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ABSTRACT Standardized clinical procedures for antibiotic administration rely on pathogen identification and antibiotic susceptibility testing, often performed on single-colony bacterial isolates. For respiratory pathogens, this could be questionable, as chronic patients may be persistently colonized by multiple clones or lineages from the same bacterial pathogen species. Indeed, multiple strains of nontypeable Haemophilus influenzae, with different antibiotic susceptibility profiles, can be co-isolated from cystic fibrosis and chronic obstructive pulmonary disease sputum specimens. Despite this clinical evidence, we lack information about the dynamics of H. influenzae polyclonal infections, which limits the optimization of therapeutics. Here, we present the engineering and validation of a plasmid toolkit (pTBH, toolbox for Haemophilus), with standardized modules consisting of six reporter genes for fluorescent or bioluminescent labeling of H. influenzae. This plasmid set was independently introduced in a panel of genomically and phenotypically different H. influenzae strains, and two of them were used as a proof of principle to analyze mixed biofilm growth architecture and antibiotic efficacy, and to visualize the dynamics of alveolar epithelial co-infection. The mixed biofilms showed a bilayer architecture, and antibiotic efficacy correlated with the antibiotic susceptibility of the respective single-species strains. Furthermore, differential kinetics of bacterial intracellular location within subcellular acidic compartments were quantified upon co-infection of cultured airway epithelial cells. Overall, we present a panel of novel plasmid tools and quantitative image analysis methods with the potential to be used in a whole range of bacterial host species, assay types, and/or conditions and generate meaningful information for clinically relevant settings. IMPORTANCE Genomic diversity of nontypeable H. influenzae strains confers phenotypic heterogeneity. Multiple strains of H. influenzae can be simultaneously isolated from clinical specimens, but we lack detailed information about polyclonal infection dynamics by this pathogen. A long-term barrier to our understanding of this host-pathogen interplay is the lack of genetic tools for strain engineering and differential labeling. Here, we present a novel plasmid toolkit named pTBH (toolbox for Haemophilus), with standardized modules for fluorescent or bioluminescent labeling, adapted to H. influenzae requirements but designed to be versatile so it can be utilized in other bacterial species. We present detailed experimental and quantitative image analysis methods, together with proof-of-principle examples, and show the ample possibilities of 3D microscopy, combined with quantitative image analysis, to model H. influenzae polyclonal infection lifestyles and unravel the co-habitation and co-infection dynamics of this respiratory pathogen.

Abstract Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83–0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.

Abstract The analysis of circulating tumor cells (CTCs) in blood is a powerful noninvasive alternative to conventional tumor biopsy. Inertial‐based separation is a promising high‐throughput, marker‐free sorting strategy for the enrichment and isolation of CTCs. Here, we present and validate a double spiral microfluidic device that efficiently isolates CTCs with a fine‐tunable cut‐off value of 9 μm and a separation range of 2 μm. We designed the device based on computer simulations that introduce a novel, customized inertial force term, and provide practical fabrication guidelines. We validated the device using calibration beads, which allowed us to refine the simulations and redesign the device. Then we validated the redesigned device using blood samples and a murine model of metastatic breast cancer. Finally, as a proof of principle, we tested the device using peripheral blood from a patient with hepatocellular carcinoma, isolating more than 17 CTCs/ml, with purity/removal values of 96.03% and 99.99% of white blood cell and red blood cells, respectively. These results confirm highly efficient CTC isolation with a stringent cut‐off value and better separation results than the state of the art.

Mesenchymal stromal cells (MSCs) have been shown to support multiple myeloma (MM) development. Here, MSCs isolated from the bone marrow of MM patients are shown to have altered DNA methylation patterns and a methyltransferase inhibitor reverts MM-associated bone loss and reduces tumour burden in MM murine models.

