but its role in the adult is not known. In this study we investigated the effect of IGF-II on beta cell regeneration.Methodology/Principal FindingsWe employed an in vivo model of ‘switchable’ c-Myc-induced beta cell ablation, pIns-c-MycERTAM, in which 90% of beta cells are lost following 11 days of c-Myc (Myc) activation in vivo. Importantly, such ablation is normally followed by beta cell regeneration once Myc is deactivated, enabling functional studies of beta cell regeneration in vivo. IGF-II was shown to be re-expressed in the adult pancreas of pIns-c-MycERTAM/IGF-II+/+ (MIG) mice, following beta cell injury. As expected in the presence of IGF-II beta cell mass and numbers recover rapidly after ablation. In contrast, in pIns-c-MycERTAM/IGF-II+/− (MIGKO) mice, which express no IGF-II, recovery of beta cell mass and numbers were delayed and impaired. Despite failure of beta cell number increase, MIGKO mice recovered from hyperglycaemia, although this was delayed.Conclusions/SignificanceOur results demonstrate that beta cell regeneration in adult mice depends on re-expression of IGF-II, and supports the utility of using such ablation-recovery models for identifying other potential factors critical for underpinning successful beta cell regeneration in vivo. The potential therapeutic benefits of manipulating the IGF-II signaling systems merit further exploration.

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Section: Resultssupporting

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Re-Expression of IGF-II Is Important for Beta Cell Regeneration in Adult Mice

et al. 2012

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“…A revised version of this manuscript was published in CMIG (PMID: 18982583 atic islet segmentation and cell counting of α and β-cells [77]. An approach which is based on the strategies described in Section 3.1 and Section 3.2 is described in [78].…”

Section: Preprint June 29 2010mentioning

confidence: 99%

Computational pathology: Challenges and promises for tissue analysis

Fuchs

Buhmann

2011

Computerized Medical Imaging and Graphics

231

127

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The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.

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“…The resultant image data consist of a stack of N grey value images ( j = 1, …, N ) where each image shows the spatial distribution of one molecule. Due to these techniques becoming ubiquitous, new computational approaches are needed to process and visualize multivariate bioimages [13], [14].…”

Section: Introductionmentioning

confidence: 99%

RAMTaB: Robust Alignment of Multi-Tag Bioimages

et al. 2012

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BackgroundIn recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.Methodology/Principal FindingsWe employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.ConclusionsFor the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

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Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue

Cited by 10 publications

References 13 publications

Re-Expression of IGF-II Is Important for Beta Cell Regeneration in Adult Mice

Re-Expression of IGF-II Is Important for Beta Cell Regeneration in Adult Mice

Computational pathology: Challenges and promises for tissue analysis

RAMTaB: Robust Alignment of Multi-Tag Bioimages

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