1932

Abstract

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

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2023-06-14
2024-04-17
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Literature Cited

  1. 1.
    Kumar V, Abbas AK, Aster JC. 2015. Robbins and Cotran Pathologic Basis of Disease Philadelphia: Elsevier/Saunders. , 9th ed..
  2. 2.
    Wood BR, Quinn MA, Tait B, Ashdown M, Hislop T et al. 1998. FTIR microspectroscopic study of cell types and potential confounding variables in screening for cervical malignancies. Biospectroscopy 4:75–91
    [Google Scholar]
  3. 3.
    Blout ER, Mellors RC. 1949. Infrared spectra of tissues. Science 110:137–38
    [Google Scholar]
  4. 4.
    Jackson M, Sowa MG, Mantsch HH. 1997. Infrared spectroscopy: a new frontier in medicine. Biophys. Chem. 68:109–25
    [Google Scholar]
  5. 5.
    Diem M, Boydston-White S, Chiriboga L. 1999. Infrared spectroscopy of cells and tissues: shining light onto a novel subject. Appl. Spectrosc. 53:148A–61A
    [Google Scholar]
  6. 6.
    Lasch P, Naumann D. 1998. FT-IR microspectroscopic imaging of human carcinoma thin sections based on pattern recognition techniques. Cell. Mol. Biol. 44:189–202
    [Google Scholar]
  7. 7.
    Chiriboga L, Xie P, Yee H, Zarou D, Zakim D, Diem M. 1998. Infrared spectroscopy of human tissue. IV. Detection of dysplastic and neoplastic changes of human cervical tissue via infrared microscopy. Cell. Mol. Biol. 44:219–29
    [Google Scholar]
  8. 8.
    Jamin N, Dumas P, Moncuit J, Fridman W, Teillaud J et al. 1998. Chemical imaging of nucleic acids, proteins and lipids of a single living cell. Application of synchrotron infrared microspectrometry in cell biology. Cell. Mol. Biol. 44:9–13
    [Google Scholar]
  9. 9.
    Argov S, Ramesh J, Salman A, Sinelnikov I, Goldstein J et al. 2002. Diagnostic potential of FTIR microspectroscopy and advanced computational methods in colon cancer patients. J. Biomed. Opt. 7:248–54
    [Google Scholar]
  10. 10.
    Lewis EN, Gorbach AM, Marcott C, Levin IW. 1996. High-fidelity Fourier transform infrared spectroscopic imaging of primate brain tissue. Appl. Spectrosc. 50:263–69
    [Google Scholar]
  11. 11.
    Kidder LH, Kalasinsky VF, Luke JL, Levin IW, Lewis EN. 1997. Visualization of silicone gel in human breast tissue using new infrared imaging spectroscopy. Nat. Med. 3:235–37
    [Google Scholar]
  12. 12.
    Lasch P, Haensch W, Lewis EN, Kidder LH, Naumann D. 2002. Characterization of colorectal adenocarcinoma sections by spatially resolved FT-IR microspectroscopy. Appl. Spectrosc. 56:1–9
    [Google Scholar]
  13. 13.
    Fernandez DC, Bhargava R, Hewitt SM, Levin IW. 2005. Infrared spectroscopic imaging for histopathologic recognition. Nat. Biotechnol. 23:469–74
    [Google Scholar]
  14. 14.
    Diem M, Romeo M, Boydston-White S, Miljković M, Matthäus C. 2004. A decade of vibrational micro-spectroscopy of human cells and tissue (1994–2004). Analyst 129:880–85
    [Google Scholar]
  15. 15.
    Levin IW, Bhargava R. 2005. Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition. Annu. Rev. Phys. Chem. 56:429–74
    [Google Scholar]
  16. 16.
    Dumas P, Sockalingum GD, Sule-Suso J. 2007. Adding synchrotron radiation to infrared microspectroscopy: What's new in biomedical applications?. Trends Biotechnol 25:40–44
    [Google Scholar]
  17. 17.
    Movasaghi Z, Rehman S, ur Rehman I. 2008. Fourier transform infrared (FTIR) spectroscopy of biological tissues. Appl. Spectrosc. Rev. 43:134–79
    [Google Scholar]
  18. 18.
