Milan Gavrilovic

Visualising individual sequence-specific protein-DNA interactions in situ

Gene expression - a key feature for modulating cell fate-is regulated in part by histone modifications, which modulate accessibility of the chromatin to transcription factors. Until now, protein-DNA interactions (PDIs) have mostly been studied in bulk without retrieving spatial information from the sample or with poor sequence resolution. New tools are needed to reveal proteins interacting with specific DNA sequences in situ for further understanding of the orchestration of transcriptional control within the nucleus. We present herein an approach to visualise individual PDIs within cells, based on the in situ proximity ligation assay (PLA). This assay, previously used for the detection of protein-protein interactions in situ, was adapted for analysis of target PDIs, using padlock probes to identify unique DNA sequences in complex genomes. As a proof-of-principle we detected histone H3 interacting with a 26 bp consensus sequence of the Alu-repeat abundantly expressed in the human genome, but absent in mice. However, the mouse genome contains a highly similar sequence, providing a model system to analyse the selectivity of the developed methods. Although efficiency of detection currently is limiting, we conclude that in situ PLA can be used to achieve a highly selective analysis of PDIs in single cells.

Quantification of colocalization and cross-talk based on spectral angles

Common methods for quantification of colocalization in fluorescence microscopy typically require cross‐talk free images or images where cross‐talk has been eliminated by image processing, as they are based on intensity thresholding. Quantification of colocalization includes not only calculating a global measure of the degree of colocalization within an image, but also a classification of each image pixel as showing colocalized signals or not. In this paper, we present a novel, automated method for quantification of colocalization and classification of image pixels. The method, referred to as SpecDec, is based on an algorithm for spectral decomposition of multispectral data borrowed from the field of remote sensing. Pixels are classified based on hue rather than intensity. The hue distribution is presented as a histogram created by a series of steps that compensate for the quantization noise always present in digital image data, and classification rules are thereafter based on the shape of the angle histogram. Detection of colocalized signals is thus only dependent on the hue, making it possible to classify also low‐intensity objects, and decoupling image segmentation from detection of colocalization. Cross‐talk will show up as shifts of the peaks of the histogram, and thus a shift of the classification rules, making the method essentially insensitive to cross‐talk. The method can also be used to quantify and compensate for cross‐talk, independent of the microscope hardware.

Automated classification of multicolored rolling circle products in dual-channel wide-field fluorescence microscopy

Specific single‐molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single‐molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost‐efficient wide‐field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point‐like signals in dual‐channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single‐molecule detection events in situ.