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Mahadevan A.,Neuropathology | Pruthi N.,National Institute of Mental Health and Neuro Sciences | Shankar S.K.,Neuropathology
Neuropathology and Applied Neurobiology | Year: 2013

Aim: Mitochondrial function and the ensuing ATP synthesis are central to the functioning of the brain and contribute to neuronal physiology. Most studies on neurodegenerative diseases have highlighted that mitochondrial dysfunction is an important event contributing to pathology. However, studies on the human brain mitochondria in various neurodegenerative disorders heavily rely on post mortem samples. As post mortem tissues are influenced by pre- and post mortem factors, we investigated the effect of these variables on mitochondrial function. Methods: We examined whether the mitochondrial function (represented by mitochondrial enzymes and antioxidant activities) in post mortem human brains (n=45) was affected by increased storage time (11.8-104.1 months), age of the donor (2 days to 80 years), post mortem interval (2.5-26h), gender difference and agonal state [based on Glasgow Coma Scale: range=3-15] in the frontal cortex, as a prototype. Results: We observed that the activities of citrate synthase, succinate dehydrogenase and mitochondrial reductase (MTT) were significantly affected only by gender difference (citrate synthase: P=0.005; succinate dehydrogenase: P=0.01; mitochondrial reductase: P=0.006), being higher in females, but not by any other factor. Mitochondrial complex I activity was significantly inhibited by increasing age (r=-0.40; P=0.05). On the other hand, the mitochondrial antioxidant enzyme glutathione reductase decreased with severe agonal state (P=0.003), while the activity of glutathione-S-transferase declined with increased storage time (P=0.005) and severe agonal state (P=0.02). Conclusion: Our data highlight the influence of pre- and post mortem factors on preservation of mitochondrial function with implications for studies on brain pathology employing stored human samples. © 2012 The Authors. Neuropathology and Applied Neurobiology © 2012 British Neuropathological Society.


PubMed | Medical Oncology, Materials Hospital, Melanoma Institute Australia, Neurosurgery and 3 more.
Type: | Journal: Case reports in oncological medicine | Year: 2014

Four cases previously treated with ipilimumab with a total of six histologically confirmed symptomatic lesions of RNB without any sign of active tumour following stereotactic irradiation of MBM are reported. These lesions were all originally thought to be disease recurrence. In two cases, ipilimumab was given prior to SRT; in the other two ipilimumab was given after SRT. The average time from first ipilimumab to RNB was 15 months. The average time from SRT to RNB was 11 months. The average time from first diagnosis of MBM to last follow-up was 20 months at which time three patients were still alive, one with no evidence of disease. These cases represent approximately three percent of the total cases of melanoma and ten percent of those cases treated with ipilimumab irradiated in our respective centres collectively. We report this to highlight this new problem so that others may have a high index of suspicion, allowing, if clinically warranted, aggressive surgical salvage, possibly resulting in increased survival. Further studies prospectively collecting data to understand the denominator of this problem are needed to determine whether this problem is just the result of longer survival or whether there is some synergy between these two modalities that are increasingly being used together.


PubMed | Tenri Hospital, Neuropathology. and Kyushu University
Type: | Journal: Brain pathology (Zurich, Switzerland) | Year: 2016

Studies of longitudinally extensive spinal cord lesions (LESCLs) in neuromyelitis optica (NMO) have focused on gray matter, where the relevant antigen, aquaporin-4 (AQP4), is abundant. Because spinal white matter pathology in NMO is not well characterized, we aimed to clarify spinal white matter pathology of LESCLs in NMO.We analyzed 50 spinal cord lesions from eleven autopsied NMO/NMO spectrum disorder (NMOSD) cases. We also evaluated LESCLs with three or fewer spinal cord attacks by 3-tesla MRI in 15 AQP4 antibody-positive NMO/NMOSD patients and in 15 AQP4 antibody-negative multiple sclerosis (MS) patients.Pathological analysis revealed seven cases of AQP4 loss and four predominantly demyelinating cases. Forty-four lesions from AQP4 loss cases involved significantly more frequently posterior columns (PC) and lateral columns (LC) than anterior columns (AC) (59.1%, 63.6%, and 34.1%, respectively). The posterior horn (PH), central portion (CP), and anterior horn (AH) were similarly affected (38.6%, 36.4% and 31.8%, respectively). Isolated perivascular inflammatory lesions with selective loss of astrocyte endfoot proteins, AQP4 and connexin 43, were present only in white matter and were more frequent in PC and LC than in AC (22.7%, 29.5% and 2.3%, PNMO frequently and extensively affects spinal white matter in addition to central gray matter, especially in PC and LC, where isolated perivascular lesions with astrocyte endfoot protein loss may emerge. Spinal white matter involvement may also appear in early NMO, similar to cerebral white matter lesions.


