Facile and Low-Cost Fabrication of SiO2-Covered Au Nanoislands for Combined Plasmonic Enhanced Fluorescence Microscopy and SERS.

An easy and low-cost way to fabricate monometallic Au nanoislands for plasmonic enhanced spectroscopy is presented. The method is based on direct thermal evaporation of Au on glass substrates to form nanoislands, with thicknesses between 2 and 15 nm, which are subsequently covered by a thin layer of silicon dioxide. We have used HR-SEM and AFM to characterize the nanoislands, and their optical transmission reveals strong plasmon resonances in the visible. The plasmonic performance of the fabricated substrates has been tested in fluorescence and Raman scattering measurements of two probe materials. Enhancement factors up to 1.8 and 9×104 are reported for confocal fluorescence and Raman microscopies, respectively, which are comparable to others obtained by more elaborated fabrication procedures.

Assessing kinetics and recruitment of DNA repair factors using high content screens.

Repair of genetic damage is coordinated in the context of chromatin, so cells dynamically modulate accessibility at DNA breaks for the recruitment of DNA damage response (DDR) factors. The identification of chromatin factors with roles in DDR has mostly relied on loss-of-function screens while lacking robust high-throughput systems to study DNA repair. In this study, we have developed two high-throughput systems that allow the study of DNA repair kinetics and the recruitment of factors to double-strand breaks in a 384-well plate format. Using a customized gain-of-function open-reading frame library ("ChromORFeome" library), we identify chromatin factors with putative roles in the DDR. Among these, we find the PHF20 factor is excluded from DNA breaks, affecting DNA repair by competing with 53BP1 recruitment. Adaptable for genetic perturbations, small-molecule screens, and large-scale analysis of DNA repair, these resources can aid our understanding and manipulation of DNA repair.

Melanoma-derived small extracellular vesicles induce lymphangiogenesis and metastasis through an NGFR-dependent mechanism.

Secreted extracellular vesicles (EVs) influence the tumor microenvironment and promote distal metastasis. Here, we analyzed the involvement of melanoma-secreted EVs in lymph node pre-metastatic niche formation in murine models. We found that small EVs (sEVs) derived from metastatic melanoma cell lines were enriched in nerve growth factor receptor (NGFR, p75NTR), spread through the lymphatic system and were taken up by lymphatic endothelial cells, reinforcing lymph node metastasis. Remarkably, sEVs enhanced lymphangiogenesis and tumor cell adhesion by inducing ERK kinase, nuclear factor (NF)-κB activation and intracellular adhesion molecule (ICAM)-1 expression in lymphatic endothelial cells. Importantly, ablation or inhibition of NGFR in sEVs reversed the lymphangiogenic phenotype, decreased lymph node metastasis and extended survival in pre-clinical models. Furthermore, NGFR expression was augmented in human lymph node metastases relative to that in matched primary tumors, and the frequency of NGFR+ metastatic melanoma cells in lymph nodes correlated with patient survival. In summary, we found that NGFR is secreted in melanoma-derived sEVs, reinforcing lymph node pre-metastatic niche formation and metastasis.

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation.

BACKGROUND AND OBJECTIVES: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. METHODS: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. RESULTS: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. CONCLUSIONS: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour.

HCS Methodology for Helping in Lab Scale Image-Based Assays.

High-content screening (HCS) automates image acquisition and analysis in microscopy. This technology considers the multiple parameters contained in the images and produces statistically significant results. The recent improvements in image acquisition throughput, image analysis, and machine learning (ML) have popularized this kind of experiments, emphasizing the need for new tools and know-how to help in its design, analysis, and data interpretation. This chapter summarizes HCS recommendations for lab scale assays and provides both macros for HCS-oriented image analysis and user-friendly tools for data mining processes. All the steps described herein are oriented to a wide variety of image cell-based experiments. The workflows are illustrated with practical examples and test images. Their use is expected to help analyze thousands of images, create graphical representations, and apply machine learning models on HCS.

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning.

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.