While single-cell whole-genome bisulfite sequencing is rapidly evolving, the associated price tag hasn’t been quite as dynamic. This lack of dynamism is primarily because current single-cell whole-genome bisulfite sequencing (scWGBS) methods are limited by the need to process each cell in individual reaction vessels and also low alignment rates. These limitations mean a high (financial) price needs to be paid to get enough information from single cells. But now, the lab of Andrew Adey at Oregon Health & Science University (USA) has slashed costs and brings forth single-cell combinatorial indexing for methylation analysis (sci-MET).
The talented team’s new sci-MET method employs their combinatorial indexing strategy that was previously applied to other DNA, RNA, and chromatin accessibility sequencing methods. A combinatorial indexing strategy eliminates the need for single-cell reaction vessels by employing two unique barcodes to tag each cell’s DNA and keep track of the single-cell it was derived from during later pooling steps.
Here’s how this new approach works:
- Cell/Nuclei preparation:
- Cells are dissociated
- Nuclei are isolated, and nucleosomes are depleted
- Tagmentation:
- Fluorescence activated nuclei sorting (FANS) is used to collect between 1 to 2000 nuclei per a well
- A transposase tags the DNA with adaptors that are depleted of cytosine and thus will not be converted by subsequent bisulfite treatment
- scWGBS library preparation:
- The samples from all wells are pooled
- FANS is used to redistribute the samples, where there are 22 nuclei per well
- Each nuclei has a unique transposase barcode from the tagmentation step
- The DNA is bisulfite converted
- Random priming is used to amplify the libraries and add on the second sequencing adaptor
- scWGBS:
- The samples are pooled, cleaned, and sequenced
The team then put sci-MET through its paces with a few interesting experiments:
- They applied sci-MET to a pure human cell population (GM12878) and found that the results closely matches the expected methylation profile
- They also applied sci-MET to a mix of three human cell lines (GM12878, HEK293, and primary inguinal fibroblast), where they:
- Achieved read alignment rates of 68 ± 8%, a figure comparable to bulk WGBS and a big step forward for scWGBS
- Demonstrated that sci-MET discriminates cell types from mixed populations
- Moving in vivo, the authors applied sci-MET to brain tissue (primary cortical tissue) from three mice, where it was able to discriminate clusters of cell types (excitatory neurons, inhibitory neurons, and non-neuronal) based on CG and CH (non-CpG) methylation
- A comparison to cortical differentially methylated regions (DMRs) identified by a different single-nuclei WGBS study revealed distinct enrichment in the neuronal clusters for different sets of excitatory and inhibitory DMRs, allowing them to further classify their sets of neuronal clusters into cell populations
- Overall, the team generated 3,282 single-cell libraries with sci-MET
Senior author Andrew Adey shares, “We can profile thousands of cells simultaneously. This technology reduces the cost to prepare single-cell DNA methylation libraries to less than 50 cents per cell from $20 to $50 per cell. It will be incredibly valuable in any environment where there is cell type heterogeneity. The major areas of interest will be cancer and neuroscience, but we are also applying it to cardiovascular disease.”
Learn how to get the most out of your single-cell budget over at Nature Biotechnology, April 2018