SUMMARY Non-apoptotic 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 non-apoptotic 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 xc−), creating a void in the antioxidant defenses of the cell, ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the non-apoptotic destruction of certain cancer cells, while inhibition of this process may protect organisms from neurodegeneration.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cell image analysis software

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

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.

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.high-content screening ͉ high-throughput image analysis ͉ phenotype T he history of biology has been dramatically shaped by classic visual screens in model organisms, including Drosophila melanogaster (1-3), Saccharomyces cerevisiae (4), Caenorhabditis elegans (5), and the zebrafish Danio rerio (6, 7). In each case, biological pathways were discovered because researchers were intrigued by groups of peculiar-looking mutants and identified the genes underlying their phenotypes. Because researchers have favored the extensive study of relatively few genes (8), classic, wide-net approaches like screening are as relevant as ever to probe known biological pathways and discover new ones. Modern technology now enables large-scale experiments in cultured cells to identify human genes that underlie biological processes via RNAi. Automation also allows the screening of chemical libraries to identify perturbants useful as research tools or drugs.Despite these advances, scoring cells in images for rare and unusual morphologies has, in general, remained a significant bottleneck (9-12). Cell image analysis allows accurate identification and measurement of cells' features, enabling automated analysis of certain phenotypes that were previously intractable (13-26). However, many interesting phenotypes require the assessment of several measured features of cells. Machine learning methods that select and combine multiple features for automated cell classification have been used to score many phenotypes (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). These methods require the provision of example cells that do and do not display the morphology of interest (i.e., positive and negative cells). Finding posi...

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.

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.

Direct and safe manipulation of neurons by external means is an increasingly studied therapeutic modality with the potential to treat many neurological diseases. Anticipating such future applications, we investigated reversible bioeffects of very low dose focused ultrasound on neuronal cell morphology and function in vitro. To test morphological changes, undifferentiated PC12 cells were serum-cultured. The culture plates were placed on an inverted optical microscope. An f/1.1 ultrasound transducer with a water-filled coupling cone was focused on the culture and excited with 30-ms 4.67-MHz 100-kPa pulses. To test functional changes, rat hippocampal slices were cultured and individually transferred to the well of a 60-channel multi electrode array. An f/2.1 ultrasound transducer with a water-filled coupling cone was focused on a culture and excited with 100-Ps 4.04-MHz 77-kPa pulses. The culture was stimulated before and after the ultrasonic stimulus with a 100-Ps 100-PA biphasic electrical stimulus. Optical microscopy of PC12 cultures under insonification revealed that cells that were clustered near the ultrasound focal region elongated by approximately 2 Pm during insonification and returned to approximately their original shapes following insonification. We conclude that the acoustic radiation force is capable of reversibly deforming cultured cells. In the rat hippocampal cultures, the ultrasonically and electrically evoked responses exhibited similar biphasic waveforms. In addition, robust electrically evoked responses following insonification indicated that the insonified cultures remained viable. We conclude that low-dose ultrasound can stimulate neurons; the mechanism is currently under investigation.

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 17β-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.