News Article | October 10, 2016
Site: www.rdmag.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data. "The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. "Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development. As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. "We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram. "The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. "Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition." The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. "At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch." After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."


News Article | October 12, 2016
Site: www.biosciencetechnology.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer's spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data--like heat maps, node link diagrams and anatomical views--to provide context for brain connectivity data. "The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before," says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. "Other tools tend to look at abstract or statistical network connections but don't do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context," says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool's functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool's development. As a neuroscientist at UCSF's Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer's and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. "We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That's the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections," says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. "For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c," Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they'd also look at fMRI data that had been reduced to a static network diagram. "The problem with this analysis process is that it's all static. If I wanted to explore another region of the brain, which would be a different pattern, I'd have to input a whole different set of data and create another set of static images," says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. "Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn't realize existed before in the dataset. Every time I use it, I find something surprising in the data," says Brown. "It is also incredibly valuable for researchers who don't know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition." The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and Seeley met at the "Computational Challenges for Precision Medicine" in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries--a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. "At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools," says Brown. "The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch." After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called "Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data." Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. "We want to make sure that this tool is useful to the community, so we will keep iterating on it," says Brown. "We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time."


News Article | October 10, 2016
Site: www.chromatographytechniques.com

Did you know that your brain processes information in a hierarchy? As you are reading this page, the signal coming in through your eyes enters your brain through the thalamus, which organizes it. That information then goes on to the primary visual cortex at the back of the brain, where populations of neurons respond to very specific basic properties. For instance, one set of neurons might fire up because the text on your screen is black and another set might activate because there are vertical lines. This population will then trigger a secondary set of neurons that respond to more complex shapes like circles, and so on until you have a complete picture. For the first time, a new tool developed at the Department of Energy’s (DOE’s) Lawrence Berkeley National Laboratory (Berkeley Lab) allows researchers to interactively explore the hierarchical processes that happen in the brain when it is resting or performing tasks. Scientists also hope that the tool can shed some light on how neurological diseases like Alzheimer’s spread throughout the brain. Created in conjunction with computer scientists at University of California, Davis (UC Davis) and with input from neuroscientists at UC San Francisco (UCSF), the software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data—like heat maps, node link diagrams and anatomical views—to provide context for brain connectivity data. “The tool provides a novel framework of visualization and new interaction techniques that explore the brain connectivity at various hierarchical levels. This method allows researchers to explore multipart observations that have not been looked at before,” says Sugeerth Murugesan, who co-led the development of Brain Modulyzer. He is currently a graduate student researcher at Berkeley Lab and a PhD candidate at UC Davis. “Other tools tend to look at abstract or statistical network connections but don’t do quite a good job at connecting back to the anatomy of the brain. We made sure that Brain