DOI: 10.7272/q6ns0s5n
title: Data from: Neutrophils actively swell to potentiate rapid migration

description: While the involvement of actin polymerization in membrane protrusion is
 well-established, we have a more limited understanding of the role of
 transmembrane water flow in cell motility. Here we investigate the role of
 water influx in neutrophil migration. These cells undergo directed
 movement to sites of injury and infection. Chemoattractant exposure
 increases cell volume and potentiates neutrophil migration, but the causal
 link between these processes is not known. Using a genome-wide CRISPR
 screen, we identify the regulators of the chemoattractant-induced
 neutrophil swelling, including NHE1, AE2, PI3K-gamma, and CA2. Through
 NHE1 inhibition in primary human neutrophils, we show that cell swelling
 is both necessary and sufficient for rapid migration following
 chemoattractant stimulation. Our data demonstrate that cell swelling
 complements cytoskeletal inputs for chemoattractant-induced potentiation
 of migration.
description: In these experiments, primary
 human neutrophils were imaged using <strong>Fluorescence Exclusion
 Microscopy (FxM)</strong>. FxM is a highly accurate method for
 measuring cell volume that relies on cells excluding a dye in a
 microfluidic chip with a flat ceiling held up by pillars. The height of
 chamber is known and can be used to determine the volume displaced by the
 cells. The FxM image is taken with a low magnification objective (in this
 case a 20x 0.75NA Plan Apo) with a depth of field larger than the height
 of the chamber to capture all of the fluorescence in the chip. For all
 experiments in this dataset, the excluded dye is a 10,000 MW dextran
 conjugated to AlexaFluor-647 and the light source is a Sutter Lambda LS
 with a Xenon Arc lamp. Two other channels were taken for segmentation
 purposes including a nuclear channel (a Hoechst stain imaged with a BFP
 filter set) and a cytoplasmic channel (Calcein Red-Orange imaged with a
 RFP filter set) also taken in epifluorescence mode. After 15 minutes of
 imaging, a high powered transmitted UV LED was flashed for 30-45 seconds
 to uncage a caged chemoattractant present in the media to acutely activate
 the cells. The cellular response was then recorded for an additional 30
 minutes. Both the raw
 movies and the processed FxM channel are included in this dataset. The raw
 data was processed using a custom Julia script (https://github.com/tlnagy/fxm-processing) that denoises, flatfield corrects, segments, extracts the cell volumes, and links the cell tracks. This script relies on a license-encumbered denoising algorithm from Boulanger, J. et al (2010) and therefore for ease of reproducibility the processed FxM channel is also included. The git hash of the script version used to process the data is included.
description: # Neutrophils actively swell to potentiate rapid migration
 Primary neutrophils were isolated
 from the blood of volunteers and then *gently* injected into the
 microfluidic chips. Mechanical stimulation activates the cells so extreme
 care was taken to keep the cells quiescent and unactivated. The FxM chips
 were prepped such that they always had 3 lanes per chip and all lanes were
 imaged concurrently to minimize the impact of timing and, if possible, all
 lanes were included in the analysis. One lane was usually an untreated
 (aka WT) condition and the other two lanes were different drug conditions
 (see `fxm_dataset_info.csv` for details). If lanes were discarded, the
 reason is included in the `fxm_dataset_info.csv` in the `notes` section.
 The primary reason for not including a lane was due to incomplete bonding
 of the microfluidic chip or due to flow in the microfluidic chip. The
 chips lack a resistive element so any bubbles in the system would lead to
 very large flows and the cells could be blown away in the matter of
 seconds. While this README lays out the high-level organization of the
 datasets, the best way to learn how to interact with the data is to look
 at the code used to generate the figures in the **Code/Software** section
 below. ## Description of the data and file structure There are two related
 FxM datasets used in the paper: 1. `fxm_uncaging_*` : This dataset is at a
 lower time resolution (every 12 seconds), but all lanes were imaged
 simultaneously. The chemoattractant was uncaged after the first 15 minutes
 and then the cell response was imaged for another 30 minutes. 2.
