title: Hyperspectral cytometry at the single-cell level using a 32-channel photodetector.
abstract: Despite recent progress in cell-analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate-up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8-15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well-separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next-generation flow cytometer based on a 32-channel PMT array detector, a phase-volume holographic grating, and a fast electronic board. The system is capable of full-spectral data collection and spectral analysis at the single-cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 μs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data-collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.
authors.affiliation: Department of Basic Medical Sciences, School of Veterinary Medicine, Purdue University, West Lafayette, Indiana 47907, USA.
authors.forename: Gérald
authors.initials: G
authors.lastname: Grégori
authors.forename: Valery
authors.initials: V
authors.lastname: Patsekin
authors.forename: Bartek
authors.initials: B
authors.lastname: Rajwa
authors.forename: James
authors.initials: J
authors.lastname: Jones
authors.forename: Kathy
authors.initials: K
authors.lastname: Ragheb
authors.forename: Cheryl
authors.initials: C
authors.lastname: Holdman
authors.forename: J Paul
authors.initials: JP
authors.lastname: Robinson
mesh_terms: D000818:Animals; D000911:Antibodies, Monoclonal / Q000737:chemistry; D005434:Flow Cytometry / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D008214:Lymphocytes / Q000166:cytology / Q000378:metabolism; D051379:Mice; D059010:Single-Cell Analysis / Q000295:instrumentation* / Q000379:methods
chemical_list: D000911:Antibodies, Monoclonal