CellVue Claret, a new far-red dye, facilitates polychromatic assessment of immune cell proliferation

Probing cell proliferation: Considerations for dye selection

Broadly speaking, cell tracking dyes are fluorescent compounds that bind stably to components on or within the cells so the fate of the labeled cells can be followed. Their staining should be bright and homogeneous without affecting cell function. For purposes of monitoring cell proliferation, each time a cell divides the intensity of cell tracking dye should diminish equally between daughter cells. These dyes can be grouped into two different classes. Protein reactive dyes label cells by reacting covalently but non-selectively with intracellular proteins. Carboxyfluorescein diacetate succinimidyl ester (CFSE) is the prototypic general protein label. Membrane intercalating dyes label cells by partitioning non-selectively and non-covalently within the plasma membrane. The PKH membrane dyes are examples of lipophilic compounds whose chemistry allows for their retention within biological membranes without affecting cellular growth, viability, or proliferation when used properly. Here we provide considerations based for labeling cell lines and peripheral blood mononuclear cells using both classes of dyes. Examples from optimization experiments are presented along with critical aspects of the staining procedures to help mitigate common risks. Of note, we present data where a logarithmically growing cell line is labeled with both a protein dye and a membrane tracking dye to compare dye loss rates over 6days. We found that dual stained cells paralleled dye loss of the corresponding single stained cells. The decrease in fluorescence intensity by protein reactive dyes, however, was more rapid than that with the membrane reactive dyes, indicating the presence of additional division-independent dye loss.

Monitoring Cell Proliferation by Dye Dilution: Considerations for Panel Design

High dimensional studies that include proliferation dyes face two inherent challenges in panel design. First, the more rounds of cell division to be monitored based on dye dilution, the greater the starting intensity of the labeled parent cells must be in order to distinguish highly divided daughter cells from background autofluorescence. Second, the greater their starting intensity, the more difficult it becomes to avoid spillover of proliferation dye signal into adjacent spectral channels, with resulting limitations on the use of other fluorochromes and ability to resolve dim signals of interest. In the third and fourth editions of this series, we described the similarities and differences between protein-reactive and membrane-intercalating dyes used for general cell tracking, provided detailed protocols for optimized labeling with each dye type, and summarized characteristics to be tested by the supplier and/or user when validating either dye type for use as a proliferation dye. In this fifth edition, we review: (a) Fundamental assumptions and critical controls for dye dilution proliferation assays; (b) Methods to evaluate the effect of labeling on cell growth rate and test the fidelity with which dye dilution reports cell division; and. (c) Factors that determine how many daughter generations can be accurately included in proliferation modeling. We also provide an expanded section on spectral characterization, using data collected for three protein-reactive dyes (CellTrace™ Violet, CellTrace™ CFSE, and CellTrace™ Far Red) and three membrane-intercalating dyes (PKH67, PKH26, and CellVue® Claret) on three different cytometers to illustrate typical decisions and trade-offs required during multicolor panel design. Lastly, we include methods and controls for assessing regulatory T cell potency, a functional assay that incorporates the "know your dye" and "know your cytometer" principles described herein.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

The Brain Microenvironment Induces DNMT1 Suppression and Indolence of Metastatic Cancer Cells

Brain metastasis is an ineffective process, and many cancer cells enter into an indolent state following extravasation in the brain. Single cell RNA sequencing of melanoma brain metastases reveals that non-proliferating brain metastatic melanoma cells exhibit a pattern of gene expression associated with inhibition of DNA methyltransferase 1 (DNMT1). The brain microenvironment, specifically the combination of reactive astrocytes and mechanically soft surroundings, suppressed DNMT1 expression in various cancer types and caused cell cycle delay. Somewhat unexpectedly, we find that DNMT1 suppression not only induces cell cycle delay but also activates pro-survival signals in brain metastatic cancer cells, including L1CAM and CRYAB. Our results demonstrate that transcriptional changes triggered by DNMT1 suppression is a key step for cancer cells to survive in the brain microenvironment and that they also restrict cancer cell proliferation. The dual consequences of DNMT1 suppression can explain the persistence of indolent cancer cells in the brain microenvironment.