Modulyzer connects to brain anatomy so that we can simultaneously appreciate the abstract information in anatomical context,” says Jesse Brown, a postdoctoral researcher at UCSF who advised the Berkeley Lab development team on the tool’s functionality. A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. Brain Modulyzer is now available on github. Murugesan and Berkeley Lab Computer Scientist Gunther Weber developed the tool together. Weber is also an adjunct professor in the Department of Computer Science at UC Davis. UCSF Associate Professor of Neurology William Seeley also advised the tool’s development. As a neuroscientist at UCSF’s Memory and Aging Center, Brown and his colleagues use neuroimaging to diagnose diseases, like Alzheimer’s and dementia, as well as monitor how the diseases progress over time. Ultimately, their goal is to build a predictive model of how a disease will spread in the brain based on where it starts. “We know that the brain is built like a network, with axons at the tip of neurons that project to other neurons. That’s the main way the neurons connect with each other, so one way to think about disease spreading in the brain is that it starts in one place and kind of jumps over along the network connections,” says Brown. To see how a brain region is connected to other brain regions, Brown and his colleagues examine the fMRIs of healthy subjects. The set of connections observed in the fMRIs are visualized as a network. “For us the connection pattern of the network in healthy subjects is valuable information, because if we then study a patient with dementia and see that the disease is starting at point a in that network, we can expect that it will soon spread through the network connections to points b and c,” Brown adds. Before Brain Modulyzer, researchers could only explore these neural networks by creating static images of the brain regions they were studying and superimposing those pictures on an anatomical diagram of the entire brain. On the same screen, they’d also look at fMRI data that had been reduced to a static network diagram. “The problem with this analysis process is that it’s all static. If I wanted to explore another region of the brain, which would be a different pattern, I’d have to input a whole different set of data and create another set of static images,” says Brown. But with Brain Modulyzer, all he has to do is input a matrix that describes the connection strengths between all of the brain regions that he is interested in studying and the tool will automatically detect the networks. Each network is colored differently in the anatomical view and the information visualized abstractly in a number of graph and matrix views. “Modulyzer is such a helpful tool for discovery because it bubbles up really important information about functional brain properties, including information that we knew was there before, but it also connects to brain regions that we didn’t realize existed before in the dataset. Every time I use it, I find something surprising in the data,” says Brown. “It is also incredibly valuable for researchers who don’t know these methods as well. It will allow them to be a lot more efficient in detecting connections between brain regions that are important for cognition.” The idea for Brain Modulyzer initiated when Berkeley Lab’s Weber and Seeley met at the “Computational Challenges for Precision Medicine” in November 2012. This workshop brought together investigators from Berkeley and UCSF to focus on computational challenges posed by precision medicine. Their initial discussions led to a collaboration with Oblong Industries—a company that builds computer interfaces--to translate laboratory data collected at UCSF into 3D visualizations of brain structures and activity. The results of this collaboration were presented at the Precision Medicine Summit in May 2013. “At the Aging and Memory Center at UCSF, our expertise is in neuroscience, neurological diseases and dementia. We are really fortunate to be in touch with Berkeley Lab scientists whose expertise in visualization, maps and working with big data exploration helped us build such amazing tools,” says Brown. “The precision medicine collaboration was such a fruitful collaboration for everyone that we decided to stay in touch.” After the Precision Medicine Summit, the team discussed possibilities for further collaboration, which led to a Laboratory Directed Research and Development (LDRD) project at Berkeley Lab called “Graph-based Analysis and Visualization of Multimodal Multi-resolution Large-scale Neuropathology Data.” Part of the funding for Brain Modulyzer development came from this LDRD, as well as grants to Seeley from the Tau Consortium and National Institutes of Health. Soon, the team hopes to present their Brain Modulyzer paper to the neuroscience community for feedback. “We want to make sure that this tool is useful to the community, so we will keep iterating on it,” says Brown. “We have plenty of ideas to improve on what we have, and we think that Modulyzer will keep getting better over time.” In addition to Brown, Weber, Murugesan and Seeley other authors on the paper are Bernd Hamann (UC Davis), Andrew Trujillo (UCSF) and Kristopher Bouchard (Berkeley Lab).


