Hyperspectral Cytometry

Hyperspectral Cytometry. Grégori, G., Rajwa, B., Patsekin, V., Jones, J., Furuki, M., Yamamoto, M., Robinson, & Paul, J. In Fienberg, H. G. & Nolan, G. P., editors, High-Dimensional Single Cell Analysis, of Current Topics in Microbiology and Immunology, pages 191--210. Springer Berlin Heidelberg, January, 2014.

Paper abstract bibtex

Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system—the Sony SP6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.

@incollection{ gregori_hyperspectral_2014,
  series = {Current Topics in Microbiology and Immunology},
  title = {Hyperspectral Cytometry},
  copyright = {©2014 Springer-Verlag Berlin Heidelberg},
  isbn = {978-3-642-54826-0, 978-3-642-54827-7},
  url = {http://link.springer.com/chapter/10.1007/82_2013_359},
  abstract = {Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system—the Sony {SP}6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.},
  language = {en},
  number = {377},
  urldate = {2014-07-21},
  booktitle = {High-Dimensional Single Cell Analysis},
  publisher = {Springer Berlin Heidelberg},
  author = {Grégori, Gérald and Rajwa, Bartek and Patsekin, Valery and Jones, James and Furuki, Motohiro and Yamamoto, Masanobu and Robinson, J. Paul},
  editor = {Fienberg, Harris G. and Nolan, Garry P.},
  month = {January},
  year = {2014},
  keywords = {Bioinformatics, Biological Techniques, Cancer Research, Immunology},
  pages = {191--210},
  file = {Snapshot:C\:\\Users\̊je\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\5wru9u0w.default\\zotero\\storage\\FA5AIKBP\\82_2013_359.html:text/html}
}

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