Characterization of a spectrally diverse set of fluorescent proteins as FRET acceptors for mTurquoise2

The performance of Förster Resonance Energy Transfer (FRET) biosensors depends on brightness and photostability, which are dependent on the characteristics of the fluorescent proteins that are employed. Yellow fluorescent protein (YFP) is often used as an acceptor but YFP is prone to photobleaching and pH changes. In this study, we evaluated the properties of a diverse set of acceptor fluorescent proteins in combination with the optimized CFP variant mTurquoise2 as the donor. To determine the theoretical performance of acceptors, the Förster radius was determined. The practical performance was determined by measuring FRET efficiency and photostability of tandem fusion proteins in mammalian cells. Our results show that mNeonGreen is the most efficient acceptor for mTurquoise2 and that the photostability is better than SYFP2. The non-fluorescent YFP variant sREACh is an efficient acceptor, which is useful in lifetime-based FRET experiments. Among the orange and red fluorescent proteins, mCherry and mScarlet-I are the best performing acceptors. Several new pairs were applied in a multimolecular FRET based sensor for detecting activation of a heterotrimeric G-protein by G-protein coupled receptors. Overall, the sensor with mNeonGreen as acceptor and mTurquoise2 as donor showed the highest dynamic range in ratiometric FRET imaging experiments with the G-protein sensor

Identifying nuclear phenotypes using semi-supervised metric learning.

In systems-based approaches for studying processes such as cancer and development, identifying and characterizing individual cells within a tissue is the first step towards understanding the large-scale effects that emerge from the interactions between cells. To this end, nuclear morphology is an important phenotype to characterize the physiological and differentiated state of a cell. This study focuses on using nuclear morphology to identify cellular phenotypes in thick tissue sections imaged using 3D fluorescence microscopy. The limited label information, heterogeneous feature set describing a nucleus, and existence of subpopulations within cell-types makes this a difficult learning problem. To address these issues, a technique is presented to learn a distance metric from labeled data which is locally adaptive to account for heterogeneity in the data. Additionally, a label propagation technique is used to improve the quality of the learned metric by expanding the training set using unlabeled data. Results are presented on images of tumor stroma in breast cancer, where the framework is used to identify fibroblasts, macrophages and endothelial cells--three major stromal cells involved in carcinogenesis

Identifying nuclear phenotypes using semi-supervised metric learning

In systems-based approaches for studying processes such as cancer and development, identifying and characterizing individual cells within a tissue is the first step towards understanding the large-scale effects that emerge from the interactions between cells. To this end, nuclear morphology is an important phenotype to characterize the physiological and differentiated state of a cell. This study focuses on using nuclear morphology to identify cellular phenotypes in thick tissue sections imaged using 3D fluorescence microscopy. The limited label information, heterogeneous feature set describing a nucleus, and existence of sub-populations within cell-types makes this a difficult learning problem. To address these issues, a technique is presented to learn a distance metric from labeled data which is locally adaptive to account for heterogeneity in the data. Additionally, a label propagation technique is used to improve the quality of the learned metric by expanding the training set using unlabeled data. Results are presented on images of tumor stroma in breast cancer, where the framework is used to identify fibroblasts, macrophages and endothelial cells - three major stromal cells involved in carcinogenesis. © 2011 Springer-Verlag

Rational design of FRET-based sensor proteins

Real-time imaging of molecular events inside living cells is important for understanding the basis of physiological processes and diseases. Genetically encoded sensors that use fluorescence resonance energy transfer (FRET) between two fluorescent proteins are attractive in this respect because they do not require cell-invasive procedures, can be targeted to different locations in the cell and are easily adapted through mutagenesis and directed evolution approaches. Most FRET sensors developed so far show a relatively small difference in emission ratio upon activation, which severely limits their application in high throughput cell-based screening applications. In our work, we try to develop strategies that allow design of FRET-based sensors with intrinsically large ratiometric changes. This rational design approach requires a better understanding and quantitative description of the conformational changes in these fusion proteins. In this chapter, I first discuss some of the key factors and strategies that determine the ratiometric response of FRET sensors, followed by an overview of our recent work in this area. Important concepts that will be discussed are (1) the conformational behavior of flexible peptide linkers to quantitatively describe the dependence of energy transfer on linker length and (2) the control of intramolecular domain interactions using the concept of effective molecular concentration

Rational design of FRET-based sensor proteins

Real-time imaging of molecular events inside living cells is important for understanding the basis of physiological processes and diseases. Genetically encoded sensors that use fluorescence resonance energy transfer (FRET) between two fluorescent proteins are attractive in this respect because they do not require cell-invasive procedures, can be targeted to different locations in the cell and are easily adapted through mutagenesis and directed evolution approaches. Most FRET sensors developed so far show a relatively small difference in emission ratio upon activation, which severely limits their application in high throughput cell-based screening applications. In our work, we try to develop strategies that allow design of FRET-based sensors with intrinsically large ratiometric changes. This rational design approach requires a better understanding and quantitative description of the conformational changes in these fusion proteins. In this chapter, I first discuss some of the key factors and strategies that determine the ratiometric response of FRET sensors, followed by an overview of our recent work in this area. Important concepts that will be discussed are (1) the conformational behavior of flexible peptide linkers to quantitatively describe the dependence of energy transfer on linker length and (2) the control of intramolecular domain interactions using the concept of effective molecular concentration

Labeling and qualification of endothelial progenitor cells for tracking in tissue engineering: An in vitro study

Purpose: In order to track location and distribution of endothelial cells (ECs) within scaffolds in vitro, we chose lentiPGK-TdTomato transduction of human endothelial progenitor cells (EPCs) isolated and differentiated from cord blood. Because transduction could have a functional impact on cell behavior, we checked different parameters for qualification of labeled-EPCs as well as their use for potential applications in the context of vascular and bone tissue engineering.Methods: After isolation and expansion, EPCs were classically characterized then transduced with the lentiviral vector containing the TdTomato protein gene under the control of the phosphoglycerate kinase (PGK) promoter. Conventional karyotyping, differentiation capacity, viability, proliferation assays were performed with labeled and unlabeled EPCs. Scaffolds and co-cultures were explored with labeled EPCs, in static or shear stress conditions.Results: Our results show that cell labeling did not affect cell adhesion nor induce cell death. Cell labeling did not induce more chromosomal aberrations. Phenotypical characterization was not affected. In the context of tissue engineering applications, labeled EPCs maintained their ability to line 2D or 3D scaffolds, withstand physiological arterial shear stress, and form tubular networks in co-cultures with human osteoblast progenitor cells.Conclusions: It is possible to label human EPCs with TdTomato without affecting their behavior by the transduction procedure. This creates an important tool for numerous applications. Our results provide a qualification of labeled EPCs in comparison with unlabeled ones for vascular and bone tissue engineering