A phasor approach analysis of multiphoton FLIM measurements of three-dimensional cell culture models

Lakner, Pirmin; Möller, Yvonne; Brucker, Sara; Schenke‐Layland, Katja; Monaghan, Michael G.

doi:10.1117/12.2220048

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Phasor plots are obtained by the numerical calculation of the discrete Fourier transform of the time domain lifetime decay curve. The conversion between two different schemes have been described previously(39–41). An alternative graphical plotting scheme can be used from the fitted values to generate these g and s values(42).…”

Section: Methodsmentioning

confidence: 99%

Autofluorescence lifetime imaging of cellular metabolism: Sensitivity toward cell density, pH, intracellular, and intercellular heterogeneity

Chacko

Eliceiri

2018

Cytometry Pt A

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Autofluorescence imaging (AFI) has greatly accelerated in the last decade, way past its origins in detecting endogenous signals in biological tissues to identify differences between samples. There are many endogenous fluorescence sources of contrast but the most robust and widely utilized have been those associated with metabolism. The intrinsically fluorescent metabolic cofactors Nicotinamide adenine dinucleotide (NAD+/NADH) and Flavin adenine dinucleotide (FAD/FADH2) have been utilized in a number of AFI applications including basic research, clinical and pharmaceutical studies. Fluorescence lifetime imaging microscopy (FLIM) has emerged as one of the more powerful AFI tools for NADH and FAD characterization due to its unique ability to non-invasively detect metabolite bound and free states and quantitate cellular redox ratio. However, despite this widespread biological use, many standardization methods are still needed to extend FLIM based AFI into a fully robust research and clinical diagnostic tools. FLIM is sensitive to a wide range of factors in the fluorophore microenvironment and there a number of analysis variables as well. To this end there has been an emphasis on developing imaging standards and ways to make the image acquisition and analysis more consistent. However biological conditions during FLIM based AFI imaging are rarely considered as key sources of FLIM variability. Here we present several experimental factors with supporting data of the cellular microenvironment such as confluency, pH, inter/intra cellular heterogeneity, and choice of cell line that need to be considered for accurate quantitative FLIM based AFI measurement of cellular metabolism.

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Section: Methodsmentioning

confidence: 99%