    Malek K, Wood BR, Bambery KR. 2014. FTIR imaging of tissues: techniques and methods of analysis. Optical Spectroscopy and Computational Methods in Biology and Medicine M Baranska 419–73. Dordrecht, Neth.: Springer
    [Google Scholar]
  19. 19.
    Pahlow S, Weber K, Popp J, Bayden RW, Kochan K et al. 2018. Application of vibrational spectroscopy and imaging to point-of-care medicine: a review. Appl. Spectrosc. 72:52–84
    [Google Scholar]
  20. 20.
    Kazarian SG. 2021. Perspectives on infrared spectroscopic imaging from cancer diagnostics to process analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc. 251:119413
    [Google Scholar]
  21. 21.
    Lasch P, Kneipp J, eds. 2008. Biomedical Vibrational Spectroscopy Hoboken, NJ: Wiley
  22. 22.
    Srinivasan G 2010. Vibrational Spectroscopic Imaging for Biomedical Applications New York: McGraw-Hill
  23. 23.
    ur Rehman I, Movasaghi Z, Rehman S. 2012. Vibrational Spectroscopy for Tissue Analysis Boca Raton, FL: CRC Press
  24. 24.
    Bhargava R. 2012. Infrared spectroscopic imaging: the next generation. Appl. Spectrosc. 66:1091–120
    [Google Scholar]
  25. 25.
    Finlayson D, Rinaldi C, Baker MJ. 2019. Is infrared spectroscopy ready for the clinic?. Anal. Chem. 91:12117–28
    [Google Scholar]
  26. 26.
    Van Leenders GJLH, Van Der Kwast TH, Grignon DJ, Evans AJ, Kristiansen G et al. 2020. The 2019 International Society of Urological Pathology (ISUP) consensus conference on grading of prostatic carcinoma. Am. J. Surg. Pathol. 44:e87–99
    [Google Scholar]
  27. 27.
    Litwin MS, Tan H-J. 2017. The diagnosis and treatment of prostate cancer: a review. JAMA 317:2532–42
    [Google Scholar]
  28. 28.
    Netto GJ. 2020. The ever changing landscape of anatomic pathology practice. Adv. Anat. Pathol. 27:1–2
    [Google Scholar]
  29. 29.
    He B, Bergenstråhle L, Stenbeck L, Abid A, Andersson A et al. 2020. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4:827–34
    [Google Scholar]
  30. 30.
    Anderson NM, Simon MC. 2020. The tumor microenvironment. Curr. Biol. 30:R921–25
    [Google Scholar]
  31. 31.
    Quail DF, Joyce JA. 2013. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19:1423–37
    [Google Scholar]
  32. 32.
    Anderson AR, Weaver AM, Cummings PT, Quaranta V. 2006. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127:905–15
    [Google Scholar]
  33. 33.
    Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M et al. 2008. Stromal gene expression predicts clinical outcome in breast cancer. Nat. Med. 14:518–27
    [Google Scholar]
  34. 34.
    Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. 2019. Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16:703–15
    [Google Scholar]
  35. 35.
    Niazi MKK, Parwani AV, Gurcan MN. 2019. Digital pathology and artificial intelligence. Lancet Oncol 20:e253–61
    [Google Scholar]
  36. 36.
    Heindl A, Nawaz S, Yuan Y. 2015. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Investig. 95:377–84
    [Google Scholar]
  37. 37.
    Musumeci G. 2014. Past, present and future: overview on histology and histopathology. J. Histol. Histopathol. 1:5
    [Google Scholar]
  38. 38.
    Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ et al. 2014. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9:1771–91
    [Google Scholar]
  39. 39.
    Greenbaum A, Zhang Y, Feizi A, Chung P-L, Luo W et al. 2014. Wide-field computational imaging of pathology slides using lens-free on-chip microscopy. Sci. Transl. Med. 6:267ra175
    [Google Scholar]
  40. 40.
    Horstmeyer R, Ou X, Zheng G, Willems P, Yang C. 2015. Digital pathology with Fourier ptychography. Comput. Med. Imaging Graph. 42:38–43
    [Google Scholar]
  41. 41.
    Fereidouni F, Harmany ZT, Tian M, Todd A, Kintner JA et al. 2017. Microscopy with ultraviolet surface excitation for rapid slide-free histology. Nat. Biomed. Eng. 1:957–66
    [Google Scholar]
  42. 42.
    Lee K, Kim K, Jung J, Heo J, Cho S et al. 2013. Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications. Sensors 13:4170–91
    [Google Scholar]
  43. 43.
