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Colorimetric histology using plasmonically active microscope slides

Abstract

The human eye can distinguish as many as 10,000 different colours but is far less sensitive to variations in intensity1, meaning that colour is highly desirable when interpreting images. However, most biological samples are essentially transparent, and nearly invisible when viewed using a standard optical microscope2. It is therefore highly desirable to be able to produce coloured images without needing to add any stains or dyes, which can alter the sample properties. Here we demonstrate that colorimetric histology images can be generated using full-sized plasmonically active microscope slides. These slides translate subtle changes in the dielectric constant into striking colour contrast when samples are placed upon them. We demonstrate the biomedical potential of this technique, which we term histoplasmonics, by distinguishing neoplastic cells from normal breast epithelium during the earliest stages of tumorigenesis in the mouse MMTV-PyMT mammary tumour model. We then apply this method to human diagnostic tissue and validate its utility in distinguishing normal epithelium, usual ductal hyperplasia, and early-stage breast cancer (ductal carcinoma in situ). The colorimetric output of the image pixels is compared to conventional histopathology. The results we report here support the hypothesis that histoplasmonics can be used as a novel alternative or adjunct to general staining. The widespread availability of this technique and its incorporation into standard laboratory workflows may prove transformative for applications extending well beyond tissue diagnostics. This work also highlights opportunities for improvements to digital pathology that have yet to be explored.

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Fig. 1: Conceptual design and fundamental principles.
Fig. 2: Testing device sensitivity using nanofabricated samples.
Fig. 3: Quantification of spectral output, comparison to Ki67, pathology scoring and statistics.
Fig. 4: Nanoslide histology images and pathology assessment of retrospective DCIS human cohort.

Data availability

All data sets are included in this article and its Supplementary Information files. Pathology scoring for Figs. 3d and 4e was conducted on the basis of visual inspection of the slides by expert breast cancer pathologists using an Olympus BX51 optical microscope. Histology images used for this study are available in figshare with the identifier https://doi.org/10.6084/m9.figshare.14897697.v2Source data are provided with this paper.

References

  1. 1.

    Judd, D. B. & Wyszecki, G. Color in Business, Science, and Industry (Wiley & Sons, 1975).

  2. 2.

    Alberts, B. Molecular Biology of the Cell (Garland Science, Taylor & Francis, 2008).

  3. 3.

    Fu, Y., Tippets, C. A., Donev, E. U. & Lopez, R. Structural colors: from natural to artificial systems. Wiley Interdiscipl. Rev. Nanomed. Nanobiotechnol. 8, 758–775 (2016).

    Article  Google Scholar 

  4. 4.

    Lochbihler, H. Colored images generated by metallic sub-wavelength gratings. Opt. Express 17, 12189–12196 (2009).

    ADS  CAS  PubMed  Article  Google Scholar 

  5. 5.

    Lee, H.-S., Yoon, Y.-T., Lee, S.-S., Kim, S.-H. & Lee, K.-D. Color filter based on a subwavelength patterned metal grating. Opt. Express 15, 15457–15463 (2007).

    ADS  PubMed  Article  Google Scholar 

  6. 6.

    Inoue, D. et al. Polarization independent visible color filter comprising an aluminum film with surface-plasmon enhanced transmission through a subwavelength array of holes. Appl. Phys. Lett. 98, 093113 (2011).

    ADS  Article  CAS  Google Scholar 

  7. 7.

    Duempelmann, L., Casari, D., Luu-Dinh, A., Gallinet, B. & Novotny, L. Color rendering plasmonic aluminum substrates with angular symmetry breaking. ACS Nano 9, 12383–12391 (2015).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Raj Shrestha, V., Lee, S.-S., Kim, E.-S. & Choi, D.-Y. Polarization-tuned dynamic color filters incorporating a dielectric-loaded aluminum nanowire array. Sci. Rep. 5, 12450 (2015).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Liddle, J. A. & Gallatin, G. M. Nanomanufacturing: a perspective. ACS Nano 10, 2995–3014 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Vala, M. & Homola, J. Flexible method based on four-beam interference lithography for fabrication of large areas of perfectly periodic plasmonic arrays. Opt. Express 22, 18778–18789 (2014).

    ADS  CAS  PubMed  Article  Google Scholar 

  11. 11.

    Wang, L. et al. Large area plasmonic color palettes with expanded gamut using colloidal self-assembly. ACS Photon. 3, 627–633 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Franklin, D. et al. Polarization-independent actively tunable colour generation on imprinted plasmonic surfaces. Nat. Commun. 6, 7337 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  13. 13.

    Solak, H. H., Dais, C. & Clube, F. Displacement Talbot lithography: a new method for high-resolution patterning of large areas. Opt. Express 19, 10686–10691 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  14. 14.

