Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under lowlight conditions: Simulation datasets
Citation
Lin, Ye; Sharifi, Fatemeh; Andersson, Sean B. (2021), Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under lowlight conditions: Simulation datasets, Dryad, Dataset, https://doi.org/10.5061/dryad.2ngf1vhnk
Abstract
The datasets store both motion and observation information of a single fluorescent subdiffraction limitsized particle moving in a threedimensional confined environment. The confined motion is following a nonlinear model driven by nonGaussian noise, the observation is formed by engineered Doublehelix (DH) point spread function (PSF) and captured by scientific complementary metaloxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo  Expectation Maximization (SMCEM), we extended it to handle the DHPSF for encoding the threedimensional position of the particle in twodimensional image plane of the camera. We focus on studying the datasets at low signal and low signaltobackground ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMCEM approach outperforms the other methods while at higher signaltobackground levels, SMCEM and the MLEbased methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMCEM approach is superior to the alternative approaches.
Methods
All datasets were simulated using Python 3.8. The camera type considered in this work is the scientific complementary metaloxide semiconductor (sCMOS) where both shot noise and the pixeldependent readout noise are common noise sources. The motion of a single fluorescent particle was generated at a time step of 1 ms. The camera was assumed to take images at a rate of 10 Hz with a shutter period of 10 ms. Motion blur is considered for all data simulation. The 10 pixelated images during the shutter period were accumulated to generate a single camera image. (See the Usage Notes for details).
Usage Notes
* DATASPECIFIC INFORMATION FOR: [data_by_sCMOS_dh.zip]
 Images were captured by sCMOS, integrating the DHPSF.
 All images are in the local region of 15by15 pixels.
 The background noise is 10 counts, while the signal level ranges from 10 to 50.
 The confinement length in three dimensions ranges from 0.1 μm to 0.5 μm.
 For each optical settings, 50 datasets were simulated (counted from 0 to 49).
 Taking the subfolder [sCMOS_N10G10Image100D0.01L0.1_double_helix] as an example, the details are as follows:
(1). Background noise N=10, signal level G=10, number of images per dataset =100, diffusion coefficient D=0.01 μm^{2}/s, confinement length L=0.1 μm;
(2). Inside the subfolder [sCMOS_N10G10Image100D0.01L0.1_double_helix], the detailed description of the files for the 0^{th} dataset is as follows:

 [local_sigma_0.csv] row*column=100*225; where row denotes the time steps, column denotes the pixel. Each pixel contains information of "sigma" characterizing the readout noise of the Hamamatsu ORCA Flash 4.0 camera.
 [photon_observation_0.csv] row*column=225*100; where the simulated images in each column containing a single image, organized sequentially from the initial time down to the final time. The 225 entries in each row are the photon counts from the 15by15 pixeled array, organized starting from the topleft pixel, going horizontally across the top five pixels, and continuing to the bottom right pixel.
 [sensor_position_0.csv] row*column=100*2; In generating images, we assumed segmentation of the full camera image was done previously and thus the location of the 15by15 array of pixels may change at each time step. This data gives the position of the pixel array starting from the initial time down to the final time, with the first column corresponding to the xcoordinate and the second column to the ycoordinate.
 [x_ground_truth_0.csv] row*column=1*100; where the entire row contains the true location of the particle in the xdirection at each time step. The data is organized starting with the first timestep and proceeding down to the last.
 [y_ground_truth_0.csv] row*column=1*100; where the entire row contains the true location of the particle in the ydirection at each time step. The data is organized as [x_ground_truth_0.csv].
 [z_ground_truth_0.csv] row*column=1*100; where the entire row contains the true location of the particle in the zdirection at each time step. The data is organized as [x_ground_truth_0.csv].
* DATASPECIFIC INFORMATION FOR: [data_for_figures_BOE.zip]
The data underlying the figures in the paper “Ye Lin, Fatemeh Sharifi and Sean B. Andersson. Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under lowlight conditions. Biomedical Optics Express (2021).”
Funding
National Institutes of Health, Award: 1R01GM11703901A1