Tissue clearing method in visualization of cancer progression and metastasis
Abstract
Since various imaging modalities have been developed, cancer metastasis can be detected from an early stage. However, limitations still exist, especially in terms of spatial resolution. Tissue-clearing technology has emerged as a new imaging modality in cancer research, which has been developed and utilized for a long time mainly in neuroscience field. This method enables us to detect cancer metastatic foci with single-cell resolution at whole mouse body/organ level. On top of that, 3D images of cancer metastasis of whole mouse organs make it easy to understand their characteristics. Recently, further applications of tissue clearing methods were reported in combination with reporter systems, labeling, and machine learning. In this review, we would like to provide an overview of this technique and current applications in cancer research and discuss their potentials and limitations.
Downloads
References
2. Fass L. Imaging and cancer: a review. Mol Oncol. 2008;2(2):115–52. doi: 10.1016/j.molonc.2008.04.001
3. Takahashi K, Nagai N, Ogura K, Tsuneyama K, Saiki I, Irimura T, et al. Mammary tissue microenvironment determines T cell-dependent breast cancer-associated inflammation. Cancer Sci. 2015;106(7):867–74. doi: 10.1111/cas.12685
4. Close DM, Xu T, Sayler GS, Ripp S. In vivo bioluminescent imaging (BLI): noninvasive visualization and interrogation of biological processes in living animals. Sensors (Basel). 2011;11(1):180–206. doi: 10.3390/s110100180
5. Iwano S, Sugiyama M, Hama H, Watakabe A, Hasegawa N, Kuchimaru T, et al. Single-cell bioluminescence imaging of deep tissue in freely moving animals. Science. 2018;359(6378):935–9. doi: 10.1126/science.aaq1067
6. Condeelis J, Weissleder R. In vivo imaging in cancer. Cold Spring Harb Perspect Biol. 2010;2(12):a003848. doi: 10.1101/cshperspect.a003848
7. Spalteholz W. Über das Durchsichtigmachen von menschlichen und tierischen Präparaten und seine theoretischen Bedingungen, nebst Anhang: Über Knochenfärbung: S. Hirzel, Leipzig; 1914.
8. Ueda HR, Dodt HU, Osten P, Economo MN, Chandrashekar J, Keller PJ. Whole-brain profiling of cells and circuits in mammals by tissue clearing and light-sheet microscopy. Neuron. 2020;106(3):369–87. doi: 10.1016/j.neuron.2020.03.004
9. Ueda HR, Erturk A, Chung K, Gradinaru V, Chedotal A, Tomancak P, et al. Tissue clearing and its applications in neuroscience. Nat Rev Neurosci. 2020;21(2):61–79. doi: 10.1038/s41583-019-0250-1
10. Dodt HU, Leischner U, Schierloh A, Jährling N, Mauch CP, Deininger K, et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat Methods. 2007;4(4):331–6. doi: 10.1038/nmeth1036
11. Ertürk A, Becker K, Jährling N, Mauch CP, Hojer CD, Egen JG, et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat Protoc. 2012;7(11):1983–95. doi: 10.1038/nprot.2012.119
12. Hama H, Kurokawa H, Kawano H, Ando R, Shimogori T, Noda H, et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat Neurosci. 2011;14(11):1481–8. doi: 10.1038/nn.2928
13. Chung K, Wallace J, Kim SY, Kalyanasundaram S, Andalman AS, Davidson TJ, et al. Structural and molecular interrogation of intact biological systems. Nature. 2013;497(7449):332–7. doi: 10.1038/nature12107
14. Ke MT, Fujimoto S, Imai T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat Neurosci. 2013;16(8):1154-61. doi: 10.1038/nn.3447
15. Tomer R, Ye L, Hsueh B, Deisseroth K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat Protoc. 2014;9(7):1682-97. doi: 10.1038/nprot.2014.123
16. Hama H, Hioki H, Namiki K, Hoshida T, Kurokawa H, Ishidate F, et al. ScaleS: an optical clearing palette for biological imaging. Nat Neurosci. 2015;18(10):1518–29. doi: 10.1038/nn.4107
17. Li W, Germain RN, Gerner MY. Multiplex, quantitative cellular analysis in large tissue volumes with clearing-enhanced 3D microscopy (Ce3D). Proc Natl Acad Sci U S A. 2017;114(35):E7321–e30. doi: 10.1073/pnas.1708981114
18. Kubota SI, Takahashi K, Nishida J, Morishita Y, Ehata S, Tainaka K, et al. Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep. 2017;20(1):236–50. doi: 10.1016/j.celrep.2017.06.010
19. Murray E, Cho JH, Goodwin D, Ku T, Swaney J, Kim SY, et al. Simple, scalable proteomic imaging for high-dimensional profiling of intact systems. Cell. 2015;163(6):1500–14. doi: 10.1016/j.cell.2015.11.025
20. Park Y-G, Sohn CH, Chen R, McCue M, Yun DH, Drummond GT, et al. Protection of tissue physicochemical properties using polyfunctional crosslinkers. Nat Biotechnol. 2019;37(1):73–83. doi: 10.1038/nbt.4281.