    Rivenson Y, Liu T, Wei Z, Zhang Y, de Haan K, Ozcan A. 2019. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl. 8:23
    [Google Scholar]
  44. 44.
    Poola PK, Afzal MI, Yoo Y, Kim KH, Chung E. 2019. Light sheet microscopy for histopathology applications. Biomed. Eng. Lett. 9:279–91
    [Google Scholar]
  45. 45.
    Barner LA, Glaser AK, Huang H, True LD, Liu JT. 2020. Multi-resolution open-top light-sheet microscopy to enable efficient 3D pathology workflows. Biomed. Opt. Express 11:6605–19
    [Google Scholar]
  46. 46.
    Liu JT, Glaser AK, Bera K, True LD, Reder NP et al. 2021. Harnessing non-destructive 3D pathology. Nat. Biomed. Eng. 5:203–18
    [Google Scholar]
  47. 47.
    Nojima S, Susaki EA, Yoshida K, Takemoto H, Tsujimura N et al. 2017. CUBIC pathology: three-dimensional imaging for pathological diagnosis. Sci. Rep. 7:9269
    [Google Scholar]
  48. 48.
    Rivenson Y, Wang H, Wei Z, de Haan K, Zhang Y et al. 2019. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3:466–77
    [Google Scholar]
  49. 49.
    Freudiger CW, Min W, Saar BG, Lu S, Holtom GR et al. 2008. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322:1857–61
    [Google Scholar]
  50. 50.
    Camp CH Jr., Cicerone MT. 2015. Chemically sensitive bioimaging with coherent Raman scattering. Nat. Photon. 9:295–305
    [Google Scholar]
  51. 51.
    Lu F-K, Basu S, Igras V, Hoang MP, Ji M et al. 2015. Label-free DNA imaging in vivo with stimulated Raman scattering microscopy. PNAS 112:11624–29
    [Google Scholar]
  52. 52.
    Sarri B, Poizat F, Heuke S, Wojak J, Franchi F et al. 2019. Stimulated Raman histology: one to one comparison with standard hematoxylin and eosin staining. Biomed. Opt. Express 10:5378–84
    [Google Scholar]
  53. 53.
    Orringer DA, Pandian B, Niknafs YS, Hollon TC, Boyle J et al. 2017. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. 1:0027
    [Google Scholar]
  54. 54.
    Liu Z, Su W, Ao J, Wang M, Jiang Q et al. 2022. Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nat. Commun. 13:4050
    [Google Scholar]
  55. 55.
    Zhang L, Zou X, Huang J, Fan J, Sun X et al. 2021. Label-free histology and evaluation of human pancreatic cancer with coherent nonlinear optical microscopy. Anal. Chem. 93:15550–58
    [Google Scholar]
  56. 56.
    Audier X, Forget N, Rigneault H. 2020. High-speed chemical imaging of dynamic and histological samples with stimulated Raman micro-spectroscopy. Opt. Express 28:15505–14
    [Google Scholar]
  57. 57.
    Ozeki Y, Umemura W, Otsuka Y, Satoh S, Hashimoto H et al. 2012. High-speed molecular spectral imaging of tissue with stimulated Raman scattering. Nat. Photon. 6:845–51
    [Google Scholar]
  58. 58.
    Cheng J-X, Min W, Ozeki Y, Polli D, eds. 2021. Stimulated Raman Scattering Microscopy: Techniques and Applications Amsterdam: Elsevier
  59. 59.
    Cable DM, Murray E, Shanmugam V, Zhang S, Zou LS et al. 2022. Cell type–specific inference of differential expression in spatial transcriptomics. Nat. Methods 19:1076–87
    [Google Scholar]
  60. 60.
    Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:436–42
    [Google Scholar]
  61. 61.
    Aichler M, Walch A. 2015. MALDI imaging mass spectrometry: current frontiers and perspectives in pathology research and practice. Lab. Investig. 95:422–31
    [Google Scholar]
  62. 62.
    Pilling M, Gardner P. 2016. Fundamental developments in infrared spectroscopic imaging for biomedical applications. Chem. Soc. Rev. 45:1935–57
    [Google Scholar]
  63. 63.
    Wrobel TP, Bhargava R. 2018. Infrared spectroscopic imaging advances as an analytical technology for biomedical sciences. Anal. Chem. 90:1444–63
    [Google Scholar]
  64. 64.
