Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer‐aided detection

Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor biology, cooperation between multidisciplinary fields is needed. Diverse mathematic computations and algorithms have been designed to find prognostic targets and enhance diagnostic assessment. In this work, we use computational digital tools to study the topology of vitronectin, a glycoprotein of the extracellular matrix. Vitronectin is linked to angiogenesis and migration, two processes closely related to tumor cell spread. Here, we investigate whether the distribution of this molecule in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, we analyze different topological features that capture the organizational cues of vitronectin in histopathological images taken from human samples. We find that the Euler number and the branching of territorial vitronectin, two topological features, could allow for a more precise pretreatment risk stratification to guide treatment strategies in neuroblastoma patients. A large amount of recently synthesized VN would create migration tracks, pinpointed by both topological features, for malignant neuroblasts, so that dramatic change in the extracellular matrix would increase tumor aggressiveness and worsen patient outcomes

Seven challenges in the multiscale modeling of multicellular tissues

The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviors, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regenerative medicine is to understand how relationships between scales produce emergent tissue-scale behaviors. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionized our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modeling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics, and signaling. Yet such models remain challenging: modeling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarize recent progress in multiscale modeling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation, and validation of such models

Unraveling the extracellular matrix-tumor cell interactions to aid better targeted therapies for neuroblastoma

Treatment in children with high-risk neuroblastoma remains largely unsuccessful due to the development of metastases and drug resistance. The biological complexity of these tumors and their microenvironment represent one of the many challenges to face. Matrix glycoproteins such as vitronectin act as bridge elements between extracellular matrix and tumor cells and can promote tumor cell spreading. In this study, we established through a clinical cohort and preclinical models that the interaction of vitronectin and its ligands, such as αv integrins, are related to the stiffness of the extracellular matrix in high-risk neuroblastoma. These marked alterations found in the matrix led us to specifically target tumor cells within these altered matrices by employing nanomedicine and combination therapy. Loading the conventional cytotoxic drug etoposide into nanoparticles significantly increased its efficacy in neuroblastoma cells. We noted high synergy between etoposide and cilengitide, a high-affinity cyclic pentapeptide αv integrin antagonist. The results of this study highlight the need to characterize cell-extracellular matrix interactions, to improve patient care in high-risk neuroblastoma

Scutoids are a geometrical solution to three-dimensional packing of epithelia

As animals develop, tissue bending contributes to shape the organs into complex three-dimensional structures. However, the architecture and packing of curved epithelia remains largely unknown. Here we show by means of mathematical modelling that cells in bent epithelia can undergo intercalations along the apico-basal axis. This phenomenon forces cells to have different neighbours in their basal and apical surfaces. As a consequence, epithelial cells adopt a novel shape that we term “scutoid”. The detailed analysis of diverse tissues confirms that generation of apico-basal intercalations between cells is a common feature during morphogenesis. Using biophysical arguments, we propose that scutoids make possible the minimization of the tissue energy and stabilize three-dimensional packing. Hence, we conclude that scutoids are one of nature's solutions to achieve epithelial bending. Our findings pave the way to understand the three-dimensional organization of epithelial organs

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.This work is supported by the project PID2019-103900GB-I00 funded by MCIN/AEI /10.13039/501100011033 and Programa Operativo FEDER Andalucía 2014–2020 (US-1380953) to L.M.E. Work by L.M.E. and J.A.A.-S.R. has been funded by the Junta de Andalucía (Consejerı´a de economı´a, conocimiento, empresas y Universidad) grant PY18-631 co-funded by FEDER funds. A.T. has been funded by a ‘‘Contrato predoctoral PIF’’ from Universidad de Sevilla. C.G.-V. has been funded by a ‘‘Contrato predoctoral para la formacio´ n de doctores’’ BES-2017-082306. G.B. was supported by a Comunidad de Madrid contract (CAM) and by an FPI grant from MINECO (BES-2022-077789). F.M.-B. was supported by MICINN (PID2020-120367GB-I00) and Fundacio´ n Ramo´ n Areces (CIVP18A3904). P.G.-G. has been funded by Margarita Salas Fellowship – NextGenerationEU. C.H.F.-E. has been funded by Marı´a Zambrano Fellowship – NextGenerationEU. I.A.-C. would like to acknowledge that his work has been partially supported by the University of the Basque Country UPV/EHU grant GIU19/027 and by grant PID2021-126701OB-I00, funded by MCIN/AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe." L.M.E. also wants to thank PIE-202120E047 – Conexiones-Life network for networking and input

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues

Author Correction: Scutoids are a geometrical solution to three-dimensional packing of epithelia.

