Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Slide Presentation

793 views

Published on

The Development of a Novel Fusion Protein to Facilitate Connectomic Analysis of Brain Networks

Published in: Science
  • Be the first to comment

  • Be the first to like this

Slide Presentation

  1. 1. The Development of a Novel Fusion Protein to Facilitate Connectomic Analysis of Brain Networks Gurion Marks Bronx High School of Science, Bronx, NY
  2. 2. The Problem: Neurodegenerative Disease • According to the World Health Organization, Alzheimer’s disease and other forms of dementia represent the fourth highest disease burden in high-income countries . – The US has the 10th highest GDP per capita and ranks 34th in the world in life expectancy, showing its increased risk of population suffering from Alzheimer’s or memory related disorders. • 35% of Europe’s health problems stem from brain disorders. • Economically, the effects are huge. – The 2010 World Alzheimer Report by Alzheimer’s Disease International reported that the cost of caring for the disease exceeded US$600 billion, globally – consuming approximately 1% of the entire world’s GDP [3]. – Worldwide, Alzheimer’s is projected to cost US$1.1 trillion and affect 114.5 million people by 2050.
  3. 3. Curing Neurodegenerative Diseases • Many neurodegenerative disorders are circuit based • To understand and cure these ailments, one must discern how neurons are affected at cellular and intercellular levels – how neurons work in circuits – Understand how the change or decrease in neural “wiring” creates diseases
  4. 4. Circuitry of the Brain – the “Connectome” • The ‘Connectome’ is a “comprehensive structural description of the network of elements and connections forming the brain.” – In short, it’s a “wiring diagram” of the brain – They show which neurons connect to which other neurons
  5. 5. A Simple Neural Circuit A Simple Model of a Neural Circuit - A simple neural system in which there is a connection from neuron 1 to neuron 2, and neuron 2 to neuron 1. Neuron 3 connects to both neurons 1 and 2; neither neuron 1 nor neuron 2 connects to neuron 3. This may only be a tiny fraction of a real neural circuit which encompasses a vastly greater number of neurons. Neuron 1 Neuron 2 Neuron 3 Photo Credit: Lichtman et al. 2008
  6. 6. Finding Connectomes • Many types of data must be utilized. – Light or fluorescence microscopy may be used to functionally identify neurons within a circuit. – Electron microscopy is used to make a high resolution stack of images. • These images are then analyzed by tracing identified neurons to form three-dimensional reconstructions.
  7. 7. The Scale of Connectomics The Scale of Connectomics – the isolation of one neuron in the larval zebrafish hindbrain. Massive sets of data are needed to utilize the resolution necessary for connectomics research. The scope of data is so large that humans will never analyze an entire brain by hand, expressing the need for machine learning strategies to reconstruct neural matrices. Endoplasmic Reticulum Mitochondrion Soma
  8. 8. Problems in Finding Connectomes • Massive amounts of data and an inability to use computer software to trace neurons effectively – Massive amounts of data have to be analyzed by hand, as machine learning algorithms do not have the pattern recognizing abilities for interpolating stacks of electron microscopy images • Neural constructs look similar near the soma – Algorithms cannot differentiate between axons and dendrites
  9. 9. Issue with Electron Microscopy Branching Dendrite The Uncertainty of Electron Microscopy – errors in electron microscopy leading to the ineffectiveness of machine learning techniques. (a) Shows issues regarding too little contrast. Without distinct boundaries between Soma 1 and Soma 2, the branch of Soma 1 may be incorrectly linked to Soma 2. There are great numbers of instances like these in any set of data. Computer algorithms cannot distinguish these errors as humans can, thus connectomes created by machine learning strategies with current imaging technology are largely erroneous. (b) Shows another instance of error – overexposure of a region or group of cells makes it nearly impossible for computer vision to trace branches of neurons. Soma 2 Soma 1 Lack of Contrast between Somae Overexposure creates uncertainty in distinguishing neural constructs (a) (b)