    Griffiths PR, de Haseth JA. 2007. Microspectroscopy and imaging. Fourier Transform Infrared Spectroscopy PR Griffiths, JA de Haseth 303–20. Hoboken, NJ: Wiley
    [Google Scholar]
  65. 65.
    Reddy RK, Walsh MJ, Schulmerich MV, Carney PS, Bhargava R. 2013. High-definition infrared spectroscopic imaging. Appl. Spectrosc. 67:93–105
    [Google Scholar]
  66. 66.
    Nasse MJ, Walsh MJ, Mattson EC, Reininger R, Kajdacsy-Balla A et al. 2011. High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams. Nat. Methods 8:413–16
    [Google Scholar]
  67. 67.
    Bhargava R. 2007. Towards a practical Fourier transform infrared chemical imaging protocol for cancer histopathology. Anal. Bioanal. Chem. 389:1155–69
    [Google Scholar]
  68. 68.
    Tiwari S, Kajdacsy-Balla A, Whiteley J, Cheng G, Jirstrom K et al. 2021. INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival. Sci. Adv. 7:eabb8292
    [Google Scholar]
  69. 69.
    Borondics F, Jossent M, Sandt C, Lavoute L, Gaponov D et al. 2018. Supercontinuum-based Fourier transform infrared spectromicroscopy. Optica 5:378–81
    [Google Scholar]
  70. 70.
    Kilgus J, Langer G, Duswald K, Zimmerleiter R, Zorin I et al. 2018. Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser. Opt. Express 26:30644–54
    [Google Scholar]
  71. 71.
    Goyal A, Myers T, Wang CA, Kelly M, Tyrrell B et al. 2014. Active hyperspectral imaging using a quantum cascade laser (QCL) array and digital-pixel focal plane array (DFPA) camera. Opt. Express 22:14392–401
    [Google Scholar]
  72. 72.
    Mittal S, Yeh K, Leslie LS, Kenkel S, Kajdacsy-Balla A, Bhargava R. 2018. Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology. PNAS 115:E5651–60
    [Google Scholar]
  73. 73.
    Schnell M, Mittal S, Falahkheirkhah K, Mittal A, Yeh K et al. 2020. All-digital histopathology by infrared-optical hybrid microscopy. PNAS 117:3388–96
    [Google Scholar]
  74. 74.
    Liu J-N, Schulmerich MV, Bhargava R, Cunningham BT. 2011. Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared spectroscopy. Opt. Express 19:24182–97
    [Google Scholar]
  75. 75.
    Kodali AK, Schulmerich M, Ip J, Yen G, Cunningham BT, Bhargava R. 2010. Narrowband midinfrared reflectance filters using guided mode resonance. Anal. Chem. 82:5697–706
    [Google Scholar]
  76. 76.
    Faist J, Capasso F, Sivco DL, Sirtori C, Hutchinson AL, Cho AY. 1994. Quantum cascade laser. Science 264:553–56
    [Google Scholar]
  77. 77.
    Hugi A, Maulini R, Faist J. 2010. External cavity quantum cascade laser. Semicond. Sci. Technol. 25:083001
    [Google Scholar]
  78. 78.
    Vodopyanov KL, Schunemann PG. 2003. Broadly tunable noncritically phase-matched ZnGeP2 optical parametric oscillator with a 2-μJ pump threshold. Opt. Lett. 28:441–43
    [Google Scholar]
  79. 79.
    Kole MR, Reddy RK, Schulmerich MV, Gelber MK, Bhargava R. 2012. Discrete frequency infrared microspectroscopy and imaging with a tunable quantum cascade laser. Anal. Chem. 84:10366–72
    [Google Scholar]
  80. 80.
    Yeh K, Kenkel S, Liu J-N, Bhargava R. 2015. Fast infrared chemical imaging with a quantum cascade laser. Anal. Chem. 87:485–93
    [Google Scholar]
  81. 81.
    Kröger N, Egl A, Engel M, Gretz N, Haase K et al. 2014. Quantum cascade laser–based hyperspectral imaging of biological tissue. J. Biomed. Opt. 19:111607
    [Google Scholar]
  82. 82.
    Bird B, Rowlette J. 2017. High definition infrared chemical imaging of colorectal tissue using a Spero QCL microscope. Analyst 142:1381–86
    [Google Scholar]
  83. 83.