News Article | December 2, 2015
Site: www.nature.com

8–10 weeks old male NMRI nude mice were used for all studies with human primary brain tumour cells. As a syngeneic astrocytoma mouse model we used Nestin-Tv-a;Tlx-GFP mice in combination with RCAS-PDGFB/AKT vectors21. All animal procedures were performed in accordance with the institutional laboratory animal research guidelines after approval of the Regierungspräsidium Karlsruhe, Germany (governmental authority). All efforts were made to minimize animal suffering and to reduce the number of animals used. Mice were clinically scored and if they showed marked neurological symptoms or weight loss of >20%, experiments were terminated. In none of the experiments these limits were exceeded. No maximum tumour size was defined for the invasive brain tumour models. Cranial window implantation in mice was done in a modification of what has been previously described49, including a custom-made titanium ring for painless head fixation during imaging. 2–3 weeks after cranial window implantation, 30,000 tumour cells were stereotactically injected into the mouse brain at a depth of 500 μm. For survival experiments we injected 50,000 tumour cells. In a subgroup of mice, a short plastic tube was glued under the glass, with one end inside and one outside, which allowed topical application of different substances under the window without the need to re-open it. For intratumoral microinjection of sulforhodamine 101 (SR101, Molecular Probes, S-359), 50 nl of 100 μM SR101 (with or without 100 μM CBX, Sigma-Aldrich, C4790) were injected with a very thin glass pipette into tumour regions of similar cellular densities, >90 days after tumour injection. Established tumours were irradiated with 7 Gy on three consecutive days (total dose 21 Gy) in regions matching in tumour cell density using a 6 MV linear accelerator with a 6 mm collimator (adjusted to the window size) at a dose rate of 3 Gy min−1 (Artiste, Siemens), or no radiation was applied (sham radiation), at day 60 (±10 days) after tumour implantation. For MRI studies, a total brain radiation with the same dose and a field size of 17 mm × 250 mm (allowing the irradiation of several mice) was used. The used radiation schedule is in the range of the commonly prescribed 60 Gy in 2 Gy fractions for malignant glioma patients, assuming an α/β of ~ 10 in the linear quadratic model and taking into account the radiation time of 3 days. MPLSM imaging was done with a Zeiss 7MP microscope (Zeiss) equipped with a Coherent Chameleon UltraII laser (Coherent). The following wavelengths were used for excitation: 750 nm (dsRed, FITC-dextrane, tdTomato), 840 nm (Fluo-4AM), 850 nm (GFP, TRITC-dextrane, Rhod-2AM), 860 nm (CFP, for Förster Resonance Energy Transfer (FRET) imaging) and 950 nm (tdTomato, YFP). Appropriate filter sets (band pass 500–550 nm/band pass 575–610 nm and band pass 460–500 nm/band pass 525–560 nm (for FRET)) were used. Standard settings for imaging were gains between 650 and 750 nm (depending on the depth, the fluorescence intensity of the fluorophore and the window quality), and a z-interval of 3 μm. Laser power was tuned as low as possible. The body temperature of mice was kept constant using a rectal thermometer and a heating pad. Isoflurane concentration (in 100% O ) was chosen as low as possible (0.5–1.5%) to avoid interference with the calcium communication between astrocytoma cells. Fluorescent dextranes (FITC (2M MW)- or TRITC (500.000 MW)-conjugated, 10 mg ml−1, Sigma) were injected intravenously to obtain angiograms. For in vivo ablation of single astrocytoma cells, only the volume of the GFP-labelled cell nucleus was exposed to continuous scanning with a high power laser beam until disintegration of the nucleus became visible. To investigate the reaction of TMs after the photodamage of a wider brain region, a larger volume (0.5–1×106 μm3) was scanned repetitively for approximately 8 min with high power, resulting in a total photon dose that was >50 times higher than during “diagnostic” imaging. The following small molecule calcium indicators were applied to the brain surface for 45 min: for GFP-transfected tumour cells, 2 mM Rhod-2AM (Life Technologies, R-1244); for RFP-transfected, 2 mM Fluo-4AM (Life Technologies, F-14201). Pharmacological gap junction inhibition was achieved by superfusion with the inhibitor CBX (100 μM; control substance: extracellular saline; n = 3 mice per group). Other superfused substances were suramin (100 μM, ATP antagonist) and 2-aminoethoxydiphenyl borate (2-APB, 100 μM, inhibitor of inositol triphosphate receptors). Two genetically encoded calcium indicators were lentivirally transduced to GBMSCs: the Lck-GCaMP3 sensor in the rrl-CAG-lGC3 vector (CAG promoter to control expression of DsRed and the Ca2+ sensor that monitors near-membrane changes in [Ca2+] 50). The ratiometric calcium sensor Twitch-3 was used to determine intracellular calcium concentrations by FRET as previously described51. MRI images were obtained at day 72 after tumour implantation for non–irradiated animals, and at day 115 for irradiated mice (60 days after radiotherapy; time points were chosen when first control animals developed neurological symptoms and/or lost 20% weight, and had to be euthanized). All scans were performed on a 9.4 T horizontal bore MR scanner (BioSpec 94/20 USR, Bruker BioSpin GmbH) with a four channel phased array surface coil. A T2-weighted rapid acquisition with refocused echoes (RARE) sequence was acquired to determine tumour volume. Tumour cell lines derived from resected glioblastomas were cultivated in DMEM-F12 under serum-free non-adherent, ‘stem-like’ conditions, including B27 supplement (12587-010, Gibco), insulin, heparin, epidermal growth factor and fibroblast growth factor17, 52 (GBMSCs: P3, S24, T1, T269, T325, WJ). These 6 GBMSC lines were selected because they were capable of growing to tumours in mouse brains; all were non-codeleted for 1p/19q, and IDH wild-type. Two oligodendroglioma cell lines harbouring the typical 1p/19q codeletion (BT088 and BT054) were kept under the same cell culture conditions53. Of note, BT054 is IDH1R132H mutated, while BT088 has lost the heterozygous IDH1R132H mutation of the patient tumour it was derived from, but still maintains its GCIMP phenotype (data not shown). Typical genetic changes of glioblastoma were confirmed for S24 using comparative genomic hybridization (CGH, see Extended Data Fig. 1i); the T1, T269, T325 and WJ lines had been characterized before52, as well as the P3 line54. Cells were regularly checked for mycoplasma infections and authenticity (species control). Tumour cells were transduced with lentiviral vectors for multicolour imaging. For cytosolic GFP expression, we used the pLKO.1-puro-CMV-TurboGFP_shnon-target-vector (SHC016 Sigma Aldrich), for cytosolic RFP (tdTomato) expression the LeGo-T2 vector (gift from A. Trumpp), and nuclear GFP expression (H2B–GFP) was achieved by transduction with pLKO.1-LV-GFP (Addgene 25999, Elaine Fuchs). Transduction with pLenti6.2 hygro/V5-Lifeact-YFP made it possible to image the in vivo dynamics of actin filaments, FUmGW (Addgene 22479, Connie Cepko) allowed in vivo illustration of cell membranes. Microtubuli were marked using the LentiBrite GFP-Tubulin Lentiviral Biosensor (17-10206, Merck Millipore). Lentiviral particles were produced as described before55. For in vivo tracking of Myosin II, a plasmid transfection with FuGENE HD (Promega) was performed with the Myosin-IIA-GFP vector (Addgene 38297, Matthew Krummel). Production of lentiviral knockdowns of Cx43 (pLKO1.1-puro-CMV-tGFP-vector, Sigma Aldrich, target sequence: GCCCAAACTGATGGTGTCAAT) and GAP-43 (pLKO1.1-puro-CMV-vector, Sigma Aldrich, target sequence: TGTAGATGAAACCAAACCTAA) by shRNA technology was carried out as described before55. Control cells were infected with the appropriate non_target shRNA-lentiviral particles (SHC016, Sigma Aldrich). For overexpression of GAP-43, the open reading frame of GAP-43 was cloned into the pCCL.PPT.SFFV.MCS.IRES.eGFP.WPRE-vector backbone. Lentiviral particle production and transduction of target cells was done as described before55. Tumour cells were incubated with the harvested virus and 8 mg ml−1 polybrene (Merck Millipore) for 24 h. Quantification revealed a 80% protein knockdown for Cx43 and a 92.5% for GAP-43 (Western Blot analyses). If