Fully-automated nuclear image segmentation is the prerequisite to ensure statistically significant, quantitative analyses of tissue preparations,applied in digital pathology or quantitative microscopy. The design of segmentation methods that work independently of the tissue type or preparation is complex, due to variations in nuclear morphology, staining intensity, cell density and nuclei aggregations. Machine learning-based segmentation methods can overcome these challenges, however high quality expert-annotated images are required for training. Currently, the limited number of annotated fluorescence image datasets publicly available do not cover a broad range of tissues and preparations. We present a comprehensive, annotated dataset including tightly aggregated nuclei of multiple tissues for the training of machine learning-based nuclear segmentation algorithms. The proposed dataset covers sample preparation methods frequently used in quantitative immunofluorescence microscopy. We demonstrate the heterogeneity of the dataset with respect to multiple parameters such as magnification, modality, signal-to-noise ratio and diagnosis. Based on a suggested split into training and test sets and additional single-nuclei expert annotations, machine learning-based image segmentation methods can be trained and evaluated.

Recently, it was shown that MYCN amplified cells spontaneously expulse extrachromosomally amplified gene copies by micronuclei formation. Furthermore, it was shown that these cells lose their malignant phenotype and start to age. We tested whether it is possible to encourage neuroblastoma tumor cells to enter the senescence pathway by low concentrations of the micronuclei-inducing drug hydroxyurea (HU). We studied the effect of HU on 12 neuroblastoma cell lines with extra- or intrachromosomally amplified MYCN copies and without amplification. Two extrachromosomally amplified neuroblastoma cell lines (with double minutes) were investigated in detail. Already after 3 weeks of HU treatment, the BrdU uptake dropped to 25% of the starting cells. After 4 weeks, enlarged and flattened cells (F-cells) and increased granularity in the majority of cells were observed. A drastic reduction of the MYCN copy number-down to one copy per cell-associated with CD44 and MHCI upregulation in up to 100% of the HU treated neuroblastoma cells was found after 5-8 weeks. Telomere length was reduced to half the length within 8 weeks of HU treatment, and telomerase activity was not detectable at this time, while being strongly expressed at the beginning. All these features and the expression of senescence-associated-beta-galactosidase (SA-beta-GAL) in up to 100% of the cells support the hypothesis that these cells entered the senescence pathway. Thus, low-dose HU is a potent senescence elicitor for tumor cells with gene amplification, possibly representing an attractive additional strategy for treatment of this subset of tumors.

MYCN amplification is associated with poor prognosis in neuroblastoma disease. To improve our understanding of the influence of the MYCN amplicon and its corresponding expression, we investigated the 2p expression pattern of MYCN amplified (n ‫؍‬ 13) and nonamplified (n ‫؍‬ 4) cell lines and corresponding primary tumors (n ‫؍‬ 3) using the comparative expressed sequence hybridization technique. All but one MYCN amplified cell line displayed overexpression at 2p. Expression peaks were observed frequently at 2pter and less frequently at 2p24 (MYCN locus), 2p23.3-23.2, and/or 2p23.1. Importantly, cell lines and two corresponding primary tumors displayed expression peaks at similar loci. No significant 2p24 expression level was observed for those cell lines displaying a low amplification rate (n ‫؍‬ 3) by comparative genomic hybridization. Only the cell lines with an enhanced peak at 2p23.2-23.3 displayed coamplification of the ALK gene (2p23.2), reported to be associated with unfavorable prognosis. Finally, two of four cell lines without MYCN amplification, both derived from patients with poor outcome, also showed an expression peak at 2p23.2. These data indicate that, besides MYCN, other genes proximal and distal to MYCN are highly expressed in neuroblastoma. The prognostic significance of expression peaks at 2p23.2-23.3, independent of MYCN and ALK status, remains to be investigated.

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Separating and labeling each nuclear instance (instance-aware segmentation) is the key challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been demonstrated to solve nuclear image segmentation tasks across different imaging modalities, but a systematic comparison on complex immunofluorescence images has not been performed. Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity. In this work, we evaluate and compare the segmentation effectiveness of multiple deep learning architectures (U-Net, U-Net ResNet, Cellpose, Mask R-CNN, KG instance segmentation) and two conventional algorithms (Iterative h-min based watershed, Attributed relational graphs) on complex fluorescence nuclear images of various types.We propose and evaluate a novel strategy to create artificial images to extend the training set. Results show that instanceaware segmentation architectures and Cellpose outperform the U-Net architectures and conventional methods on complex images in terms of F1 scores, while the U-Net architectures achieve overall higher mean Dice scores. Training with additional artificially generated images improves recall and F1 scores for complex images, thereby leading to top F1 scores for three out of five sample preparation types. Mask R-CNN trained on artificial images achieves the overall highest F1 score on complex images of similar conditions to the training set images while Cellpose achieves the overall highest F1 score on complex images of new imaging conditions. We provide quantitative results demonstrat-Manuscript resubmitted on February 13, 2021.

PTX resulted in a significant reduction of oral mucositis during fractionated irradiation, which may be attributed to stimulation of mucosal repopulation processes. The biological basis of this effect, however, needs to be clarified in further, detailed mechanistic studies.

PurposeEarly inflammation is a major factor of mucosal reactions to radiotherapy. Pentoxifylline administration resulted in a significant amelioration of radiation-induced oral mucositis in the mouse tongue model. The underlying mechanisms may be related to the immunomodulatory properties of the drug. The present study hence focuses on the manifestation of early inflammatory changes in mouse tongue during daily fractionated irradiation and their potential modulation by pentoxifylline.Materials and methodsDaily fractionated irradiation with 5 fractions of 3 Gy/week (days 0–4, 7–11) was given to the snouts of mice. Groups of 3 animals per day were euthanized every second day between day 0 and 14. Pentoxifylline (15 mg/kg, s. c.) was administered daily from day 5 to the day before sacrifice. The expression of the inflammatory proteins TNFα, NF-κB, and IL-1β were analysed.ResultsFractionated irradiation increased the expression of all inflammatory markers. Pentoxifylline significantly reduced the expression of TNFα and IL-1β, but not NF-κB.ConclusionEarly inflammation, as indicated by the expression of the inflammatory markers TNFα, NF-κB, and IL-1β, is an essential component of early radiogenic oral mucositis. Pentoxifylline differentially modulated the expression of different inflammatory markers. The mucoprotective effect of pentoxifylline does not appear to be based on modulation of NF-κB-associated inflammation.

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