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Cell Cycle Mapping Using Multiplexed Immunofluorescence

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Cell Cycle Control

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2740))

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Abstract

The development of technologies that allow measurement of the cell cycle at the single-cell level has revealed novel insights into the mechanisms that regulate cell cycle commitment and progression through DNA replication and cell division. These studies have also provided evidence of heterogeneity in cell cycle regulation among individual cells, even within a genetically identical population. Cell cycle mapping combines highly multiplexed imaging with manifold learning to visualize the diversity of “paths” that cells can take through the proliferative cell cycle or into various states of cell cycle arrest. In this chapter, we describe a general protocol of the experimental and computational components of cell cycle mapping. We also provide a comprehensive guide for the design and analysis of experiments, discussing key considerations in detail (e.g., antibody library preparation, analysis strategies, etc.) that may vary depending on the research question being addressed.

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References

  1. Nurse P (2000) A long twentieth century of the cell cycle and beyond. Cell 100:71–78. https://doi.org/10.1016/s0092-8674(00)81684-0

    Article  CAS  PubMed  Google Scholar 

  2. Sakaue-Sawano A, Kurokawa H, Morimura T et al (2008) Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132:487–498. https://doi.org/10.1016/j.cell.2007.12.033

    Article  CAS  PubMed  Google Scholar 

  3. Gut G, Tadmor MD, Pe’er D et al (2015) Trajectories of cell-cycle progression from fixed cell populations. Nat Methods 12:951–954. https://doi.org/10.1038/nmeth.3545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gookin S, Min M, Phadke H et al (2017) A map of protein dynamics during cell-cycle progression and cell-cycle exit. PLoS Biol 15:e2003268. https://doi.org/10.1371/journal.pbio.2003268

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zerjatke T, Gak IA, Kirova D et al (2017) Quantitative cell cycle analysis based on an endogenous all-in-one reporter for cell tracking and classification. Cell Rep 19:1953–1966. https://doi.org/10.1016/j.celrep.2017.05.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rappez L, Rakhlin A, Rigopoulos A et al (2020) DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks. Mol Syst Biol 16:e9474. https://doi.org/10.15252/msb.20209474

    Article  PubMed  PubMed Central  Google Scholar 

  7. Stallaert W, Kedziora KM, Taylor CD et al (2022) The structure of the human cell cycle. Cell Syst 13:230–240.e3. https://doi.org/10.1016/j.cels.2021.10.007

    Article  CAS  PubMed  Google Scholar 

  8. Spencer SL, Cappell SD, Tsai FC et al (2013) The proliferation-quiescence decision is controlled by a bifurcation in CDK2 activity at mitotic exit. Cell 155:369–383. https://doi.org/10.1016/j.cell.2013.08.062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Overton KW, Spencer SL, Noderer WL et al (2014) Basal p21 controls population heterogeneity in cycling and quiescent cell cycle states. Proc Natl Acad Sci U S A 111:E4386–E4393. https://doi.org/10.1073/pnas.1409797111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Stallaert W, Taylor SR, Kedziora KM et al (2022) The molecular architecture of cell cycle arrest. Mol Syst Biol 18:e11087. https://doi.org/10.15252/msb.202211087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Liu C, Konagaya Y, Chung M et al (2020) Altered G1 signaling order and commitment point in cells proliferating without CDK4/6 activity. Nat Commun 11:5305. https://doi.org/10.1038/s41467-020-18966-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Knudsen ES, Kumarasamy V, Nambiar R et al (2022) CDK/cyclin dependencies define extreme cancer cell-cycle heterogeneity and collateral vulnerabilities. Cell Rep 38:110448. https://doi.org/10.1016/j.celrep.2022.110448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Stallaert W, Kedziora KM, Chao HX, Purvis JE (2019) Bistable switches as integrators and actuators during cell cycle progression. FEBS Lett 593:2805–2816. https://doi.org/10.1002/1873-3468.13628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zou T, Lin Z (2021) The involvement of ubiquitination machinery in cell cycle regulation and cancer progression. Int J Mol Sci 22. https://doi.org/10.3390/ijms22115754

  15. Maiani E, Milletti G, Nazio F et al (2021) AMBRA1 regulates cyclin D to guard S-phase entry and genomic integrity. Nature 592:799. https://doi.org/10.1038/s41586-021-03422-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Simoneschi D, Rona G, Zhou N et al (2021) CRL4AMBRA1 is a master regulator of D-type cyclins. Nature 592:789. https://doi.org/10.1038/s41586-021-03445-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gut G, Herrmann MD, Pelkmans L (2018) Multiplexed protein maps link subcellular organization to cellular states. Science 361. https://doi.org/10.1126/science.aar7042