Achilles tendon rupture is a frequent injury with an increasing incidence. After clinical surgical repair, aimed at suturing the tendon stumps back into their original position, the repaired Achilles tendon is often plastically deformed and mechanically less strong than the pre-injured tissue, with muscle fatty degeneration contributing to function loss. Despite clinical outcomes, pre-clinical research has mainly focused on tendon structural repair, with a lack of knowledge regarding injury progression from tendon to muscle and its consequences on muscle degenerative/regenerative processes and function. Here, we characterize the morphological changes in the tendon, the myotendinous junction and muscle belly in a mouse model of Achilles tendon complete rupture, finding cellular and fatty infiltration, fibrotic tissue accumulation, muscle stem cell decline and collagen fiber disorganization. We use novel imaging technologies to accurately relate structural alterations in tendon fibers to pathological changes, which further explain the loss of muscle mechanical function after tendon rupture. The treatment of tendon injuries remains a challenge for orthopedics. Thus, the main goal of this study is to bridge the gap between clinicians’ knowledge and research to address the underlying pathophysiology of ruptured Achilles tendon and its consequences in the gastrocnemius. Such studies are necessary if current practices in regenerative medicine for Achilles tendon ruptures are to be improved.

The accurate estimation of the number and size of cells provides relevant information on the kinetics of growth and the physiological status of a given tissue or organ. Here, we present Adiposoft, a fully automated open-source software for the analysis of white adipose tissue cellularity in histological sections. First, we describe the sequence of image analysis routines implemented by the program. Then, we evaluate our software by comparing it with other adipose tissue quantification methods, namely, with the manual analysis of cells in histological sections (used as gold standard) and with the automated analysis of cells in suspension, the most commonly used method. Our results show significant concordance between Adiposoft and the other two methods. We also demonstrate the ability of the proposed method to distinguish the cellular composition of three different rat fat depots. Moreover, we found high correlation and low disagreement between Adiposoft and the manual delineation of cells. We conclude that Adiposoft provides accurate results while considerably reducing the amount of time and effort required for the analysis.

Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.

Summary: How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

Costimulation has been shown to be required for optimal activation of T cells and it could be delivered either in trans with respect to the source of CD3-TCR ligation or in cis on the same cell. Here the authors show that CD137 costimulation is more effective when delivered in cis to enhance T cell proliferation and activation.

Chronic obstructive pulmonary disease (COPD) patients undergo infectious exacerbations whose frequency identifies a clinically meaningful phenotype. Mouse models have been mostly used to separately study both COPD and the infectious processes, but a reliable model of the COPD frequent exacerbator phenotype is still lacking. Accordingly, we first established a model of single bacterial exacerbation by nontypeable Haemophilus influenzae (NTHi) infection on mice with emphysema-like lesions. We characterized this single exacerbation model combining both noninvasive in vivo imaging and ex vivo techniques, obtaining longitudinal information about bacterial load and the extent of the developing lesions and host responses. Bacterial load disappeared 48 hours post-infection (hpi). However, lung recovery, measured using tests of pulmonary function and the disappearance of lung inflammation as revealed by micro-computed X-ray tomography, was delayed until 3 weeks post-infection (wpi). Then, to emulate the frequent exacerbator phenotype, we performed two recurrent episodes of NTHi infection on the emphysematous murine lung. Consistent with the amplified infectious insult, bacterial load reduction was now observed 96 hpi, and lung function recovery and disappearance of lesions on anatomical lung images did not happen until 12 wpi. Finally, as a proof of principle of the use of the model, we showed that azithromycin successfully cleared the recurrent infection, confirming this macrolide utility to ameliorate infectious exacerbation. In conclusion, we present a mouse model of recurrent bacterial infection of the emphysematous lung, aimed to facilitate investigating the COPD frequent exacerbator phenotype by providing complementary, dynamic information of both infectious and inflammatory processes.

The blood–brain barrier is a unique physiological structure acting as a filter for every molecule reaching the brain through the blood. For this reason, an effective pharmacologic treatment supplied to a patient by systemic circulation should first be capable of crossing the barrier. Standard cell cultures (or those based on microfluidic devices) and animal models have been used to study the human blood–brain barrier. Unfortunately, these tools have not yet reached a state of maturity because of the complexity of this physiological process aggravated by a high heterogeneity that is not easily recapitulated experimentally. In fact, the extensive research that has been performed and the preclinical trials carried out provided sometimes contradictory results, and the functionality of the barrier function is still not fully understood. In this study, we have combined tissue clarification, advanced microscopy and image analysis to develop a one-dimensional computational model of the microvasculature hemodynamics inside the mouse brain. This model can provide information about the flow regime, the pressure field and the wall shear stress among other fluid dynamics variables inside the barrier. Although it is a simplified model of the cerebral microvasculature, it allows a first insight on into the blood–brain barrier hemodynamics and offers several additional possibilities to systematically study the barrier microcirculatory processes.

