Spatially resolved tissue imaging to analyze the tumor immune microenvironment: beyond cell-type densities.

INTRODUCTION: The tissue immune microenvironment is associated with key aspects of tumor biology. The interaction between the immune system and cancer cells has predictive and prognostic potential across different tumor types. Spatially resolved tissue-based technologies allowed researchers to simultaneously quantify different immune populations in tumor samples. However, bare quantification fails to harness the spatial nature of tissue-based technologies. Tumor-immune interactions are associated with specific spatial patterns that can be measured. In recent years, several computational tools have been developed to increase our understanding of these spatial patterns. TOPICS COVERED: In this review, we cover standard techniques as well as new advances in the field of spatial analysis of the immune microenvironment. We focused on marker quantification, spatial intratumor heterogeneity analysis, cell‒cell spatial interaction studies and neighborhood analyses.

Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease.

BACKGROUND: There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. PURPOSE: Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. STUDY TYPE: Prospective. SUBJECTS: Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). FIELD STRENGTH/SEQUENCE: 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). ASSESSMENT: N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. STATISTICAL TESTS: Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. RESULTS: N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5 ; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10-5 ; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5 ; Iron-nVolPD = 2.82 × 10-5 ± 2.04 × 10-5 ). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status (ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. DATA CONCLUSION: This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling.

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.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids.

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.

Prussian Blue nanoparticles: An FDA-approved substance that may quickly degrade at physiological pH.

Prussian blue (PB) is a coordination polymer based on the Fe2+…CN…Fe3+ sequence. It is an FDA-approved drug, intended for oral use at the acidic pH of the stomach and of most of the intestine track. However, based on FDA approval, a huge number of papers proposed the use of PB nanoparticles (PBnp) under "physiological conditions", meaning pH buffered at 7.4 and high saline concentration. While most of these papers report that PBnp are stable at this pH, a small number of papers describes instead PBnp degradation at the same or similar pH values, i.e. in the 7-8 range. Here we give a definitively clear picture: PBnp are intrinsically unstable at pH ≥ 7, degrading with the fast disappearance of their 700 nm absorption band, due to the formation of OH- complexes from the labile Fe3+ centers. However, we show also that the presence of a polymeric coating (PVP) can protect PBnp at pH 7.4 for over 24 h. Moreover, we demonstrate that when "physiological conditions" include serum, a protein corona is rapidly formed on PBnp, efficiently avoiding degradation. We also show that the viability of PBnp-treated EA.hy926, NCI-H1299, and A549 cells is not affected in a wide range of conditions that either prevent or promote PBnp degradation.

Synplex: In Silico Modeling of the Tumor Microenvironment From Multiplex Images.

Multiplex immunofluorescence is a novel, high-content imaging technique that allows simultaneous in situ labeling of multiple tissue antigens. This technique is of growing relevance in the study of the tumor microenvironment, and the discovery of biomarkers of disease progression or response to immune-based therapies. Given the number of markers and the potential complexity of the spatial interactions involved, the analysis of these images requires the use of machine learning tools that rely for their training on the availability of large image datasets, extremely laborious to annotate. We present Synplex, a computer simulator of multiplexed immunofluorescence images from user-defined parameters: i. cell phenotypes, defined by the level of expression of markers and morphological parameters; ii. cellular neighborhoods based on the spatial association of cell phenotypes; and iii. interactions between cellular neighborhoods. We validate Synplex by generating synthetic tissues that accurately simulate real cancer cohorts with underlying differences in the composition of their tumor microenvironment and show proof-of-principle examples of how Synplex could be used for data augmentation when training machine learning models, and for the in silico selection of clinically relevant biomarkers. Synplex is publicly available at https://github.com/djimenezsanchez/Synplex.

Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.

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.

Design and validation of a tunable inertial microfluidic system for the efficient enrichment of circulating tumor cells in blood.

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.

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images.

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

Tumor ENPP1 (CD203a)/Haptoglobin Axis Exploits Myeloid-Derived Suppressor Cells to Promote Post-Radiotherapy Local Recurrence in Breast Cancer.

