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The diagnosis of intrathoracic non-tuberculous mycobacteriosis (NTM) is challenging. We report a case of a pediatric pulmonary NTM with endobronchial lesion and lymphadenitis in a child with HIV infection diagnosed by bronchoscopic biopsy, EBUS-TBNA and probe-based confocal laser endomicroscopy (pCLE). The pCLE showed a large number of highly fluorescent cells and zones of density and disorganized elastin fibers at alveolar areas. A combination of diagnostic endoscopic procedures is required to establish the diagnosis of NTM.

Abstract Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate whether CyTOF identifies cellular and molecular predictors of tumor behavior. We developed and validated a CyTOF panel of 34 antibodies in four ADC cell lines and PBMC. We tested our panel in a set of 10 ADCs, classified into long- (LPS) (n = 4) and short-predicted survival (SPS) (n = 6) based on radiomics features. We identified cellular subpopulations of epithelial cancer cells (ECC) and their microenvironment and validated our results by multiplex immunofluorescence (mIF) applied to a tissue microarray (TMA) of LPS and SPS ADCs. The antibody panel captured the phenotypical differences in ADC cell lines and PBMC. LPS ADCs had a higher proportion of immune cells. ECC clusters (ECCc) were identified and uncovered two ADC groups. ECCc with high HLA-DR expression were correlated with CD4+ and CD8+ T cells, with LPS samples being enriched for those clusters. We confirmed a positive correlation between HLA-DR expression on ECC and T cell number by mIF staining on TMA slides. Spatial analysis demonstrated shorter distances from T cells to the nearest ECC in LPS. Our results demonstrate a distinctive cellular profile of ECC and their microenvironment in ADC. We showed that HLA-DR expression in ECC is correlated with T cell infiltration, and that a set of ADCs with high abundance of HLA-DR+ ECCc and T cells is enriched in LPS samples. This suggests new insights into the role of antigen presenting tumor cells in tumorigenesis.

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.

Cancer associated fibroblasts (CAF) play a key role in cancer progression and metastasis. Diminished TGFβ response on CAF correlates with poor outcome and recurrence in cancer patients. Mechanisms behind lost TGFβ signaling on CAF are poorly understood, but, utilizing MMTV-PyMT mouse model, we have previously demonstrated that in tumor microenvironment myeloid cells, producing adenosine, contribute to downregulated TGFβ signaling on CAFs. In the current work, we performed serial in vitro studies to investigate the role of adenosine/TGFβ axis in mouse mammary fibroblast functions, i.e., proliferation, protein expression, migration, and contractility. We found that adenosine analog NECA diminished TGFβ-induced CCL5 and MMP9 expression. Additionally, we discovered that NECA completely inhibited effect of TGFβ to upregulate αSMA, key protein of cytoskeletal rearrangements, necessary for migration and contractility of fibroblasts. Our results show that TGFβ increases contractility of mouse mammary fibroblasts and human fibroblast cell lines, and NECA attenuates theses effects. Using pharmacological approach and genetically modified animals, we determined that NECA effects on TGFβ pathway occur via A2A/A2B adenosine receptor-AC-PKA dependent manner. Using isolated CD11b+ cells from tumor tissue of CD73-KO and CD39-KO animals in co-culture experiments with ATP and AMP, we confirmed that myeloid cells can affect functions of mammary fibroblasts through adenosine signaling. Our data suggest a novel mechanism of interaction between adenosine and TGFβ signaling pathways that can impact phenotype of fibroblasts in a tumor microenvironment.

Open-source software tools are often used for the analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a worldwide community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for the development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension, which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here, we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

[This corrects the article DOI: 10.1371/journal.pone.0056062.].

Pax3cre-mediated deletion of fibroblast growth factor receptor 2 (Fgfr2) broadly in renal and urinary tract mesenchyme led to ureteric bud (UB) induction defects and vesicoureteral reflux (VUR), although the mechanisms were unclear. Here, we investigated whether Fgfr2 acts specifically in peri-Wolffian duct stroma (ST) to regulate UB induction and development of VUR and the mechanisms of Fgfr2 activity.We conditionally deleted Fgfr2 in ST (Fgfr2(ST-/-)) using Tbx18cre mice. To look for ureteric bud induction defects in young embryos, we assessed length and apoptosis of common nephric ducts (CNDs). We performed 3D reconstructions and histological analyses of urinary tracts of embryos and postnatal mice and cystograms in postnatal mice to test for VUR. We performed in situ hybridization and real-time PCR in young embryos to determine mechanisms underlying UB induction defects.We confirmed that Fgfr2 is expressed in ST and that Fgfr2 was efficiently deleted in this tissue in Fgfr2(ST-/-) mice at embryonic day (E) 10.5. E11.5 Fgfr2(ST-/-) mice had randomized UB induction sites with approximately 1/3 arising too high and 1/3 too low from the Wolffian duct; however, apoptosis was unaltered in E12.5 mutant CNDs. While ureters were histologically normal, E15.5 Fgfr2(ST-/-) mice exhibit improper ureteral insertion sites into the bladder, consistent with the ureteric induction defects. While ureter and bladder histology appeared normal, postnatal day (P) 1 mutants had high rates of VUR versus controls (75% versus 3%, p = 0.001) and occasionally other defects including renal hypoplasia and duplex systems. P1 mutant mice also had improper ureteral bladder insertion sites and shortened intravesicular tunnel lengths that correlated with VUR. E10.5 Fgfr2(ST-/-) mice had decreases in Bmp4 mRNA in stromal tissues, suggesting a mechanism underlying the ureteric induction and VUR phenotypes.Mutations in FGFR2 could possibly cause VUR in humans.

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques., (© The Author(s) 2018. Published by Oxford University Press on behalf of the National Academy of Sciences. All rights reserved. For permissions, please email: journals.permissions@oup.com.)