Monitoring Cell Proliferation by Dye Dilution: Considerations for Probe Selection

In the third edition of this series, we described protocols for labeling cell populations with tracking dyes, and addressed issues to be considered when combining two different tracking dyes with other phenotypic and viability probes for the assessment of cytotoxic effector activity and regulatory T cell functions. We summarized key characteristics of and differences between general protein and membrane labeling dyes, discussed determination of optimal staining concentrations, and provided detailed labeling protocols for both dye types. Examples of the advantages of two-color cell tracking were provided in the form of protocols for: (a) independent enumeration of viable effector and target cells in a direct cytotoxicity assay; and (b) an in vitro suppression assay for simultaneous proliferation monitoring of effector and regulatory T cells.The number of commercially available fluorescent cell tracking dyes has expanded significantly since the last edition, with new suppliers and/or new spectral properties being added at least annually. In this fourth edition, we describe evaluations to be performed by the supplier and/or user when characterizing a new cell tracking dye and by the user when selecting one for use in multicolor proliferation monitoring. These include methods for: (a) Assessment of the dye's spectral profile on the laboratory's flow cytometer(s) to optimize compatibility with other employed fluorochromes and minimize compensation problems; (b) Evaluating the effect of labeling on cell growth rate;

A method for evaluating the use of fluorescent dyes to track proliferation in cell lines by dye dilution

Labeling nonquiescent cells with carboxyfluorescein succinimidyl ester (CFSE)-like dyes gives rise to a population width exceeding the threshold for resolving division peaks by flow cytometry. Width is a function of biological heterogeneity plus extrinsic and intrinsic error sources associated with the measurement process. Optimal cytometer performance minimizes extrinsic error, but reducing intrinsic error to the point of facilitating peak resolution requires careful fluorochrome selection and fluorescent cell sorting. In this study, we labeled the Jurkat and A549 cell lines with CFSE, CellTraceViolet (CTV), and eFluor 670 proliferation dye (EPD) to test if we could resolve division peaks in culture after reducing the labeled input widths by cell sorting. Reanalysis of the sorted populations to ascertain the level of reduction achieved always led to widths exceeding the gated limits due to the contribution of errors. Measuring detector-specific extrinsic error by sorting uniform fluorescent particles with similar spectral properties to the tracking dyes allowed us to determine the intrinsic error for each dye and cell type using a simple mathematical approach. We found that cell intrinsic error ultimately dictated whether we could resolve division peaks, and that as this increased, the required sort gate width to resolve any division peaks decreased to the point whereby issues with yield made A549 unsuitable for this approach. Finally, attempts to improve yields by setting two concurrent sort gates on the fluorescence distribution enriched for cells in different stages of the cell cycle that had nonequivalent proliferative properties in culture and thus should be practiced with caution.

Homeostatic proliferation of mature T cells

Under normal circumstances, the secondary lymphoid tissues contain a predictable number of T cells with a diverse T cell receptor (TCR) repertoire. Such a T cell pool must be of sufficient size to confer maximum protection of the host from infectious pathogens and cancer, but small enough not to overburden the host. The T cell pool is maintained by a combination of de novo T cell production by the thymus and by the long-term survival and gradual turnover of mature T cells in the periphery. The latter process, termed homeostatic proliferation, has been intensely investigated over the past 20 years, and a few techniques have been developed to facilitate these studies. In this chapter, we describe the experimental procedures that allow conspicuous visualization of homeostatic proliferation, which have been instrumental in facilitating recent advances in the study of T cell homeostasis.

Optimized staining and proliferation modeling methods for cell division monitoring using cell tracking dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.(1-5) Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of: stem and progenitor cell quiescence, proliferation and/or differentiation(6-8) antigen-driven membrane transfer(9) and/or precursor cell proliferation(3,4,10-18) and immune regulatory and effector cell function(1,18-21). Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes(1,2,11). "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins(4,16,18). Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class(2-4,16,18,24). The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are: Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies. Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 10(2) - 10(3) times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used. Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile. Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Homo sapiens systemic RNA interference-defective-1 transmembrane family member 1 (SIDT1) protein mediates contact-dependent small RNA transfer and microRNA-21-driven chemoresistance

Locally initiated RNA interference (RNAi) has the potential for spatial propagation, inducing posttranscriptional gene silencing in distant cells. In Caenorhabditis elegans, systemic RNAi requires a phylogenetically conserved transmembrane channel, SID-1. Here, we show that a human SID-1 orthologue, SIDT1, facilitates rapid, contact-dependent, bidirectional small RNA transfer between human cells, resulting in target-specific non-cell-autonomous RNAi. Intercellular small RNA transfer can be both homotypic and heterotypic. We show SIDT1-mediated intercellular transfer of microRNA-21 to be a driver of resistance to the nucleoside analog gemcitabine in human adenocarcinoma cells. Documentation of a SIDT1-dependent small RNA transfer mechanism and the associated phenotypic effects on chemoresistance in human cancer cells raises the possibility that conserved systemic RNAi pathways contribute to the acquisition of drug resistance. Mediators of non-cell-autonomous RNAi may be tractable targets for novel therapies aimed at improving the efficacy of current cytotoxic agents.

