About Software DataSamples Movies Publications
www.mashanov.uk
Dear Reader,
You can use this
website to download freeware applications (see links below) written by
G. Mashanov (The Francis Crick Institute, UK). This software was developed for the
detection/tracking, analysis, and modelling of single molecule dynamics (movement and binding kinetics)
in live cells, but it can be used for other purposes. You can download our
real and simulated data samples and ImageJ Plugins to import and export your data
files.
The software was
compiled using CBuilder_XE7. It will run under Win32 or Win64 OS and does not
require installation or registration:
1. Download required
.zip file (see below). The archives contain ".exe" files (32 or 64 bit), and corresponding “.dll”
files (“.bpl” libraries).
2.
Unzip these files
into selected folder on your computer.
3.
Run the required “.exe”
file.
You may manually
associate “.gmv” data files (using Windows Explorer) with GMimPro and “.gmi” files with Motility to open data files by clicking
on it. I am happy to answer your specific questions gmashanov@gmail.com but, please, read the help
files (.pdf) (and corresponding publications) first.
Yours
Gregory Mashanov
New: GMvCell is
a development of GMcellModel (see below). It is a combination of a 3D matrix
and continuum space models used to simulate complicated, randomly
shaped and placed cellular structures and some dynamic cellular
structures (e.g., moving vesicles fusing with cell membrane). Single
molecule objects (upto 50000 units of each class) move with
floating-point precision in a continuum space but only in voxels of
correct type in discrete space. Objects of the same or different
classes can interact with each other according to the
binding/dissociation rates set by operator - the probability of binding
depends on the distance/mobility of eligible pair and continuity of the
correct voxel space between these objects. During simulation model
produces sequences of fluorescence light microscopy images built
according to the simulated imaging conditions (e.g., illumination
method, microscope magnification, objective numerical aperture, and
camera settings) which can be used for data ananlysis. The model executable file (GMvCell.exe), required
libraries, help file (GmvCell-Help.pdf), pre-defined scenarios, and satellite software
(GMimPro and Motility) can be downloaded below (64bit files). Please note, this is 64bit only software because it requires large memory volume to simulate virtual cell.
GMimPro is an image sequence processor designed for automatic
single particle/molecule detection and tracking. It can track up to 10000 objects
for up to 10000 frames. You can use ImageJ Plugins to convert your data
files into GMimPro format (“.gmv”) or import RAW data files in GMimPro (File/Import
Data). You can export the results of tracking or other measurements using
“.txt” or “.gmi” data format. See Biophysical Journal, 2007 publication for full description of the employed
algorithms.
Update: New, 64-bit only version of GMimPro2023 is included in 64bit download file.
This version works faster because it loads the whole record into RAM.
GMimPro2023 has more features (e.g. analysis of two-colour pairs of
records).
GMcellModel is a computer model simulating
mobility and binding kinetics of the single fluorescent molecules (both
cytoplasm and membrane associated) in a virtual cell. It generates a sequence of images
(8-bit “.bmp” or “.gmv” format), each containing summed images of all
fluorescent objects emitting light under given illumination conditions with
realistic levels of noise and emission fluctuations. These sequences can be
analysed by GMimPro or other imaging software (e.g., ImageJ). You
can load few basic scenarios (downloaded folder GMcellModelScenario) and run
the model to test it. See JRS Interface 2014 publication for full description of the
employed algorithms.
Motility is a satellite software designed
for statistical analysis of tracking data (.gmi) generated by GMimPro. You can add many
individual “.gmi” files together and create distributions of: intensity,
mobility, velocity, and other paprameters. You can create plots of average
intensity, mobility, and "distance from the origin" versus time, generate MSD
versus dT plots, and others. You can apply thresholds to separate slow-fast,
dim-bright, short-long lived objects, and so on. The graphs can be printed,
saved as “.bmp”, and exported as “.txt” files for future analysis or
publishing.
ImageJ plug-ins are written by Prof. J.E. Molloy (University of Warwick, UK).
1. Copy plugins into ImageJ
plugins folder
2. Open ImageJ and load your image
sequence
3. Input scales and time interval
to the sequence properties if needed
4. Use“GMV Writer” in “Plugins”
menu to save your data as “.gmv” file.
Alternatively you can save your data as RAW data file and use File/Import
Data in GMimPro to convert data into GMimPro format (".gmv").
