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gene expression

A GPS-like System for Single-Cell Analysis

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Courtesy of the Chen and Macosko labs

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?

Reference:

[1] Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, Macosko EZ. Science. 2019 Mar 29;363(6434):1463-1467.

Links:

Single Cell Analysis (NIH)

Macosko Lab (Broad Institute of Harvard and MIT, Cambridge)

Chen Lab (Broad Institute)

NIH Support: National Institute on Aging; Common Fund


Gene Duplication: New Analysis Shows How Extra Copies Split the Work

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Word cloudThe human genome contains more than 20,000 protein-coding genes, which carry the instructions for proteins essential to the structure and function of our cells, tissues and organs. Some of these genes are very similar to each other because, as the genomes of humans and other mammals evolve, glitches in DNA replication sometimes result in extra copies of a gene being made. Those duplicates can be passed along to subsequent generations and, on very rare occasions, usually at a much later point in time, acquire additional modifications that may enable them to serve new biological functions. By starting with a protein shape that has already been fine-tuned for one function, evolution can produce a new function more rapidly than starting from scratch.

Pretty cool! But it leads to a question that’s long perplexed evolutionary biologists: Why don’t duplicate genes vanish from the gene pool almost as soon as they appear? After all, instantly doubling the amount of protein produced in an organism is usually a recipe for disaster—just think what might happen to a human baby born with twice as much insulin or clotting factor as normal. At the very least, duplicate genes should be unnecessary and therefore vulnerable to being degraded into functionless pseudogenes as new mutations arise over time

An NIH-supported team offers a possible answer to this question in a study published in the journal Science. Based on their analysis of duplicate gene pairs in the human and mouse genomes, the researchers suggest that extra genes persist in the genome because of rapid changes in gene activity. Instead of the original gene producing 100 percent of a protein in the body, the gene duo quickly divvies up the job [1]. For instance, the original gene might produce roughly 50 percent and its duplicate the other 50 percent. Most importantly, organisms find the right balance and the duplicate genes can easily survive to be passed along to their offspring, providing fodder for continued evolution.


Snapshots of Life: Fish Awash in Color

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Skin cells from a genetically engineered zebrafish

Credit: Chen-Hui Chen, Duke University

If this image makes you think of a modern art, you’re not alone. But what you’re actually seeing are hundreds of live cells from a tiny bit (0.0003348 square inches) of skin on the tail fin of a genetically engineered adult zebrafish. Zebrafish are normally found in tropical freshwater and are a favorite research model to study vertebrate development and tissue regeneration. The cells have been labeled with a cool, new fluorescent imaging tool called Skinbow. It uniquely color codes cells by getting them to express genes encoding red, green, and blue fluorescent proteins at levels that are randomly determined. The different ratios of these colorful proteins mix to give each cell a distinctive hue when imaged under a microscope. Here, you can see more than 70 detectable Skinbow colors that make individual cells as visually distinct from one another as jellybeans in a jar.

Skinbow is the creation of NIH-supported scientists Chen-Hui Chen and Kenneth Poss at Duke University, Durham, NC, with imaging computational help from collaborators Stefano Di Talia and Alberto Puliafito. As reported recently in the journal Developmental Cell [1], Skinbow’s distinctive spectrum of color occurs primarily in the outermost part of the skin in a layer of non-dividing epithelial cells. Using Skinbow, Poss and colleagues tracked these epithelial cells, individually and as a group, over their entire 2 to 3 week lifespans in the zebrafish. This gave them an unprecedented opportunity to track the cellular dynamics of wound healing or the regeneration of lost tissue over time. While Skinbow only works in zebrafish for now, in theory, it could be adapted to mice and maybe even humans to study skin and possibly other organs.