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±10.3years old, 34 female/37 male); 30 HC (62.7±7.8years old, 17 female/13 male).FIELD STRENGTH/SEQUENCE: 3T 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 (rhoNM-CR =-0.31; rhoiron-CR =0.43; rhoiron-nVol =0.46) and the motor status (rhoNM-nVol =-0.27; rhoiron-CR =0.33; rhoiron-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.

Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.

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.

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.

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 (kappa = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.

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.

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.

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. Keywords: Cellular neighborhoods; Deep learning; Imaging mass cytometry; Interpretable machine learning; Multiplex imaging; Self supervised learning; Spatial biology; Tumor microenvironment; Weakly supervised learning.

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.

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. (c) 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.