Metabolic Dynamics of In Vitro CD8+ T Cell Activation

Edwards-Hicks, Joy; Mitterer, Michael; Pearce, Erika L.; Buescher, Joerg M.

doi:10.3390/metabo11010012

Cited by 21 publications

(26 citation statements)

References 42 publications

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Section: Methodsmentioning

confidence: 99%

“…Metabolite analysis was carried out using an Agilent 1290 Infinity II UHPLC in line with a Bruker Impact II QTOF (resolution > 50,000 FSR). Details of LC-MS methods are described by Edwards-Hicks et al (2020). In brief, chromatographic separation of metabolites was performed using a Phenomenex Luna NH2 column (50 3 2 mm, 3 mm particles) using a solvent gradient of 100% buffer B (5 mM ammonium carbonate in 90% acetonitrile) to ll OPEN ACCESS Cell 184, 1-17.e1-e6, August 5, 2021 e4 Please cite this article in press as: Puleston et al, Polyamine metabolism is a central determinant of helper T cell lineage fidelity, Cell (2021), https://doi.org/10.1016/j.cell.2021.06.007 Article 90% buffer A (10 mM NH4 in water).…”

Section: Metabolite Tracingmentioning

confidence: 99%

See 1 more Smart Citation

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

Puleston

Baixauli

Sanin

et al. 2021

Cell

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174

132

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Polyamine synthesis represents one of the most profound metabolic changes during T cell activation, but the biological implications of this are scarcely known. Here, we show that polyamine metabolism is a fundamental process governing the ability of CD4 + helper T cells (T H ) to polarize into different functional fates. Deficiency in ornithine decarboxylase, a crucial enzyme for polyamine synthesis, results in a severe failure of CD4 + T cells to adopt correct subset specification, underscored by ectopic expression of multiple cytokines and lineage-defining transcription factors across T H cell subsets. Polyamines control T H differentiation by providing substrates for deoxyhypusine synthase, which synthesizes the amino acid hypusine, and mice in which T cells are deficient for hypusine develop severe intestinal inflammatory disease. Polyamine-hypusine deficiency caused widespread epigenetic remodeling driven by alterations in histone acetylation and a re-wired tricarboxylic acid (TCA) cycle. Thus, polyamine metabolism is critical for maintaining the epigenome to focus T H cell subset fidelity. ll

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Section: Methodsmentioning

confidence: 99%

Section: Metabolite Tracingmentioning

confidence: 99%

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

Puleston

Baixauli

Sanin

et al. 2021

Cell

Self Cite

174

132

View full text Add to dashboard Cite

show abstract

“…Despite the fact that the metabolism of T cells is known to evolve dynamically during an immune response, few studies to date have tried to characterize T-cell metabolism from a dynamic viewpoint. To date, these efforts were mostly based on elaborate time-resolved, bulk-level metabolic measurements, and were thus limited to the more tractable case of in vitro activating T cells (Edwards-Hicks et al, 2020; Wang et al, 2011). With the advent of single-cell omics, it is now not only possible to swiftly resolve the heterogeneity of cell states present in asynchronously-evolving cell populations (such as T cells progressing along the immune response cascade) (Grün, 2018; Stubbington et al, 2017), but also to simultaneously probe their metabolic states (Artyomov and Van den Bossche, 2020).…”

Section: Discussionmentioning

confidence: 99%

CD8⁺ T-Cell Metabolic Rewiring Defined by Single-Cell RNA-Sequencing Identifies a Critical Role of ASNS Expression Dynamics in T-Cell Differentiation