    Choudhury, K. R., Yagle, K. J., Swanson, P. E., Krohn, K. A. & Rajendran, J. G. A robust automated measure of average antibody staining in immunohistochemistry images. J. Histochem. Cytochem. 58, 95–107 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Wong, T. T. W. et al. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy. Sci. Adv. 3, e1602168 (2017).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Fereidouni, F. et al. Microscopy with ultraviolet surface excitation for rapid slide-free histology. Nat. Biomed. Eng. 1, 957–966 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Neilson, K. A. et al. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11, 535–553 (2011).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Lu, F.-K. et al. Label-free DNA imaging in vivo with stimulated Raman scattering microscopy. Proc. Natl Acad. Sci. USA 112, 11624–11629 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Ji, M. et al. Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy. Science Transl. Med. 5, 201ra119 (2013).

    Article  CAS  Google Scholar 

  20. 20.

    Lee, M. et al. Label-free optical quantification of structural alterations in Alzheimer’s disease. Sci. Rep. 6, 31034 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Langley, D. P., Balaur, E., Hwang, Y., Sadatnajafi, C. & Abbey, B. Optical chemical barcoding based on polarization controlled plasmonic nanopixels. Adv. Funct. Mater. 28, 1704842 (2018).

    Article  CAS  Google Scholar 

  22. 22.

    Langley, D., Balaur, E., Sadatnajafi, C. & Abbey, B. Dual pitch plasmonic devices for polarization enhanced colour based sensing. Proc. SPIE 10013, 1001338 (2016).

    Article  Google Scholar 

  23. 23.

    Huth, M. et al. Focused electron beam induced deposition: a perspective. Beilstein J. Nanotechnol. 3, 597–619 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Ding, X. et al. Surface plasmon resonance enhanced light absorption and photothermal therapy in the second near-infrared window. J. Am. Chem. Soc. 136, 15684–15693 (2014).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Kawata, S., Ichimura, T., Taguchi, A. & Kumamoto, Y. Nano-Raman scattering microscopy: resolution and enhancement. Chem. Rev. 117, 4983–5001 (2017).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Duivenvoorden, H. M., Spurling, A., O’Toole, S. A. & Parker, B. S. Discriminating the earliest stages of mammary carcinoma using myoepithelial and proliferative markers. PLoS ONE 13, e0201370 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Wang, Z., Tangella, K., Balla, A. & Popescu, G. Tissue refractive index as marker of disease. J. Biomed. Opt. 16, 116017 (2011).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Shan, M., Kandel, M. E. & Popescu, G. Refractive index variance of cells and tissues measured by quantitative phase imaging. Opt. Express 25, 1573–1581 (2017).

    ADS  PubMed  Article  Google Scholar 

  29. 29.

    Giannios, P. et al. Visible to near-infrared refractive properties of freshly-excised human-liver tissues: marking hepatic malignancies. Sci. Rep. 6, 27910 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Shi, P. et al. Automated Ki-67 quantification of immunohistochemical staining image of human nasopharyngeal carcinoma xenografts. Sci. Rep. 6, 32127 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    van Seijen, M. et al. Ductal carcinoma in situ: to treat or not to treat, that is the question. Br. J. Cancer 121, 285–292 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Elmore, J. G. et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. J. Am. Med. Assoc. 313, 1122–1132 (2015).

    CAS  Article  Google Scholar 

  33. 33.

    Simpson, P. T., Reis-Filho, J. S., Gale, T. & Lakhani, S. R. Molecular evolution of breast cancer. J. Pathol. 205, 248–254 (2005).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Ramkumar, C., Prakash, C., Madhav, L., Kumar, A. & Basavaraj, C. Assessment of Ki67 as a prognostic marker in hormone receptor positive breast cancer: a retrospective study on an Indian cohort. J. Mol. Biomark. Diagn. (2017).

  35. 35.

    Balaur, E., Sadatnajafi, C. & Abbey, B. Large-scale fabrication of optically active plasmonic arrays via displacement Talbot lithography. J. Phys. Conf. Ser. 1455, 012005 (2020).

    CAS  Article  Google Scholar 

  36. 36.

    Balaur, E., Sadatnajafi, C., Kou, S. S., Lin, J. & Abbey, B. Continuously tunable, polarization controlled, colour palette produced from nanoscale plasmonic pixels. Sci. Rep. 6, 28062 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Balaur, E., Sadatnajafi, C., Langley, D. & Abbey, B. Optimisation of polarization controlled colour tuning using nanoscale cross-shaped apertures in silver films. Proc. SPIE 10013, 100132F (2016).

    Article  Google Scholar 

  38. 38.

