An interacting multiple model filter-based autofocus strategy for confocal time-lapse microscopy

Gene expression and other cellular processes are stochastic, thus their study requires observing multiple events in multiple cells. Therefore, confocal microscopy cell imaging has recently gained much interest. In time-lapse imaging, adjustments are needed at short intervals to compensate for focus drift. There are several automated methods for this purpose. In general, before acquiring higher resolution images, software-based autofocus algorithms require a set of low-resolution images along the z-axis to determine the plane for which a predefined focusing function is maximized. These algorithms require 10100 z-slices each time, and there is no fixed number or upper limit of required z-slices that ensures optimal focusing. The higher is this number, the stronger is photo bleaching, hampering the feasibility of long-time series measurements. We propose a new focusing strategy in time-lapse imaging. The algorithm relies on the nature and predictability of the focus drift. We first show that the focus drift curve is predictable within a small error bound in standard experimental setups. We, then, exploit the interactingmultiple model filter algorithm to predict the drift at time, t, based on the measurement at time t - 1. This allows a drastic reduction of thenumberof required z-slices for focusdrift correction, largely overcoming the problem of photo bleaching. In addition, we propose a new set of functions for focusing in time-lapse imaging, derived from preexisting ones. We demonstrate the method's efficiency in time-lapse imaging of Escherichia coli cells expressing MS2d-GFP tagged RNA molecules.