Lin Shao

Bio: Lin Shao is an academic researcher from Texas A&M University. The author has contributed to research in topics: Irradiation & Ion. The author has an hindex of 48, co-authored 380 publications receiving 12737 citations. Previous affiliations of Lin Shao include Howard Hughes Medical Institute & University of Southern California.

Lattice Light Sheet Microscopy: Imaging Molecules to Embryos at High Spatiotemporal Resolution

Abstract: Although fluorescence microscopy provides a crucial window into the physiology of living specimens, many biological processes are too fragile, are too small, or occur too rapidly to see clearly with existing tools. We crafted ultrathin light sheets from two-dimensional optical lattices that allowed us to image three-dimensional (3D) dynamics for hundreds of volumes, often at subsecond intervals, at the diffraction limit and beyond. We applied this to systems spanning four orders of magnitude in space and time, including the diffusion of single transcription factor molecules in stem cell spheroids, the dynamic instability of mitotic microtubules, the immunological synapse, neutrophil motility in a 3D matrix, and embryogenesis in Caenorhabditis elegans and Drosophila melanogaster. The results provide a visceral reminder of the beauty and the complexity of living systems.

Subdiffraction Multicolor Imaging of the Nuclear Periphery with 3D Structured Illumination Microscopy

Abstract: Fluorescence light microscopy allows multicolor visualization of cellular components with high specificity, but its utility has until recently been constrained by the intrinsic limit of spatial resolution. We applied three-dimensional structured illumination microscopy (3D-SIM) to circumvent this limit and to study the mammalian nucleus. By simultaneously imaging chromatin, nuclear lamina, and the nuclear pore complex (NPC), we observed several features that escape detection by conventional microscopy. We could resolve single NPCs that colocalized with channels in the lamin network and peripheral heterochromatin. We could differentially localize distinct NPC components and detect double-layered invaginations of the nuclear envelope in prophase as previously seen only by electron microscopy. Multicolor 3D-SIM opens new and facile possibilities to analyze subcellular structures beyond the diffraction limit of the emitted light.

Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics

Abstract: Super-resolution fluorescence microscopy is distinct among nanoscale imaging tools in its ability to image protein dynamics in living cells. Structured illumination microscopy (SIM) stands out in this regard because of its high speed and low illumination intensities, but typically offers only a twofold resolution gain. We extended the resolution of live-cell SIM through two approaches: ultrahigh numerical aperture SIM at 84-nanometer lateral resolution for more than 100 multicolor frames, and nonlinear SIM with patterned activation at 45- to 62-nanometer resolution for approximately 20 to 40 frames. We applied these approaches to image dynamics near the plasma membrane of spatially resolved assemblies of clathrin and caveolin, Rab5a in early endosomes, and α-actinin, often in relationship to cortical actin. In addition, we examined mitochondria, actin, and the Golgi apparatus dynamics in three dimensions.

Super-resolution 3D microscopy of live whole cells using structured illumination

Abstract: Three-dimensional (3D) structured-illumination microscopy (SIM) can double the lateral and axial resolution of a wide-field fluorescence microscope but has been too slow for live imaging. Here we apply 3D SIM to living samples and record whole cells at up to 5 s per volume for >50 time points with 120-nm lateral and 360-nm axial resolution. We demonstrate the technique by imaging microtubules in S2 cells and mitochondria in HeLa cells.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

UCSF ChimeraX: Meeting modern challenges in visualization and analysis.

Abstract: UCSF ChimeraX is next-generation software for the visualization and analysis of molecular structures, density maps, 3D microscopy, and associated data. It addresses challenges in the size, scope, and disparate types of data attendant with cutting-edge experimental methods, while providing advanced options for high-quality rendering (interactive ambient occlusion, reliable molecular surface calculations, etc.) and professional approaches to software design and distribution. This article highlights some specific advances in the areas of visualization and usability, performance, and extensibility. ChimeraX is free for noncommercial use and is available from http://www.rbvi.ucsf.edu/chimerax/ for Windows, Mac, and Linux.

When is nearest neighbor meaningful

Abstract: We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.