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In his 1979 Scientific American article, Francis Crick wrote: "a method that would make it possible to inject one neuron with a substance that would then clearly stain all the neurons connected to it, and no others, would be invaluable. So would a method by which all neurons of just one type could be inactivated, leaving the others more or less unaltered." The Callaway lab has developed both of these methods and is beginning to use them to reveal the organization and function of the brain circuits responsible for visual perception.
The need for these methods arises from the "impossible jungle" that composes the circuitry of the brain. In many parts of the brain, particularly the neocortex, there are numerous distinct neuron types whose complex axonal and dendritic arbors are intimately intertwined. Recent work in the Callaway lab has shown that each distinct cell type is connected differently from it neighbors and even two adjacent cells of the same type can receive input from different sources. These observations obviate the need for the methods proposed by Crick. One must have experimental tools that manipulate and unravel neural circuits with the same level of precision at which they are organized.
A traditional way that researchers have tested the function of different regions of the visual system is to selectively interfere with the flow of information in specific brain areas, typically through irreversible gross lesions. A new genetic technique developed in the Callaway lab takes this approach to the level of single cell types, which is the equivalent of having a "molecular scalpel" and allows reversible inactivation. The Callaway laboratory recently published a paper describing a method based on expression of an insect neuropeptide receptor in mammalian neurons. This receptor can be expressed in specific neuron types making them susceptible to inactivation following exposure to the insect neuropeptide. More recently they manufactured AAV-expressing the receptor and find that it works beautifully for selective, quickly reversible silencing of visual responses and spontaneous activity in several brain areas and species including the thalamus and cortex of rats, ferrets and monkeys. Future studies will use this technology to inactivate specific visual pathways in primates to test the effects on visual responses of higher cortical neurons and on perception in awake monkeys. In collaboration with the Goulding laboratory at the Salk Institute, this method has revealed the role of specific types of inhibitory spinal cord neurons in the regulation of locomotor rhythms.
Labeling of "one neuron with a substance that would then clearly stain all the neurons connected to it, and no others" has been a sort of holy grail in the field of neural circuitry and many attempts have failed. But recent studies in the Callaway lab have overcome these difficulties. To accomplish this they developed a strategy which uses a neurotropic virus that has been modified so that its genome is missing a gene required to make infectious viral particles. And in collaboration with the Salk laboratory of John Young they further modified the virus so that it would only be able to infect cells expressing a receptor for an avian virus. By expressing the avian virus receptor, as well as the missing viral gene, it was possible to initiate viral infection in that one cell, which by virtue of complementation of the missing gene, was also able to produce infectious viral particles. These viral particles then spread to and labeled all of the cells connected to the first neuron, but the virus could spread no further because the new cells did not express the missing gene. This method will be invaluable for teasing apart the detailed circuitry of the brain, one cell at a time. The method is also compatible with in vivo physiological assays that will allow the visual responses of single neurons to be directly correlated with their detailed connectivity.
The overall goal of this research is to study the systems and networks regulated by transcriptional complexes in neuronal differentiation and stem cell maintenance, which includes the downstream alternative splicing and microRNA networks. Nervous system development depends crucially on signaling events that lead to the maintenance of a stem cell and progenitor state, and differentiation from the stem cell or progenitor state to a neuronal or glial lineage. The neuron restrictive silencing factor (NRSF/REST) is thought to be a master regulator of neuronal cell fate, repressing the transcription of neuronal genes in non-neuronal cells and in neural progenitors. NRSF binds to a 21-23 bp motif in the promoter and introns of target genes. We have identified at least 600 binding sites conserved across mammals with comparative genomic analysis.
Of particular interest is the regulation of NRSF target genes in the context of adult neuronal stem cell differentiation to neurons, as well as human embryonic stem cell differentiation to neurons. The goal is to elucidate a mapping between trans-acting repressive complexes seeded by NRSF and gene expression in neuronal cell fate. NRSF regulates microRNAs and alternative splicing. MicroRNAs are important in cell fate and development. Alternative splicing is prevalent in the nervous system and important in neurogenesis. We are identifying potential NRSF targets that control networks of RNA binding proteins that are important in nervous system development.
