Monitoring Lactate Dynamics in Individual Macrophages with a Genetically Encoded Probe

Baeza-Lehnert, Felipe; Flores, Carlos A.; Guequén, Anita; Barros, L. Felipe

doi:10.1007/978-1-0716-0802-9_2

Cited by 1 publication

(2 citation statements)

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“…Laconic 22 is a genetically-encoded, FRET-based lactate biosensor comprised of the lactate binding domain of the bacterial transcription factor, LldR, flanked by mTFP and Venus (Figure S1A). Proximity of the fluorophores in the absence of lactate binding (low [lactate]) results in constitutive mTFP–Venus FRET, which is abrogated when the sensor binds lactate (high [lactate]) 22, 23 (Figure S1A). Therefore, changes in cytosolic [lactate] are inversely related to mTFP–Venus FRET efficiency in cells expressing Laconic, which we refer to as the Laconic ratio 22, 23 .…”

Section: Resultsmentioning

confidence: 99%

“…Proximity of the fluorophores in the absence of lactate binding (low [lactate]) results in constitutive mTFP–Venus FRET, which is abrogated when the sensor binds lactate (high [lactate]) 22, 23 (Figure S1A). Therefore, changes in cytosolic [lactate] are inversely related to mTFP–Venus FRET efficiency in cells expressing Laconic, which we refer to as the Laconic ratio 22, 23 . Here, we expressed UAS-Laconic 24 under the control of the d42-GAL4 glutamatergic/motor neuron driver 25, 26 ( d42>Laconic ).…”

Section: Resultsmentioning

confidence: 99%

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Intracellular Lactate Dynamics Reveal the Metabolic Diversity ofDrosophilaGlutamatergic Neurons

Price,

Moore,

Venkatachalam

2024

Preprint

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SUMMARYLactate, an intermediary between glycolysis and mitochondrial oxidative phosphorylation, reflects the metabolic state of neurons. Here, we utilized a genetically-encoded lactate FRET biosensor to uncover subpopulations of distinct metabolic states amongDrosophilaglutamatergic neurons. Neurons within specific subpopulations exhibited correlated lactate flux patterns that stemmed from inherent cellular properties rather than neuronal interconnectivity. Further, individual neurons exhibited consistent patterns of lactate flux over time such that stimulus-evoked changes in lactate were correlated with pre-treatment fluctuations. Leveraging these temporal autocorrelations, deep-learning models accurately predicted post-stimulus responses from pre-stimulus fluctuations. These findings point to the existence of distinct neuronal subpopulations, each characterized by unique lactate dynamics, and raise the possibility that neurons with correlated metabolic activities might synchronize across different neural circuits. Such synchronization, rooted in neuronal metabolic states, could influence information processing in the brain.

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