Abstract Airway infection by nontypeable Haemophilus influenzae (NTHi) associates to chronic obstructive pulmonary disease (COPD) exacerbation and asthma neutrophilic airway inflammation. Lipids are key inflammatory mediators in these disease conditions and consequently, NTHi may encounter free fatty acids during airway persistence. However, molecular information on the interplay NTHi-free fatty acids is limited, and we lack evidence on the importance of such interaction to infection. Maintenance of the outer membrane lipid asymmetry may play an essential role in NTHi barrier function and interaction with hydrophobic molecules. VacJ/MlaA-MlaBCDEF prevents phospholipid accumulation at the bacterial surface, being the only system involved in maintaining membrane asymmetry identified in NTHi. We assessed the relationship among the NTHi VacJ/MlaA outer membrane lipoprotein, bacterial and exogenous fatty acids, and respiratory infection. The vacJ/mlaA gene inactivation increased NTHi fatty acid and phospholipid global content and fatty acyl specific species, which in turn increased bacterial susceptibility to hydrophobic antimicrobials, decreased NTHi epithelial infection, and increased clearance during pulmonary infection in mice with both normal lung function and emphysema, maybe related to their shared lung fatty acid profiles. Altogether, we provide evidence for VacJ/MlaA as a key bacterial factor modulating NTHi survival at the human airway upon exposure to hydrophobic molecules.

The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing. Here, we present a comprehensive, quantitative analysis of the role of cell-ECM interactions in cancer cell migration within a highly physiological environment consisting of mixed Matrigel-collagen hydrogel scaffolds of increasing complexity that mimic the tumor microenvironment at the leading edge of cancer invasion. We quantitatively show that the presence of Matrigel increases hydrogel stiffness, which promotes β1 integrin expression and metalloproteinase activity in H1299 lung cancer cells. Then, we show that ECM remodeling activity causes matrix alignment and compaction that favors higher tractions exerted by the cells. However, these traction forces do not linearly translate into increased motility due to a biphasic role of cell adhesions in cell migration: at low concentration Matrigel promotes migration-effective tractions exerted through a high number of small sized focal adhesions. However, at high Matrigel concentration, traction forces are exerted through fewer, but larger focal adhesions that favor attachment yielding lower cell motility.

Understanding how cancer cells migrate, and how this migration is affected by the mechanical and chemical composition of the extracellular matrix (ECM) is critical to investigate and possibly interfere with the metastatic process, which is responsible for most cancer-related deaths. In this article we review the state of the art about the use of hydrogel-based three-dimensional (3D) scaffolds as artificial platforms to model the mechanobiology of cancer cell migration. We start by briefly reviewing the concept and composition of the extracellular matrix (ECM) and the materials commonly used to recreate the cancerous ECM. Then we summarize the most relevant knowledge about the mechanobiology of cancer cell migration that has been obtained using 3D hydrogel scaffolds, and relate those discoveries to what has been observed in the clinical management of solid tumors. Finally, we review some recent methodological developments, specifically the use of novel bioprinting techniques and microfluidics to create realistic hydrogel-based models of the cancer ECM, and some of their applications in the context of the study of cancer cell migration.