  10. 10. Axon and Dendrite Near the Soma (a) (b) Branching Axon Branching Dendrite Differentiating Axons and Dendrites – (a) shows an axon branching off the soma, while (b) shows a branching dendrite. Both constructs appear very similar and are not easily differentiated close to the soma. This spurs a need to create a tag to mark only one of the two structures. Both constructs look essentially the same, showing why computers are not able to tell the difference
  11. 11. How Can We Fix Those Issues? • Using genetically encoded tags to increase EM contrast at only the axon, close to the soma. – This region is called the axon initial segment – Engineered Ascorbate Peroxidase (APEX) is a recently created tag that increases EM contrast – The protein AnkyrinG is localized to the axon initial segment
  12. 12. Components of the Plasmid • APEX is a 28 kDa monomeric peroxidase. The peroxidase, upon the addition of H2O2, catalyzes the oxidation of diaminobenzidine to create a local precipitate, which when treated with OsO4 gives local electron microscopy contrast. • Fluorescent proteins – ‘X’FP for checking expression • The protein AnkyrinG is localized to the axon initial segment • The Tol1 transposon system for incorporating the plasmid DNA into the zebrafish genome
  13. 13. Methods pDon122 Vector BackbonepEGFP-N1 Vector Backbone AnkG-XFP XbaI HindIII AnkG-XFP XbaIHindIII pDon122-mCherry-GCaMP6f Connexin43-GFP- APEX2 XbaI Connexin43-EGFP APEX2 AscI Starting Plasmids, AnkG-XFP, pDon122-mCherry-GCaMP6f, Connexin43-GFP-APEX2 – plasmids were amplified in DH5 and extracted via Qiagen Maxi-prep, then digested at appropriate restriction sites for subsequent gel extraction pcDNA3 Vector Backbone Starting Plasmids
  14. 14. Methods Continued HindIII AnkG-XFP XbaIHindIII XbaI pDon122 Vector Backbone AscI APEX2 XbaI Cut and Purified DNA Fragments – AnkG-XFP and pDon122-mCherry-GCaMP6f were cut at HindIII and XbaI. Connexin43-GFP-APEX2 was cut at AscI and XbaI 10 kb 8.5 kb 3.024 kb 2 kb .4 kb AnkG-mCherry AnkG-GFP pDon122 2-Log DNA Ladder (NEB) 10 kb 7.2 kb 3 kb 1 kb .793 kb 1 kb DNA Ladder (NEB) APEX2 APEX2 Gel Purification of (a) AnkG-XFP and pDon122 Vector Backbone; (b) APEX2 – The 8.5 kb AnkG-XFP constructs were extracted via a QiaQUICK gel extraction kit after running a 0.8% agarose gel at 100 V for 1 hr. The 0.793 kb APEX2 was extracted via the same methods, after a 1.0% agarose gel at 100 V for 45 min. (a) (b)
  15. 15. Methods Continued APEX2 XbaI AnkG-XFP AnkG-XFP XbaI AscI APEX2 HindIII HindIII pDon122 Vector Backbone AscI APEX2 XbaI Ligation Steps to Obtain pDon122-AnkG-XFP-APEX2 – APEX2 was ligated into the pDon122 vector backbone. Then AnkG-XFP ws ligated into pDon122-APEX2. Finally, the plasmid pDon122-AnkG-XFP- APEX2 was created by blunt ligating the construct to fuse the site formerly XbaI on AnkG-XFP with the site formerly AscI on APEX2. pDon122 Vector Backbone XbaI pDon122 Vector Backbone HindIII Ligation of Plasmid
  16. 16. Results >10 kb ≈ 12.5 kb 10 kb 5 kb 1 kb Supercoiled DNA Ladder (NEB) pDon122-AnkG-GFP-APEX2 pDon122-AnkG-mCherry-APEX2 Ligation Product pDon122-AnkG-XFP-APEX2 – Creation of pDon122-AnkG- XFP-APEX2 was confirmed after running the ligation product on a 0.8% agarose gel for 1 hr. Confirmation of Ligation
  17. 17. Significance • This construct will allow for greater contrast to be created in specific areas of neurons, allowing for machine learning algorithms to be used in connectomics, greatly expediting the process of finding connectomes. • Determining axons, rather than dentrites, will allow for macroscale connectomes that show the linkage of brain regions • Faster creation of connectomes will allow for a more comprehensive picture of neural circuitry, and a more informed view into neurodegenerative disease
  18. 18. Significance Continued • Significance of AnkyrinG – In abnormal animals, AnkG has been seen to migrate out of the axon initial segment, and accumulate in beta-amyloid plaque • A main cause of Alzheimer’s Disease – This construct will allow for pinpointing of AnkG and the tracing of AnkG beta-amyloid plaque – Opens the possibility of AnkG mediated therapeutics
  19. 19. Further Research • Testing in zebrafish for expression • Testing of algorithms on tagged EM data • Functional Connectome – Tag synaptic vesicles to determine the strength of synapses between neurons (step after finding the structural “wiring diagram” connectome) • Studies testing link of AnkG and beta-amyloid plaque
