Dynamic and geometric analysis of short time series: a new comparative approach to cell-based biosensors

This Letter describes two approaches for sensing changes in spiking cells when only a limited amount of spike data is available. The first method detects changes in dynamically constructed local expansion rates, and the other uses spike area distributions. These two methods were tested on time series from cultured neurons. Over-sampled data was generated from experiments on single cells before and after being treated with a small concentration of channel blocker, but relatively few spikes were recorded. In the spontaneously spiking cells, the local expansion rates showed a sensitivity that correlated with the channel concentration level, while the driven cells showed no such correlation. Spike area distributions showed measurable differences between control and treated conditions for both types of spiking, and a much higher degree of sensitivity. Because these methods are based on analysis of short time series analysis, they might provide novel means for cell drug and toxin detection. Published by Elsevier Science B.V.