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1. Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity

   https://doi.org/10.1073/pnas.2118241119  

   Shirshin, Evgeny A. ; Shirmanova, Marina V. ; Gayer, Alexey V. ; Lukina, Maria M. ; Nikonova, Elena E. ; Yakimov, Boris P. ; Budylin, Gleb S. ; Dudenkova, Varvara V. ; Ignatova, Nadezhda I. ; Komarov, Dmitry V. ; et al ( March 2022 , Proceedings of the National Academy of Sciences)

   Molecular, morphological, and physiological heterogeneity is the inherent property of cells which governs differences in their response to external influence. Tumor cell metabolic heterogeneity is of a special interest due to its clinical relevance to tumor progression and therapeutic outcomes. Rapid, sensitive, and noninvasive assessment of metabolic heterogeneity of cells is a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique, is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters of endogenous fluorophores, such as NAD(P)H. To achieve accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this paper, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity is demonstrated. This has been achieved using an approach to data analysis based on the nonparametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations compared to more traditional approaches of FLIM measurements and analysis. The approach was further validated for imaging cultured cancer cells treated with chemotherapy. These results pave the way for accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilitiesmore »and predicting clinical outcomes.« less
2. Sorting by interfacial tension (SIFT): label-free selection of live cells based on single-cell metabolism

   https://doi.org/10.1039/c8lc01328d  

   Pan, Ching W. ; Horvath, Daniel G. ; Braza, Samuel ; Moore, Trevor ; Lynch, Annabella ; Feit, Cameron ; Abbyad, Paul ( April 2019 , Lab on a Chip)

   Selection of live cells from a population is critical in many biological studies and biotechnologies. We present here a novel droplet microfluidic approach that allows for label-free and passive selection of live cells using the glycolytic activity of individual cells. It was observed that with the use of a specific surfactant utilized to stabilize droplet formation, the interfacial tension of droplets was very sensitive to pH. After incubation, cellular lactate release results in droplets containing a live cell to attain a lower pH than other droplets. This enables the sorting of droplets containing live cells when confined droplets flow over a microfabricated trench oriented diagonally with respect to the direction of flow. The technique is demonstrated with human U87 glioblastoma cells for the selection of only droplets containing a live cell while excluding either empty droplets or droplets containing a dead cell. This label-free sorting method, dubbed sorting by interfacial tension (SIFT) presents a new strategy to sort diverse cell types based on metabolic activity.
3. Combined noninvasive metabolic and spindle imaging as potential tools for embryo and oocyte assessment

   https://doi.org/10.1093/humrep/dez210  

   Sanchez, Tim ; Venturas, Marta ; Aghvami, S. Ali ; Yang, Xingbo ; Fraden, Seth ; Sakkas, Denny ; Needleman, Daniel J. ( December 2019 , Human Reproduction)

   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?

   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.

   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.

   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.

   Experiments were performed in mouse embryos and oocytes. Samples weremore »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.

   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).

   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.

   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.

   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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4. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

   https://doi.org/10.1186/s40168-021-01213-8  

   Zhou, Zhichao ; Tran, Patricia Q. ; Breister, Adam M. ; Liu, Yang ; Kieft, Kristopher ; Cowley, Elise S. ; Karaoz, Ulas ; Anantharaman, Karthik ( February 2022 , Microbiome)

   Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

   We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, ormore »single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

   METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 athttps://github.com/AnantharamanLab/METABOLIC.

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5. Measurement of oxygen consumption rates of human renal proximal tubule cells in an array of organ-on-chip devices to monitor drug-induced metabolic shifts

   https://doi.org/10.1038/s41378-022-00442-7  

   Kann, Samuel H. ; Shaughnessey, Erin M. ; Coppeta, Jonathan R. ; Azizgolshani, Hesham ; Isenberg, Brett C. ; Vedula, Else M. ; Zhang, Xin ; Charest, Joseph L. ( September 2022 , Microsystems & Nanoengineering)

   Measurement of cell metabolism in moderate-throughput to high-throughput organ-on-chip (OOC) systems would expand the range of data collected for studying drug effects or disease in physiologically relevant tissue models. However, current measurement approaches rely on fluorescent imaging or colorimetric assays that are focused on endpoints, require labels or added substrates, and lack real-time data. Here, we integrated optical-based oxygen sensors in a high-throughput OOC platform and developed an approach for monitoring cell metabolic activity in an array of membrane bilayer devices. Each membrane bilayer device supported a culture of human renal proximal tubule epithelial cells on a porous membrane suspended between two microchannels and exposed to controlled, unidirectional perfusion and physiologically relevant shear stress for several days. For the first time, we measured changes in oxygen in a membrane bilayer format and used a finite element analysis model to estimate cell oxygen consumption rates (OCRs), allowing comparison with OCRs from other cell culture systems. Finally, we demonstrated label-free detection of metabolic shifts in human renal proximal tubule cells following exposure to FCCP, a drug known for increasing cell oxygen consumption, as well as oligomycin and antimycin A, drugs known for decreasing cell oxygen consumption. The capability to measure cellmore »OCRs and detect metabolic shifts in an array of membrane bilayer devices contained within an industry standard microtiter plate format will be valuable for analyzing flow-responsive and physiologically complex tissues during drug development and disease research.

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