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.

Regeneration of myelin is a dynamic, yet enigmatic process. Dombrowski et al. uncover a central role for regulatory T (Treg) cells in driving oligodendrocyte differentiation, in part via CCN3, a novel factor in Treg function and oligodendrocyte biology. This identifies Treg cells as key cellular players in efficient remyelination. Regeneration of CNS myelin involves differentiation of oligodendrocytes from oligodendrocyte progenitor cells. In multiple sclerosis, remyelination can fail despite abundant oligodendrocyte progenitor cells, suggesting impairment of oligodendrocyte differentiation. T cells infiltrate the CNS in multiple sclerosis, yet little is known about T cell functions in remyelination. We report that regulatory T cells (Treg) promote oligodendrocyte differentiation and (re)myelination. Treg-deficient mice exhibited substantially impaired remyelination and oligodendrocyte differentiation, which 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 oligodendrocyte progenitor cell differentiation and myelination in vitro. We identified CCN3 as a 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 the cells were originally named 'Treg' to reflect immunoregulatory roles, this also captures emerging, regenerative Treg functions.

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.