HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables.

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC's implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants' homes. Our results demonstrate HEROIC's capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Standardizing analysis of intra-tumoral heterogeneity with computational pathology.

Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their regional biology to survive and thrive under diverse microenvironmental and therapeutic pressures. While the concept of intra-tumoral heterogeneity has become a major paradigm in cancer research and care, systematic approaches to assess and document bio-variation in cancer are still in their infancy. Here we discuss existing approaches and challenges to documenting intra-tumoral heterogeneity and emerging computational approaches that leverage artificial intelligence to begin to overcome these limitations. We propose how these emerging techniques can be coupled with a diversity of molecular tools to address intra-tumoral heterogeneity more systematically in research and in practice, especially across larger specimens and longitudinal analyses. Systematic documentation and characterization of heterogeneity across entire tumor specimens and their longitudinal evolution has the potential to improve our understanding and treatment of cancer.

Towards deciphering glioblastoma intra-tumoral heterogeneity: The importance of integrating multidimensional models.

Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.

Distinguishing Gastric/Esophageal Adenocarcinoma from Pancreatic Adenocarcinoma Using Methylation-Based Droplet Digital PCR.

The goal of this study was to develop a methylation-based droplet digital PCR to separate 2 cancer classes that do not have sensitive and specific immunohistochemical stains: gastric/esophageal and pancreatic adenocarcinomas. The assay used methylation-independent primers and methylation-dependent probes to assess a single differentially methylated CpG site; analyses of array data from The Cancer Genome Atlas network showed that high methylation at the cg06118999 probe supports the presence of cells originating from the stomach or esophagus (eg, as in gastric metastasis), whereas low methylation suggests that these cells are rare to absent (eg, pancreatic metastasis). On validation using formalin-fixed paraffin-embedded primary and metastatic samples from our institution, methylation-based droplet digital PCR targeting the corresponding CpG dinucleotide generated evaluable data for 60 of the 62 samples (97%) and correctly classified 50 of the 60 evaluable cases (83.3%), mostly adenocarcinomas from the stomach or pancreas. This ddPCR was created to be easy-to-interpret, rapid, inexpensive, and compatible with existing platforms at many clinical laboratories. We suggest that similarly accessible PCRs could be developed for other differentials in pathology that do not have sensitive and specific immunohistochemical stains.

The promise of organoids for unraveling the proteomic landscape of the developing human brain.

Cerebral organoids offer an opportunity to bioengineer experimental avatars of the developing human brain and have already begun garnering relevant insights into complex neurobiological processes and disease. Thus far, investigations into their heterogeneous cellular composition and developmental trajectories have been largely limited to transcriptional readouts. Recent advances in global proteomic technologies have enabled a new range of techniques to explore dynamic and non-overlapping spatiotemporal protein-level programs operational in these humanoid neural structures. Here we discuss these early protein-based studies and their potentially essential role for unraveling critical secreted paracrine signals, processes with poor proteogenomic correlations, or neurodevelopmental proteins requiring post-translational modification for biological activity. Integrating emerging proteomic tools with these faithful human-derived neurodevelopmental models could transform our understanding of complex neural cell phenotypes and neurobiological processes, not exclusively driven by transcriptional regulation. These insights, less accessible by exclusive RNA-based approaches, could reveal new knowledge into human brain development and guide improvements in neural regenerative medicine efforts.

Integrating computational pathology and proteomics to address tumor heterogeneity.

Despite numerous advances in our molecular understanding of cancer biology, success in precision medicine trials has remained elusive for many malignancies. Emerging evidence now supports that these challenges are partly driven by proteogenomic discordances across molecular readouts and heterogeneous biology that is spatially distributed across tumors. Here we discuss these key limitations and how integrating the promise of mass-spectrometry-based global proteomics and computational imaging can help prioritize and direct regional sampling to help overcome these important challenges of biologic variation in cancer. © 2022 The Pathological Society of Great Britain and Ireland.

The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue.

The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.

Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?

One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imaging can now be resolved. This article briefly reviews the problem and how eventually both supervised and unsupervised AI technologies could help to overcome it.

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and limitation in terms of interpretability are significant obstacles for their widespread clinical utility. This study investigates the visualization of deep features (DFs) to characterize two lung cancer subtypes, adenocarcinoma and squamous cell carcinoma. It demonstrates that a subset of DFs, called prominent DFs, can accurately distinguish these two cancer subtypes. Visualization of such individual DFs allows for a better understanding of histopathologic patterns at both the whole-slide and patch levels, and discrimination of these cancer types. These DFs were visualized at the whole slide image level through DF-specific heatmaps and at tissue patch level through the generation of activation maps. In addition, these prominent DFs can distinguish carcinomas of organs other than the lung. This framework may serve as a platform for evaluating the interpretability of any deep network for diagnostic decision making.

