While humans have them, too, the Purkinje cells pictured here are in the brain of a mouse. The head-like shapes you see in the image are the so-called soma, or the neurons’ cell bodies. Extending downwards are the heavily branched dendrites, which act like large antennae, receiving thousands of inputs from the rest of the body.
One reason this picture is such a standout, explains Busch, is because of what you don’t see. You’ll notice that only a few cells are fluorescently labeled and therefore lit up in green, leaving the rest in shadows. As a result, it’s possible to trace the detailed branches of individual Purkinje cells and make out their intricate forms. As it turns out, this ability to trace Purkinje cells so precisely led Busch, who is a graduate student in Christian Hansel’s lab focused on the neurobiology of learning and memory, to a surprising discovery, which the pair recently reported in Science.1
Purkinje cells connect to nerve fibers that “climb up” from the brain stem, which connects your brain and spinal cord to help control breathing, heart rate, balance and more. Scientists thought that each Purkinje cell received only one of these climbing fibers from the brain stem on its single primary branch.
However, after carefully tracing thousands of Purkinje cells in brain tissue from both mice and humans, the researchers have now shown that Purkinje cells and climbing fibers don’t always have a simple one-to-one relationship. In fact, Busch and Hansel found more than 95 percent of human Purkinje cells have multiple primary branches, not just one. In mice, that ratio is closer to 50 percent.
The researchers went on to show that mouse Purkinje cells with multiple primary branches often also receive multiple climbing fibers. The discovery rewrites the textbook idea of how Purkinje cells in the brain and climbing fibers from the brainstem are anatomically arranged.
Not surprisingly, those extra connections in the cerebellum (located in the back of the brain) also have important functional implications. When Purkinje cells have just one climbing fiber input, the new study shows, the whole cell receives each signal equally and responds accordingly. But, in cells with multiple climbing fiber inputs, the researchers could detect differences across a single cell depending on which primary branch received an input.
What this means is that Purkinje cells in the brain have much more computational power than had been appreciated. That extra brain power has important implications for understanding how brain circuits can adapt and respond to changes outside the body that now warrant further study. The new findings may have implications also for understanding the role of these Purkinje cell connections in some neurological and developmental disorders, including autism2 and a movement disorder known as cerebellar ataxia.
As they say, a picture is worth a thousand words. And this winning image comes as a reminder that we still have more to learn from careful study of basic brain anatomy, with important implications for human health and disease.
A few years ago, I highlighted a really cool technology called Drop-seq for simultaneously analyzing the gene expression activity inside thousands of individual cells. Today, one of its creators, Evan Macosko, reports significant progress in developing even better tools for single-cell analysis—with support from an NIH Director’s New Innovator Award.
In a paper in the journal Science, Macosko, Fei Chen, and colleagues at the Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, recently unveiled another exciting creation called Slide-seq [1]. This technology acts as a GPS-like system for mapping the exact location of each of the thousands of individual cells undergoing genomic analysis in a tissue sample.
This 3D video shows the exquisite precision of this new cellular form of GPS, which was used to generate a high-resolution map of the different cell types found in a tiny cube of mouse brain tissue. Specifically, it provides locations of the cell types and gene expression in the hippocampal regions called CA1 (green), CA2/3 (blue), and dentate gyrus (red).
Because using Slide-seq in the lab requires no specialized imaging equipment or skills, it should prove valuable to researchers across many different biomedical disciplines who want to look at cellular relationships or study gene activity in tissues, organs, or even whole organisms.
How does Slide-seq work? Macosko says one of the main innovations is an inexpensive rubber-coated glass slide nicknamed a puck. About 3 millimeters in diameter, pucks are studded with tens of thousands of 10 micron-sized beads, each one decorated with a random snippet of genetic material—an RNA barcode—that serves as its unique identifier of the bead.
The barcodes are sequenced en masse, and the exact location of each barcoded bead is indexed using innovative software developed by a team led by Chen, who is an NIH Director’s Early Independence awardee.
Then, the researchers place a sample of fresh-frozen tissue (typically, 10 micrometers, or 0.00039 inches, thick) on the puck and dissolve the tissue, lysing the cells and releasing their messenger RNA (mRNA). That leaves only the barcoded beads binding the mRNA transcripts expressed by the cells in the tissue—a biological record of the genes that were turned on at the time the sample was frozen.
The barcoded mRNA is then sequenced. The spatial position of each mRNA molecule can be inferred, using the reference index on the puck. This gives researchers a great deal of biological information about the cells in the tissue, often including their cell type and their gene expression pattern. All the data can then be mapped out in ways similar to those seen in this video, which was created using data from 66 pucks.
Slide-seq has been tested on a range of tissues from both mouse and human, replicating results from similar maps created using existing approaches, but also uncovering new biology. For example, in the mouse cerebellum, Slide-seq allowed the researchers to detect bands of variable gene activity across the tissues. This intriguing finding suggests that there may be subpopulations of cells in this part of the brain that have gene activity influenced by their physical locations.
Such results demonstrate the value of combining cell location with genomic information. In fact, Macosko now hopes to use Slide-seq to study the response of brain cells that are located near the buildup of damaged amyloid protein associated with the early-stage Alzheimer’s disease. Meanwhile, Chen is interested in pursuing cell lineage studies in a variety of tissues to see how and where changes in the molecular dynamics of tissues can lead to disease.
These are just a few examples of how Slide-seq will add to the investigative power of single-cell analysis in the years ahead. In meantime, the Macosko and Chen labs are working hard to develop even more innovative approaches to this rapidly emerging areas of biomedical research, so who knows what “seq” we will be talking about next?
