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13 results for “ZeroCostDL4Mic”
ZeroCostDL4Mic - CARE (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CARE (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low and high signal-to-noise ratio</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective </p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP</p> <p><strong>File format</strong>: .tif (32-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 40 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (paxillin-GFP) </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective </p> <p><strong>Cell type</strong>: U-251 glioma cells, endogenously expressing paxillin-GFP</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512 (Pixel size: 248 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Aki Stubb<sup>1</sup>, Guillaume Jacquemet<sup>1,2</sup> and Johanna Ivaska<sup>1</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p>2) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p><br> </p> <p><strong>Associated publication</strong>: Stubb <em>et al.</em> 2020, Nano letters DOI: 10.1021/acs.nanolett.9b04083</p> <p><strong>Funding bodies</strong>: A.S. has been supported by the University of Turku Doctoral programme for Molecular Medicine (TuDMM).</p>
ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence and transmitted light)</p> <p><strong>Microscopy data type</strong>: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)</p> <p><strong>Microscope</strong>: Leica SP8, HC PL APO 63x 1.40 NA oil objective </p> <p><strong>Cell type</strong>: HeLa (fixed using an organelle-preserving protocol)</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 x 32 (Pixel size: x and y: 90 nm pixel size, z: 150 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn</p> <p><strong>Contact email</strong>: c.spahn@chemie.uni-frankfurt.de, heilemann@chemie.uni-frankfurt.de</p> <p><strong>Affiliation</strong>: Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><br> <strong>Funding body(ies)</strong>: M.H. and C.S.: German Science Foundation (grant nr. SFB1177). C.S.: European Molecular Biology Organization (short term fellowship 8589)</p>
ZeroCostDL4Mic / DeepBacs - Multi-label U-Net training dataset (Bacillus subtilis) and pretrained model
<p>Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP, the manually annotated instance segmentation mask and the corresponding 2-label semantic segmentation mask used for model training.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired fluorescence and segmented mask images</p> <p><strong>Microscopy data type</strong>: 2D widefield images (fluorescence) </p> <p><strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)</p> <p><strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads</p> <p><strong>File format</strong>: .tiff (8-bit)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm)</p> <p><strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin and mask images with labeled cytosol and cell boundaries were created using a custom Fiji macro (see our <a href="https://github.com/HenriquesLab/DeepBacs/tree/main/ImageJ-macros">github repository</a>).</p> <p> </p> <p><strong>Multi-label U-Net model</strong>:</p> <p>The U-Net (2D) multilabel model was generated using the ZeroCostDL4Mic platform (Chamier & Laine et al., 2021). It was trained from scratch for 200 epochs on 733 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (256 x 256 px²)) with a batch size of 8 and a categorical_crossentrop loss function, using the U-Net (2D) multilabel ZeroCostDL4Mic notebook (v 1) (Chamier & Laine et al., 2021). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.1.105). The training was accelerated using a Tesla P100GPU.</p> <p> </p> <p><strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup></p> <p><strong>Contact email</strong>: <a href="mailto:Seamus.Holden@newcastle.ac.uk">Seamus.Holden@newcastle.ac.uk</a></p> <p> </p> <p><strong>Affiliation</strong>:</p> <p>1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK</p> <p>2) ORCID: 0000-0002-7169-907X</p> <p> </p> <p> <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235</p>
Breast Cancer Nuclei images for DL Training + ZeroCostDL4Mic StarDist Model
<div> <p><strong>Training dataset:</strong><br>Paired microscopy images (fluorescence) and corresponding masks</p> <p>Microscopy data type: Fluorescence microscopy and masks obtained via manual correction of automatic segmentation with pre-trained StarDist model (see https://github.com/qupath/models/tree/main/stardist) </p> <p>Cells were imaged using a 20x objective with a 1x camera adapter was used in conjunction with a pco.edge 4.2 4MP camera on Pannoramic SCAN 150 scanner.</p> <p>Cell type: FFPE tissue sections were sliced from all cancer-containing paraffin blocks</p> <p>File format: .tif (8-bit for fluorescence and 16-bit for the masks)</p> <p> </p> <p><strong>StarDist Model:</strong><br>The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 100 epochs using 80 manually annotated paired images (image dimensions: (257, 257)) with a batch size of 2, an augmentation factor of 10 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 2.2.0), Keras (v 1.1.2), CSBdeep (v 0.7.2), NumPy (v 1.21.6), Cuda (v 11..1.105). The training was accelerated using a Tesla P100GPU.<br>The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. a QuPath-compatible model is also provided.</p> <p> </p> <p> </p> </div>
