Comparative microelectrode array dataset of functional development of human pluripotent stem cell-derived and rat neuronal networks
Fikret Emre Kapucu, Andrey Vinogradov, Tanja Hyvärinen, Laura Ylä-Outinen, Susanna Narkilahti
Neuro Group, Faculty of Medicine and Health Technology, Tampere University
We provide a dataset of microelectrode array recordings (MEA) from human pluripotent stem cell (hPSC)-derived and rat embryonic cortical neurons during in vitro maturation and pharmacological treatment at their mature stage (Kapucu et al., 2022). Together with the recorded raw MEA data, we share the analysis code that produces the key scientific findings published previously with this dataset (Hyvärinen et al., 2019), including PCA analysis to reveal the distinction between rat and human cell activity features. The data enables evaluation and comparison of hPSC-derived and rat cortical cultures in terms of functional development and response to pharmacological treatment. The 12500 Hz sampled data is recorded with Axion 12-well plates (for developmental tracking) and 48-well plates (for pharmacological experiments). Raw data constitute time series of extracellularly recorded voltage values in Volts. The duration of the recordings varies from 10 to 30 minutes depending on the experimental set-up. Each raw MEA data file contains organised meta-data. This dataset is valuable for experimental and computational neuroscientists and any signal analyst with interest for neuronal signalling. The accurate detailed description of data is delivered in the corresponding publication (Kapucu et al., 2022). Please, refer to the publication, as this README.md file deliver only a brief introduction of the data and basic procedures of reading it.
Downloading the data
There are several ways to upload the data using GIN command line client, git annex and web browser. Short instructions for web browser and GIN CLI are provided below.
The clear way to get a particular file from the repository with the web interface is to proceed to its folder, click the file and then click the "Download" link below it. The files containing raw MEA signals are quite large, so the downloading time may be extensive in case of slow Internet connection. The robustness of the connection is important, as it happes so, that browsers sometimes can not continue the file downloading after abrupt connection loss.
Create an account on GIN and download the GIN client (instructions). Log in using CLI:
Clone the repository:
gin get NeuroGroup_TUNI/Comparative_MEA_dataset
Big data files are not downloaded automatically. The following command should be used to get their contents:
gin get-content <filename>
Downloaded big files will be locked. You have to unlock the file with the following command:
gin unlock <filename>
To remove the contents of a big file:
gin lock <filename>
gin remove-content <filename>
Please, refer to the detailed GIN CLI Usage Tutorial.
Data directory embeds the following sub-directories
- hPSC_MEA1, hPSC_MEA2, Rat_MEA1 <- developmental tracking
- hPSC_MEA3_Pharmacology, Rat_MEA2_Pharmacology <- pharmacological experiments
- PCA <- data sets for MEA plates, used in PCA analysis
PCA folder contains 7 sub-folders with pre-selected MEA sets used in PCA analysis (3 timepoints for each set): hPSC_MEA1_PCA, hPSC_MEA2_PCA, hPSC_MEA4_PCA, hPSC_MEA5_PCA, Rat_MEA1_PCA, Rat_MEA3_PCA, Rat_MEA4_PCA. PCA folder also includes PCA_table.xlsx file which contains extracted meaRtools output features of the sets for running MATLAB PCA analysis code (may be found in the Codes directory : /Codes/PCA_code/PCA.m).
Each folder contains raw MEA recordings in HDF5 format (used, for example as an input for MATLAB spike detection). Each .h5 file accomodate two groups: Data and DataInfo. Data group embeds subgroups for each well of MEA plate. Each of these subgroups contains a number of datasets for each electrode in the well. For example, to access the recording of electode 11 of well B3 of ... .h5 in MATLAB, the following command should be executed:
DataInfo group provides meta-data for the recording. It contains two datasets: ExcludedWells and InactiveChannels and a list of attributes (SamplingFrequencyInHz, DurationInSec, RecordingUnits, DIV, and Plate type which together deliver the detailed description of the raw MEA file. To load the dataset of excluded wells the following command should be exectuted:
The attribute reading command is a bit different. For example, to read MEA plate type of the same file, the following command works:
h5readatt('...path/hPSC_20517_MEA1_DIV3.h5', '/DataInfo', 'Plate type')
Each folder also contains two sub-folders:
- <data label>_spikes_noise_explogs <- the folder with detected spike .csv files (MATLAB outputs) and explog and noisy electrodes .csv files (which all together comprise meaRtools input).
- <data label>_meaRtools_output <- the folder with selected meaRtools outputs (those, used in Hyvärinen et al., 2019)
Pharmacology folders contain separate sub-folders for each stage of the pharmacological experiment (Baseline Pharmacology and TTX treatment). To check which well of MEA plate has undergone which specific treatment (e.g. drug or control) during Pharmacology and TTX stage it is recommended to take a glance into explog .csv file (to be found in the corresponding <data label>_spikes_noise_explogs sub-folder).
