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Record W4389613108 · doi:10.1186/s13287-023-03606-y

2P-FLIM unveils time-dependent metabolic shifts during osteogenic differentiation with a key role of lactate to fuel osteogenesis via glutaminolysis identified

2023· article· en· W4389613108 on OpenAlexaff
Nuno Neto, Meenakshi Suku, David A. Hoey, Michael G. Monaghan

Bibliographic record

VenueStem Cell Research & Therapy · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsTrinity College
FundersEngineering and Physical Sciences Research CouncilScience Foundation Ireland
KeywordsGlutaminolysisKey (lock)ChemistryStem cellCell biologyBiochemistryBiologyMetabolismGlycolysisEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Human mesenchymal stem cells (hMSCs) utilize discrete biosynthetic pathways to self-renew and differentiate into specific cell lineages, with undifferentiated hMSCs harbouring reliance on glycolysis and hMSCs differentiating towards an osteogenic phenotype relying on oxidative phosphorylation as an energy source. METHODS: In this study, the osteogenic differentiation of hMSCs was assessed and classified over 14 days using a non-invasive live-cell imaging modality-two-photon fluorescence lifetime imaging microscopy (2P-FLIM). This technique images and measures NADH fluorescence from which cellular metabolism is inferred. RESULTS: During osteogenesis, we observe a higher dependence on oxidative phosphorylation (OxPhos) for cellular energy, concomitant with an increased reliance on anabolic pathways. Guided by these non-invasive observations, we validated this metabolic profile using qPCR and extracellular metabolite analysis and observed a higher reliance on glutaminolysis in the earlier time points of osteogenic differentiation. Based on the results obtained, we sought to promote glutaminolysis further by using lactate, to improve the osteogenic potential of hMSCs. Higher levels of mineral deposition and osteogenic gene expression were achieved when treating hMSCs with lactate, in addition to an upregulation of lactate metabolism and transmembrane cellular lactate transporters. To further clarify the interplay between glutaminolysis and lactate metabolism in osteogenic differentiation, we blocked these pathways using BPTES and α-CHC respectively. A reduction in mineralization was found after treatment with BPTES and α-CHC, demonstrating the reliance of hMSC osteogenesis on glutaminolysis and lactate metabolism. CONCLUSION: In summary, we demonstrate that the osteogenic differentiation of hMSCs has a temporal metabolic profile and shift that is observed as early as day 3 of cell culture using 2P-FLIM. Furthermore, extracellular lactate is shown as an essential metabolite and metabolic fuel to ensure efficient osteogenic differentiation and as a signalling molecule to promote glutaminolysis. These findings have significant impact in the use of 2P-FLIM to discover potent approaches towards bone tissue engineering in vitro and in vivo by engaging directly with metabolite-driven osteogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.295
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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