ZeroCostDL4Mic - CARE (3D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CARE (3D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence) of low and high signal-to-noise ratio</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective </p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP</p> <p><strong>File format</strong>: .tif (32-bit)</p> <p><strong>Image size</strong>: 1024x1024x33 (Pixel size: x,y: 40 nm, z: 125 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective </p> <p><strong>Cell type</strong>: A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512x13 (Pixel size: x,y: 195 nm , z: 500 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Kaukonen <em>et al.</em> 2017, Nature Protocol DOI: 10.1038/nprot.2017.107</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Stardist example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Stardist example training and test dataset</p> <p>(see <a href="http://https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - pix2pix example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - pix2pix example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells</p> <p><strong>File format</strong>: .png (RGB images)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p><br> <strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - YoloV2 example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - YoloV2 example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format. </p> <p><strong>Microscopy data type</strong>: Phase contrast microscopy data (brightfield)</p> <p><strong>Microscope</strong>: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%). </p> <p><strong>Cell type</strong>: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts. </p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 1388 x 1040 px (323 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup>, Lucas von Chamier<sup>4,5</sup></p> <p><strong>Contact email</strong>: <a href="mailto:lucas.chamier.13@ucl.ac.uk">lucas.chamier.13@ucl.ac.uk</a> and guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong>:<strong> </strong></p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p>4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK</p> <p>5) ORCID: 0000-0002-9243-912X</p> <p> </p> <p><strong>Associated publications</strong>: Jacquemet et al 2016. DOI: 10.1038/ncomms13297</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p> <p> </p>
ZeroCostDL4Mic - CycleGAN example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CycleGAN example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy. </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 100x 1.4 NA objective. SRRF reconstructions were performed using the latest generation of SRRF and 200 frames.</p> <p><strong>Cell type</strong>: U2OS cells</p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 62.5 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p> </p> <p><strong>Associated publications</strong>: Jacquemet et al 2020, Journal of Cell Science DOI: https://doi.org/10.1242/jcs.240713</p> <p> </p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - DeepSTORM training and example dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - DeepSTORM training and example dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset). Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset). </p> <p><strong>Microscopy data type</strong>: Simulated SMLM and experimental dSTORM super-resolution microscopy data. The experimental dataset was temporally binned in groups of 4 (BIN4) or 10 (BIN10) consecutive time frames in order to simulate higher density of emitters.</p> <p><strong>Microscope</strong>: Nikon N-STORM</p> <p><strong>Cell type</strong>: Glial cell in a culture of rat hippocampal neurons, fixed and stained with phalloidin-Alexa Fluor 647.</p> <p><strong>File format</strong>: .tif (16-bit or 32-bit)</p> <p><strong>Image size</strong>: 64x64 px (160 nm / pixel), 20 frames (Training dataset),</p> <p> 256 x 256 px (160 nm / pixel), 14,975 frames (Example dataset)</p> <p><strong>Author(s)</strong>: Christophe Leterrier<sup>1,2</sup>, Romain F. Laine<sup>3,4,5</sup></p> <p><strong>Contact email</strong>: christophe.leterrier@univ-amu.fr, r.laine@ucl.ac.uk</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France</p> <p>2) ORCID: 0000-0002-2957-2032</p> <p>3) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK</p> <p>4) The Francis Crick Institute, London, UK</p> <p>5) ORCID: 0000-0002-2151-4487</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: C.L. acknowledges CNRS ATIP-AO2016. R.F.L. acknowledges a MRC Skills development fellowship (MR/T027924/1).</p>
ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)
<p><strong>Name</strong>: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)</p> <p>(see <a href="http://https//github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
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