Cytometry. Part A : the journal of the International Society for Analytical Cytology

In recent years, a tremendous effort has been devoted to the detailed characterization of the phenotype and function of distinct T cell subpopulations in humans, as well as to their pathway(s) of differentiation and role in immune responses. But these studies seem to have generated more questions than definitive answers. To clarify issues related to the function and differentiation of T cell subsets, one session of the MASIR 2008 conference was dedicated to this topic. Several points of consensus and discord were highlighted in the work presented during this session. We provide here an account of these points, including the relative heterogeneity of T cell subpopulations during infections with distinct pathogens, the relationship between phenotypic and functional T cell attributes, and the pathway(s) of T cell differentiation. Finally, we discuss the problems which still limit general agreement. Published 2008 Wiley‐Liss, Inc.

Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with intervals between scheduled maintenance must be accounted for and then normalized. Here, samples were mixed with polystyrene beads embedded with metal lanthanides, allowing monitoring of mass cytometry instrument performance over multiple days of data acquisition. The protocol described here includes simultaneous measurements of beads and cells on the mass cytometer, subsequent extraction of the bead‐based signature, and the application of an algorithm enabling correction of both short‐ and long‐term signal fluctuations. The variation in the intensity of the beads that remains after normalization may also be used to determine data quality. Application of the algorithm to a one‐month longitudinal analysis of a human peripheral blood sample reduced the range of median signal fluctuation from 4.9‐fold to 1.3‐fold. © 2013 International Society for Advancement of Cytometry

A frequent goal of flow cytometric analysis is to classify cells as positive or negative for a given marker, or to determine the precise ratio of positive to negative cells. This requires good and reproducible instrument setup, and careful use of controls for analyzing and interpreting the data. The type of controls to include in various kinds of flow cytometry experiments is a matter of some debate and discussion. In this tutorial, we classify controls in various categories, describe the options within each category, and discuss the merits of each option. © 2006 International Society for Analytical Cytology

The study of T cell biology has been accelerated by substantial progress at the technological level, particularly through the continuing advancement of flow cytometry. The conventional approach of observing T cells as either T helper or T cytotoxic is overly simplistic and does not allow investigators to clearly identify immune mechanisms or alterations in physiological processes that impact on clinical outcomes. The complexity of T cell sub‐populations, as we understand them today, combined with the immunological and functional diversity of these subsets represent significant complications for the study of T cell biology. In this article, we review the use of classical markers in delineating T cell sub‐populations, from “truly naïve” T cells (recent thymic emigrants with no proliferative history) to “exhausted senescent” T cells (poorly proliferative cells that display severe functional abnormalities) wherein the different phenotypes of these populations reflect their disparate functionalities. In addition, since persistent infections and chronological aging have been shown to be associated with significant alterations in human T cell distribution and function, we also discuss age‐associated and cytomegalovirus‐driven alterations in the expression of key subset markers. © 2013 International Society for Advancement of Cytometry

Evaluation of the potential hazard of man‐made nanomaterials has been hampered by a limited ability to observe and measure nanoparticles in cells. In this study, different concentrations of TiO₂ nanoparticles were suspended in cell culture medium. The suspension was then sonicated and characterized by dynamic light scattering and microscopy. Cultured human‐derived retinal pigment epithelial cells (ARPE‐19) were incubated with TiO₂ nanoparticles at 0, 0.1, 0.3, 1, 3, 10, and 30 μg/ml for 24 hours. Cellular reactions to nanoparticles were evaluated using flow cytometry and dark field microscopy. A FACSCalibur™ flow cytometer was used to measure changes in light scatter after nanoparticle incubation. Both the side scatter and forward scatter changed substantially in response to the TiO₂. From 0.1 to 30 μg/ml TiO₂, the side scatter increased sequentially while the forward scatter decreased, presumably due to substantial light reflection by the TiO₂ particles. Based on the parameters of morphology and the calcein‐AM/propidium iodide viability assay, TiO₂ concentrations below 30 μg/ml TiO₂ caused minimal cytotoxicity. Microscopic analysis was done on the same cells using an E‐800 Nikon microscope containing a xenon light source and special dark field objectives. At the lowest concentrations of TiO₂ (0.1–0.3 μg/ml), the flow cytometer could detect as few as 5–10 nanoparticles per cell due to intense light scattering by TiO₂. Rings of concentrated nanoparticles were observed around the nuclei in the vicinity of the endoplasmic reticulum at higher concentrations. These data suggest that the uptake of nanoparticles within cells can be monitored with flow cytometry and confirmed by dark field microscopy. This approach may help fulfill a critical need for the scientific community to assess the relationship between nanoparticle dose and cellular toxicity Such experiments could potentially be performed more quickly and easily using the flow cytometer to measure both nanoparticle uptake and cellular health. Published 2010 Wiley‐Liss, Inc.

Mass cytometry is a recently introduced technology that utilizes transition element isotope‐tagged antibodies for protein detection on a single‐cell basis. By circumventing the limitations of emission spectral overlap associated with fluorochromes utilized in traditional flow cytometry, mass cytometry currently allows measurement of up to 40 parameters per cell. Recently, a comprehensive mass cytometry analysis was described for the hematopoietic differentiation program in human bone marrow from a healthy donor. The current study describes approaches to delineate cell cycle stages utilizing 5‐iodo‐2‐deoxyuridine (IdU) to mark cells in S phase, simultaneously with antibodies against cyclin B1, cyclin A, and phosphorylated histone H3 (S28) that characterize the other cell cycle phases. Protocols were developed in which an antibody against phosphorylated retinoblastoma protein (Rb) at serines 807 and 811 was used to separate cells in G0 and G1 phases of the cell cycle. This mass cytometry method yielded cell cycle distributions of both normal and cancer cell populations that were equivalent to those obtained by traditional fluorescence cytometry techniques. We applied this to map the cell cycle phases of cells spanning the hematopoietic hierarchy in healthy human bone marrow as a prelude to later studies with cancers and other disorders of this lineage. © 2012 International Society for Advancement of Cytometry

We have developed flowMeans, a time‐efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K‐means clustering. Unlike traditional K‐means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub‐populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state‐of‐the‐art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor. © 2010 International Society for Advancement of Cytometry