Multiparameter cell cycle analysis

Analysis of the multiparametric cell cycle data

This chapter was originally written in 2011. The idea was to give some history of cell cycle analysis before and after flow cytometry became widely accessible; provide references to educational material for single parameter DNA content analysis, introduce and discuss multiparameter cell cycle analysis in a methodological style, and in a casual style, discuss aspects of the work over the last 40years that we have given thought, performing some experiments, but didn't publish. It feels like there is a linear progression that moves from counting cells for growth curves, to counting labeled mitotic cells by autoradiography, to DNA content analysis, to cell cycle states defined by immunofluorescence plus DNA content analysis, to extraction of cell cycle expression profiles, and finally to probability state modeling, which should be the "right" way to analyze cytometric cell cycle data. This is the sense of this chapter. In 2023, we have updated it, but the exciting, expansive aspects brought about by spectral and mass cytometry are still young and developing, and thus have not been vetted, reviewed, and presented in mature form.

Multiparameter Cell Cycle Analysis

Cell cycle cytometry and analysis are essential tools for studying cells of model organisms and natural populations (e.g., bone marrow). Methods have not changed much for many years. The simplest and most common protocol is DNA content analysis, which is extensively published and reviewed. The next most common protocol, 5-bromo-2-deoxyuridine S phase labeling detected by specific antibodies, is also well published and reviewed. More recently, S phase labeling using 5'-ethynyl-2'-deoxyuridine incorporation and a chemical reaction to label substituted DNA has been established as a basic, reliable protocol. Multiple antibody labeling to detect epitopes on cell cycle regulated proteins, which is what this chapter is about, is the most complex of these cytometric cell cycle assays, requiring knowledge of the chemistry of fixation, the biochemistry of antibody-antigen reactions, and spectral compensation. However, because this knowledge is relatively well presented methodologically in many papers and reviews, this chapter will present a minimal Methods section for one mammalian cell type and an extended Notes section, focusing on aspects that are problematic or not well described in the literature. Most of the presented work involves how to segment the data to produce a complete, progressive, and compartmentalized cell cycle analysis from early G1 to late mitosis (telophase). A more recent development, using fluorescent proteins fused with proteins or peptides that are degraded by ubiquitination during specific periods of the cell cycle, termed "Fucci" (fluorescent, ubiquitination-based cell cycle indicators) provide an analysis similar in concept to multiple antibody labeling, except in this case cells can be analyzed while living and transgenic organisms can be created to perform cell cycle analysis ex or in vivo (Sakaue-Sawano et al., Cell 132:487-498, 2007). This technology will not be discussed.

Comprehensive Mass Cytometry Analysis of Cell Cycle, Activation, and Coinhibitory Receptors Expression in CD4 T Cells from Healthy and HIV-Infected Individuals

RATIONALE

Mass cytometry allows large multiplex analysis of cell cycle stages together with differentiation, activation, and exhaustion markers, allowing further assessment of the quiescence status of resting CD4 T cells.

METHODS

Peripheral blood CD4 T lymphocytes from 8 individuals, 4 healthy donors, and 4 HIV-infected on antiretroviral treatment (T) were stained with the same 26 monoclonal antibodies and dyes targeting surface and intracellular markers of differentiation, activation, exhaustion, and cell cycle stages. Samples were run on a CYTOF-2.

RESULTS

Patterns of naïve [TN] CD4 T cells strongly differed from all other memory subsets central-memory (CM), transitional-memory (TM), effector-memory (EM), and terminally differentiated RA-expressing (TEMRA) subsets, while stem-cell memory (SCM) and T follicular-helper cells (TfH) were close to CM and TM cells with the highest percentages in cell cycle. EM and TEMRA were the most altered by HIV infection, with an increased frequency of activated and cycling cells. Activation markers and coinhibitory receptor expression differed among cell cycle stages, with HLA-DR fitting better than CD25 or CD38 with cycle, and opposite PD-1 gradients along differentiation and cell cycle. "Resting" DR-CD25- CD4+ T cells contained similar amounts of cells in G1 than the activated DR ± CD25± ones but three fold lower cells in S-G2-M.