    Pilling MJ, Henderson A, Gardner P. 2017. Quantum cascade laser spectral histopathology: breast cancer diagnostics using high throughput chemical imaging. Anal. Chem. 89:7348–55
    [Google Scholar]
  84. 84.
    Kuepper C, Kallenbach-Thieltges A, Juette H, Tannapfel A, Großerueschkamp F, Gerwert K. 2018. Quantum cascade laser-based infrared microscopy for label-free and automated cancer classification in tissue sections. Sci. Rep. 8:7717
    [Google Scholar]
  85. 85.
    Kröger-Lui N, Gretz N, Haase K, Kränzlin B, Neudecker S et al. 2015. Rapid identification of goblet cells in unstained colon thin sections by means of quantum cascade laser-based infrared microspectroscopy. Analyst 140:2086–92
    [Google Scholar]
  86. 86.
    Yoon Y, Breshike CJ, Kendziora CA, Furstenberg R, McGill RA. 2019. Reduction of speckle noise and mitigation of beam wander in tunable external cavity quantum cascade lasers using rotating diamond/KBr pellet coupled with multimode fiber. Opt. Express 27:8011–20
    [Google Scholar]
  87. 87.
    Schönhals A, Kröger-Lui N, Pucci A, Petrich W. 2018. On the role of interference in laser-based mid-infrared widefield microspectroscopy. J. Biophoton. 11:e201800015
    [Google Scholar]
  88. 88.
    Ran S, Berisha S, Mankar R, Shih W-C, Mayerich D. 2018. Mitigating fringing in discrete frequency infrared imaging using time-delayed integration. Biomed. Opt. Express 9:832–43
    [Google Scholar]
  89. 89.
    Tiwari S, Raman J, Reddy V, Ghetler A, Tella RP et al. 2016. Towards translation of discrete frequency infrared spectroscopic imaging for digital histopathology of clinical biopsy samples. Anal. Chem. 88:10183–90
    [Google Scholar]
  90. 90.
    Liberda D, Hermes M, Koziol P, Stone N, Wrobel TP. 2020. Translation of an esophagus histopathological FT-IR imaging model to a fast quantum cascade laser modality. J. Biophoton. 13:e202000122
    [Google Scholar]
  91. 91.
    Phal Y, Yeh K, Bhargava R. 2021. Design considerations for discrete frequency infrared microscopy systems. Appl. Spectrosc. 75:1067–92
    [Google Scholar]
  92. 92.
    Dazzi A, Prater CB. 2017. AFM-IR: technology and applications in nanoscale infrared spectroscopy and chemical imaging. Chem. Rev. 117:5146–73
    [Google Scholar]
  93. 93.
    Schwartz JJ, Jakob DS, Centrone A. 2022. A guide to nanoscale IR spectroscopy: resonance enhanced transduction in contact and tapping mode AFM-IR. Chem. Soc. Rev. 51:5248–67
    [Google Scholar]
  94. 94.
    Perez-Guaita D, Kochan K, Batty M, Doerig C, Garcia-Bustos J et al. 2018. Multispectral atomic force microscopy-infrared nano-imaging of malaria infected red blood cells. Anal. Chem. 90:3140–48
    [Google Scholar]
  95. 95.
    Kenkel S, Mittal A, Mittal S, Bhargava R. 2018. Probe–sample interaction-independent atomic force microscopy–infrared spectroscopy: toward robust nanoscale compositional mapping. Anal. Chem. 90:8845–55
    [Google Scholar]
  96. 96.
    Kenkel S, Mittal S, Bhargava R. 2020. Closed-loop atomic force microscopy-infrared spectroscopic imaging for nanoscale molecular characterization. Nat. Commun. 11:3225
    [Google Scholar]
  97. 97.
    Wang H, Xie Q, Xu XG. 2022. Super-resolution mid-infrared spectro-microscopy of biological applications through tapping mode and peak force tapping mode atomic force microscope. Adv. Drug Deliv. Rev. 180:114080
    [Google Scholar]
  98. 98.
    Bai Y, Yin J, Cheng J-X. 2021. Bond-selective imaging by optically sensing the mid-infrared photothermal effect. Sci. Adv. 7:eabg1559
    [Google Scholar]
  99. 99.