  20. 20. References • Axer, M., Amunts, K., Grässel, D., Palm, C., Dammers, J., Axer, H., ... Zilles, K. (2011). A Novel Approach to the Human Connectome: Ultra-high Resolution Mapping of Fiber Tracts in the Brain. NeuroImage, 1091-1101. • Bertram, L., Blacker, D., Mullin, K., Keeney, D., Jones, J., Basu, S., ... Tanzi, R. (2000). Evidence for Genetic Linkage of Alzheimer's Disease to Chromosome 10q. Science, 22, 2302-2303 • Brain Disorders: By the Numbers. (2014). McGovern Institute for Brain Research at MIT. • Buckner, R., Sepulcre, J., Talukdar, T., Krienen, F., Liu, H., Hedden, T., ... Johnson, K. (2009). Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease. Journal of Neuroscience, 29(6), 1860- 1873. • Denk, W., & Horstmann, H. (2004). Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure. PLoS Biology, 2, E329-E329. • GDP Per Capita. (2013). The World Bank. • Gray, E.G. (1959) Axo-somatic and Axo-dendritic Synapses of the Cerebral Cortex: an Electron Microscope Study. Journal of Anatomy. 93:420–433. • Hill, S., Wang, Y., Riachi, I., Schurmann, F., & Markram, H. (2012). Statistical Connectivity Provides a Sufficient Foundation for Specific Functional Connectivity in Neocortical Neural Microcircuits. Proceedings of the National Academy of Sciences, E2885-E2894 • Jabr, F. (2012, October 2). The Connectome Debate: Is Mapping the Mind of a Worm Worth It? Scientific American. • Jain, V., Seung, H., & Turaga, S. (2010). Machines that Learn to Segment Images: A Crucial Technology for Connectomics. Current Opinion in Neurobiology, 653-666. • Knott, G., Marchman, H., Wall, D., & Lich, B. (2008). Serial Section Scanning Electron Microscopy of Adult Brain Tissue Using Focused Ion Beam Milling. Journal of Neuroscience, 28(12), 2959-2964. • Koga, A., Cheah, F., Hamaguchi, S., Yeo, G., & Chong, S. (2008). Germline Transgenesis of Zebrafish Using the Medaka Tol1 Transposon System. Developmental Dynamics, 237(9), 2466-2474. • Kosik, K. (2013). Diseases: Study neuron networks to tackle Alzheimer's. Nature, 503, 31-32. • Kotter, R., & Wanke, E. (2005). Mapping Brains without Coordinates. Philosophical Transactions of the Royal Society B: Biological Sciences, 751-766. • Lehrer, J. (2009). Neuroscience: Making connections. Nature, 457, 524-527. • Lewis, D., & Sweet, R. (2009). Schizophrenia from a neural circuitry perspective: Advancing toward rational pharmacological therapies. Journal of Clinical Investigation, 119(4), 706-716.
  21. 21. References Continued • Lichtman, J., Livet, J., & Sanes, J. (2008). A Technicolour Approach to the Connectome. Nature Reviews Neuroscience, 9, 417-422. • Life Expectancy at Birth. (2012). World Health Organization. • Martell, J., Deerinck, T., Sancak, Y., Poulos, T., Mootha, V., Sosinsky, G., ... Ting, A. (2012). Engineered Ascorbate Peroxidase as a Genetically Encoded Reporter for Electron Microscopy. Nature Biotechnology, 1143-1148. • Morgan, A., Turic, D., Jehu, L., Hamilton, G., Hollingworth, P., Moskvina, V., ... Williams, J. (2007). Association studies of 23 positional/functional candidate genes on chromosome 10 in late-onset Alzheimer's disease. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 762-770. • Olesen, J., & Leonardi, M. (2003). The Burden of Brain Diseases in Europe. European Journal of Neurology, 471-477. • Santuccione, A., Merlini, M., Shetty, A., Tackenberg, C., Bali, J., Ferretti, M., ... Nitsch, R. (2013). Active Vaccination with Ankyrin G Reduces B-amyloid Pathology in APP Transgenic Mice. Nature Molecular Psychiatry, 18, 358-368. • Seung, HS. (2009). Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes. Neuron, 62, 17-29. • Sporns, O. (2011). The Human Connectome: A Complex Network. Annals of the New York Academy of Sciences, 109-125. • Sporns, O., Tononi, G., & Kötter, R. (2005). The Human Connectome: A Structural Description of the Human Brain. PLoS Computational Biology, 1(4), E42-E42. • The Global Burden of Disease: 2004 Update. (2004). World Health Organization. • White, J., Southgate, E., Thomson, J., & Brenner, S. (1986). The Structure Of The Nervous System Of The Nematode Caenorhabditis Elegans. Philosophical Transactions of the Royal Society B: Biological Sciences,1-340. • World Alzheimer Report 2010: The Global Economic Impact of Dementia. (2010, September 21). Alzheimer’s Disease International. • Xu, M., Jarrell, T., Wang, Y., Cook, S., Hall, D., Emmons, S., & Gómez, S. (2013). Computer Assisted Assembly of Connectomes from Electron Micrographs: Application to Caenorhabditis elegans. PLoS ONE, E54050-E54050. • Young, M. (1993). The Organization of Neural Systems in the Primate Cerebral Cortex. Proceedings of the Royal Society B: Biological Sciences, 13-18. • Zhou, D., Lambert, S., Malen, P., Carpenter, S., Boland, L., & Bennett, V. (1998). AnkyrinG Is Required for Clustering of Voltage- gated Na Channels at Axon Initial Segments and for Normal Action Potential Firing. The Journal of Cell Biology, 143(5), 1295-1304.

×