Advancing preclinical in vitro pulmonary models for ventilation and Inhalation assays

Please click Download on the upper right corner to see the full descriptio

Microfluidic acini-on-chip platforms as a tool to study bacterial lung exposure

Bacterial invasion of the respiratory system leads to complex immune responses involving many cell types. In the alveolar regions, the first line of defense includes the alveolar epithelium, secreted surfactant, alveolar lining fluid and alveolar macrophages. The epithelium consists of alveolar type I and type II cells. Both cell types are known to have immuno-modulatory functions characterized by the secretion of pro-inflammatory cytokines. Epithelial in vitro models offer attractive platforms to investigate biological functionality, but have typically relied on traditional well plate assays that come short of mimicking the complexity of the airway environment and do not capture physiological flows or relevant anatomical features. In the last decade, microfluidics have gained significant momentum in laying the foundations for constructing in vitro models that mimic physiologically-relevant organ functions. Here we propose to use acinus-on-chip platforms that mimic more closely native acinar microflows at true scale in a multi-generation alveolated tree. Acinar chips are cultured with human Alveolar Epithelial Lentivirus immortalized (hAELVi) cells at an air-liquid interface (ALI); such cells show alveolar type I like characteristics and maintained barrier function, leading to high trans-epithelial electrical resistance (TEER) in analogy to primary cells harvested from human tissue. To model bacterial infection, i.e. a strong stimulator of the innate arm of the immune system, lipopolysaccharides (LPS) will be used. LPS is a major outer surface membrane protein expressed on Gram-negative bacteria. The alveolar epithelium is exposed to LPS-laden aerosols and cell response is monitored mainly by secretion of pro-inflammatory cytokines. Our acinus-on-chip allows quantitative on-line measurements of alveolar barrier function, absorption kinetics and immunologically relevant responses, giving further insight to the role played by type I alveolar cells in lung immunity. Please click Additional Files below to see the full abstract

Visualization of low Reynolds boundary-driven cavity flows in thin liquid shells

ISSN:1343-8875ISSN:1875-897

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays

Crawling & Swimming of Nematodes in Complex Fluids

C. elegans is a small (~ 1mm long), free-living nematode that is extensively used as a model organism for biological research, including genomics, cell biology, and neuroscience. In this video, we show how C. elegans adapts its motility gaits to move on or through different fluidic environments. This video shows the nematode swimming & crawling behavior in a variety of environments including water, granular materials, and microfluidic systems using high-speed imaging microscopy. Fundamental understanding of the remarkable adaptability of such nematode to different environments can be potentially useful in fields such as medicine, robotics, and biophysics

Crawling & Swimming of Nematodes in Complex Fluids

C. elegans is a small (~ 1mm long), free-living nematode that is extensively used as a model organism for biological research, including genomics, cell biology, and neuroscience. In this video, we show how C. elegans adapts its gaits to move on or through different fluidic environments. This video shows the nematode swimming & crawling behavior in a variety of environments including water, granular materials, and microfluidic systems using high-speed imaging microscopy. Fundamental understanding of the remarkable adaptability of such nematode to different environments can be potentially useful in fields such as medicine, robotics, and biophysics

Caenorhabditis elegant segmentation using texture-based models for motility phenotyping

With widening interests in using model organisms for reverse genetic approaches and biomimmetic micro-robotics, motility phenotyping of the nematode Caenorhabditis elegans is expanding across a growing array of locomotive environments. One ongoing bottleneck lies in providing users with automatic ne- matode segmentations of C. elegans in image sequences featuring complex and dynamic visual cues, a first and necessary step prior to extracting motility phenotypes. Here, we propose to tackle such automatic segmentation challenges by introducing a novel Texture Feature Model (TFM). Our approach revolves around the use of combined intensity- and texture-based features integrated within a probabilistic framework. This strategy first provides a coarse nematode segmentation from which a Markov Random Field (MRF) model is used to refine the segmentation by inferring pixels belonging to the nematode using an approximate inference technique. Finally, informative priors can then be estimated and integrated in our framework to provide coherent segmentations across image sequences. We validate our TFM method across a wide range of motility environments. Not only does TFM assure comparative performances to existing segmentation methods on traditional environments featuring static backgrounds, it importantly provides state-of-the-art C. elegans segmentations for dynamic environments such as the recently introduced wet granular media. We show how such segmentations may be used to compute nematode “skeletons” from which motility phenotypes can then be extracted. Overall, our TFM method provides users with a tangible solution to tackle the growing needs of C. elegans segmentation in challenging motility environments

Innovative preclinical models for pulmonary drug delivery research

Introduction: Pulmonary drug delivery is a complex field of research combining physics which drive aerosol transport and deposition and biology which underpins efficacy and toxicity of inhaled drugs. A myriad of preclinical methods, ranging from in- silico to in-vitro, ex–vivo and in-vivo, can be implemented.Areas covered: The present review covers in-silico mathematical and computational fluid dynamics modelization of aerosol deposition, cascade impactor technology to estimated drug delivery and deposition, advanced in-vitro cell culture methods and associated aerosol exposure, lung-on-chip technology, ex–vivo modeling, in-vivo inhaled drug delivery, lung imaging, and longitudinal pharmacokinetic analysis.Expert opinion: No single preclinical model can be advocated; all methods are fundamentally complementary and should be implemented based on benefits and drawbacks to answer specific scientific questions. The overall best scientific strategy depends, among others, on the product under investigations, inhalation device design, disease of interest, clinical patient population, previous knowledge. Preclinical testing is not to be separated from clinical evaluation, as small proof-of-concept clinical studies or conversely large-scale clinical big data may inform preclinical testing. The extend of expertise required for such translational research is unlikely to be found in one single laboratory calling for the setup of multinational large-scale research consortiums