Abstract
We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse.More information is available at https://pgpresearch.med.harvard.edu/poolseq/
Highlights
The screening of many cell lines for specific phenotypes is commonly performed to discover factors that confer donor cell specific effects
An algorithm that accurately predicts the proportion of individuals within a simulated mixed pool To test the efficacy of our algorithm, we designed and implemented a simulation program to generate simulated data for testing the robustness of the prediction given the number of donors, number of Single nucleotide polymorphism (SNP) as well as sequencing read-depth
Taking these parameters as input, the program first randomly simulates the true proportion for each donor within the mixed pool
Summary
The screening of many cell lines for specific phenotypes is commonly performed to discover factors that confer donor cell specific effects. Other studies have used primary cells from different donors to identify genetic variants associated with various cellular phenotypes. The authors reported six loci associated with immune response to pathogens by measuring cytokine production in peripheral blood mononuclear cells from hundreds of different donors [4]. Other groups measured the transcriptional response to pathogenic stimulus in primary monocytes obtained from many African and European individuals [5, 6]. In these studies, the experiments were performed on cells from each individual donor separately. With increasing numbers of donors, generating data from cells from more donors would require more research
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