Computational insights into the influence of substrate stiffness on collective cell migration

Critically important biological phenomena in health and disease, such as wound healing, cancer metastasis, and embryonic development, are governed by collective cell migration. This highly complex process depends not only on cellular features, but also on different stimuli from the local cell environment. Cell migration is promoted by the combination of physico-chemical cues, including the mechanical properties of the extracellular matrix (ECM). Stiffness gradients within ECM have recently been demonstrated to result into preferred directions of cell migration. However, the specific mechanisms driving this directed collective cell migration and their relative roles remain unclear. Here, we develop a continuum formulation and its finite element (FE) implementation to test different hypotheses on the cause of spatial heterogeneities during cell migration on heterogeneous-stiffness substrates. We evaluate two key hypotheses: (i) cell polarisation is promoted by stiffness gradients within the ECM and; (ii) propulsion forces are weighted by ECM stiffness. Ultimately, we provide a robust in silico framework to explain experimental observations and guide future research.The authors thank Denis Wirtz (Johns Hopkins University) for relevant discussion. The authors acknowledge support from Programa de Apoyo a la Realizacion de Proyectos Interdiscisplinares de I+D para Jovenes Investigadores de la Universidad Carlos III de Madrid and Comunidad de Madrid, Spain (project: BIOMASKIN). DGG acknowledges support from the Talent Attraction grant, Spain (CM 2018 - 2018-T2/IND-9992) from the Comunidad de Madrid. This work was partially funded by projects TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy and a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation, Spain

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)

Molecular imaging of pulmonary tuberculosis in an ex-vivo mouse model using spectral photon-counting computed tomography and micro-CT

Artículo escrito por un elevado número de autores. Sólo se referencian el que aparece en primer lugar y los autores pertenecientes a la UC3MThe results of this study support the idea that photon-counting CT imaging is capable of molecular imaging when enhanced by high-Z pharmaceuticals. The Medipix3RX detector operating four CSM energy bins provided sufficient spectral information for the simultaneous differentiation of iodine, water, and lipid (and a second high-Z contrast). In an ex-vivo mouse model of chronic TB, detection of iodine contrast enabled segmentation and volume quantification of healthy and disease-related lung tissue. The results demonstrated the potential clinical utility of photon-counting CT imaging for molecular imaging in infectious lung diseases. In the future, if a TB-specific drug were to be incorporated with a high-Z nanoparticle, spectral CT could provide non-invasive evaluation of drug delivery and response to treatment. Such an imaging platform would have the potential to assist diagnosis and accelerate the development of novel therapies, which is essential for the eradication of TB. Photon-counting CT technology offers improved spatial and energy resolution. Thus, it is a promising next step in the evolution of CT.The authors would like to acknowledge the Medipix2, Medipix3, and Medipix4 collaborations. They would also like to take this opportunity to acknowledge the generous support of the MARS Collaboration. They would also like to acknowledge Dr. Guembe from CIMA-Universidad de Navarra for preparing and staining the tissue sections

Innovations in ex vivo light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) has revolutionized how optical imaging of biological specimens can be performed as this technique allows to produce 3D fluorescence images of entire samples with a high spatiotemporal resolution. In this manuscript, we aim to provide readers with an overview of the field of LSFM on ex vivo samples. Recent advances in LSFM architectures have made the technique widely accessible and have improved its acquisition speed and resolution, among other features. These developments are strongly supported by quantitative analysis of the huge image volumes produced thanks to the boost in computational capacities, the advent of Deep Learning techniques, and by the combination of LSFM with other imaging modalities. Namely, LSFM allows for the characterization of biological structures, disease manifestations and drug effectivity studies. This information can ultimately serve to develop novel diagnostic procedures, treatments and even to model the organs physiology in healthy and pathological conditions.This work was produced with the support of the Spanish Ministry of Science, Innovation and Universities (TEC2016-78052-R, RTC-2017-6600-1, PID2019-109820RB-100, FPU19/02854)

