(C) I. Bronshtein, E. Kepten, I. Kanter, S. Berezin, M. Lindner,
A.B. Redwood, S. Mai, S. Gonzalo, R. Foisner, Y. Shav-Tal, and Y. Garini,
Loss of lamin A function increases chromatin dynamics in the nuclear interior.
Nature Communications 6, 8044 (2015).
DISCLAIMER: These data have been kindly provided by Dr. Y. Garini and
posted onto the INADILIC website according to the authors' permission
(02/10/2016). These data can be used for testing statistical methods
and biophysical models under the condition of providing the appropriate
credits to the authors and citing the above paper which presents and
describes the data, as well as the references below. The password that
is required to download the data, can be requested directly from
Dr. Y. Garini by email: yuval.garini@biu.ac.il
Three-dimensional trajectories of telomeres in 18 cells, in total 500
trajectories of 50 steps recorded at time step of 20 seconds.
The Excel file contains the single particle tracking data for 18 cells,
each one is described in a different sheet. It contains 6 columns:
A: telomere index
B: X coordinate, in microns
C: Y coordinate, in microns
D: Z coordinate, in microns
E: elapsed time index
F: time step, in seconds
All cells were measured under similar transfection conditions and
telomeres were labeled with DsRed-TRF1 or GFP-TRF2 proteins. The
measurements were done on an Olympus confocal microscope FV 1000
using a high magnification of 63X.
References:
1) I. Bronshtein, E. Kepten, I. Kanter, S. Berezin, M. Lindner,
A.B. Redwood, S. Mai, S. Gonzalo, R. Foisner, Y. Shav-Tal, and Y. Garini,
Loss of lamin A function increases chromatin dynamics in the nuclear interior.
Nature Communications 6, 8044 (2015).
Author's Comment:
Here we present the significance of a protein (Lamin A) in maintaining
the genome organization. It uses many measurements and the attached data
is part of it.
2) E. Kepten, I. Bronshtein, and Y. Garini,
Ergodicity convergence test suggests telomere motion obeys fractional dynamics.
Physical Review E 83, 041919 (2011).
Author's Comment:
Here we present our exploration for the type of the anomalous diffusion
we found.
3) E. Kepten, I. Bronshtein, and Y. Garini,
Improved estimation of anomalous diffusion exponents in single-particle tracking experiments.
Physical Review E 87, 052713 (2013).
Author's Comment:
This paper shows an improved method for calculating the anomality of
single particle tracking data.
4) E. Kepten, A. Weron, G. Sikora, K. Burnecki, and Y. Garini,
Guidelines for the fitting of anomalous diffusion mean square displacement graphs from single particle tracking experiments.
PLoS One 10, e0117722 (2015).
Author's Comment:
This explains how to analyze and fit anomalous data.
Matlab hints:
The excel file can be read in Matlab by using the following commands
[A, DESCR, FORMAT] = xlsfinfo('ALL WTLMNA cells.xls'); % the names of sheets are returns in DESCR
% to read the n-th sheet, use
[NUMERIC,TXT,RAW] = xlsread('ALL WTLMNA cells.xls', DESCR{n});