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

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.

A map of protein dynamics during cell-cycle progression and cell-cycle exit

The cell-cycle field has identified the core regulators that drive the cell cycle, but we do not have a clear map of the dynamics of these regulators during cell-cycle progression versus cell-cycle exit. Here we use single-cell time-lapse microscopy of Cyclin-Dependent Kinase 2 (CDK2) activity followed by endpoint immunofluorescence and computational cell synchronization to determine the temporal dynamics of key cell-cycle proteins in asynchronously cycling human cells. We identify several unexpected patterns for core cell-cycle proteins in actively proliferating (CDK2-increasing) versus spontaneously quiescent (CDK2-low) cells, including Cyclin D1, the levels of which we find to be higher in spontaneously quiescent versus proliferating cells. We also identify proteins with concentrations that steadily increase or decrease the longer cells are in quiescence, suggesting the existence of a continuum of quiescence depths. Our single-cell measurements thus provide a rich resource for the field by characterizing protein dynamics during proliferation versus quiescence.

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Acute myeloid leukaemia is characterized by marked inter- and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.

Cell Cycle Dynamics of Proteins and Post-translational Modifications Using Quantitative Immunofluorescence

Immunofluorescence can be a powerful tool to detect protein levels, intracellular localization, and post-translational modifications. However, standard immunofluorescence provides only a still picture and thus lacks temporal information. Here, we describe a method to extract temporal information from immunofluorescence images of fixed cells. In addition, we provide an optional protocol that uses micropatterns, which increases the accuracy of the method. These methods allow assessing how protein levels, intracellular localization, and post-translational modifications change through the cell cycle.

Initiation and termination of DNA replication during S phase in relation to cyclins D1, E and A, p21WAF1, Cdt1 and the p12 subunit of DNA polymerase δ revealed in individual cells by cytometry

During our recent studies on mechanism of the regulation of human DNA polymerase δ in preparation for DNA replication or repair, multiparameter imaging cytometry as exemplified by laser scanning cytometry (LSC) has been used to assess changes in expression of the following nuclear proteins associated with initiation of DNA replication: cyclin A, PCNA, Ki-67, p21(WAF1), DNA replication factor Cdt1 and the smallest subunit of DNA polymerase δ, p12. In the present review, rather than focusing on Pol δ, we emphasize the application of LSC in these studies and outline possibilities offered by the concurrent differential analysis of DNA replication in conjunction with expression of the nuclear proteins. A more extensive analysis of the data on a correlation between rates of EdU incorporation, likely reporting DNA replication, and expression of these proteins, is presently provided. New data, specifically on the expression of cyclin D1 and cyclin E with respect to EdU incorporation as well as on a relationship between expression of cyclin A vs. p21(WAF1) and Ki-67 vs. Cdt1, are also reported. Of particular interest is the observation that this approach makes it possible to assess the temporal sequence of degradation of cyclin D1, p21(WAF1), Cdt1 and p12, each with respect to initiation of DNA replication and with respect to each other. Also the sequence or reappearance of these proteins in G2 after termination of DNA replication is assessed. The reviewed data provide a more comprehensive presentation of potential markers, whose presence or absence marks the DNA replicating cells. Discussed is also usefulness of these markers as indicators of proliferative activity in cancer tissues that may bear information on tumor progression and have a prognostic value.

A data-driven, mathematical model of mammalian cell cycle regulation

Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours) calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

Assessing kinetics from fixed cells reveals activation of the mitotic entry network at the S/G2 transition

During the cell cycle, DNA duplication in S phase must occur before a cell divides in mitosis. In the intervening G2 phase, mitotic inducers accumulate, which eventually leads to a switch-like rise in mitotic kinase activity that triggers mitotic entry. However, when and how activation of the signaling network that promotes the transition to mitosis occurs remains unclear. We have developed a system to reduce cell-cell variation and increase accuracy of fluorescence quantification in single cells. This allows us to use immunofluorescence of endogenous marker proteins to assess kinetics from fixed cells. We find that mitotic phosphorylations initially occur at the completion of S phase, showing that activation of the mitotic entry network does not depend on protein accumulation through G2. Our data show insights into how mitotic entry is linked to the completion of S phase and forms a quantitative resource for mathematical models of the human cell cycle.

The kinetics of G2 and M transitions regulated by B cyclins

B cyclins regulate G2-M transition. Because human somatic cells continue to cycle after reduction of cyclin B1 (cycB1) or cyclin B2 (cycB2) by RNA interference (RNAi), and because cycB2 knockout mice are viable, the existence of two genes should be an optimization. To explore this idea, we generated HeLa BD™ Tet-Off cell lines with inducible cyclin B1- or B2-EGFP that were RNAi resistant. Cultures were treated with RNAi and/or doxycycline (Dox) and bromodeoxyuridine. We measured G2 and M transit times and 4C cell accumulation. In the absence of ectopic B cyclin expression, knockdown (kd) of either cyclin increased G2 transit. M transit was increased by cycB1 kd but decreased by cycB2 depletion. This novel difference was further supported by time-lapse microscopy. This suggests that cycB2 tunes mitotic timing, and we speculate that this is through regulation of a Golgi checkpoint. In the presence of endogenous cyclins, expression of active B cyclin-EGFPs did not affect G2 or M phase times. As previously shown, B cyclin co-depletion induced G2 arrest. Expression of either B cyclin-EGFP completely rescued knockdown of the respective endogenous cyclin in single kd experiments, and either cyclin-EGFP completely rescued endogenous cyclin co-depletion. Most of the rescue occurred at relatively low levels of exogenous cyclin expression. Therefore, cycB1 and cycB2 are interchangeable for ability to promote G2 and M transition in this experimental setting. Cyclin B1 is thought to be required for the mammalian somatic cell cycle, while cyclin B2 is thought to be dispensable. However, residual levels of cyclin B1 or cyclin B2 in double knockdown experiments are not sufficient to promote successful mitosis, yet residual levels are sufficient to promote mitosis in the presence of the dispensible cyclin B2. We discuss a simple model that would explain most data if cyclin B1 is necessary.