Evolution of mass over time and the corresponding microscopy images are shown for a time span of 20min. The mass data was acquired every 10ms (data shown in black), overlaid in red is the rolling mean with a window of 1000. Images taken every 3 min over the observed times span, we see on average a steady increase of the cell mass.
With the introduction of a picoscopic cell balance that is compatible with optical microscopy, a new tool for the investigation of the cell state-dependent cell mass regulation is available for use in biophysics, cell biology, physiology and medicine. However, the analysis of the data can be challenging due to a) the amount of high resolution data or b) the structure of low-stress measurement (low resolution) data. Here, we introduce the software pyIMD, which allows to easily extract the mass as a function of time for non-moving cells out of the raw data. pyIMD Stands for Python inertial mass determination.
This documentation of pyIMD describes the API as well as gives provides a sample data set as well as sample scripts to run pyIMD from Jupyter or the Python console but it also contains a tutorial about how pyIMD is used with the user interface. The aim of this module is the calculation of the total/inertial mass for measurements taken in the continuous sweep or in the phase-lock loop (PLL) mode.
If you download the portable for your operating system there is no need for any installation and you can use pyIMD directly trough the user interface.
Otherwise, to install this module from source, type on your terminal:
>>> git clone https://gitlab.com/csb.ethz/pyIMD.git >>> cd pyIMD >>> pip install .
Example data files can be found in this repo. We provide example data of the various devices used (Cytomass and Nanonis) in various formats.
Go to Example_data.
Go to Example_scripts.
To use this module in a Python script, write:
>>> from pyIMD.imd import InertialMassDetermination
Set path to files either absolute or relative to where you run the script from, i.e. using the provided show case data:
>>> file_path1 = "/pyIMD/examples/data/show_case/0190110_ShowCase_PLL_B.txt"
>>> file_path2 = "/pyIMD/examples/data/show_case/20190110_ShowCase_PLL_A.txt"
>>> file_path3 = "/pyIMD/examples/data/show_case/20190110_ShowCase_PLL_LongTerm.txt"
>>> imd.create_pyimd_project(file_path1, file_path2, file_path3, '\t', 23, 'PLL', figure_width=16.5, figure_height=20,
initial_parameter_guess=[60.0, 2.0, 0.0, 0.0], cell_position=9.5, figure_format='pdf')
>>> imd.run_intertial_mass_determination()
List default settings for calculation:
>>> imd.print_pyimd_project()
Change settings for calculation and figure output the following way before calling run_intertial_mass_determination():
>>> imd.settings.spring_constant = 4
More info and examples can be found in the online Documentation
Use tab completion to access the object's attributes (e.g. to get the calculated mass):
>>> mass = imd.calculated_cell_mass
In a IPython notebook the progress bar works not properly but has no effect on the calculations. In a IPython notebook the calculation speed is much slower.
Please do contribute! Issues and pull requests are welcome. Thank you for your help improving software one changelog at a time!
If you use pyIMD in your academic work we would appreciate if you cite us. To do so please use:
@article{Cuny2019,
title = {pyIMD: Automated analysis of inertial mass measurements of single cells},
journal = {SoftwareX},
volume = {10},
pages = {100303},
year = {2019},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2019.100303},
url = {https://www.sciencedirect.com/science/article/pii/S2352711019300871},
author = {Andreas P. Cuny and David Martínez-Martín and Gotthold Fläschner},
keywords = {Single cell, Mass, Picobalance, Oscillators},
abstract = {The total mass of single cells can be accurately monitored in real time under physiological conditions with our recently developed picobalance. It is a powerful tool to investigate crucial processes in biophysics, cell biology or medicine, such as cell growth or hydration dynamics. However, processing of the raw data can be challenging, as computation is needed to extract the mass and long-term measurements can generate large amounts of data. Here, we introduce the software package pyIMD that automates raw data processing, particularly when investigating non-migrating cells. pyIMD is implemented in Python and can be used as a command line tool or as a stand-alone version including a graphical user interface.}
}