├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 guijacquemet 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cell Migration Lab Datasets 2 | 3 | Welcome to the Cell Migration Lab's datasets repository. Here, you will find a comprehensive list of openly available datasets generated by our lab or by Guillaume Jacquemet before the cell migration lab started. 4 | 5 | For any inquiries or further information about these datasets, please get in touch with us! 6 | 7 | # Table of Contents 8 | - [Deep Learning training datasets and models](https://github.com/CellMigrationLab/ModelZoo) 9 | - [Materials at Addgene](#Materials-at-Addgene) 10 | - [Proteomic Data](#Proteomic-Data) 11 | - [Sequencing Data](#Sequencing-Data) 12 | - [Image Data](#Image-Data) 13 | 14 | # Materials at Addgene 15 | 16 |
Guillaume Jacquemet Lab
Find and request materials through
Addgene
17 | 18 | # Proteomic Data 19 | 20 | Our lab has generated and published a series of proteomic datasets focusing on protein interactions and cellular fractionation. Below is a summary of these datasets, providing insights into various proteins and their binding partners in different cellular contexts. 21 | 22 | | Dataset Name | Description | View Dataset | Reference | 23 | | ------------ | ----------- | ------------ | --------- | 24 | | TLNRD1-GFP Pulldown in HEK293T Cells | Pulldown of human TLNRD1-GFP and GFP in HEK cells for mass spectrometry analysis of binding partners. | [View Dataset](https://www.ebi.ac.uk/pride/archive/projects/PXD045258) | [Ball et al., 2023](https://www.biorxiv.org/content/10.1101/2023.09.29.559344v1) | 25 | | Talin1-GFP Pulldown in U2OS Cells | Study of human Talin1-GFP and GFP pulldown from U2OS cells plated on fibronectin, using mass spectrometry. | [View Dataset](https://www.ebi.ac.uk/pride/archive/projects/PXD024634) | [Gough et al., 2021](https://www.jbc.org/article/S0021-9258(21)00635-9/fulltext) | 26 | | Sharpin-GFP Pulldown in HEK293T Cells | Analysis of human Sharpin-GFP and GFP pulldown from HEK293T cells, identifying binding partners through mass spectrometry. | [View Dataset](https://www.ebi.ac.uk/pride/archive/projects/PXD004734) | [Khan et al., 2017](https://journals.biologists.com/jcs/article/130/18/3094/56377/The-Sharpin-interactome-reveals-a-role-for-Sharpin) | 27 | | Plasma Membrane, Endosomal, and Cytoplasmic Fractions in Mouse Embryonic Fibroblast | Cellular fractionation experiments to identify novel endosomal proteins in mouse embryonic fibroblast. | [View Dataset](https://www.ebi.ac.uk/pride/archive/projects/PXD001870) | [Alanko et al., 2015](https://www.nature.com/articles/ncb3250) | 28 | | Proteomic analysis of filamin-A, IQGAP1, Rac1 and RCC2 binding partners | Analysis of human filamin-A-GFP, IQGAP1-GFP, Rac1 and RCC2-GFP and GFP pulldown from HEK293T cells, identifying binding partners through mass spectrometry. | [View Dataset](https://www.ebi.ac.uk/pride/archive/projects/PRD000726) | [Jacquemet et al., 2013](https://journals.biologists.com/jcs/article/126/18/4121/53831/Rac1-is-deactivated-at-integrin-activation-sites) | 29 | 30 | # Sequencing Data 31 | 32 | Our lab has been actively generating and publishing sequencing datasets. 33 | 34 | | Dataset Name | Sequencing Type | Description | View Dataset | Reference | 35 | |-----------------------------------------------------|-----------------|-------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|------------------------------------------------------------------------------| 36 | | MYO10-Filopodia Breast Tumor Xenograft Expression Dataset | RNA-Seq | mRNA sequencing data from subcutaneous breast tumor xenografts of MCF10DCIS.com cell lines expressing non-targeting control shRNA (4 tumors) or Myosin-X targeting shRNA (4 tumors). | [View Dataset](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166898) | [Peuhu et al., 2022](https://www.sciencedirect.com/science/article/pii/S1534580722007134#sec4) | 37 | 38 | 39 | 40 | 41 | # Image Data 42 | 43 | This section overviews our publicly available image datasets, encompassing various studies. 44 | 45 | ## [Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers](https://github.com/CellMigrationLab/PDAC_DL/tree/main) 46 | 47 | All data and code associated with the manuscript [Follain et al., 2024](https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1) are available in a dedicated [Zenodo community](https://zenodo.org/communities/pdac_dl) 48 | 49 | | Dataset Name | Description | Link | Reference | 50 | | ------------ | ----------- | ---- | --------- | 51 | | Fast label-free live imaging reveals regulation of cancer cell endothelium adhesion by flow and CD44-HA interaction | This repository contains all the data used to make the figure shown in the paper | [View Dataset on Zenodo](https://zenodo.org/records/13846276) | [Follain et al., 2024](https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1) | 52 | 53 | #### Segmentation models 54 | | Model Name | Imaging Modality | Performance | Purpose and Associated Figure | Training Dataset Link | 55 | |------------|------------------|-------------|-------------------------------|-----------------------| 56 | | Flow chamber dataset | Brightfield | IoU = 0.813
