Strong Correlation of Genome-Wide Expression after Traumatic Brain Injury In Vitro and In Vivo Implicates a Role for SORLA.

The utility of in vitro models of traumatic brain injury (TBI) depends on their ability to recapitulate the in vivo TBI cascade. In this study, we used a genome-wide approach to compare changes in gene expression at several time points post-injury in both an in vitro model and an in vivo model of TBI. We found a total of 2073 differentially expressed genes in our in vitro model and 877 differentially expressed genes in our in vivo model when compared to noninjured controls. We found a strong correlation in gene expression changes between the two models (r = 0.69), providing confidence that the in vitro model represented at least part of the in vivo injury cascade. From these data, we searched for genes with significant changes in expression over time (analysis of covariance) and identified sorting protein-related receptor with A-type repeats (SORLA). SORLA directs amyloid precursor protein to the recycling pathway by direct binding and away from amyloid-beta producing enzymes. Mutations of SORLA have been linked to Alzheimer's disease (AD). We confirmed downregulation of SORLA expression in organotypic hippocampal slice cultures by immunohistochemistry and Western blotting and present preliminary data from human tissue that is consistent with these experimental results. Together, these data suggest that the in vitro model of TBI used in this study strongly recapitulates the in vivo TBI pathobiology and is well suited for future mechanistic or therapeutic studies. The data also suggest the possible involvement of SORLA in the post-traumatic cascade linking TBI to AD.

Bioorthogonal chemical imaging of metabolic activities in live mammalian hippocampal tissues with stimulated Raman scattering.

Brain is an immensely complex system displaying dynamic and heterogeneous metabolic activities. Visualizing cellular metabolism of nucleic acids, proteins, and lipids in brain with chemical specificity has been a long-standing challenge. Recent development in metabolic labeling of small biomolecules allows the study of these metabolisms at the global level. However, these techniques generally require nonphysiological sample preparation for either destructive mass spectrometry imaging or secondary labeling with relatively bulky fluorescent labels. In this study, we have demonstrated bioorthogonal chemical imaging of DNA, RNA, protein and lipid metabolism in live rat brain hippocampal tissues by coupling stimulated Raman scattering microscopy with integrated deuterium and alkyne labeling. Heterogeneous metabolic incorporations for different molecular species and neurogenesis with newly-incorporated DNA were observed in the dentate gyrus of hippocampus at the single cell level. We further applied this platform to study metabolic responses to traumatic brain injury in hippocampal slice cultures, and observed marked upregulation of protein and lipid metabolism particularly in the hilus region of the hippocampus within days of mechanical injury. Thus, our method paves the way for the study of complex metabolic profiles in live brain tissue under both physiological and pathological conditions with single-cell resolution and minimal perturbation.

A Combination Therapy of 17ß-Estradiol and Memantine Is More Neuroprotective Than Monotherapies in an Organotypic Brain Slice Culture Model of Traumatic Brain Injury.

Combination therapies are a promising therapeutic option for traumatic brain injury (TBI) owing to the clinical failure of monotherapy treatments, such as progesterone. Organotypic hippocampal slice cultures (OHSCs) from Sprague-Dawley rats were subjected to an in vitro TBI, and the neuroprotective effects of 17ß-estradiol (E2) or memantine (MEM) monotherapies were quantified. Several combination treatments at different concentrations of both drugs were tested, with 100 pM of E2 and 10 µM of MEM statistically and significantly reducing cell death over either monotherapy when administered immediately after injury. This combination was also significantly neuroprotective when administered 1 h postinjury, possibly supporting future in vivo studies. Further, we hypothesized that this synergy could be the result of MEM blocking a potentially deleterious effect of E2, specifically E2 enhancement of N-methyl-D-aspartate (NMDA) currents. Evoked electrophysiological responses in OHSCs were potentiated by E2 treatment, whereas this potentiation was significantly reduced by MEM. In conclusion, a combination therapy of E2 and memantine was significantly more neuroprotective than both monotherapy treatments, and this synergy may be the result of MEM blocking a deleterious E2-mediated enhancement of NMDA receptors.

GPR30 activation is neither necessary nor sufficient for acute neuroprotection by 17ß-estradiol after an ischemic injury in organotypic hippocampal slice cultures.

In this study, we investigated the role of GPR30 in 17ß-estradiol- (E2) mediated neuroprotection after an ischemic injury in an organotypic hippocampal slice culture (OHSC) model. We report that after oxygen-glucose deprivation (OGD), a physiological concentration of 100 pM E2 provided the greatest significant reduction in cell death while supra-physiological levels were less effective. The canonical estrogen receptor (ER) inhibitor ICI 182,780 completely abrogated the therapeutic effect of E2 while the GPR30 antagonist G-15 effected a slight but not significant reduction in neuroprotection. Only supra-physiological levels of E2 led to significantly increased phosphorylation of Akt and Erk which are well known downstream effects of GPR30 activation. We conclude that GPR30 activation may facilitate acute E2 mediated neuroprotection after OGD, but is neither necessary nor sufficient.

Ferroptosis: an iron-dependent form of nonapoptotic cell death.

Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration.

Genome-scale RNAi on living-cell microarrays identifies novel regulators of Drosophila melanogaster TORC1-S6K pathway signaling.

The evolutionarily conserved target of rapamycin complex 1 (TORC1) controls cell growth in response to nutrient availability and growth factors. TORC1 signaling is hyperactive in cancer, and regulators of TORC1 signaling may represent therapeutic targets for human diseases. To identify novel regulators of TORC1 signaling, we performed a genome-scale RNA interference screen on microarrays of Drosophila melanogaster cells expressing human RPS6, a TORC1 effector whose phosphorylated form we detected by immunofluorescence. Our screen revealed that the TORC1-S6K-RPS6 signaling axis is regulated by many subcellular components, including the Class I vesicle coat (COPI), the spliceosome, the proteasome, the nuclear pore, and the translation initiation machinery. Using additional RNAi reagents, we confirmed 70 novel genes as significant on-target regulators of RPS6 phosphorylation, and we characterized them with extensive secondary assays probing various arms of the TORC1 pathways, identifying functional relationships among those genes. We conclude that cell-based microarrays are a useful platform for genome-scale and secondary screening in Drosophila, revealing regulators that may represent drug targets for cancers and other diseases of deregulated TORC1 signaling.

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.

CellProfiler: free, versatile software for automated biological image analysis.

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).