Multiplexed Nucleic Acid Programmable Protein Arrays

Yu, Xiaobo; Song, Lusheng; Petritis, Brianne; Bian, Xiaofang; Wang, Haoyu; Viloria, Jennifer; Park, Jin; Bui, Hoang; Li, Han; Wang, Jie; Liu, Lei; Yang, Liuhui; Duan, Hu; McMurray, David N.; Achkar, Jacqueline M.; Magee, Mitch; Qiu, Ji; LaBaer, Joshua

The optimal number of proteins to multiplex depends upon several factors, including hit frequency, cost, array space, and number of proteins. As the frequency of hits in the screen increases, more proteins will need to be tested as individual features during the verification step. Taken to the extreme, if one protein per multiplexed feature were a hit (hit rate = 1/M), all multiplexed features would require deconvolution, making the multiplexing approach impractical. However, such a high hit rate is not reflected by data collected by numerous studies; for example, the hit rate was < 5% in most of our previous NAPPA-based screening studies with 10k human genes ( Figure 1B) . We generated a mathematical model (Materials and Methods) to find the optimal M that would take into consideration array space and the cost of screening and verifying hits using our 10k protein human collection at different hit rates. In Figure 1C , the x-axis represents the number of genes per spot (M) while the y-axis represents the number of spots or proteins that are needed for the two-step screening process. Notably, when the hit rate is < 5% for 10k proteins, a relatively small number of spots would be needed for the entire study (screening + verification) with 5 proteins multiplexed per feature in the initial screen, thus representing a good compromise between the number of initial features screened and the subsequent number of features that would be needed for deconvolution and verification.