DOI: 10.5061/dryad.7wm37pw0r
title: Data from: Live imaging of SARS-CoV-2 infected airway epithelium cultures

description: SARS-CoV-2 infects the conducting airways, where mucociliary clearance
 inhibits pathogen penetrance. Mucociliary clearance is a dynamic system,
 and both the host and the pathogen can influence it. To better understand
 how SARS-CoV-2 changes MCC, we performed live imaging of infected
 differentiated primary human bronchial epithelium cultures over multiple
 days. We used a fluorescent reporter virus and fluorescent markers for
 tubulin and apoptotic cells to understand changes in the culture in both
 infected and bystander cells. In whole culture movies, we saw that
 SARS-CoV-2 infection foci traced the motion of mucus, suggesting that
 mucociliary clearance shapes the spread of the virus. We then monitored
 how mucociliary clearance changed during infection. We found that
 SARS-CoV-2 infection induced defects in ciliary motion in both infected
 and bystander cells, taking ~4 days for the infected cells to become
 numerous and old enough to impact culture-wide ciliary motion.
description: Live imaging of inverted ALI cultures was performed on a
 DeltaVision Elite microscope outfitted with a stagetop incubator. For
 movies of entire cultures, imaging was done in a 5x5 panel of 1024 x 1024
 FOVs across 4 Z planes spanning 80 um with a 4x air lens (Olympus
 UPLXAPO4X), with panels acquired every 2 hours. For ciliary motion movies,
 256x256 pixel FOVs were acquired with a 10x air lens at 125 frames per
 second. Centered on the smaller FOV of each ciliary motion movie, 1024 x
 1024 reference images of all channels spanning approximately 20 microns of
 Z were also acquired with the same 10x lens. Channels include
 SPY650-Tubulin (far red), NucView 530 (orange), GFP (green), and white
 light. Details of each experiment are provided in the accompanying
 .csv. Ciliary motion movies were denoised using
 cellpose 3 cytoplasm denoising model. Ciliary beat frequency was
 calculated using scipy.signal.periodogram on ciliary motion movies. Whole
 culture movies were stitched in ImageJ using the Grid/Collection stitching
 plugin (doi: 10.1093/bioinformatics/btp184), Z projected and concatenated across multiple days of imaging, registered using the Linear Stack Alignment with SIFT Multichannel, and cropped to remove areas outside the culture. The code used for these tasks is provided in the GitHub repository accompanying the manuscript (link in Related Works).
description: # Data from: Live imaging of SARS-CoV-2 infected airway epithelium
 cultures This dataset consists of live microscopy data of SARS-CoV-2
 infected primary human bronchial epithelium cultures. We wanted to
 understand how SARS-CoV-2 spreads in the airway, so we infected these
 cultures with a fluorescent reporter virus and then imaged the entire
 culture surface for some days. We also used fluorescent dyes to monitor
 parameters for culture health. In this repository, we share data relating
 to how the virus changes ciliary motion over time. We find that changes in
 infected cell ciliary motion take a few days to become noticeable and that
 it takes 4 days for culturewide changes in ciliary motion to be
 significant. Any questions should be directed to the corresponding author:
 Thomas Hope, [thope@northwestern.edu](mailto:thope@northwestern.edu). ##
 Description of the data and file structure Data are organized by file
 type. The bullet points below mirror the file structure. * keys: .csv
 files containing information linking files to experimental info. *
 'exptlog_iii.csv' contains details of every live-imaged culture.
 Each row is one culture. * 'filename' = name of .tif file
 containing stitched, concatenated, and registered movie * 'csv'
 = name of .csv file containing trackmate data from the .tif file *
 'expt.name' = name of the experiment as recorded in each .tif
 file * 'date' = date of experiment (start of infection) *
 'usable' = whether the culture is valid for use in analysis. 