Codes directory contains the following folders:
- MATLAB spike detection
- Connectivity Analysis
- PCA code
Code Usage Notes
To launch the MATLAB spike detection code, the user needs to open the Main.m file and then add the whole folder containing the MATLAB analysis code to the MATLAB path (so that the program is capable of finding the functions that the code requires). Next, by pressing MATLAB’s green “Run” button, the analysis is launched. The selection of the .h5 raw data files for the analysis is implemented via a pop-up window that opens as the user launches the code. One or more files can be selected for analysis. The output folder selection is performed in the same way. The code sequentially analyses the selected files, providing the user with spike .csv files as an output. If the user decides to use different parameters than described in the methods section for spike detection, the parameters to be changed are located in Main.m and amp_detect.m.
To implement the analyses in meaRtools, the user needs to follow the steps specified below:
i. First, the user needs to open MEA_analysis_Axion.R file.
ii. Then, by clicking the “Source” button, the code is launched. The user sees pop-up windows for selecting the code-containing folder, the output folder, the spike .csv files, the noisy electrode file and the expLog file.
iii. The last pop-up window enables the selection of the analyzed MEA type. There are 12- and 48-well MEA plates available, and the user only needs to enter an integer that corresponds to the analyzed plate type.
The folder selection windows sometimes do not appear on top of the RStudio window; then, they are found in the Windows taskbar. If the user wants to avoid the code directory selection step, it is possible to remove the first pop-up window by replacing the first "choose.dir" function with the code directory address.
It should be noted that electrodes that
- have no detected spikes (are not mentioned in the MATLAB-generated .csv spike files)
- are listed in the noisy electrode .csv file
- are eliminated by the minimum-spikes-per-minute criterion
are not included in the meaRtools analysis. Essentially, if for these abovementioned reasons all electrodes in a particular well are cancelled for all DIVs included in the analysis, this well is not displayed in the analysis output files.
There is a possibility of implementing the code in segments by performing segment selection and pressing the “Run” button.
The PCA and connectivity analysis MATLAB code packages are delivered in their corresponding folders. To run the PCA code, the user needs to open the PCA.m code in the "PCA" folder and add the whole folder to the MATLAB path. The folder contains a table with preselected activity features of the cell populations obtained during the provided analysis path. The next step is to click the "Run" button to launch PCA.
To launch the connectivity analysis, the user needs to open the Connectivity.m file in the "Connectivity analysis" folder and add this folder to the MATLAB path. After clicking the "Run" button, the pop-up window for .h5 file selection appears. After selecting the desired file, a new pop-up window for MEA well selection appears. The user needs to take into account the list of excluded wells, which are automatically displayed in the command window after the file selection, and avoid selecting them. For the connectivity analysis, the threshold value for connectivity strength can be changed in the script cross_selection_correlated_channels.m. More information on the CorSE and analysis guidelines can be found at https://se.mathworks.com/matlabcentral/fileexchange/59626-spectral-entropy-based-neuronal-network-synchronization-analysis-corse.
- Kapucu, F.E., Vinogradov A., Hyvärinen T., Ylä-Outinen L., Narkilahti S (2022). Comparative microelectrode array dataset of functional development of human PSC-derived and rat neuronal networks. Scientific Data volume 9, 120. https://doi.org/10.1038/s41597-022-01242-4
- Hyvärinen, T., Hyysalo, A., Kapucu, F.E., Aarnos, L., Vinogradov, A., Eglen, J.E., Ylä-Outinen, L., Narkilahti, S. (2019). Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures. Scientific Reports volume 9, 17125. https://doi.org/10.1038/s41598-019-53647-8
- Academy of Finland, 332693 (Fikret Emre Kapucu), 286990/326436 (Laura Ylä-Outinen), 312414 and 311017 (Susanna Narkilahti)
- Orion Research Foundation, grant for postdoctoral research 2019 (Fikret Emre Kapucu), grant for doctoral dissertation work 2020 (Andrey Vinogradov)
Comparative microelectrode array dataset of functional development of human pluripotent stem cell-derived and rat neuronal networks in the directory
Data by Neuro Group, Faculty of Medicine and Health Technology, Tampere University (TUNI), Tampere, Finland is licensed under a Creative Commons Attribution 4.0 International License.
Codes\MATLAB spike detection contains folder
Functions from F. Lieb's SpikeDetection-Toolbox which embeds functions imported from SpikeDetection-Toolbox distributed under GNU General Public License v3.0.
Codes contains folder
meaRtools with modified version of meaRtools package distributed under GNU General Public License v3.0. Modifications imply user interface, input file reading and selection process and the addition of LogISI algorithm for burst detection into the package. The LogISI algorithm originally received from its author Valentina Pasquale was modified for our data and the modifications are described in the publication Kapucu et al., submitted.
Codes contains folder
Connectivity Analysis with Spectral entropy based neuronal network synchronization analysis: CorSE code distributed under the 3-Clause BSD License.
All remaining codes in the directory
Codes are published under the 3-Clause BSD License.
LICENSE files in the corresponding directories for the full license.