Development of one-step single-cell RT-PCR for the massively parallel detection of gene expression

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 87-91).The United Nations estimates that over 35 million people are afflicted with HIV/AIDS in the world. Highly active antiretroviral treatments (HAART) that use a combination of drugs that target the virus at different stages of its life cycle are effective at reducing the HIV plasma levels below levels detectable by the most sensitive assays. However, upon termination of HAART, HIV RNA transcripts are measurable in the blood after 2-3 weeks. This relapse is attributed to the presence of a reservoir of latently infected cells, such as resting CD4+ T-cells. The latent reservoir in resting memory CD4+ T-cells has been estimated to decay with a half-life of as long as 44 months, thus hindering the eradication of HIV. Current knowledge of latent reservoirs came from the isolation of possible reservoir populations by cell surface markers and querying each population for the presence of HIV RNA. These measurements do not have single cell resolution so the exact frequencies of latently infected cells are not known. In this thesis, we developed and optimized a method to detect cellular transcripts of single cells in an array of nanowells. The limit of detection of the assay was approximately 1.4 copies of DNA in a 125 pL well (18.6 fM) with a false positive rate as low as 4.6x10-5. Combining this assay along with image-based cytometry and microengraving, we generated a multivariate dataset on single cells to understand the relationships between cell phenotype, transcribed genes, and secreted products. We showed that gene expression could not be a surrogate measure for antibody secretion. We were also able to detect rare cells in a population at a frequency as low as 1 in 10,000. We then applied the technology to samples from a patient on HAART for more than 1.5 years. We were able to detect an infection rate of 1:3000 cells that had low levels of HIV RNA in bulk.by Yuan Gong.Ph. D