Non-beta-cell progenitors in pregnant mice and the origin and functionality of beta-cells after diabetic recovery in a c-Myc ablation model

The debate regarding the contribution of adult stem/progenitor cells during normal growth and beta-cell regeneration is far from being resolved. Therefore, we addressed in two distinct situations the origin of new beta-cells. We exploited a Cre/loxP lineage tracing system to efficiently label beta-cells in double transgenic mice (Z/AP; RIPCreERTAM) to address the origin of new beta-cell during the beta-cell mass expansion in response to one and two pregnancies. Similarly, we examined origin of new beta-cell after diabetic recovery in triple transgenic mouse (Z/AP; RIPCreERTAM; pIns-c-MycERTAM). Finally we evaluated the functionality of regenerated beta-cells after diabetic recovery in the single pIns-c-MycERTAM mouse model by microfluorimetry, in collaboration with Dr P. Squires. We showed that the beta-cell functionality in the pIns-c-MycERTAM line was abnormal. Second, we showed that the human placental alkaline phosphatase label (HPAP) in the double and triple transgenic mice was 1) specific to beta-cells, 2) irreversible and heritable and 3) tamoxifen dose-dependant. Third, the analysis of the proportion of beta-cells labelled for HPAP in one pregnancy, showed that the HPAP labelling index of the non-pregnant animals (0.44±0.05) was greater that in the pregnant group (0.33±0.06),(paired two-tailed t-test, P-value 0.021), indicating a dilution of the label in pregnant animal pancreata. Furthermore the combined results of the mean HPAP labelling index in non-pregnant animals (0.44±0.12) and pregnant animals (0.33±0.09) in one and two pregnancies reinforced our results above by indicating that the difference between the two groups was considered extremely significant (paired, two-sided student t-test, P-value 0.0007). Likewise, we showed that two to three months after the tamoxifen pulse, beta-cells do not fully lose differentiation or transdifferentiate into other lineages of either endocrine or exocrine compartment. In conclusion, we demonstrated for the first time that non-beta-cell progenitors contribute significantly to the increase of the beta-cell mass in response to pregnancy in combination with pre-existing beta-cell self-duplication

Non-beta-cell progenitors in pregnant mice and the origin and functionality of beta-cells after diabetic recovery in a c-Myc ablation model

The debate regarding the contribution of adult stem/progenitor cells during normal growth and beta-cell regeneration is far from being resolved. Therefore, we addressed in two distinct situations the origin of new beta-cells. We exploited a Cre/loxP lineage tracing system to efficiently label beta-cells in double transgenic mice (Z/AP; RIPCreERTAM) to address the origin of new beta-cell during the beta-cell mass expansion in response to one and two pregnancies. Similarly, we examined origin of new beta-cell after diabetic recovery in triple transgenic mouse (Z/AP; RIPCreERTAM; pIns-c-MycERTAM). Finally we evaluated the functionality of regenerated beta-cells after diabetic recovery in the single pIns-c-MycERTAM mouse model by microfluorimetry, in collaboration with Dr P. Squires. We showed that the beta-cell functionality in the pIns-c-MycERTAM line was abnormal. Second, we showed that the human placental alkaline phosphatase label (HPAP) in the double and triple transgenic mice was 1) specific to beta-cells, 2) irreversible and heritable and 3) tamoxifen dose-dependant. Third, the analysis of the proportion of beta-cells labelled for HPAP in one pregnancy, showed that the HPAP labelling index of the non-pregnant animals (0.44±0.05) was greater that in the pregnant group (0.33±0.06),(paired two-tailed t-test, P-value 0.021), indicating a dilution of the label in pregnant animal pancreata. Furthermore the combined results of the mean HPAP labelling index in non-pregnant animals (0.44±0.12) and pregnant animals (0.33±0.09) in one and two pregnancies reinforced our results above by indicating that the difference between the two groups was considered extremely significant (paired, two-sided student t-test, P-value 0.0007). Likewise, we showed that two to three months after the tamoxifen pulse, beta-cells do not fully lose differentiation or transdifferentiate into other lineages of either endocrine or exocrine compartment. In conclusion, we demonstrated for the first time that non-beta-cell progenitors contribute significantly to the increase of the beta-cell mass in response to pregnancy in combination with pre-existing beta-cell self-duplication.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Brief inactivation of c-Myc is not sufficient for sustained regression of c-Myc-induced tumours of pancreatic islets and skin epidermis

