Fluorescence Probing Live Single-cell Mass Spectrometry for Direct Analysis of Organelle Metabolism

Esaki, Tsuyoshi; Masujima, Tsutomu

doi:10.2116/analsci.31.1211

Cited by 29 publications

(27 citation statements)

References 13 publications

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“…The individual tumor cells, sorted by flow cytometry, were introduced into a nano-tip, and then subjected to in-tip super-sonication for homogenization and MS analysis. In another example, fluorescence probes were used to reveal the location of mitochondria in live single HepG2 cells, which were aspirated into a fine tip with ionization solvents and analyzed by ESI-MS. 51…”

Section: Characterization Methods In Small-volume Neurometabolomicsmentioning

confidence: 99%

Single Cell Neurometabolomics

Meng

Philip

Yang

et al. 2017

ACS Chem. Neurosci.

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Metabolomics, the characterization of metabolites and their changes within biological systems, has seen great technological and methodological progress over the past decade. Most metabolomic experiments involve the characterization of the small-molecule content of fluids or tissue homogenates. While these microliter and larger volume metabolomic measurements can characterize hundreds to thousands of compounds, the coverage of molecular content decreases as sample sizes are reduced to the nanoliter and even to the picoliter volume range. Recent progress has enabled the ability to characterize the major molecules found within specific individual cells. Especially within the brain, a myriad of cell types are colocalized, and oftentimes only a subset of these cells undergo changes in both healthy and pathological states. Here we highlight recent progress in mass spectrometry-based approaches used for single cell metabolomics, emphasizing their application to neuroscience research. Single cell studies can be directed to measuring differences between members of populations of similar cells (e.g., oligodendrocytes), as well as characterizing differences between cell types (e.g., neurons and astrocytes), and are especially useful for measuring changes occurring during different behavior states, exposure to diets and drugs, neuronal activity, and disease. When combined with other omics approaches such as transcriptomics, and with morphological and physiological measurements, single cell metabolomics aids fundamental neurochemical studies, has great potential in pharmaceutical development, and should improve the diagnosis and treatment of brain diseases.

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Section: Characterization Methods In Small-volume Neurometabolomicsmentioning

confidence: 99%

Single Cell Neurometabolomics

Meng

Philip

Yang

et al. 2017

ACS Chem. Neurosci.

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“…Recent technical advances in single cell metabolomics with cultured cells demonstrate the feasibility of reaching cellular resolution also for cells smaller than early embryonic blastomeres with subcellular sampling on the horizon (Esaki and Masujima, 2015); reviewed in Armbrecht and Dittrich (2017), Yang et al (2017), Qi et al (2018). Although challenging, microdissections using microneedles or capillaries on tissues from zebrafish will be ideally suited for single cell metabolomics facilitated by the rich resource of reporter transgenic lines for the identification of embryonic and larval anatomical structures.…”

Section: Recent Advances In Nano-sampling For Metabolomicsmentioning

confidence: 99%

Nano-Sampling and Reporter Tools to Study Metabolic Regulation in Zebrafish

Dickmeis

Feng

Mione

et al. 2019

Front. Cell Dev. Biol.

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In the past years, evidence has emerged that hallmarks of human metabolic disorders can be recapitulated in zebrafish using genetic, pharmacological or dietary interventions. An advantage of modeling metabolic diseases in zebrafish compared to other “lower organisms” is the presence of a vertebrate body plan providing the possibility to study the tissue-intrinsic processes preceding the loss of metabolic homeostasis. While the small size of zebrafish is advantageous in many aspects, it also has shortcomings such as the difficulty to obtain sufficient amounts for biochemical analyses in response to metabolic challenges. A workshop at the European Zebrafish Principal Investigator meeting in Trento, Italy, was dedicated to discuss the advantages and disadvantages of zebrafish to study metabolic disorders. This perspective article by the participants highlights strategies to achieve improved tissue-resolution for read-outs using “nano-sampling” approaches for metabolomics as well as live imaging of zebrafish expressing fluorescent reporter tools that inform on cellular or subcellular metabolic processes. We provide several examples, including the use of reporter tools to study the heterogeneity of pancreatic beta-cells within their tissue environment. While limitations exist, we believe that with the advent of new technologies and more labs developing methods that can be applied to minimal amounts of tissue or single cells, zebrafish will further increase their utility to study energy metabolism.

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“…Traditionally, cellular metabolism has been regarded as a deterministic process on the basis that metabolites appear in large numbers that filter out stochastic phenomena 8 . But this view is changing rapidly thanks to a growing number of single-cell measurements of metabolites and co-factors [9][10][11][12][13][14][15][16][17] that suggest that cell-to-cell metabolite variation is much more pervasive than previously thought. The functional implications of this heterogeneity are largely unknown but likely to be substantial given the roles of metabolism in many cellular processes, including growth 18 , gene regulation 19 , epigenetic control 20 and immunity 21 .…”

Section: Introductionmentioning

confidence: 99%

Computation of single-cell metabolite distributions using mixture models

Tonn

Thomas

Barahona

et al. 2020

Preprint

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Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

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Fluorescence Probing Live Single-cell Mass Spectrometry for Direct Analysis of Organelle Metabolism

Cited by 29 publications

References 13 publications

Single Cell Neurometabolomics

Single Cell Neurometabolomics

Nano-Sampling and Reporter Tools to Study Metabolic Regulation in Zebrafish

Computation of single-cell metabolite distributions using mixture models

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