CONCLUSION

This broad multiplex mass cytometry analysis demonstrates some subsets of the so-called "resting" CD25-DR- CD4+ T cells contain noticeable amounts of cells into cycle or expressing coinhibitory receptors, opening new avenues for a redefinition of resting peripheral blood CD4 T cells harboring the HIV reservoirs. © 2016 International Clinical Cytometry Society.

Cytokinetic effects of Wee1 disruption in pancreatic cancer

The Wee1 kinase, which is activated in response to DNA damage, regulates exit from G2 through inhibitory phosphorylation of Cdk1/Cdc2, and is an attractive drug target. However, recent work has highlighted effects of Cdk2 phosphorylation by Wee1 on movement through S-phase, suggesting the potential to sensitize to S-phase specific agents by Wee1 inhibitors. In this paper we applied multiparametric flow cytometry to patient-derived pancreatic cancer xenograft tumor cells to study the cell cycle perturbations of Wee1 disruption via the small molecule inhibitor MK-1775, and genetic knockdown. We find that in vitro treatment with MK-1775, and to a lesser degree, Wee1 RNA transcript knockdown, results in the striking appearance of S-phase cells prematurely entering into mitosis. This effect was not seen in vivo in any of the models tested. Here, although we noted an increase of S-phase cells expressing the damage response marker γH2AX, treatment with MK-1775 did not significantly sensitize cells to the cytidine analog gemcitabine. Treatment with MK-1775 did result in a transient but large increase in cells expressing the mitotic marker phosphorylated H3S10 that reached a peak 4 hours after treatment. This suggests a role for Wee1 regulating the progression of genomically unstable cancer cells through G2 in the absence of extrinsically-applied DNA damage. A single dose of 8Gy ionizing radiation resulted in the time-dependent accumulation of Cyclin A2 positive/phosphorylated H3S10 negative cells at the 4N position, which was abrogated by treatment with MK-1775. Consistent with these findings, a genome-scale pooled RNA interference screen revealed that toxic doses of MK-1775 are suppressed by CDK2 or Cyclin A2 knockdown. These findings support G2 exit as the more significant effect of Wee1 inhibition in pancreatic cancers.

Cell cycle synchronization and flow cytometry analysis of mammalian cells

Analysis of cellular DNA content and measurement of pulse-labeled newly replicated DNA by flow cytometry are useful techniques for cell cycle studies. In this chapter, we describe the protocols for cell cycle synchronization of mammalian cells, including time course designs and consideration of cell types to achieve successful experiments, along with the methods for detection of DNA. Some selected applications dealing with siRNA-mediated knockdown are also presented.

Effect of p53 activity on the sensitivity of human glioblastoma cells to PARP-1 inhibitor in combination with topoisomerase I inhibitor or radiation

Poly (ADP-Ribose) polymerase-1 (PARP-1) is involved in the DNA repairing system by sensing and signaling the presence of DNA damage. Inhibition of PARP-1 is tested in combination with DNA damaging agents such as topoisomerase I inhibitors or ionizing radiations (RT) for the treatment of glioblastoma (GBM). Disruption of p53, widely prevalent in GBMs, plays a major role in DNA repairing system. The current study investigates whether p53 activity has an effect on the sensitivity of human GBM cells to PARP-1 inhibitors in combination with topoisomerase I inhibitor topotecan (TPT) and/or RT. Human GBM cell lines carrying a different functional status of p53 were treated with PARP-1 inhibitor NU1025, in combination with TPT and/or RT. Cytotoxic effects were examined by analyzing the antiproliferative activity, the cell cycle perturbations, and the DNA damage induced by combined treatments. PARP inhibition enhanced the antiproliferative activity, the cell cycle perturbations and the DNA damage induced by both TPT or RT in GBM cells. These effects were influenced by the p53 activity: cells carrying an active p53 were more sensitive to the combination of PARP inhibitor and RT, while cells carrying an inactive p53 displayed a higher sensitivity to the combination of PARP inhibitor and TPT. Our study suggests that p53 activity influences the differential sensitivity of GBM cells to combined treatments of TPT, RT, and PARP inhibitors. © 2014 International Society for Advancement of Cytometry.

