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.

Low-grade neuroepithelial tumors (LGNTs) are diverse CNS tumors presenting in children and young adults, often with a history of epilepsy. While the genetic profiles of common LGNTs, such as the pilocytic astrocytoma and ‘adult-type’ diffuse gliomas, are largely established, those of uncommon LGNTs remain to be defined. In this study, we have used massively parallel sequencing and various targeted molecular genetic approaches to study alterations in 91 LGNTs, mostly from children but including young adult patients. These tumors comprise dysembryoplastic neuroepithelial tumors (DNETs; n=22), diffuse oligodendroglial tumors (d-OTs; n=20), diffuse astrocytomas (DAs; n=17), angiocentric gliomas (n=15), and gangliogliomas (n=17). Most LGNTs (84%) analyzed by whole-genome sequencing (WGS) were characterized by a single driver genetic alteration. Alterations of FGFR1 occurred frequently in LGNTs composed of oligodendrocyte-like cells, being present in 82% of DNETs and 40% of d-OTs. In contrast, a MYB-QKI fusion characterized almost all angiocentric gliomas (87%), and MYB fusion genes were the most common genetic alteration in DAs (41%). A BRAF:p.V600E mutation was present in 35% of gangliogliomas and 18% of DAs. Pathogenic alterations in FGFR1/2/3, BRAF, or MYB/MYBL1 occurred in 78% of the series. Adult-type d-OTs with an IDH1/2 mutation occurred in four adolescents, the youngest aged 15 years at biopsy. Despite a detailed analysis, novel genetic alterations were limited to two fusion genes, EWSR1-PATZ1 and SLMAP-NTRK2, both in gangliogliomas. Alterations in BRAF, FGFR1, or MYB account for most pathogenic alterations in LGNTs, including pilocytic astrocytomas, and alignment of these genetic alterations and cytologic features across LGNTs has diagnostic implications. Additionally, therapeutic options based upon targeting the effects of these alterations are already in clinical trials.

BackgroundDe novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes.MethodsTo accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk.ResultsUsing currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model.ConclusionsValidation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.

Summary Neuronal intranuclear inclusion disease (NIID) is a neurodegenerative disease characterized by the presence of intranuclear inclusions of unknown origin. NIID is caused by an expansion of GGC repeats in the 5′ UTR of the NOTCH2NLC (N2C) gene. We found that these repeats are embedded in a small upstream open reading frame (uORF) (uN2C), resulting in their translation into a polyglycine-containing protein, uN2CpolyG. This protein accumulates in intranuclear inclusions in cell and mouse models and in tissue samples of individuals with NIID. Furthermore, expression of uN2CpolyG in mice leads to locomotor alterations, neuronal cell loss, and premature death of the animals. These results suggest that translation of expanded GGC repeats into a novel and pathogenic polyglycine-containing protein underlies the presence of intranuclear inclusions and neurodegeneration in NIID.

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifact that limit interpretation. Stimulated Raman histology (SRH) is a novel label-free imaging technique that provides intraoperative histologic images of fresh, unprocessed surgical specimens. Here we evaluate the capacity of SRH for use in the intraoperative diagnosis of pediatric type brain tumors. SRH revealed key diagnostic features in fresh tissue specimens collected from 33 prospectively enrolled pediatric type brain tumor patients, preserving tumor cytology and histoarchitecture in all specimens. We simulated an intraoperative consultation for 25 patients with specimens imaged using both SRH and standard hematoxylin and eosin histology. SRH-based diagnoses achieved near-perfect diagnostic concordance (Cohen's kappa, > 0.90) and an accuracy of 92% to 96%. We then developed a quantitative histologic method using SRH images based on rapid image feature extraction. Nuclear density, tumor-associated macrophage infiltration, and nuclear morphology parameters from 3337 SRH fields of view were used to develop and validate a decision-tree machine-learning model. Using SRH image features, our model correctly classified 25 fresh pediatric type surgical specimens into normal versus lesional tissue and low-grade versus high-grade tumors with 100% accuracy. Our results provide insight into how SRH can deliver rapid diagnostic histologic data that could inform the surgical management of pediatric brain tumors. A new imaging method simplifies diagnosis and informs decision making during pediatric brain tumor surgery. .