Leukoencephalopathy with brain calcifications and cysts (Labrune syndrome) case report: diagnosis and management of a rare neurological disease.

BACKGROUND: Leukoencephalopathy with brain calcifications and cysts (LCC; also known as Labrune syndrome) is a rare genetic microangiopathy caused by biallelic mutations in SNORD118. The mechanisms by which loss-of-function mutations in SNORD118 lead to the phenotype of leukoencephalopathy, calcifications and intracranial cysts is unknown. CASE PRESENTATION: We present the histopathology of a 36-year-old woman with ataxia and neuroimaging findings of diffuse white matter abnormalities, cerebral calcifications, and parenchymal cysts, in whom the diagnosis of LCC was confirmed with genetic testing. Biopsy of frontal white matter revealed microangiopathy with small vessel occlusion and sclerosis associated with axonal loss within the white matter. CONCLUSIONS: These findings support that the white matter changes seen in LCC arise as a consequence of ischemia rather than demyelination.

Three-dimensional modeling of human neurodegeneration: brain organoids coming of age.

The prevalence of dementia and other neurodegenerative diseases is rapidly increasing in aging nations. These relentless and progressive diseases remain largely without disease-modifying treatments despite decades of research and investments. It is becoming clear that traditional two-dimensional culture and animal model systems, while providing valuable insights on the major pathophysiological pathways associated with these diseases, have not translated well to patients' bedside. Fortunately, the advent of induced-pluripotent stem cells and three-dimensional cell culture now provide tools that are revolutionizing the study of human diseases by permitting analysis of patient-derived human tissue with non-invasive procedures. Specifically, brain organoids, self-organizing neural structures that can mimic human fetal brain development, have now been harnessed to develop alternative models of Alzheimer's disease, Parkinson's disease, motor neuron disease, and Frontotemporal dementia by recapitulating important neuropathological hallmarks found in these disorders. Despite these early breakthroughs, several limitations need to be vetted in brain organoid models in order to more faithfully match human tissue qualities, including relative tissue immaturity, lack of vascularization and incomplete cellular diversity found in this culture system. Here, we review current brain organoid protocols, the pathophysiology of neurodegenerative disorders, and early studies with brain organoid neurodegeneration models. We then discuss the multiple engineering and conceptual challenges surrounding their use and provide possible solutions and exciting avenues to be pursued. Altogether, we believe that brain organoids models, improved with classical and emerging molecular and analytic tools, have the potential to unravel the opaque pathophysiological mechanisms of neurodegeneration and devise novel treatments for an array of neurodegenerative disorders.

Defining Protein Pattern Differences Among Molecular Subtypes of Diffuse Gliomas Using Mass Spectrometry.

Molecular characterization of diffuse gliomas has thus far largely focused on genomic and transcriptomic interrogations. Here, we utilized mass spectrometry and overlay protein-level information onto genomically defined cohorts of diffuse gliomas to improve our downstream molecular understanding of these lethal malignancies. Bulk and macrodissected tissues were utilized to quantitate 5,496 unique proteins over three glioma cohorts subclassified largely based on their IDH and 1p19q codeletion status (IDH wild type (IDHwt), n = 7; IDH mutated (IDHmt), 1p19q non-codeleted, n = 7; IDH mutated, 1p19q-codeleted, n = 10). Clustering analysis highlighted proteome and systems-level pathway differences in gliomas according to IDH and 1p19q-codeletion status, including 287 differentially abundant proteins in macrodissection-enriched tumor specimens. IDHwt tumors were enriched for proteins involved in invasiveness and epithelial to mesenchymal transition (EMT), while IDHmt gliomas had increased abundances of proteins involved in mRNA splicing. Finally, these abundance changes were compared with IDH-matched GBM stem-like cells (GSCs) to better pinpoint protein patterns enriched in putative cellular drivers of gliomas. Using this integrative approach, we outline specific proteins involved in chloride transport (e.g. chloride intracellular channel 1, CLIC1) and EMT (e.g. procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3, PLOD3, and serpin peptidase inhibitor clade H member 1, SERPINH1) that showed concordant IDH-status-dependent abundance differences in both primary tissue and purified GSC cultures. Given the downstream position proteins occupy in driving biology and phenotype, understanding the proteomic patterns operational in distinct glioma subtypes could help propose more specific, personalized, and effective targets for the management of patients with these aggressive malignancies.

Deep learning for image analysis: Personalizing medicine closer to the point of care.