necessary, tumour cells were selected for the fluorophores by FACS sorting (BD FACSAria II Cell Sorter) or antibiotics. For tracking of mitochondria, the BacMam 2.0 technology was used (CellLight Mitochondria-GFP, BacMam 2.0, C10600, Life Technologies). For IHCs and ICCs, standard protocols were used. For human brain analyses, thin (3 μm) formalin-fixed paraffin-embedded human tissue sections from resected primary gliomas were obtained from the Department of Neuropathology in Heidelberg in accordance with local ethical approval. Human sections were incubated with anti-BRAF-V600E (VE1, Ventana), anti-IDH1 R132H (H09, Dianova), anti-Cx43 (C6219, Sigma), anti-GAP-43 (8945, Cell Signaling), anti-NGF (ab52918, Abcam), anti-NT4 (ab150437, Abcam), anti-TrkA (ab76291, Abcam) and anti-TrkB (ab134155, Abcam) antibodies. If not explicitly stated, all oligodendrogliomas had a 1p/19q codeletion, and all astrocytomas were non-codeleted for 1p/19q. To detect contralateral tumour cells in human brains, large sections were analysed as previously described56. For mouse brain analyses, animals were transcardially perfused with PBS followed by 4.5% paraformaldehyde (PFA). For ICCs, cells were grown on glass slides for 4 days and fixed with PFA. The following antibodies were used for 10 μm cryotome sections and ICCs: anti-nestin (ab6320, Abcam, specific staining of GBMSCs, no signal detectable in normal mouse brain), in combination with anti-β-catenin (ab16051, Abcam), anti-β-parvin (sc-50775, Santa Cruz), anti-beta tubulin III (ab18207, Abcam), anti-Cx26 (ab59020, Abcam), anti-Cx31 (ab156582, Abcam), anti-Cx37 (ab185820, Abcam), anti-Cx43, anti-GAP-43, anti-GFAP (Z0334, Dako), anti-Ki-67 (M7240, Dako), anti-myosin IIa (ab24762, Abcam), anti-myosin X (22430002, Novus Bioscience), anti-N-cadherin (ab18203, Abcam), and anti-PDI (ab3672, Abcam). Ten S24 GBMSC brain tumour tissue blocks were prepared for photo-oxidation as described previously57. Serial 70 nm thick sections of photo-oxidated epon-embedded tissue were produced using 3D SEM as described before58. A volume of 747 μm3 was imaged. Specimens were imaged with a Zeiss 1530 scanning electron microscope. Images were aligned manually. Western blots were performed according to standard protocols. Total protein lysates (20–50 μg) were electrophoretically separated using a 10% SDS–PAGE. After blotting and blocking, the primary antibodies (see above) were incubated over night at 4 °C. As loading control, anti-GAPDH antibody (C4780, Linaris), or anti-alpha-tubulin antibody (T9026, Sigma) was used. Horizontal acute brain slices were obtained from 2 NMRI nude mice with 131 days old S24 as described59. Patch electrodes with resistances of 5–10 MΩ were filled with Lucifer yellow (5 mg ml−1, L0259, Sigma) and approached to identified tumour cells under visual control using a 63×, NA 1.0 dipping lens (Zeiss). The dye was transferred into tumour cells with an Axoporator 800A (Axon instruments) by 1-ms square voltage pulses at 50 Hz. Pulse amplitude was adjusted between −5 V and −20 V and train duration was adjusted up to 3 s to receive sufficient labelling of the target cell. MPLSM images were acquired by the ZEISS ZEN Software (Zeiss, Germany). After primary image calculation (for example, subtraction of different channels to remove unspecific background), images were transferred to Imaris (Bitplane, Switzerland) to allow 3D visualization, rendering and analysis of the data. For illustration of different aspects single planes, maximum intensity projections (MIPs) or 3D images were used. For exemplary illustration of tumour cell interconnectivity and TM branches, z-stacks were rendered manually (tumour cell bodies with surface function; TMs with filament tracker function). When a TM started at one cell and ended at another, these cells were defined as connected. Serial electron micrographs were reconstructed using OpenCAR software. 