SAN ANTONIO, TX (GenomeWeb) – Researchers have more and more single-cell sequencing approaches to choose from to use in their work, speakers at this year's Association for Biomolecular Resource Facilities meeting said.
Last year, the ABRF's Genomics Research Group reported on the performance of various single-cell RNA sequencing platforms on a set of treated and untreated breast cancer cell lines to find that each had their plusses and minuses.
At this year's meeting, speakers discussed some of those advantages and drawbacks, and how different single-cell technologies complement each other and other omics techniques. "Single-cell sequencing is changing our understanding of biological processes," said ETH Zurich's Emilio Yángüez during an ABRF session. He further noted there is now a plethora of different protocols and solutions to isolate and analyze single cells and some may be better suited for certain types of studies.
"What platform you choose depends on your goals," John Ashton, an assistant professor of microbiology and immunology and co-director of the Functional Genomics Center at the University of Rochester, said in another session.
Some researchers are even choosing more than one platform for the same project, or are combining tools from different fields to meet their needs.
Yángüez said that sometimes the best approach for a given project might be to rely on a combination of single-cell platforms. The Functional Genomics Center Zurich, where he works as a single-cell sequencing specialist, has a number of platforms for various aspects of the single-cell sequencing workflow, including the Fluidigm C1, as well as 10X Genomics' Chromium, Illumina's Smart-Seq2, and the SPLiT-seq method developed at the University of Washington.
At his center, Yángüez has found that some types of samples are better suited to certain platforms, based on, among a number of factors, the number of cells, their heterogeneity, the amount of coverage needed, and what downstream analyses are being pursued. For instance, he said that highly heterogeneous or clinical samples were best suited for the 10X platform, while projects with low numbers of samples or that need high sequencing coverage were best for Smart-Seq2.
Yángüez has also used multiple platforms for the same project. For example, for a project that aimed to study tumor cells, he initially used the 10X platform to characterize the cells and then used the identified cell markers to sort them for more in-depth characterization of the different cell populations using the Smart-Seq2 protocol.
The University of Auburn's Fernando Biase, meanwhile, combined a cell lineage tracing approach with single-cell sequencing as part of his work studying differentiation in early development to form an approach he's dubbed Rainbow-seq.
Rainbow-seq relies on Brainbow, a gene construct developed by a group at Harvard University, that contains four different fluorescent protein genes and can be integrated into cells' genomes. Then, when treated, the cells fluoresce and can be traced as they divide. By combining this with Smart-Seq2, Biase followed which cell developed into what part of the blastocyst and looked for corresponding changes in gene expression. Whether cells are homogenous or differ early in development has been an outstanding question, he said.
Biase found that, in mice, cell lineages have divergent molecular profiles as early as the four-cell stage. He further noted that cells appear to differ even at the two-cell stage, as there is an unbalanced distribution of daughter cells from those cells, with one lineage contributing more cells to the inner cell mass relative to the trophectoderm.
This increase of single-cell approaches and application also means there is a need to optimize more steps and develop best practices.
"Single-cell RNA-seq technology and methods are rapidly evolving and improving and we basically have to accommodate for that," Ashton said.
These new technologies, he said, need to be "optimized and tested to see how they work in your hands."