Mechanical Characterization of Human Brain Tissue and SoftDynamic Gels Exhibiting Electromechanical Neuro-Mimicry

Synthetic hydrogels are an important class of materialsin tissue engineering, drug delivery, and other biomedicalfields. Their mechanical and electrical properties can betuned to match those of biological tissues. In this work,we report on hydrogels that exhibit both mechanical andelectrical biomimicry. The presented dual networks consistof supramolecular networks formed from 2:1 homoternarycomplexes of imidazolium-based guest molecules in cucu-bit[8]uril and covalent networks of oligoethylene glycol-(di)methacrylate. We also investigate the viscoelastic prop-erties of human brain tissues. The mechanical properties ofthe dual network gels are benchmarked against the humantissue, and we find that they both are neuro-mimetic and ex-hibit cytocompatiblity in a neural stem cell model.The Winston Churchill Foundation of the United States. The Newton International Fellowship

SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm-1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.This work has been supported in part by the European Commission through the FP7 FET Open Programme ICT-2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant Agreement 618080. This work has been also supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperespectral identification of Brain tumors” under Grant Agreement ProID2017010164. Additionally, this work has been supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria. Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%))

Cucurbit[8]uril-derived graphene hydrogels

The scalable production of uniformly distributed graphene (GR)-based composite materials remains a sizable challenge. While GR-polymer nanocomposites can be manufactured at large scale, processing limitations result in poor control over the homogeneity of hydrophobic GR sheets in the matrices. Such processes often result in difficulties controlling stability and avoiding aggregation, therefore eliminating benefits that might have otherwise arisen from the nanoscopic dimensions of GR. Here, we report an exfoliated and stabilized GR dispersion in water. Cucurbit[8]uril (CB[8])-mediated host guest chemistry was used to obtain supramolecular hydrogels consisting of uniformly distributed GR and guest-functionalized macromolecules. The obtained GR-hydrogels show superior bioelectrical properties over identical systems produced without CB[8]. Utilizing such supramolecular interactions with biologically-derived macromolecules is a promising approach to stabilize graphene in water and avoid oxidative chemistry.Marie Sklodowska-Curie individual research grant (H2020-MSCAIF- 2017, P.ID: 797106) The Winston Churchill Foundation of the United States EPSRC Doctoral Training Grant EP/N509620/1 EPSRC Programme Grant NOtCH (EP/L027151/1

Mitochondrial DNA and traumatic brain injury.

OBJECTIVE: Traumatic brain injury (TBI) is a multifactorial pathology with great interindividual variability in response to injury and outcome. Mitochondria contain their own DNA (mtDNA) with genomic variants that have different physiological and pathological characteristics, including susceptibility to neurodegeneration. Given the central role of mitochondria in the pathophysiology of neurological injury, we hypothesized that its genomic variants may account for the variability in outcome following TBI. METHODS: We undertook an analysis of mitochondrial haplogroups in a large, well-characterized cohort of 1,094 TBI patients. A proportional odds model including age, brain computed tomography characteristics, injury severity, pupillary reactivity, mitochondrial haplogroups, and APOE was applied to Glasgow Outcome Score (GOS) data. RESULTS: mtDNA had a significant association with 6-month GOS (p=0.008). Haplogroup K was significantly associated with favorable outcome (odds ratio=1.64, 95% confidence interval=1.08-2.51, p=0.02). There was also a significant interaction between mitochondrial genome and age (p=0.002), with a strong protective effect of both haplogroups T (p=0.015) and K (p=0.017) with advancing age. We also found a strong interaction between APOE and mitochondrial haplogroups (p=0.001), indicating a protective effect of haplogroup K in carriers of the APOE ε4 allele. INTERPRETATION: These findings reveal an interplay between mitochondrial DNA, pathophysiology of TBI, and aging. Haplogroups K and T, which share a common maternal ancestor, are shown as protective in TBI. The data also suggest that the APOE pathways interact with genetically regulated mitochondrial functions in the response to acute injury, as previously reported in Alzheimer disease

Applying support-vector machine learning algorithms toward predicting host-guest interactions with cucurbit[7]uril.

