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Category: Immunology
Future Cytometric Technologies and Applications, Page 1 of 2
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The past several years have seen a rapid evolution of flow cytometry technology, moving toward more parameters with the development of instruments containing smaller and less expensive lasers and with the discovery of new dyes that extend the range of emissions that can be detected by those lasers. This is exemplified by the emergence of violet diode lasers (1) and associated quantum dot nanocrystals (2) and brilliant violet dyes (3) that are excited by violet (and most recently by UV) lasers. These developments make flow cytometry in the range of 15 or so parameters more practical than ever.
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Mass cytometry workflow overview. A cell suspension is first stained with antibodies labeled with heavy metal ions, via covalently coupled chelator molecules. The stained cell suspension is then nebulized and directed into a plasma torch in the mass cytometer, using a stream of argon gas. The extremely hot plasma breaks cellular components into elemental ions, and the heavier ions are focused into a time-of-flight detector. The signals from this detector are recorded over time, and pulses corresponding to cell events are quantitated. The data for each cell event is transcribed into a Flow Cytometry Standard (FCS) file, such that it can be gated and analyzed with traditional flow cytometry software.
Mass cytometry workflow overview. A cell suspension is first stained with antibodies labeled with heavy metal ions, via covalently coupled chelator molecules. The stained cell suspension is then nebulized and directed into a plasma torch in the mass cytometer, using a stream of argon gas. The extremely hot plasma breaks cellular components into elemental ions, and the heavier ions are focused into a time-of-flight detector. The signals from this detector are recorded over time, and pulses corresponding to cell events are quantitated. The data for each cell event is transcribed into a Flow Cytometry Standard (FCS) file, such that it can be gated and analyzed with traditional flow cytometry software.
Comparison of fluorescence emission spectra (left) and mass spectrometry (right). The latter allows for the detection of many more discrete labels, with very little spillover between detector channels.
Comparison of fluorescence emission spectra (left) and mass spectrometry (right). The latter allows for the detection of many more discrete labels, with very little spillover between detector channels.
Spillover across the mass window of the CyTOF. Antibody capture beads were labeled with antibody conjugates for each of the indicated metals and collected on the CyTOF in cell mode. The relative percentage of signal in each detector channel is as shown. Note the major signals outside of the primary channel tend to occur in M+1 and (for certain isotopes) M+16 channels. The former is largely due to detector infidelity, while the latter is due to oxidation, a partially preventable consequence of incomplete plasma ionization. Occasional signals are seen in other channels, and these are generally a result of specific isotopic impurities. Data courtesy of Michael Leipold, Stanford University.
Spillover across the mass window of the CyTOF. Antibody capture beads were labeled with antibody conjugates for each of the indicated metals and collected on the CyTOF in cell mode. The relative percentage of signal in each detector channel is as shown. Note the major signals outside of the primary channel tend to occur in M+1 and (for certain isotopes) M+16 channels. The former is largely due to detector infidelity, while the latter is due to oxidation, a partially preventable consequence of incomplete plasma ionization. Occasional signals are seen in other channels, and these are generally a result of specific isotopic impurities. Data courtesy of Michael Leipold, Stanford University.
Example of SPADE analysis. Peripheral blood mononuclear cells from a healthy donor and a cancer patient were stained for surface and intracellular markers and analyzed by CyTOF. Clustering was performed in SPADE ( 15 ) using major cell surface lineage markers. The major cell lineages were annotated by examination of the staining patterns of these markers. The distribution of other markers can then be visualized, as shown here for granzyme B. Note the lack of granzyme B expression in the cancer patient relative to the control. pctile, percentile. Data courtesy of Serena Chang and Holbrook Kohrt, Stanford University.
Example of SPADE analysis. Peripheral blood mononuclear cells from a healthy donor and a cancer patient were stained for surface and intracellular markers and analyzed by CyTOF. Clustering was performed in SPADE ( 15 ) using major cell surface lineage markers. The major cell lineages were annotated by examination of the staining patterns of these markers. The distribution of other markers can then be visualized, as shown here for granzyme B. Note the lack of granzyme B expression in the cancer patient relative to the control. pctile, percentile. Data courtesy of Serena Chang and Holbrook Kohrt, Stanford University.
Example of heat map calculation in Cytobank. Upon analysis of phospho-CyTOF data, readout of phosphorylation signal changes in each sample can be displayed as a heat map. Here we see pSTAT5 induction in five major cell lineages (columns) with 12 different stimulation conditions (rows), for a healthy control peripheral blood mononuclear cell sample. For CyTOF data, an arcsinh transformation of the median intensity signal was performed in the software and the arcsinh ratio relative to the unstimulated sample was displayed using the color scale indicated. Relative change using medians or percentile statistics, along with other data transformations, are also available in the software. IFN, interferon; IL, interleukin; LPS, lipopolysaccharide; PMA, phorbol myristate acetate. Data courtesy of Rosemary Fernandez, Stanford University.
Example of heat map calculation in Cytobank. Upon analysis of phospho-CyTOF data, readout of phosphorylation signal changes in each sample can be displayed as a heat map. Here we see pSTAT5 induction in five major cell lineages (columns) with 12 different stimulation conditions (rows), for a healthy control peripheral blood mononuclear cell sample. For CyTOF data, an arcsinh transformation of the median intensity signal was performed in the software and the arcsinh ratio relative to the unstimulated sample was displayed using the color scale indicated. Relative change using medians or percentile statistics, along with other data transformations, are also available in the software. IFN, interferon; IL, interleukin; LPS, lipopolysaccharide; PMA, phorbol myristate acetate. Data courtesy of Rosemary Fernandez, Stanford University.
Phospho-CyTOF panel optimized in the author's laboratory a
Phospho-CyTOF panel optimized in the author's laboratory a