Quantitative Nuclear Grading: An Objective, Artificial Intelligence-Facilitated Foundation for Grading Noninvasive Papillary Urothelial Carcinoma.

In nonmuscle invasive bladder cancer, grade drives important treatment and management decisions. However, grading is complex and qualitative, and it has considerable interobserver and intraobserver variability. Previous literature showed that nuclear features quantitatively differ between bladder cancer grades, but these studies were limited in size and scope. In this study, we aimed to measure morphometric features relevant to grading criteria and build simplified classification models that objectively distinguish between the grades of noninvasive papillary urothelial carcinoma (NPUC). We analyzed 516 low-grade and 125 high-grade 1.0-mm diameter image samples from a cohort of 371 NPUC cases. All images underwent World Health Organization/International Society of Urological Pathology 2004 consensus pathologist grading at our institution that was subsequently validated by expert genitourinary pathologists from 2 additional institutions. Automated software segmented the tissue regions and measured the nuclear features of size, shape, and mitotic rate for millions of nuclei. Then, we analyzed differences between grades and constructed classification models, which had accuracies up to 88% and areas under the curve as high as 0.94. Variation in the nuclear area was the best univariate discriminator and was prioritized, along with the mitotic index, in the top-performing classifiers. Adding shape-related variables improved accuracy further. These findings indicate that nuclear morphometry and automated mitotic figure counts can be used to objectively differentiate between grades of NPUC. Future efforts will adapt the workflow to whole slides and tune grading thresholds to best reflect time to recurrence and progression. Defining these essential quantitative elements of grading has the potential to revolutionize pathologic assessment and provide a starting point from which to improve the prognostic utility of grade.

Protocol to quantify immune cell distribution from the vasculature to the glioma microenvironment on sequential immunofluorescence multiplex images.

Although myeloid-derived immune cells can be dispersed throughout the tumor microenvironment (TME), anti-tumor effector cells are confined to the perivascular space. Here, we present a protocol to quantify immune cell distribution from tumor vasculature to its glioma microenvironment on sequential immunofluorescence multiplex images. We describe steps for sequential immunofluorescence multiplex staining, image generation, and storage. We then detail the procedures for tissue, vessel, and nuclei segmentation; cell phenotyping; data extraction; and training using RStudio and Spyder.

Homeostatic regulation of perisynaptic matrix metalloproteinase 9 (MMP9) activity in the amblyopic visual cortex.

Dark exposure (DE) followed by light reintroduction (LRx) reactivates robust synaptic plasticity in adult mouse primary visual cortex (V1), which allows subsequent recovery from amblyopia. Previously we showed that perisynaptic proteolysis by MMP9 mediates the enhancement of plasticity by LRx in binocular adult mice (Murase et al., 2017). However, it was unknown if a visual system compromised by amblyopia could engage this pathway. Here we show that LRx to adult amblyopic mice induces perisynaptic MMP2/9 activity and extracellular matrix (ECM) degradation in deprived and non-deprived V1. Indeed, LRx restricted to the amblyopic eye is sufficient to induce robust MMP2/9 activity at thalamo-cortical synapses and ECM degradation in deprived V1. Two-photon live imaging demonstrates that the history of visual experience regulates MMP2/9 activity in V1, and that DE lowers the threshold for the proteinase activation. The homeostatic reduction of the MMP2/9 activation threshold by DE enables visual input from the amblyopic pathway to trigger robust perisynaptic proteolysis.