Back

Potassium-Selective Nanoelectrode Arrays for Single-Cell Profiling of human iPSC-Derived Cardiomyocytes

Meganathan, D. P.; Banzon, R.; Casanova, A.; Sarikhani, E.; Mahato, K.; Vu, H.; Reade, S.; Ambika Devarajan, I.; Tahir, A.; Sasi, L.; Spain, L.; Wang, J.; Jahed, Z.

2026-02-18 pharmacology and toxicology
10.64898/2026.02.13.705576 bioRxiv
Show abstract

Potassium ion (K) dynamics are central to cardiac electrophysiology, with early disruptions in K flux often preceding arrhythmia and contractile dysfunction. However, current sensing technologies, such as patch-clamp, Microelectrode arrays (MEAs), and fluorescent indicators, either lack chemical specificity for K or are unsuitable for long-term, single-cell analysis. Conventional ion-selective electrodes (ISEs), while more selective, are limited by bulk-phase design and poor spatial resolution. To address these limitations, we present KINESIS (K-Ion Nano-Electrode Selective Interface System), a nanofabricated, cell-compliant platform that enables direct, label-free potentiometric measurement of K gradients with subcellular precision. KINESIS features high-aspect-ratio nanopillars coated with a valinomycin-based K recognition membrane, forming a stable, non-invasive interface with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This architecture allows localized, Nernstian sensing of K efflux or depletion without disrupting cell membranes. Pharmacological validation shows distinct potential shifts in response to caffeine and ouabain. KINESIS thus offers a highly selective, spatially resolved approach for studying K handling in cardiotoxicity screening and patient-specific disease modeling.

Published in ACS Nano (predicted rank #4) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.