Differences between FLIM phasor analyses for data collected with the Becker and Hickl SPC830 card and with the FLIMbox card

Ranjit, Suman; Malacrida, Leonel; Gratton, Enrico

doi:10.1002/jemt.23061

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…Due to the non-linearity of the time to amplitude converter (TAC), often only 9–10 ns of this time can be used. The intensity decays of biological fluorophores with lifetime above 2 ns do not reach background levels during this timescale (here exemplified by using Rhodamine 110 in water with a 4.0 ns lifetime ( Figure 5 a, [ 40 ]). The transformation of this decay to phasor can lead to improper transformation with a resultant breakdown of phasor linearity.…”

Section: Breakdown Of Linearity and Improper Transformation To Phasor...mentioning

confidence: 99%

“…Please see Figures 7 and 8 from the original [ 39 ] for more details and explanation of the mathematical quantities. (Panels a , b and c – e are adapted with permission from [ 40 ] and [ 39 ], respectively).…”

Section: Figurementioning

confidence: 99%

“…There are situations wherein linear combination breaks down, often due to improper transformation of the time correlated single photon counting (TCSPC) intensity decays to the phasor space [ 39 , 40 ] or due to application of further restrictions, e.g., diffusion phasor. Similar non-linearity can be observed when only a part of the fluorescence spectra is transformed in the case of spectral phasors.…”

Section: Introductionmentioning

confidence: 99%

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Linear Combination Properties of the Phasor Space in Fluorescence Imaging

Torrado

Malacrida

Ranjit

2022

Sensors

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The phasor approach to fluorescence lifetime imaging, and more recently hyperspectral fluorescence imaging, has increased the use of these techniques, and improved the ease and intuitiveness of the data analysis. The fit-free nature of the phasor plots increases the speed of the analysis and reduces the dimensionality, optimization of data handling and storage. The reciprocity principle between the real and imaginary space—where the phasor and the pixel that the phasor originated from are linked and can be converted from one another—has helped the expansion of this method. The phasor coordinates calculated from a pixel, where multiple fluorescent species are present, depends on the phasor positions of those components. The relative positions are governed by the linear combination properties of the phasor space. According to this principle, the phasor position of a pixel with multiple components lies inside the polygon whose vertices are occupied by the phasor positions of these individual components and the distance between the image phasor to any of the vertices is inversely proportional to the fractional intensity contribution of that component to the total fluorescence from that image pixel. The higher the fractional intensity contribution of a vertex, the closer is the resultant phasor. The linear additivity in the phasor space can be exploited to obtain the fractional intensity contribution from multiple species and quantify their contribution. This review details the various mathematical models that can be used to obtain two/three/four components from phasor space with known phasor signatures and then how to obtain both the fractional intensities and phasor positions without any prior knowledge of either, assuming they are mono-exponential in nature. We note that other than for blind components, there are no restrictions on the type of the decay or their phasor positions for linear combinations to be valid—and they are applicable to complicated fluorescence lifetime decays from components with intensity decays described by multi-exponentials.

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Section: Breakdown Of Linearity and Improper Transformation To Phasor...mentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linear Combination Properties of the Phasor Space in Fluorescence Imaging

Torrado

Malacrida

Ranjit

2022

Sensors

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“…Further, if the calibration decay is not acquired properly, this can lead to improper conversions of data to phasors [208]. Additionally, if the phasor transformation is not done with data acquired in entire period of the laser repetition rate, advantages of phasor plot analysis can be diminished [209]. The consequences of such errors as well as ways to avoid them are outlined elsewhere [185,209].…”

Section: Fluorescence Lifetime Imaging Microscopy As a Tool To Measure In Situ Brain Metabolismmentioning

confidence: 99%

Altered substrate metabolism in neurodegenerative disease: new insights from metabolic imaging

Cleland

Al-Juboori

Dobrinskikh

et al. 2021

J Neuroinflammation

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Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and multiple sclerosis (MS), are relatively common and devastating neurological disorders. For example, there are 6 million individuals living with AD in the United States, a number that is projected to grow to 14 million by the year 2030. Importantly, AD, PD and MS are all characterized by the lack of a true disease-modifying therapy that is able to reverse or halt disease progression. In addition, the existing standard of care for most NDs only addresses the symptoms of the disease. Therefore, alternative strategies that target mechanisms underlying the neuropathogenesis of disease are much needed. Recent studies have indicated that metabolic alterations in neurons and glia are commonly observed in AD, PD and MS and lead to changes in cell function that can either precede or protect against disease onset and progression. Specifically, single-cell RNAseq studies have shown that AD progression is tightly linked to the metabolic phenotype of microglia, the key immune effector cells of the brain. However, these analyses involve removing cells from their native environment and performing measurements in vitro, influencing metabolic status. Therefore, technical approaches that can accurately assess cell-specific metabolism in situ have the potential to be transformative to our understanding of the mechanisms driving AD. Here, we review our current understanding of metabolism in both neurons and glia during homeostasis and disease. We also evaluate recent advances in metabolic imaging, and discuss how emerging modalities, such as fluorescence lifetime imaging microscopy (FLIM) have the potential to determine how metabolic perturbations may drive the progression of NDs. Finally, we propose that the temporal, regional, and cell-specific characterization of brain metabolism afforded by FLIM will be a critical first step in the rational design of metabolism-focused interventions that delay or even prevent NDs.

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“…Furthermore, the phasor formulation is independent of the sample and hence allows for the identification of fluorescent species that may not have been accounted for in a particular study's imaging protocol. However, although the phasor method provides a graphical interface that simplifies FLI data interpretation, the mathematics underlying its computation can be challenging, its user-friendly interface requires some level of proficiency for accurate quantification (17), the approach may Significance Fluorescence lifetime imaging (FLI), although capable of providing powerful biomedical insight, necessitates computationally expensive inverse solvers to obtain parameters of interest, which has limited its broad dissemination, especially in clinical settings. Hence, a real-time, user-friendly approach has the potential to transform this field.…”

mentioning

confidence: 99%

Fast fit-free analysis of fluorescence lifetime imaging via deep learning

Smith

Yao

Sinsuebphon

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

123

124

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Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.

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Differences between FLIM phasor analyses for data collected with the Becker and Hickl SPC830 card and with the FLIMbox card

Cited by 24 publications

References 17 publications

Linear Combination Properties of the Phasor Space in Fluorescence Imaging

Linear Combination Properties of the Phasor Space in Fluorescence Imaging

Altered substrate metabolism in neurodegenerative disease: new insights from metabolic imaging

Fast fit-free analysis of fluorescence lifetime imaging via deep learning

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