A

MACROPHAGE POLARIZATION ASSAYS

Espacenet Family 92295778

Abstract

Computer-implemented methods for identifying the macrophage polarization state of one or more macrophage cells in live cell culture, and drug screening methods making use of such methods are provided. The methods include obtaining single cell label-free images obtained using an optical label-free imaging technology, quantifying a plurality of single cell image-derived features, wherein the plurality of single cell image-derived features comprise: one or more intensity features, one or more shape features and one or more texture features; and identifying a macrophage polarization state associated with the single cell image using a trained machine learning classification model, trained to classify cells between a plurality of classes each associated with a different macrophage polarization state selected from: M0, M1, M2, and a plurality of M2 subtypes.

Inventors 8

  1. Chatrian, Andrea
  2. Sörmann Paulsson, Elsa
  3. Keselman, Paul
  4. Austerjost, Jonas
  5. Barnes, Kalpana
  6. Linda van den Berk Aachen author as filed: van der Berk, Linda
  7. Richard P. Davis Aachen author as filed: Davis, Richard
  8. Sörmann, Paulsson Elsa

Inventors are linked to an Aachen author when surname and first name match exactly one author.

Applicants

  • Sartorius Stedim Data Analytics AB
  • Academisch Ziekenhuis Leiden GBS Leiden

Cited papers 6 in the database

Non-patent literature as cited (20)
  • - STRINGER CWANG TMICHAELOS MPACHITARIU M.: "Cellpose: a generalist algorithm for cellular segmentation", NATURE METHODS, vol. 18, no. 1, January 2021 (2021-01-01), pages 100 - 6, XP037330201, DOI: 10.1038/s41592-020-01018-x
  • - EDLUND, C.JACKSON, T.R.KHALID, N. ET AL.: "LIVECell-A large-scale dataset for label-free live cell segmentation", NAT METHODS, vol. 18, 2021, pages 1038 - 1045, XP037563031, DOI: 10.1038/s41592-021-01249-6
  • - YU T.: "scikit-image: image processing in Python", PEERJ., vol. 2, 19 June 2014 (2014-06-19), pages e453
  • - COELHO LP. MAHOTAS: "Open source software for scriptable computer vision", JOURNAL OF OPEN RESEARCH SOFTWARE, vol. 1, 2013
  • - HU MK: "Visual pattern recognition by moment invariants", IRE TRANSACTIONS ON INFORMATION THEORY, vol. 8, no. 2, February 1962 (1962-02-01), pages 179 - 87, XP000195704
  • - HARALICK RMSHANMUGAM KDINSTEIN IH: "Textural features for image classification", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, no. 6, November 1973 (1973-11-01), pages 610 - 21, XP011192771, DOI: 10.1109/TSMC.1973.4309314
  • - HALL-BEYER M.: "GLCM texture: a tutorial", NATIONAL COUNCIL ON GEOGRAPHIC INFORMATION AND ANALYSIS REMOTE SENSING CORE CURRICULUM, vol. 3, no. 1, March 2000 (2000-03-01), pages 75
  • - CHEN TGUESTRIN C.: "Xgboost: A scalable tree boosting system", PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 13 August 2016 (2016-08-13), pages 785 - 794, XP058631191, DOI: 10.1145/2939672.2939785
  • - RONNEBERGER OFISCHER PBROX T: "Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference", 5 October 2015, SPRINGER INTERNATIONAL PUBLISHING, article "U-net: Convolutional networks for biomedical image segmentation", pages: 234 - 241
  • - CHRISTIANSEN EMYANG SJANDO DMJAVAHERIAN ASKIBINSKI GLIPNICK SMOUNT EO'NEIL ASHAH KLEE AK: "silico labeling: predicting fluorescent labels in unlabeled images", CELL, vol. 173, no. 3, 19 April 2018 (2018-04-19), pages 792 - 803, XP002788720, DOI: 10.1016/j.cell.2018.03.040
  • - CAO XVAN DEN HIL FEMUMMERY CLORLOVA VV: "Generation and Functional Characterization of Monocytes and Macrophages Derived from Human Induced Pluripotent Stem Cells", CURR PROTOC STEM CELL BIOL., vol. 52, no. 1, March 2020 (2020-03-01), pages e108
  • - ARANGO DUQUE, G.DESCOTEAUX, A.: "Macrophage Cytokines: Involvement in Immunity and Infectious Diseases", FRONTIERS IN IMMUNOLOGY, vol. 5, no. 491, 7 October 2014 (2014-10-07)
  • - CAO, X.YAKALA, G. K.VAN DEN HIL, F. E.COCHRANE, A.MUMMERY, C. L.ORLOVA, V. V.: "Differentiation and Functional Comparison of Monocytes and Macrophages from hiPSCs with Peripheral Blood Derivatives", STEM CELL REPORTS., vol. 12, no. 6, 11 June 2019 (2019-06-11), pages 1282 - 1297, XP055838672, DOI: 10.1016/j.stemcr.2019.05.003
  • - ROSTAM HMREYNOLDS PMALEXANDER MRGADEGAARD NGHAEMMAGHAMI AM: "Image based machine learning for identification of macrophage subsets", SCIENTIFIC REPORTS, vol. 7, no. 1, 14 June 2017 (2017-06-14), pages 3521
  • - HASTIE TTIBSHIRANI RFRIEDMAN JHFRIEDMAN JH: "The elements of statistical learning: data mining, inference, and prediction", August 2009, SPRINGER, article "Relative Importance of Predictor Variables", pages: 367
  • - ANAND NPEH KHKOLESAR JM: "Macrophage Repolarization as a Therapeutic Strategy for Osteosarcoma", INT J MOL SCI., vol. 24, no. 3, 2 February 2023 (2023-02-02), pages 2858
  • - LONG, F.: "Microscopy cell nuclei segmentation with enhanced U-Net", BMC BIOINFORMATICS, vol. 21, no. 8, 2020
  • - PHASEFOCUS, QUANTIFYING MACROPHAGE ACTIVATION AND PROLIFERATION APPLICATION NOTE, September 2021 (2021-09-01), Retrieved from the Internet <URL:https://www.phasefocus.com/application/files/2916/3293/1965/Phasefocus_-_AN017_-_Quantifying_Macrophage_Activation_and_Proliferation.pdf>
  • - PAVILLON NICOLAS ET AL: "Noninvasive detection of macrophage activation with single-cell resolution through machine learning", PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES (PNAS), vol. 115, no. 12, 20 March 2018 (2018-03-20), XP093241512, ISSN: 0027-8424, DOI: 10.1073/pnas.1711872115
  • - DANNHAUSER DAVID ET AL: "Single cell classification of macrophage subtypes by label-free cell signatures and machine learning", ROYAL SOCIETY OPEN SCIENCE, vol. 9, no. 9, 1 September 2022 (2022-09-01), XP093241544, ISSN: 2054-5703, Retrieved from the Internet <URL:https://royalsocietypublishing.org/doi/full-xml/10.1098/rsos.220270> DOI: 10.1098/rsos.220270

Cited patents 0