Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Novel Image Analysis Approach Quantifies Morphological Characteristics of 3D Breast Culture Acini with Varying Metastatic Potentials

Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology. A set of statistically significant features for the characterization of acini morphology are exploited for the automated grading of six (MCF10 series) cell line cultures mimicking three grades of breast cancer along the metastatic cascade. In addition to capturing both expected and visually differentiable changes, we quantify subtle differences that pose a challenge to assess through microscopic inspection. Our method achieves 89.0% accuracy in grading the acinar structures as nonmalignant, noninvasive carcinoma, and invasive carcinoma grades. We further demonstrate that the proposed methodology can be successfully applied for the grading of in vivo tissue samples albeit with additional constraints. These results indicate that the proposed features can be used to describe the relationship between the acini morphology and cellular function along the metastatic cascade

Gene-body hypermethylation of ATM in peripheral blood DNA of bilateral breast cancer patients

Bilaterality of breast cancer is an indicator of constitutional cancer susceptibility; however, the molecular causes underlying this predisposition in the majority of cases is not known. We hypothesize that epigenetic misregulation of cancer-related genes could partially account for this predisposition. We have performed methylation microarray analysis of peripheral blood DNA from 14 women with bilateral breast cancer compared with 14 unaffected matched controls throughout 17 candidate breast cancer susceptibility genes including BRCA1, BRCA2, CHEK2, ATM, ESR1, SFN, CDKN2A, TP53, GSTP1, CDH1, CDH13, HIC1, PGR, SFRP1, MLH1, RARB and HSD17B4. We show that the majority of methylation variability is associated with intragenic repetitive elements. Detailed validation of the tiled region around ATM was performed by bisulphite modification and pyrosequencing of the same samples and in a second set of peripheral blood DNA from 190 bilateral breast cancer patients compared with 190 controls. We show significant hypermethylation of one intragenic repetitive element in breast cancer cases compared with controls (P = 0.0017), with the highest quartile of methylation associated with a 3-fold increased risk of breast cancer (OR 3.20, 95% CI 1.78–5.86, P = 0.000083). Increased methylation of this locus is associated with lower steady-state ATM mRNA level and correlates with age of cancer patients but not controls, suggesting a combined age–phenotype-related association. This research demonstrates the potential for gene-body epigenetic misregulation of ATM and other cancer-related genes in peripheral blood DNA that may be useful as a novel marker to estimate breast cancer risk

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Experts by experience in mental health nursing education: What have we learned from the COMMUNE project?

The COMMUNE (co-produced mental health nursing education) was an international project established to embed EBE perspectives in mental health nursing education by developing and delivering a specific mental health nursing module. The underlying intention of this project was to go well beyond ad hoc implementation and tokenistic approaches to EBE involvement. Standards for co-production of Education (Mental Health Nursing) (SCo-PE [MHN]) was developed to provide guidance to the increasing number of academics seeking genuine and meaningful involvement of Experts by Experience in the education of health professionals. These standards were recently published in the Journal of Mental Health and Psychiatric Nursing (Horgan et al., 2020): https://onlinelibrary.wiley.com/doi/abs/10.1111/jpm.12605 and prompted this Editorial to discuss the COMMUNE project more fully, including the lessons learned

Practice Guidelines for Co-Production of Mental Health Nursing Education

COMMUNE (Co-production of Mental Health Nursing Education) is an Erasmus+ Strategic Partnership Project based on the collaboration of experts by experience (EBE) and mental health nursing academics from six European universities and the University of Canberra in Australia. Its purpose was to advance the involvement of those who have experiences of mental health service use (EBE) in mental health nursing education. The project combined experiential and academic knowledge, with the aim of co-producing a module on ‘mental health recovery’ for undergraduate nursing students; a module that was taught to the students by EBE. Principles of co-production where followed as much as possible, involving EBE in all stages of the process, from grant application to dissemination. The project tried to move beyond typical service user involvement and towards co-creation of knowledge, where power differentials are acknowledged and equity issues addressed. Barriers to meeting these goals were experienced and will be discussed in this Guidelines. We hope that these Practice Guidelines will be useful for those who intend to co-produce learning programs or modules in mental health nursing and inspire others to follow similar paths and learn from our experiences, positive or otherwise. These Guidelines provide an overview of our experiences, learnings, limitations and barriers.The Commune team decided on the term ‘Expert by Experience’ (EBE) to describe the members of the team and other collaborators who has lived experience of mental distress. Other more commonly used terms are ‘service user’, ‘consumer’ and ‘people with mental illness.’ As not all experts by experience are mental health care users, and what constitutes an illness is highly debated, the team decided on a term that more correctly describes and value lived experience.Erasmus

REGIONAL DIFFERENCES OF REACTIVE RESPONSES AGAINST SILICON NEURAL PROBE IMPLANTED INTO DEEP BRAIN REGIONS

This study was supported by the International Collaboration Program of NBS-ERC/KOSEF and NIH/NIBIB, R01- EB-000359

Cellular Responses to Micromachined Neuroprosthetic Device Insertion into the Brain

Insertion of prosthetic device is elicits reactive responses from both nervous tissue and vasculature that prevent successful integration of these devices. Their chronic use is limited due to glial encapsulation that electrically isolates devices from cellular networks. We examined time-dependent changes of reactive responses in neocortex, hippocampus, and thalamus using immunohistochemistry and confocal microscopy. Results show dramatic differences in the magnitude of cellular response in different brain regions and time-courses. These experiments will provide important new information for the design of improved biomaterials and nano/micro-device to control dynamic biological events in the central nervous system.This study was supported by the International Collaboration Program of NBSERC/ KOSEF and NIH/NIBIB, R01-EB- 000359