21. Tainaka K, Murakami TC, Susaki EA, Shimizu C, Saito R, Takahashi K, et al. Chemical landscape for tissue clearing based on hydrophilic reagents. Cell Rep. 2018;24(8):2196–210.e9. doi: 10.1016/j.celrep.2018.07.056
22. Jing D, Zhang S, Luo W, Gao X, Men Y, Ma C, et al. Tissue clearing of both hard and soft tissue organs with the PEGASOS method. Cell Res. 2018;28(8):803–18. doi: 10.1038/s41422-018-0049-z.
23. Richardson DS, Guan W, Matsumoto K, Pan C, Chung K, Ertürk A, et al. Tissue clearing. Nat Rev Methods Primers. 2021;1(1): 81. doi: 10.1038/s43586-021-00080-9
24. Susaki EA, Tainaka K, Perrin D, Kishino F, Tawara T, Watanabe TM, et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell. 2014;157(3):726–39. doi: 10.1016/j.cell.2014.03.042
25. Tainaka K, Kuno A, Kubota SI, Murakami T, Ueda HR. Chemical principles in tissue clearing and staining protocols for whole-body cell profiling. Annu Rev Cell Dev Biol. 2016;32:713-41. doi: 10.1146/annurev-cellbio-111315-125001
26. Tainaka K, Kubota SI, Suyama TQ, Susaki EA, Perrin D, Ukai-Tadenuma M, et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell. 2014;159(4):911–24. doi: 10.1016/j.cell.2014.10.034
27. Zhao S, Todorov MI, Cai R, Maskari RA, Steinke H, Kemter E, et al. Cellular and molecular probing of intact human organs. Cell. 2020;180(4):796–812.e19. doi: 10.1016/j.cell.2020.01.030
28. Mai H, Rong Z, Zhao S, Cai R, Steinke H, Bechmann I, et al. Scalable tissue labeling and clearing of intact human organs. Nat Protoc. 2022;17(10):2188–215. doi: 10.1038/s41596-022-00712-8
29. Susaki EA, Shimizu C, Kuno A, Tainaka K, Li X, Nishi K, et al. Versatile whole-organ/body staining and imaging based on electrolyte-gel properties of biological tissues. Nat Commun. 2020;11(1):1982. doi: 10.1038/s41467-020-15906-5
30. Cai R, Pan C, Ghasemigharagoz A, Todorov MI, Förstera B, Zhao S, et al. Panoptic imaging of transparent mice reveals whole-body neuronal projections and skull–meninges connections. Nat Neurosci. 2019;22(2):317–27. doi: 10.1038/s41593-018-0301-3
31. Messal HA, Almagro J, Zaw Thin M, Tedeschi A, Ciccarelli A, Blackie L, et al. Antigen retrieval and clearing for whole-organ immunofluorescence by FLASH. Nat Protoc. 2021;16(1):239–62. doi: 10.1038/s41596-020-00414-z
32. Weiss KR, Voigt FF, Shepherd DP, Huisken J. Tutorial: practical considerations for tissue clearing and imaging. Nat Protoc. 2021;16(6):2732–48. doi: 10.1038/s41596-021-00502-8
33. Glaser AK, Reder NP, Chen Y, Yin C, Wei L, Kang S, et al. Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues. Nat Commun. 2019;10(1):2781. doi: 10.1038/s41467-019-10534-0
34. Wei M, Shi L, Shen Y, Zhao Z, Guzman A, Kaufman LJ, et al. Volumetric chemical imaging by clearing-enhanced stimulated Raman scattering microscopy. Proc Natl Acad Sci U S A. 2019;116(14):6608–17. doi: 10.1073/pnas.1813044116
35. Miyazono K, Katsuno Y, Koinuma D, Ehata S, Morikawa M. Intracellular and extracellular TGF-β signaling in cancer: some recent topics. Front Med. 2018;12(4):387-411. doi: 10.1007/s11684-018-0646-8
36. Katsuno Y, Meyer DS, Zhang Z, Shokat KM, Akhurst RJ, Miyazono K, et al. Chronic TGF-β exposure drives stabilized EMT, tumor stemness, and cancer drug resistance with vulnerability to bitopic mTOR inhibition. Sci Signal. 2019;12(570). doi: 10.1126/scisignal.aau8544
37. Takahashi K, Kubota SI, Ehata S, Ueda HR, Miyazono K. Protocol for imaging and analysis of mouse tumor models with CUBIC tissue clearing. STAR Protoc. 2020;1(3):100191. doi: 10.1016/j.xpro.2020.100191
