The ventral habenulae of zebrafish develop in prosomere 2 dependent on Tcf7l2 function

BACKGROUND: The conserved habenular neural circuit relays cognitive information from the forebrain into the ventral mid- and hindbrain. In zebrafish, the bilaterally formed habenulae in the dorsal diencephalon are made up of the asymmetric dorsal and symmetric ventral habenular nuclei, which are homologous to the medial and lateral nuclei respectively, in mammals. These structures have been implicated in various behaviors related to the serotonergic/dopaminergic neurotransmitter system. The dorsal habenulae develop adjacent to the medially positioned pineal complex. Their precursors differentiate into two main neuronal subpopulations which differ in size across brain hemispheres as signals from left-sided parapineal cells influence their differentiation program. Unlike the dorsal habenulae and despite their importance, the ventral habenulae have been poorly studied. It is not known which genetic programs underlie their development and why they are formed symmetrically, unlike the dorsal habenulae. A main reason for this lack of knowledge is that the vHb origin has remained elusive to date. RESULTS: To address these questions, we applied long-term 2-photon microscopy time-lapse analysis of habenular neural circuit development combined with depth color coding in a transgenic line, labeling all main components of the network. Additional laser ablations and cell tracking experiments using the photoconvertible PSmOrange system in GFP transgenic fish show that the ventral habenulae develop in prosomere 2, posterior and lateral to the dorsal habenulae in the dorsal thalamus. Mutant analysis demonstrates that the ventral habenular nuclei only develop in the presence of functional Tcf7l2, a downstream modulator of the Wnt signaling cascade. Consistently, photoconverted thalamic tcf7l2(exl/exl) mutant cells do not contribute to habenula formation. CONCLUSIONS: We show in vivo that dorsal and ventral habenulae develop in different regions of prosomere 2. In the process of ventral habenula formation, functional tcf7l2 gene activity is required and in its absence, ventral habenular neurons do not develop. Influenced by signals from parapineal cells, dorsal habenular neurons differentiate at a time at which ventral habenular cells are still on their way towards their final destination. Thus, our finding may provide a simple explanation as to why only neuronal populations of the dorsal habenulae differ in size across brain hemispheres

cudaMap: a GPU accelerated program for gene expression connectivity mapping

BACKGROUND: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. RESULTS: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. CONCLUSION: Emerging ‘omics’ technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap

Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Characterization of a murine mixed neuron-glia model and cellular responses to regulatory T cell-derived factors

Abstract One of the unmet clinical needs in demyelinating diseases such as Multiple Sclerosis (MS) is to provide therapies that actively enhance the process of myelin regeneration (remyelination) in the central nervous system (CNS). Oligodendrocytes, the myelinating cells of the CNS, play a central role in remyelination and originate from oligodendrocyte progenitor cells (OPCs). We recently showed that depletion of regulatory T cells (Treg) impairs remyelination in vivo, and that Treg-secreted factors directly enhance oligodendrocyte differentiation. Here we aim to further characterize the dynamics of Treg-enhanced oligodendrocyte differentiation as well as elucidate the cellular components of a murine mixed neuron-glia model. Murine mixed neuron-glia cultures were generated from P2–7 C57BL/6 mice and characterized for percentage of neuronal and glial cell populations prior to treatment at 7 days in vitro (div) as well as after treatment with Treg-conditioned media at multiple timepoints up to 12 div. Mixed neuron-glia cultures consisted of approximately 30% oligodendroglial lineage cells, 20% neurons and 10% microglia. Furthermore, a full layer of astrocytes, that could not be quantified, was present. Treatment with Treg-conditioned media enhanced the proportion of MBP+ oligodendrocytes and decreased the proportion of PDGFRα+ OPCs, but did not affect OPC proliferation or survival. Treg-enhanced oligodendrocyte differentiation was not caused by Treg polarizing factors, was dependent on the number of activation cycles Treg underwent and was robustly achieved by using 5% conditioned media. These studies provide in-depth characterization of a murine mixed neuron-glia model as well as further insights into the dynamics of Treg-enhanced oligodendrocyte differentiation

Experiences of open science & software

Open source image analysis software plays a crucial role in biological research. For more than 20 years biologists have relied upon ImageJ to analyze microscopy data, and an ecosystem of open bioimage analysis tools (including Fiji, CellProfiler, ilastik, KNIME, icy) has since grown up to help researchers deal with the wide diversity of data across the field. However, until recently there was no established open source platform designed to handle whole slide images. These are ultra-large scans of entire glass slides (typically up to 50 GB per 2D image), the size and complexity of which pose unique computational challenges. Whole slide images are already the mainstay of digital pathology and are becoming increasingly common in other areas of biomedical research. I created QuPath to address this need (https://qupath.github.io), with the aim of making the analysis of complex tissue images containing millions of cells both fast and intuitive. Since its release less than 2 years ago, QuPath has becom

QuPath algorithm accurately identifies MLH1 deficient inflammatory bowel disease-associated colorectal cancers in a tissue microarray

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression

QuPath Edu and OpenMicroanatomy::Open-source virtual microscopy tools for medical education

Virtual microscopy is becoming increasingly common in both medical education and routine clinical practice. Virtual microscopy software is typically designed either for 1) training students in anatomy, histology, and histopathology, or 2) quantitative analysis – but not both simultaneously. QuPath is one of the most widely used software applications for histopathology image analysis in research and provides a comprehensive set of computational tools to evaluate histology slides. We have enhanced QuPath by developing a new extension, QuPath Edu, which adapts the software to function as an intuitive microanatomy learning environment. Additionally, we have created an entirely new, complementary software platform called OpenMicroanatomy, which provides an alternative way to access QuPath Edu teaching content through a web interface. These tools have been used in teaching of first year medical and dentistry students at the University of Oulu Medical Faculty and we conducted a user survey for the Class of 2023 to assess the usability and student experience. In general, the introduced annotation and quiz features were appreciated by the students and the system usability of OpenMicroanatomy was considered excellent (SUS score 84.8). Together, these freely available tools enable teachers to develop and deploy innovative training material for anatomy, histopathology, quantitative analysis, and artificial intelligence in a wide range of contexts. This unique combination can provide the next generation of students with essential multidisciplinary skills

Recent Advances in Pathology: the 2022 Annual Review Issue of The Journal of Pathology

The 2022 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 15 invited reviews on research areas of growing importance in pathology. This year, the articles include those that focus on digital pathology, employing modern imaging techniques and software to enable improved diagnostic and research applications to study human diseases. Thissubject area includes the ability to identify specific genetic alterations through the morphological changes they induce, as well as integrating digital and computational pathology with ‘omics technologies. Other reviews in this issue include an updated evaluation of mutational patterns (mutation signatures) in cancer, the applications of lineage tracing in human tissues, and single cell sequencing technologies to uncover tumour evolution and tumour heterogeneity. The tissue microenvironment is covered in reviews specifically dealing with proteolytic control of epidermal differentiation, cancer associated fibroblasts, field cancerisation, and host factors that determine tumour immunity. All of the reviews contained in this issue are the work of invited experts selected to discuss the considerable recent progress in their respective fields and are freely available online (https://onlinelibrary.wiley.com/journal/10969896)

AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements made manually from EM images-a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth