1932

Abstract

Vibrational spectroscopy has contributed to the understanding of biological materials for many years. As the technology has advanced, the technique has been brought to bear on the analysis of whole organisms. Here, we discuss advanced and recently developed infrared and Raman spectroscopic instrumentation to whole-organism analysis. We highlight many of the recent contributions made in this relatively new area of spectroscopy, particularly addressing organisms associated with disease with emphasis on diagnosis and treatment. The application of vibrational spectroscopic techniques to entire organisms is still in its infancy, but new developments in imaging and chemometric processing will likely expand in the field in the near future.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061318-115117
2019-06-12
2024-05-12
Loading full text...

Full text loading...

/deliver/fulltext/ac/12/1/annurev-anchem-061318-115117.html?itemId=/content/journals/10.1146/annurev-anchem-061318-115117&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Josien ML, Fuson N, Cary AS 1951. Infrared spectroscopy of compounds having biological interest. I. A comparative study of normal and i-steroids. J. Am. Chem. Soc. 73:4445–49
    [Google Scholar]
  2. 2.
    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]
  3. 3.
    Childs DTD, Hogg RA, Revin DG, Rehman IU, Cockburn JW, Matcher SJ 2015. Sensitivity advantage of QCL tunable-laser mid-infrared spectroscopy over FTIR spectroscopy. Appl. Spectrosc. Rev. 50:822–39
    [Google Scholar]
  4. 4.
    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]
  5. 5.
    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]
  6. 6.
    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]
  7. 7.
    Hirschmugl CJ, Gough KM. 2012. Fourier transform infrared spectrochemical imaging: review of design and applications with a focal plane array and multiple beam synchrotron radiation source. Appl. Spectrosc. 66:475–91
    [Google Scholar]
  8. 8.
    Tobin MJ, Vongsvivut J, Martin DE, Sizeland KH, Hackett MJ et al. 2018. Focal plane array IR imaging at the Australian Synchrotron. Infrared Phys. Technol. 94:85–90
    [Google Scholar]
  9. 9.
    Kozicki M, Creek DJ, Sexton A, Morahan BJ, Wesełucha-Birczyńska A, Wood BR 2015. An attenuated total reflection (ATR) and Raman spectroscopic investigation into the effects of chloroquine on Plasmodium falciparum-infected red blood cells. Analyst 140:2236–46
    [Google Scholar]
  10. 10.
    Roy S, Perez-Guaita D, Andrew DW, Richards JS, McNaughton D et al. 2017. Simultaneous ATR-FTIR based determination of malaria parasitemia, glucose and urea in whole blood dried onto a glass slide. Anal. Chem. 89:5238–45
    [Google Scholar]
  11. 11.
    Khoshmanesh A, Christensen D, Perez-Guaita D, Iturbe-Ormaetxe I, O'Neill SL et al. 2017. Screening of Wolbachia endosymbiont infection in Aedes aegypti mosquitoes using attenuated total reflection mid-infrared spectroscopy. Anal. Chem. 89:5285–93
    [Google Scholar]
  12. 12.
    Khoshmanesh A, Dixon MWA, Kenny S, Tilley L, McNaughton D, Wood BR 2014. Detection and quantification of early-stage malaria parasites in laboratory infected erythrocytes by attenuated total reflectance infrared spectroscopy and multivariate analysis. Anal. Chem. 86:4379–86
    [Google Scholar]
  13. 13.
    Dazzi A, Prater CB. 2016. AFM-IR: technology and applications in nanoscale infrared spectroscopy and chemical imaging. Chem. Rev. 117:5146–73
    [Google Scholar]
  14. 14.
    Pancani E, Mathurin J, Bilent S, Bernet-Camard MF, Dazzi A et al. 2018. High-resolution label-free detection of biocompatible polymeric nanoparticles in cells. Particle Particle Syst. Charact. 35:1700457
    [Google Scholar]
  15. 15.
    Slater AF, Swiggard WJ, Orton BR, Flitter WD, Goldberg DE et al. 1991. An iron-carboxylate bond links the heme units of malaria pigment. PNAS 88:2325–29
    [Google Scholar]
  16. 16.
    Webster GT, De Villiers KA, Egan TJ, Deed S, Tilley L et al. 2009. Discriminating the intraerythrocytic lifecycle stages of the malaria parasite using synchrotron FT-IR microspectroscopy and an artificial neural network. Anal. Chem. 81:2516–24
    [Google Scholar]
  17. 17.
    Perez-Guaita D, Andrew D, Heraud P, Beeson J, Anderson D et al. 2016. High resolution FTIR imaging provides automated discrimination and detection of single malaria parasite infected erythrocytes on glass. Faraday Disc 187:341–52
    [Google Scholar]
  18. 18.
    Martin M, Perez-Guaita D, Andrew DW, Richards JS, Wood BR, Heraud P 2017. The effect of common anticoagulants in detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy. Analyst 142:81192–99
    [Google Scholar]
  19. 19.
    Martin M, Pérez-Guaita D, Andrew DW, Richards JS, Wood BR, Heraud P 2018. Detection and quantification of Plasmodium falciparum in aqueous red blood cells by attenuated total reflection infrared spectroscopy and multivariate data analysis. J. Vis. Exp. 141:e56797
    [Google Scholar]
  20. 20.
    Le Ru EC, Blackie E, Meyer M, Etchegoin PG 2007. Surface enhanced Raman scattering enhancement factors: a comprehensive study. J. Phys. Chem. C 111:13794–803
    [Google Scholar]
  21. 21.
    Legesse FB, Rüger J, Meyer T, Krafft C, Schmitt M, Popp J 2018. Investigation of microalgal carotenoid content using coherent anti-Stokes Raman scattering (CARS) microscopy and spontaneous Raman spectroscopy. Chem. Phys. Chem. 19:1048–55
    [Google Scholar]
  22. 22.
    Karanja CW, Hong W, Younis W, Eldesouky HE, Seleem MN, Cheng JX 2017. Stimulated Raman imaging reveals aberrant lipogenesis as a metabolic marker for azole-resistant Candida albicans. Anal. Chem 89:9822–29
    [Google Scholar]
  23. 23.
    Segawa H, Okuno M, Kano H, Leproux P, Couderc V, Hamaguchi H-O 2012. Label-free tetra-modal molecular imaging of living cells with CARS, SHG, THG and TSFG (coherent anti-Stokes Raman scattering, second harmonic generation, third harmonic generation and third-order sum frequency generation). Opt. Express 20:9551–57
    [Google Scholar]
  24. 24.
    Rusciano G, Zito G, Pesce G, Sasso A 2017. Cell imaging by spontaneous and amplified Raman spectroscopies. J. Spectrosc. 2017.2193656
    [Google Scholar]
  25. 25.
    Jorio A, Mueller NS, Reich S 2017. Symmetry-derived selection rules for plasmon-enhanced Raman scattering. Phys. Rev. B 95:155409
    [Google Scholar]
  26. 26.
    Perez-Guaita D, Kochan K, Rüther A, Heraud P, Quintas G, Wood B 2017. Multimodal imaging of cells and tissues: all photons are welcome. Spectrosc. Eur. 29:6–9
    [Google Scholar]
  27. 27.
    Perez-Guaita D, Kochan K, Martin M, Andrew DW, Heraud P et al. 2017. Multimodal vibrational imaging of cells. Vib. Spectrosc. 91:46–58
    [Google Scholar]
  28. 28.
    Brémard C, Girerd JJ, Merlin JC, Moreau S 1992. Iron (III) porphyrin aggregates grafted on agarose gel as models of hemoglobin degradation products. J. Mol. Struct. 267:117–22
    [Google Scholar]
  29. 29.
    Ong CW, Shen ZX, Ang KKH, Kara UAK, Tang SH 1999. Resonance Raman microspectroscopy of normal erythrocytes and Plasmodium berghei-infected erythrocytes. Appl. Spectrosc. 53:1097–101
    [Google Scholar]
  30. 30.
    Ong CW, Shen ZX, Ang KKH, Kara UAK, Tang SH 2002. Raman microspectroscopy of normal erythrocytes and Plasmodium berghei-infected erythrocytes. Appl. Spectrosc. 56:1126–31
    [Google Scholar]
  31. 31.
    Wood BR, Langford SJ, Cooke BM, Glenister FK, Lim J, McNaughton D 2003. Raman imaging of hemozoin within the food vacuole of Plasmodium falciparum trophozoites. FEBS Lett 554:247–52
    [Google Scholar]
  32. 32.
    Frosch T, Koncarevic S, Zedler L, Schmitt M, Schenzel K et al. 2007. In situ localization and structural analysis of the malaria pigment hemozoin. J. Phys. Chem. B 111:11047–56
    [Google Scholar]
  33. 33.
    Bonifacio A, Finaurini S, Krafft C, Parapini S, Taramelli D, Sergo V 2008. Spatial distribution of heme species in erythrocytes infected with Plasmodium falciparum by use of resonance Raman imaging and multivariate analysis. Anal. Bioanal. Chem. 392:1277–82
    [Google Scholar]
  34. 34.
    Wood BR, Hermelink A, Lasch P, Bambery KR, Webster GT et al. 2009. Resonance Raman microscopy in combination with partial dark-field microscopy lights up a new path in malaria diagnostics. Analyst 134:1119–25
    [Google Scholar]
  35. 35.
    Hobro AJ, Konishi A, Coban C, Smith NI 2013. Raman spectroscopic analysis of malaria disease progression via blood and plasma samples. Analyst 138:3927–33
    [Google Scholar]
  36. 36.
    Kozicki M, Czepiel J, Biesiada G, Nowak P, Garlicki A, Wesełucha-Birczyńska A 2015. The ring-stage of Plasmodium falciparum observed in RBCs of hospitalized malaria patients. Analyst 140:8007–16
    [Google Scholar]
  37. 37.
    Frame L, Brewer J, Lee R, Faulds K, Graham D 2018. Development of a label-free Raman imaging technique for differentiation of malaria parasite infected from non-infected tissue. Analyst 143:157–63
    [Google Scholar]
  38. 38.
    Webster GT, Tilley L, Deed S, McNaughton D, Wood BR 2008. Resonance Raman spectroscopy can detect structural changes in haemozoin (malaria pigment) following incubation with chloroquine in infected erythrocytes. FEBS Lett 582:1087–92
    [Google Scholar]
  39. 39.
    Puskar L, Tuckermann R, Frosch T, Popp J, Ly V et al. 2007. Raman acoustic levitation spectroscopy of red blood cells and Plasmodium falciparum trophozoites. Lab Chip 7:1125–31
    [Google Scholar]
  40. 40.
    Brückner M, Becker K, Popp J, Frosch T 2015. Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells. Anal. Chim. Acta 894:76–84
    [Google Scholar]
  41. 41.
    Perez-Guaita D, Marzec KM, Hudson A, Evans C, Chernenko T et al. 2018. Parasites under the spotlight: applications of vibrational spectroscopy to malaria research. Chem. Rev. 118:5330–58
    [Google Scholar]
  42. 42.
    Kang JW, Lue N, Kong CR, Barman I, Dingari NC et al. 2011. Combined confocal Raman and quantitative phase microscopy system for biomedical diagnosis. Biomed. Opt. Express 2:2484–92
    [Google Scholar]
  43. 43.
    Chen F, Flaherty BR, Cohen CE, Peterson DS, Zhao Y 2016. Direct detection of malaria infected red blood cells by surface enhanced Raman spectroscopy. Nanomed. Nanotechnol. Biol. Med. 12:1445–51
    [Google Scholar]
  44. 44.
    Chen K, Xiong A, Yuen C, Preiser P, Liu Q 2016. Investigation of surface enhanced Raman spectroscopy for hemozoin detection in malaria diagnosis. Proc. SPIE 9715, Opt. Diagn. Sens., 97150F. https://doi.org/10.1117/12.2210804
    [Crossref] [Google Scholar]
  45. 45.
    Wood BR, Bailo E, Khiavi MA, Tilley L, Deed S et al. 2011. Tip-enhanced Raman scattering (TERS) from hemozoin crystals within a sectioned erythrocyte. Nano Lett 11:1868–73
    [Google Scholar]
  46. 46.
    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]
  47. 47.
    Amaral KB, Silva TP, Malta KK, Carmo LAS, Dias FF et al. 2016. Natural Schistosoma mansoni infection in the wild reservoir Nectomys squamipes leads to excessive lipid droplet accumulation in hepatocytes in the absence of liver functional impairment. PLOS ONE 11:e0166979
    [Google Scholar]
  48. 48.
    Toledo DAM, Roque NR, Teixeira L, Milán-Garcés EA, Carneiro AB et al. 2016. Lipid body organelles within the parasite Trypanosoma cruzi: a role for intracellular arachidonic acid metabolism. PLOS ONE 11:e0160433
    [Google Scholar]
  49. 49.
    Naemat A, Elsheikha HM, Al-Sandaqchi A, Kong K, Ghita A, Notingher I 2015. Analysis of interaction between the apicomplexan protozoan Toxoplasma gondii and host cells using label-free Raman spectroscopy. Analyst 140:756–64
    [Google Scholar]
  50. 50.
    Naemat A, Elsheikha HM, Boitor RA, Notingher I 2016. Tracing amino acid exchange during host-pathogen interaction by combined stable-isotope time-resolved Raman spectral imaging. Sci. Rep. 6:20811
    [Google Scholar]
  51. 51.
    Stewart S, McClelland L, Maier J 2005. A fast method for detecting Cryptosporidium parvum oocysts in real world samples. Proc. SPIE 5692 Adv. Biomed. Clin. Diagn. Syst. III
    [Google Scholar]
  52. 52.
    Grow AE, Wood LL, Claycomb JL, Thompson PA 2003. New biochip technology for label-free detection of pathogens and their toxins. J. Microbiol. Methods 53:221–33
    [Google Scholar]
  53. 53.
    Murugkar S, Evans CL, Xie XS, Anis H 2009. Chemically specific imaging of cryptosporidium oocysts using coherent anti-Stokes Raman scattering (CARS) microscopy. J. Microsc. 233:244–50
    [Google Scholar]
  54. 54.
    Rule K, Vikesland PJ. 2009. Surface-enhanced resonance Raman spectroscopy for the rapid detection of Cryptosporidium parvum and Giardia lamblia. Environ. Sci. Technol 43:1147–52
    [Google Scholar]
  55. 55.
    Lau K, Hobro A, Smith T, Thurston T, Lendl B 2012. Label-free non-destructive in situ biochemical analysis of nematode Steinernema kraussei using FPA-FTIR and Raman spectroscopic imaging. Vib. Spectrosc. 60:34–42
    [Google Scholar]
  56. 56.
    Hobro AJ, Lendl B. 2011. Fourier-transform mid-infrared FPA imaging of a complex multicellular nematode. Vib. Spectrosc. 57:213–19
    [Google Scholar]
  57. 57.
    Engelkirk PG, Duben-Engelkirk JL, Burton GRW 2011. Burton's Microbiology for the Health Sciences Philadelphia: Lippincott Williams & Wilkins
  58. 58.
    Schalk R, Geoerg D, Staubach J, Raedle M, Methner FJ, Beuermann T 2017. Evaluation of a newly developed mid-infrared sensor for real-time monitoring of yeast fermentations. J. Biosci. Bioeng. 123:651–57
    [Google Scholar]
  59. 59.
    Kochan K, Peng H, Wood BR, Haritos VS 2018. Single cell assessment of yeast metabolic engineering for enhanced lipid production using Raman and AFM-IR imaging. Biotechnol. Biofuels 11:106
    [Google Scholar]
  60. 60.
    Canal C, Ozen B. 2017. Monitoring of wine process and prediction of its parameters with mid-infrared spectroscopy. J. Food Process Eng. 40:e12280
    [Google Scholar]
  61. 61.
    Mihoubi W, Sahli E, Gargouri A, Amiel C 2017. FTIR spectroscopy of whole cells for the monitoring of yeast apoptosis mediated by p53 over-expression and its suppression by Nigella sativa extracts. PLOS ONE 12:e0180680
    [Google Scholar]
  62. 62.
    Kosa G, Kohler A, Tafintseva V, Zimmermann B, Forfang K et al. 2017. Microtiter plate cultivation of oleaginous fungi and monitoring of lipogenesis by high-throughput FTIR spectroscopy. Microb. Cell Factor. 16:101
    [Google Scholar]
  63. 63.
    Arici M, Ozulku G, Yildirim RM, Sagdic O, Durak MZ 2018. Biodiversity and technological properties of yeasts from Turkish sourdough. Food Sci. Biotechnol. 27:499–508
    [Google Scholar]
  64. 64.
    Kogkaki EA, Sofoulis M, Natskoulis P, Tarantilis PA, Pappas CS, Panagou EZ 2017. Differentiation and identification of grape-associated black aspergilli using Fourier transform infrared (FT-IR) spectroscopic analysis of mycelia. Int. J. Food Microbiol. 259:22–28
    [Google Scholar]
  65. 65.
    Pereyra C, Gil S, Cristofolini A, Bonci M, Makita M et al. 2018. The production of yeast cell wall using an agroindustrial waste influences the wall thickness and is implicated on the aflatoxin B1 adsorption process. Food Res. Int. 111:306–13
    [Google Scholar]
  66. 66.
    Nguyen TD, Guyot S, Pénicaud C, Passot S, Sandt C et al. 2017. Understanding the responses of Saccharomyces cerevisiae yeast strain during dehydration processes using synchrotron infrared spectroscopy. Analyst 142:3620–28
    [Google Scholar]
  67. 67.
    Silge A, Heinke R, Bocklitz T, Wiegand C, Hipler UC et al. 2018. The application of UV resonance Raman spectroscopy for the differentiation of clinically relevant Candida species. Anal. Bioanal. Chem. 410:5839–47
    [Google Scholar]
  68. 68.
    Montanari LB, Sartori FG, Ribeiro DBM, Leandro LF, Pires RH et al. 2018. Yeast isolation and identification in water used in a Brazilian hemodialysis unit by classic microbiological techniques and Raman spectroscopy. J. Water Health 16:311–20
    [Google Scholar]
  69. 69.
    Ibelings MS, Maquelin K, Endtz HP, Bruining HA, Puppels GJ 2005. Rapid identification of Candida spp. in peritonitis patients by Raman spectroscopy. Clin. Microbiol. Infect. 11:353–58
    [Google Scholar]
  70. 70.
    Voss JP, Mittelheuser NE, Lemke R, Luttmann R 2017. Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods. Eng. Life Sci. 17:1281–94
    [Google Scholar]
  71. 71.
    Schalk R, Braun F, Frank R, Rädle M, Gretz N et al. 2017. Non-contact Raman spectroscopy for in-line monitoring of glucose and ethanol during yeast fermentations. Bioprocess Biosyst. Eng. 40:1519–27
    [Google Scholar]
  72. 72.
    Noothalapati H, Sasaki T, Kaino T, Kawamukai M, Ando M et al. 2016. Label-free chemical imaging of fungal spore walls by Raman microscopy and multivariate curve resolution analysis. Sci. Rep. 6:27789
    [Google Scholar]
  73. 73.
    Colabella C, Corte L, Roscini L, Shapaval V, Kohler A et al. 2017. Merging FT-IR and NGS for simultaneous phenotypic and genotypic identification of pathogenic Candida species. PLOS ONE 12:e0188104
    [Google Scholar]
  74. 74.
    El-Sayed ST, Ali AM, El-Sayed ESM, Shousha WG, Omar NI 2017. Characterization and potential antimicrobial effect of novel chitooligosaccharides against pathogenic microorganisms. J. Appl. Pharm. Sci. 7:006–012
    [Google Scholar]
  75. 75.
    Roscini L, Vassiliou A, Corte L, Pierantoni DC, Robert V et al. 2018. Yeast biofilm as a bridge between medical and environmental microbiology across different detection techniques. Infect. Dis. Ther. 7:27–34
    [Google Scholar]
  76. 76.
    Pacia MZ, Pukalski J, Turnau K, Baranska M, Kaczor A 2016. Lipids, hemoproteins and carotenoids in alive Rhodotorula mucilaginosa cells under pesticide decomposition–Raman imaging study. Chemosphere 164:1–6
    [Google Scholar]
  77. 77.
    Farías-Álvarez L, Gschaedler-Mathis A, Sánchez-Ortiz A, Femat R, Cervantes-Martínez J et al. 2018. Xanthophyllomyces dendrorhous physiological stages determination using combined measurements from dielectric and Raman spectroscopies, a cell counter system and fluorescence flow cytometry. Biochem. Eng. J. 136:1–8
    [Google Scholar]
  78. 78.
    Uusitalo S, Popov A, Ryabchikov YV, Bibikova O, Alakomi H-L et al. 2017. Surface-enhanced Raman spectroscopy for identification and discrimination of beverage spoilage yeasts using patterned substrates and gold nanoparticles. J. Food Eng. 212:47–54
    [Google Scholar]
  79. 79.
    Sujith A, Itoh T, Abe H, Yoshida K, Kiran MS et al. 2009. Imaging the cell wall of living single yeast cells using surface-enhanced Raman spectroscopy. Anal. Bioanal. Chem. 394:1803–9
    [Google Scholar]
  80. 80.
    Culha M, Kahraman M, Çam D, Sayın I, Keseroǧlu K 2010. Rapid identification of bacteria and yeast using surface‐enhanced Raman scattering. Surface Interface Anal 42:462–65
    [Google Scholar]
  81. 81.
    Kitahama Y, Hayashi H, Itoh T, Ozaki Y 2017. Measurement of pH-dependent surface-enhanced hyper-Raman scattering at desired positions on yeast cells via optical trapping. Analyst 142:3967–74
    [Google Scholar]
  82. 82.
    Brackmann C, Norbeck J, Åkeson M, Bosch D, Larsson C et al. 2009. CARS microscopy of lipid stores in yeast: the impact of nutritional state and genetic background. J. Raman Spectrosc. 40:748–56
    [Google Scholar]
  83. 83.
    van Zutphen T, Todde V, de Boer R, Kreim M, Hofbauer HF et al. 2014. Lipid droplet autophagy in the yeast Saccharomyces cerevisiae. Mol. Biol. Cell 25:290–301
    [Google Scholar]
  84. 84.
    Radulovic M, Knittelfelder O, Cristobal-Sarramian A, Kolb D, Wolinski H, Kohlwein SD 2013. The emergence of lipid droplets in yeast: current status and experimental approaches. Curr. Genet. 59:231–42
    [Google Scholar]
  85. 85.
    Wolinski H, Bredies K, Kohlwein SD 2012. Quantitative imaging of lipid metabolism in yeast: from 4D analysis to high content screens of mutant libraries. Methods Cell Biol 108:345–65
    [Google Scholar]
  86. 86.
    Wolinski H, Hofbauer HF, Hellauer K, Cristobal-Sarramian A, Kolb D et al. 2015. Seipin is involved in the regulation of phosphatidic acid metabolism at a subdomain of the nuclear envelope in yeast. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1851:1450–64
    [Google Scholar]
  87. 87.
    Furuta R, Kurake N, Ishikawa K, Takeda K, Hashizume H et al. 2017. Intracellular-molecular changes in plasma-irradiated budding yeast cells studied using multiplex coherent anti-Stokes Raman scattering microscopy. Phys. Chem. Chem. Phys. 19:13438–42
    [Google Scholar]
  88. 88.
    Garrett NL, Singh B, Jones A, Moger J 2017. Imaging microscopic distribution of antifungal agents in dandruff treatments with stimulated Raman scattering microscopy. J. Biomed. Opt. 22:66003
    [Google Scholar]
  89. 89.
    Lin H, Liao C-S, Wang P, Kong N, Cheng J-X 2018. Spectroscopic stimulated Raman scattering imaging of highly dynamic specimens through matrix completion. Light Sci. Appl. 7:17179
    [Google Scholar]
  90. 90.
    Niu C, Yuan Y, Guo H, Wang X, Wang X, Yue T 2018. Recognition of osmotolerant yeast spoilage in kiwi juices by near-infrared spectroscopy coupled with chemometrics and wavelength selection. RSC Adv 8:222–29
    [Google Scholar]
  91. 91.
    Li J, Sang H, Guo H, Popko JT, He L et al. 2017. Antifungal mechanisms of ZnO and Ag nanoparticles to Sclerotinia homoeocarpa. Nanotechnology 28:155101
    [Google Scholar]
  92. 92.
    Da Silva JC, Queiroz A, Oliveira A, Kartnaller V 2017. Advances in the application of spectroscopic techniques in the biofuel area over the last few decades. Frontiers in Bioenergy and Biofuels E Jacob-Lopez London: InTech https://.doi.org/10.5772/65552
    [Crossref] [Google Scholar]
  93. 93.
    Kochan K, Peng H, Gwee E, Izgorodina E, Haritos V, Wood BR 2019. Raman spectroscopy as a tool for tracking cyclopropane fatty acids in genetically engineered Saccharomyces cerevisiae. Analyst 144:901–12
    [Google Scholar]
  94. 94.
    Aguiar JC, Mittmann J, Ferreira I, Ferreira-Strixino J, Raniero L 2015. Differentiation of Leishmania species by FT-IR spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc. 142:80–85
    [Google Scholar]
  95. 95.
    Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW et al. 2013. The global distribution and burden of dengue. Nature 496:504–7
    [Google Scholar]
  96. 96.
    Aliota MT, Peinado SA, Velez ID, Osorio JE 2016. The wMel strain of Wolbachia reduces transmission of Zika virus by Aedes aegypti. Sci. Rep 6:28792
    [Google Scholar]
  97. 97.
    Aliota MT, Walker EC, Uribe Yepes A, Dario Velez I, Christensen BM, Osorio JE 2016. The wMel strain of Wolbachia reduces transmission of chikungunya virus in Aedes aegypti.PLOS Negl. Trop. Dis 10:e0004677
    [Google Scholar]
  98. 98.
    Gautam R, Vanga S, Madan A, Gayathri N, Nongthomba U, Umapathy S 2015. Raman spectroscopic studies on screening of myopathies. Anal. Chem. 87:2187–94
    [Google Scholar]
  99. 99.
    Bernardi RC, Firmino ELB, Pereira MC, Andrade LHC, Cardoso CAL et al. 2014. Fourier transform infrared photoacoustic spectroscopy as a potential tool in assessing the role of diet in cuticular chemical composition of Ectatomma brunneum. Genet. Mol. Res 13:10035–48
    [Google Scholar]
  100. 100.
    Rosencwaig A. 1980. Photoacoustics and Photoacoustic Spectroscopy New York: John Wiley & Sons
  101. 101.
    Moore TS, Moody AS, Payne TD, Sarabia GM, Daniel AR, Sharma B 2018. In vitro and in vivo SERS biosensing for disease diagnosis. Biosensors 8:46
    [Google Scholar]
  102. 102.
    Lasch P, Stämmler M, Zhang M, Baranska M, Bosch A, Majzner K 2018. FT-IR hyperspectral imaging and artificial neural network analysis for identification of pathogenic bacteria. Anal. Chem. 90:8896–904
    [Google Scholar]
  103. 103.
    Wiercigroch E, Szafraniec E, Czamara K, Pacia MZ, Majzner K et al. 2017. Raman and infrared spectroscopy of carbohydrates: a review. Spectrochim. Acta A Mol. Biomol. Spectrosc. 185:317–35
    [Google Scholar]
  104. 104.
    Czamara K, Majzner K, Pacia MZ, Kochan K, Kaczor A, Baranska M 2014. Raman spectroscopy of lipids: a review. J. Raman Spectrosc. 46:4–20
    [Google Scholar]
  105. 105.
    Rygula A, Majzner K, Marzec KM, Kaczor A, Pilarczyk M, Baranska M 2013. Raman spectroscopy of proteins: a review. J. Raman Spectrosc. 44:1061–76
    [Google Scholar]
  106. 106.
    Garcia-Rico E, Alvarez-Puebla RA, Guerrini L 2018. Direct surface-enhanced Raman scattering (SERS) spectroscopy of nucleic acids: from fundamental studies to real-life applications. Chem. Soc. Rev. 47:4909–23
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-061318-115117
Loading
/content/journals/10.1146/annurev-anchem-061318-115117
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error