New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications

A fluorophore-tagged RGD peptide to control endothelial cell adhesion to micropatterned surfaces

The long-term patency rates of vascular grafts and stents are limited by the lack of surface endothelialisation of the implanted materials. We have previously reported that GRGDS and WQPPRARI peptide micropatterns increase the endothelialisation of prosthetic materials in vitro. To investigate the mechanisms by which the peptide micropatterns affect endothelial cell adhesion and proliferation, a TAMRA fluorophore-tagged RGD peptide was designed. Live cell imaging revealed that the micropatterned surfaces led to directional cell spreading dependent on the location of the RGD-TAMRA spots. Focal adhesions formed within 3 h on the micropatterned surfaces near RGD-TAMRA spot edges, as expected for cell regions experiencing high tension. Similar levels of focal adhesion kinase phosphorylation were observed after 3 h on the micropatterned surfaces and on surfaces treated with RGD-TAMRA alone, suggesting that partial RGD surface coverage is sufficient to elicit integrin signaling. Lastly, endothelial cell expansion was achieved in serum-free conditions on gelatin-coated, RGD-TAMRA treated or micropatterned surfaces. These results show that these peptide micropatterns mainly impacted cell adhesion kinetics rather than cell proliferation. This insight will be useful for the optimization of micropatterning strategies to improve vascular biomaterials

Dynamics of endothelial cell responses to laminar shear stress on surfaces functionalized with fibronectin-derived peptides

Surface endothelialization could improve the long-term performance of vascular grafts and stents. We previously demonstrated that aerosol-generated fibronectin-derived peptide micropatterns consisting of GRGDS spots over a WQPPRARI background increase endothelial cell yields in static cultures. We developed a novel fluorophore-tagged RGD peptide (RGD-TAMRA) to visualize cell–surface interactions in real-time. Here, we studied the dynamics of endothelial cell response to laminar flow on these peptide-functionalized surfaces. Endothelial cells were exposed to 22 dyn/cm² wall shear stress while acquiring time-lapse images. Cell surface coverage and cell alignment were quantified by undecimated wavelet transform multivariate image analysis. Similar to gelatin-coated surfaces, surfaces with uniform RGD-TAMRA distribution led to cell retention and rapid cell alignment (∼63% of the final cell alignment was reached within 1.5 h), contrary to the micropatterned surfaces. The RGD-TAMRA peptide is a promising candidate for endothelial cell retention under flow, and the spray-based micropatterned surfaces are more promising for static cultures

The ATLAS 3D project - XI : Dense molecular gas properties of CO-luminous early-type galaxies

Surveying 18 12CO-bright galaxies from the ATLAS 3D early-type galaxy sample with the Institut de Radio Astronomie Millimétrique (IRAM) 30-m telescope, we detect 13CO(1-0) and 13CO(2-1) in all 18 galaxies, HCN(1-0) in 12/18 and HCO +(1-0) in 10/18. We find that the line ratios 12CO(1-0)/ 13CO(1-0) and 12CO(1-0)/HCN(1-0) are clearly correlated with several galaxy properties: total stellar mass, luminosity-weighted mean stellar age, molecular-to-atomic gas ratio, dust temperature and dust morphology. We suggest that these correlations are primarily governed by the optical depth in the 12CO lines; interacting, accreting and/or starbursting early-type galaxies have more optically thin molecular gas while those with settled dust and gas discs host optically thick molecular gas. The ranges of the integrated line intensity ratios generally overlap with those of spirals, although we note some outliers in the 12CO(1-0)/ 13CO(1-0), 12CO(2-1)/ 13CO(2-1) and HCN/HCO +(1-0) ratios. In particular, three galaxies are found to have very low 12CO(1-0)/ 13CO(1-0) and 12CO(2-1)/ 13CO(2-1) ratios. Such low ratios may signal particularly stable molecular gas which creates stars less efficiently than 'normal' (i.e. below Schmidt-Kennicutt prediction), consistent with the low dust temperatures seen in these galaxies. © 2012 The Authors Monthly Notices of the Royal Astronomical Society © 2012 RAS.Peer reviewedFinal Accepted Versio

The DESI experiment part I: science, targeting, and survey design

DESI (Dark Energy Spectroscopic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations (BAO) and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. To trace the underlying dark matter distribution, spectroscopic targets will be selected in four classes from imaging data. We will measure luminous red galaxies up to $z=1.0$. To probe the Universe out to even higher redshift, DESI will target bright [O II] emission line galaxies up to $z=1.7$. Quasars will be targeted both as direct tracers of the underlying dark matter distribution and, at higher redshifts ($2.1 < z < 3.5$), for the Ly-$\alpha$ forest absorption features in their spectra, which will be used to trace the distribution of neutral hydrogen. When moonlight prevents efficient observations of the faint targets of the baseline survey, DESI will conduct a magnitude-limited Bright Galaxy Survey comprising approximately 10 million galaxies with a median $z\approx 0.2$. In total, more than 30 million galaxy and quasar redshifts will be obtained to measure the BAO feature and determine the matter power spectrum, including redshift space distortions