Trends in Immunology
Volume 33, Issue 7, July 2012, Pages 323-332
Journal home page for Trends in Immunology

Review
A deep profiler's guide to cytometry

https://doi.org/10.1016/j.it.2012.02.010Get rights and content

In recent years, advances in technology have provided us with tools to quantify the expression of multiple genes in individual cells. The ability to measure simultaneously multiple genes in the same cell is necessary to resolve the great diversity of cell subsets, as well as to define their function in the host. Fluorescence-based flow cytometry is the benchmark for this; with it, we can quantify 18 proteins per cell, at >10 000 cells/s. Mass cytometry is a new technology that promises to extend these capabilities significantly. Immunophenotyping by mass spectrometry provides the ability to measure >36 proteins at a rate of 1000 cells/s. We review these cytometric technologies, capable of high-content, high-throughput single-cell assays.

Section snippets

The case for deep profiling

To understand the biological actions of cells and their mechanisms of differentiation, we must understand how phenotype and function are structured across diverse cell types and tissues. This structure can be perturbed by innate or infectious sources, which may drive disease pathogenesis; therefore, understanding it is crucial for identifying treatments and preventions. Great cellular diversity underlies this organization, so measurements taken at the single-cell level that encompass RNA,

Technical achievements that led to PFC

The development of PFC required multiple stepwise advancements in hardware and reagents. For example, the earliest fluorescence-based cytometers used arc lamps, developed originally for microscopy, emitting light at a broad spectrum of wavelengths [1]. This light interfered with fluorochrome-derived signals, therefore, arc-lamps were not easily used for multi-color detection. By 1974, in the Herzenberg Laboratory at Stanford University, argon lasers, emitting a single wavelength (488 nm) were

The post-fluorescence era: mass cytometry

A new platform has been developed that couples flow cytometry with mass spectrometry. This technology, known as mass cytometry, offers single-cell analysis of at least 45 simultaneous parameters without fluorescent agents or interference from spectral overlap (Figure 2). For this, stable (nonradioactive) isotopes of nonbiological, rare earth metals are used as reporters. By exploiting the resolution, sensitivity and dynamic range of mass spectrometry on a time-scale that allows the measurement

Analysis of multiplexed, multiparametric data

Rapid increases in the numbers of measurable single-cell parameters, both in flow and mass cytometry, have brought a daunting increase in the complexity of the data. Analysis of flow cytometry data is typically manual, performed in one or two dimensions at a time by selecting subsets of interest from parent populations. This approach is not scalable, and suffers from individual user bias (Figure 5a). Moreover, it requires prior knowledge of the cell type of interest, so unexpected cell types

Concluding remarks

Over the past 40 years, continual improvement in single-cell analysis technologies has driven our investigation and understanding of immunology and stem cell biology. Pushing the multiparameter limits of fluorescence-based analysis has led to unprecedented studies of regulatory signaling in both the healthy and diseased hematopoietic system. It has also identified many distinct immune cell subsets – most of which have no assigned function. Now, next-generation, mass cytometry instrumentation

Conflict of interest

M.R. receives royalties on the sale of FlowJo software and Cy7APC fluorescent reagents. G.P.N. owns stock and is a paid consultant with DVS Sciences (CyTOF manufacturer) and is a paid consultant with Becton Dickinson, a purveyor of reagents central to both cytometry platforms.

Acknowledgments

M.R. and P.K.C. are supported by the Intramural Research Program of the NIAID, NIH, and by the Collaboration for AIDS Vaccine Discovery (CAVD), Grant #OPP1032325, from the Bill and Melinda Gates Foundation. S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09). G.P.N. is supported by the Rachford and Carlota A. Harris Endowed Professorship and grants from U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, CIRM DR1-01477 and RB2-01592, NCI RFA CA

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