348
Views
0
CrossRef citations to date
0
Altmetric
Technical report

Finding and following: a deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon, ORCID Icon & ORCID Icon show all
Article: 2344512 | Received 12 Oct 2023, Accepted 30 Mar 2024, Published online: 09 May 2024

References

  • Ono A, Westein E, Hsiao S, Nesbitt WS, Hamilton JR, Schoenwaelder SM, Jackson SP. Identification of a fibrin-independent platelet contractile mechanism regulating primary hemostasis and thrombus growth. Blood J Am Soc Hematol. 2008;112(1):90–10. doi:10.1182/blood-2007-12-127001.
  • Frenette PS, Johnson RC, Hynes RO, Wagner DD. Platelets roll on stimulated endothelium in vivo: an interaction mediated by endothelial P-selectin. Proc Natl Acad Sci. 1995;92(16):7450–4. doi:10.1073/pnas.92.16.7450.
  • Claesson K, Lindahl TL, Faxälv L. Counting the platelets: a robust and sensitive quantification method for thrombus formation. Thromb Haemostasis. 2016;115(6):1178–90. doi:10.1160/TH15-10-0799.
  • Weigert M, Royer L, Jug F, Myers G, editors. Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2017; Quebec (Canada): Springer.
  • Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Böhm A, Deubner J, Jäckel Z, Seiwald K, et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1):67–70. doi:10.1038/s41592-018-0261-2.
  • Seifert J, von Eysmondt H, Chatterjee M, Gawaz M, Schäffer TE. Effect of oxidized LDL on platelet shape, spreading, and migration investigated with deep learning platelet morphometry. Cells. 2021;10(11):2932. doi:10.3390/cells10112932.
  • Alhazmi L, Rokaya D. Detection of WBC, RBC, and platelets in blood samples using deep learning. Biomed Res Int. 2022;2022:1–0. doi:10.1155/2022/1499546.
  • Crocker JC, Grier DG. Methods of digital video microscopy for colloidal studies. J Colloid Interface Sci. 1996;179(1):298–310. doi:10.1006/jcis.1996.0217.
  • Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18; 2015; Springer.
  • Cui W. Visual analytics: a comprehensive overview. IEEE Access. 2019;7:81555–73. doi:10.1109/ACCESS.2019.2923736.
  • Larsson P, Tarlac V, Wang T-Y, Bonnard T, Hagemeyer CE, Hamilton JR, Medcalf RL, Cody SH, Boknäs N. Scanning laser-induced endothelial injury: a standardized and reproducible thrombosis model for intravital microscopy. Sci Rep. 2022;12(1):3955. doi:10.1038/s41598-022-07892-z.
  • Maxwell MJ, Yuan Y, Anderson KE, Hibbs ML, Salem HH, Jackson SP. SHIP1 and Lyn kinase negatively regulate integrin αIIbβ3 signaling in platelets. J Biol Chem. 2004;279(31):32196–204. doi:10.1074/jbc.M400746200.
  • Schurr Y, Sperr A, Volz J, Beck S, Reil L, Kusch C, Eiring P, Bryson S, Sauer M, Nieswandt B, et al. Platelet lamellipodium formation is not required for thrombus formation and stability. Blood J Am Society Hematol. 2019;134(25):2318–29. doi:10.1182/blood.2019002105.
  • Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980. 2014.
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. Pytorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:191201703. 2019.
  • Sofroniew N, Lambert T, Evans K, Nunez-Iglesias J, Bokota G, Winston P, Peña-Castellanos G, Yamauchi K, Bussonnier M, Pop DD, et al. Napari: a multi-dimensional image viewer for Python. Zenodo. doi:10.5281/zenodo.8076424. 2022.
  • Van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T. Scikit-image: image processing in Python. PeerJ. 2014;2:e453. doi:10.7717/peerj.453.
  • Kong D, Fonseca J. Quantification of the morphology of shelly carbonate sands using 3D images. Géotechnique. 2018;68(3):249–61. doi:10.1680/jgeot.16.P.278.
  • Allan DB, Caswell T, Keim NC, van der Wel CM. trackpy: Trackpy v0. 4.1. Geneva (Switzerland): Zenodo CERN; 2018.
  • Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. doi:10.1038/s41592-019-0686-2.
  • Waskom ML. seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021. doi:10.21105/joss.03021.
  • Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(3):90–5. doi:10.1109/MCSE.2007.55.
  • Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. 2019;4:4. doi:10.12688/wellcomeopenres.15191.1.
  • Fabian N, Moreland K, Thompson D, Bauer AC, Marion P, Gevecik B, Rasquin M, Jansen KE, editors. The paraview coprocessing library: A scalable, general purpose in situ visualization library. 2011 IEEE Symposium on Large Data Analysis and Visualization; 2011; Providence (RI): IEEE.
  • Meilă M. Comparing clusterings—an information based distance. J Multivar Anal. 2007;98(5):873–95. doi:10.1016/j.jmva.2006.11.013.
  • Boulanger J, Kervrann C, Bouthemy P, Elbau P, Sibarita J-B, Salamero J. Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Trans Med Imaging. 2009;29(2):442–54. doi:10.1109/TMI.2009.2033991.
  • Matula P, Maška M, Sorokin DV, Matula P, Ortiz-de-Solórzano C, Kozubek M. Cell tracking accuracy measurement based on comparison of acyclic oriented graphs. PLOS ONE. 2015;10(12):e0144959. doi:10.1371/journal.pone.0144959.
  • Massberg S, Gawaz M, Grüner S, Schulte V, Konrad I, Zohlnhöfer D, Heinzmann U, Nieswandt B. A crucial role of glycoprotein VI for platelet recruitment to the injured arterial wall in vivo. J Exp Med. 2003;197(1):41–9. doi:10.1084/jem.20020945.
  • Jackson SP. The growing complexity of platelet aggregation. Blood J Am Soc Hematol. 2007;109(12):5087–95. doi:10.1182/blood-2006-12-027698.
  • Kuwahara M, Sugimoto M, Tsuji S, Matsui H, Mizuno T, Miyata S, Yoshioka A. Platelet shape changes and adhesion under high shear flow. Arterioscler Thromb Vasc Biol. 2002;22(2):329–34. doi:10.1161/hq0202.104122.
  • Shin E-K, Park H, Noh J-Y, Lim KM, Chung JH. Platelet shape changes and cytoskeleton dynamics as novel therapeutic targets for anti-thrombotic drugs. Biomol Ther. 2017;25(3):223. doi:10.4062/biomolther.2016.138.
  • Nesbitt WS, Westein E, Tovar-Lopez FJ, Tolouei E, Mitchell A, Fu J, Carberry J, Fouras A, Jackson SP. A shear gradient–dependent platelet aggregation mechanism drives thrombus formation. Nat Med. 2009;15(6):665–73. doi:10.1038/nm.1955.