Barcoding Gets the Drop on Single-Cell Transcriptomes

May 26, 2015
Barcoding Gets the Drop on Single-Cell Transcriptomes
Source: iStock/© goldi

Averages shout, and individuals whisper. That, in a nutshell, is the frustration of single-cell transcriptomics. Although innumerable cell types have unique gene-expression profiles, they are hard to discern. Great, thundering herds of cells typically give up their RNA to be sequenced all in one batch. It is possible to effectively cut cells from the herd with a technique called RNA-seq, which enables RNA sequencing with single-cell resolution. Still, RNA-seq does not provide an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing.

This limitation, however, may soon be lifted. According to two teams of scientists at Harvard Medical School, microfluidics can be combined with genetic barcoding to bring about high-throughput single-cell transcriptomics. One team, in the laboratory of Steven McCarroll, developed a technique called Drop-seq. The other team, in the laboratory of Marc Kirschner, developed a technique called inDrops.

Both methods use microfluidic devices to co-encapsulate cells in nanometer-sized water droplets along with genetic-barcoding beads. The droplets get created in a tiny assembly line, streaming along a channel the width of a human hair. The bead barcodes get attached to the genes in each cell, so that scientists can sequence the genes all in one batch and still trace each gene back to the cell it came from.

The McCarroll and Kirschner labs were able to advance work initiated by the researchers Evan Macosko and Allon Klein, respectively. Macosko and Klein make their beads in different ways. The droplets get broken up at different steps in the process. Other aspects of the chemistry diverge. But the result is the same.

After running a single batch of cells through Drop-seq or inDrops, scientists "can see which genes are expressed in the entire sample--and can sort by each individual cell," said Klein. They can then use computer software to uncover patterns in the mix, including which cells have similar gene expression profiles. That provides a way to classify what cell types were in the original tissue—and to possibly discover new ones.

Current methods allow researchers to generate 96 single-cell expression profiles in a day for several thousand dollars. Drop-seq, by comparison, enables 10,000 profiles a day for 6.5 cents each.

"If you're a biologist with an interesting question in mind, this approach could shine a light on the problem without bankrupting you," said Macosko. "It finally makes gene expression profiling on a cell-by-cell level tractable and accessible. I think it's something biologists in a lot of fields will want to use."

Both teams published their work May 21 in the journal Cell. The McCarroll team published an article entitled, “Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.” The Kirshner team published an article entitled, “Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells.”

“We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes,” wrote the McCarroll team. “Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.”

“The [inDrops] method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays,” wrote the Kirschner team. “We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal.”

McCarroll, Macosko, and their colleagues are excited to explore the brain with Drop-seq. With luck, that will include discovering new cell types, constructing a global architecture of those cell types in the brain and understanding brain development and function as they relate to disease.

Among the questions they want to pursue are: What are all the cell types that make the brain work? How do these cell types vary in their functions and responses to stimuli? What cell populations are missing or malfunctioning in schizophrenia, autism and other disorders of the brain?

Kirschner, Klein, and their colleagues, meanwhile, are keenly interested in other areas, including stem cell development. "Does a population of cells that we initially think is uniform actually have some substructure?" Klein wants to know; he's trying to find out by studying immune cells and different kinds of adult stem cells. "What is the nature of an early developing stem cell? What endows those cells with a pluripotent state? Is gene expression more plastic or does it have a well-defined state that's different from a more mature cell? How is its fate determined?"

Using inDrops, Klein and team have confirmed prior findings that suggest even embryonic stem cells are not uniform. They found previously undiscovered cell types in the population they studied, as well as cells in intermediate stages that they suspect are converting from one type to another.

Although both teams are excited by the massive amounts of data they and other researchers will obtain from Drop-seq and inDrops, they realize the sheer volume of information poses a problem as well. "We have thousands of cells expressing tens of thousands of genes. We can't look in 20,000 directions to pick out interesting features," explained Klein.

Machine learning is able to do some of that, and the teams have already employed new statistical techniques. Still, Kirschner has called on mathematicians and computer scientists to develop new ideas about how to analyze and extract useful information about our biology from the mountains of data that are on the horizon.