LabDia Labordiagnostik GmbH
LabDia Labordiagnostik GmbH
Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-2013-ITN | Award Amount: 3.59M | Year: 2013
ImResFun shall provide state-of-the-art training in infectious disease research and medical immunology targeting the most common human fungal pathogens, the opportunistic Candida species. The key objectives of ImResFun are: (i) to understand how immune cells and infected organs respond to invasion by Candida spp, (ii) to decipher host-defense mechanisms mediating pathogen elimination, and (iii) to identify genetic networks driving the dynamics of host-pathogen interplay. ImResFun will exploit cutting-edge technologies to unravel the basic mechanisms of fungal pathogenesis and host immunity, and to improve diagnosis and identify novel biomarkers of infection. Importantly, ImResFun will translate research into clinical practice and identify potential targets for antifungal drug discovery. ImResFun has seven WPs. In addition to coordination (WP7), research will cover molecular mechanisms of host-pathogen interactions using dual-system infection biology in vitro and in vivo (WP1), clinical patient setting and age-related infections (WP2), chemical biology and antifungal drug development (WP3), and bioinformatics and genome-wide data analysis (WP4). A compulsory and tailor-made practical course (WP5) and complementary skills (WP6) program will boost hypothesis-driven projects. Meaningful exposure to the private sector is ensured by extensive secondments of all ESRs/ERs. The resulting reciprocal technology transfer will be beneficial for both SMEs and ESR/ER hosts and sustain collaborations among partners. ImResFun will use personalized career development plans for each ESR/ER to train entrepreneurial scientists capable of translating frontier research into clinical practice, biotechnology and drug discovery. Taken together, ImResFun offers a best-practice example for interdisciplinary, intersectorial and supradisciplinary training in understanding the immunology of microbial infectious diseases, since most approaches are amenable to other microbial pathogens.
Ban S.A.,Austrian Academy of Sciences |
Salzer E.,Austrian Academy of Sciences |
Eibl M.M.,Immunology Outpatient Clinic |
Linder A.,Immunology Outpatient Clinic |
And 11 more authors.
Journal of Clinical Immunology | Year: 2014
Purpose: Idiopathic CD4 lymphopenia constitutes a heterogeneous group of immunodeficiencies with characteristically low CD4+ T-cell counts with largely unknown genetic etiology. We here sought to determine the underlying molecular cause in an index family with two patients suffering from combined immunodeficiency that evolved into predominant CD4+ lymphopenia. The more severely affected index patient also presented with selective antibody deficiency against bacterial polysaccharide antigens.Methods: For the genetic analysis, we used combined homozygosity mapping and exome sequencing. Functional assays included immunoblot analysis, flow cytometry and TCR Vβ spectratyping.Results: A novel homozygous missense mutation was revealed in the kinase domain of JAK3 (c.T3196C, p.Cys1066Arg). Further analysis showed revertant chimerism in CD8+ T-cells in both patients. The additional presence of revertant CD4+ T-cells was associated with a milder clinical and immunological phenotype in the second patient, although the role somatic chimerism plays in amelioration of disease phenotype is uncertain, as presence of revertant cells had no effect on residual CD4 cell JAK3 signaling function. Residual activity of JAK3-dependent STAT3 and STAT5 signaling was also found in immortalized B-cell lines indicating a hypomorphic nature of the described mutation which likely contributes to the milder clinical phenotype.Conclusions: We here present the first case of revertant mosaicism in JAK3 deficiency, manifesting as combined immunodeficiency evolving into predominant CD4+ lymphopenia. Revertant chimerism or hypomorphic mutations in genes typically associated with more severe T-cell deficiency should be considered when assessing patients with milder forms of combined immunodeficiencies. © 2014, The Author(s).