  18. Radtke AJ, Chu CJ, Yaniv Z et al (2022) IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nat Protoc 1–26:378. https://doi.org/10.1038/s41596-021-00644-9

    Article  CAS  Google Scholar 

  19. Black S, Phillips D, Hickey JW et al (2021) CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 16:3802. https://doi.org/10.1038/s41596-021-00556-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lin J-R, Izar B, Wang S et al (2018) Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. elife 7. https://doi.org/10.7554/eLife.31657

  21. Lee J-Y, Kitaoka M (2018) A beginner’s guide to rigor and reproducibility in fluorescence imaging experiments. Mol Biol Cell 29:1519–1525. https://doi.org/10.1091/mbc.E17-05-0276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ogama T (2020) A beginner’s guide to improving image acquisition in fluorescence microscopy. Biochemist 42:22–27. https://doi.org/10.1042/BIO20200075

    Article  Google Scholar 

  23. Montero Llopis P, Senft RA, Ross-Elliott TJ et al (2021) Best practices and tools for reporting reproducible fluorescence microscopy methods. Nat Methods 18:1463–1476. https://doi.org/10.1038/s41592-021-01156-w

    Article  CAS  PubMed  Google Scholar 

  24. van der Walt S, Schönberger JL, Nunez-Iglesias J et al (2014) Scikit-image: image processing in Python. PeerJ 2:e453. https://doi.org/10.7717/peerj.453

    Article  PubMed  PubMed Central  Google Scholar 

  25. Chiu C-L, Clack N, The Napari Community (2022) Napari: a python multi-dimensional image viewer platform for the research community. Microsc Microanal 28:1576–1577. https://doi.org/10.1017/S1431927622006328

    Article  Google Scholar 

  26. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  PubMed  Google Scholar 

  27. Schapiro D, Sokolov A, Yapp C et al (2021) MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods 19:311–315. https://doi.org/10.1038/s41592-021-01308-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. McQuin C, Goodman A, Chernyshev V et al (2018) CellProfiler 3.0: next-generation image processing for biology. PLoS Biol 16:e2005970. https://doi.org/10.1371/journal.pbio.2005970

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Stringer C, Wang T, Michaelos M, Pachitariu M (2021) Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18:100–106. https://doi.org/10.1038/s41592-020-01018-x

    Article  CAS  PubMed  Google Scholar 

  30. Bankhead P, Loughrey MB, Fernández JA et al (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7:16878. https://doi.org/10.1038/s41598-017-17204-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Palla G, Spitzer H, Klein M et al (2022) Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19:171–178. https://doi.org/10.1038/s41592-021-01358-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bannon D, Moen E, Schwartz M et al (2021) DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes. Nat Methods 18:43–45. https://doi.org/10.1038/s41592-020-01023-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Peng T, Thorn K, Schroeder T et al (2017) A BaSiC tool for background and shading correction of optical microscopy images. Nat Commun 8:14836. https://doi.org/10.1038/ncomms14836

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. BaSiC: a BaSiC tool for background and shading correction of optical microscopy images. Github. Available from: https://github.com/marrlab/BaSiC

  35. BaSiCPy. Github. Available from: https://github.com/peng-lab/BaSiCPy

  36. Chalfoun J, Majurski M, Blattner T et al (2017) MIST: accurate and scalable microscopy image stitching tool with stage modeling and error minimization. Sci Rep 7:1–10. https://doi.org/10.1038/s41598-017-04567-y

    Article  CAS  Google Scholar 

  37. Muhlich JL, Chen Y-A, Yapp C et al (2022) Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR. Bioinformatics 38:4613–4621. https://doi.org/10.1093/bioinformatics/btac544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. MIST: microscopy image stitching tool. Github. Available from: https://github.com/usnistgov/MIST

  39. Fukai YT m2stitch: MIST-inspired microscope image stitching package. Github. Available from: https://github.com/yfukai/m2stitch

  40. ashlar: ASHLAR: alignment by simultaneous harmonization of layer/adjacency registration. Github. Available from: https://github.com/labsyspharm/ashlar

  41. Thévenaz P, Ruttimann UE, Unser M (1998) A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process 7:27–41. https://doi.org/10.1109/83.650848

    Article  PubMed  Google Scholar 

  42. StackReg. http://bigwww.epfl.ch/thevenaz/stackreg/. Accessed 2 May 2023

  43. Lichtner G pystackreg: a python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame. Github. Available from: https://github.com/glichtner/pystackreg

