The flow of events: How the sequence of molecular interactions is seen by the latest, user-friendly high throughput flow cytometric FRET

FRET causing misleading signal from fluorescein excited by the violet laser in flow cytometry

Multiple immunolabeling introduces high risks of interferences between fluorescences. As an example, in analyzing T cell clonality, we recently reported a fluorescence resonance energy transfer (FRET) effect providing an unexpected signal on B770 (PE-Cy7) detector, on the Vβ-PE positive CD3 APC-Alexa750+ T cell subsets. Here, we report another FRET effect produced by the violet laser in Vβ-FITC positive CD3-Pacific Blue (PB) T cells providing signal on V550 (Krome Orange; KrO) detector. The study was performed on fresh whole blood, labeled with anti-CD3-PB, CD8-KrO, Vbeta FITC, Vbeta PE, CD4 AA750 then fixed, treated for erythrolysis, and washed before analysis on DxFlex cytometer from Beckman Coulter. Data were analyzed using Kaluza software. Using this panel, we repeatedly observed an added CD8dim-KrO (V550) cell population on all Vβ FITC positive T cells. The unexpected green signal excited by the violet laser was still observed after removing anti-CD8-KrO (FMO) but disappeared where either anti-CD3-PB or anti-Vβ-FITC was removed. The effect was also observed with an anti-TCR gamma delta-FITC labeling, but not with another FITC labeled antibody targeting a protein out of the CD3-TCR complex. The analysis of fluorochrome spectra confirms that PB emission and FITC excitation spectra partly overlap. This observation clearly reminds users that FRET can give misleading results in case of labeling of very close markers with complementary fluorochromes. This risk has to be considered in panel design. These observations clearly highlight the potential for FRET to give misleading results in cases where very close markers are labeled with complementary fluorochromes. This risk must be considered when designing panels. To our knowledge, this is the first description of a FRET between PB and FITC as acceptor thus excited by the violet laser.

Flow cytometry based-FRET: basics, novel developments and future perspectives

Förster resonance energy transfer (FRET) is a widespread technology used to analyze and quantify protein interactions in multiple settings. While FRET is traditionally measured by microscopy, flow cytometry based-FRET is becoming popular within the last decade and more commonly used. Flow cytometry based-FRET offers the possibility to assess FRET in a short time-frame in a high number of cells thereby allowing stringent and statistically robust quantification of FRET in multiple samples. Furthermore, established, simple and easy to implement gating strategies facilitate the adaptation of flow cytometry based-FRET measurements to most common flow cytometers. We here summarize the basics of flow cytometry based-FRET, highlight recent novel developments in this field and emphasize on exciting future perspectives.

Fluorescent energy transfer causing misleading signal in multicolor flow cytometry

Multiple immunolabeling introduces high risks of interferences between fluorochromes. In an intend to analyze T cell clonality using CD3-APC Alexa750, CD4-Pac Blue, CD8-Krome Orange, CD56-PE-Cy7 and Vbeta clonotypes FITC and PE, we repeatedly observed a clear, unexpected signal on B770 (PE-Cy7) detector on the Vb subset mimicking a lymphoproliferative disorder. The aim of this study was to identify and prevent this source of artifact. The study was performed on a seven color panel performed on fresh whole blood, labeled, fixed, lyzed and analyzed on Navios Cytometer Beckman Coulter. Data were reanalyzed using Kaluza. Eleven tubes tested two clonotypes each with the same T cell backbone. Only one representative combination is presented. Using this panel, we observed repeatedly a strong CD56 PE-Cy7 (B755 LP) on all Vbeta1 T cell subsets but not on Vbeta 2-FITC T cells. The effect was still observed after removing CD56-PE-Cy7 (Full Minus One). Changing anti-CD3 APC-Alexa 750 with CD3APC, the B755 LP signal disappeared but a B695/30 signal appeared. Shifting to CD3-FITC abolished any unexpected red signal. This demonstrates a fluorescent energy transfer (FRET) between PE excited by the blue laser and Alexa750 to be excited by the red laser. Accordingly, the Vbeta PE fluorescence intensity was reduced when FRET happened and clearly increased when CD3-FITC was used instead. This observation clearly reminds that FRET can give misleading results in case of labeling of very close markers with complementary fluorochromes. This risk has to be considered in panel design.

Analysis of GPI-Anchored Receptor Distribution and Dynamics in Live Cells by Tag-mediated Enzymatic Labeling and FRET

The analysis of glycosylphosphatidylinositol (GPI)-anchored receptor distribution and dynamics in live cells is challenging, because their clusters exhibit subdiffraction-limited sizes and are highly dynamic. However, the cellular response depends on the GPI-anchored receptor clusters' distribution and dynamics. Here, we compare three approaches to GPI-anchored receptor labeling (with antibodies, fluorescent proteins, and enzymatically modified small peptide tags) and use several variants of Förster resonance energy transfer (FRET) detection by confocal microscopy and flow cytometry in order to obtain insight into the distribution and the ligand-induced dynamics of GPI-anchored receptors. We found that the enzyme-mediated site-specific fluorescence labeling of T-cadherin modified with a short peptide tag (12 residues in length) have several advantages over labeling by fluorescent proteins or antibodies, including (i) the minimized distortion of the protein's properties, (ii) the possibility to use a cell-impermeable fluorescent substrate that allows for selective labeling of surface-exposed proteins in live cells, and (iii) superior control of the donor to acceptor molar ratio. We successfully detected the FRET of GPI-anchored receptors, T-cadherin, and ephrin-A1, without ligands, and showed in real time that adiponectin induces stable T-cadherin cluster formation. In this paper (which is complementary to our recent research (Balatskaya et al., 2019)), we present the practical aspects of labeling and the heteroFRET measurements of GPI-anchored receptors to study their dynamics on a plasma membrane in live cells.

Advanced FRET normalization allows quantitative analysis of protein interactions including stoichiometries and relative affinities in living cells

FRET (Fluorescence Resonance Energy Transfer) measurements are commonly applied to proof protein-protein interactions. However, standard methods of live cell FRET microscopy and signal normalization only allow a principle assessment of mutual binding and are unable to deduce quantitative information of the interaction. We present an evaluation and normalization procedure for 3-filter FRET measurements, which reflects the process of complex formation by plotting FRET-saturation curves. The advantage of this approach relative to traditional signal normalizations is demonstrated by mathematical simulations. Thereby, we also identify the contribution of critical parameters such as the total amount of donor and acceptor molecules and their molar ratio. When combined with a fitting procedure, this normalization facilitates the extraction of key properties of protein complexes such as the interaction stoichiometry or the apparent affinity of the binding partners. Finally, the feasibility of our method is verified by investigating three exemplary protein complexes. Altogether, our approach offers a novel method for a quantitative analysis of protein interactions by 3-filter FRET microscopy, as well as flow cytometry. To facilitate the application of this method, we created macros and routines for the programs ImageJ, R and MS-Excel, which we make publicly available.

Cell and small animal models for phenotypic drug discovery

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.