    Pavlovetc IM, Aleshire K, Hartland GV, Kuno M. 2020. Approaches to mid-infrared, super-resolution imaging and spectroscopy. Phys. Chem. Chem. Phys. 22:4313–25
    [Google Scholar]
  100. 100.
    Zhang D, Li C, Zhang C, Slipchenko MN, Eakins G, Cheng J-X. 2016. Depth-resolved mid-infrared photothermal imaging of living cells and organisms with submicrometer spatial resolution. Sci. Adv. 2:e1600521
    [Google Scholar]
  101. 101.
    Li Z, Aleshire K, Kuno M, Hartland GV. 2017. Super-resolution far-field infrared imaging by photothermal heterodyne imaging. J. Phys. Chem. B 121:8838–46
    [Google Scholar]
  102. 102.
    Zhang S, Kniazev K, Pavlovetc IM, Zhang S, Stevenson RL, Kuno M. 2021. Deep image restoration for infrared photothermal heterodyne imaging. J. Chem. Phys. 155:214202
    [Google Scholar]
  103. 103.
    Furstenberg R, Kendziora CA, Papantonakis MR, Nguyen V, McGill R. 2012. Chemical imaging using infrared photothermal microspectroscopy. Proc. SPIE 8374:837411
    [Google Scholar]
  104. 104.
    Tamamitsu M, Toda K, Shimada H, Honda T, Takarada M et al. 2020. Label-free biochemical quantitative phase imaging with mid-infrared photothermal effect. Optica 7:359–66
    [Google Scholar]
  105. 105.
    Bai Y, Zhang D, Lan L, Huang Y, Maize K et al. 2019. Ultrafast chemical imaging by widefield photothermal sensing of infrared absorption. Sci. Adv. 5:eaav7127
    [Google Scholar]
  106. 106.
    Mankar R, Gajjela CC, Bueso-Ramos CE, Yin CC, Mayerich D, Reddy RK. 2022. Polarization sensitive photothermal mid-infrared spectroscopic imaging of human bone marrow tissue. Appl. Spectrosc. 76:508–18
    [Google Scholar]
  107. 107.
    Kato R, Yano T-A, Minamikawa T, Tanaka T. 2022. High-sensitivity hyperspectral vibrational imaging of heart tissues by mid-infrared photothermal microscopy. Anal. Sci. 38:1497–503
    [Google Scholar]
  108. 108.
    Shi J, Wong TT, He Y, Li L, Zhang R et al. 2019. High-resolution, high-contrast mid-infrared imaging of fresh biological samples with ultraviolet-localized photoacoustic microscopy. Nat. Photon. 13:609–15
    [Google Scholar]
  109. 109.
    Phal Y, Yeh K, Bhargava R. 2020. Concurrent vibrational circular dichroism measurements with infrared spectroscopic imaging. Anal. Chem. 93:1294–303
    [Google Scholar]
  110. 110.
    Sato H, Shimizu M, Watanabe K, Yoshida J, Kawamura I, Koshoubu J. 2021. Vibrational circular dichroism system equipped with quantum cascade laser for microscopic scanning. Chem. Lett. 50:1543–45
    [Google Scholar]
  111. 111.
    Wiens R, Findlay CR, Baldwin SG, Kreplak L, Lee JM et al. 2016. High spatial resolution (1.1 μm and 20 nm) FTIR polarization contrast imaging reveals pre-rupture disorder in damaged tendon. Faraday Discuss 187:555–73
    [Google Scholar]
  112. 112.
    Kosowska K, Koziol P, Liberda D, Wrobel TP. 2021. Spatially resolved macromolecular orientation in biological tissues using FT-IR imaging. Clin. Spectrosc. 3:100013
    [Google Scholar]
  113. 113.
    Wrobel TP, Mukherjee P, Bhargava R. 2017. Rapid visualization of macromolecular orientation by discrete frequency mid-infrared spectroscopic imaging. Analyst 142:75–79
    [Google Scholar]
  114. 114.
    Koziol P, Kosowska K, Liberda D, Borondics F, Wrobel TP. 2022. Super-resolved 3D mapping of molecular orientation using vibrational techniques. J. Am. Chem. Soc. 144:14278–87
    [Google Scholar]
  115. 115.
    Dam JS, Tidemand-Lichtenberg P, Pedersen C. 2012. Room-temperature mid-infrared single-photon spectral imaging. Nat. Photon. 6:788–93
    [Google Scholar]
  116. 116.
