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Mass Cytometry: Single Cells, Many Features
Open accessAbstract
Summary Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis as well as thoughts on future developments and applications.
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Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Self-citationAmir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550
Self-citationQuantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183
Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age
Pang, W. W., Price, E. A., Sahoo, D., Beerman, I., Maloney, W. J., Rossi, D. J., Schrier, S. L., & Weissman, I. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116110108 · Cited by 991
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., & Plevritis, S. K. (2011) · Nature Biotechnology · doi:10.1038/nbt.1991 · Cited by 956
Self-citationBendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906
Self-citationMultiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators
Bodenmiller, B., Zunder, E. R., Finck, R., Chen, T. J., Savig, E. S., Bruggner, R. V., Simonds, E. F., Bendall, S. C., Sachs, K., Krutzik, P. O., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2317 · Cited by 546
Self-citationHighly multiplexed simultaneous detection of RNAs and proteins in single cells
Frei, A. P., Bava, F.-A., Zunder, E. R., Hsieh, E. W. Y., Chen, S.-Y., Nolan, G. P., & Gherardini, P. F. (2016) · Nature Methods · doi:10.1038/nmeth.3742 · Cited by 330
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Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P. (2014) · Science · doi:10.1126/science.1250689 · Cited by 225
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Metadata
- Authors
- Spitzer, M. H., & Nolan, G. P.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 165
- Issue
- 4
- Pages
- 780-791
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2346039638
- Cited by
- 1,199
- References
- 10
- FWCI
- 54.32
- Type
- journal article
- Language
- en
- Open access
- bronze
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