Agency: European Commission | Branch: FP7 | Program: MC-IAPP | Phase: FP7-PEOPLE-2013-IAPP | Award Amount: 1.64M | Year: 2014
Acute Lymphoblastic Leukaemia is the most frequent leukaemia entity in children and adolescents. Despite continued progress and refinement of therapeutic approaches, disease relapse due to insufficient extinction of leukaemic blasts still remains the number one cause of treatment failure. About 15-20% of paediatric patients with the disease still suffer from relapse. Flow cytometry (FCM) is one of the methodologies most useful in this respect, because it is widely available and applicable to most patients. While sample preparation, antibody panels, staining procedures, and FCM acquisition can be harmonized straightforwardly, data analysis and interpretation rely largely on operator skills and experience. These assessments are time-consuming and costly to be attained via staff-training, online support between twinning laboratories, and continued quality control. Hence, these requirements represent the current bottleneck of safely applying the FCM-Minimal Residual Desease (MRD) methodology in a growing community of diagnostic laboratories to the benefit of an increasing number of patients with leukaemia. To address this bottleneck, AutoFLOW aims at developing an objective and automated tool for multi-parameter FCM data analysis with robust and reliable MRD quantification. The consortium aims at engaging professionals from the medical and ICT fields in a network where the exchange of knowledge will culminate in a valid solution for automated FCM analysis for clinical follow-up assessment of Acute Lymphoblastic Leukaemia (ALL). Additionally, AutoFLOW is expected to gain better career perspectives for the involved personnel and increase the competitiveness of the participating SMEs.
Kromp F.,Childrens Cancer Research Institute |
Kromp F.,Vienna University of Technology |
Kromp F.,Labdia Labordiagnostik GmbH. |
Taschner-Mandl S.,Childrens Cancer Research Institute |
And 8 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015
We propose a user-driven method for the segmentation of neuroblastoma nuclei in microscopic fluorescence images involving the gradient energy tensor. Multispectral fluorescence images contain intensity and spatial information about antigene expression, fluorescence in situ hybridization (FISH) signals and nucleus morphology. The latter serves as basis for the detection of single cells and the calculation of shape features, which are used to validate the segmentation and to reject false detections. Accurate segmentation is difficult due to varying staining intensities and aggregated cells. It requires several (meta-) parameters, which have a strong influence on the segmentation results and have to be selected carefully for each sample (or group of similar samples) by user interactions. Because our method is designed for clinicians and biologists, who may have only limited image processing background, an interactive parameter selection step allows the implicit tuning of parameter values. With this simple but intuitive method, segmentation results with high precision for a large number of cells can be achieved by minimal user interaction. The strategy was validated on handsegmented datasets of three neuroblastoma cell lines. © 2015 SPIE.
Bareck E.,Vienna University Hospital |
Ba-Ssalamah A.,Medical University of Vienna |
Brodowicz T.,Medical University of Vienna |
Eisterer W.,Innsbruck Medical University |
And 11 more authors.
Wiener Medizinische Wochenschrift | Year: 2013
Optimal treatment for patients suffering from gastrointestinal stromal tumors (GIST) is based on an interdisciplinary treatment approach. Austrian representatives of Medical and Surgical Oncology, Pathology, Radiology, Nuclear Medicine, Gastroenterology, and Laboratory Medicine issued this manuscript on a consensual base within the context of currently available and published literature. This paper contains guidelines and recommendations for diagnosis, therapy, and follow- up of GIST patients in Austria.
Kastner R.,Childrens Cancer Research Institute |
Kastner R.,Labdia Labordiagnostik GmbH |
Zopf A.,Red Cross |
Preuner S.,Childrens Cancer Research Institute |
And 9 more authors.
European Journal of Cancer | Year: 2014
An emerging problem in patients with Philadelphia (Ph)-positive leukaemias is the occurrence of cells with multiple mutations in the BCR-ABL1 tyrosine kinase domain (TKD) associated with high resistance to different tyrosine kinase inhibitors. Rapid and sensitive detection of leukaemic subclones carrying such changes, referred to as compound mutations, is therefore of increasing clinical relevance. However, current diagnostic methods including next generation sequencing (NGS) of short fragments do not optimally meet these requirements. We have therefore established a long-range (LR) NGS approach permitting massively parallel sequencing of the entire TKD length of 933 bp in a single read using 454 sequencing with the GS FLX+ instrument (454 Life Sciences). By testing a series of individual and consecutive specimens derived from six patients with chronic myeloid leukaemia, we demonstrate that long-range NGS analysis permits sensitive identification of mutations and their assignment to the same or to separate subclones. This approach also facilitates readily interpretable documentation of insertions and deletions in the entire BCR-ABL1 TKD. The long-range NGS findings were reevaluated by an independent technical approach in select cases. Polymerase chain reaction (PCR) amplicons of the BCR-ABL1 TKD derived from individual specimens were subcloned into pGEM®-T plasmids, and >100 individual clones were subjected to analysis by Sanger sequencing. The NGS results were confirmed, thus documenting the reliability of the new technology. Long-range NGS analysis therefore provides an economic approach to the identification of compound mutations and other genetic alterations in the entire BCR-ABL1 TKD, and represents an important advancement of the diagnostic armamentarium for rapid assessment of impending resistant disease. © 2013 Elsevier Ltd. All rights reserved.