  44. Johnson HJ, McCormick MM, Ibanez L (2015) The ITK software guide Book 2: design and functionality, 4th edn. Kitware, Incorporated

    Google Scholar 

  45. Yaniv Z, Lowekamp BC, Johnson HJ, Beare R (2018) SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research. J Digit Imaging 31:290–303. https://doi.org/10.1007/s10278-017-0037-8

    Article  PubMed  Google Scholar 

  46. Moen E, Bannon D, Kudo T et al (2019) Deep learning for cellular image analysis. Nat Methods 16:1233–1246. https://doi.org/10.1038/s41592-019-0403-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bilodeau A, Bouchard C, Lavoie-Cardinal F (2022) Automated microscopy image segmentation and analysis with machine learning. Methods Mol Biol 2440:349–365. https://doi.org/10.1007/978-1-0716-2051-9_20

    Article  CAS  PubMed  Google Scholar 

  48. Ravindran S (2022) Five ways deep learning has transformed image analysis. Nature 609:864–866. https://doi.org/10.1038/d41586-022-02964-6

    Article  CAS  PubMed  Google Scholar 

  49. Schmidt U, Weigert M, Broaddus C, Myers G (2018) Cell detection with star-convex polygons. In: Medical image computing and computer assisted intervention—MICCAI 2018. Springer International Publishing, pp 265–273

    Google Scholar 

  50. stardist: StarDist—object detection with star-convex shapes. Github. Available from: https://github.com/stardist/stardist

  51. Schmidt U, Weigert M StarDist. In: ImageJ Wiki. https://imagej.net/plugins/stardist. Accessed 2 May 2023. Available from: https://imagej.net/plugins/stardist

  52. qupath-extension-stardist: QuPath extension to run StarDist nucleus detection. Github. Available from: https://github.com/qupath/qupath-extension-stardist

  53. cellpose: a generalist algorithm for cellular segmentation with human-in-the-loop capabilities. Github. Available from: https://github.com/MouseLand/cellpose

  54. qupath-extension-cellpose: an extension that wraps a Cellpose environment such that WSI can be analyzed using Cellpose through QuPath. Github. Available from: https://github.com/BIOP/qupath-extension-cellpose

  55. ijl-utilities-wrappers: wrappers for external software calls (Cellpose, Elastix, Ilastix...) and java utilities (object conversions and display). Github. Available from: https://github.com/BIOP/ijl-utilities-wrappers

  56. deepcell-tf: deep learning library for single cell analysis. Github. Available from: https://github.com/vanvalenlab/deepcell-tf

  57. kiosk-console: DeepCell kiosk distribution for kubernetes on GKE and AWS. Github. Available from: https://github.com/vanvalenlab/kiosk-console

  58. Yang P, Huang H, Liu C (2021) Feature selection revisited in the single-cell era. Genome Biol 22:321. https://doi.org/10.1186/s13059-021-02544-3

    Article  PubMed  PubMed Central  Google Scholar 

  59. Moon KR, van Dijk D, Wang Z et al (2019) Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol 37:1482–1492. https://doi.org/10.1038/s41587-019-0336-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. PHATE: PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data. Github. Available from: https://github.com/KrishnaswamyLab/PHATE

  61. Pardee AB (1974) A restriction point for control of normal animal cell proliferation. Proc Natl Acad Sci U S A 71:1286–1290. https://doi.org/10.1073/pnas.71.4.1286

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zetterberg A, Larsson O (1985) Kinetic analysis of regulatory events in G1 leading to proliferation or quiescence of Swiss 3T3 cells. Proc Natl Acad Sci U S A 82:5365–5369. https://doi.org/10.1073/pnas.82.16.5365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. BenchSci BenchSci: reimagining research. https://www.benchsci.com/. Accessed 8 May 2023

  64. Kramer BA, Sarabia Del Castillo J, Pelkmans L (2022) Multimodal perception links cellular state to decision-making in single cells. Science 377:642–648. https://doi.org/10.1126/science.abf4062

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Wayne Stallaert .

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Kedziora, K.M., Stallaert, W. (2024). Cell Cycle Mapping Using Multiplexed Immunofluorescence. In: Castro, A., Lacroix, B. (eds) Cell Cycle Control. Methods in Molecular Biology, vol 2740. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3557-5_15

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  • DOI: https://doi.org/10.1007/978-1-0716-3557-5_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3556-8

  • Online ISBN: 978-1-0716-3557-5

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