Cell Migration Lab Datasets

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.

For any inquiries or further information about these datasets, please get in touch with us!

Table of Contents

• Deep Learning training datasets and models
• Materials at Addgene
• Proteomic Data
• Sequencing Data
• Image Data

Materials at Addgene

Guillaume Jacquemet Lab
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Proteomic Data

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.

Dataset Name	Description	View Dataset	Reference
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	Ball et al., 2023
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	Gough et al., 2021
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	Khan et al., 2017
Plasma Membrane, Endosomal, and Cytoplasmic Fractions in Mouse Embryonic Fibroblast	Cellular fractionation experiments to identify novel endosomal proteins in mouse embryonic fibroblast.	View Dataset	Alanko et al., 2015
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	Jacquemet et al., 2013

Sequencing Data

Our lab has been actively generating and publishing sequencing datasets.

Dataset Name	Sequencing Type	Description	View Dataset	Reference
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	Peuhu et al., 2022

Image Data

This section overviews our publicly available image datasets, encompassing various studies.

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers

All data and code associated with the manuscript Follain et al., 2024 are available in a dedicated Zenodo community

Dataset Name	Description	Link	Reference
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	Follain et al., 2024

Segmentation models

Model Name	Imaging Modality	Performance	Purpose and Associated Figure	Training Dataset Link
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
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
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
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
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
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
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
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
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
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

Artificial labeling models

Model Name	Performance	Purpose and Associated Figure	Training Dataset Link
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
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
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

Tracking datasets

Dataset name	Purpose and Associated Figure	Link to dataset
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
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
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
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
Hyaluronidase treatment perfusion tracking dataset	This dataset was used to analyze the attachment of PDACs to the endothelium in Fig.8.	Link to dataset

Structural Repetition Detector: multi-scale quantitative mapping of molecular complexes through microscopy

Dataset Name	Description	Link	Reference
SReD - Figure's data	This repository contains all the data related to the SReD paper	View Dataset on Zenodo	Mendes et al., 2024

PhotoFiTT: A Quantitative Framework for Assessing Phototoxicity in Live-Cell Microscopy Experiments

Dataset Name	Description	Link	Reference
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	Del Rosario et al., 2024

CellTracksColab —A platform for compiling, analyzing, and exploring tracking data

Dataset Name	Description	Link	Reference
CellTracksColab - breast cancer cell dataset	Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data"	View Dataset on Zenodo	Gómez-de-Mariscal et al., 2024
CellTracksColab - Filopodia dataset	Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data"	View Dataset on Zenodo	Gómez-de-Mariscal et al., 2024
CellTracksColab - T cell dataset (full)	Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data"	View Dataset on Zenodo	Gómez-de-Mariscal et al., 2024

NanoPyx: super-fast bioimage analysis powered by adaptive machine learning

Dataset Name	Description	Link	Reference
NanoPyx - Figures' Data	NanoPyx - Figures' Data	View Dataset on Zenodo	Saraiva et al., 2023

TLNRD1 is a CCM complex component and regulates endothelial barrier integrity

Dataset Name	Description	Link	Reference
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	Ball et al., 2023

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

Dataset Name	Description	Link	Reference
eSRRF - Supplementary Data	eSRRF datasets used in the manuscript	View Dataset on Zenodo	Laine et al., 2023

Fast4DReg: Fast registration of 4D microscopy datasets

Dataset Name	Description	Link	Reference
Fast4DRegistration	Data used in the manuscript	View Dataset on Zenodo	Pylvänäinen et al., 2023
Training dataset for Fast4DReg workshop	Fast4DReg workshop data	View Dataset on Zenodo	Pylvänäinen et al., 2023

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines

Dataset Name	Description	Link	Reference
Tracking label images with TrackMate	Dataset used in a tutorial on tracking label images with TrackMate.	View Dataset on Zenodo	Ershov et al., 2022
Tracking with TrackMate using mask images of cell migration	Dataset used in a tutorial on tracking mask images with TrackMate.	View Dataset on Zenodo	Ershov et al., 2022
Tracking cell migration with the TrackMate threshold detector	Dataset used in a tutorial on using the TrackMate threshold detector.	View Dataset on Zenodo	Ershov et al., 2022
T cells migration followed with TrackMate	Dataset of T cells migrating on ICAM-1, tracked using StarDist in TrackMate.	View Dataset on Zenodo	Ershov et al., 2022
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	Ershov et al., 2022
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	Ershov et al., 2022
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	Ershov et al., 2022
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	Ershov et al., 2022
Cancer cell migration followed with TrackMate	Dataset of migrating breast cancer cells for analysis with TrackMate. tutorial.	View Dataset on Zenodo	Ershov et al., 2022
Tracking Glioblastoma-astrocytoma cells with TrackMate-Cellpose	Dataset of Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel.	View Dataset on Zenodo	Ershov et al., 2022
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	Ershov et al., 2022
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	Ershov et al., 2022

Cargo-specific recruitment in clathrin- and dynamin-independent endocytosis

Dataset Name	Description	Link	Reference
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	Moreno-Layseca et al., 2022

Democratising deep learning for microscopy with ZeroCostDL4Mic

Dataset Name	Description	Link	Reference
ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset	A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - DeepSTORM training and example dataset	Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - Stardist example training and test dataset	Description not provided	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - YoloV2 example training and test dataset	MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset	Hela labeled with TOM20	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset	U-251 glioma cells, endogenously expressing paxillin-GFP	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - CycleGAN example training and test dataset	Unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)	View Dataset on Zenodo	von Chamier et al., 2021
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	von Chamier et al., 2021
ZeroCostDL4Mic - CARE (2D) example training and test dataset	Paired microscopy images (fluorescence) of low and high signal-to-noise ratio	View Dataset on Zenodo	von Chamier et al., 2021
ZeroCostDL4Mic - pix2pix example training and test dataset	Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA	View Dataset on Zenodo	von Chamier et al., 2021

Mapping the Localization of Proteins Within Filopodia Using FiloMap

Dataset Name	Description	Link	Reference
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	Jacquemet et al., 2019 and Jacquemet et al., 2023

Automated cell tracking using StarDist and TrackMate

Dataset Name	Description	Link	Reference
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
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
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

FiloQuant reveals increased filopodia density during breast cancer progression

Dataset Name	Description	Link	Reference
S-JCBD-201704045	Raw data from figures	View Dataset	Jacquemet et al., 2017

RCP-driven α5β1 recycling suppresses Rac and promotes RhoA activity via the RacGAP1–IQGAP1 complex

Dataset Name	Description	Link	Reference
S-JCBD-201302041	Raw data from figures	View Dataset	Jacquemet et al., 2013