Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed
- Award ID(s):
- 2013771
- NSF-PAR ID:
- 10440824
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 9
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract flimGANE (f luorescencel ifetimeim aging based onG enerativeA dversarialN etworkE stimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE ) and thatflimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability,flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical. -
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Abstract STUDY QUESTION Is the combined use of fluorescence lifetime imaging microscopy (FLIM)-based metabolic imaging and second harmonic generation (SHG) spindle imaging a feasible and safe approach for noninvasive embryo assessment?
SUMMARY ANSWER Metabolic imaging can sensitively detect meaningful metabolic changes in embryos, SHG produces high-quality images of spindles and the methods do not significantly impair embryo viability.
WHAT IS KNOWN ALREADY Proper metabolism is essential for embryo viability. Metabolic imaging is a well-tested method for measuring metabolism of cells and tissues, but it is unclear if it is sensitive enough and safe enough for use in embryo assessment.
STUDY DESIGN, SIZE, DURATION This study consisted of time-course experiments and control versus treatment experiments. We monitored the metabolism of 25 mouse oocytes with a noninvasive metabolic imaging system while exposing them to oxamate (cytoplasmic lactate dehydrogenase inhibitor) and rotenone (mitochondrial oxidative phosphorylation inhibitor) in series. Mouse embryos (n = 39) were measured every 2 h from the one-cell stage to blastocyst in order to characterize metabolic changes occurring during pre-implantation development. To assess the safety of FLIM illumination, n = 144 illuminated embryos were implanted into n = 12 mice, and n = 108 nonilluminated embryos were implanted into n = 9 mice.
PARTICIPANTS/MATERIALS, SETTING, METHODS Experiments were performed in mouse embryos and oocytes. Samples were monitored with noninvasive, FLIM-based metabolic imaging of nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Between NADH cytoplasm, NADH mitochondria and FAD mitochondria, a single metabolic measurement produces up to 12 quantitative parameters for characterizing the metabolic state of an embryo. For safety experiments, live birth rates and pup weights (mean ± SEM) were used as endpoints. For all test conditions, the level of significance was set at P < 0.05.
MAIN RESULTS AND THE ROLE OF CHANCE Measured FLIM parameters were highly sensitive to metabolic changes due to both metabolic perturbations and embryo development. For oocytes, metabolic parameter values were compared before and after exposure to oxamate and rotenone. The metabolic measurements provided a basis for complete separation of the data sets. For embryos, metabolic parameter values were compared between the first division and morula stages, morula and blastocyst and first division and blastocyst. The metabolic measurements again completely separated the data sets. Exposure of embryos to excessive illumination dosages (24 measurements) had no significant effect on live birth rate (5.1 ± 0.94 pups/mouse for illuminated group; 5.7 ± 1.74 pups/mouse for control group) or pup weights (1.88 ± 0.10 g for illuminated group; 1.89 ± 0.11 g for control group).
LIMITATIONS, REASONS FOR CAUTION The study was performed using a mouse model, so conclusions concerning sensitivity and safety may not generalize to human embryos. A limitation of the live birth data is also that although cages were routinely monitored, we could not preclude that some runt pups may have been eaten.
WIDER IMPLICATIONS OF THE FINDINGS Promising proof-of-concept results demonstrate that FLIM with SHG provide detailed biological information that may be valuable for the assessment of embryo and oocyte quality. Live birth experiments support the method’s safety, arguing for further studies of the clinical utility of these techniques.
STUDY FUNDING/COMPETING INTEREST(S) Supported by the Blavatnik Biomedical Accelerator Grant at Harvard University and by the Harvard Catalyst/The Harvard Clinical and Translational Science Center (National Institutes of Health Award UL1 TR001102), by NSF grants DMR-0820484 and PFI-TT-1827309 and by NIH grant R01HD092550-01. T.S. was supported by a National Science Foundation Postdoctoral Research Fellowship in Biology grant (1308878). S.F. and S.A. were supported by NSF MRSEC DMR-1420382. Becker and Hickl GmbH sponsored the research with the loaning of equipment for FLIM. T.S. and D.N. are cofounders and shareholders of LuminOva, Inc., and co-hold patents (US20150346100A1 and US20170039415A1) for metabolic imaging methods. D.S. is on the scientific advisory board for Cooper Surgical and has stock options with LuminOva, Inc.
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Abstract The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co‐enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell‐cycle status of tumor cells. Heterogeneity in tumor cell‐cycle status (e. g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell‐cycle status is closely linked to cellular metabolism. Thus, this study applies cell‐level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two‐photon microscopy and time‐correlated single photon counting are used to measure optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), NAD(P)H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P)H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares ‐ discriminant analysis (PLS‐DA) are used to exploit all measurements together. Leave‐one‐out cross validation of the model yielded high classification accuracies (92.4 and 90.1 % for two and three populations, respectively). OMI and PLS‐DA also identifies each sub‐population within heterogeneous samples. These results establish single‐cell analysis with OMI and PLS‐DA as a label‐free method to distinguish cell‐cycle status within intact samples. This approach could be used to incorporate cell‐level tumor heterogeneity in cancer drug development.
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Abstract Motivation Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these ‘radiogenomic’ studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. Results We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. Availability and implementation https://github.com/novasmedley/deepRadiogenomics. Contact whsu@mednet.ucla.edu Supplementary information Supplementary data are available at Bioinformatics online.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2118241119
