READ ME File For 'Data for "Real time activation profiles of single T cell arrays following controlled interaction with antigen-presenting cells"'

Dataset DOI: 10.5258/SOTON/D1129

ReadMe Author: Anna Desalvo, University of Southampton, ORCID ID orcid.org/0000-0003-1357-3222

This dataset supports the thesis entitled "Real time activation profiles of single T cell arrays following controlled interaction with antigen-presenting cells"
AWARDED BY: University of Southampton
DATE OF AWARD: 2019

DESCRIPTION OF THE DATA

This Dataset contains the data underlying each figure in the thesis. Data were obtained as described in the relevant session of the manuscript.
Data are divided in chapters, and the files were named after their figure number in the thesis.
The files come in different formats: GraphPad Prism 7 XML Project (.pzfx), FCS File (.fcs), Microsoft Excel 97-2003 Worksheet (.xls), COMSOL Application File (.mph), MATLAB Figure (.fig), Text Document (.txt) and MATLAB Code (.m).
Software required to access all the data: GraphPad Prism, any FCS software (e.g. FlowJo), Microsoft Excel, COMSOL and MATLAB.


This dataset contains:

Chapter 3 zip file. This file includes data associated with the figures:
    Fig 3.1 - Results from the specificity test ran on two 96-wells plates of the B3Z subclones.
    Fig 3.2 - Sensitivity assay of the best subclones.
    Fig 3.3 - MFI of B3Z populations stained with different concentrations of Fluo8-AM.


Chapter 4 zip file. This file includes data associated with the figures:
    Fig 4.2 - Ionomycin titration results.
    Fig 4.3 - Double stimulation using ionomycin.
    Fig 4.4-4.5-4.23 - Data related to experiments ran in buffers containing different calcium levels.
    Fig 4.6 top - Comparison between the ionomycin responses and sensitivity of subclones populations.
    Fig 4.6 - Comparison between the ionomycin responses and sensitivity of subclones populations.
    Fig 4.8-4.9- Soluble antiCD3 titration.
    Fig 4.10 - Comparison between soluble and beads-conjugated antiCD3 antibodies.
    Fig 4.11 - Comparison of B3Z responses to peptide-loaded K89 in the presence or absence of PKH26 staining.
    Fig 4.14 Raw FACS data - B3Z biological stimulations time points using flow cytometry.
    Fig 4.15 - Sensitivity assays comparing different APC cell lines.
    Fig 4.16 - H-2Kb and H-2Kb/SL8 staining of various antigen presenting cells.
    Fig 4.17 Raw FlowJo Data (xlsx)- Antigen presentation on K89 surface over time.
    Fig 4.18 Raw FACS Data - FL-1 histograms of single B3Z after 1:1 addition of 10μM SL8 loaded K89 and spinning of the mixed population at different centrifugation speeds and times.
    Fig 4.19 - Doublets percentages after B3Z:K89 centrifugation and pipette disruption of the pellet, gated on the FSC-A:FSC-H graph.
    Fig 4.20-4.21-4.22 Raw FACS Data - B3Z activations against K89 pulsed with different SL8 concentrations.
    Fig 4.21-4.22 - B3Z activations against K89 pulsed with different SL8 concentrations.


Chapter 5 zip file. This file includes data associated with the figures:
    Fig 5.2 - B3Z size characterization using single cell microfluidic impedance cytometry.
    Fig 5.6 Comsol simulation - Calcium ion diffusion into agarose trapping plate.
    Fig 5.8 - B3Z viability in presence of different hydrogels.
    Fig 5.11 - B3Z occupancies using different cell:well seeding ratios.
    Fig 5.13 - Comparison of the cell:well occupancies of B3Z and K89 cell lines.
    Fig 5.20 - Comparison between flow cytometry and fluorescence microscopy in the analysis of a sample of calibration particles having 8 different fluorescent peaks.
    Fig 5.21-5.22-5.23 - Responses of single B3Z to ionomycin stimulations (various analyses).
    Fig 5.24 - An example of single B3Z responses to subsequent stimulation with antiCD3 and ionomycin.
    Fig 5.25-5.26-5.27 Raw data - Normalized fluorescent signals (MFI-MFI0)/MFI0 of the entire B3Z population from which responders were gated in Figure 5.25 and Figure 5.26 (columns are the individual cells, rows are the frame num).
    Fig 5.28-5.29 - Analysis of single cell responses to double stimulation with antiCD3 and ionomycin.


