Flow Cytometry Quantification of Transient Transfections in Mammalian Cells
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Abstract We provide an overview of the methods used to label circulating cells for fluorescence detection by in vivo flow cytometry. These methods are useful for cell tracking in small animals without the need to draw blood samples and are particularly useful for the detection of circulating cancer cells and quantification of circulating immune cells. © 2011 International Society for Advancement of Cytometry.
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This chapter contains sections titled: The Cell and Cytometry Flow and Image Cytometry Chemical Cytometry Chemical Cytometry of DNA and mRNA Metabolic Cytometry Future Perspectives on Instrumentation for Chemical Cytometry Acknowledgments References
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This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.
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Abstract Mass cytometry offers the advantage of allowing the simultaneous measurement of a greater number parameters than conventional flow cytometry. However, to date, mass cytometry has lacked a reliable alternative to the light scatter properties that are commonly used as a cell size metric in flow cytometry (forward scatter intensity—FSC). Here, we report the development of two plasma membrane staining assays to evaluate mammalian cell size in mass cytometry experiments. One is based on wheat germ agglutinin (WGA) staining and the other on Osmium tetroxide (OsO 4 ) staining, both of which have preferential affinity for cell membranes. We first perform imaging and flow cytometry experiments to establish a relationship between WGA staining intensity and traditional measures of cell size. We then incorporate WGA staining in mass cytometry analysis of human whole blood and show that WGA staining intensity has reproducible patterns within and across immune cell subsets that have distinct cell sizes. Lastly, we stain PBMCs or dissociated lung tissue with both WGA and OsO 4 ; mass cytometry analysis demonstrates that the two staining intensities correlate well with one another. We conclude that both WGA and OsO 4 may be used to acquire cell size‐related parameters in mass cytometry experiments, and expect these stains to be broadly useful in expanding the range of parameters that can be measured in mass cytometry experiments. © 2016 International Society for Advancement of Cytometry
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Abstract Simultaneous monitoring of biomarkers as well as single‐cell analyses based on flow cytometry and mass cytometry are important for investigations of disease mechanisms, drug discovery, and signaling‐network studies. Flow cytometry and mass cytometry are complementary to each other; however, probes that can satisfy all the requirements for these two advanced technologies are limited. In this study, we report a probe of lanthanide‐coordinated semiconducting polymer dots (Pdots), which possess fluorescence and mass signals. We demonstrated the usage of this dual‐functionality probe for both flow cytometry and mass cytometry in a mimetic cell mixture and human peripheral blood mononuclear cells as model systems. The probes not only offer high fluorescence signal for use in flow cytometry, but also show better performance in mass cytometry than the commercially available counterparts.
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Abstract Imaging flow cytometry shows significant potential for increasing our understanding of heterogeneous and complex life systems and is useful for biomedical applications. Ghost cytometry is a recently proposed approach for directly analyzing compressively measured signals of cells, thereby relieving a computational bottleneck for real‐time data analysis in high‐throughput imaging cytometry. In our previous work, we demonstrated that this image‐free approach could distinguish cells from two cell lines prepared with the same fluorescence staining method. However, the demonstration using different cell lines could not exclude the possibility that classification was based on non‐morphological factors such as the speed of cells in flow, which could be encoded in the compressed signals. In this study, we show that GC can classify cells from the same cell line but with different fluorescence distributions in space, supporting the strength of our image‐free approach for accurate morphological cell analysis. © 2020 International Society for Advancement of Cytometry
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Abstract Mass and fluorescence cytometry are quantitative single cell flow cytometry approaches that are powerful tools for characterizing diverse tissues and cellular systems. Here mass cytometry was directly compared with fluorescence cytometry by studying phenotypes of healthy human peripheral blood mononuclear cells (PBMC) in the context of superantigen stimulation. One mass cytometry panel and five fluorescence cytometry panels were used to measure 20 well‐established lymphocyte markers of memory and activation. Comparable frequencies of both common and rare cell subpopulations were observed with fluorescence and mass cytometry using biaxial gating. The unsupervised high‐dimensional analysis tool viSNE was then used to analyze data sets generated from both mass and fluorescence cytometry. viSNE analysis effectively characterized PBMC using eight features per cell and identified similar frequencies of activated CD4+ T cells with both technologies. These results suggest combinations of unsupervised analysis programs and extended multiparameter cytometry will be indispensable tools for detecting perturbations in protein expression in both health and disease. © 2015 International Society for Advancement of Cytometry