 `fxm_highres_* `: After the uncaging dataset was collected, certain lanes
 were then immediately imaged at a higher time resolution (every 3 seconds)
 to capture the motility-driven volume fluctuations. Each lane was imaged
 sequentially, not in parallel. ### Uncaging FxM Dataset The uncaging FxM
 microscopy dataset consists of 30 sets of movies organized by date,
 volunteer, and condition. The details of these datasets and any datasets
 that were discarded for any reason are in `fxm_dataset_info.csv`. Each of
 the 30 sets include: 1. `fxm_uncaging...raw_rgb.tif` A RGB TIFF file of
 the raw movie with the nuclear (Hoechst), cytoplasmic (Calcein
 Red-Orange), and FxM (AlexaFluor-647 10k MW dextran) channels encoded as
 red, green, and blue, respectively. 2. `fxm_uncaging...fxmcorr.tif` A
 fully processed, denoised, and flatfield corrected version of the FxM
 channel that was used for segmentation also as a TIFF file. Additionally,
 there are two CSV files* that contained the extracted information from the
 movies 1. `fxm_uncaging_augmented.csv` - contains the extracted data from
 the FxM movies. Key columns include `particle` (the ID of a cell), `x` and
 `y` (the position of the cell), `time_s` (the time in seconds since the
 start of the movie), `abs_volume_um3`(the absolute volume of the cell in
 um^3), `area_um2` (the footprint area of the cell in um^2),
 `isencapsulated` (whether the cell is fully separated from other cells),
 2. `fxm_uncaging_augmented_w_footprints.csv` - same as above but with an
 additional column called `footprint_cart` which is a list of the XY
 coordinates of pixels "belonging" to the cell. Useful for
 plotting cell footprints over the original movies. *All CSVs can have
 missing values (i.e. two consecutive delimiters `,,`) if a value is absent
 or could not be computed for a situation. This values are treated as
 [missing](https://docs.julialang.org/en/v1/manual/missing/) in Julia. ###
 FxM HighRes dataset The highres dataset (described in
 `fxm_highres_datasets.csv`) consists of 4 sets of movies organized the
 same way as the uncaging dataset from above with both
 `fxm_highres...rawrgb.tif` and `fxm_highres...fxmcorr.tif` versions for
 each set. The extracted information from these datasets is available in
 `fxm_highres_augmented_w_localities.csv` which is identical to the CSVs
 described above with the important addition of `locality_cart`, which is a
 list of pixels "belonging" to the local area around a cell. This
 local area is used to "fill in" the cells footprint to compute
 the counterfactual of what the signal would be if the cells *wasn't*
 there. ### Coulter dataset All in-suspension volume measurements were
 taken on a Beckman-Coulter Z2 machine. For each run, the cell volumes were
 measured three times prior to stimulation with chemoattractant and then
 was sampled every minute for the following 12+ minutes. The
 `coulter_datasets.csv` file contains the metadata for each run with the
 `date`, `volunteer`, and condition (aka `sample`). It also includes a
 `SampleCode` which is the file name prefix for each measurement in a run.
 Finally, `DataPath` is the path to the folder containing the raw data
 files in `coulter_data.zip`. The raw files are Beckman-Coulter Z2 files,
 which can be loaded into Julia using
 [tlnagy/Coulter.jl](https://github.com/tlnagy/Coulter.jl). ##
 Code/Software The best way to interact with the data is to explore the
 code used to generate the figures in the paper. The code is available as a
 mini-site ( ) which
 is built automatically on Github Actions to validate reproducibility and
 portability. To run the analysis locally, first git [download the
 repository](https://github.com/tlnagy/Nagy_2023_SwellMigration) and then
 download this dataset into the `data` folder. You'll need a recent
 version of Julia installed (tested on 1.9.3) and then you can run the
 following code in the terminal from the root of the repository: `julia
 --project="." -e 'using Pkg; Pkg.instantiate()'` which
 will install all the packages you need, followed by `julia
 --project="." site/make.jl` This will run all the analysis code
 and build the website. You can follow this by running `julia
 --project=build -e 'using Pkg; Pkg.instantiate()'` `julia
 --project=build -e 'using LiveServer; serve(dir =
 "site/build")'` which will fire up a mini-server and allow
 you to browse the site locally if you navigate to the URL that is printed
 out.