38. Almagro J, Messal HA, Zaw Thin M, van Rheenen J, Behrens A. Tissue clearing to examine tumour complexity in three dimensions. Nat Rev Cancer. 2021;21(11):718–30. doi: 10.1038/s41568-021-00382-w
39. Nojima S, Susaki EA, Yoshida K, Takemoto H, Tsujimura N, Iijima S, et al. CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci Rep. 2017;7(1):9269. doi: 10.1038/s41598-017-09117-0
40. Cai R, Kolabas ZI, Pan C, Mai H, Zhao S, Kaltenecker D, et al. Whole-mouse clearing and imaging at the cellular level with vDISCO. Nat Protoc. 2023;18(4):1197-242. doi: 10.1038/s41596-022-00788-2
41. Pan C, Schoppe O, Parra-Damas A, Cai R, Todorov MI, Gondi G, et al. Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body. Cell. 2019;179(7):1661–76.e19. doi: 10.1016/j.cell.2019.11.013
42. Takahashi K, Tanabe R, Ehata S, Kubota SI, Morishita Y, Ueda HR, et al. Visualization of the cancer cell cycle by tissue-clearing technology using the Fucci reporter system. Cancer Sci. 2021;112(9):3796–809. doi: 10.1111/cas.15034
43. Takahashi K, Abe K, Kubota SI, Fukatsu N, Morishita Y, Yoshimatsu Y, et al. An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis. Nat Commun. 2022;13(1):5239. doi: 10.1038/s41467-022-32848-2
44. Sakaue-Sawano A, Kurokawa H, Morimura T, Hanyu A, Hama H, Osawa H, et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell. 2008;132(3):487–98. doi: 10.1016/j.cell.2007.12.033
45. Sakaue-Sawano A, Kobayashi T, Ohtawa K, Miyawaki A. Drug-induced cell cycle modulation leading to cell-cycle arrest, nuclear mis-segregation, or endoreplication. BMC Cell Biol. 2011;12:2. doi: 10.1186/1471-2121-12-2
46. Sakaue-Sawano A, Yo M, Komatsu N, Hiratsuka T, Kogure T, Hoshida T, et al. Genetically encoded tools for optical dissection of the mammalian cell cycle. Mol Cell. 2017;68(3):626–40.e5. doi: 10.1016/j.molcel.2017.10.001
47. Aceto N, Bardia A, Miyamoto DT, Donaldson MC, Wittner BS, Spencer JA, et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell. 2014;158(5):1110–22. doi: 10.1016/j.cell.2014.07.013
48. Cheung KJ, Ewald AJ. A collective route to metastasis: seeding by tumor cell clusters. Science. 2016;352(6282):167–9. doi: 10.1126/science.aaf6546
49. Hunter KW, Amin R, Deasy S, Ha N-H, Wakefield L. Genetic insights into the morass of metastatic heterogeneity. Nat Rev Cancer. 2018;18(4):211–23. doi: 10.1038/nrc.2017.126
50. Kok SY, Oshima H, Takahashi K, Nakayama M, Murakami K, Ueda HR, et al. Malignant subclone drives metastasis of genetically and phenotypically heterogenous cell clusters through fibrotic niche generation. Nat Commun. 2021;12(1):863. doi: 10.1038/s41467-021-21160-0
51. Kubota SI, Takahashi K, Mano T, Matsumoto K, Katsumata T, Shi S, et al. Whole-organ analysis of TGF-β-mediated remodelling of the tumour microenvironment by tissue clearing. Commun Biol. 2021;4(1):294. doi: 10.1038/s42003-021-01786-y
52. Tanaka N, Kanatani S, Tomer R, Sahlgren C, Kronqvist P, Kaczynska D, et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat Biomed Eng. 2017;1(10):796–806. doi: 10.1038/s41551-017-0139-0
53. de Visser KE, Joyce JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374–403. doi: 10.1016/j.ccell.2023.02.016
54. Mano T, Murata K, Kon K, Shimizu C, Ono H, Shi S, et al. CUBIC-Cloud provides an integrative computational framework toward community-driven whole-mouse-brain mapping. Cell Rep Methods. 2021;1(2):100038. doi: 10.1016/j.crmeth.2021.100038