    Fishman DA, Cirloganu CM, Webster S, Padilha LA, Monroe M et al. 2011. Sensitive mid-infrared detection in wide-bandgap semiconductors using extreme non-degenerate two-photon absorption. Nat. Photon. 5:561–65
    [Google Scholar]
  117. 117.
    Potma EO, Knez D, Ettenberg M, Wizeman M, Nguyen H et al. 2021. High-speed 2D and 3D mid-IR imaging with an InGaAs camera. APL Photon 6:096108
    [Google Scholar]
  118. 118.
    Hanninen AM, Prince RC, Ramos R, Plikus MV, Potma EO. 2018. High-resolution infrared imaging of biological samples with third-order sum-frequency generation microscopy. Biomed. Opt. Express 9:4807–17
    [Google Scholar]
  119. 119.
    Mayerich D, Walsh MJ, Kadjacsy-Balla A, Ray PS, Hewitt SM, Bhargava R. 2015. Stain-less staining for computed histopathology. Technology 3:27–31
    [Google Scholar]
  120. 120.
    Zimmermann E, Mukherjee SS, Falahkheirkhah K, Gryka MC, Kajdacsy-Balla A et al. 2021. Detection and quantification of myocardial fibrosis using stain-free infrared spectroscopic imaging. Arch. Pathol. Lab. Med. 145:1526–35
    [Google Scholar]
  121. 121.
    Goertzen N, Pappesch R, Fassunke J, Bruning T, Ko YD et al. 2021. Quantum cascade laser-based infrared imaging as a label-free and automated approach to determine mutations in lung adenocarcinoma. Am. J. Pathol. 191:1269–80
    [Google Scholar]
  122. 122.
    Shi L, Liu X, Shi L, Stinson HT, Rowlette J et al. 2020. Mid-infrared metabolic imaging with vibrational probes. Nat. Methods 17:844–51
    [Google Scholar]
  123. 123.
    Tai F, Koike K, Kawagoe H, Ando J, Kumamoto Y et al. 2021. Detecting nitrile-containing small molecules by infrared photothermal microscopy. Analyst 146:2307–12
    [Google Scholar]
  124. 124.
    Zhang Y, Zong H, Zong C, Tan Y, Zhang M et al. 2021. Fluorescence-detected mid-infrared photothermal microscopy. J. Am. Chem. Soc. 143:11490–99
    [Google Scholar]
  125. 125.
    Razumtcev A, Li M, Rong J, Teng CC, Pfluegl C et al. 2022. Label-free autofluorescence-detected mid-infrared photothermal microscopy of pharmaceutical materials. Anal. Chem. 94:6512–20
    [Google Scholar]
  126. 126.
    Kwak JT, Reddy R, Sinha S, Bhargava R. 2012. Analysis of variance in spectroscopic imaging data from human tissues. Anal. Chem. 84:1063–69
    [Google Scholar]
  127. 127.
    Byrne HJ, Knief P, Keating ME, Bonnier F. 2016. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. Chem. Soc. Rev. 45:1865–78
    [Google Scholar]
  128. 128.
    Beleites C, Neugebauer U, Bocklitz T, Krafft C, Popp J. 2013. Sample size planning for classification models. Anal. Chim. Acta 760:25–33
    [Google Scholar]
  129. 129.
    Falahkheirkhah K, Yeh K, Mittal S, Pfister L, Bhargava R. 2021. Deep learning-based protocols to enhance infrared imaging systems. Chemom. Intell. Lab. Syst. 217:104390
    [Google Scholar]
  130. 130.
    Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning Cambridge, MA: MIT Press
  131. 131.
    He H, Yan S, Lyu D, Xu M, Ye R et al. 2021. Deep learning for biospectroscopy and biospectral imaging: state-of-the-art and perspectives. Anal. Chem. 93:83653–65
    [Google Scholar]
  132. 132.
    Phal Y, Pfister L, Carney PS, Bhargava R. 2022. Resolution limit in infrared chemical imaging. J. Phys. Chem. C 126:9777–83
    [Google Scholar]
  133. 133.
    Falahkheirkhah K, Tiwari S, Yeh K, Gupta S, Herrera-Hernandez L et al. 2022. Deepfake histological images for enhancing digital pathology. arXiv:2206.08308 [eess.IV]
  134. 134.
