Regionally defined proteomic profiles of human cerebral tissue and organoids reveal conserved molecular modules of neurodevelopment.

Cerebral organoids have emerged as robust models for neurodevelopmental and pathological processes, as well as a powerful discovery platform for less-characterized neurobiological programs. Toward this prospect, we leverage mass-spectrometry-based proteomics to molecularly profile precursor and neuronal compartments of both human-derived organoids and mid-gestation fetal brain tissue to define overlapping programs. Our analysis includes recovery of precursor-enriched transcriptional regulatory proteins not found to be differentially expressed in previous transcriptomic datasets.

Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning.

Background: Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation.

Methods: To address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies.

Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity.

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments.

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.

Cerebral organoids: emerging ex vivo humanoid models of glioblastoma.

Glioblastoma is an aggressive form of brain cancer that has seen only marginal improvements in its bleak survival outlook of 12-15 months over the last forty years. There is therefore an urgent need for the development of advanced drug screening platforms and systems that can better recapitulate glioblastoma's infiltrative biology, a process largely responsible for its relentless propensity for recurrence and progression. Recent advances in stem cell biology have allowed the generation of artificial tridimensional brain-like tissue termed cerebral organoids.

Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network.

Purpose: Applications of deep learning to histopathology have proven capable of expert-level performance, but approaches have largely focused on supervised classification tasks requiring context-specific training and deployment. More generalizable workflows that can be easily shared across subspecialties could help accelerate and broaden adoption. Here, we hypothesized that histology-optimized feature representations, generated by a convolutional neural network (CNN) during supervised learning, are transferable and can resolve meaningful differences in large-scale, discovery-type unsupervised analyses.

Unsupervised Machine Learning in Pathology: The Next Frontier.

Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised machine learning workflows can be deployed in existing pathology workflows to begin learning autonomously through exploration and without the need for extensive direction. Although still in its infancy, this type of machine learning, which more closely mirrors human intelligence, stands to add another exciting layer of innovation to computational pathology and accelerate the transition to autonomous pathologic tissue analysis.

Shifts in Ribosome Engagement Impact Key Gene Sets in Neurodevelopment and Ubiquitination in Rett Syndrome.

Regulation of translation during human development is poorly understood, and its dysregulation is associated with Rett syndrome (RTT). To discover shifts in mRNA ribosomal engagement (RE) during human neurodevelopment, we use parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNA sequencing (RNA-seq) on control and RTT human induced pluripotent stem cells, neural progenitor cells, and cortical neurons. We find that 30% of transcribed genes are translationally regulated, including key gene sets (neurodevelopment, transcription and translation factors, and glycolysis).

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), = 7; IDH mutated (IDHmt), 1p19q non-codeleted, = 7; IDH mutated, 1p19q-codeleted, = 10).

Physician perspectives on integration of artificial intelligence into diagnostic pathology.

Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large.

Proteomic analysis of meningiomas reveals clinically distinct molecular patterns.

Background: Meningiomas represent one of the most common brain tumors and exhibit a clinically heterogeneous behavior, sometimes difficult to predict with classic histopathologic features. While emerging molecular profiling efforts have linked specific genomic drivers to distinct clinical patterns, the proteomic landscape of meningiomas remains largely unexplored.

Methods: We utilize liquid chromatography tandem mass spectrometry with an Orbitrap mass analyzer to quantify global protein abundances of a clinically well-annotated formalin-fixed paraffin embedded (FFPE) cohort (n = 61) of meningiomas spanning all World Health Organization (WHO) grades and various degrees of clinical aggressiveness.

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.

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.

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.

The pluripotency factor Nanog regulates pericentromeric heterochromatin organization in mouse embryonic stem cells.

An open and decondensed chromatin organization is a defining property of pluripotency. Several epigenetic regulators have been implicated in maintaining an open chromatin organization, but how these processes are connected to the pluripotency network is unknown. Here, we identified a new role for the transcription factor NANOG as a key regulator connecting the pluripotency network with constitutive heterochromatin organization in mouse embryonic stem cells.

MECP2e1 isoform mutation affects the form and function of neurons derived from Rett syndrome patient iPS cells.

MECP2 mutations cause the X-linked neurodevelopmental disorder Rett Syndrome (RTT) by consistently altering the protein encoded by the MECP2e1 alternative transcript. While mutations that simultaneously affect both MECP2e1 and MECP2e2 isoforms have been widely studied, the consequence of MECP2e1 deficiency on human neurons remains unknown. Here we report the first isoform-specific patient induced pluripotent stem cell (iPSC) model of RTT.

Open and closed domains in the mouse genome are configured as 10-nm chromatin fibres.

The mammalian genome is compacted to fit within the confines of the cell nucleus. DNA is wrapped around nucleosomes, forming the classic "beads-on-a-string" 10-nm chromatin fibre. Ten-nanometre chromatin fibres are thought to condense into 30-nm fibres.

NLRP7, a nucleotide oligomerization domain-like receptor protein, is required for normal cytokine secretion and co-localizes with Golgi and the microtubule-organizing center.

A hydatidiform mole (HM) is a human pregnancy with hyperproliferative placenta and abnormal embryonic development. Mutations in NLRP7, a member of the nucleotide oligomerization domain-like receptor family of proteins with roles in inflammation and apoptosis, are responsible for recurrent HMs. However, little is known about the functional role of NLRP7.

Constitutive heterochromatin reorganization during somatic cell reprogramming.

Induced pluripotent stem (iPS) cell reprogramming is a gradual epigenetic process that reactivates the pluripotent transcriptional network by erasing and establishing repressive epigenetic marks. In contrast to loci-specific epigenetic changes, heterochromatin domains undergo epigenetic resetting during the reprogramming process, but the effect on the heterochromatin ultrastructure is not known. Here, we characterize the physical structure of heterochromatin domains in full and partial mouse iPS cells by correlative electron spectroscopic imaging.

Epigenetics of induced pluripotency, the seven-headed dragon.

Induction of pluripotency from somatic cells by exogenous transcription factors is made possible by a variety of epigenetic changes that take place during the reprogramming process. The derivation of fully reprogrammed induced pluripotent stem (iPS) cells is achieved through establishment of embryonic stem cell (ESC)-like epigenetic architecture permitting the reactivation of key endogenous pluripotency-related genes, establishment of appropriate bivalent chromatin domains and DNA hypomethylation of genomic heterochromatic regions. Restructuring of the epigenetic landscape, however, is a very inefficient process and the vast majority of the induced cells fail to complete the reprogramming process.