Suliana Manley graduated with honors from Rice University in 1997, with degrees in physics and mathematics. She joined the group of David Weitz for her doctoral work (Harvard University), then moved to the group of Alice Gast for a postdoc (MIT). Her expedition into the world of biology and super-resolution microscopy began when she moved as a National Research Council Fellow to work with Jennifer-Lippincott Schwartz (NIH). There, she developed a method using photoactivation of fluorescent proteins in combination with tracking, sptPALM, which allowed mobility measurements of thousands of single molecules in a single cell. In 2009, she started her own group as a professor of physics at EPFL, where she was promoted with tenure in 2016. The scientific focus of her group is on developing tools such as high-throughput and “smart” super-resolution microscopies. Her group has used this approach to investigate with collaborators a wide range of biophysical questions, such as how the cytokinetic machinery is organized in bacteria, how telomeres and other genomic loci are packaged, and how centriolar proteins are arranged in three dimensions. In recognition of her achievements, she has been awarded the 2019 Medal for Light Microscopy by the Royal Microscopical Society. She is also the recipient of a European Research Council Starting Grant (2009) and Consolidator Grant (2019).
Single molecule localization microscopies (SMLM) occupy a special niche in the biologist's toolbox because they can achieve among the highest resolutions in fluorescence microscopy. Yet, many important biological questions remain out of reach due to challenges in acquiring and analyzing statistically significant SMLM datasets. Previously, we created high-throughput PALM by building an automated microscope to image hundreds of bacteria cells, live, 3D, and across cell cycle. To complement this, we created a uniform illumination scheme to enable large field of view images. More recently, we have been working toward autonomous microscopy, reducing the number of user inputs and increasing the responsiveness of the microscope to the sample using machine learning and engineering approaches. We demonstrate the power of this approach for studying macromolecular complexes within cells. To study the organization of such complexes, particle-based analysis has proven to be powerful, but has been limited so far by difficulties in generating large multi-color particle libraries, as well as the complexity of orientational alignment. We have addressed both challenges and, as a result, present a novel framework and software for deciphering the 3D organization of protein complexes composed of multiple components.