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WIREs Nanomed Nanobiotechnol
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The use of quantum dot nanocrystals in multicolor flow cytometry

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Abstract Because of their unique fluorescence properties, quantum dots (QDs) represent a promising new technology in the realm of multicolor flow cytometry. Although commercial reagents and applications for the technology are still in the early phases of their development, the strategies and considerations necessary for successful use are becoming known. This article discusses the value of QDs in multicolor flow cytometry, introduces strategies to successfully incorporate QDs into routine use, and highlights emerging applications of the technology. WIREs Nanomed Nanobiotechnol 2010 2 334–348 This article is categorized under: Diagnostic Tools > In Vitro Nanoparticle-Based Sensing Nanotechnology Approaches to Biology > Nanoscale Systems in Biology

13‐color flow cytometry reveals the heterogeneity of naïve and memory T‐cells. Peripheral blood mononuclear cells (PBMC) were stained with a panel of reagents that included antibodies against the T‐cell differentiation antigens CD45RO, CCR7, CD27, CD127, CCR5, CD28, and CD57. For this panel, CD45RO and CD57 were conjugated to QD545 and QD585, respectively. (a) The patterns of expression for these seven markers, within the CD4+ T‐lymphocytes of a representative subject, are depicted. (b) The frequency of each subset is shown, within cell populations that are broadly defined as naïve, central memory, other memory, and effector by the markers CD45RO and CCR7. Each bar represents a distinct subset of cells, and the most frequent cell subset within each group is listed below the chart. Many distinct subsets are detectable within each CD45RO/CCR7 cell population, as indicated by the numbers in parentheses at the top of the panel. For example, in the naïve (CD45RO− CCR7+) population, 22 subsets, defined by various combinations of CD27, CD127, CCR5, CD28, and CD57, exist. However, the predominant population is CD45RO− CCR7+ CD27+ CD127+ CCR5− CD28+ CD57− (approximately 35% of CD4+ T‐cells).

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Multiplexed analysis of antigen‐specific CD8+ T‐cells from an HIV‐infected individual. The expression of various phenotypic markers within total CD8+ T‐cells is shown in the top row; the left column shows the staining and frequency of each pMHCI–QD‐defined population. Subsequent rows compare the expression of various phenotypic markers within each antigen‐specific population (in color) to the overall CD8+ T‐cell population (light gray). The bottom row overlays the staining patterns of all antigen‐specific cell populations studied. Axes indicate the fluorescence intensity of each marker. Within bulk CD8+ T‐cells (top row), bright, dim, and negative populations could be distinguished for nearly every marker, indicating the heterogeneity of proteins expressed on the surface of CD8+ T‐cells and the need for multiparameter technology. pMHCI–QD multimers clearly identified HIV Nef‐, HIV Gag‐, EBV GLC‐ and CMV NLV‐specific cells among CD8+ T‐cells (left column). Note that CD45RA QD655 and CD57 QD585 were used in this example.

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The effect of various fixation and permeabilization buffers and conditions on QD fluorescence. Peripheral blood mononuclear cells (PBMC) were stained with CD3 Cy7APC, and CD4 QD565, and then treated with fixation/permeabilization kits manufactured by E‐Biosciences, BD, or Invitrogen. To test the effect of temperature, all steps were performed on ice or at room temperature. Results were compared to stained, but otherwise untreated, cells.

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Size and composition of quantum dots (QDs). QD fluorochromes are approximately the same size as phycoerythrin (PE) (as indicated by the scale on the axis), once they are coated with the inorganic shell, organic crust, and functional groups necessary for flow cytometric applications. Dyes are not drawn to scale.

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Detection of quantum dot (QD) fluorescence. (a) Configuration of octagonal photomultiplier tube (PMT) system in a modified12 LSRII flow cytometer. Dichroic long‐pass (LP) filters transmit light at a certain wavelength to band pass filters, which further purify the light before transmission to the detector. Light below a particular wavelength is reflected to the next dichroic filter in the sequence. (b) Fluorescence intensity of anti‐IgK beads stained with QD‐labeled antibodies. QD655 is brightest, followed by QD605 and QD585. Note that PMT voltages were optimized prior to this analysis, using the technique described in Ref [20].