Preuner S.,Childrens Cancer Research Institute |
Preuner S.,Labdia Labordiagnostik GmbH |
Danzer M.,Red Cross |
Proll J.,Red Cross |
And 6 more authors.
Journal of Molecular Diagnostics | Year: 2014
The availability of high-quality germline DNA is an important prerequisite for a variety of genetic analyses. We have shown previously that fingernail clippings provide an optimal source of autologous, constitutional DNA for PCR-based applications. However, most existing protocols for nucleic acid purification from nails do not provide sufficiently high yields of pure and intact DNA for more demanding downstream analyses such as next generation sequencing (NGS). We have extensively tested and systematically modified a number of different protocols for DNA purification from nail material to optimize the yield and quality. The integrity of DNA was determined by PCR amplification of short (<300 bp), mid-range (>400 bp), and long-range (>2 kb) sequences using different target genes. Among the methods tested, the Prepfiler Forensic DNA Extraction kit was identified as the most appropriate approach to isolation of high-quality DNA from nail clippings. A standardized input of 20 mg nail material (1 to 10 pieces of fingernail clippings) yielded a mean of 1 μg DNA (range, 0.5 to 2.3 μg). Subsequent PCR-analysis revealed efficient amplifiability of short and mid-range targets in 93% and 90%, and long-range fragments in 60% of the samples tested. The adequacy for next generation sequencing applications was demonstrated by successful high-resolution HLA-typing in ten transplant recipients. Hence, the protocol presented facilitates the exploitation of fingernail material even for demanding genomic analyses both in research and diagnostics. © 2014 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.
Reiter M.,Labdia Labordiagnostik GmbH. |
Rota P.,Labdia Labordiagnostik GmbH. |
Kleber F.,Labdia Labordiagnostik GmbH. |
Diem M.,Labdia Labordiagnostik GmbH. |
And 2 more authors.
Pattern Recognition | Year: 2016
We propose a supervised learning approach to automatic quantification of cell populations in flow cytometric samples. One sample contains up to millions of measurement vectors with a dimensionality between 10 and 20. Normally, each measurement vector corresponds to a single cell in the biological sample. Identifying biologically meaningful cell populations is essentially a clustering problem, however, standard clustering methods are impractical, because size, shape and location of corresponding clusters may vary strongly between samples mainly due to phenotypic differences and inter-laboratory variations. In our holistic approach, we implicitly employ the structural information (such as relative locations and shape of sub-populations). A new input sample is reconstructed by a linear combination of artificial reference samples each represented by a Gaussian Mixture Model (GMM), in which for each Gaussian component the class label of the corresponding cluster of observations is known. The reference samples are calculated from a larger set of training samples by non-negative matrix factorization and can be regarded as the basis of a lower dimensional feature space, in which input samples are reconstructed. We show a method for calculating the feature space transformation based on minimization the L 2 distance defined between two GMM. The feature space representation of the sample is then used to assign each observation to one of the specified sub-populations by a Bayes decision. We present classification results on a database of about 170 patients with Acute Lymphoblastic Leukemia (ALL), where high accuracy in the prediction of relatively small leukemic populations is crucial. The approach is not limited to our application. It can be employed wherever analysis of large, multi-dimensional, numerical data of a specific class of samples with related structure has to be performed. © 2016 Elsevier Ltd.