ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
04 10 2023
04 10 2023
Historique:
received:
21
02
2023
accepted:
05
09
2023
medline:
2
11
2023
pubmed:
5
10
2023
entrez:
4
10
2023
Statut:
epublish
Résumé
Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.
Identifiants
pubmed: 37794110
doi: 10.1038/s41597-023-02540-1
pii: 10.1038/s41597-023-02540-1
pmc: PMC10551030
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
677Informations de copyright
© 2023. Springer Nature Limited.
Références
Maddalena, L. & Petrosino, A. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Processing 17, 1168–1177, https://doi.org/10.1109/TIP.2008.924285 (2008).
doi: 10.1109/TIP.2008.924285
Nguyen, D. T., Li, W. & Ogunbona, P. O. Human detection from images and videos: A survey. Pattern Recognition 51, 148–175, https://doi.org/10.1016/j.patcog.2015.08.027 (2016).
doi: 10.1016/j.patcog.2015.08.027
Huh, S., Ker, D. F. E., Bise, R., Chen, M. & Kanade, T. Automated mitosis detection of stem cell populations in phase-contrast microscopy images. IEEE Trans. Med. Imaging 30, 586–596, https://doi.org/10.1109/TMI.2010.2089384 (2011).
doi: 10.1109/TMI.2010.2089384
pubmed: 21356609
Coutu, D. L. & Schroeder, T. Probing cellular processes by long-term live imaging–historic problems and current solutions. Journal of Cell Science 126, 3805–3815, https://doi.org/10.1242/jcs.118349 (2013).
doi: 10.1242/jcs.118349
pubmed: 23943879
Chessel, A. & Carazo Salas, R. From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Essays in Biochemistry 63, 197–208, https://doi.org/10.1042/EBC20180044 (2019).
doi: 10.1042/EBC20180044
pubmed: 31243141
pmcid: 6610450
Asteriti, I. A. et al. The Aurora-A inhibitor MLN8237 affects multiple mitotic processes and induces dose-dependent mitotic abnormalities and aneuploidy. Oncotarget 5, 6229–6242, https://doi.org/10.18632/oncotarget.2190 (2014).
doi: 10.18632/oncotarget.2190
pubmed: 25153724
pmcid: 4171625
Sebestyén, E. et al. Sammy-seq reveals early alteration of heterochromatin and deregulation of bivalent genes in Hutchinson-Gilford progeria syndrome. Nat. Commun. 11, https://doi.org/10.1038/s41467-020-20048-9 (2020).
Penna, L. S., Henriques, J. A. P. & Bonatto, D. Anti-mitotic agents: Are they emerging molecules for cancer treatment. Pharmacol. Ther. 173, 67–82, https://doi.org/10.1016/j.pharmthera.2017.02.007 (2017).
doi: 10.1016/j.pharmthera.2017.02.007
pubmed: 28174095
Gascoigne, K. E. & Taylor, S. S. How do anti-mitotic drugs kill cancer cells. Journal of Cell Science 122, 2579–2585, https://doi.org/10.1242/jcs.039719 (2009).
doi: 10.1242/jcs.039719
pubmed: 19625502
Boutros, M., Heigwer, F. & Laufer, C. Microscopy-based high-content screening. Cell 163, 1314–1325, https://doi.org/10.1016/j.cell.2015.11.007 (2015).
doi: 10.1016/j.cell.2015.11.007
pubmed: 26638068
Mattiazzi Usaj, M. et al. High-content screening for quantitative cell biology. Trends Cell Biol. 26, 598–611, https://doi.org/10.1016/j.tcb.2016.03.008 (2016).
doi: 10.1016/j.tcb.2016.03.008
pubmed: 27118708
Neumann, B. et al. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727, https://doi.org/10.1038/nature08869 (2010).
doi: 10.1038/nature08869
pubmed: 20360735
pmcid: 3108885
Funk, L. et al. The phenotypic landscape of essential human genes. Cell 185, https://doi.org/10.1016/j.cell.2022.10.017 (2022).
Maddalena, L., Antonelli, L., Albu, A., Hada, A. & Guarracino, M. R. Artificial intelligence for cell segmentation, event detection, and tracking for label-free microscopy imaging. Algorithms 15, https://doi.org/10.3390/a15090313 (2022).
Ulman, V. et al. An objective comparison of cell-tracking algorithms. Nature Methods 14, https://doi.org/10.1038/nmeth.4473 (2017).
Schwendy, M., Unger, R. E. & Parekh, S. H. EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation. Bioinformatics 36, 3863–3870, https://doi.org/10.1093/bioinformatics/btaa225 (2020).
doi: 10.1093/bioinformatics/btaa225
pubmed: 32239126
pmcid: 7320615
Edlund, C. et al. LIVECell—A large-scale dataset for label-free live cell segmentation. Nature Methods 18, 1038–1045, https://doi.org/10.1038/s41592-021-01249-6 Previously included in thesis in manuscript form (2021).
Ker, D. et al. Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations. Sci. Data 13, https://doi.org/10.1038/sdata.2018.237 (2018).
Antonelli, L. et al. ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cellsree time-lapse imaging data from cultured human cells, figshare, https://doi.org/10.6084/m9.figshare.c.6436958.v1 (2023).
Gallini, S. et al. NuMA Phosphorylation by Aurora-A orchestrates spindle orientation. Curr. Biol. 26, 458–469, https://doi.org/10.1016/j.cub.2015.12.051 (2016).
doi: 10.1016/j.cub.2015.12.051
pubmed: 26832443
Naso, F. D. et al. Excess tpx2 interferes with microtubule disassembly and nuclei reformation at mitotic exit. Cells 9, 374, https://doi.org/10.3390/cells9020374 (2020).
doi: 10.3390/cells9020374
pubmed: 32041138
pmcid: 7072206
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat Meth 9, 676–682, https://doi.org/10.1038/nmeth.2019 (2012).
doi: 10.1038/nmeth.2019
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat Meth 9, 671–675, https://doi.org/10.1038/nmeth.2089 (2012).
doi: 10.1038/nmeth.2089
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nature Methods https://doi.org/10.1038/s41592-019-0582-9 (2019).
doi: 10.1038/s41592-019-0582-9
pubmed: 31570887
Skalski, P. Make Sense. https://github.com/SkalskiP/make-sense (2019).
Breiman, L. Random forests. Machine Learning 45, https://doi.org/10.1023/A:1010933404324 (2001).