f1 = 0.933 | StarDist model to detect cancer cells in BSA-coated channels. Used to measure perfusion speed inside the channels (Fig S1). | [Link](https://zenodo.org/records/4034939) | 57 | | StarDist_Fluorescent_cells | Fluorescence | IoU = 0.646
f1 = 0.877 | StarDist model to detect cancer cells from fixed samples. Used in Fig. 1 to count the number of attached cells | [Link](https://doi.org/10.5281/zenodo.10572310) | 58 | | StarDist_BF_cancer_cell_dataset_20x | Brightfield | IoU = 0.793
f1 = 0.921 | StarDist model capable of segmenting cancer cells on endothelial cells (20x magnification). This model was used to segment cancer cells prior to tracking in Fig 1. | [Link](https://doi.org/10.5281/zenodo.10572122) | 59 | | StarDist_BF_Neutrophil_dataset | Brightfield | IoU = 0.914
f1 = 0.969 | StarDist model capable of segmenting neutrophils on endothelial cells. This model was used to segment neutrophils prior to tracking in Fig 2. | [Link](https://doi.org/10.5281/zenodo.10572231) | 60 | | StarDist_BF_Monocytes_dataset | Brightfield | IoU = 0.831
f1 = 0.941 | StarDist model capable of segmenting mononucleated cells on endothelial cells. This model was used to segment mononucleated cells prior to tracking in Fig 2. | [Link](https://doi.org/10.5281/zenodo.10572200) | 61 | | StarDist_HUVEC_nuclei_dataset | Fluorescence | IoU = 0.927
f1 = 0.976 | StarDist model capable of segmenting endothelial nuclei while ignoring cancer cells. Used to segment endothelial nuclei in Fig 4. | [Link](https://doi.org/10.5281/zenodo.10617532) | 62 | | StarDist_BF_cancer_cell_dataset_10x | Brightfield | IoU = 0.882
f1 = 0.968 | StarDist model capable of segmenting cancer cells on endothelial cells (10x magnification). This model used in figure 7, 8 + associated supplementary figures. | [Link](https://zenodo.org/uploads/13304399) | 63 | | StarDist_AsPC1_Lifeact | Fluorescence | IoU = 0.884
f1 = 0.967 | StarDist model capable of segmenting AsPC1 cells from AsPC1 channel, in addition to segmenting from background, model also segments individual cells from clusters. Used in figure 6.| [Link](https://zenodo.org/records/13442128) | 64 | | Stardist_MiaPaCa2_from_CD44 | Fluorescence | IoU = 0.884
f1 = 0.950 | StarDist model capable of segmenting MiaPaCa2 cells from CD44 channel while ignoring endothelial cells. Used in figure 6. | [Link](https://doi.org/10.5281/zenodo.13442877) | 65 | | StarDist_TumorCell_nuclei | Fluorescence | IoU = 0.558
f1 = 0.793 | StarDist model capable of segmenting tumor cell nuclei from the nuclei channel while ignoring endothelial nuclei. | [Link](https://doi.org/10.5281/zenodo.13443221) | 66 | 67 | #### Artificial labeling models 68 | 69 | | Model Name | Performance | Purpose and Associated Figure | Training Dataset Link | 70 | |------------|-------------|-------------------------------|-----------------------| 71 | | pix2pix_HUVEC_nuclei_cancer_cells_dataset | SSIM = 0.755
lpips = 0.120 | This model was used in Fig. 4 to artificially label nulcei from BF images with cancer and endothelial cells. | [Link](https://doi.org/10.5281/zenodo.10621667) | 72 | | pix2pix_HUVEC_nuclei_immuno_cells_dataset | SSIM = 0.756
lpips = 0.130 | This model was used in Fig. 4 to artificially label nulcei from BF images with immuno and endothelial cells. | [Link](https://doi.org/110.5281/zenodo.10617565) | 73 | | pix2pix_HUVEC_juctions_dataset | SSIM = 0.270
lpips = 0.360 | This model was used in Fig. 4 to artificially label cell-cell juctions from BF images with immuno or cancer and endothelial cells. | [Link](https://doi.org/10.5281/zenodo.10611092) | 74 | 75 | #### Tracking datasets 76 | | Dataset name | Purpose and Associated Figure | Link to dataset | 77 | |------------|-------------|-------------------------------| 78 | | PDAC cells vs Immune cells perfusion tracking dataset | This dataset was used to analyze the attachment of PDAC and immune cells to the endothelium in Fig.2, Fig.3 Fig.4 and SFig.5. | [Link to dataset](https://doi.org/10.5281/zenodo.13643590) | 79 | | PDAC cells CD44 siRNA perfusion tracking dataset | This dataset was used to analyze the attachment of PDACs to the endothelium in Fig.7, SFig.7 and SFig8. | [Link to dataset](https://doi.org/10.5281/zenodo.13379627) | 80 | | HUVEC CD44 siRNA perfusion tracking dataset | This dataset was used to analyze the attachment of PDACs to the endothelium in Fig.7, SFig.7 and SFig8. | [Link to dataset](https://doi.org/10.5281/zenodo.13377961) | 