    Namatsu, H. et al. Three-dimensional siloxane resist for the formation of nanopatterns with minimum linewidth fluctuations. J. Vac. Sci. Technol. B 16, 69 (1998).

    CAS  Article  Google Scholar 

  39. 39.

    Giannuzzi, L. A. Introduction to Focused Ion Beams 1st edn (Springer US, 2005).

  40. 40.

    Jung, L. S., Campbell, C. T., Chinowsky, T. M., Mar, M. N. & Yee, S. S., Quantitative interpretation of the response of surface plasmon resonance sensors to adsorbed films. Langmuir 14, 5636–5648 (1998). 

    CAS  Article  Google Scholar 

  41. 41.

    Maier, S. A. Plasmonics: Fundamentals and Applications (Springer US, 2007).

  42. 42.

    Balaur, E. et al. Plasmon-induced enhancement of ptychographic phase microscopy via sub-surface nanoaperture arrays. Nat. Photon. 15, 222–229 (2021).

    ADS  CAS  Article  Google Scholar 

  43. 43.

    Sadatnajafi, C., Balaur, E. & Abbey, B. Bimodal plasmonic color filters enable direct optical imaging of ion implantation in thin films. Adv. Funct. Mater. https://doi.org/10.1002/adfm.202009419 (2021).

    Article  Google Scholar 

  44. 44.

    Simmons, D. M. & Swanson, L. W. Comparing histological data from different brains: sources of error and strategies for minimizing them. Brain Res. Rev. 60, 349–367 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Niazi, M. K. K. et al. Relationship between the Ki67 index and its area based approximation in breast cancer. BMC Cancer 18, 867 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46.

    Davie, S. A. et al. Effects of FVB/NJ and C57Bl/6J strain backgrounds on mammary tumor phenotype in inducible nitric oxide synthase deficient mice. Transgenic Res. 16, 193–201 (2007).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Orian, J. M. et al. Insertional mutagenesis inducing hypomyelination in transgenic mice. J. Neurosci. Res. 39, 604–612 (1994).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Matenaers, C., Popper, B., Rieger, A., Wanke, R. & Blutke, A. Practicable methods for histological section thickness measurement in quantitative stereological analyses. PLoS ONE 13, e0192879 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Thurby, C., White, M. R., Simutis, F., Gillham, S. & Slinker, D. An improved method of tissue adhesion on glass microslides for immunohistochemical evaluation by the use of selected tissues from the dog and monkey. J. Histotechnol. 32, 198–201 (2009).

    Article  Google Scholar 

  50. 50.

    Cruz-Roa, A. et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

B.A., E.B. and K.A.N. acknowledge the support of the Australian Research Council through the Centre of Excellence in Advanced Molecular Imaging (CE140100011). Fellowship funding from the Victorian Cancer Agency and grant funding from the National Breast Cancer Foundation NBCF (IIRS-21-069) is acknowledged by B.S.P. Funding from the National Breast Cancer Foundation (NBCF Prac. 16-006) and Sydney Breast Cancer Foundation is acknowledged by S.O’T. B.A. acknowledges support from the La Trobe Biomedical and Environmental Sensor Technology (BEST) Research Centre. The authors gratefully acknowledge X. Li from the La Trobe Statistics Consultancy Platform for help and advice with the statistical analysis of the data. This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF).

Author information

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Authors

Contributions

E.B. carried out the research, performed the measurements, analysed the data and generated the figures for the manuscript. E.B., B.S.P. and B.A. conceived the idea and are responsible for the conceptual design of the study. E.B. and B.A. jointly developed the original concept for ‘nanoslide’ and ‘histoplasmonics’. B.S.P. was responsible for design and management of cancer histological studies. B.M. and B.Y. sourced patient samples and assisted with diagnostic sample identification for study. C.S. assisted with experiments. E.H., B.S.P. and A.S. prepared the biological samples and microtome slices. J.O. prepared the optic nerve samples. S.O’T. and B.S.P. performed the pathology scoring and, along with K.H., prepared patient samples. B.A. directed the project. K.A.N. discussed the results and analysis. B.A., B.S.P. and E.B. wrote the paper. All authors discussed the results and contributed to the manuscript.

Corresponding authors

Correspondence to Belinda S. Parker or Brian Abbey.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Roberto Salgado and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Finite element methods (FEM) optical transmission simulations.

a, FEM model of a section of the nanoslide showing the glass substrate (dark blue), nanoaperture in the metallic film, the capping layer, and air (light blue) constructed in the COMSOL Multiphysics software program. Simulated transmission spectra from each of the four lines (I-IV) which make up the chevron sample (see Fig. 2) and the background (B) spectrum generated using b, FEM model of the device for circular apertures (diameter = 160 nm). c, Shows the simulated background spectrum (no sample) for the crosses (arm length = 160 nm), ellipse (major axis = 210 nm, minor axis = 160 nm), and circular apertures. The peaks represent the plasmon resonances that occur within the active layer of the device. d, Example FEM simulations of a cross section of the electric field generated by a single circular nanoaperture at the peak resonant wavelength of the spectra shown in b

Source data.