Validation of cell-based fluorescence assays: practice guidelines from the ICSH and ICCS - part III - analytical issues

Clinical diagnostic assays, may be classified as quantitative, quasi-quantitative or qualitative. The assay's description should state what the assay needs to accomplish (intended use or purpose) and what it is not intended to achieve. The type(s) of samples (whole blood, peripheral blood mononuclear cells (PBMC), bone marrow, bone marrow mononuclear cells (BMMC), tissue, fine needle aspirate, fluid, etc.), instrument platform for use and anticoagulant restrictions should be fully validated for stability requirements and specified. When applicable, assay sensitivity and specificity should be fully validated and reported; these performance criteria will dictate the number and complexity of specimen samples required for validation. Assay processing and staining conditions (lyse/wash/fix/perm, stain pre or post, time and temperature, sample stability, etc.) should be described in detail and fully validated.

Cabazitaxel-induced stabilization of microtubules enhances radiosensitivity in ovarian cancer cells

Background: Up to 40% of women with ovarian cancer have short disease-free intervals due to molecular mechanisms of chemotherapy resistance. New therapeutic strategies are sought. Ovarian cancers are sensitive to radiochemotherapy. The taxane cabazitaxel (XRP6258, Jevtana) promotes tubulin assembly and stabilizes microtubules against depolymerization in cells, acting similarly in mechanism to paclitaxel. Here, sequences of cabazitaxel-radiation co-administration are tested for drug-alone cytotoxicity and optimal radiosensitization.

Materials and Methods: SKOV3, OVCAR3, and TOV-112D ovarian cancer cells were administered cabazitaxel 24 h before (first), 18 h before (second), together (third), or 24 h after (fourth) a single radiation dose, and then, investigated by clonogenic assay and flow cytometric assays. Radiation dose-cell survival data were fitted by two-stage multivariate analyses of variance. High-content flow cytometry partitioned cabazitaxel effects into G2-phase versus M-phase events by DNA content, cyclin A2, and phospho-S10-histone H3 (PHH3). Paclitaxel served as a comparator.

Findings: Cabazitaxel cytotoxicity and radiosensitization were dose dependent. Cabazitaxel added 24 h before radiation was the most lethal schedule. DNA content measurements by flow cytometry showed that cabazitaxel-treated cells accumulated in the radiosensitive G2/M 4C DNA complement compartment. Cytometry also showed that surviving cabazitaxel-induced cell cycle arrested cells resolve the arrest by entering 4C or by 8C DNA complement cell cycles.

Interpretation: The radiosensitizing effect of cabazitaxel was schedule dependent, due to cell cycle redistribution, and best when cabazitaxel was given 24 h before radiation. Clinical trials of administering both cabazitaxel and radiation should be explored in women with chemoresistant ovarian cancer.

Dynamic expression profiles from static cytometry data: component fitting and conversion to relative, "same scale" values

Background

Cytometry of asynchronous proliferating cell populations produces data with an extractable time-based feature embedded in the frequency of clustered, correlated events. Here, we present a specific case of general methodology for calculating dynamic expression profiles of epitopes that oscillate during the cell cycle and conversion of these values to the same scale.

Methods

Samples of K562 cells from one population were labeled by direct and indirect antibody methods for cyclins A2 and B1 and phospho-S10-histone H3. The same indirect antibody was used for both cyclins. Directly stained samples were counter-stained with 4′6-diamidino-2-phenylindole and indirectly stained samples with propidium to label DNA. The S phase cyclin expressions from indirect assays were used to scale the expression of the cyclins of the multi-variate direct assay. Boolean gating and two dimensional, sequential regions set on bivariate displays of the directly conjugated sample data were used to untangle and isolate unique, unambiguous expression values of the cyclins along the four-dimensional data path through the cell cycle. The median values of cyclins A2 and B1 from each region were correlated with the frequency of events within each region.

