Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions. We found a weak negative correlation between tissue cellularity and the fluorescence intensity of PpIX across all imaged specimens. Semi-supervised clustering of the TPEF images revealed five distinct patterns of PpIX fluorescence, and spatial transcriptomic analyses of the imaged tissue showed that myeloid cells predominate in areas where PpIX accumulates in the intracellular space. Further analysis of external spatially resolved metabolomics, transcriptomics and RNA-sequencing datasets from glioblastoma specimens confirmed that myeloid cells preferentially accumulate and metabolize PpIX. Our findings question 5-ALA-induced fluorescence in glioma cells and show how 5-ALA and TPEF imaging can provide a window into the immune microenvironment of gliomas.

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy.

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method - stimulated Raman histology (SRH) - which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

Restoring visual function to the blind retina with a potent, safe and long-lasting photoswitch.

Photoswitch compounds such as DENAQ confer light-sensitivity on endogenous neuronal ion channels, enabling photocontrol of neuronal activity without genetic manipulation. DENAQ treatment restores both retinal light responses and visual behaviors in rodent models of Retinitis pigmentosa. However, retinal photosensitization requires a high dose of DENAQ and disappears within several days after treatment. Here we report that BENAQ, an improved photoswitch, is 20-fold more potent than DENAQ and persists in restoring visual responses to the retina for almost 1 month after a single intraocular injection. Studies on mice and rabbits show that BENAQ is non-toxic at concentrations 10-fold higher than required to impart light-sensitivity. These favorable properties make BENAQ a potential drug candidate for vision restoration in patients with degenerative blinding diseases.

Compound classification using image-based cellular phenotypes.

Compounds with similar target specificities and modes of inhibition cause similar cellular phenotypes. Based on this observation, we hypothesized that we could quantitatively classify compounds with diverse mechanisms of action using cellular phenotypes and identify compounds with unintended cellular activities within a chemical series. We have developed Cytometrix technologies, a highly automated image-based system capable of quantifying, clustering, and classifying changes in cellular phenotypes for this purpose. Using this system, 45 out of 51 known compounds were accurately classified into 12 distinct mechanisms of action. We also demonstrate microtubule-binding activity in one of seven related cytochalasin actin poisons. This technology can be used for a variety of drug discovery applications, including high-throughput primary screening of chemical and siRNA libraries and as a secondary assay to detect unintended activities and toxicities.

An unbiased cell morphology-based screen for new, biologically active small molecules.

We have implemented an unbiased cell morphology-based screen to identify small-molecule modulators of cellular processes using the Cytometrix (TM) automated imaging and analysis system. This assay format provides unbiased analysis of morphological effects induced by small molecules by capturing phenotypic readouts of most known classes of pharmacological agents and has the potential to read out pathways for which little is known. Four human-cancer cell lines and one noncancerous primary cell type were treated with 107 small molecules comprising four different protein kinase-inhibitor scaffolds. Cellular phenotypes induced by each compound were quantified by multivariate statistical analysis of the morphology, staining intensity, and spatial attributes of the cellular nuclei, microtubules, and Golgi compartments. Principal component analysis was used to identify inhibitors of cellular components not targeted by known protein kinase inhibitors. Here we focus on a hydroxyl-substituted analog (hydroxy-PP) of the known Src-family kinase inhibitor PP2 because it induced cell-specific morphological features distinct from all known kinase inhibitors in the collection. We used affinity purification to identify a target of hydroxy-PP, carbonyl reductase 1 (CBR1), a short-chain dehydrogenase-reductase. We solved the X-ray crystal structure of the CBR1/hydroxy-PP complex to 1.24 A resolution. Structure-based design of more potent and selective CBR1 inhibitors provided probes for analyzing the biological function of CBR1 in A549 cells. These studies revealed a previously unknown function for CBR1 in serum-withdrawal-induced apoptosis. Further studies indicate CBR1 inhibitors may enhance the effectiveness of anticancer anthracyclines. Morphology-based screening of diverse cancer cell types has provided a method for discovering potent new small-molecule probes for cell biological studies and anticancer drug candidates.