3D analysis of electron microscopy images was done using the Amira 5.4.6 software (Visage Imaging, Richmond, Australia). Some of the data (for example, calcium imaging) were transferred to the ImageJ software (Rasband, W.S., ImageJ, NIH). Videos were extracted from ZEN or Imaris and edited in Adobe Premiere Pro CS6. In patient tumour tissue (only from primary tumour resections), maximum TM length was measured in standard 3 μm thin IDH1R132H IHC sections. Here, TMs were divided into 3 groups: <50 μm (not qualifying as definite TM, because other cellular structures might still be confused with filamentous structures of this length); shorter TMs of 50–100 μm, and longer TMs of >100 μm length. Quantitative analysis of human IHCs was done by a Histoscore (range 0–300) as described before60, 61. For in vivo imaging data, TM numbers, branches per TM and connections per cell were counted manually, and TM lengths were measured manually in the slice mode in Imaris. Cells without a TM connection were defined as “non-connected” and cells with at least one TM-connection as “connected”. TMs were also classified as connecting when the connected cell was outside the region of interest. To analyse the number of TMs before and after irradiation, the TMs of individual, identical cells were counted at both time points. The mean speed of tumour cell invasion in S24 shControl versus shGAP-43 tumours was determined by analysis of three consecutive imaging time points within a 24 h time interval in vivo. Distances of tumour cells to the main tumour mass (defined as a radius of 0.5 mm around the middle of the main tumour) were analysed and grouped, or displayed as individual distances to the main tumour core in tumours that were much less invasive (oligodendrogliomas). Nuclei and mitochondria (time-lapse imaging data) were marked using the spot function of Imaris. They were connected to tracks and the mean track speed was calculated. For quantification and analysis of fluorescence intensity after SR101 application, all GFP-expressing tumour cells in a volume were marked using the spot function of Imaris and then mean intensities of the SR101-channel of these spots were calculated and compared with each other. For quantification of tumour volumes two regions per animal were marked using the surface function of Imaris. Tumour cells and non-malignant brain astrocytes were identified by GFP/RFP expression and uptake of the chemical calcium indicator, and marked manually by the use of the region of interest manager of ImageJ. Mean grey values were measured over time. This data was processed by the program GNU Octave 3.8.1 (John W. Eaton, GPL): images were normalized to the background fluorescence using a sliding interval of ±10 images. Local maxima of calcium signals were detected by the findpeaks function (signal package, Octave-Forge). Thus the number of calcium peaks of each cell (N) could be determined and the frequency (f) was calculated. The frequency was standardized for the cell number of each region. Synchronous cells, the number of synchronous communications, and the time point of the synchronous firing were determined. Analysis was done in a window of 2 frames around each peak. This allowed to assess the synchronicity S ( ), which was defined by us as the fraction of the whole number of synchronous cells (N ) divided by the number of calcium peaks for the given cell (N ). In case the cells were not active, a synchronicity of zero was allotted. Hence, synchronicity states the average number of interactions at the same time point. For example, in a system with a synchronicity of 1, a firing cell interacts with a second one; for a synchronicity of 10, one cell is communicating with 10 other cells. For the comparison between different blockers in vivo, the synchronicity was normalized to the baseline level. Finally, the results were summed up by a heat map. The number of calcium peaks of these cells were coded by a colour map. Synchronic cells were connected by lines, whereat the colour described the time point of the synchronic firing. For measurement of relative changes in fluorescence intensity, tumour cells were again marked manually and relative changes were calculated (ΔF/F ). F was defined as the average intensity of the 20% lowest grey values in a region of interest. The slice with the largest tumour area per mouse was chosen, and both the tumour (hyperintense on T2-weighted images) and the whole brain were segmented manually. The