Ultrahigh-Throughput Single-Molecule Spectroscopy and Multidimensional Super-Resolution Microscopy

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

With support from the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Professor Ke Xu and his research group at the University of California-Berkeley are developing ultrahigh-throughput approaches for single-molecule spectroscopy and multidimensional super-resolution microscopy. The rise of super-resolution microscopy over the past decade has revolutionized how researchers study chemical, biological, and materials systems at the nanometer scale. This project extends super-resolution microscopy to high-dimensional measurements in order to enable new capabilities for chemical imaging at the single-molecule level. New approaches include the use of expandable hydrogels to control diffusion, incorporation of microfluidic techniques, and the application of machine learning approaches to analyze data from the high-throughput measurements. The measurements enabled by this research have the potential to reveal new knowledge about the behavior of single molecules and to provide new tools and concepts that will benefit diverse research fields in fundamental ways. Moreover, the project provides advanced student training in modern experimental technologies and big-data analysis methods that are increasingly important skills for employment in both academia and industry. The project also supports Professor Xu in providing an inclusive environment through his commitment to teaching, mentoring, and outreach activities. The rise of super-resolution microscopy based on the detection of single-molecule fluorescence, often known as single-molecule localization microscopy (SMLM), has revolutionized the study of chemical, biological, and materials systems over the past decade. In this project, the research team led by Professor Xu is developing new experimental approaches to enable high-dimensional and high-throughput measurements of single-molecule properties in order to expand the capabilities of SMLM. In addition to experimental approaches that take advantage of expandable hydrogels and microfluidic techniques to record diffusion rates and fluorescence spectra for a very large number of single molecules, the team is also working to apply machine learning algorithms to analyze the data from the ultrahigh-throughput single-molecule spectroscopy measurements for millions of molecules. Through the integration of these diverse techniques, the project aims to provide high temporal resolution for recording the fast dynamics of untethered single molecules, to enable the extraction of high-dimensional molecular information from single-molecule images for super-resolution mapping, and to elucidate size effects in macromolecule diffusion. Broader impacts of the work include potential applications of ultrahigh-throughput and multidimensional single-molecule measurements across a wide range of research areas. Students working on the project also gain valuable cross-disciplinary training based on the new knowledge, tools, and concepts that are being developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.