This repository contains datasets and all analysis scripts necessary to generate the results presented in the paper published in Nature Methods by Urbanska, Muñoz et al.:
A comparison of microfluidic methods for high-throughput cell deformability measurements
The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with potential value in clinical diagnostics. Several microfluidic-based methods developed in recent years have enabled single-cell mechanophenotyping at throughputs comparable to flow cytometery. Here we present a highly standardized cross-laboratory study comparing three leading microfluidic-based approaches to measure cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC), and extensional flow deformability cytometry (xDC). We show that all three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, other cell components dominate the response. The direct comparison presented here serves to unify deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.
cell mechanics, deformability cytometry, real-time deformability cytometry, suspended microchannel resonator, microfluidics, osmotic pressure, actin cytoskeleton
Urbanska, M., Muñoz, H.E., Shaw Bagnall, J. et al. A comparison of microfluidic methods for high-throughput cell deformability measurements. Nat Methods (2020). https://doi.org/10.1038/s41592-020-0818-8
├───Code Code needed for complete analysis
│ ├───LatB Matlab scripts/R notebooks for LatB data analysis
│ ├───Osm Matlab scripts/R notebooks for osmolarity data analysis
│ ├───Strain Matlab scripts to estimate strain per method
│ └───Utils 3rd party Matlab functions
│
├───renv Files associated with recreating R virtual environment
│
├───Data_Raw Original measurements to be analyzed
│ ├───LatB LatB-treated cell measurement data
│ ├───Osm Osmolarity-treated cell measurement data
│ ├───Strain Control cell measurement data for strain calculation
│ ├───LatB_FlowRate LatB-treated cell measurement data with different flow rates
│ ├───LatB_HighDose High LatB-treated cell measurement data
│ └───Osm_Time_Response Time response to osmolarity treatment of cells
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├───Data_Processed Processed data for figures or statistical analysis
│ ├───LatB LatB-treated cell measurement with Relative Deformability
│ ├───Osm Osmolarity-treated cell measurement with Relative Deformability
│ └───Strain Calculated strain values
│
├───Figures Automatically generated figures for publication
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└───Results Statistical results
├───LatB_ANOVA LatB-treated ANOVA comparison results
├───LatB_Regression LatB-treated curve fitting results
├───Osm_ANOVA Osmolarity-treated ANOVA comparison results
└───Osm_Regression Osmolarity-treated curve fitting results
Both Matlab scripts and R notebooks are used to generate results.
The original data is contained within Data_Raw folder.
All analysis scripts are contained within Code folder.
Downloading complete repository provides user with all final and intermediate results of the analysis, which allows for running scripts in arbitrary order.
Without the intermediate results, the scripts are in big part independent of one another. However, please note the following dependencies:
to run any of the
Rscripts, summary of the raw data has to be generated first using:
Osm_Generate_Summary_CSV.m for osmolarity analysis
LatB_Generate_Summary_CSV.m for LatB analysis
to plot response functions in
Matlab, fitting usingRhas to be performed first:
Osm_Response_Curves.m requires results from Osm_Dose_Curves.Rmd
Osm_SI_GOF_Plots.m requires results from Osm_Dose_Curves.Rmd
LatB_Response_Curve.m requires results from LatB_Dose_Curves.Rmd
Matlab scripts (.m) should be executed in the directory where they exist, as the scripts are built on relative paths. Each script starts with a line that changes the current working directory to the directory
of the script evaluated to ensure correct definition of data and destination directories.
Matlab code was evaluated with R2016B and R2019B.
R notebooks (.Rmd) should be executed in the directory where they exist, as the scripts are built on relative paths.
R code was evaluated with 3.6.1
Opening this folder in RStudio will load the project details saved in MetaDeformability.Rproj.
This project uses renv to document and create the environment in which the analysis is performed. Opening the project will install the package renv if not already installed. Recorded package versions are stored in renv.lock.
To install the recorded packages and versions run in R:
renv::restore()
This will not affect the other packages you have installed on your computer.
Last updated: April 27, 2020