Measuring enzyme activity in single cells - PubMed (original) (raw)

Review

Measuring enzyme activity in single cells

Michelle L Kovarik et al. Trends Biotechnol. 2011 May.

Abstract

Seemingly identical cells can differ in their biochemical state, function and fate, and this variability plays an increasingly recognized role in organism-level outcomes. Cellular heterogeneity arises in part from variation in enzyme activity, which results from interplay between biological noise and multiple cellular processes. As a result, single-cell assays of enzyme activity, particularly those that measure product formation directly, are crucial. Recent innovations have yielded a range of techniques to obtain these data, including image-, flow- and separation-based assays. Research to date has focused on easy-to-measure glycosylases and clinically-relevant kinases. Expansion of these techniques to a wider range and larger number of enzymes will answer contemporary questions in proteomics and glycomics, specifically with respect to biological noise and cellular heterogeneity.

Copyright © 2011 Elsevier Ltd. All rights reserved.

PubMed Disclaimer

Figures

Figure 1

Figure 1

Examples of pathway- and enzyme-level cellular heterogeneity. (a) ATP-induced calcium signaling in mouse thymocytes results from a multi-step process in which the release of intracellular calcium stores triggers calcium influx through Ca2+ release-activated Ca2+ (CRAC) channels [77]. The cells were all stimulated with the same ATP concentration, but variability in this pathway results in dramatic cell-to-cell differences in calcium influx. The heat map indicates intracellular Ca2+ concentration (blue: 0 μM, red: 1.0 μM). Reproduced with permission from [78]. (b) Mating events in yeast are initiated by a complex response to the pheromone α-factor that involves scaffold proteins, enzymes, and transcription factors. Mating response varies widely between cells stimulated with the same α-factor concentration. Reprinted with permission from [79]. (c) Directed single-cell enzyme assays probe the activities of enzymes that contribute to complex cell functions. These assays might examine a specific enzyme activity, such as α-glucosidase II-catalyzed conversion of a rhodamine-labeled disaccharide to monosaccharide in Sf9 cells (reprinted with permission from [55], or (d) the total enzymatic activity of a broad class of enzymes, such as the net proteolytic activity of individual TF-1 cells toward β-amyloid precursor protein (β-APP). Each data point represents one cell. Reprinted with permission from [80].

Figure 2

Figure 2

Origins of cellular heterogeneity in enzyme activity. The sources of heterogeneity are diverse and complex, including variation at both the nucleic acid (a-c) and protein (d-f) levels of gene expression. These biomolecular events affect enzyme activity by changing the chemical identity (a,e) and concentration (b-d, f) of an enzyme within a single cell. In each schematic, the biochemical source of variability is highlighted in red. (a) Genetic mutations change the corresponding amino acid sequence and consequently alter the activity of the gene product. (b) Epigenetic modifications, including DNA methylation, are implicated in varying levels of gene expression. (c) Activator and repressor proteins interact with eukaryotic transcription machinery to up-regulate or down-regulate, respectively, the transcription of specific genes. (d) Translational regulation, including variation in the initiation rate of translation events, affects the production of protein from mRNA transcripts. (e) Post-translation modifications, including phosphorylation, commonly regulate enzyme activity and binding partners through changes in conformation, hydrophobicity, and/or charge. (f) Rates of degradation by the proteasome (pictured) and proteases influence the concentration and lifetime of enzymes in a cell.

Figure 3

Figure 3

Image-based methods for single-cell enzyme assays. (a) Two FRET pairs [enhanced cyanine fluorescent protein (ECFP) paired with citrine and mOrange paired with mCherry] permit simultaneous measurements of Src kinase and MT1-MMP as a function of time after exposure to EGF. The heat maps show the FRET ratio for each pair and reflect enzyme activity. Scale bar, 30 μm. The white circles are pertinent to another panel of the original figure which was not reproduced here. Reprinted with permission from [23]. (b) Microfabricated cell arrays trap individual cells and lysates at high density in known locations. Scale bar, 50 μm. Reprinted with permission from [35]. (c) SECM shows benzoquinone production from the peroxidase-catalyzed reaction of hydroquinone and hydrogen peroxide. Current (i) from reduction of benzoquinone is directly related to peroxidase activity in the two perforated cells. Reproduced with permission from [37].

Figure 4

Figure 4

Flow- and separation-based methods for single-cell enzyme assays. (a) Flow cytometry data on glucocerebrosidase activity in polymorphonuclear cells (PMN), monocytes (Mo) and lymphocytes (Ly). Cell types are determined by forward (FS) and side scatter (SS), and enzyme activity by fluorescence signal (FITC) from cleavage of a fluorogenic substrate. Blue arrows indicate the change in activity upon treatment with the inhibitor conduritol B epoxide (CBE), and signals for an unspecific isotypic control (IC) are shown for reference. Reprinted with permission from [47]. (b) An alternative capillary electrophoresis technique separates fluorescent reporters from lysed cells. PI3K and phospholipase C in rat basophilic leukemia cells convert a Bodipy–fluorescein-labeled reporter, phosphatidyl-inositol 4,5-bisphosphate (Bodipy Fl PIP2), into Bodipy Fl PIP3 and Bodipy Fl diacylglycerol (DAG). Reproduced with permission from [57].

Figure 5

Figure 5

Comparison of the distribution of enzymatic reactions with published single-cell assays to those known in humans. (a) Enzymatic reactions for which single-cell activity assays have been published, and (b) the total number of enzyme-catalyzed reactions in humans from the BRENDA are grouped by their top-level EC number. (c,d) Break-out pie charts of hydrolase (EC 3) reactions with (c) published single-cell activity assays compared to (d) all human hydrolases reactions in BRENDA. Although every published single-cell enzyme assay might not be included, the data shown here are reasonably complete and representative. Additionally, because EC numbers technically correspond to a reaction rather than to a specific enzyme molecule, two or more closely related enzymes may share a single EC number. Overall, glycosylases are notably overrepresented owing to the ready availability of fluorogenic substrates for these enzymes, whereas oxidoreductases, lyases and isomerases are underrepresented.

Similar articles

Cited by

References

    1. Altschuler SJ, Wu LF. Cellular heterogeneity: do differences make a difference? Cell. 2010;141:559–563. - PMC - PubMed
    1. Spiller DG, et al. Measurement of single-cell dynamics. Nature. 2010;465:736–745. - PubMed
    1. Tawfik DS. Messy biology and the origins of evolutionary innovations. Nat. Chem. Biol. 2010;6:692–696. - PubMed
    1. Zernicka-Goetz M, et al. Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo. Nat. Rev. Genet. 2009;10:467–477. - PubMed
    1. Prussin C, et al. TH2 heterogeneity: Does function follow form? J. Allergy Clin. Immunol. 2010;126:1094–1098. - PMC - PubMed

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources