GICTrack 0.0.2

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GIC-Track

This dashboard is designed for the spatial and temporal analysis of fluorescently tagged transcription factors within cell nuclei.

Requirement

This dashboard requires MATLAB with Statistics and Curve Fitting toolbox installed.

Dashboard Usage

1. More than one file (.tif for pre-processed files, .mat for post-processed files) could be uploaded at the same time from the "Upload" tab using the respective buttons.
2. The files could be in any directory; however, they follow a specific naming convention. The dashboard would classify the files uploaded based on the naming convention; with the string/text before the first "_" being used as condition type to group the file/cell data. Below are some example of the file name convention for comparing between 2 conditions of 3 files each:
   Condition-1_Cell_1.tiff
   Condition-1_Cell_2.tiff
   Condition-1_Cell_3.tiff
   Condition-2_Cell_1.tiff
   Condition-2_Cell_2.tiff
   Condition-2_Cell_3.tiff
   
3. If you have a background image that you want to use as overlay and/or comparison (DAPI or Hoist stain compare to trajectories, you can upload the DAPI/Hoechst file together with previous step, naming the file as WT_Cell1_DAPI.tif (even if it's Hoechst stain, currently it looks for "_DAPI" to existing file name and mark it as a background file).
4. After pressing the "Upload" button and selecting the files. The dashboard will process all the data on its own (it may show a MATLAB info tab on the progress, I have not tested if this will happen on a Mac/Linux system). Once it's done processing all the files, you should be able to go to any tabs in any order and you can view the visualisation of the data it processed.

A few known issues that are being worked on and workaround are as follows:

1. If the dashboard crashes when visiting certain tabs, it could be that some file has mistakenly got recorded onto the dashboard.db despite it failing the Quality Control (QC) that was marked on the Upload tab. Thsis result in the dashboard crashes when attempting to view these data. Workaround: Delete the database.db and re-upload new data with lower QC requirement (the "Clip Factor", "Trajectory Length" and "Minimum Number of Trajectory" undering the "Generic Parameters" are the most important parameters. Try changing the "Analysis Type" to "Percentage", "Clip Factor" to 100, "Trajectory Length" to 1 and "Minimum Number of Trajectory" to 1 to accept all data.
2. "Angle Plots" tab can crash when data with too short duration or translocation distance exist in the database.

Data Table Format

FileList

Parameter	Type	Description
filename	TEXT	The filename that contribute to the dataset.
mutation	TEXT	The condition of the file/cell data, created based on file naming convention.
acquisition_rate	TEXT	"fast" or "slow", used to differentiate the data acquisition rate to prevent mixing of data types.
exposure_time	REAL	Exposure time in ms.
pixelSize	REAL	Pixel size of the data (in μm).
cellSize	REAL	The size of the cell/nucleus based on segmented data (in pixels).
psfScale	REAL	Point spread function value of the microscope (user input in dashboard).
wvlngth	REAL	The wavelength of the emission used by the microscope (user input in dashboard).
iNA	REAL	The "Numerical Aperture" of the microscope used to gather the data (user input in dashboard).
psfStd	REAL	The deviation of the "point spread function" based on "psfScale", "wvlnth", "iNA" and "pixelSize". $psfStd = \frac{psf\textunderscore scale * \left( 0.55 * wvlnth \right)}{2 * iNA * pixelSize * 1.17}$
wn	REAL	Spatial sliding window width used for particle detection (in pixels).
errorRate	REAL	The maximum difference between $H_{0}$ and $H_{1}$ hypothesis that are still accepted as a detected point. Refer to Sergé et al. Nature Methods 5:687-694 (2008)'s Supplementary Text and Figures's Equation [4].
dfltnLoops	REAL	Number of deflation loops (usually 0, but useful if the image density is too high).
minInt	REAL	Minimum intensity to be classified as a point.
optim_MaxIter	REAL	The maximum number of iteration allowed during localization optimization.
optim_termTol	REAL	The termination tolerance (the value is 10 to the power of the input value). If the variation of x and y coordinates is lesser than this, the iteration will stop (variation of Gaussian radius will be checked as well if "optim_isRadTol" is activated).
optim_isRadTol	INTEGER	Is Gaussian radius variation tolerance feature used for the localization algorithm (user input in dashboard).
optim_radiusTol	REAL	The Gaussian radius tolerance (in percentage) with respect to "point spread function deviation" (psfStd).
optim_posTol	REAL	The tolerance for the x and y coordinates of the point detected during the localization (in pixels).
isThreshLocPrec	INTEGER	Currently not being used. Is radius tolerance feature used for the tracking algorithm (user input in dashboard).
minLoc	REAL	Currently not being used.
maxLoc	REAL	Currently not being used.
isThreshSNR	INTEGER	Currently not being used. Is signal-to-noise threshold feature used for the tracking algorithm (user input in dashboard).
minSNR	REAL	Currently not being used.
maxSNR	REAL	Currently not being used.
isThreshDensity	INTEGER	Currently not being used. Is density threshold feature used for the tracking algorithm (user input in dashboard).
trackStart	REAL	Currently not being used.
trackEnd	REAL	Currently not being used.
Dmax	REAL	The maximum expected diffusion coefficient (in μm2/s) of the uploaded file (user input in dashboard).
searchExpFac	REAL	Search exploration factor. The maximum amount a trajectory can move is computed as $searchExpFac * \frac{Dmax}{pixelSize^{2} * 4 * \frac{exposureTime}{1000}}$.
statWin	REAL	Number of frames data to be used in computing trajectories data.
maxComp	REAL	Maximum number of trajectories a point can belong to (during trajectories forming stage).
maxOffTime	REAL	Maximum number of frames allowed to be in-between a trajectory for it to still be classify as the same trajectory (for situation where particles could fade out of focus).
intLawWeight	REAL	The intensity probability law weighting, value ranges from 0 to 1.0, with 1.0 accounting for intensity staying on and 0 accounting for blinking state. The reconnection procedure take into account the point's intensity, diffusion and blinking.
diffLawWeight	REAL	The diffusion probability law weighting, value ranges from 0 to 1.0, with 1.0 accounting for local diffusion (based on estimated standard deviation of diffusion based on "statWin" number of past frames information) and 0 accounting for free diffusion. The reconnection procedure take into account the point's intensity, diffusion and blinking, a value of 0.9 with emphasizes on local behaviour while allowing the possibility of a sudden increase towards free diffusion.
bleach_rate	REAL	The initial value used to estimate long dwell time activity. Long dwell time is computed as follows: $t = \frac{\left( bleach\textunderscore rate - 1 \right)}{\frac{bleach\textunderscore rate}{max\left( \tau_{s}, \tau_{ns} \right) - 1}}$ where $\tau_{s}$ is the long dwell time from the fitting model and $\tau_{ns}$ is short dwell time from the fitting model.
traj_length	INTEGER	Quality Control: The minimum number of tracks in a trajectory (trajectories with fewer number than this will be discarded).
min_traj	INTEGER	Quality Control: The minimum number of trajectories in a file (files with fewer trajectories than this number will be discarded).
clip_factor	INTEGER	The number/percentage (depending on "Analysis Type:" selected in the dashboard) of a trajectories (from when it first being formed) to be used in the "mean square displacement" analysis.
tol	INTEGER	The number of decimals to keep during during computation of "mean square displacement".
twoParN	REAL	The number of tracks that are used in two parameters fitting (used for jump distance plots in the dashboard).
twoPardN	REAL	The number of tracks variation that are used in two parameters fitting (used for jump distance plots in the dashboard).
twoParD1	REAL	The "immobile" diffusion coefficient (in μm2/s) computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoPardD1	REAL	The variation of "immobile" diffusion coefficient (in μm2/s) computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoParD2	REAL	The variation of "mobile" diffusion coefficient (in μm2/s) computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoPardD2	REAL	The variation of "mobile" diffusion coefficient (in μm2/s) computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoParf1	REAL	The fraction of "immobile" tracks computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoPardf1	REAL	The variation of fraction of "immobile" tracks computed from two parameters fitting model (used for jump distance plots in the dashboard).
twoParSSR	REAL	The "sum of squares due to regression" of the two parameters fitting model (used for jump distance plots in the dashboard).
threeParN	REAL	The number of tracks that are used in three parameters fitting (used for jump distance plots in the dashboard).
threePardN	REAL	The number of tracks variation that are used in three parameters fitting (used for jump distance plots in the dashboard).
threeParD1	REAL	The "immobile" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threePardD1	REAL	The variation of "immobile" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threeParD2	REAL	The variation of "mixed" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threePardD2	REAL	The variation of "mixed" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threeParD3	REAL	The variation of "mobile" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threePardD3	REAL	The variation of "mobile" diffusion coefficient (in μm2/s) computed from three parameters fitting model (used for jump distance plots in the dashboard).
threeParf1	REAL	The fraction of "immobile" tracks computed from three parameters fitting model (used for jump distance plots in the dashboard).
threePardf1	REAL	The variation of fraction of "immobile" tracks computed from three parameters fitting model (used for jump distance plots in the dashboard).
threeParf2	REAL	The fraction of "mixed" tracks computed from three parameters fitting model (used for jump distance plots in the dashboard).
threePardf2	REAL	The variation of fraction of "mixed" tracks computed from three parameters fitting model (used for jump distance plots in the dashboard).
threeParSSR	REAL	The "sum of squares due to regression" of the three parameters fitting model (used for jump distance plots in the dashboard).

SSR is computed as follows:
$$\sum_{i=1}^n \left( \hat{y}_{i}-\bar{y})^{2} \right)$$

TrajectoryList

Parameter	Type	Description
filename	TEXT	The filename that contribute to the dataset.
trajID	TEXT	The ID used to identify this trajectory.
traj_length	INTEGER	The total number of frames this trajectory existed.
msd	REAL	The average value of all the tracks' "mean square displacement" in the trajectory.
D	REAL	The diffusion coefficient of the trajectory (in Log10(μm2)).
startTime	REAL	The time stamp the trajectory first appear.
endTime	REAL	The time stamp the trajectory is last seen.
meanX	REAL	The mean X-coordinate of the trajectory.
meanY	REAL	The mean Y-coordinate of the trajectory.
maxDistance	REAL	The maximum distance a track (that's belong to this trajectory) travelled.
meanDistance	REAL	The mean distance a track (that's belong to this trajectory) travelled.
medianDistance	REAL	The median distance a track (that's belong to this trajectory) travelled.

TrackList

Parameter	Type	Description
trajID	TEXT	The ID used to identify this trajectory.
Frame	REAL	The time the track is being observed (computed from the frame number it's being detected in and "exposure time").
x	REAL	The X-coordinate of the track at "Frame".
y	REAL	The Y-coordinate of the track at "Frame".
msd	REAL	The "mean square displacement" of the track with respect to its previous position (in the previous "Frame").
distance	REAL	The distance this track moved with respect to its previous position (in the previous "Frame").
angle	REAL	The angle between the track at "Frame" with respect to its two previous position.

The angle is calculated by its “diversion” from the previous track (how many degree it branched from previous movement [refer to the Figure below, where the track was moving from “1” to “2” in the first frame and “diverted” to “3” at second frame with the angle theta calculated]). Note that the angle diversion do not account for whether it's clockwise or anti-clockwise diversion.

