# General information

Authors: Schlegel J.; Porebski B.; Andronico L.; Hanke L.; Edwards S.; Brismar H.; Murrell B.; McInerney G.M.; Fernandez-Capetillo; Sezgin E.
Publisher: Karolinska Institute, Stockholm, Sweden (https://ki.se/)
Item DOI: 10.17044/scilifelab.20517336
DOI of the peer-reviewed paper: 10.1021/acs.nanolett.2c04884
Contact e-mail: erdinc.sezgin@ki.se

License: CC BY 4.0
This readme file was last updated: 31-03-2023

Please cite as: Schlegel J, Porebski B, Andronico L, Hanke L, Edwards S, Brismar H, Murrell B, McInerney GM, Fernandez-Capetillo O, Sezgin E. A Multiparametric and High-Throughput Platform for Host-Virus Binding Screens. Nano Lett. 2023 Mar 9. doi: 10.1021/acs.nanolett.2c04884. Epub ahead of print. PMID: 36892970.

# Dataset description

This dataset is connected to the following article, https://pubs.acs.org/doi/10.1021/acs.nanolett.2c04884

The article is available at a link: https://pubs.acs.org/doi/10.1021/acs.nanolett.2c04884.

## Abstract
Speed is key during infectious disease outbreaks. It
is essential, for example, to identify critical host binding factors to
pathogens as fast as possible. The complexity of host plasma
membrane is often a limiting factor hindering fast and accurate
determination of host binding factors as well as high-throughput
screening for neutralizing antimicrobial drug targets. Here, we
describe a multiparametric and high-throughput platform tackling
this bottleneck and enabling fast screens for host binding factors as
well as new antiviral drug targets. The sensitivity and robustness of
our platform were validated by blocking SARS-CoV-2 particles
with nanobodies and IgGs from human serum samples.

## Dataset items

### .csv-files: LSM & LLSM Quantification
Figure_01C: folder "LSM": 24x .csv files
Figure_02B: folder "LLSM": 30x .csv files


### .fcs Flow-Cytometry Data
Figure_02_C: 6x .fcs files

### .ijm Macro
Figure_01C: LSM_VLP-quantification_BSLBs.ijm
Figure_02B: LLSM_VLP-quantification_BSLBs.ijm

### .lsm LSM Images
Figure_01B: 12x .lsm files

### .pdf Main Figures
Figure_01: Figure 1.pdf
Figure_02: Figure 2.pdf
Figure_03: Figure 3 V2.pdf
Figure_04: Figure 4 V2.pdf

### .pzfx GraphPad LSM LLSM
Figure_01C: Analysis LSM.pzfx
Figure_02B: Analysis LLSM.pzfx

### .svg Supplementary Figures
SFigure_01: SupplementaryFigure_01.svg
SFigure_02: SupplementaryFigure_02.svg
SFigure_03: SupplementaryFigure_03.svg
SFigure_04: SupplementaryFigure_04.svg
SFigure_05: SupplementaryFigure_05.svg
SFigure_06: SupplementaryFigure_06.svg

### .tif MIPs LLSM
Figure_02B: 15x .tif files

### .tif MIPs LSM
Figure_01B-C: 12x .tif files

### .xlsz Excel SuperPlotsOfData
Figure_03A: Figure_03A_Single Receptors_+S_SuperPlotsOfData.xlsx
Figure_03B: Figure_03B_Analysis Single Receptors_-S_SuperPlotsOfData.xlsx
Figure_03C: Figure_03C_Dual Receptors_+S_SuperPlotsOfData.xlsx

Researchers are welcome to use the data contained in the dataset for any projects. 
Please cite this metadata record upon use or when published. We encourage reuse using the same CC BY 4.0 License.

# Software to open files:
.csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel 
.xlsx: Microsoft Excel 
.tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads)
.pzfx: GraphPad Prism
.svg: Inkscape (https://inkscape.org/)
.fcs: FCS Express
.pdf: AdobeAcrobat or Mozilla Firefox
.ijm: Fiji (https://imagej.net/software/fiji/downloads)