55. Yang B, Treweek JB, Kulkarni RP, Deverman BE, Chen CK, Lubeck E, et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell. 2014;158(4):945-58. doi: 10.1016/j.cell.2014.07.017
56. Stoltzfus CR, Filipek J, Gern BH, Olin BE, Leal JM, Wu Y, et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 2020;31(3):107523. doi: 10.1016/j.celrep.2020.107523
57. Cuccarese MF, Dubach JM, Pfirschke C, Engblom C, Garris C, Miller MA, et al. Heterogeneity of macrophage infiltration and therapeutic response in lung carcinoma revealed by 3D organ imaging. Nat Commun. 2017;8:14293. doi: 10.1038/ncomms14293
58. Si Y, Merz SF, Jansen P, Wang B, Bruderek K, Altenhoff P, et al. Multidimensional imaging provides evidence for down-regulation of T cell effector function by MDSC in human cancer tissue. Sci Immunol. 2019;4(40):eaaw9159. doi: 10.1126/sciimmunol.aaw9159
59. Kirst C, Skriabine S, Vieites-Prado A, Topilko T, Bertin P, Gerschenfeld G, et al. Mapping the fine-scale organization and plasticity of the brain vasculature. Cell. 2020;180(4):780–95.e25. doi: 10.1016/j.cell.2020.01.028
60. Todorov MI, Paetzold JC, Schoppe O, Tetteh G, Shit S, Efremov V, et al. Machine learning analysis of whole mouse brain vasculature. Nat Methods. 2020;17(4):442–9. doi: 10.1038/s41592-020-0792-1
61. Miyawaki T, Morikawa S, Susaki EA, Nakashima A, Takeuchi H, Yamaguchi S, et al. Visualization and molecular characterization of whole-brain vascular networks with capillary resolution. Nat Commun. 2020;11(1):1104. doi: 10.1038/s41467-020-14786-z
62. Kirschnick N, Drees D, Redder E, Erapaneedi R, Pereira da Graca A, Schäfers M, et al. Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures. iScience. 2021;24(6):102650. doi: 10.1016/j.isci.2021.102650
63. Sommer C, Straehle C, Koethe U, Hamprecht FA, editors. Ilastik: Interactive learning and segmentation toolkit. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233 (IEEE, 2011) doi: 10.1109/ISBI.2011.5872394
64. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16(12):1226–32. doi: 10.1038/s41592-019-0582-9
65. Wang Q, Ding SL, Li Y, Royall J, Feng D, Lesnar P, et al. The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell. 2020;181(4):936–53.e20. doi: 10.1016/j.cell.2020.04.007
66. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82. doi: 10.1126/science.aaf2403
67. Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021;596(7871):211–20. doi: 10.1038/s41586-021-03634-9
68. Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods. 2022;19(5):534–46. doi: 10.1038/s41592-022-01409-2
69. Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature. 2019;568(7751):235–9. doi: 10.1038/s41586-019-1049-y
70. Li W, Germain RN, Gerner MY. High-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging. Nat Protoc. 2019;14(6):1708–33. doi: 10.1038/s41596-019-0156-4
71. Murakami TC, Heintz N. Multiplexed and scalable cellular phenotyping toward the standardized three-dimensional human neuroanatomy. bioRxiv. 2022:2022.11.23.517711. doi: https://doi.org/10.1101/2022.11.23.517711
72. Bhatia HS, Brunner AD, Öztürk F, Kapoor S, Rong Z, Mai H, et al. Spatial proteomics in three-dimensional intact specimens. Cell. 2022;185(26):5040–58.e19. doi: 10.1016/j.cell.2022.11.021
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to Upsala Medical Society. Read the full Copyright- and Licensing Statement.