Background Tumour regression observed in many conditional mouse models following oncogene inactivation provides the impetus to develop, and a platform to preclinically evaluate, novel therapeutics to inactivate specific oncogenes. Inactivating single oncogenes, such as c-Myc, can reverse even advanced tumours. Intriguingly, transient c-Myc inactivation proved sufficient for sustained osteosarcoma regression; the resulting osteocyte differentiation potentially explaining loss of c-Myc's oncogenic properties. But would this apply to other tumours? Results We show that brief inactivation of c-Myc does not sustain tumour regression in two distinct tissue types; tumour cells in pancreatic islets and skin epidermis continue to avoid apoptosis after c-Myc reactivation, by virtue of Bcl-xL over-expression or a favourable microenvironment, respectively. Moreover, tumours progress despite reacquiring a differentiated phenotype and partial loss of vasculature during c-Myc inactivation. Interestingly, reactivating c-Myc in β-cell tumours appears to result not only in further growth of the tumour, but also re-expansion of the accompanying angiogenesis and more pronounced β-cell invasion (adenocarcinoma). Conclusions Given that transient c-Myc inactivation could under some circumstances produce sustained tumour regression, the possible application of this potentially less toxic strategy in treating other tumours has been suggested. We show that brief inactivation of c-Myc fails to sustain tumour regression in two distinct models of tumourigenesis: pancreatic islets and skin epidermis. These findings challenge the potential for cancer therapies aimed at transient oncogene inactivation, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context fail to reinstate apoptosis. Together, these results suggest that treatment schedules will need to be informed by knowledge of the molecular basis and environmental context of any given cancer

Re-expression of IGF-II is important for beta cell regeneration in adult mice

Background The 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 Findings We 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/Significance Our 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

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application

Analyzing Multi-Tag Bioimages with BIOIMAX colocation mining tools

Kölling J, Rathke M, Abouna S, Khan M, Nattkemper TW. Analyzing Multi-Tag Bioimages with BIOIMAX colocation mining tools. Presented at the IEEE International Symposium on BIOMEDICAL IMAGING (ISBI), Barcelona.The application of multi-tag protocols in fluorescence microscopy allows the visualization of a large number (> 10) of molecules (i. e. proteins) in a sample (like a tissue section). However, the analysis of such high dimensional bioimages is a difficult task for most of the labs, since software solutions for particular data mining steps are difficult to use or just not available. In this paper we present two new free online tools: MICOLT (Multivariate Image COlocation Tool) and MIFIST (Multivariate Image Frequent Item Set Tool). Both tools can be used via our recently proposed online bioimage analysis platform BioIMAX, so users can upload their bioimage data, apply the tools and share the results with other invited users based on BioIMAX’ concept of shared virtual projects. Data mining with these tools includes the computation and visualization colocation factors well established in the microscopy community (like Mander’s score) and association rule mining following the frequent item set principle, thereby supporting large and small scale analysis

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

Langenkämper D, Kölling J, Abouna S, Khan M, Niehaus K, Nattkemper TW. TICAL - a web-tool for multivariate image clustering and data topology preserving visualization. Presented at the Microscopic Image Analysis with Applications in Biology (MIAAB), Heidelberg, Germany.In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Conception et réalisation d'un circuit d'émission ultrasonore autonome et souple d'utilisation

posterXIIème Collloque National de la Recherche dans les IUT - CNRIUT'06Ultrasound techniques have applications in various areas: fault detection, medical ultrasound, underwater acoustics, telemetry. It is within this last application that we have developed a system for measuring distance using an ultrasonic transducer to emit conditioning pulses at 40 kHz. The transmitted and received signals are operated under the software MATLA

A novel framework for molecular co-expression pattern analysis in multi-channel toponome fluorescence images

Bioimage computing is rapidly emerging as an important area in image based systems biology with an emphasis on spatiotemporal localization of subcellular bio-molecules, most importantly proteins. A key problem in this domain is analysis of protein co-localization or co expression of protein molecules. Imaging techniques, such as the Toponome Imaging System (TIS) [1], with the ability to localize several different proteins in the same tissue specimen are only becoming available recently. Traditional co-localization studies and some of the modern coexpression studies have serious limitations when analyzing this kind of data. Here we present a framework for the analysis of molecular co-expression patterns (MCEPs) in TIS image dat