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The effect of spreading error. Peripheral blood mononuclear cells (PBMC) were stained with anti‐CD8 Cy7APC or QD800, and spreading error into other channels was examined after compensation and bi‐exponential transformation of data. Cy7APC contributes significant spreading error into APC, which can mask populations exhibiting dim APC fluorescence. In contrast, QD800 contributes no spreading error into the QD655 channel, allowing better discrimination of dim QD655+ cells.

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Emission spectra for organic dyes and quantum dots (QDs). The colored bars indicate the range of wavelengths for filters used to detect each fluorochrome.12 (a) Phycoerythrin (PE) and PE‐based tandems overlap significantly, such that all of these organic dyes are detected in the PE channel and high compensation values are required. Also, these fluorochromes have long tails of emission in the red region of the spectrum, and which can induce significant spreading error. (b) In contrast, QD emission spectra are narrow and symmetrical, resulting in better sensitivity. For example, only light from adjacent QDs contaminates the QD585 channel. QDs also have broad (and roughly equivalent) absorption spectra (solid gray line), so multiple QDs can be excited by the same laser. However, because higher wavelength QDs have even broader absorption spectra (dashed gray line), these QDs are excited by multiple lasers (depicted as circles in the figure) and therefore emit light in channels commonly used for organic dyes. Note that the dashed gray line roughly describes absorption spectrum; in reality, the absorption spectrum of an individual QD cannot extend pass its emission wavelength.

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Properties of quantum dots (QDs). (a) Schematic showing potential energy states for electrons in a fluorescent molecule. At resting state, electrons may differ slightly in energy (vibrational states) but collectively remain at various low energy states within the valence band. Upon excitation, electrons in the valence band jump to a higher energy state within the conduction band. The separation between the valence and conduction bands is known as the band gap. As electrons return to the valence band, energy is released as fluorescence. (b) For smaller nanocrystal cores, band gaps are larger and energy differences are higher. Therefore, longer wavelengths of light are emitted (resulting in redder fluorescence). (c) A wide variety of QDs are commercially available; these differ by the diameter of their nanocrystal cores. As the core diameter increases, fluorescence appears redder. (QD525 and QD800 are not shown here, but follow this pattern.) QDs were aliquoted into a 96‐well plate for this photograph, which was taken with a digital camera, and cropped. Magnification is minimal.

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Limitation of 2‐color approaches to identify T‐cell differentiation stages. To compare different approaches for identifying naïve and memory T‐cell subsets, peripheral blood mononuclear cells (PBMC) were stained with a panel of reagents that included antibodies against CD7, CD27, CD28, CD45RA, CD62L, and CCR7. For this panel, CD45RA and CD62L were conjugated to QD605 and QD705, respectively. The top row shows the strategy used to identify CD8+ T‐cells; the remaining graphs in the grid below (a–d) show only the gated CD3+ CD4− CD8+ lymphocyte population. In each column of the grid, graphs show expression of a pair of markers commonly used to describe T‐cell differentiation stages. In each row of the grid, the lone dot‐plot shows the total CD8+ population, along with gates that might be used to identify differentiation stages (gates are named according to the various schemas). The contour plots in each row show overlays of the cells belonging to each of the gates from the dot‐plot. By examining CD45RA and CD62L in the first row (a), four stages of cells can be identified (naïve, central, effector, and terminal). The expression of the other markers for these four subsets is shown in the rest of the rows. ‘Central’ memory cells identified by row (a)'s scheme are CD45RA− CD62L+; however, a significant proportion of these cells lack expression of another marker of central memory cells, CCR7 [see black contour plot, second histogram, row (a)]. Similar inconsistencies are observed across the other classification schemes, suggesting that the T‐cell compartment cannot be adequately described by only two or three markers.

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