81 | | CD44 Blocking Antibody perfusion tracking dataset | This dataset was used to analyze the attachment of PDACs to the endothelium in Fig.7, SFig.7 and SFig8. | [Link to dataset](https://doi.org/10.5281/zenodo.13584215) | 82 | | Hyaluronidase treatment perfusion tracking dataset | This dataset was used to analyze the attachment of PDACs to the endothelium in Fig.8. | [Link to dataset](https://doi.org/10.5281/zenodo.13627037) | 83 | 84 | 85 | ## [Structural Repetition Detector](https://github.com/HenriquesLab/SReD): multi-scale quantitative mapping of molecular complexes through microscopy 86 | 87 | | Dataset Name | Description | Link | Reference | 88 | | ------------ | ----------- | ---- | --------- | 89 | | SReD - Figure's data | This repository contains all the data related to the SReD paper | [View Dataset on Zenodo](https://zenodo.org/records/13764726) | [Mendes et al., 2024](https://www.biorxiv.org/content/10.1101/2024.09.16.613204v1.full) | 90 | 91 | 92 | ## [PhotoFiTT](https://github.com/HenriquesLab/PhotoFiTT?tab=readme-ov-file): A Quantitative Framework for Assessing Phototoxicity in Live-Cell Microscopy Experiments 93 | 94 | | Dataset Name | Description | Link | Reference | 95 | | ------------ | ----------- | ---- | --------- | 96 | | PhotoFiTT: A Quantitative Framework for Assessing Phototoxicity in Live-Cell Microscopy Experiments | This repository contains all the data related to the study PhotoFiTT (Phototoxicity Fitness Time Trial) as well as example data for PhotoFiTT computational framework | [View Dataset on the BioImage Archive](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1269) | [Del Rosario et al., 2024](https://www.biorxiv.org/content/10.1101/2024.07.16.603046v2) | 97 | 98 | 99 | ## [CellTracksColab](https://github.com/guijacquemet/CellTracksColab/tree/main) —A platform for compiling, analyzing, and exploring tracking data 100 | 101 | | Dataset Name | Description | Link | Reference | 102 | | ------------ | ----------- | ---- | --------- | 103 | | CellTracksColab - breast cancer cell dataset | Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data" | [View Dataset on Zenodo](https://zenodo.org/record/10539020) | [Gómez-de-Mariscal et al., 2024](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002740) | 104 | | CellTracksColab - Filopodia dataset | Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data" | [View Dataset on Zenodo](https://zenodo.org/record/10539196) | [Gómez-de-Mariscal et al., 2024](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002740) | 105 | | CellTracksColab - T cell dataset (full) | Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data" | [View Dataset on Zenodo](https://zenodo.org/record/10539720) | [Gómez-de-Mariscal et al., 2024](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002740) | 106 | 107 | ## [NanoPyx](https://github.com/HenriquesLab/NanoPyx): super-fast bioimage analysis powered by adaptive machine learning 108 | 109 | | Dataset Name | Description | Link | Reference | 110 | | ------------ | ----------- | ---- | --------- | 111 | | NanoPyx - Figures' Data | NanoPyx - Figures' Data | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.8318395) | [Saraiva et al., 2023](https://www.biorxiv.org/content/10.1101/2023.08.13.553080v2) | 112 | 113 | ## TLNRD1 is a CCM complex component and regulates endothelial barrier integrity 114 | | Dataset Name | Description | Link | Reference | 115 | | ------------ | ----------- | ---- | --------- | 116 | | TLNRD1 figures | Raw microscopy images used to make the figures displayed in the article "TLNRD1 is a CCM complex component and regulates endothelial barrier integrity." | [View Dataset on Zenodo](https://zenodo.org/records/8377287) | [Ball et al., 2023](https://www.biorxiv.org/content/10.1101/2023.09.29.559344v1) | 117 | 118 | ## High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation 119 | 120 | | Dataset Name | Description | Link | Reference | 121 | | ------------ | ----------- | ---- | --------- | 122 | | eSRRF - Supplementary Data | eSRRF datasets used in the manuscript | [View Dataset on Zenodo](https://zenodo.org/records/8325164) | [Laine et al., 2023](https://www.nature.com/articles/s41592-023-02057-w) | 123 | 124 | ## [Fast4DReg](https://github.com/CellMigrationLab/Fast4DReg): Fast registration of 4D microscopy datasets 125 | | Dataset Name | Description | Link | Reference | 126 | | ------------ | ----------- | ---- | --------- | 127 | | Fast4DRegistration | Data used in the manuscript | [View Dataset on Zenodo](https://zenodo.org/records/6534570) | [Pylvänäinen et al., 2023](https://journals.biologists.com/jcs/article/136/4/jcs260728/287682/Fast4DReg-fast-registration-of-4D-microscopy)| 128 | | Training dataset for Fast4DReg workshop | Fast4DReg workshop data | [View Dataset on Zenodo](https://zenodo.org/records/8347798) | [Pylvänäinen et al., 2023](https://journals.biologists.com/jcs/article/136/4/jcs260728/287682/Fast4DReg-fast-registration-of-4D-microscopy)| 129 | 130 | ## [TrackMate 7](https://imagej.net/plugins/trackmate/): integrating state-of-the-art segmentation algorithms into tracking pipelines 131 | 132 | | Dataset Name | Description | Link | Reference | 133 | | ------------ | ----------- | ---- | --------- | 134 | | Tracking label images with TrackMate | Dataset used in a [tutorial](https://imagej.net/plugins/trackmate/trackmate-label-image-detector) on tracking label images with TrackMate. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5221424) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 135 | | Tracking with TrackMate using mask images of cell migration | Dataset used in a [tutorial](https://imagej.net/plugins/trackmate/trackmate-mask-detector) on tracking mask images with TrackMate. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5243127) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 136 | | Tracking cell migration with the TrackMate threshold detector | Dataset used in a [tutorial](https://imagej.net/plugins/trackmate/trackmate-thresholding-detector) on using the TrackMate threshold detector. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5220796) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 137 | | T cells migration followed with TrackMate | Dataset of T cells migrating on ICAM-1, tracked using StarDist in TrackMate. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5206119) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 138 | | Segmenting cells in a spheroid in 3D using 2D StarDist within TrackMate | Dataset for segmenting cells in a 3D spheroid using 2D StarDist in TrackMate. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5220610) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 139 | | Tracking focal adhesions with TrackMate and Weka - tutorial dataset 1 | Dataset of MDA-mb-231 cells expressing GFP-paxillin for tracking focal adhesions. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5226842) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 140 | | Tracking focal adhesions with TrackMate and Weka - tutorial dataset 2 | Dataset of human dermal microvascular blood endothelial cells for tracking focal adhesions. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5978940) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 141 | | Tracking breast cancer cells migrating collectively with TrackMate-Cellpose | Dataset for tracking collective migration of breast cancer cells with TrackMate-Cellpose. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5864646) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 142 | | Cancer cell migration followed with TrackMate | Dataset of migrating breast cancer cells for analysis with TrackMate. [tutorial](https://imagej.net/plugins/trackmate/trackmate-stardist). | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5206107) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 143 | | Tracking Glioblastoma-astrocytoma cells with TrackMate-Cellpose | Dataset of Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5863317) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 144 | | Cell migration with ERK signalling | Movie following cells expressing ERK and a nuclei staining, tracked with TrackMate and later analyzed with MATLAB. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5205935) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 145 | | Quantitative comparison of tracking performance using TrackMate-Helper. | we used TrackMate-Helper to assess the performance of TrackMate on four datasets that cover a wide range of biological and imaging situations | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.6087729) | [Ershov et al., 2022](https://www.nature.com/articles/s41592-022-01507-1) | 146 | 147 | ## Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis 148 | 149 | | Dataset Name | Description | Link | Reference | 150 | | ----------- | ---------- | ------- | --------- | 151 | | Cancer cell migration followed with TrackMate | Stardist model and training dataset for automated tracking of MDA-MB-231 and BT20 cells | [View Dataset on Zenodo](https://zenodo.org/records/4812018) | [Moreno-Layseca et al., 2022](https://www.nature.com/articles/s41556-021-00767-x#Sec13) | 152 | 153 | 154 | ## Democratising deep learning for microscopy with [ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic) 155 | | Dataset Name | Description | Link | Reference | 156 | | ------------ | ----------- | ---- | --------- | 157 | | ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset | A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3713326) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 158 | | ZeroCostDL4Mic - DeepSTORM training and example dataset | Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3959089) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 159 | | ZeroCostDL4Mic - Stardist example training and test dataset | Description not provided | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3713307) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 160 | | ZeroCostDL4Mic - YoloV2 example training and test dataset | MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3941908) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 161 | | ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset | Hela labeled with TOM20 | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3748967) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 162 | | ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset | U-251 glioma cells, endogenously expressing paxillin-GFP | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3713315) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 163 | | ZeroCostDL4Mic - CycleGAN example training and test dataset | Unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images) | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3941884) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 164 | | ZeroCostDL4Mic - CARE (3D) example training and test dataset | 3D paired microscopy images (fluorescence) of low and high signal-to-noise ratio | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.3713337) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 165 | | ZeroCostDL4Mic - CARE (2D) example training and test dataset | Paired microscopy images (fluorescence) of low and high signal-to-noise ratio | [View Dataset on Zenodo](https://zenodo.org/records/3713330) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 166 | | ZeroCostDL4Mic - pix2pix example training and test dataset | Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA | [View Dataset on Zenodo](https://zenodo.org/records/3941889) | [von Chamier et al., 2021](https://www.nature.com/articles/s41467-021-22518-0) | 167 | 168 | ## Mapping the Localization of Proteins Within Filopodia Using [FiloMap](https://github.com/guijacquemet/FiloMAP) 169 | | Dataset Name | Description | Link | Reference | 170 | | ------------ | ----------- | ---- | --------- | 171 | | FiloMap Test Dataset | Dataset for testing and validation in FiloMap, a tool that can be used to map the localization of proteins within filopodia from microscopy images. | [View Dataset on Zenodo](https://doi.org/10.5281/zenodo.5912949) | [Jacquemet et al., 2019](https://www.cell.com/current-biology/fulltext/S0960-9822(18)31552-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0960982218315525%3Fshowall%3Dtrue) and [Jacquemet et al., 2023](https://link.springer.com/protocol/10.1007/978-1-0716-2887-4_4) | 172 | 173 | ## Automated cell tracking using StarDist and TrackMate 174 | | Dataset Name | Description | Link | Reference | 175 | | ------------ | ----------- | ---- | --------- | 176 | Combining StarDist and TrackMate example 1 - Breast cancer cell dataset | Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic | https://doi.org/10.5281/zenodo.4034976| [Fazeli et al., 2020](https://f1000research.com/articles/9-1279/v1)| 177 | Combining StarDist and TrackMate example 2 - T cell dataset | Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic | https://doi.org/10.5281/zenodo.4034929 | [Fazeli et al., 2020](https://f1000research.com/articles/9-1279/v1)| 178 | Combining StarDist and TrackMate example 3 - Flow chamber dataset | Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic | https://doi.org/10.5281/zenodo.4034939 | [Fazeli et al., 2020](https://f1000research.com/articles/9-1279/v1)| 179 | 180 | 181 | ## [FiloQuant](https://github.com/CellMigrationLab/FiloQuant) reveals increased filopodia density during breast cancer progression 182 | | Dataset Name | Description | Link | Reference | 183 | | ------------ | ----------- | ---- | --------- | 184 | | S-JCBD-201704045 | Raw data from figures | [View Dataset](https://www.ebi.ac.uk/biostudies/jcb/studies/S-JCBD-201704045) | [Jacquemet et al., 2017](https://rupress.org/jcb/article/216/10/3387/38936/FiloQuant-reveals-increased-filopodia-density) | 185 | 186 | ## RCP-driven α5β1 recycling suppresses Rac and promotes RhoA activity via the RacGAP1–IQGAP1 complex 187 | | Dataset Name | Description | Link | Reference | 188 | | ------------ | ----------- | ---- | --------- | 189 | | S-JCBD-201302041 | Raw data from figures | [View Dataset](https://www.ebi.ac.uk/biostudies/jcb/studies/S-JCBD-201302041) | [Jacquemet et al., 2013](https://rupress.org/jcb/article/216/10/3387/38936/FiloQuant-reveals-increased-filopodia-density) | 190 | 191 | 192 | 193 | --------------------------------------------------------------------------------