QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

QuPath is new bioimage analysis software designed to meet the growing need

Regeneration of central nervous system (CNS) myelin involves differentiation of oligodendrocytes from oligodendrocyte progenitor cells (OPC). In multiple sclerosis (MS), remyelination can fail despite abundant OPC, suggesting impairment of oligodendrocyte differentiation. T cells infiltrate the CNS during MS, yet little is known about T cell functions in remyelination. Here, we report that regulatory T cells (Treg) promote oligodendrocyte differentiation and (re)myelination. Treg-deficient mice exhibited significantly impaired remyelination and oligodendrocyte differentiation that was rescued by adoptive transfer of Treg. In brain slice cultures, Treg accelerated developmental myelination and remyelination, even in the absence of overt inflammation. Treg directly promoted OPC differentiation and myelination in vitro. We identified CCN3 as a novel Treg-derived mediator of oligodendrocyte differentiation and myelination in vitro. These findings reveal a new regenerative function of Treg in the CNS, distinct from immunomodulation. Although originally named ‘Treg’ to reflect immunoregulatory roles, this also captures emerging, regenerative Treg functions aptly.

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There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

Purpose: A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled.Experimental Design: We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients.Results: We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR ¼ 2.914 (confidence interval 0.9286-9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stemlike biology have undergone a widespread epithelial-mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed.Conclusions: Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer.

SJ. Virtual microscopy and digital pathology in training and education. APMIS 2012; 120: 305-15.Traditionally, education and training in pathology has been delivered using textbooks, glass slides and conventional microscopy. Over the last two decades, the number of web-based pathology resources has expanded dramatically with centralized pathological resources being delivered to many students simultaneously. Recently, whole slide imaging technology allows glass slides to be scanned and viewed on a computer screen via dedicated software. This technology is referred to as virtual microscopy and has created enormous opportunities in pathological training and education. Students are able to learn key histopathological skills, e.g. to identify areas of diagnostic relevance from an entire slide, via a web-based computer environment. Students no longer need to be in the same room as the slides. New human-computer interfaces are also being developed using more natural touch technology to enhance the manipulation of digitized slides. Several major initiatives are also underway introducing online competency and diagnostic decision analysis using virtual microscopy and have important future roles in accreditation and recertification. Finally, researchers are investigating how pathological decision-making is achieved using virtual microscopy and modern eyetracking devices. Virtual microscopy and digital pathology will continue to improve how pathology training and education is delivered.Key words: Digital pathology; education; review; training; virtual microscopy. Peter W. Hamilton, G64 Health Sciences Building, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK. e-mail: p.hamilton@qub.ac.uk Education and training in histology, anatomical pathology and cytopathology remain essential to both undergraduate and postgraduate courses in pathology and for residency training in pathology. Previously, this would have been delivered using textbooks, glass slides and conventional microscopy, but increasingly web-based resources have been developed to supplement or replace the more traditional methodologies. Web-resources in pathology have expanded dramatically in the last 20 years. Although the quality has often been variable, there have been clear benefits in centralizing resources that can be delivered to many students simultaneously. This has greatly aided departments, colleges and professional organisations in delivering histology training to a wider audience in a more cost-effective way.Recently, however, an additional tool, 'Whole Slide Imaging' has revolutionized the way in which histology and histopathology can be delivered on-line by providing the ability to scan entire glass slides at diagnostic resolution. This creates a digital slide of the tissue section, which, using software can be viewed on a computer screen. The slide can be tracked in the x and y axes and magnified in

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