Extended Data Fig. 2 AFM line-scan height profiles of the fabricated samples.

a, Schematics of the chevron structure and b, the ‘staircase’ sample. c, The AFM line-scan height profile of the chevron structure sample, the thinnest sample stripe is 3 ± 1 nm. d, The AFM line-scan height profile across the steps and stripes respectively of the ‘staircase’ sample

Source data.

Extended Data Fig. 3 Pathology workflow for small-animal study and schematic showing serial sections used.

a, Pathology workflow for small-animal, MMTV-PyMT mouse model study. b, Schematic showing how serial sections were taken to enable a direct comparison of nanoslide, H&E, and Ki67.

Extended Data Fig. 4 Large field-of-view example images from H&E, Ki67, and nanoslide.

The Ki67 and nanoslide positive areas are overlaid. Large-field-of-view (3.8 × 3.8 mm2) areas of tissue stained with H&E and Ki67 compared to the results obtained directly on nanoslide. The areas of positivity of both nanoslide (light blue) and Ki67 (bright green) were identified based on their respective HSL colour space values for example, ref. 32 (also see Methods).

Extended Data Fig. 5 Comparison of histoplasmonics to H&E staining of different tissues.

Bright-field optical images: H&E stained images and histoplasmonic images. Tissue slices were placed on the slides in a sequential manner.

Extended Data Fig. 6 Colorimetric analysis and pathology assessment of mouse data.

a, Histology images (200× magnification) were sub-categorized into four different stages for both nanoslide (1st column) and Ki67 (3rd column). The HSL image pixel colour space values were compared against ground truth pathology annotations and classified as True Positive (TP - green), False Negative (FN - red), False Positive (FP - yellow), and True Negative (TN - blue). The white space in the Ki67 image in the top row is an area where no stain adhered. Scale bars = 15 μm. b, H&E images for neoplastic regions –yellow outline (1st column), nanoslide intensity (2nd column), and Ki67 (3rd column) positivity.

Extended Data Fig. 7 Cell counting Ki67 vs nanoslide using brightfield microscopy.

Example histoplasmonic images of 1 μm thick sequential cancerous breast tissues (PyMT mice). a, Low magnification ( × 100) images. Left: contouring of cancerous regions on Nanoslide. Middle: contouring of the same cancerous regions using Ki67. Right: the contours for Nanoslide (blue) is overlapped with the contour for Ki67 (red). b, High magnification ( × 200) images. Left: Nanoslide image of MIN region; Middle: Ki67 image of the same region. Right: Overlap of positive cells on Nanoslide and MIN (92% concordance). c, High magnification (×200) images. Left: Nanoslide image of MIN region; Middle: Ki67 image of the same region. Right: Overlap of positive cells on Nanoslide and MIN (93% concordance).

Extended Data Fig. 8 Schematic showing serial sections used for DCIS breast cancer patient study.

Schematic showing how serial sections were taken to enable a direct comparison of nanoslide, H&E, ER, and CK 5/6 for human tissue.

Extended Data Fig. 9 Bright-field microscopy of 70 nm thick TEM optic nerve tissue sections imaged using toluidine staining and with histoplasmonics.

Top: Bright-field optical image of toluidine stained 70 nm thick optic nerve sections. Nanoslide results: middle – at 0° incident polarization, bottom – at 90° incident polarization reveals axons, glia and the myelin sheath as different colours. The black scale bar in both the left and right column images is 5 μm.

Extended Data Fig. 10 Bright-field microscopy of tissues of different thicknesses.

Left: Histoplasmonic images of cancerous breast tissues (PyMT mice) sectioned at 4 and 5 μm slice thickness showing almost no difference in colour contrast. Right: Bright-field optical images of optic nerve sections sectioned at 70 and 200 nm thickness showing dramatic difference in colour contrast. The scale bar is 15 μm.

Extended Data Table 1 Comparison of key label-free techniques for histology

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-6 and Supplementary Tables 1-4.

Reporting Summary

Supplementary Video 1

Example video of pathologist using nanoslide for histoplasmonics. Video shows an example of breast tissue being examined on nanoslide. Note that the colours as they appear in the video look slightly different to when images are captured directly using the microscope.

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Balaur, E., O’ Toole, S., Spurling, A.J. et al. Colorimetric histology using plasmonically active microscope slides. Nature 598, 65–71 (2021). https://doi.org/10.1038/s41586-021-03835-2

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