Name: Péter Horváth
Affiliation: Director, Institute of Biochemistry Biological Research Centre
Primary research interest: microscopy imaging, single-cell analysis, deep visual proteomics, digital pathology.
Title of the lecture: Life beyond the pixels: single-cell analysis using deep learning and image analysis methods
Keywords: microscopy imaging,single-cell analysis, image segmentation, deep visual proteomics, AI-driven tumor diagnostics, digital pathology
Summary: In this talk I will give an overview of the computational steps in the analysis of single cell-based large-scale microscopy experiments. First, I will present a novel microscopic image correction method designed to eliminate illumination and uneven background effects which, left uncorrected, corrupt intensity-based measurements. New, single-cell image segmentation methods will be presented using differential geometry, energy minimization and deep learning methods. I will discuss machine learning software tools capable of identifying cellular phenotypes based on features extracted from the image. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. To improve the learning speed and accuracy, we propose an active learning scheme that selects the most informative cell samples. Our recently developed Deep Visual Proteomics (Method of the year 2024) for single-cell isolation methods, based on laser-microcapturing and patch clamping, utilizes the selection and extraction of specific cell(s) using the above machine learning models. I will show that we successfully performed DNA and RNA sequencing, proteomics, lipidomics and targeted electrophysiology measurements on the selected cells and their usage in personalized precision cancer therapies.