More info after the jump.

Press release:

Dear Colleague,

I am writing to let you know that we have just launched our first data set mapping gene expression across an adult human brain.   As with all Allen Institute Atlases, the data is freely available at www.brain-map.org For this first release, we have included:

· Spatially mapped microarray data for over 700 distinct anatomic locations throughout the brain and containing information for over 62,000 gene probes with 93% of known genes represented by at least 2 probes

· A hierarchical anatomic naming system (ontology) integrating leading schemes for different brain regions

· Searches by anatomic region, probe, or gene, as well as queries for comparing expression among pre-selected structure sets

· Expert neuroanatomic annotation of brain structures and delineation of areas sampled for microarray analysis

· An in situ hybridization study from a separate adult human brain that characterizes 55 genes in subcortical regions extending from the front of the caudate through posterior substantia nigra, and a smaller set of 10 genes through the hypothalamus. The 55-gene set focuses on the glutamatergic and GABAergic systems.

We hope that you might find these to be valuable resources, and will be continuing to add data from additional brains (8-10 in total) and improving our tools for analysis over the next few years.  As always, we appreciate your feedback, both good and bad.  If the tools and data have helped in some meaningful way, please let us know…though we can track basic usage of the data through web hits, your personal stories really help justify the continued support of the existing resources and our ability to create more.

Allan R. Jones, PhD
Chief Executive Officer
Allen Institute for Brain Science
551 N. 34th St.
Seattle WA 98103

My colleague Robert Wang and I created an online collaborative DNA Vector analysis program called everyVECTOR. We were initially motivated because all of the existing commercial software are really expensive and the free ones are not as nicely designed/intuitive to use. Also, I was always frustrated with collaborators sending me text files of DNA sequences that weren’t annotated and confusing to read.

[...] You can also the public interface (without registration) by visiting here.

We released everyVECTOR last week and so far we have received good responses from people. We have around 200 users now from the past week, mostly from the Stanford and bay area universities.

I hope all of you molecular biologists can give everyVECTOR a try and give Feng some feedback. It certainly seems much more affordable (ie. free) than its well-known competitors. I’m a big fan of web-based tools myself and find them invaluable in doing simple sequence calculations for my own projects (one of my favs is the Sequence Manipulation Suite).

Also, apologies for the decreased posting frequency… I’m trying to graduate these days and there just doesn’t seem to be enough hours for everything. I hope to return to full force soon.

I’m a fan of it because it is an open-access journal featuring a “tiered system” and more.  From their website:

The Frontiers Journal Series is not just another journal. It is a new approach to scientific publishing. As service to scientists, it is driven by researchers for researchers but it also serves the interests of the general public. Frontiers disseminates research in a tiered system that begins with original articles submitted to Specialty Journals. It evaluates research truly democratically and objectively based on the reading activity of the scientific communities and the public. And it drives the most outstanding and relevant research up to the next tier journals, the Field Journals.

I’m a big fan of the variety of specialty journals they have:

• Aging Neuroscience
• Behavioral Neuroscience
• Cellular Neuroscience
• Computational Neuroscience
• Enteric Neuroscience
• Evolutionary Neuroscience
• Human Neuroscience
• Integrative Neuroscience
• Molecular Neuroscience
• Neural Circuits
• Neuroanatomy
• Neuroenergetics
• Neuroengineering
• Neurogenesis
• Neurogenomics
• Neuroinformatics
• Neuromethods
• Neuropharamacology
• Neuroprosthetics
• Neurorobotics
• Synaptic Neuroscience
• Systems Neuroscience

In this work, Cambridge et al. generated a dox analog, cyanodoxycycline, that is better retained inside cells (reduced membrane permeability) and put it inside a stable photoactivatable (UV) cage. Instead of making the system entirely genetic, they use small molecule uncaging (an established technology) to make problem simpler. In this case, standard Tet genetics can be used (thus leveraging existing model organism Tet lines) and the novel caged Dox can be easily added to food, etc. I think this is a great example of finding the simplest solution to a problem that at first seems very complex.

And the uncaging results are quite impressive. Here are two spatial patterns of gene expression in hippocampal slice culture, a smiley face and a single neuron, achieved by clamping down the field stop before illumination:

We mentioned the innovative MARCM technique in a previous post. Here, Lee and colleagues extend MARCM (Mosaic Analysis with a Repressible Cell Marker, pronounced mark-em) to twin-spot MARCM, where both cells from a mitotic event are labeled with different colors fluorescent proteins. In regular MARCM, only one cell is labeled and the other daughter cell remained unlabeled. Like the original MARCM, this technique lets you distinguish between what would otherwise be identical pairs/clonal populations of cells during development and gain insight into the (lack of) stereotypy in development. Under the hood, twin-spot MARCM is a bit different: Instead of relying on GAL80 suppression of GAL4-driven transcription (regular MARCM), twin-spot MARCM uses RNAi directed against the protein-coding transcripts.