Microfluidic devices are becoming mainstream tools to recapitulate in vitro the behavior of cells and tissues. In this study, we use microfluidic devices filled with hydrogels of mixed collagen-Matrigel composition to study the migration of lung cancer cells under different cancer invasion microenvironments. We present the design of the microfluidic device, characterize the hydrogels morphologically and mechanically and use quantitative image analysis to measure the migration of H1299 lung adenocarcinoma cancer cells in different experimental conditions. Our results show the plasticity of lung cancer cell migration, which turns from mesenchymal in collagen only matrices, to lobopodial in collagen-Matrigel matrices that approximate the interface between a disrupted basement membrane and the underlying connective tissue. Our quantification of migration speed confirms a biphasic role of Matrigel. At low concentration, Matrigel facilitates migration, most probably by providing a supportive and growth factor retaining environment. At high concentration, Matrigel slows down migration, possibly due excessive attachment. Finally, we show that antibody-based integrin blockade promotes a change in migration phenotype from mesenchymal or lobopodial to amoeboid and analyze the effect of this change in migration dynamics, in regards to the structure of the matrix. In summary, we describe and characterize a robust microfluidic platform and a set of software tools that can be used to study lung cancer cell migration under different microenvironments and experimental conditions. This platform could be used in future studies, thus benefitting from the advantages introduced by microfluidic devices: precise control of the environment, excellent optical properties, parallelization for high throughput studies and efficient use of therapeutic drugs.

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, genetic alterations. We analyzed markers of DNA damage response (DDR), proliferative stress, telomeric stress: δ-H2AX, p16, p53, TERT. Lung cancer-related epigenetic, genetic alterations, including promoter hypermethylation status of p16(CDKN2A), APC, CDH13, Rassf1, Nore1A, as well as mutations of Tp53, epidermal growth factor receptor, K-ras, N-ras, c-H-ras, have been also studied. Our results showed DDR pathway activation in preneoplastic lesions, in association with inducible nitric oxide synthase, p53 induction. p16 was also induced in early tumorigenic progression, was inactivated in bronchiolar dysplasias, tumors. Remarkably, lack of mutations of Ras, epidermal growth factor receptor, 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, APC, but not in Rassf1, 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.

Traction Force Microscopy (TFM) is a widespread method used to recover cellular tractions from the deformation that they cause in their surrounding substrate. Particle Image Velocimetry (PIV) is commonly used to quantify the substrate's deformations, due to its simplicity and efficiency. However, PIV relies on a block-matching scheme that easily underestimates the deformations. This is especially relevant in the case of large, locally non-uniform deformations as those usually found in the vicinity of a cell's adhesions to the substrate. To overcome these limitations, we formulate the calculation of the deformation of the substrate in TFM as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one. In particular, we propose to use a B-spline -based Free Form Deformation (FFD) algorithm that uses a connected deformable mesh to model a wide range of flexible deformations caused by cellular tractions. Our FFD approach is validated in 3D fields using synthetic (simulated) data as well as with experimental data obtained using isolated endothelial cells lying on a deformable, polyacrylamide substrate. Our results show that FFD outperforms PIV providing a deformation field that allows a better recovery of the magnitude and orientation of tractions. Together, these results demonstrate the added value of the FFD algorithm for improving the accuracy of traction recovery.

The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes. In this paper, we propose a novel algorithm for blind spectral separation that addresses some of the shortcomings of existing solutions: namely, their dependency on the initialization and their slow convergence. We apply this new algorithm to two relevant problems in fluorescence microscopy: autofluorescence elimination and spectral unmixing of multi-labeled samples. Our results show that our new algorithm performs well when compared with the state-of-the-art approaches for a much faster implementation.

RationaleLow-grade inflammation and emphysema have been shown to be associated with an increased risk of lung cancer. However, the systemic inflammatory response in patients with emphysema is still unknown.ObjectiveTO COMPARE THE PLASMA CYTOKINE PROFILES IN TWO GROUPS OF CURRENT OR FORMER SMOKERS WITHOUT AIRWAY OBSTRUCTION: a control group of individuals without computed tomography (CT) detected emphysema vs. a study group of individuals with CT detected emphysema.MethodsSubjects underwent a chest CT, spirometry, and determination of EGF, IL-15, IL-1ra, IL-8, MCP-1, MIP-1β, TGFα, TNFα, and VEGF levels in plasma. Cytokine levels in each group were compared adjusting for confounding factors.Results160 current smokers and former smokers without airway obstruction participated in the study: 80 without emphysema and 80 subjects with emphysema. Adjusted group comparisons revealed significant reductions in EGF (-0.317, p = 0.01), IL-15 (-0.21, p = 0.01), IL-8 (-0.180, p = 0.02) and IL-1ra (-0.220, p = 0.03) in subjects with emphysema and normal spirometry.ConclusionsCurrent or former smokers expressing a well-defined disease characteristic such as emphysema, has a specific plasma cytokine profile. This includes a decrease of cytokines mainly implicated in activation of apoptosis or decrease of immunosurveillance. This information should be taken into account when evaluated patients with tobacco respiratory diseases.