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DNA measurements of 130 melanomas were carried out by flow cytometry (FCM) and image cytometry (ICM). ICM was applied to cytological preparations of fresh material (cICM) and to sections of formalin-fixed paraffin embedded tissue (sICM). The DNA ploidy, the DNA index of G0/G1 peaks (DI), and the proliferation index (PI) were used to compare all the methods. The following parameters reflecting malignancy were calculated only from ICM histograms: the 5c exceeding rate (5cER) and the malignancy grade (MG). In cases found to be DNA aneuploid by FCM, the PI values (FCM versus cICM) and the DIs (between all methods) showed a high correlation, and the concordance in relation to the DNA ploidy status was 96% (FCM versus cICM) and 94% (FCM versus sICM). However, we ascertained essential differences between FCM and ICM in melanomas classified as DNA diploid by FCM. The concordance in DNA ploidy was only 66% (FCM versus cICM) and 64% (FCM versus sICM). In contrast, cICM and sICM yielded similar results in most cases. With the exception of the near diploid range, ICM is superior to FCM in detecting DNA aneuploidy. In particular, DNA tetraploid stem lines can easily be overlooked by FCM. Therefore, DNA measurements of tumours judged to be DNA diploid by FCM must be verified by ICM. ICM on sections proved to be applicable and yielded reliable results provided that a suitable thickness was used, and the measuring of sectioned and overlapping nuclei was largely avoided by careful focusing in either direction. © 1996 Wiley-Liss, Inc.
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Cytometry is interdisciplinary in that it measures the physical or chemical signals of cells or particles flowing in a fluidic stream.1 The first generation of commercial flow cytometry performs impedance measurements for particle counting. With the invention of laser technology, light scattering from single cells or particles in flow can be measured, making the optical flow cytometry attractive. Compared with label-free light scattering measurements, the first fluorescence flow cytometer was developed in 1969. However, it is not until the groundbreaking development of engineered green fluorescence probe2 and the many other fluorescence labels that optical flow cytometry was widely used in the field of biomedicine. Recently, we have witnessed the burgeoning multidisciplinary single-cell cytometry of mechanical cytometry,3 photoacoustic cytometry,4 and mass cytometry,5 with more on the novel developments of optical cytometric technologies.6, 7 In the June issue of Cytometry Part A in 2021, “Quantitative Single-Cell Optical Technologies”,8 we embraced the studies of single cells with various optical technologies such as flow cytometry and super-resolution microscopy. For example, Wei et al. have further developed the in-vivo flow cytometry by adopting 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d-glucose (2-NBDG) to label tumor cells, and found the CTCs of mice in-vivo have a high level of 2-NBDG.9 Su and colleagues have shown that the deep learning technology is potentially important for intelligent diagnosis of cervical cancer by developing a light scattering pattern-specific convolution net cytometer (LSPS-net Cytometer).10 This issue will focus on the interdisciplinary fields of nanotechnology, deep learning and microfluidics, which echoes the intrinsic multidisciplinary property of cytometry. Nanotechnology, also called nanoscience, studies matter with size ranging from 1 to 100 nm by multidisciplinary approaches. The amalgamation of nanotechnology and cytometry heavily resides in the labeling of the cells or particles, in order to help for the cytometric measurements with a higher sensitivity or resolution. Due to the quantum effects, different sizes of semiconductor nanoparticles (quantum dots) emit variant colors, which can be used for biological labelling. Compared with fluorescence labelling, the quantum dots are not limited to blinking and provide multi-color labels with higher emitting intensity; however, they may suffer from the fabrication complexity, decoration problems, and high toxicity.11 Gold nanoparticles have been widely used as versatile bio-labels, thanks to their strong light scattering and surface plasmon resonance. The nanoparticles have been shown in cytometry for many interesting works such as three-dimensional intracellular visualization.12 In flow cytometry, the cells are flowing in a single cell profile by hydrodynamic focusing, where the diameter of the sample fluidic stream can be narrowed to the size of a cell. Thus, conventional flow cytometry explores the microfluidics. Moreover, it is known to all that microfluidics controls the fluidics at micro-scale or nano-scale. In flow cytometry, the samples measured are usually in the