Since MARCM can be difficult to understand, here’s an excellent, detailed yet easy-to-understand description written for a bio lab class from Richard Vogt at the University of South Carolina:

1. A fly is constructed with the following genotype: (promotor)Gal4; UAS-GFP. In this fly, the promoter drives the expression of a transcription factor called Gal4, and Gal4 binds to and activates a regulatory site referred to as “UAS” (upstream activating sequence). Activation of the UAS site drives expression of GFP (green fluorescent protein) which fluoresces green when stimulated by blue light.
2. This fly also contains a gene encoding and expressing a protein called “Gal80″; Gal80 suppresses the action of Gal4. If Gal80 is expressed, no GFP is made and no green fluorescence can occur.
3. This fly also contains a complex of genes referred to as the FLP/FRT system; FLP is a transcription factor that activates the FRT site, which is situated adjacent to the Gal80 site. Further more, at least in our case, the FLP is driven by a “heat shock” promoter (hs). All this means is that when you raise the temperature of the animal to 37^oC, this activates the hs promoter which activates the expression of FLP which activates the FRT site.

           Something I’ve not mentioned yet… there is also an FRT site adjacent to the UAS-GFP site. Something else I’ve not mentioned yet, the FRT-UAS-GFP site and the FRT-Gal80 site are on the same chromosome, but importantly on different chromatids.

4. So we make a bunch of fly embryos that have all this stuff in them. Procedurally this is really easy, since the genes have already been put in the flies, and all we have to do is take virgin females of one stain (FRT-Gal80) and mate them to males of another strain (FRT-UAS-GFP) and… POW… we have fly embryos that have all this stuff in them.
5. All the cells in the embryos we now have are capable of expressing GFP except for the one problem… all the cells are expressing Gal80 which is blocking the expression of GFP. We need to turn off Gal80 expression. We do this by activating the FLP/FRT system.
6. Normally, a cell has two copies of each chromosome called chromatids. In our case, the chromatids are different, one containing by FRT-Gal80 and the other containing FRT-UAS-GFP. This cell can not express GFP because Gal80 is present. During mitosis, the chromatids are duplicated and sort to produce two identical chromatid pairs, both pairs consisting of a FRT-Gal80 chromatid and a FRT-UAS-GFP chromatid. Like their mother, neither daughter cell would be able to express GFP, again because Gal80 is present.

           HOWEVER, AND HERE IS THE TRICK… if the FRT is activated during mitosis, it induces a recombination event (recombination normally only occurs during meiosis), creating one chromatid pair that contains only UAS-GFP and another chromatid pair that contains only Gal80. One of the resulting daughter cells now contains no Gal80, and suddenly is able to express GFP and fluoresce green light. And any additional cells produced by this daughter will also express GFP.

From the News and Views, I was intrigued to learn that previous transcriptome analyses of adult human brains found very little difference in gene expression between brain areas:

[...] this suggests that it is the gene expression during development that largely determines higher brain functions by specifying the complexity of neural connections. Numerically, the most important genes relating to cognitive differences between species may be genes that specify how the machinery is put together. In support of this hypothesis, many of the identified differentially expressed genes in this study are related to processes involved in connection formation, such as axonal guidance and cell adhesion.

An impressive 76% of all human genes are expressed in the developing fetal brain. Of those, 33% are differentially expressed over brain regions (13 regions were examined) and 28% are alternatively spliced. The differentially expressed genes are also ones that seem to have evolved the most recently. Even in these early (midgestation) stages, left-right asymmetry was seen, such as the localization of the language-associated FOXP2 genes to Broca’s area.

Of interest to computational folks, they find that gene expression follows power-law scaling (as many other naturally occurring “small-worlds” networks do) with certain hub genes connected to many others and certain spoke genes with relatively few connections. Unsupervised hierarchical clustering is used in this analysis.

Two approaches

Broadly, there are two approaches to synaptic tagging: 1) mRNA is distributed widely and translated locally at tagged locations; 2) protein products are distributed widely in the bodies of dendrites but only contact/off-load at tagged synaptic specializations. This News & Views gives a nice overview of the two papers, which find approach 1) in Aplysia cultures with sensorin mRNA and approach 2) in rat hippocampal neurons with Vesl-1S/Homer-1a protein. It amazes me that both were found pretty much simultaneously, but that might have more to do with the use of the photoconvertible Dendra2 protein than anything else.

With both approaches, we still don’t know why mRNA/protein is directed to a certain location. That is, we can visualize synaptic tagging but we don’t know what is the tag, its ontogeny, or the mechanism of tagging. But that might not be so important to understanding more about neural function. These new tools might allow us to image plasticity at many synapses at once, perhaps even in vivo. But before that, more work is needed… does the optical signal (from the Dendra fusion protein) correlate with degree of potentiation? Can we detect plasticity in the opposite direction, ie. synaptic depression, through another tag?  (As a sidenote to approach 1), the use of 5′ and 3′ UTRs as a sort of molecular zipcode is also intriguing.)