A semiconductor 96-microplate platform for electrical-imaging based high-throughput phenotypic screening.

High-content imaging for compound and genetic profiling is popular for drug discovery but limited to endpoint images of fixed cells. Conversely, electronic-based devices offer label-free, live cell functional information but suffer from limited spatial resolution or throughput. Here, we introduce a semiconductor 96-microplate platform for high-resolution, real-time impedance imaging. Each well features 4096 electrodes at 25 µm spatial resolution and a miniaturized data interface allows 8× parallel plate operation (768 total wells) for increased throughput. Electric field impedance measurements capture >20 parameter images including cell barrier, attachment, flatness, and motility every 15 min during experiments. We apply this technology to characterize 16 cell types, from primary epithelial to suspension cells, and quantify heterogeneity in mixed co-cultures. Screening 904 compounds across 13 semiconductor microplates reveals 25 distinct responses, demonstrating the platform's potential for mechanism of action profiling. The scalability and translatability of this semiconductor platform expands high-throughput mechanism of action profiling and phenotypic drug discovery applications.

A semiconductor 96-microplate platform for electrical-imaging based high-throughput phenotypic screening.

Profiling compounds and genetic perturbations via high-content imaging has become increasingly popular for drug discovery, but the technique is limited to endpoint images of fixed cells. In contrast, electronic-based devices offer label-free, functional information of live cells, yet current approaches suffer from low-spatial resolution or single-well throughput. Here, we report a semiconductor 96-microplate platform designed for high-resolution real-time impedance "imaging" at scale. Each well features 4,096 electrodes at 25 µm spatial resolution while a miniaturized data interface allows 8× parallel plate operation (768 total wells) within each incubator for enhanced throughputs. New electric field-based, multi-frequency measurement techniques capture >20 parameter images including tissue barrier, cell-surface attachment, cell flatness, and motility every 15 min throughout experiments. Using these real-time readouts, we characterized 16 cell types, ranging from primary epithelial to suspension, and quantified heterogeneity in mixed epithelial and mesenchymal co-cultures. A proof-of-concept screen of 904 diverse compounds using 13 semiconductor microplates demonstrates the platform's capability for mechanism of action (MOA) profiling with 25 distinct responses identified. The scalability of the semiconductor platform combined with the translatability of the high dimensional live-cell functional parameters expands high-throughput MOA profiling and phenotypic drug discovery applications.

Apolipoprotein L1 (APOL1) risk variant toxicity depends on the haplotype background.

The Apolipoprotein L1 (APOL1) risk variants G1 and G2 are associated with high rates of kidney disease in African Americans in genetic studies. However, our understanding of APOL1 biology has lagged far behind. Here we report that engineering G1 and G2 mutations on unnatural haplotype backgrounds instead of on the specific G1 and G2 haplotype backgrounds that occur in nature profoundly alters APOL1-mediated cytotoxicity in experimental systems. Thus, in addition to helping resolve some important controversies in the APOL1 field, our demonstration of the critical influence of haplotype background may apply more generally to the study of other genetic variants that cause or predispose to human disease.

APOL1 Kidney Risk Variants Induce Cell Death via Mitochondrial Translocation and Opening of the Mitochondrial Permeability Transition Pore.

BACKGROUND: Genetic Variants in Apolipoprotein L1 (APOL1) are associated with large increases in CKD rates among African Americans. Experiments in cell and mouse models suggest that these risk-related polymorphisms are toxic gain-of-function variants that cause kidney dysfunction, following a recessive mode of inheritance. Recent data in trypanosomes and in human cells indicate that such variants may cause toxicity through their effects on mitochondria. METHODS: To examine the molecular mechanisms underlying APOL1 risk variant-induced mitochondrial dysfunction, we generated tetracycline-inducible HEK293 T-REx cells stably expressing the APOL1 nonrisk G0 variant or APOL1 risk variants. Using these cells, we mapped the molecular pathway from mitochondrial import of APOL1 protein to APOL1-induced cell death with small interfering RNA knockdowns, pharmacologic inhibitors, blue native PAGE, mass spectrometry, and assessment of mitochondrial permeability transition pore function. RESULTS: We found that the APOL1 G0 and risk variant proteins shared the same import pathway into the mitochondrial matrix. Once inside, G0 remained monomeric, whereas risk variant proteins were prone to forming higher-order oligomers. Both nonrisk G0 and risk variant proteins bound components of the mitochondrial permeability transition pore, but only risk variant proteins activated pore opening. Blocking mitochondrial import of APOL1 risk variants largely eliminated oligomer formation and also rescued toxicity. CONCLUSIONS: Our study illuminates important differences in the molecular behavior of APOL1 nonrisk and risk variants, and our observations suggest a mechanism that may explain the very different functional effects of these variants, despite the lack of consistently observed differences in trafficking patterns, intracellular localization, or binding partners. Variant-dependent differences in oligomerization pattern may underlie APOL1's recessive, gain-of-function biology.

Domain-domain interactions in filamin A (16-23) impose a hierarchy of unfolding forces.

The quaternary structure of Filamin A (FLNa) 16-23 was recently shown to exhibit multiple domain-domain interactions that lead to a propeller-like construction. Here we present single-molecule force spectroscopy experiments to show a wide variety of mechanical responses of this molecule and compare it with its linear counterpart FLNa 1-8. The compact structure of FLNa 16-23 leads to a broad distribution of rupture forces and end-to-end lengths in the force-extension mode and multiple unraveling timescales in the force-clamp mode. Moreover, a subset of force-extension trajectories reveals a mechanical hierarchy in which the rupture of domain-domain interactions at high forces (>200 pN) liberates the unfolding of individual domains at low forces (∼100 pN). This mechanism may also explain the order-of-magnitude difference in the rates of the biexponential fits to the distribution of unfolding dwell times under force-clamp. Overall, FLNa 16-23 under a force of 100 pN is more compliant than the linear FLNa 1-8. Because a physiological role of FLNa is to crosslink actin filaments, this range of responses allows it to accommodate a broad spectrum of forces exerted by the cell and its environment.

Force-clamp analysis techniques give highest rank to stretched exponential unfolding kinetics in ubiquitin.

Force-clamp spectroscopy reveals the unfolding and disulfide bond rupture times of single protein molecules as a function of the stretching force, point mutations, and solvent conditions. The statistics of these times reveal whether the protein domains are independent of one another, the mechanical hierarchy in the polyprotein chain, and the functional form of the probability distribution from which they originate. It is therefore important to use robust statistical tests to decipher the correct theoretical model underlying the process. Here, we develop multiple techniques to compare the well-established experimental data set on ubiquitin with existing theoretical models as a case study. We show that robustness against filtering, agreement with a maximum likelihood function that takes into account experimental artifacts, the Kuiper statistic test, and alignment with synthetic data all identify the Weibull or stretched exponential distribution as the best fitting model. Our results are inconsistent with recently proposed models of Gaussian disorder in the energy landscape or noise in the applied force as explanations for the observed nonexponential kinetics. Because the physical model in the fit affects the characteristic unfolding time, these results have important implications on our understanding of the biological function of proteins.