About Software DataSamples Movies Publications
Download 32bit “.exe” files and libraries
Download 64bit “.exe” files and libraries
Download ImageJ Plugins for GMimPro
Download GMinfectionSpreadModel
(model simulating infection spread in a structured enviroment)
About Software DataSamples Movies Publications
Download Data samples
GFP_inVitro - single GFP molecules attached to glass via antiGFP ab
(in vitro, TIRF microscopy)
Cy3B_inVitro - single fluorescent molecules of Cy3B dye attached to
coverslip (in vitro, TIRF microscopy)
GFP_A1_HEK - Adenosine GPCR A1
receptors (GFP tagged) at plasma membrane of live HEK cell (37°C, TIRF microscopy)
Cy3B-Tz_M1_CHO
- Muscarinic Acetylcholine
GPCR M1 receptors at plasma membrane of live CHO cell (23°C, labelled with Cy3B-telenzepine, TIRF microscopy)
Cy3B-Tz_M2_CHO
- Muscarinic Acetylcholine
M2 receptors at plasma membrane of live CHO cell (23°C, labelled with Cy3B-telenzepine, TIRF microscopy)
Cy3B-Tz_M2_HL1
- Muscarinic
Acetylcholine GPCR M2 receptors at plasma membrane of live HL1 cell @23˚C (37°C,
labelled with Cy3B-telenzepine, TIRF microscopy)
Cy3B-Tz_M2_HeartSlice
- Muscarinic
Acetylcholine GPCR M2 receptors at plasma membrane of ex-vivo mice
heart slice (23°C, labelled with Cy3B-telenzepine, TIRF
microscopy)
GFP_KCNQ1_HEK - KCNQ1 potassium channels. GFP
tagged at plasma membrane of live HEK cell (37°C, TIRF microscopy)
GFP_KIR6.2_HEK – KIR6.2 potassium channels. GFP tagged at plasma membrane of live HEK cell (37°C, TIRF microscopy)
Myosin-2b in HUVEC – Myosin-2b molecules (GFP tagged) binding to stress fibres in Endothelial cell (37°C, TIRF microscopy)
Myosin-10 in HeLa – Myosin-10 molecules moving to the tips of filopodia in HeLa cell (37°C, TIRF microscopy)
Modelled Data - Few samples of GMcellModel output (.gmv files) made in different conditions.
About Software DataSamples Movies Publications
Here is a short list of YouTube movies - examples of real and modelled data
(use DataSamples to download non-compressed data or download GMcellModel to generate simulated data)
Single molecules of M1 muscarinic ACh (GPCR) receptor moving on the plasma membrane of CHO cell @23°C
Single molecules of KCNQ1 potassium channel in HEK293 cell (KCNQ1 containing vesicle fusing with membrane)
Molecules of potassium channel (KIR6.2-GFP) in HL1 cell @37°C
A1 (GPCR) receptors (A1-GFP) on the membrane of HEK293 cell @37°C
M2 receptors (labelled with Cy3B-Telenzepine). Adult mouse heart slice @23°C
PH12-GFP (domains of Myosin-10) in Endothelial cell@37°C
Myosin-10 (GFP tagged) in HeLa@37°C
Myosin-2b (GFP tagged) in HeLa@37°C
Amoeba cells moving in Dunn chamber (cAMP gradient). Low-mag, dark-field imaging and single cell tracking
Tracking individual aphids (insects) in a Petri dish
GMcellModel - virtual cell Z-scan in Confocal and Epi-Fluorescene mode
GMcellModel - tracking single molecules at cell membrane
GMcellModel - interaction of moving membrane molecules with "lipid rafts"
GMcellModel - molecules in tubular network
GMcellModel - Kinesin molecules stepping on microtubules in a virtual cell
GMvCell - Constructing virtual cell in 3D voxilated matrix
GMvCell - Simulation of single molecule movements and interactions in a virtual cell
About Software DataSamples Movies Publications
Mashanov,
G.I., Tacon, D., Knight, A.E., Peckham, M., and J.E. Molloy. (2003)
Visualizing single molecules inside living cells using total internal
reflection fluorescence microscopy. Methods,
Academ. Press., 29:142-152.
Mashanov,
G.I., Tacon, D., Peckham, M., and J.E. Molloy. (2004) The spatial and
temporal dynamics of pleckstrin homology domain binding at the plasma membrane
measured by imaging single molecules in live mouse myoblasts. J. Biol. Chem., 279:15274-15280.
Mashanov
G.I., Molloy J.E. (2007) Automatic detection of single fluorophores in
live cells. Biophys.J., 92:2199-2211.
Mashanov
G.I., Nobles M., Harmer S.C., Molloy J.E., Tinker A. (2010) Direct
Observation of Individual KCNQ1 Potassium Channels Reveals Their Distinctive
Diffusive Behavior, J. of Biol. Chem.,
285:3664-3675
Nenasheva T.A., Carter T., Mashanov
G.I. (2012) Automatic tracking of individual migrating cells using
low-magnification dark-field microscopy. J.
of Microscopy, 246:83–88.
Mashanov
G.I. (2014) Single molecule dynamics in a virtual cell: a
three-dimensional model that produces simulated fluorescence video-imaging data,
JRS Interface, 11:1-11
Baboolal T.G., Mashanov G.I., NenashevaT.A., Peckham M., Molloy J.E. (2016) A Combination of Diffusion and Active Translocation
Localizes Myosin 10 to the Filopodial Tip J. Biol. Chem., 291:22373-22385
Mashanov G.I., NenashevaT.A., Mashanova T., Maclachlan C., Birdsall N.J.M., Molloy J.E. (2020) A method for imaging single molecules at the plasma membrane of live cells within tissue slices J.G.P., 153:1-11
Mashanov G.I., NenashevaT.A., Mashanova A., Lape R., Birdsall N.J.M. Sivilotti L., Molloy J.E. (2021) Heterogeneity of cell membrane structure studied by single molecule tracking Faraday Discussions
Hellen N., Mashanov G.I., Conte J.L., ...... Carter T. (2022) P-selectin mobility undergoes a sol-gel transition as it diffuses from exocytosis sites into the cell membrane Nature Comm., 13:1-11
Mashanov
G.I., Molloy J.E. (2024) Single molecule dynamics in a virtual cell combining a 3‑dimensional matrix model with random walks Scientific Reports, 14:1-14