AngleList

Parameter	Type	Description
filename	TEXT	The filename that contribute to the dataset.
trajID	TEXT	The ID used to identify this trajectory.
A1	INTEGER	Number of angles with tracks between 0 - 10° in this trajectory.
A2	INTEGER	Number of angles with tracks between 10 - 20° in this trajectory.
A3	INTEGER	Number of angles with tracks between 20 - 30° in this trajectory.
A4	INTEGER	Number of angles with tracks between 30 - 40° in this trajectory.
A5	INTEGER	Number of angles with tracks between 40 - 50° in this trajectory.
A6	INTEGER	Number of angles with tracks between 50 - 60° in this trajectory.
A7	INTEGER	Number of angles with tracks between 60 - 70° in this trajectory.
A8	INTEGER	Number of angles with tracks between 70 - 80° in this trajectory.
A9	INTEGER	Number of angles with tracks between 80 - 90° in this trajectory.
A10	INTEGER	Number of angles with tracks between 90 - 100° in this trajectory.
A11	INTEGER	Number of angles with tracks between 100 - 110° in this trajectory.
A12	INTEGER	Number of angles with tracks between 110 - 120° in this trajectory.
A13	INTEGER	Number of angles with tracks between 120 - 130° in this trajectory.
A14	INTEGER	Number of angles with tracks between 130 - 140° in this trajectory.
A15	INTEGER	Number of angles with tracks between 140 - 150° in this trajectory.
A16	INTEGER	Number of angles with tracks between 150 - 160° in this trajectory.
A17	INTEGER	Number of angles with tracks between 160 - 170° in this trajectory.
A18	INTEGER	Number of angles with tracks between 170 - 180° in this trajectory.

JDList

Parameter	Type	Description
filename	TEXT	The filename that contribute to the dataset.
jump_distance	REAL	The jump distance tick (used for jump distance plots in the dashboard).
sharedFrequency	REAL	The number of tracks that fall within the "jump_distance" tick (used for jump distance plots in the dashboard).
twoParFrequency	REAL	The number of tracks that fall within the "jump_distance" tick based on two parameters fitting (used for jump distance plots in the dashboard).
threeParFrequency	REAL	The number of tracks that fall within the "jump_distance" tick based on three parameters fitting (used for jump distance plots in the dashboard).
twoParD1Values	REAL	The number of tracks that are "immobile" fall within the "jump_distance" tick based on two parameters fitting (used for jump distance plots in the dashboard).
twoParD2Values	REAL	The number of tracks that are "mobile" fall within the "jump_distance" tick based on two parameters fitting (used for jump distance plots in the dashboard).
threeParD1Values	REAL	The number of tracks that are "immobile" fall within the "jump_distance" tick based on three parameters fitting (used for jump distance plots in the dashboard).
threeParD2Values	REAL	The number of tracks that are "mixed" fall within the "jump_distance" tick based on three parameters fitting (used for jump distance plots in the dashboard).
threeParD3Values	REAL	The number of tracks that are "immobile" fall within the "jump_distance" tick based on three parameters fitting (used for jump distance plots in the dashboard).

Reference

Previously published algorithms, analysis, and scripts that are utilized in the dashboard can be found below:

Localization and Tracking:

D. M. McSwiggen et al. (2019) Evidence for DNA-mediated nuclear compartmentalization distinct from phase separation. eLife. 8:e47098.
A. Sergé et al. (2008) Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nature methods, 5(8):687.

MSD-Based Diffusion Plot

J. Chen et al. (2014) Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell. 156(6):1274 - 1285.

Jump Distance Plot:

D. Mazza et al. (2013) Monitoring dynamic binding of chromatin proteins in vivo by single-molecule tracking. Methods Mol Biol. 1042:117-37.

Angle Plots:

I. Izeddin et. al. (2014), Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus. eLife. 3:e02230.

Heat Map:

J. O. Andrews et al. (2018) qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells. Sci Rep. 7424 (2018).

Dwell Time:

A.J. McCann et al. (2021) A dominant-negative SOX18 mutant disrupts multiple regulatory layers essential to transcription factor activity. Nucleic Acids Res. 49(19):10931-10955. Developed by Zhe Liu in Janelia Research Campus.