ABSTRACT: Locoregional failure (LRF) in patients with breast cancer post-surgery and post-irradiation is linked to a dismal prognosis. In a refined new model, we identified ectonucleotide pyrophosphatase/phosphodiesterase 1/CD203a (ENPP1) to be closely associated with LRF. ENPP1hi circulating tumor cells (CTC) contribute to relapse by a self-seeding mechanism. This process requires the infiltration of polymorphonuclear myeloid-derived suppressor cells and neutrophil extracellular trap (NET) formation. Genetic and pharmacologic ENPP1 inhibition or NET blockade extends relapse-free survival. Furthermore, in combination with fractionated irradiation, ENPP1 abrogation obliterates LRF. Mechanistically, ENPP1-generated adenosinergic metabolites enhance haptoglobin (HP) expression. This inflammatory mediator elicits myeloid invasiveness and promotes NET formation. Accordingly, a significant increase in ENPP1 and NET formation is detected in relapsed human breast cancer tumors. Moreover, high ENPP1 or HP levels are associated with poor prognosis. These findings unveil the ENPP1/HP axis as an unanticipated mechanism exploited by tumor cells linking inflammation to immune remodeling favoring local relapse. SIGNIFICANCE: CTC exploit the ENPP1/HP axis to promote local recurrence post-surgery and post-irradiation by subduing myeloid suppressor cells in breast tumors. Blocking this axis impairs tumor engraftment, impedes immunosuppression, and obliterates NET formation, unveiling new opportunities for therapeutic intervention to eradicate local relapse and ameliorate patient survival. This article is highlighted in the In This Issue feature, p. 1171.

Complement C5a induces the formation of neutrophil extracellular traps by myeloid-derived suppressor cells to promote metastasis.

Myeloid-derived suppressor cells (MDSCs) play a major role in cancer progression. In this study, we investigated the mechanisms by which complement C5a increases the capacity of polymorphonuclear MDSCs (PMN-MDSCs) to promote tumor growth and metastatic spread. Stimulation of PMN-MDSCs with C5a favored the invasion of cancer cells via a process dependent on the formation of neutrophil extracellular traps (NETs). NETosis was dependent on the production of high mobility group box 1 (HMGB1) by cancer cells. Moreover, C5a induced the surface expression of the HMGB1 receptors TLR4 and RAGE in PMN-MDSCs. In a mouse lung metastasis model, inhibition of C5a, C5a receptor-1 (C5aR1) or NETosis reduced the number of circulating-tumor cells (CTCs) and the metastatic burden. In support of the translational relevance of these findings, C5a was able to stimulate migration and NETosis in PMN-MDSCs obtained from lung cancer patients. Furthermore, myeloperoxidase (MPO)-DNA complexes, as markers of NETosis, were elevated in lung cancer patients and significantly correlated with C5a levels. In conclusion, C5a induces the formation of NETs from PMN-MDSCs in the presence of cancer cells, which may facilitate cancer cell dissemination and metastasis.

CD137 (4-1BB) costimulation of CD8+ T cells is more potent when provided in cis than in trans with respect to CD3-TCR stimulation.

CD137 (4-1BB; TNFSR9) is an activation-induced surface receptor that through costimulation effects provide antigen-primed T cells with augmented survival, proliferation and effector functions as well as metabolic advantages. These immunobiological mechanisms are being utilised for cancer immunotherapy with agonist CD137-binding and crosslinking-inducing agents that elicit CD137 intracellular signaling. In this study, side-by-side comparisons show that provision of CD137 costimulation in-cis with regard to the TCR-CD3-ligating cell is superior to that provided in-trans in terms of T cell activation, proliferation, survival, cytokine secretion and mitochondrial fitness in mouse and human. Cis ligation of CD137 relative to the TCR-CD3 complex results in more intense canonical and non-canonical NF-κB signaling and provides a more robust induction of cell cycle and DNA damage repair gene expression programs. Here we report that the superiority of cis versus trans CD137-costimulation is readily observed in vivo and is relevant for understanding the immunotherapeutic effects of CAR T cells and CD137 agonistic therapies currently undergoing clinical trials, which may provide costimulation either in cis or in trans.

CRMP2 as a Candidate Target to Interfere with Lung Cancer Cell Migration.

Collapsin response mediator protein 2 (CRMP2) is an adaptor protein that adds tubulin dimers to the growing tip of a microtubule. First described in neurons, it is now considered a ubiquitous protein that intervenes in processes such as cytoskeletal remodeling, synaptic connection and trafficking of voltage channels. Mounting evidence supports that CRMP2 plays an essential role in neuropathology and, more recently, in cancer. We have previously described a positive correlation between nuclear phosphorylation of CRMP2 and poor prognosis in lung adenocarcinoma patients. In this work, we studied whether this cytoskeleton molding protein is involved in cancer cell migration. To this aim, we evaluated CRMP2 phosphorylation and localization in the extending lamella of lung adenocarcinoma migrating cells using in vitro assays and in vivo confocal microscopy. We demonstrated that constitutive phosphorylation of CRMP2 impaired lamella formation, cell adhesion and oriented migration. In search of a mechanistic explanation of this phenomenon, we discovered that CRMP2 Ser522 phospho-mimetic mutants display unstable tubulin polymers, unable to bind EB1 plus-Tip protein and the cortical actin adaptor IQGAP1. In addition, integrin recycling is defective and invasive structures are less evident in these mutants. Significantly, mouse xenograft tumors of NSCLC expressing CRMP2 phosphorylation mimetic mutants grew significantly less than wild-type tumors. Given the recent development of small molecule inhibitors of CRMP2 phosphorylation to treat neurodegenerative diseases, our results open the door for their use in cancer treatment.