CellMap is an entirely new way to organize information about cell function. The new format is based not on lists or chemical similarities but rather on graphical models of cell structures and biological machines that work together; these machines could be complex organelles or simpler chemical complexes that carry out specific functions. CellMap is based on the comparison of the sequences of genes in multiple species of animals. The CellMap database resulting from these comparisons provides a means for understanding the distribution of cellular components and their interactions at different levels of organization. A great challenge and opportunity for CellMap lies in its application to understanding the brain. The central nervous system contains a large number of different cell types defined by different gene expression patterns. In addition, we will also need to move beyond the description of the individual cells and their connections.
We will also need to understand what happens when hundreds of proteins become organized to form macromolecular machines capable of carrying out the cell's functions. Some of these proteins will be key players in carrying out specific cell functions, while others will be regulatory. Cascades of protein-protein interactions will form natural hierarchies, but feedback between them will make it difficult to sort out the direct flow of information from the indirect regulatory systems. These include cell signaling pathways studied in Dr Tony Hunter's lab and electrical signaling in neurons studied by Dr. Paul Slesinger and others.
Molecular processes within cells occur in highly organized subcellular spaces, where ligand and effector molecules participate in complex signaling pathways. At this level of resolution, one cannot consider molecular signals to be well-mixed, but instead attention must center on subcellular microdomains, which include both intra- and extracellular regions such as cell and organelle membranes, which are complex barriers to ligand diffusion. MCell is a program that uses Monte Carlo techniques to simulate microdomains and their associated biochemical signaling mechanisms.
The 3-D structure of the cell is itself an important part of the computation and needs to be accurately included in the simulations. This is accomplished through reconstructing subcellular structure from serial electron microscope sections. Coupled with the rapidly expanding experimental database of three-dimensional structure, molecular constituents, biochemical pathways, and physiological measurements, this new approach to subcellular signaling will help provide quantitative answers to many existing questions centered on the organization organelles such as the endoplasmic reticulum within cells, the functional relationships between neurons and glia, and the modulation of information transmission at synapses.
The goal of this work is to identify the nature of computations carried out by specific parts of the brain. The approach is to combine theory and constraints provided by the structure and function of neural circuits to describe the mathematical operations performed by structures such as the primary visual cortex. The theories that result always make specific predictions that can be tested by comparison with experimental observations.
One important set of constraints arises from the fact that the vertebrate brain has a scalable architecture, and clues about the nature of computations used by the brain are contained in the laws that govern brain scaling. An important part of the program, then, is to determine experimentally the scaling laws to which neural circuits conform, and to understand the computational significance of these laws. Scaling laws - such as the rules that govern axon arbors when they are made larger - are established first in fish (fish brains, like the whole fish, continue to grow larger throughout life), and then extended to mammals.
The approach used generally is not modeling but rather theory more in the style of physics where general properties of the system are used to determine its behavior. In biology, every system has to carry out a particular job, and general properties must be derived from insights into how the system does what it has evolved to do. One must, then, identify properties that are optimized or conserved, and use these properties to make quantitative predictions about the system's behavior. An example of a theory of this sort would be one in which the receptive field structure of, say, retinal ganglion cells is derived from the requirement that the receptive fields cover the visual space smoothly in spite of some randomness in the growth of dendritic arbors.
Tatyana Sharpee is an assistant professor at the Crick-Jacobs Center for Theoretical and Computational Biology. She received her PhD in theoretical physics from Michigan State University in 2001, and completed her postdoctoral training in computational and theoretical neuroscience at the University of California, San Francisco. Tatyana uses methods from statistical physics and information theory to study how the brain encodes sensory stimuli. She is interested in how sensory processing in the brain is matched to the statistics of real-world signals, why might the evolved hierarchy of neural representations be optimal, and how it can be best adapted to track rapid changes in the statistics of inputs.