Accurate and quantitative single cell sequencing with digital droplet MDA PI: Adam R. Abate Abstract: Accurate single cell genomic sequencing is important for applications ranging from copy number variation analysis in cancer to the investigation of uncultivable microbes. To provide sufficient DNA for sequencing, a single cell's genome must be massively amplified. However, existing methods introduce errors (MALBAC) or result in significant bias (MDA), yielding low-confidence genomes with gaps in coverage. In this proposal, we will develop single cell digital droplet MDA (sc-ddMDA), a method that will combine the uniform amplification of MALBAC with the high copying accuracy of MDA. This project will extend our preliminary results in which we have demonstrated the ability to reliably and uniformly amplify the genomes of just ten bacterial cells. Using novel biochemical and microfluidic methods, we will push the limit to a single cell and will implement automation for high-throughput processing of, ultimately, thousands of single cells. Importantly, we will provide two versions of the technology, one using no specialized equipment accessible to any lab with molecular biology expertise, and a higher-throughput version using microfluidics and robotic automation.
Aim 1 : Optimize methods to uniformly amplify and sequence the genomes of single cells.
Aim 2 : Develop high-throughput single cell ddMDA.
Aim 3 : Implement microfluidic pre-amplification for accurate, high-throughput single cell sequencing.
The objective of the proposed study will be to enable accurate, quantitative, and high throughput sequencing of single cells. The technologies developed will advance the fields of genomics and single cell analysis and will provide new tools to understand pathogen evolution and human disease. These technologies should ultimately have broad biomedical, biotechnological, and industrial applications.
| Siltanen, Christian A; Cole, Russell H; Poust, Sean et al. (2018) An Oil-Free Picodrop Bioassay Platform for Synthetic Biology. Sci Rep 8:7913 |
| Hatori, Makiko N; Kim, Samuel C; Abate, Adam R (2018) Particle-Templated Emulsification for Microfluidics-Free Digital Biology. Anal Chem 90:9813-9820 |
| Delley, Cyrille L; Liu, Leqian; Sarhan, Maen F et al. (2018) Combined aptamer and transcriptome sequencing of single cells. Sci Rep 8:2919 |
| Kim, Samuel C; Clark, Iain C; Shahi, Payam et al. (2018) Single-Cell RT-PCR in Microfluidic Droplets with Integrated Chemical Lysis. Anal Chem 90:1273-1279 |
| Demaree, Benjamin; Weisgerber, Daniel; Lan, Freeman et al. (2018) An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing. J Vis Exp : |
| Shahi, Payam; Kim, Samuel C; Haliburton, John R et al. (2017) Abseq: Ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep 7:44447 |
| Haliburton, J R; Kim, S C; Clark, I C et al. (2017) Efficient extraction of oil from droplet microfluidic emulsions. Biomicrofluidics 11:034111 |
| Lim, Shaun W; Lance, Shea T; Stedman, Kenneth M et al. (2017) PCR-activated cell sorting as a general, cultivation-free method for high-throughput identification and enrichment of virus hosts. J Virol Methods 242:14-21 |
| Haliburton, John R; Shao, Wenjun; Deutschbauer, Adam et al. (2017) Genetic interaction mapping with microfluidic-based single cell sequencing. PLoS One 12:e0171302 |
| Lan, Freeman; Demaree, Benjamin; Ahmed, Noorsher et al. (2017) Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat Biotechnol 35:640-646 |
Showing the most recent 10 out of 14 publications