    Mittal S, Kim J, Bhargava R. 2022. Statistical considerations and tools to improve histopathologic protocols with spectroscopic imaging. Appl. Spectrosc. 76:428–38
    [Google Scholar]
  135. 135.
    Mittal S, Bhargava R. 2019. A comparison of mid-infrared spectral regions on accuracy of tissue classification. Analyst 144:2635–42
    [Google Scholar]
  136. 136.
    Davis BJ, Carney PS, Bhargava R. 2010. Theory of mid-infrared absorption microspectroscopy: II. Heterogeneous samples. Anal. Chem. 82:3487–99
    [Google Scholar]
  137. 137.
    Mittal S, Wrobel TP, Walsh M, Kajdacsy-Balla A, Bhargava R. 2021. Breast cancer histopathology using infrared spectroscopic imaging: the impact of instrumental configurations. Clin. Spectrosc. 3:100006
    [Google Scholar]
  138. 138.
    Ergin A, Großerüschkamp F, Theisen O, Gerwert K, Remiszewski S et al. 2015. A method for the comparison of multi-platform spectral histopathology (SHP) data sets. Analyst 140:2465–72
    [Google Scholar]
  139. 139.
    Ogunleke A, Bobroff V, Chen H-H, Rowlette J, Delugin M et al. 2017. Fourier-transform versus quantum-cascade-laser infrared microscopes for histo-pathology: from lab to hospital?. Trends Anal. Chem. 89:190–96
    [Google Scholar]
  140. 140.
    Leslie LS, Wrobel TP, Mayerich D, Bindra S, Emmadi R, Bhargava R. 2015. High definition infrared spectroscopic imaging for lymph node histopathology. PLOS ONE 10:e0127238
    [Google Scholar]
  141. 141.
    Sandt C, Nadaradjane C, Richards R, Dumas P, Sée V. 2016. Use of infrared microspectroscopy to elucidate a specific chemical signature associated with hypoxia levels found in glioblastoma. Analyst 141:870–83
    [Google Scholar]
  142. 142.
    Le Naour F, Bralet M-P, Debois D, Sandt C, Guettier C et al. 2009. Chemical imaging on liver steatosis using synchrotron infrared and ToF-SIMS microspectroscopies. PLOS ONE 4:e7408
    [Google Scholar]
  143. 143.
    Nazeer SS, Sreedhar H, Varma VK, Martinez-Marin D, Massie C, Walsh MJ. 2017. Infrared spectroscopic imaging: label-free biochemical analysis of stroma and tissue fibrosis. Int. J. Biochem. Cell Biol. 92:14–17
    [Google Scholar]
  144. 144.
    Liu K-Z, Man A, Shaw RA, Liang B, Xu Z, Gong Y 2006. Molecular determination of liver fibrosis by synchrotron infrared microspectroscopy. Biochim. Biophys. Acta Biomembr. 1758:960–67
    [Google Scholar]
  145. 145.
    Sreedhar H, Varma VK, Gambacorta FV, Guzman G, Walsh MJ. 2016. Infrared spectroscopic imaging detects chemical modifications in liver fibrosis due to diabetes and disease. Biomed. Opt. Express 7:2419–24
    [Google Scholar]
  146. 146.
    Kallenbach-Thieltges A, Großerüschkamp F, Mosig A, Diem M, Tannapfel A, Gerwert K. 2013. Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. J. Biophoton. 6:88–100
    [Google Scholar]
  147. 147.
    Bird B, Miljković M, Remiszewski S, Akalin A, Kon M, Diem M. 2012. Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer. Lab. Investig. 92:1358–73
    [Google Scholar]
  148. 148.
    Gazi E, Baker M, Dwyer J, Lockyer NP, Gardner P et al. 2006. A correlation of FTIR spectra derived from prostate cancer biopsies with Gleason grade and tumour stage. Eur. Urol. 50:750–61
    [Google Scholar]
  149. 149.
    Kwak JT, Kajdacsy-Balla A, Macias V, Walsh M, Sinha S, Bhargava R. 2015. Improving prediction of prostate cancer recurrence using chemical imaging. Sci. Rep. 5:8758
    [Google Scholar]
  150. 150.
    Hanna MG, Reuter VE, Ardon O, Kim D, Sirintrapun SJ et al. 2020. Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod. Pathol. 33:2115–27
    [Google Scholar]
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