NeuroSeq and cell diversity in the nervous system

The central nervous system has the most cellular diversity of any organ in the body, but how does this diversity arise?

While the presumption is that genetic programs specify each neuron type, our understanding of these programs is in its infancy. To begin uncovering the underlying design principles of neuronal architecture in the brain, scientists from the Nelson Lab at Brandeis University and the HHMI Janelia Research Campus jointly formed the NeuroSeq project to profile genetic programs in a monumental number of neurons throughout the nervous system. Selected neurons were from transgenic animals to facilitate access among the scientific community for future functional studies. While single cell sequencing is the most popular method for transcriptome profiling, its technical limitations only provide a shallow view of molecular profiles. To go deeper, the NeuroSeq program assessed transcription in pools of nearly 200 genetically identified mouse cell types. NeuroSeq captured 80% of single gene copies and could even assess splice isoforms.

What did the NeuroSeq effort find?

Interestingly, two unique classes of genes lie at the heart of adult neuronal identity. Homeobox transcription factors and long genes explain a great deal of the neuronal diversity in the central nervous system. This extends the role of homeobox genes well beyond development and into neuronal identity maintenance. It also highlights long genes as an important class of neuronal identity effectors. Long genes are long due to insertion of foreign elements, and they come with costs, namely increased energy consumption and risk of mutations. These costs seem to be overcome by the benefits of neuronal diversification. We are excited to spotlight the NeuroSeq project in providing a unique resource for future discoveries concerning neuronal diversity and function.

The data resource is available at, and the findings are described in a recent paper in eLife. Brandeis-affiliated authors on the paper include Professor Sacha Nelson, former postdoc Ken Sugino PhD ’05 (now at HHMI Janelia), current postdoc Erin Clark, and former research scientist Yasuyuki Shima.

Genome illustration

Trapping individual cell types in the mouse brain

Lines labeling cortical subplate, mesencephalic, and diencephalic cell types

Lines labeling cortical subplate, mesencephalic, and diencephalic cell types (see Fig. 7 in Shima et al.)

The complexity of the human brain depends upon the many thousands of individual types of nerve cells it contains. Even the much simpler mouse brain probably contains 10,000 or more different neuronal cell types. Brandeis scientists Yasu Shima, Sacha Nelson and colleagues report in the journal eLife on a new approach for genetically identifying and manipulating these cell types.

Cells in the brain have different functions and therefore express different genes. Important instructions for which genes to express, in which cell types, lie not only in the genes themselves, but in small pieces of DNA called enhancers found in the large spaces between genes. The Brandeis group has found a way to highjack these instructions to express other artificial genes in particular cell types in the mouse brain. Some of these artificially expressed genes (also called transgenes) simply make the cells fluorescent so they can be seen under the microscope. Other transgenes are master regulators that can be used to turn on or off any other gene of interest. This will allow scientists to activate or deactivate the cells to see how they alter behavior, or to study the function of specific genes by altering them only in some cell types without altering them everywhere in the body. In addition to developing the approach, the Brandeis group created a resource of over 150 strains of mice in which different brain cell types can be studied.


Shima Y, Sugino K, Hempel C, Shima M, Taneja P, Bullis JB, Mehta S, Lois C, Nelson SB. A mammalian enhancer trap resource for discovering and manipulating neuronal cell types. eLife. 2016;5.

Fishing for neurons

Let’s say you’re a fisherman/woman trawling for tuna out on the azure-blue waters of the Pacific. Tuna’s your desired catch, but as you drag your net through the water you notice that all manner of aquatic life gets ensnared, to say nothing of styrofoam flotsam, plastic bottles, used automotive parts, and syringes. The FDA has guidelines about these sorts of things and the folks back at Trader Joe’s won’t tolerate even trace amounts of dolphin in their tuna. Bottom line is – you need your tuna to be pure. However, fishing individual tuna out of the sea one by one is extremely labor intensive, and though it may achieve high purity, you’ll be hard pressed to meet your production quotas.  The point of all this?

Scientists in the Nelson Lab at Brandeis fish for neurons. And not just any neurons, mind you, but very specific types. The end goal is to harvest their mRNA in order to “read out” their global gene expression using microarrays or sequencing based methods. They’re not alone in these pursuits; on the contrary, cell-type-specific gene expression profiling is a burgeoning field. However, like the analogy of fish in the sea, neurons exist in a complex and crowded environment, and isolating specific cell types requires some ingenuity. Different labs have used very different methods. In a recent study published in PLoS ONE, Okaty et al. compiled and re-analyzed all of the publicly available mouse brain, cell-type-specific microarray data (including their own) in order to ask the question: can they detect evidence of contamination, “stress effects” (more on that below), or any other kind of peculiar artifacts stemming from the purification (“fishing”) methods themselves? The short answer: Yes they can.

Some methods are fairly low throughput – fishing out one cell at a time.  The Manual cell sorting method (a home grown method) dissociates brain tissue, keeps the cells alive in artificial cerebrospinal fluid (almost literally seawater), and then the cell fisherman/woman hand picks labeled cells from the cell suspension with a glass pipette under a microscope (how they’re labeled isn’t terribly relevant to this discussion). This would be like collecting seawater, transferring the fish to less dense holding tanks with artificial seawater and then sorting the yellowfin tuna from the chub mackerel, etcetera. Another of the lower throughput methods is called Laser Capture Microdissection (LCM), where the extracted mouse brain is preserved through formalin fixation or flash freezing. Then thin tissue sections are made with a microtome, and individual cells are carved out of these tissue sections with a laser beam. This would be roughly approximate to freezing a volume of seawater, and then carving out the frozen fish of choice with a laser beam (sounds complicated). The primary difference between these two methods is that Manual sorts dissociated cells, whereas LCM extracts cells from intact, but preserved tissue.  Methods like fluorescence activated cell sorting (FACS) and immunopanning (PAN) also sort dissociated cells, and with the aid of flow cytometry, automated fluorometry, and/or the power of antibody selection (cell-type-specific bait), these methods greatly exceed the yields afforded by Manual cell sorting (imagine a dense network of narrow canals in which each fish is entrained in a high velocity stream, and an automated detection system diverts tuna into one channel, chub mackerel into another, and dolphin into another). Finally, a method called translating ribosome affinity purification (TRAP) bypasses the need to sort cells and “pulls down” tagged ribosomes, mRNAs in tow, from non-preserved tissue homogenate (a process which defies fishing analogy).

As you might expect, Manual cell sorting, along with FACS and PAN, achieve the highest purity (lowest amount of contamination), whereas LCM and TRAP show strong evidence of contamination from off-target cell types. Another concern is that the stress of dissociating cells or maintaining them in artificial media may perturb gene expression (think nervous, angry, wild fish in a cramped fish tank). However, only in the case of PAN data is there evidence of these effects (elevated levels of stress-response, cell death, and immediate early genes). Finally, the TRAP method extracts only mRNAs that are actively being translated, thus differences between TRAP data and data obtained by other methods may also reveal patterns of posttranscriptional regulation. For the full story, please refer to the paper.

addendum: see also Okaty et al. J.Neurosci. 31(19):6939-6943, 2011

Protected by Akismet
Blog with WordPress

Welcome Guest | Login (Brandeis Members Only)