BackgroundThe aim of this article is to present an optimized acquisition and analysis protocol for the echocardiographic evaluation of left ventricle (LV) remodeling in a mouse model of myocardial infarction (MI).Methodology13 female DBA/2J mice underwent permanent occlusion of the left anterior descending (LAD) coronary artery leading to MI. Mice echocardiography was performed using a Vevo 770 (Visualsonics, Canada) before infarction, and 7, 14, 30, 60, 90 and 120 days after LAD ligation. LV systolic function was evaluated using different parameters, including the fractional area change (FAC%) computed in four high-temporal resolution B-mode short axis images taken at different ventricular levels, and in one parasternal long axis. Pulsed wave and tissue Doppler modes were used to evaluate the diastolic function and Tei Index for global cardiac function. The echocardiographic measurements of infarct size were validated histologically using collagen deposition labeled by Sirius red staining. All data was analyzed using Shapiro-Wilk and Student's t-tests.Principal findingsOur results reveal LV dilation resulting in marked remodeling an severe systolic dysfunction, starting seven days after MI (LV internal apical diameter, basal = 2.82±0.24, 7d = 3.49±0.42; pConclusions/significanceIn summary, we have performed a complete characterization of LV post-infarction remodeling in a DBA/2J mouse model of MI, using parameters adapted to the particular characteristics of the model In the future, this well characterized model will be used in both investigative and pharmacological studies that require accurate quantitative monitoring of cardiac recovery after myocardial infarction.

Objective. To define the sensitivity of microcomputed tomography- (micro-CT-) derived descriptors for the quantification of lung damage caused by elastase instillation. Materials and Methods. The lungs of 30 elastase treated and 30 control A/J mice were analyzed 1, 6, 12, and 24 hours and 7 and 17 days after elastase instillation using (i) breath-hold-gated micro-CT, (ii) pulmonary function tests (PFTs), (iii) RT-PCR for RNA cytokine expression, and (iv) histomorphometry. For the latter, an automatic, parallel software toolset was implemented that computes the airspace enlargement descriptors: mean linear intercept (Lm) and weighted means of airspace diameters (D0, D1, and D2). A Support Vector Classifier was trained and tested based on three nonhistological descriptors using D2 as ground truth. Results. D2 detected statistically significant differences (P

Biofilm formation by the pathobiont Haemophilus influenzae is associated with human nasopharynx colonization, otitis media in children, and chronic respiratory infections in adults suffering from chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD). β-lactam and quinolone antibiotics are commonly used to treat these infections. However, considering the resistance of biofilm-resident bacteria to antibiotic-mediated killing, the use of antibiotics may be insufficient and require being replaced or complemented with novel strategies. Moreover, unlike the standard minimal inhibitory concentration assay used to assess antibacterial activity against planktonic cells, standardization of methods to evaluate anti-biofilm drug activity is limited. In this work, we detail a panel of protocols for systematic analysis of drug antimicrobial effect on bacterial biofilms, customized to evaluate drug effects against H. influenzae biofilms. Testing of two cinnamaldehyde analogs, (E)-trans-2-nonenal and (E)-3-decen-2-one, demonstrated their effectiveness in both H. influenzae inhibition of biofilm formation and eradication or preformed biofilms. Assay complementarity allowed quantifying the dynamics and extent of the inhibitory effects, also observed for ampicillin resistant clinical strains forming biofilms refractory to this antibiotic. Moreover, cinnamaldehyde analog encapsulation into poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles allowed drug vehiculization while maintaining efficacy. Overall, we demonstrate the usefulness of cinnamaldehyde analogs against H. influenzae biofilms, present a test panel that can be easily adapted to a wide range of pathogens and drugs, and highlight the benefits of drug nanoencapsulation towards safe controlled release.