Chromatin structure and nucleosome positioning play a crucial role in gene expression regulation. Nucleosome positioning is often inferred by the protection of underlying DNA to nucleases. Because nucleases are excluded by plasma membranes, chromatin mapping requires isolating nuclei from cells and digesting the chromatin in situ with nucleases. The quality of this data is highly dependent on the nuclei preparation. Here we describe a method to isolate nuclei from the budding yeast Saccharomyces cerevisiae and the use of micrococcal nuclease to map the chromatin structure at the RNR3 gene. Nuclei isolated by this procedure are competent for many of the common chromatin mapping and detection procedures.

Our understanding of the input-output function of single cells has been substantially advanced by biophysically accurate multi-compartmental models. The large number of parameters needing hand tuning in these models has, however, somewhat hampered their applicability and interpretability. Here we propose a simple and well-founded method for automatic estimation of many of these key parameters: 1) the spatial distribution of channel densities on the cell’s membrane; 2) the spatiotemporal pattern of synaptic input; 3) the channels’ reversal potentials; 4) the intercompartmental conductances; and 5) the noise level in each compartment. We assume experimental access to: a) the spatiotemporal voltage signal in the dendrite (or some contiguous subpart thereof, e.g. via voltage sensitive imaging techniques), b) an approximate kinetic description of the channels and synapses present in each compartment, and c) the morphology of the part of the neuron under investigation. The key observation is that, given data a)-c), all of the parameters 1)-4) may be simultaneously inferred by a version of constrained linear regression; this regression, in turn, is efficiently solved using standard algorithms, without any “local minima” problems despite the large number of parameters and complex dynamics. The noise level 5) may also be estimated by standard techniques. We demonstrate the method’s accuracy on several model datasets, and describe techniques for quantifying the uncertainty in our estimates.