ratio of these two areas were determined and compared between the different groups (n = 6 mice per group, t-tests). RNA sequencing raw data (mapped to genes) and curated IDH-1/2 mutation data were downloaded from The Cancer Genome Atlas (TCGA) data portal on 30 January 2015, and last updated on 6 May 2015. Additionally, copy-number calls (using GISTIC 2.0) from the cBioPortal62 and 450k- as well as CNV-NMF-clustering results from the Broad GDAC Firehose (http://gdac.broadinstitute.org/) were acquired. Only IDH mutant samples which clearly clustered to either the 1p/19q codeleted or 1p/19q non-codeleted group (and had the respective copy-number profile; 194 samples: 124 non-codeleted, 70 codeleted) were kept for further analysis. The rationale to restrict the primary analysis on IDH mutated gliomas was that the IDH mutation itself has a profound impact on epigenetic and gene expression patterns in gliomas2, 3. First, normalization and differential gene expression analysis of RNA sequencing counts was performed using the edgeR package63, which assumes a negative binomial distribution of count data, filtering lowly expressed transcripts. Differentially activated signalling pathways and downstream effects between codeleted and non-codeleted IDH mutated tumours were analysed with the proprietary Ingenuity Pathway Analysis (Qiagen) using a fold change filter of |1.5| and FDR-q < 0.0564 for the input list. Briefly, the software calculates both an overlap P value (based on Fisher’s exact test) and an activation z score, which is based on the expression state of activating and inhibiting genes, for manually curated pathways and downstream biological functions. For this exploratory, hypothesis-generating study, results with both P < 0.1 and a z score > |1.5| were kept. To confirm the relevance of the results for IDH wild-type astrocytomas, we also analysed functional transcriptomic differences between IDH wild-type, non-codeleted gliomas (n = 56) and IDH mutated, 1p/19q codeleted gliomas (n = 70) from the TCGA RNASeq data using the analysis strategy from above. As this was a secondary, exploratory analysis, we did not perform multiple-testing adjustments for the results of our primary analysis. The results of image analyses were transferred to the SigmaPlot Software (Systat Software, Inc.) to test the statistical significance with the appropriate tests (data were tested for normality using the Shapiro–Wilk test and for equal variance). Statistical significance was assessed by the two-sided Student’s t-test for normally distributed data. Otherwise a Mann–Whitney test was used for non-normal distributions. For more than two groups a one way ANOVA or an ANOVA on the ranks was performed. For contingency tables, a Fisher’s exact test was used. For Kaplan–Meier survival analysis, a log rank test was performed. Results were considered statistically significant if the P value was below 0.05. Quantifications were done blinded by two independent investigators. Animal group sizes were as low as possible and empirically chosen, and longitudinal measurements allowed a reduction of animal numbers by maintaining an adequate power. No statistical methods were used to predetermine sample size. If treatments were applied, animals were randomized to these procedures. Quantitative in vivo data are normally depicted as mean ± standard deviation. The calculated calcium imaging frequency and synchronicity values were corrected for outliers using the Nalimov test.


PubMed | CHR Citadelle, Neuropathology and University of Liège
Type: Case Reports | Journal: Neuro-Chirurgie | Year: 2015

Failure of the anterior neuropore can lead to three main types of anomalies: nasal dermal sinus, encephalocele and nasal glioma or heterotopia. In this report, we describe a case of intracranial and extracranial glial heterotopia that probably resulted from a common failure of anterior neuropore development. We describe the prenatal radiological assessment based on ultrasound and MRI results, and consider their limitation for early fetal diagnosis. We also discuss the embryogenesis and the possible pathogenic mechanisms involved.

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