IFN-γ promotes muscle damage in the mdx mouse model of Duchenne muscular dystrophy by suppressing M2 macrophage activation and inhibiting muscle cell proliferation

Duchenne muscular dystrophy is a degenerative disorder that leads to death by the third decade of life. Previous investigations have shown that macrophages that invade dystrophic muscle are a heterogeneous population consisting of M1 and M2 macrophages that promote injury and repair, respectively. In the present investigation, we tested whether IFN-γ worsens the severity of mdx dystrophy by activating macrophages to a cytolytic M1 phenotype and by suppressing the activation of proregenerative macrophages to an M2 phenotype. IFN-γ is a strong inducer of the M1 phenotype and is elevated in mdx dystrophy. Contrary to our expectations, null mutation of IFN-γ caused no reduction of cytotoxicity of macrophages isolated from mdx muscle and did not reduce muscle fiber damage in vivo or improve gross motor function of mdx mice at the early, acute peak of pathology. In contrast, ablation of IFN-γ reduced muscle damage in vivo during the regenerative stage of the disease and increased activation of the M2 phenotype and improved motor function of mdx mice at that later stage of the disease. IFN-γ also inhibited muscle cell proliferation and differentiation in vitro, and IFN-γ mutation increased MyoD expression in mdx muscle in vivo, showing that IFN-γ can have direct effects on muscle cells that could impair repair. Taken together, the findings show that suppression of IFN-γ signaling in muscular dystrophy reduces muscle damage and improves motor performance by promoting the M2 macrophage phenotype and by direct actions on muscle cells.

Tracking immune cell proliferation and cytotoxic potential using flow cytometry

In the second edition of this series, we described the use of cell tracking dyes in combination with tetramer reagents and traditional phenotyping protocols to monitor levels of proliferation and cytokine production in antigen-specific CD8(+) T cells. In particular, we illustrated how tracking dye fluorescence profiles could be used to ascertain the precursor frequencies of different subsets in the T-cell pool that are able to bind tetramer, synthesize cytokines, undergo antigen-driven proliferation, and/or carry out various combinations of these functional responses.Analysis of antigen-specific proliferative responses represents just one of many functions that can be monitored using cell tracking dyes and flow cytometry. In this third edition, we address issues to be considered when combining two different tracking dyes with other phenotypic and viability probes for the assessment of cytotoxic effector activity and regulatory T-cell functions. We summarize key characteristics of and differences between general protein- and membrane-labeling dyes, discuss determination of optimal staining concentrations, and provide detailed labeling protocols for both dye types. Examples of the advantages of two-color cell tracking are provided in the form of protocols for (a) independent enumeration of viable effector and target cells in a direct cytotoxicity assay and (b) simultaneous monitoring of proliferative responses in effector and regulatory T cells.

Tracking antigen-driven responses by flow cytometry: monitoring proliferation by dye dilution

Cell-tracking reagents such as the green-fluorescent protein labeling dye CFSE and the red-fluorescent lipophilic membrane dye PKH26 are commonly used to monitor cell proliferation by flow cytometry in heterogeneous cell populations responding to immune stimuli. Both reagents stain cells with a bright homogeneous fluorescence, which is partitioned between daughter cells during each cell division. Because daughter cell fluorescence intensities are approximately halved after each division, the intensity of a cell relative to its intensity at the time of staining provides information about how many divisions it has undergone. Knowing how many rounds of division have occurred and the relative number of cells in each daughter generation, one can back-calculate the number of cells in the original population (i.e., cells present at the time of stimulus) that went on to respond by proliferating. Using this information, the precursor cell frequencies and extent of expansion to a specific antigen or mitogen of interest can be calculated. Concurrently, the phenotype of the cells can be determined, as well as their ability to bind antigen or synthesize cytokines, providing more detailed characterization of all cells responding to the antigen, not just effector cells. In multiparameter flow cytometric experiments to simultaneously analyze antigen-specific tetramer binding, cytokine production and T-cell proliferation, we found that only approximately half of the cells that exhibited specific binding to influenza tetramer also proliferated, as measured by dye dilution, and synthesized IFNgamma in response to antigen. We expect the advent of new cell tracking dyes emitting from the violet to the near infrared combined with the increasing number of lasers and detectors on contemporary flow cytometers to further expand the usefulness of this approach to characterization of complex antigen-driven immunological responses.