The contribution of microfluidics to the fight against tuberculosis

The high mortality associated with tuberculosis brings forward the urgency of developing new therapies and strategies against the disease. With the advance of drug-resistant strains, traditional techniques have proven insufficient to manage the disease appropriately. Microfluidic devices have characteristics that can enhance treatment prescription and significantly advance our knowledge about the disease and its interaction within the human body. In addition, microfluidic systems provide advantages in terms of time and costs, which are particularly important in countries with low income and resources. This review will highlight how microdevices can help bridge the gaps in disease management, including their use for drug testing and development, drug susceptibility, basic research, and novel approaches to anti-TB vaccines and organ-on-chip studies.This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non-profit organisation, Bill & Melinda Gates Foundation and University of Dundee. This work was partially funded by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/, co-finance by European Regional Development Fund (ERDF), "A way of making Europe

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis

Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, "ABANICCO" (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC-NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO's accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.This research was funded by the Ministerio de Ciencia, Innovacción y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB, MCIN/AEI/10.13039/501100011033 co-financed by the European Regional Development Fund (ERDF) "A way of making Europe" to A.M.-B. and L.N.-S.Publicad

Search for temporal cell segmentation robustness in phase-contrast microscopy videos

Proceeding of: Medical Imaging with Deep Learning (MIDL 2022), Zürich, Switzerland, 6-8 July 2022This work presents a deep learning-based workflow to segment cancer cells embedded in D collagen matrices and imaged with phase-contrast microscopy under low magnification and strong background noise conditions. Due to the experimental and imaging setup, cell and protrusion appearance change largely from frame to frame. We use transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information and provide temporally stable results. Our results show that the proposed approach is robust to weight initialization and training data sampling.This work was co-financed by ERDF, "A way of making Europe" (AMB), partially funded under Grant PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/; the US NIH under Grants UO1AG060903 (DW) and U54CA143868 (DW). We acknowledge NVIDIA Corporation for the donation of the Titan X (Pascal) GPU

Deep-learning-based segmentation of small extracellular vesicles in transmission electron microscopy images

Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.We want to acknowledge the support of NVIDIA Corporation with the donation of the Titan X (Pascal) GPU used for this research. This work was supported by the Spanish Ministry of Economy and Competitiveness (TEC2013-48552-C2-1-R, TEC2015-73064-EXP, TEC2016-78052-R) (EGM-AMB), a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (EGM-AMB), and the Czech Science Foundation (GA17-05048S)(MM-PM) and (GJ17-11776Y) (AK-VP)

X-ray-based virtual slicing of TB-infected lungs

Hollow organs such as the lungs pose a considerable challenge for post-mortem imaging in preclinical research owing to their extremely low contrast and high structural complexity. The aim of our study was to enhance the contrast of tuberculosis lesions for their stratification by 3D x-ray&-based virtual slicing. Organ samples were taken from five control and five tuberculosis-infected mice. Micro-Computed Tomography (CT) scans of the subjects were acquired in vivo (without contrast agent) and post-mortem (with contrast agent). The proposed contrast-enhancing technique consists of x-ray contrast agent uptake (silver nitrate and iodine) by immersion. To create the histology ground-truth, the CT scan of the paraffin block guided the sectioning towards specific planes of interest. The digitalized histological slides reveal the presence, extent, and appearance of the contrast agents in lung structures and organized aggregates of immune cells. These findings correlate with the contrast-enhanced micro-CT slice. The abnormal densities in the lungs due to tuberculosis disease are concentrated in the right tail of the lung intensity histograms. The increase in the width of the right tail (~376%) indicates a contrast enhancement of the details of the abnormal densities. Postmortem contrast agents enhance the x-ray attenuation in tuberculosis lesions to allow 3D visualization by polychromatic x-ray CT, providing an advantageous tool for virtual slicing of whole lungs. The proposed contrast-enhancing technique combined with computational methods and the diverse micro-CT modalities will open the doors to the stratification of lesion types associated with infectious diseases.The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union Seventh Framework Programme (FP7/2007–2013) and EFPIA companies in kind contribution. This work was partially funded by projects RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK. This study (was supported by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013–2016) and co-financed by the European Social Fund (ESF) “ESF investing in your future”. The authors thank Dr.Guembe from CIMA-Universidad de Navarra for preparing and staining the tissue sections, and to Dr. Guerrero-Aspizua and Prof. Conti of the Department of Bioengineering, Universidad Carlos III de Madrid for the pathology evaluation

Unsupervised CT lung image segmentation of a mycobacterium tuberculosis infection model

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. This work was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministerio de Economía, Industria y Competitividad, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK