Yiming Li

        Yiming Li

Associate Professor of Southern University of Science and Technology

Software

Optimal-Sampling-Rate-of-Different-PSFs-in-SMLM

Resolution of single molecule localization microscopy (SMLM) depends on the localization accuracy, which can be improved by utilizing engineered point spread functions (PSF) with delicate shapes. However, the intrinsic pixelation effect of the detector sensor will deteriorate PSFs under different sampling rates. The influence of the pixelation effect to the achieved 3D localization accuracy for different PSF shapes under different signal to background ratio (SBR) and pixel dependent readout noise has not been investigated in detail so far. In this work, we proposed a framework to characterize the 3D localization accuracy of pixelated PSF at different sampling rates. Four different PSFs (astigmatic PSF, double helix (DH) PSF, Tetrapod PSF and 4Pi PSF) were evaluated and the pixel size with optimal 3D localization performance were derived. This work provides a theoretical guide for the optimal design of sampling rate for 3D super resolution imaging.

GPU-based-B-spline-Fitter

The recent development of single molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information of the single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU)-based B-spline PSF modeling method which could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single molecule data including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).

FD-DeepLoc

Field dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.Single-molecule localization microscopy (SMLM) in a typical wide-field setup has been widely used for investigating sub-cellular structures with super resolution. However, field-dependent aberrations restrict the field of view (FOV) to only few tens of micrometers. Here, we present a deep learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit (GPU) based vectorial PSF fitter, we can fast and accurately fit the spatially variant point spread function (PSF) of a high numerical aperture (NA) objective in the entire FOV. Combined with deformable mirror based optimal PSF engineering, we demonstrate high-accuracy 3D SMLM over a volume of ~180 × 180 × 5 μm3, allowing us to image mitochondria and nuclear pore complex in the entire cells in a single imaging cycle without hardware scanning - a 100-fold increase in throughput compared to the state-of-the-art.

GlobLoc

GlobLoc is a graphics processing unit (GPU) based global fitting algorithm with flexible PSF modeling and parameter sharing, to extract maximum information from multi-channel single molecule data. Global fitting can substantially improve the 3D localization precision for biplane and 4Pi SMLM and color assignment for ratiometric multicolor imaging. The fitting speeds achieve ~35,000 fits/s on a standard GPU (NVIDIA RTX3090) for regions of interest (ROI) with a size of 13×13 pixels.

DM_based_PSF_optimization

Point spread function (PSF) engineering is an important technique to encode the properties (e.g., 3D positions, color, and orientation) of a single molecule in the shape of the PSF, often with the help of a programmable phase modulator. A deformable mirror (DM) is currently the most widely used phase modulator for fluorescence detection as it shows negligible photon loss. However, it relies on careful calibration for precise wavefront control. Therefore, design of an optimal PSF not only relies on the theoretical calculation of the maximum information content, but also the physical behavior of the phase modulator, which is often ignored during the optimization process. Here, we develop a framework for PSF engineering which could generate a device specific optimal PSF for 3D super-resolution imaging using a DM. We use our method to generate two types of PSFs with depths of field comparable to the widely used astigmatism and tetrapod PSFs, respectively. We demonstrate the superior performance of the DM specific optimal PSF over the conventional astigmatism and tetrapod PSF both theoretically and experimentally.

Ratiometric-4Pi

Ratiometric-4Pi is a graphics processing unit (GPU) based global fitting algorithm for 4Pi-SMLM with flexible PSF modeling and parameter sharing, to extract maximum information from 4Pi single molecule data and achieved both good color separation and optimal 3D resolution. By partially linking the photon parameters between channels with interference difference of π during global fitting of the multi-channel 4Pi single molecule data, we showed on simulated data that the loss of the localization precision is minimal compared with the theoretical minimum uncertainty, the Cramer-Rao lower bound (CRLB). Our algorithm is implemented in GPU and the fitting speeds is more than 38 times faster than the CPU based code.

fit3Dcspline

fit3Dcspline is a GPU based 3D single molecule fitter for arbitrary, experimental point spread functions (PSF). The fitting speeds achieves more than 10^5 fits/s on a consumer graphic card GTX 1070. The implmentation of the fitting algorithm is based on maximum likelihood estimation and employs Levenberg-Marquardt optimization routine, which reaches theoretical minimum uncertainty. Both the EMCCD and sCMOS noise model are included. The softare package also includes tools to robustly model beads based experimental PSFs of different modality and correct for depth induce aberrations.