Sequencing the Connectome: An entirely new approach to neuroanatomy at the finest level

Neuroanatomy can happen at many scales. At the highest end, we can ask if certain areas of the brain have connections between them: for example, does the lateral geniculate nucleus (LGN) send projections to primary visual cortex? (hint: yes). Through electrical stimulation and tract-tracing methods, we’ve gotten pretty good at finding this out. We can then look at connections within these areas: which layers of visual cortex connect to each other? Cell-staining and microscopy make this investigation possible. And we can even go further and try to learn about what kind of connections exist within a single layer of cortex (an area that is only fractions of a millimeter thick). Advanced, automated imaging techniques have allowed much progress here. Not only that, we can even look across scales by investigating, for example, which layer of LGN sends connections to which layer of visual cortex. Importantly, the tight relationship between structure and function in the brain means that learning about all these connections provides functional insights in addition to purely anatomical ones.

Now, taking this connectivity quest to its logical extreme, the most we could ask to know is every connection made by every cell in the brain. This information is called the connectome, and it has been causing quite a buzz recently in the neuro-world. What that level of detail could tell us about how the brain computes and how it differs across species and individuals is an area of hot debate. Some people feel the effort to investigate the connectome is a waste of resources, and useless in any case because a single network with constant connectivity can still show vastly different behavior under different conditions (inputs, modulators, etc.). Others feel that much information about a network and its functions is stored in how cells connect to each other. The connectome’s most prominent proponent, Sebastian Seung has almost religious-like zeal for the connectome as the be all and end all in determining who we are.

For the imaging approach, programs like NeuroTrace automatically scan layers of EM images trying to trace neuronal projections.

But if Seung’s mantra of “we are our connectome” is true, then the vast majority of us are going through a major identity crisis. The fact is, there is only one creature for which we have deciphered the connectome: C. elegans. The 7000 synapses between the 302 neurons of this little worm took over 50 person-years to obtain using available imaging techniques. Advances in automated analysis of electron microscopy data are occurring rapidly and can speed up and simplify this process. But scaling this up to, say, a mouse brain (~100 billion synapses) or a human brain (~100 trillion synapses) still seems pretty impractical. Our desire to know the connectome is nowhere near our ability to obtain it.

Enter Anthony Zador, a Cold Spring Harbor researcher with a different approach. His recent paper outlines the delightfully acronymed BOINC (barcoding of individual neuronal connections) method of seeing synapses. Actually, the value of BOINC is in the fact that it takes the “seeing” out of the process. Rather than relying on imaging methods to determine the existence of a synapse, BOINC harnesses the power of genetic sequencing. As the paper points out, DNA sequencing speeds have been increasing nearly as quickly as the prices have been dropping. This powerful combination makes it ideal for such an immensely large project as connectome mapping.

So how does it work? As the name suggests, the process requires each neuron be artificially tagged with its own “barcode”, or sequence of nucleotides.  Hosting these barcodes in specific types of viral vectors inside the neuron can allow them to be transferred via synapses to other neurons. Thus, a single neuron will contain its own barcode as well as the barcodes of all the neurons with which it synapses. Next, these barcode sequences will need to be joined inside the cell so that their association can be made known later via sequencing. So, if in the process of sequencing you come across a chunk of DNA with cell A’s unique nucleotide sequence followed by cell X’s unique nucleotide sequence, then you can infer that there’s a connection between cell A and cell X. Do this for all the DNA chunks, get all the connections, and you’ve made yourself a connectome. (Fittingly, the word ‘connectome’ was actually inspired by ‘genome.’ This method should’ve been obvious!).

A schematic of the new approach (BOINC)

Now of course this idea is only in its very early stages. The exact implementation is yet to be determined and plenty of questions about the specifics already abound. To start, giving each neuron a unique DNA label is not a trivial problem. The authors suggest a similar method to the combinatorial one used in creating all the different fluorescent colors for Brainbow, but replacing the florophores with DNA sequences. The next stage, the act of transferring the barcodes across synapses, is luckily not as complicated as it may seem. Viruses specialize in spreading themselves, and rabies and pseudorabies viruses have been used in conjunction with dyes and other markers to trace neural connections for years. This method has its difficulties of course, such as  ensuring that the barcode-carrying virus stops transporting itself once it reaches the post-synaptic cell (lest it replicate and invade all cells, giving a lot of false positives). And once the synapse-jumping is accomplished, there is the matter of getting the barcodes to join together in the right way to ensure that which cell is pre- and which is post-synaptic remains decipherable.

And even if all these specifics are successfully tackled, the method itself has its limitations. Essentially what it provides is a connectivity matrix, a list of cells which are defined solely by their connections. We can’t say much else about the type of cells (their location, the neurotransmitter they use), the type of synapse (excitatory v. inhibitory, it’s location on the post-synaptic cell, the morphology), or activity levels. All these aspects are potentially important to our understanding of neuronal wiring. But that’s no reason to dismiss BOINC. The current methodology also has its limiatations and if BOINC works it will be a vast improvement in terms of output rates. It also has the potential to be combined with other traditional techniques to investigate the above properties, yielding a far more complete picture than we have right now.

Overall, we don’t know how important knowing the connectome is for our understanding of average brain function or differences amongst individuals. But making claims about its role when we know so little about it is illogical. Even “anti-connectomists” should recognize that getting results which validate their dismissal of such fine-level anatomical analysis requires that we have the fine-level anatomy to test in the first place. Basically, when it comes to the connectome, we don’t know enough to say that we don’t need to know it. Furthermore, the sequencing approach promises a smaller time and money commitment, quieting those who worry about the resources going into connectomism. So breakthroughs in approaches like this should be applauded by all, both for their potential to advance the field and for encouraging others to think a little outside of their methodological box.

Zador, A., Dubnau, J., Oyibo, H., Zhan, H., Cao, G., & Peikon, I. (2012). Sequencing the Connectome PLoS Biology, 10 (10) DOI: 10.1371/journal.pbio.1001411