Chapter 6 zip file. This file includes data associated with the figures:
    Fig 6.3 - Superparamagnetic nanoparticles uptake in B3Z and K89 cell lines.
    Fig 6.5 - Velocity distribution of magnetically-loaded cells in presence of a magnetic gradient.
    Fig 6.9 (a) Normalized traces - single T cells signalling in absence of any stimulation (being in contact with the agarose trapping device only. Columns are the individual cells, rows are the frame num).
    Fig 6.9 (b) Normalized traces - single T cells responses when pushed against a PDMS surface (columns are the individual cells, rows are the frame num).
    Fig 6.9 (c) Normalized traces - single T cell responses when pushed against a tissue-culture Petri dish (columns are the individual cells, rows are the frame num).
    Fig 6.10 (a) Normalized traces - Signalling of single T cells paired to K89, in absence of peptide (columns are the individual cells, rows are the frame num).
    Fig 6.10 (b) and 6.11 Normalized traces - Signalling of single T cells paired to K89, in presence of peptide (columns are the individual cells, rows are the frame num).
    Fig 6.11 (b) - Selection of the first activators against SL8-pulsed antigen presenting cells.
    Fig 6.12 (a) - Time of peak of all the responders, that were sorted based on their peak values (from the highest to the lowest).
    Fig 6.12 (b,c) - Two subpopulations of responders.
    Fig 6.14 Normalized traces - Example of activation profiles from the integrated system, time traces of the entire array of single cells (columns are the individual cells, rows are the frame num).
    Fig 6.15 Normalized and corrected traces - Combinatorial study of B3Z and K89 ROIs (columns are the individual cells, rows are the frame num).
    Fig 6.16 Normalized traces - Different activation patterns (columns are the individual cells, rows are the frame num).
    Fig 6.17-6.18 - Single and multiple B3Z responders analysis.
    Fig 6.19 (a) Normalized traces - activators stimulated with 100 μM peptide (columns are the individual cells, rows are the frame num).
    Fig 6.19 (b) Normalized traces - activators stimulated with 1 μM peptide (columns are the individual cells, rows are the frame num).
    Fig 6.19 (c) Normalized traces - activators stimulated with 10 nM peptide (columns are the individual cells, rows are the frame num).
    Fig 6.20 Matlab figures - Multiple and single responders. A selection of the traces from Figure 6.19 (a, c).
    Fig 6.31 (a) Comsol simulation - 2D Comsol simulation to evaluate the pressure drop across a pressure channel.
    Fig 6.31 (b) Comsol simulation - 2D Comsol simulation to evaluate the pressure drop across a pressure channel.
    Fig 6.37 - Fluorescence intensity profile of 3 channels during the membrane deformation experiments using fluorescein.
    Fig 6.38 (a) - Membrane deformation assessment using blue food colouring dye.
    Fig 6.38 (b,c) - Membrane deformation assessment using blue food colouring dye.


Chapter 7 zip file. This file includes data associated with the figures:
    Fig 7.3 - Well deformation rate due to PA gel drying.


Matlab and ImageJscript zip file. This file includes:

    AnnaROI (adapted from http://imagej.1557.x6.nabble.com/Add-ROIs-from-a-list-of-coordinates-in-a-CSV-file-td5009300.html) and AnnaSave - txt files used as Macros in ImageJ to draw the ROIs around the cells and save the results (adapted).
    
    DATAANALYSIS - Main script to launch to start the process, it relies on several functions.
    cutresultsfunction - Reads Results.csv and divides it in nfiles called 'Results1.csv' etc, as ImageJ won't process large amounts of data at once, so it needs reiteration.
    renameresultsfunction - Changes name of 'ResultsX.csv' to 'Results.csv' and back upon click, to iteratively extract the timetraces from ImageJ (that will only read files named 'Results.csv').
    timeseriesmergefunction1 - Reads the timeseries files named '1.txt' etc and merges all files in one matrix.
    heatscatterfunction (adapted from https://uk.mathworks.com/matlabcentral/fileexchange/47165-heatscatter-plot-for-variables-x-and-y) - Plots the scatter density plot of the timetraces in input. It relies on a second function, 'heatscatter.m'(downloaded), that needs to be saved in the same folder.
    plotaveragefunction - Plots all the traces, and their average and standard deviation
    cellselectionfunction (adapted from code provided at the Single Cell Analysis Course - CSHL, 2016) - It lets you manually select relevant traces by plotting all of them in parallel in several screens. You can click on traces you want to keep (they will turn red) and proceed. Responders and not responders will also be saved in Excel files to visualize their positions in the plate via ImageJ.
    plotheatmapfunction - Plots the heatmap of the selected traces
    shadedErrorBar (downloaded from https://uk.mathworks.com/matlabcentral/fileexchange/26311-raacampbell-shadederrorbar) - Makes a 2-d line plot with a pretty shaded error bar
    saveFigs (adapted from https://uk.mathworks.com/matlabcentral/fileexchange/71433-savefigs)- Saves open figures to files
    thresholdfunction - Defines the activators from the traces that pass a manually-selected threshold. Requires plotheatmapfunction in the same folder
    slopefunction - Defines the activators from the traces that have a shift between to subsequent frames that is greater than the set threshold. Requires plotheatmapfunction in the same folder.
    
    cellselectionfunction1 - alternatives to cellselectionfuncion
    gridLegend (downloaded from https://uk.mathworks.com/matlabcentral/fileexchange/66464-gridlegend) - plots a legend in a multi comlumn format
    LineSelected (adapted from https://uk.mathworks.com/matlabcentral/answers/376144-selecting-lines-from-plot) - highlights selected line by changing its width
    timeseriesmergefunction - alternative to timeseriesmergefunction1

    sorting - Script to sort the timetraces matrix - select the appropriate section to run to sort in the desired way. It relied on multiple functions
    maxsort - Sorts based on maximum value of each column (cell during the recording)
    heatmap - generates an heatmap of the timetraces
    meansort - Sorts based on maximum value of each column (cell during the recording)
    sortfirst - Selects all traces that exceeded a threshold during a manually selected time window.
    displayselectedcells (adapted from code provided at the Single Cell Analysis Course - CSHL, 2016) - It lets you manually select relevant traces by plotting all of them in parallel in several screens. You can click on traces you want to keep (they will turn red) and proceed. Responders and not responders will also be saved in Excel files to visualize their positions in the plate via ImageJ. 

Note that part of the presented functions were downloaded and/or adapted from scripts found online (as stated).

Date of data collection: May 2015 - May 2018

Information about geographic location of data collection: University of Southampton

Related projects/Funders: This project was kindly funded by Dr Norman Godinho.

Date that the file was created: October, 2019