Data for: The energetics of rapid mechanotransduction

Cells throughout the human body detect mechanical forces. While it is known that the rapid (millisecond) detection of mechanical forces is mediated by force-gated ion channels, a detailed quantitative understanding of cells as sensors of mechanical energy is still lacking. Here, we combine atomic force microscopy with patch-clamp electrophysiology to determine the physical limits of cells expressing the force-gated ion channels Piezo1, Piezo2, TREK1, and TRAAK. We find that, depending on the ion channel expressed, cells can function either as proportional or non-linear transducers of mechanical energy, detect mechanical energies as little as ~100 fJ, and with a resolution of up to ~1 fJ. These specific energetic values depend on cell size, channel density, and cytoskeletal architecture. We also make the surprising discovery that cells can transduce forces either nearly instantaneously (<1 ms), or with substantial time delay (~10 ms). Using a chimeric experimental approach and simulat..., AFM data was collected on a modified Digital Instruments Bioscope and passed through a breakout box to be integrated with electrophysiology data in a HEKA EPC10 amplifier. Details regarding the collection of raw data are given below. Raw data were preprocessed in batches using the package EphysAFM using the preprocess module. The preprocessed output is saved into an Arrow file ending in the suffix \"_preprocessed.arrow\" This data was then further summarized for downstream analysis using the module summarize in the same package. Details for package use can be found in the repository.
AFM Stimulation of HEK293T Cells
All stimuli were applied at 5 s intervals. Prior to initial contact, a coarse step-motor moved the cantilever into contact with the cell in 1-5 µm steps. Contact was determined by a sudden increase in the photodetector signal. Following contact, the step-motor moved the cantilever towards the cell in 0.2-1 µm increments until mechanotransduction currents were observed. Stimul..., All data was preprocessed and summarized using a package developed in Python. The full reproducible environment along with installation and usage instructions can be found at EphysAFM.