| Autor: |
Ma J; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada., Tran G; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada., Wan AMD; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada., Young EWK; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada., Kumacheva E; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.; Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada., Iscove NN; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada., Zandstra PW; School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada. peter.zandstra@ubc.ca.; Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. peter.zandstra@ubc.ca. |
| Abstrakt: |
Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications. |
