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A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. ...

Karolinska Institutet

An aptamer can specifically bind to its target molecule, or hybridize with its complementary strand. A target bound aptamer complex has difficulty to hybridize with its complementary strand. It is possible to determine the concentration of target based on affinity separation system for the protein detection. Here, we exploited this property using C-reactive protein (CRP) specific RNA aptamers as probes that were immobilized by physical adsorption on carbon nanotubes (CNTs) activated gold interdigitated electrodes of capacitors. The selective binding ability of RNA aptamer with its target molecule was determined by change in capacitance after allowing competitive binding with CRP and complementary RNA (cRNA) strands in pure form and co-mixtures (CRP:cRNA=0:1, 1:0, 1:1, 1:2 and 2:1). The sensor showed significant capacitance change with pure forms of CRP/cRNA while responses reduced considerably in presence of CRP:cRNA in co-mixtures (1:1 and 1:2) because of the binding competition. At a critical CRP:cRNA ratio of 2:1, the capacitance response was dramatically lost because of the dissociation of adsorbed aptamers from the sensor surface to bind when excess CRP. Binding assays showed that the immobilized aptamers had strong affinity for cRNA (K(d)=1.98 μM) and CRP molecules (K(d)=2.4 μM) in pure forms, but low affinity for CRP:cRNA ratio of 2:1 (K(d)=8.58 μM). The dynamic detection range for CRP was determined to be 1-8 μM (0.58-4.6 μg/capacitor). The approach described in this study is a sensitive label-free method to detect proteins based on affinity separation of target molecules that can potentially be used for probing molecular interactions.

An aptamer based competition assay for protein detection using CNT activated gold-interdigitated capacitor arrays.

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography-high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine.

Irena Roci

Department of Medicine,
Unit of Computational Medicine,
Center for Molecular Medicine,
Department of Medicine, Unit of Computational Medicine

Karolinska Institutet

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Irena Roci

Department of Medicine,Unit of Computational Medicine,Center for Molecular Medicine,Department of Medicine, Unit of Computational Medicine

Karolinska Institutet

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Department of Medicine,
Unit of Computational Medicine,
Center for Molecular Medicine,
Department of Medicine, Unit of Computational Medicine