dc.contributor.author
de Jong-Bolm, Daniëlle
dc.contributor.author
Sadeghi, Mohsen
dc.contributor.author
Bao, Guobin
dc.contributor.author
Klaehn, Gabriele
dc.contributor.author
Hoff, Merle
dc.contributor.author
Mittelmeier, Lucas
dc.contributor.author
Basmanav, F. Buket
dc.contributor.author
Opazo, Felipe
dc.contributor.author
Noé, Frank
dc.contributor.author
Rizzoli, Silvio O.
dc.date.accessioned
2023-06-13T09:14:21Z
dc.date.available
2023-06-13T09:14:21Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/39793
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-39512
dc.description
This dataset contains multiplexed fluorescence microscopy images of the protein nanobarcodes with the immunostaining protocol described in the manuscript.
en
dc.description
Data are provided for 4 different expression times, namely overnight, 24h, 48h, and 72h, for all the constructs used in the research. The datasets used in for training, validation, and testing of the deep network used for identification of nanobarcodes are solely obtained from the provided images.
en
dc.description.abstract
Multiplexed cellular imaging typically relies on the sequential application of detection probes, such as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations.
en
dc.publisher
Freie Universität Berlin
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
fluorescence imaging
en
dc.subject
confocal microscopy
en
dc.subject
deep learning
en
dc.subject
immunostaining
en
dc.subject
multiplexed imaging
en
dc.subject.ddc
500 Natural sciences and mathematics::570 Life sciences::570 Life sciences
dc.subject.ddc
500 Natural sciences and mathematics::570 Life sciences::572 Biochemistry
dc.subject.ddc
000 Computer science, information, and general works::000 Computer Science, knowledge, systems::004 Data processing and Computer science
dc.title
Protein nanobarcodes enable single-step multiplexed fluorescence imaging
dc.contributor.type
data_manager
dc.contributor.type
project_leader
dc.title.subtitle
Image data
refubium.affiliation
Mathematik und Informatik
refubium.funding.funder
dfg
refubium.funding.funder
fund_eu
refubium.funding.funder
institution
refubium.funding.project
European Union Horizon 2020
Deutsche Forschungsgemeinschaft (DFG)
European Research Council Consolidator Grant (CoG)
Berlin Institute for Foundations in Learning and Data (BIFOLD)
Deutsche Forschungsgemeinschaft (DFG) through Cluster of Excellence Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB)
Campus Laboratory for Advanced Imaging, Microscopy and Spectroscopy (AIMS)
refubium.funding.projectId
Horizon 2020 grant agreement No. 964016 (FET-OPEN Call 2020, IMAGEOMICS project)
DFG SFB 958/Project A04
DFG SFB 1114/Project C03
ERC CoG 772230
refubium.isSupplementTo.doi
https://doi.org/10.1101/2022.06.03.494744
refubium.isSupplementedBy.url
https://refubium.fu-berlin.de/handle/fub188/40380
refubium.note.author
The authors acknowledge Ms. Christina Zeising for helping with general laboratoray procedures.
en
dcterms.accessRights.openaire
open access
refubium.funding.stream
Horizon research and innovation programme
DFG Sonderforschungsbereich (SFB) 958 and 1114
