Quantitative Assessment of Emphysema Severity in Histological Lung Analysis

Published onlineEmphysema is a characteristic component of chronic obstructive pulmonary disease (COPD), which has been pointed out as one of the main causes of mortality for the next years. Animal models of emphysema are employed to study the evolution of this disease as well as the effect of treatments. In this context, measures such as the mean linear intercept (Lm) and the equivalent diameter (d) have been proposed to quantify the airspace enlargement associated with emphysematous lesions in histological sections. The parameter D2 , which relates the second and the third moments of the variable d , has recently shown to be a robust descriptor of airspace enlargement. However, the value of D2 does not provide a direct evaluation of emphysema severity. In our research, we suggest a Bayesian approach to map D2 onto a novel emphysema severity index (SI) reflecting the probability for a lung area to be emphysematous. Additionally, an image segmentation procedure was developed to compute the severity map of a lung section using the SI function. Severity maps corresponding to 54 lung sections from control mice, mice induced with mild emphysema and mice induced with severe emphysema were computed, revealing differences between the distribution of SI in the three groups. The proposed methodology could then assist in the quantification of emphysema severity in animal models of pulmonary disease.This work has been partly funded by the grants ‘‘MINECO DPI2012-38090-C03-02’’ and ‘‘TEC2013-48552-C2-1-R’’ from the Spanish Ministry of Economy and CompetitivenessPublicad

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However, the identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of cells, organized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features. We validate NaroNet using synthetic patient cohorts with adjustable incidence of different TMEs and two cancer patient datasets. In both synthetic and real datasets, NaroNet unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of biomarker signatures.Comment: 37 pages, 4 figure

Applying watershed algorithms to the segmentation of clustered nuclei

Cluster division is a critical issue in fluor escence micr oscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking cluster ed nuclei and analyzing only isolated nuclei may dramatically incr ease analysis time or af fect the statistical validation of the r esults. Automatic segmentation of cluster ed nuclei r equir es the implementation of specific image segmentation tools. Most algorithms are inspired by one of the two following strategies: 1) cluster division by the detection of inter nuclei gradients; or 2) division by definition of domains of influence (geometrical approach). Both strategies lead to completely different implementations, and usually algorithms based on a single view strategy fail to corr ectly segment most cluster ed nuclei, or per for m well just for a specific type of sample. An algorithm based on morphological watersheds has been implemented and tested on the segmentation of micr oscopic nuclei clusters. This algorithm pr ovides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, mor e importantly, for the implementation of mixed (gradient- and shape-based) algorithms. Using this algorithm, almost 90% of the test clusters wer e corr ectly segmented in peripheral blood and bone marr ow pr eparations. The algorithm was valid for both types of samples, using the appr opriate markers and transfor mations.Contract grant sponsor: ARCADIM Project; Contract grant number: CICYT TIC92-0922-C02-01 (Comisión Interministerial de Ciencia y Tecnología); Contract grant sponsor: European Concerted Action CA-AMCA; Contract grant number: BMH1-CT92-1307; Contract grant sponsor: Comunidad Autónoma de Madrid (CAM); Contract grant sponsor: Universidad Politécnica de Madrid (UPM).Publicad

Preclinical evaluation of the antimicrobial-immunomodulatory dual action of xenohormetic molecules against haemophilus influenzae respiratory infection

Chronic obstructive pulmonary disease (COPD) is characterized by abnormal inflammation and impaired airway immunity, providing an opportunistic platform for nontypeable Haemophilus influenzae (NTHi) infection. In this context, therapies targeting not only overactive inflammation without significant adverse effects, but also infection are of interest. Increasing evidence suggests that polyphenols, plant secondary metabolites with anti-inflammatory and antimicrobial properties, may be protective. Here, a Cistus salviifolius plant extract containing quercetin, myricetin, and punicalagin was shown to reduce NTHi viability. Analysis of these polyphenols revealed that quercetin has a bactericidal effect on NTHi, does not display synergies, and that bacteria do not seem to develop resistance. Moreover, quercetin lowered NTHi airway epithelial invasion through a mechanism likely involving inhibition of Akt phosphorylation, and reduced the expression of bacterially-induced proinflammatory markers il-8, cxcl-1, il-6, pde4b, and tnfα. We further tested quercetin’s effect on NTHi murine pulmonary infection, showing a moderate reduction in bacterial counts and significantly reduced expression of proinflammatory genes, compared to untreated mice. Quercetin administration during NTHi infection on a zebrafish septicemia infection model system showed a bacterial clearing effect without signs of host toxicity. In conclusion, this study highlights the therapeutic potential of the xenohormetic molecule quercetin against NTHi infection

Molecular analysis of a multistep lung cancer model induced by chronic inflammation reveals epigenetic regulation of p16 and activation of the DNA damage response pathway

The molecular hallmarks of inflammation-mediated lung carcinogenesis have not been fully clarified, mainly due to the scarcity of appropriate animal models. We have used a silica-induced multistep lung carcinogenesis model driven by chronic inflammation to study the evolution of molecular markers and genetic alterations. We analyzed markers of DNA damage response (DDR), proliferative stress, and telomeric stress: gamma-H2AX, p16, p53, and TERT. Lung cancer-related epigenetic and genetic alterations, including promoter hypermethylation status of p16(CDKN2A), APC, CDH13, Rassf1, and Nore1A, as well as mutations of Tp53, epidermal growth factor receptor, K-ras, N-ras, and c-H-ras, have been also studied. Our results showed DDR pathway activation in preneoplastic lesions, in association with inducible nitric oxide synthase and p53 induction. p16 was also induced in early tumorigenic progression and was inactivated in bronchiolar dysplasias and tumors. Remarkably, lack of mutations of Ras and epidermal growth factor receptor, and a very low frequency of Tp53 mutations suggest that they are not required for tumorigenesis in this model. In contrast, epigenetic alterations in p16(CDKN2A), CDH13, and APC, but not in Rassf1 and Nore1A, were clearly observed. These data suggest the existence of a specific molecular signature of inflammation-driven lung carcinogenesis that shares some, but not all, of the molecular landmarks of chemically induced lung cancer

From ANAIS-25 towards ANAIS-250

The ANAIS (Annual modulation with NaI(Tl) Scintillators) experiment aims at the confirmation of the DAMA/LIBRA signal using the same target and technique at the Canfranc Underground Laboratory (LSC). 250 kg of ultra pure NaI(Tl) crystals will be used as target, divided into 20 modules, 12.5 kg mass each, and coupled to two high efficiency photomultiplier tubes from Hamamatsu. The ANAIS-25 set-up at the LSC consists of two prototypes, amounting 25 kg NaI(Tl), grown from a powder having a potassium level under the limit of our analytical techniques, and installed in a convenient shielding at the LSC. The background has been carefully analyzed and main results will be summarized in this paper, focusing on the alpha contamination identified in the prototypes and the related background contributions. Status of fulfillment of ANAIS experimental goals and prospects for the building of ANAIS-250 experiment will be also revised