Machine learning is a valuable tool in the development of chemical technologies but its applications into supramolecular chemistry have been limited. Here, the utility of kernel-based support vector machine learning using density functional theory calculations as training data is evaluated when used to predict equilibrium binding coefficients of small molecules with cucurbit[7]uril (CB[7]). We find that utilising SVMs may confer some predictive ability. This algorithm was then used to predict the binding of drugs TAK-580 and selumetinib. The algorithm did predict strong binding for TAK-580 and poor binding for selumetinib, and these results were experimentally validated. It was discovered that the larger homologue cucurbit[8]uril (CB[8]) is partial to selumetinib, suggesting an opportunity for tunable release by introducing different concentrations of CB[7] or CB[8] into a hydrogel depot. We qualitatively demonstrated that these drugs may have utility in combination against gliomas. Finally, mass transfer simulations show CB[7] can independently tune the release of TAK-580 without affecting selumetinib. This work gives specific evidence that a machine learning approach to recognition of small molecules by macrocycles has merit and reinforces the view that machine learning may prove valuable in the development of drug delivery systems and supramolecular chemistry more broadly.A.T. and M.P.S. thank The Winston Churchill Foundation of the United States. A.T. thanks the National Science Foundation graduate research fellowship, the MIT Chemical Engineering first year fellowship, and the Churchill College post-graduate grant program. G.W. thanks the Leverhulme Trust (project: ‘Natural material innovation for sustainable living’). V.K.R. thanks the Swiss National Science Foundation (P2EZP2_168784). O.A.S. acknowledges EPSRC Programme grant Nano-Optics to controlled Nano- Chemistry (NOtCH, EP/L027151/1) for funding

Glioblastoma Stem Cells Respond to Differentiation Cues but Fail to Undergo Commitment and Terminal Cell-Cycle Arrest.

Glioblastoma (GBM) is an aggressive brain tumor whose growth is driven by stemcell-like cells. BMP signaling triggers cell-cycle exit and differentiation of GBM stemcells (GSCs) and, therefore, might have therapeutic value. However, the epigenetic mechanisms that accompany differentiation remain poorly defined. It is also unclear whether cell-cycle arrest is terminal. Herewe find only a subset ofGSCcultures exhibit astrocyte differentiation in response to BMP. Although overtly differentiated non-cycling astrocytes are generated, they remain vulnerable to cell-cycle re-entry and fail to appropriately reconfigure DNA methylation patterns. Chromatin accessibility mapping identified loci that failed to alter in response to BMP and these were enriched in SOX transcription factor-binding motifs. SOX transcription factors, therefore, may limit differentiation commitment. A similar propensity for cell-cycle re-entry and de-differentiation was observed in GSC-derived oligodendrocyte-like cells. These findings highlight significant obstacles to BMP-induced differentiation as therapy forGBM

Elevated FOXG1 and SOX2 in glioblastoma enforces neural stem cell identity through transcriptional control of cell cycle and epigenetic regulators

Glioblastoma multiforme (GBM) is an aggressive brain tumor driven by cells with hallmarks of neural stem (NS) cells. GBM stem cells frequently express high levels of the transcription factors FOXG1 and SOX2. Here we show that increased expression of these factors restricts astrocyte differentiation and can trigger dedifferentiation to a proliferative NS cell state. Transcriptional targets include cell cycle and epigenetic regulators (e.g., Foxo3, Plk1, Mycn, Dnmt1, Dnmt3b, and Tet3). Foxo3 is a critical repressed downstream effector that is controlled via a conserved FOXG1/SOX2-bound cis-regulatory element. Foxo3 loss, combined with exposure to the DNA methylation inhibitor 5-azacytidine, enforces astrocyte dedifferentiation. DNA methylation profiling in differentiating astrocytes identifies changes at multiple polycomb targets, including the promoter of Foxo3 In patient-derived GBM stem cells, CRISPR/Cas9 deletion of FOXG1 does not impact proliferation in vitro; however, upon transplantation in vivo, FOXG1-null cells display increased astrocyte differentiation and up-regulate FOXO3. In contrast, SOX2 ablation attenuates proliferation, and mutant cells cannot be expanded in vitro. Thus, FOXG1 and SOX2 operate in complementary but distinct roles to fuel unconstrained self-renewal in GBM stem cells via transcriptional control of core cell cycle and epigenetic regulators.H.B. was supported by a Wellcome Trust Clinician Research Training Fellowship. E.J. was supported by the Biotechnology and Biological Sciences Research Council. M.A.M.-T. is supported by an EMBO training fellowship. K.F. is supported by a studentship from Cancer Research UK (A19680). R.B. is supported by a studentship from the Science Without Borders Program (CAPES, Brazil). D.S. is a Cancer Research UK Career Development Fellow (reference C47648/A20837), and work in his laboratory is also supported by a Medical Research Council University grant to the MRC Human Genetics Unit. S.M.P. is a Cancer Research UK Senior Research Fellow (A17368)

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples

A map of transcriptional heterogeneity and regulatory variation in human microglia.

Microglia, the tissue-resident macrophages of the central nervous system (CNS), play critical roles in immune defense, development and homeostasis. However, isolating microglia from humans in large numbers is challenging. Here, we profiled gene expression variation in primary human microglia isolated from 141 patients undergoing neurosurgery. Using single-cell and bulk RNA sequencing, we identify how age, sex and clinical pathology influence microglia gene expression and which genetic variants have microglia-specific functions using expression quantitative trait loci (eQTL) mapping. We follow up one of our findings using a human induced pluripotent stem cell-based macrophage model to fine-map a candidate causal variant for Alzheimer's disease at the BIN1 locus. Our study provides a population-scale transcriptional map of a critically important cell for human CNS development and disease