Heterogenous presence of neutrophil extracellular traps in human solid tumours is partially dependent on IL-8.

Neutrophil extracellular traps (NETs) are webs of extracellular nuclear DNA extruded by dying neutrophils infiltrating tissue. NETs constitute a defence mechanism to entrap and kill fungi and bacteria. Tumours induce the formation of NETs to the advantage of the malignancy via a variety of mechanisms shown in mouse models. Here, we investigated the presence of NETs in a variety of human solid tumours and their association with IL-8 (CXCL8) protein expression and CD8+ T-cell density in the tumour microenvironment. Multiplex immunofluorescence panels were developed to identify NETs in human cancer tissues by co-staining with the granulocyte marker CD15, the neutrophil marker myeloperoxidase and citrullinated histone H3 (H3Cit), as well as IL-8 protein and CD8+ T cells. Three ELISA methods to detect and quantify circulating NETs in serum were optimised and utilised. Whole tumour sections and tissue microarrays from patients with non-small cell lung cancer (NSCLC; n = 14), bladder cancer (n = 14), melanoma (n = 11), breast cancer (n = 31), colorectal cancer (n = 20) and mesothelioma (n = 61) were studied. Also, serum samples collected retrospectively from patients with metastatic melanoma (n = 12) and NSCLC (n = 34) were ELISA assayed to quantify circulating NETs and IL-8. NETs were detected in six different human cancer types with wide individual variation in terms of tissue density and distribution. At least in NSCLC, bladder cancer and metastatic melanoma, NET density positively correlated with IL-8 protein expression and inversely correlated with CD8+ T-cell densities. In a series of serum samples from melanoma and NSCLC patients, a positive correlation between circulating NETs and IL-8 was found. In conclusion, NETs are detectable in formalin-fixed human biopsy samples from solid tumours and in the circulation of cancer patients with a considerable degree of individual variation. NETs show a positive association with IL-8 and a trend towards a negative association with CD8+ tumour-infiltrating lymphocytes. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Modeling the Mechanobiology of Cancer Cell Migration Using 3D Biomimetic Hydrogels.

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.

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease.

Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD.

The use of mixed collagen-Matrigel matrices of increasing complexity recapitulates the biphasic role of cell adhesion in cancer cell migration: ECM sensing, remodeling and forces at the leading edge of cancer invasion.

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 ß1 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.

Preclinical Evaluation of the Antimicrobial-Immunomodulatory Dual Action of Xenohormetic Molecules against Haemophilus influenzae Respiratory Infection.

Chronic obstructive pulmonary disease (COPD) is characterized by abnormal inflammation and impaired airway immunity, providing an opportunistic platform for nontypeable Haemophilus influenzae (NTHi) infection. In this context, therapies targeting not only overactive inflammation without significant adverse effects, but also infection are of interest. Increasing evidence suggests that polyphenols, plant secondary metabolites with anti-inflammatory and antimicrobial properties, may be protective. Here, a Cistus salviifolius plant extract containing quercetin, myricetin, and punicalagin was shown to reduce NTHi viability. Analysis of these polyphenols revealed that quercetin has a bactericidal effect on NTHi, does not display synergies, and that bacteria do not seem to develop resistance. Moreover, quercetin lowered NTHi airway epithelial invasion through a mechanism likely involving inhibition of Akt phosphorylation, and reduced the expression of bacterially-induced proinflammatory markers il-8, cxcl-1, il-6, pde4b, and tnfα. We further tested quercetin's effect on NTHi murine pulmonary infection, showing a moderate reduction in bacterial counts and significantly reduced expression of proinflammatory genes, compared to untreated mice. Quercetin administration during NTHi infection on a zebrafish septicemia infection model system showed a bacterial clearing effect without signs of host toxicity. In conclusion, this study highlights the therapeutic potential of the xenohormetic molecule quercetin against NTHi infection.

3-D Quantification of Filopodia in Motile Cancer Cells.

We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.

Modulation of Haemophilus influenzae interaction with hydrophobic molecules by the VacJ/MlaA lipoprotein impacts strongly on its interplay with the airways.

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.