Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry

Abstract

Immune responses arise from a wide variety of cells expressing unique combinations of multiple cell-surface proteins. Detailed characterization is hampered, however, by limitations in available probes and instrumentation. Here, we use the unique spectral properties of semiconductor nanocrystals (quantum dots) to extend the capabilities of polychromatic flow cytometry to resolve 17 fluorescence emissions. We show the need for this power by analyzing, in detail, the phenotype of multiple antigen-specific T-cell populations, revealing variations within complex phenotypic patterns that would otherwise remain obscure. For example, T cells specific for distinct epitopes from one pathogen, and even those specific for the same epitope, can have markedly different phenotypes. The technology we describe, encompassing the detection of eight quantum dots in conjunction with conventional fluorophores, should expand the horizons of flow cytometry, as well as our ability to characterize the intricacies of both adaptive and innate cellular immune responses.

Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data

“…generated by these techniques is difficult to analyze and thus computer assistance and mathematical algorithms are absolutely necessary for data interpretation. This problem is enhanced in polychromatic flow cytometry because the analysis is performed on single cells; as a consequence, hundreds of subsets can be identified in a specific cell population and each of them can bear a different function (5,14,15) or play a specific role in the development of diseases (16)(17)(18)(19)(20). A general approach to the visualization of flow cytometric data is the utilization of bivariate plots.…”

A flow cytometer for the measurement of Raman spectra

“…To account for the broad background that underlies the Raman features, the intensity of adjacent but featureless regions of the spectra were subtracted to give new parameters that corresponded to the peak intensity distinguishing Raman spectral features. 21 ). The frequency histograms for these parameters were used to set markers and define gates that identified each of the SERS tags used in this study according to the logic outlined in Table 1.…”

“…Standard conditions were 15 mW laser power, 300 ls integration time, and 83 horizontal pixel binning unless otherwise noted. Insets: the integrated SERS intensity of the 563 cm 21 peak is plotted as a function of transit time (A), pixel binning (B), and laser power (C). Figure 7.…”

“…In many cases it is possible to identify a distinguishing spectral feature or features that allow a particular SERS tag to be identified. For example, Ag-Thionin has a strong peak at $450 cm 21 and a relatively weak peak at $1,620 cm 21 , whereas Ag-Rhodamine800 has a relatively weak peak at 450 cm 21 and a stronger peak at 1,620 cm 21 . Ag-Ox170 and Ag-NB are more similar, showing strong vibrations at $650 cm 21 and 1,620 cm 21 but with Nile Blue having a moderate intensity peak at 1,150 cm 21 that Ag-Ox170 lacks The presence and absence of these spectral features can be considered to comprise a spectral ''barcode'' for identifying specific SERS tags, a concept we sought to exploit using standard flow cytometry data analysis methods.…”

“…7). Because a broad (presumably fluorescent) signal underlies the spectral features, the signal from an adjacent, but featureless region of the spectrum was also calculated (e.g., 1,000 cm 21 and 1,685 cm 21 ) and subtracted as a background from each Raman feature. The resulting intensities were displayed as one parameter histograms (Fig.…”