Results

The sequential runs of data were plotted as continuous multi-line linear equations of the form y  =  [(y_i+1−y_i)/(x_i+1−x_i)]x + y_i−[(y_i+1−y_i)/(x_i+1−x_i)]x_i (line between points (x_i,y_i) and (x_i+1, y_i+1)) to capture the dynamic expression profile of the two cyclins.

Conclusions

This specific approach demonstrates the general methodology and provides a rule set from which the cell cycle expression of any other epitopes could be measured and calculated. These expression profiles are the “state variable” outputs, useful for calibrating mathematical cell cycle models.

Single-cell mass cytometry adapted to measurements of the cell cycle

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.

Dynamic epitope expression from static cytometry data: principles and reproducibility

Background

An imprecise quantitative sense for the oscillating levels of proteins and their modifications, interactions, and translocations as a function of the cell cycle is fundamentally important for a cartoon/narrative understanding for how the cell cycle works. Mathematical modeling of the same cartoon/narrative models would be greatly enhanced by an open-ended methodology providing precise quantification of many proteins and their modifications, etc. Here we present methodology that fulfills these features.

Methodology

Multiparametric flow cytometry was performed on Molt4 cells to measure cyclins A2 and B1, phospho-S10-histone H3, DNA content, and light scatter (cell size). The resulting 5 dimensional data were analyzed as a series of bivariate plots to isolate the data as segments of an N-dimensional “worm” through the data space. Sequential, unidirectional regions of the data were used to assemble expression profiles for each parameter as a function of cell frequency.

Results

Analysis of synthesized data in which the true values where known validated the approach. Triplicate experiments demonstrated exceptional reproducibility. Comparison of three triplicate experiments stained by two methods (single cyclin or dual cyclin measurements with common DNA and phospho-histone H3 measurements) supported the feasibility of combining an unlimited number of epitopes through this methodology. The sequential degradations of cyclin A2 followed by cyclin B1 followed by de-phosphorylation of histone H3 were precisely mapped. Finally, a two phase expression rate during interphase for each cyclin was robustly identified.

Conclusions

Very precise, correlated expression profiles for important cell cycle regulating and regulated proteins and their modifications can be produced, limited only by the number of available high-quality antibodies. These profiles can be assembled into large information libraries for calibration and validation of mathematical models.

CytoSys: a tool for extracting cell-cycle-related expression dynamics from static data

Computational models of biological processes are important building blocks in Systems Biology studies. Calibration and validation are two important steps for moving a mathematical model to a computational model. While calibration refers to finding numerical value of the coefficients such as rate constants in a mathematical model, validation refers to verifying that the calibrated model behaves the same as the biological system under previously unseen conditions such as environmental changes (e.g., drug treatment) or mutations. In lieu of direct measurements of rate constants, modeling of the molecular mechanisms that govern biological behaviors may be able to use dynamic expression profiles of reactant biomolecules for calibration. For validation, similar data, obtained under new conditions, are probably better than direct measurements of rate constants. In any case, direct measurement of rate constants is almost always impractical and difficult or impossible. Here, we show a computer-assisted methodology to extract embedded dynamic profiles of cell-cycle proteins from statically sampled, multivariate cytometry data guided by heuristics assembled from canonical cell-cycle knowledge. The methodology is implemented using standard "list mode" cytometry data-processing software followed by CytoSys - a software tool with an easy-to-use graphical interface. We demonstrate the use of CytoSys with a case study of exponentially growing, human erythroleukemia cells and extract the dynamic expression profiles of cyclin A for calibrating an existing deterministic mathematical model of the cell cycle.