Daniel Levner

Barcoding cells using cell-surface programmable DNA-binding domains

We report an approach to barcode cells through cell-surface expression of programmable zinc-finger DNA-binding domains (surface zinc fingers, sZFs). We show that sZFs enable sequence-specific labeling of living cells by dsDNA, and we develop a sequential labeling approach to image more than three cell types in mixed populations using three fluorophores. We demonstrate the versatility of sZFs through applications in which they serve as surrogate reporters, function as selective cell capture reagents and facilitate targeted cellular delivery of viruses.

How accurate can genetic predictions be

BackgroundPre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution.ResultsHere, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features.ConclusionKnowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.