scale of microliters or nanoliters, which requires the technology of microfluidics. The microfluidics also couple well with optics and electronics. In this case, the microfluidic technology may help to advance the core technology of flow cytometry and make flow cytometry compact, portable, and high-efficiency.13, 14 Deep learning, which is inspired by the neuron network of the human brain for studying, is an important step forward in machine learning toward the artificial intelligence, especially in biomedicine. The deep learning technology incorporates well with flow cytometry for two main reasons. Firstly, big data from a large number of cells in a due time can be obtained by high-throughput flow cytometry, for example, several thousand cell images per second by an imaging flow cytometer. This requires robust and automatic algorithms for big data analysis. Secondly, the development of novel methods to extract more information of cells such as label-free flow cytometry relies on data mining that may be better achieved by deep learning.10, 15, 16 The integration of nanotechnology in cytometry is presented in this issue with nanoparticles and fluorescence resonance energy transfer. Plasmonic nanoparticle (PNP) may provide high sensitivity for disease diagnosis. Evans et al. have reviewed the recent development of PNP-based technologies, and described their PNP-based “digital” cytometry methodology for neurological disorder characterization (Evans et al., pp. 1067–1078). Toward the development of efficient biomarkers, biofunctionalized nanospheres are combined with imaging flow cytometry to measure immune cell signaling in subcellular regions of interest (Thaunat et al., pp. 1079–1090). Fluorescence resonance energy transfer (FRET), the energy transfer between two adjacent molecules (less than 10 nm), has been shown with great applications in biomedicine.2 Davis et al. have reported the measurements of FRET in living cells by using both conventional flow cytometry and the spectra cytometry17. With quantitative FRET imaging, the inhibitory priority of Bcl-xL to Bad, tBid and Bax has been evaluated by using live-cell imaging assay (Chen et al., pp. 1091–1101). However, concerns have been raised when performing FRET measurements with flow cytometry (Lambert et al., pp. 1102–1106). The incorporation of microfluidics with optics is demonstrated by developing a microfluidic cytometer for white blood cell counting (Li et al., pp. 1107–1113). For their microfluidic cytometer, polydimethylsiloxane is used for chip fabrication and optical fibers and microlens are integrated with the microfluidics, where the small angle forward scattering, side scattering and fluorescence signals of single cells controlled by on-chip hydrodynamic focusing are measured. Combining impedance and optics in flow cytometry, Yue et al. have reported that specific membrane capacitance and cytoplasmic conductivity as intrinsic bioelectrical markers can be measured to classify tumor subtypes by their constriction channel (with a width and a height that were smaller than the cell diameter) based impedance cytometry (Yue et al., pp. 1114–1122). Deep learning is adopted to cytometry for effective analysis of the big cytometric imaging data and for label-free cell classification in the current issue. Liu et al. have combined flow cytometry with an artificial neural network (MCellNet) to classify the species that have similar morphologies with high accuracy (Liu et al., pp. 1123–1133). In order to measure cell viability, the 2D light scattering method is combined with the deep learning algorithm for label-free classification of live and dead colonic adenocarcinoma cells (Yang et al., pp. 1134–1142). Key Research and Development Program of Shandong Province (Major Science and Technology Innovation Project), Grant number: 2019JZZY011016; National Natural Science Foundation of China (NSFC), Grant number: 91859114. The peer review history for this article is available at https://publons.com/publon/10.1002/cyto.a.24513.
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Abstract We have determined the DNA content, the ploidy levels, and the percentages of different cell types present in small and large mouse mammary tumors as well as in young and old mouse livers by using absorption and flow cytometry. Absorption cytometry data indicated a significant increase in the proportion of transformed G0/G1 cells in the tumors as compared to that of the stromal G0/G1 cells with progressive tumor growth. This increase was not detected by flow cytometry. In both young and old mouse livers, a small number of cells of higher ploidy (8C and 16C) were detected by absorption cytometry but were not apparent in histograms obatined by flow cytometry. Furthermore, changes in the proportions of liver cells of different ploidy with age were apparent in absorption cytometry data but not in flow cytometry data. In one mouse liver experiment, a 6C cell peak appeared in the flow cytometry histogram, but a direct measurement of DNA content by absorption cytometry failed to detect cells with such a peak. We therefore believe that some caution may be warranted in the use of flow cytometry alone for evaluation of DNA distributions and of the proportions of different types of cells in complex solid tissues.
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