Information-Rich 6D Single-Molecule Imaging Using Optimization

Abstract: When studying biochemical processes, localizing the targets alone will not paint a complete picture. We also need techniques to probe how biomolecules interact with one another, including how molecules are organized into larger structures, how they are orientated with respect to surrounding molecules, and how local chemical parameters, like pH and hydrophobicity, vary spatially and temporally. My dissertation focuses on single-molecule orientation localization microscopy (SMOLM). The objective is to develop imaging techniques for measuring how molecules are oriented with respect to their surrounding molecules, namely, 3D orientation.

Achieving optimal imaging performance requires careful design of both the forward process (optical hardware) and the inverse process (estimation algorithm). I first introduce our engineered microscope, pixOL, which I designed to optimally modulate the emission light collected from single molecules. The pixOL microscope encodes the 3D orientation and 3D position information of molecules into the shape of the dipole-spread functions, the vectorial analog of scalar point-spread functions, captured by a camera.

With the new pixOL microscope in hand, whose images change dramatically as emitters translate and rotate in 3D, I will next present a machine learning-based estimation algorithm, termed Deep-SMOLM, to decode information contained within the captured images. Deep-SMOLM is designed to deconvolve a set of intensity patterns, which we term basis images, from raw data rather than directly estimate 3D orientations. This architecture allows us to leverage the physical forward model to estimate robustly and simultaneously the 3D orientation and 2D position of single molecules.

Lastly, I will present our endeavor to map nanoscale structures inside biomolecular condensates. Using environmentally sensitive fluorogenic dyes, we visualized heterogeneous structures inside condensates called hubs, which arise from transient physical crosslinks formed through protein interactions. SMOLM maps the orientation of dyes at the interface of condensates. Our images show that proteins at the interface are organized with specific orientations rather than simply oriented randomly.

Information-Rich 6D Single-Molecule Imaging Using Optimization

Abstract: When studying biochemical processes, localizing the targets alone will not paint a complete picture. We also need techniques to probe how biomolecules interact with one another, including how molecules are organized into larger structures, how they are orientated with respect to surrounding molecules, and how local chemical parameters, like pH and hydrophobicity, vary spatially and temporally. My dissertation focuses on single-molecule orientation localization microscopy (SMOLM). The objective is to develop imaging techniques for measuring how molecules are oriented with respect to their surrounding molecules, namely, 3D orientation.

Achieving optimal imaging performance requires careful design of both the forward process (optical hardware) and the inverse process (estimation algorithm). I first introduce our engineered microscope, pixOL, which I designed to optimally modulate the emission light collected from single molecules. The pixOL microscope encodes the 3D orientation and 3D position information of molecules into the shape of the dipole-spread functions, the vectorial analog of scalar point-spread functions, captured by a camera.

With the new pixOL microscope in hand, whose images change dramatically as emitters translate and rotate in 3D, I will next present a machine learning-based estimation algorithm, termed Deep-SMOLM, to decode information contained within the captured images. Deep-SMOLM is designed to deconvolve a set of intensity patterns, which we term basis images, from raw data rather than directly estimate 3D orientations. This architecture allows us to leverage the physical forward model to estimate robustly and simultaneously the 3D orientation and 2D position of single molecules.

Lastly, I will present our endeavor to map nanoscale structures inside biomolecular condensates. Using environmentally sensitive fluorogenic dyes, we visualized heterogeneous structures inside condensates called hubs, which arise from transient physical crosslinks formed through protein interactions. SMOLM maps the orientation of dyes at the interface of condensates. Our images show that proteins at the interface are organized with specific orientations rather than simply oriented randomly.