The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. Management at, or closer to, the point of care still heavily relies on the subjective qualitative interpretation of clinical and diagnostic imaging findings. New and emerging technological advances in artificial intelligence (AI) now appear poised to help bring objectivity and precision to these traditionally qualitative analytic tools. In particular, one specific form of AI, known as deep learning, is achieving expert-level disease classifications in many areas of diagnostic medicine dependent on visual and image-based findings. Here, we briefly review concepts of deep learning, and more specifically recent developments in convolutional neural networks (CNNs), to highlight their transformative potential in personalized medicine and, in particular, diagnostic histopathology. Understanding the opportunities and challenges of these quantitative machine-based decision support tools is critical to their widespread introduction into routine diagnostics.

Spatiotemporal Proteomic Profiling of Human Cerebral Development.

Mass spectrometry (MS) analysis of human post-mortem central nervous system (CNS) tissue and induced pluripotent stem cell (iPSC)-based directed differentiations offer complementary avenues to define protein signatures of neurodevelopment. Methodological improvements of formalin-fixed, paraffin-embedded (FFPE) protein isolation now enable widespread proteomic analysis of well-annotated archival tissue samples in the context of development and disease. Here, we utilize a shotgun label-free quantification (LFQ) MS method to profile magnetically enriched human cortical neurons and neural progenitor cells (NPCs) derived from iPSCs. We use these signatures to help define spatiotemporal protein dynamics of developing human FFPE cerebral regions. We show that the use of high resolution Q Exactive mass spectrometers now allow simultaneous quantification of >2700 proteins in a single LFQ experiment and provide sufficient coverage to define novel biomarkers and signatures of NPC maintenance and differentiation. Importantly, we show that this abbreviated strategy allows efficient recovery of novel cytoplasmic, membrane-specific and synaptic proteins that are shared between both in vivo and in vitro neuronal differentiation. This study highlights the discovery potential of non-comprehensive high-throughput proteomic profiling of unfractionated clinically well-annotated FFPE human tissue from a diverse array of development and diseased states.

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. RESULTS: Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. CONCLUSION: Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

Posterior fossa ependymoma: current insights.

BACKGROUND: Ependymoma is the third most common malignant tumor of the posterior fossa and is a major cause of neurological morbidity and mortality in children. Current treatments, particularly surgery and external beam irradiation result in relatively poor outcomes with significant neurological and cognitive sequelae from treatment. Historical approaches have considered all ependymomas as similar entities based on their morphological appearance. RESULTS: Recent advances in genomics and epigenetics have revealed, however, that ependymomas from different CNS locations represent distinct entities. Moreover, ependymoma of the posterior fossa, the most common location in children, is actually comprised of two distinct molecular variants. These two variants have marked differences in demographics, transcriptomes, structure, methylation patterns, and clinical outcomes. This allows for the development of new biology-based clinical risk stratification, which can both prioritize patients for de-escalation of therapy and identify those who will benefit from novel therapeutic strategies. Indeed, the identification of these two variants allows an opportunity for robust preclinical modeling for development of novel therapeutic strategies. CONCLUSIONS: Herein, we have summarized our current clinical approach to diagnosis and treatment of posterior fossa ependymoma, recent advances in understanding the biology of posterior fossa ependymoma and how these new insights can be translated into the clinic to form the basis of the next generation of clinical trials.

Differences in methylation profiles between long-term survivors and short-term survivors of IDH-wild-type glioblastoma.

Background: Patients with glioblastoma (GBM) have a median overall survival (OS) of approximately 16 months. However, approximately 5% of patients survive >5 years. This study examines the differences in methylation profiles between long-term survivors (>5 years, LTS) and short-term survivors (<1 year, STS) with isocitrate dehydrogenase (IDH)-wild-type GBMs. Methods: In a multicenter retrospective analysis, we identified 25 LTS with a histologically confirmed GBM. They were age- and sex-matched to an STS. The methylation profiles of all 50 samples were analyzed with EPIC 850k, classified according to the DKFZ methylation classifier, and the methylation profiles of LTS versus STS were compared. Results: After methylation profiling, 16/25 LTS and 23/25 STS were confirmed to be IDH-wild-type GBMs, all with +7/-10 signature. LTS had significantly increased O6-methylguanine methyltransferase (MGMT) promoter methylation and higher prevalence of FGFR3-TACC3 fusion (P = .03). STS were more likely to exhibit CDKN2A/B loss (P = .01) and higher frequency of NF1 (P = .02) mutation. There were no significant CpGs identified between LTS versus STS at an adjusted P-value of .05. Unadjusted analyses identified key pathways involved in both LTS and STS. The most common pathways were the Hippo signaling pathway and the Wnt pathway in LTS, and GPCR ligand binding and cell-cell signaling in STS. Conclusions: A small group of patients with IDH-wild-type GBM survive more than 5 years. While there are few differences in the global methylation profiles of LTS compared to STS, our study highlights potential pathways involved in GBMs with a good or poor prognosis.