The original version of this Article contained an error in ref. 39, which incorrectly cited 'Fristrom, D. & Fristrom, J. W. in The Development of Drosophila melanogaster (eds. Bate, M. & Martinez-Arias, A.) II, (Cold spring harbor laboratory press, 1993)'. The correct reference is 'Condic, M.L, Fristrom, D. & Fristrom, J.W. Apical cell shape changes during Drosophila imaginal leg disc elongation: a novel morphogenetic mechanism. Development 111: 23-33 (1991)'. Furthermore, the last sentence of the fourth paragraph of the introduction incorrectly omitted citation of work by Rupprecht et al. The correct citation is given below. These errors have now been corrected in both the PDF and HTML versions of the Article. Rupprecht, J.F., Ong, K.H., Yin, J., Huang, A., Dinh, H.H., Singh, A.P., Zhang, S., Yu, W. & Saunders, T.E. Geometric constraints alter cell arrangements within curved epithelial tissues. Mol. Biol. Cell 28, 3582-3594 (2017)

Lipid profile, cardiovascular disease and mortality in a Mediterranean high-risk population: the ESCARVAL-RISK study

The potential impact of targeting different components of an adverse lipid profile in populations with multiple cardiovascular risk factors is not completely clear. This study aims to assess the association between different components of the standard lipid profile with all cause mortality and hospitalization due to cardiovascular events in a high-risk population. Methods This prospective registry included high risk adults over 30 years old free of cardiovascular disease (2008±2012). Diagnosis of hypertension, dyslipidemia or diabetes mellitus was inclusion criterion. Lipid biomarkers were evaluated. Primary endpoints were all-cause mortality and hospital admission due to coronary heart disease or stroke. We estimated adjusted rate ratios (aRR), absolute risk differences and population attributable risk associated with adverse lipid profiles. Results 51,462 subjects were included with a mean age of 62.6 years (47.6% men). During an average follow-up of 3.2 years, 919 deaths, 1666 hospitalizations for coronary heart disease and 1510 hospitalizations for stroke were recorded. The parameters that showed an increased rate for total mortality, coronary heart disease and stroke hospitalization were, respectively, low HDL-Cholesterol: aRR 1.25, 1.29 and 1.23; high Total/HDL-Cholesterol: aRR 1.22, 1.38 and 1.25; and high Triglycerides/HDL-Cholesterol: aRR 1.21, 1.30, 1.09. The parameters that showed highest population attributable risk (%) were, respectively, low HDL-Cholesterol: 7.70, 11.42, 8.40; high Total/HDL-Cholesterol: 6.55, 12.47, 8.73; and high Triglycerides/ HDL-Cholesterol: 8.94, 15.09, 6.92. Conclusions In a population with cardiovascular risk factors, HDL-cholesterol, Total/HDL-cholesterol and triglycerides/HDL-cholesterol ratios were associated with a higher population attributable risk for cardiovascular disease compared to other common biomarkers

Clinical inertia in poorly controlled elderly hypertensive patients: a cross-sectional study in Spanish physicians to ascertain reasons for not intensifying treatment

Background Clinical inertia, the failure of physicians to initiate or intensify therapy when indicated, is a major problem in the management of hypertension and may be more prevalent in elderly patients. Overcoming clinical inertia requires understanding its causes and evaluating certain factors, particularly those related to physicians. Objective The objective of our study was to determine the rate of clinical inertia and the physician-reported rea- sons for it. Conclusion Physicians provided reasons for not intensi- fying treatment in poorly controlled patients in only 30 % of instances. Main reasons for not intensifying treatment were borderline BP values, co-morbidity, suspected white coat effect, or perceived difficulty achieving target. nJCI was associated with high borderline BP values and car- diovascular diseas