Protein-bound NAD(P)H Lifetime is Sensitive to Multiple Fates of Glucose Carbon

Sharick, Joe T.; Favreau, Peter F.; Gillette, Amani A.; Sdao, Sophia M.; Merrins, Matthew J.; Skala, Melissa C.

doi:10.1038/s41598-018-23691-x

Cited by 97 publications

(144 citation statements)

References 60 publications

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“…For simplicity of the analysis we are not portraying the differences between the breast cells and pancreatic cells, but the respective differences within these groups. For understanding the differences between the two cell types, comparison between HPV16 cells ( HPDE cells ) and MCF10A cells under metabolic reagents can be found here:(15). …”

Section: Resultsmentioning

confidence: 99%

“…This reaction uses NADH as a cofactor and generates NAD + . B) NADH Lifetime curve with addition of LDH: [ Figure adapted from Sharick et al ( 15 ) ] the IRF (black curve) is convolved with the fluorescence of NADH (purple) and compared as an overlay. LDH was added to obtain 1.4uM, 3.5uM and 7.3uM and respective lifetime curves are plotted as an overlay in colors blue, green and red respectively.…”

Section: Figure 1)mentioning

confidence: 99%

“…For understanding the differences between the two cell types, comparison between HPV16 cells (HPDE cells) and MCF10A cells under metabolic reagents can be found in Ref. (15). MCF 10 cell line series constitute of different mutant cell lines, which have multiple representative cell lines for breast cancer progression: benign hyperplasia-atypical hyperplasiacarcinoma-malignant tumor.…”

Section: Intracellular Heterogeneity Among Cell Types and Selection Omentioning

confidence: 99%

“…This rise in LDH levels in a solution-based measurement as presented in Sharick et.al., increases the mean measured fluorescence lifetime. Multiple techniques and methods re-established these lifetime based observations, sometimes even reporting equivocal absolute values of bound lifetimes for same enzyme (NADH-LDH as 1.8ns ,2.8, 3.2ns) (15–17). Another prevailing explanation is the possibility of four NADH lifetimes in a cell (0.4 free, 1-conformation of free NADH, 1.7-bound NADH, 3.2-NADH bound to an enzyme in ETC(Electron Transport Chain)(18).…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Figure 1)mentioning

confidence: 99%

Section: Intracellular Heterogeneity Among Cell Types and Selection Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Many FLIM studies including intravital (28,29,50,51) or in vitro approaches (37,(52)(53)(54) have established the significance of NAD(P)H lifetime as a reliable metric of metabolic fluctuations. Many FLIM studies including intravital (28,29,50,51) or in vitro approaches (37,(52)(53)(54) have established the significance of NAD(P)H lifetime as a reliable metric of metabolic fluctuations.…”

mentioning

confidence: 99%

Fluorescence lifetime shifts of NAD(P)H during apoptosis measured by time‐resolved flow cytometry

et al. 2018

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Autofluorescence from the intracellular metabolite, NAD(P)H, is a biomarker that is widely used and known to reliably screen and report metabolic activity as well as metabolic fluctuations within cells. As a ubiquitous endogenous fluorophore, NAD(P)H has a unique rate of fluorescence decay that is altered when bound to coenzymes. In this work we measure the shift in the fluorescence decay, or average fluorescence lifetime (1–3 ns), of NAD(P)H and correlate this shift to changes in metabolism that cells undergo during apoptosis. Our measurements are made with a flow cytometer designed specifically for fluorescence lifetime acquisition within the ultraviolet to violet spectrum. Our methods involved culture, treatment, and preparation of cells for cytometry and microscopy measurements. The evaluation we performed included observations and quantification of the changes in endogenous emission owing to the induction of apoptosis as well as changes in the decay kinetics of the emission measured by flow cytometry. Shifts in NAD(P)H fluorescence lifetime were observed as early as 15 min post‐treatment with an apoptosis inducing agent. Results also include a phasor analysis to evaluate free to bound ratios of NAD(P)H at different time points. We defined the free to bound ratios as the ratio of ‘short‐to‐long’ (S/L) fluorescence lifetime, where S/L was found to consistently decrease with an increase in apoptosis. With a quantitative framework such as phasor analysis, the short and long lifetime components of NAD(P)H can be used to map the cycling of free and bound NAD(P)H during the early‐to‐late stages of apoptosis. The combination of lifetime screening and phasor analyses provides the first step in high throughput metabolic profiling of single cells and can be leveraged for screening and sorting for a range of applications in biomedicine. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Identification of rare cell populations in autofluorescence lifetime image data

Cardona

Walsh

2022

Cytometry Pt A

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Drug-resistant cells and anti-inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live-cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single-cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures

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Protein-bound NAD(P)H Lifetime is Sensitive to Multiple Fates of Glucose Carbon

Cited by 97 publications

References 60 publications

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

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

Fluorescence lifetime shifts of NAD(P)H during apoptosis measured by time‐resolved flow cytometry

Identification of rare cell populations in autofluorescence lifetime image data

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