 see 'notes' for details. * 'sample.no' = # of each
 culture. This is in each .tif and .csv. * 'donor' = donor id # *
 'virus' = which virus/whether the culture was infected *
 'dyes' = which dye combination the culture received *
 'condition' = any experiment-specific factor. *
 'notes' = notes on culture appearance, experimental procedure,
 etc. * 'jammed' = whether the culture exhibited cell migration,
 and if so to what extent. Assessed by eye. * 'crypts' = whether
 the culture exhibited crypts, and if so what kind. Assessed by eye. *
 'cyst' = whether the culture exhibited cysts, and if so what
 kind. Assessed by eye. * 'infxn.in.crypt' = whether there were
 foci in crypts. * 'mucus.simple' = mucus motion type, simplified
 into rotary/disorganized/static. * 'mucus.mvmt.type' = mucus
 motion type, more complex (ex. rotary.many = multiple spinning mucus
 discs) * 'mucus.stop.frame' = what frame the mucus last moved on
 (ex. if mucus.stop.frame = 60, there was a change from 59-60 and no change
 from 60-61) * 'video.end.frame' = last frame of video *
 'mucus.stop.frame.end' = was the mucus.stop.frame the same as
 the last frame of the video?  * 'focus.type' = dominant focus
 morphology within the culture (detailed) * 'focus.num' = Number
 code for the dominant focus morphology within the culture (detailed) *
 'focus.simple' = Dominant focus morphology, simplified to
 mock/plaque/comet/diffuse. The major categorization used in the
 manuscript. * '20hpi.spots' = # of cell-sized GFP foci evident
 at 20 hours post-infection. * '48hpi.focicount' = The number of
 distinct foci evident at 48 hours post-infection. A fuzzy metric, as at
 this timepoint foci often blur together. * 'n.mm.2a',
 'n.mm.2b', 'n.mm.2mean' = # of N RNA copies per mm^2
 culture area. 'a' and 'b' are technical duplicates,
 'mean' is the mean of the technical duplicates. *
 'fromold' = was this culture infected in the old bsl3 facility
 or the new one (the bsl3 core)? * 'piv.*': particle image
 velocitometry data based on the farthest red channel of the .tif. this is
 only informative when that channel is SPY650-tubulin; otherwise, it
 reflects mucus motion which is typically much faster than the framerate of
 whole culture videos can capture. * 'piv.total' = # of particle
 image velocitometry sectors, total. * 'piv.fxn.3',
 'piv.fxn.6', etc. =  # of particle image velocitometry sectors
 with speed above N microns/hour (N = 3, 6, 12, 24, 48). *
 'piv.mean.speed' = mean speed of particle image velocitometry
 data. * 'piv.median.speed' = medianspeed of particle image
 velocitometry data. * 'maxgfp0', 'maxgfp850', etc. = #
 of pixels with GFP intensities above N (N = 0, 850, 900, 1000, 1100) at
 the frame with the highest value for each threshold. *
 'frame.maxgfp0', 'frame.maxgfp850', etc. = The frame
 with the highest # of pixels with GFP intensities above N (N = 0, 850,
 900, 1000, 1100) for each threshold. * 't0spy0',
 't0spy350', etc. = # of pixels in the farthest red channel with
 signal intensity above N (N = 0, 350, 400, 500) at a given frame t (t = 0
 or 20). * na = not applicable (i.e. the given column is not informative or
 meaningful for a culture) * nd = not determined (i.e. file was available
 but a given metric was not measured) * undetermined = qpcr was run &
 no target detected, for 'n.mm.*' columns * none = no notes
 taken, for 'notes' column * \-- = script failed to quantify, for
 'piv.*' columns. * ciliakeys contains .csv files necessary for
 telling which culture and timepoint each ciliary beat frequency video
 corresponds to. These files are used in the Python code to concatenate
 summarized ciliary beat frequency data for analysis. * *timepoints.csv
 files: Each row is one timepoint. * 'timepoints' column = number
 code corresponding to each timepoint in raw data files, 'hpi'
 column = hours post-infection each number code corresponds to * *pts.csv
 files: Each row is one consecutive run of imaging ciliary beat frequency
 videos/reference images for a single culture on the microscope. For this
 imaging, additional points were appended at successive time points; thus,
 there may be more than one row for each culture. *
 'culturepoint' = which individual culture,
 'vidpoint.start' = number corresponding to the first beat
 frequency movie point for a given culture, 'vidpoint.end' =
 number corresponding to the last beat frequency movie point for a given
 culture, 'donor' = donor for that culture, 'condition'
 = experimental condition for that culture, 'experiment' = which
 experiment that culture was a part of. * multitest_unprocessed:
 Unprocessed reference images and ciliary beat frequency movies plus
 denoised ciliary beat frequency movies from a single experiment. The
 unprocessed images themselves are in .dv format; logs containing info
 about imaging conditions are .dv.log. Denoised movies are .tif. Owing to
 the prohibitively large size of the full dataset, only one experiment is
 provided. * cbf_bulk is concatenated data from each pixel of each ciliary
 beat frequency movie, with data from reference images appended in
 additional columns where applicable. Each .csv is from one experiment. *
 cbf_vis contains .tifs for each ciliary beat frequency movie, with
 channels ordered like so: power, beat frequency (Hz), sum spy650 tubulin
 signal across the ciliary beat frequency movie, max intensity projection
 of spy650-tubulin signal in the reference image, max intensity projection
 of NucView 530 signal in the reference image, max intensity projection of
 GFP signal in the reference image, and max intensity projection of
 brightfield signal in the reference image. Files are grouped in folders by
 experiment. * trackmate_spotdetection contains trackmate spot detection
 files (.csv format) for whole culture movies. * d7d8 contains processed
 whole culture movies for the experiment where cultures were rinsed at 120
 hours post-infection. ## Code/Software Code used for data processing and
 smaller summary data is available on GitHub (see Related Works for the
 link).