The cerebral cortex of the human brain contains more than 160 trillion synapses, or sites of communication between neurons. Each neuron receives synaptic inputs from hundreds or even thousands of different neurons, and each sends outputs to a similar number of target neurons, spread out over a large distance. Thus, deciphering the “connectome,” or complete connectivity diagram, of even one type of neuron in the cortex poses enormous challenges.
Dr. Lichtman’s research focuses on the study of neural connectivity and how it changes as animals develop and age. With his colleagues he has developed a number of tools that permit synaptic level analysis of neural connections. These include activity dependent uptake of fluorescent dyes, transgenic approaches to label individual nerve cells, and “combinatoric” methods (e.g., DiOlistics, Brainbow, and NPS) to label many nerve cells in the same tissue. In addition, he has helped develop automated electron microscopy approaches for large scale neural circuit reconstruction. These connectomic methods seek to make it routine to acquire neural circuit data in any nervous system. The central focus of his work is to describe the ways in which developing nervous systems change to accommodate information that is acquired by experience.
The Lichtman Lab and Zhuang Lab in our center have been developing novel imaging methods to overcome these challenges. One method that the Lichtman Lab has optimized significantly in recent years is serial electron microscopy-based reconstruction of brain tissue. While it has for some time been possible to image ultrathin slices of brain tissue by electron microscopy and piece together three dimensional reconstructions, it’s an incredibly cumbersome process that has until recently allowed only very small, fragmented glimpses into connectome. But thanks to the recent acceleration of several steps in the pipeline from brain slicing and imaging to analysis, it’s now possible to reconstruct neural circuitry at a much more rapid pace and gain increasingly larger glimpses into the connectome.
Complementing this approach is a super-resolution light microscopy technique invented in the Zhuang lab - called stochastic optical reconstruction microscopy (STORM). This method allows fluorescent labeling and analysis of the molecular architecture of synapses at a resolution that is not possible with conventional light microscopy - in which, due to the diffraction of light, structures smaller than several hundred nanometers cannot be distinguished from one another. STORM overcomes this limitation by using photo-switchable fluorescent probes to temporally separate the otherwise spatially overlapping images of individual molecules, and it has recently been used by the Zhuang lab to begin mapping the precise synaptic fields of neurons.
As part of the Conte Center, the labs are using these, as well as viral tracing methods, to visualize the connectome of PV-cells in the prefrontal cortex, believed to be particularly vulnerable in mental illness. Of particular interest are changes in the PV-cell connectome across normal cortical development, and alternations in the PV-cell connectome in mouse models of early life stress or mental illness.
Saturated Reconstruction of a Volume of Neocortex.
Kasthuri N, Hayworth KJ, Berger DR, Schalek RL, Conchello JA, Knowles-Barley S, Lee D, Vázquez-Reina A, Kaynig V, Jones TR, Roberts M, Morgan JL, Tapia JC, Seung HS, Roncal WG, Vogelstein JT, Burns R, Sussman DL, Priebe CE, Pfister H, Lichtman JW.
Cell. 2015 Jul 30;162(3):648-61. doi: 10.1016/j.cell.2015.06.054.
Multispectral labeling technique to map many neighboring axonal projections in the same tissue.
Tsuriel S, Gudes S, Draft RW, Binshtok AM, Lichtman JW.
Nat Methods. 2015 Apr 27. doi: 10.1038/nmeth.3367. [Epub ahead of print]
Correlative stochastic optical reconstruction microscopy and electron microscopy.
Kim D, Deerinck TJ, Sigal YM, Babcock HP, Ellisman MH, Zhuang X.
PLoS One. 2015 Apr 15;10(4):e0124581.
Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.
Hayworth KJ, Xu CS, Lu Z, Knott GW, Fetter RD, Tapia JC, Lichtman JW, Hess HF.
Nat Methods. 2015 Apr;12(4):319-22. doi: 10.1038/nmeth.3292. Epub 2015 Feb 16.
The big data challenges of connectomics.
Lichtman JW, Pfister H, Shavit N.
Nat Neurosci. 2014 Nov;17(11):1448-54. doi: 10.1038/nn.3837. Epub 2014 Oct 28. Review.
Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits.
Hayworth KJ, Morgan JL, Schalek R, Berger DR, Hildebrand DG, Lichtman JW.
Front Neural Circuits. 2014 Jun 27;8:68. doi: 10.3389/fncir.2014.00068. eCollection 2014.
Exploring the connectome: petascale volume visualization of microscopy data streams.
Beyer J, Hadwiger M, Al-Awami A, Jeong WK, Kasthuri N, Lichtman JW, Pfister H.
IEEE Comput Graph Appl. 2013 Jul-Aug;33(4):50-61.
Characterization and development of photoactivatable fluorescent proteins for single-molecule-based superresolution imaging.
Wang S, Moffitt JR, Dempsey GT, Xie XS, Zhuang X.
Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8452-7
Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex.
Tomassy GS, Berger DR, Chen HH, Kasthuri N, Hayworth KJ, Vercelli A, Seung HS, Lichtman JW, Arlotta P.
Science. 2014 Apr 18;344(6181):319-24. doi: 10.1126/science.1249766.
ConnectomeExplorer: query-guided visual analysis of large volumetric neuroscience data.
Beyer J, Al-Awami A, Kasthuri N, Lichtman JW, Pfister H, Hadwiger M.
IEEE Trans Vis Comput Graph. 2013 Dec;19(12):2868-77. doi: 10.1109/TVCG.2013.142.
Improved tools for the Brainbow toolbox.
Cai D, Cohen KB, Luo T, Lichtman JW, Sanes JR.
Nat Methods. 2013 Jun;10(6):540-7.
Why not connectomics?
Morgan JL, Lichtman JW.
Nat Methods. 2013 Jun;10(6):494-500.
3D multicolor super-resolution imaging offers improved accuracy in neuron tracing.
Lakadamyali M, Babcock H, Bates M, Zhuang X, Lichtman J.
PLoS One. 2012;7(1):e30826.