Over the past two decades, synergistic innovations in imaging technology have resulted in a revolution in which a range of biomedical applications are now benefiting from fluorescence imaging. Specifically, advances in fluorophore chemistry and imaging hardware, and the identification of targetable biomarkers have now positioned intraoperative fluorescence as a highly specific real-time detection modality for surgeons in oncology. In particular, the deeper tissue penetration and limited autofluorescence of near-infrared (NIR) fluorescence imaging improves the translational potential of this modality over visible-light fluorescence imaging. Rapid developments in fluorophores with improved characteristics, detection instrumentation, and targeting strategies led to the clinical testing in the early 2010s of the first targeted NIR fluorophores for intraoperative cancer detection. The foundations for the advances that underline this technology continue to be nurtured by the multidisciplinary collaboration of chemists, biologists, engineers, and clinicians. In this Review, we highlight the latest developments in NIR fluorophores, cancer-targeting strategies, and detection instrumentation for intraoperative cancer detection, and consider the unique challenges associated with their effective application in clinical settings.

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.

Objective Glioblastoma (GBM) is the most malignant primary brain tumor. Collagen is present in low amounts in normal brain, but in GBMs, collagen gene expression is reportedly upregulated. However, to the authors’ knowledge, direct visualization of collagen architecture has not been reported. The authors sought to perform the first direct visualization of GBM collagen architecture, identify clinically relevant collagen signatures, and link them to differential patient survival. Methods Second-harmonic generation microscopy (SHG) was used to detect collagen in a GBM patient tissue microarray. Focal and invasive GBM mouse xenografts were stained with Picrosirius red. Quantitation of collagen fibers was performed using custom software. Multivariate survival analysis was done to determine if collagen is a survival marker for patients. Results In focal xenografts, collagen was observed at tumor brain boundaries. For invasive xenografts, collagen was intercalated with tumor cells. Quantitative analysis showed significant differences in collagen fibers for focal and invasive xenografts. The authors also found that GBM patients with more organized collagen had longer median survival than those with less organized collagen. Conclusions Collagen architecture can be directly visualized and is different in focal versus invasive GBMs. The authors also demonstrate that collagen signature is associated with patient survival. These findings suggest that there are collagen differences in focal versus invasive GBMs and that collagen is a survival marker for GBM.