Fernández-García

Franco

Parik

et al. 2021

Preprint

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Cytotoxic T cells dynamically rewire their metabolism during the course of an immune response. While T cell metabolism has been extensively studied at phenotypic endpoints of activation and differentiation, the underlying dynamics remain largely elusive. Here, we leverage on single-cell RNA-sequencing (scRNA-seq) measurements of in vitro activated and differentiated CD8+ T cells cultured in physiological media to resolve these metabolic dynamics. We find that our scRNA-seq analysis identifies most metabolic changes previously defined in in vivo experiments, such as a rewiring from an oxidative to an anabolism-promoting metabolic program during activation to an effector state, which is later reverted upon memory polarization. Importantly, our scRNA-seq data further provide a dynamic description of these changes. In this sense, our data predict a differential time-dependent reliance of CD8+ T cells on the synthesis versus uptake of various non-essential amino acids during T cell activation, which we corroborate with additional functional in vitro experiments. We further exploit our scRNA-seq data to identify metabolic genes that could potentially dictate the outcome of T cell differentiation, by ranking them based on their expression dynamics. Among the highest-ranked hits, we find asparagine synthetase (Asns), whose expression sharply peaks for effector CD8+ T cells and further decays towards memory polarization. We then confirm that these in vitro Asns expression dynamics are representative of an in vivo situation in a mouse model of viral infection. Moreover, we find that disrupting these expression dynamics in vitro, by depleting asparagine from the culture media, delays central-memory polarization. Accordingly, we find that preventing the decay of ASNS by stable overexpression at the protein level in vivo leads to a significant increase in effector CD8+ T cell expansion, and a concomitant decrease in central-memory formation, in a mouse model of viral infection. This shows that ASNS expression dynamics dictate the fate of CD8+ T cell differentiation. In conclusion, we provide a resource of dynamic expression changes during CD8+ T cell activation and differentiation that is expected to increase our understanding of the dynamic metabolic requirements of T cells progressing along the immune response cascade.

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“…Upon activation, T cells undergo a metabolic switch from oxidation of fatty acids to glycolysis to enable effector function (Chang & Pearce, 2016;Edwards-Hicks, Mitterer, Pearce, & Buescher, 2020). The switch into effector function is critical for immune surveillance however is undesirable during ex vivo manufacturing due to the loss of memory function of T cell and its association with reduced therapeutic efficacy and persistence (Delgoffe et al, 2021;Eyles et al, 2019).…”

Section: Inhibiting Glycolysis Skews T Cells Towards a More Memory Ph...mentioning

confidence: 99%

A closed, autologous bioprocess optimized for TCR-T cell therapies

Stevens

Liu

Zah

et al. 2022

Preprint

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Autologous cell therapy has proven to be an effective treatment for hematological malignancies. Cell therapies for solid tumors are on the horizon, however the high cost and complexity of manufacturing these therapies remain a challenge. Routinely used open steps to transfer cells and reagents through unit operations further burden the workflow reducing efficiency and increasing the chance for human error. Here we describe a fully closed, autologous bioprocess generating MAGE-B2 TCR-T cells. This bioprocess yielded 5 – 12e9 MAGE-B2-specific TCR-expressing T cells, transduced at low MOIs, within 7 to 10 days, and cells exhibited an enriched memory T cell phenotype and enhanced metabolic fitness. It was demonstrated that activating, transducing, and expanding leuko-apheresed cells in a single bioreactor without a T cell enrichment step promoted lentivirus transduction efficiency while resulting in comparable level of T cell purity (~97%) as that of leukopak cells that went through CD8+ and CD4+ positive selection. The critical process parameters of the bioreactor, including culturing at a high cell density (7e6 cells/mL), adjusting rocking agitations during phases of scale up, lowering glycolysis through addition of 2-Deoxy-D-glucose (2-DG), and modulating IL-2 levels, were shown to positively regulate TCR expression and cell doubling time, and promote resistance to effector-associated apoptosis of TCR-T cells. The bioprocess described herein supports scale-out feasibility by enabling processing of multiple patients’ batches in parallel within a Grade C cleanroom.

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Metabolic Dynamics of In Vitro CD8+ T Cell Activation

Cited by 21 publications

References 42 publications

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

CD8⁺ T-Cell Metabolic Rewiring Defined by Single-Cell RNA-Sequencing Identifies a Critical Role of ASNS Expression Dynamics in T-Cell Differentiation

A closed, autologous bioprocess optimized for TCR-T cell therapies

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Metabolic Dynamics of In Vitro CD8+ T Cell Activation

Cited by 21 publications

References 42 publications

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

Polyamine metabolism is a central determinant of helper T cell lineage fidelity

CD8+ T-Cell Metabolic Rewiring Defined by Single-Cell RNA-Sequencing Identifies a Critical Role of ASNS Expression Dynamics in T-Cell Differentiation

A closed, autologous bioprocess optimized for TCR-T cell therapies

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Product

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CD8⁺ T-Cell Metabolic Rewiring Defined by Single-Cell RNA-Sequencing Identifies a Critical Role of ASNS Expression Dynamics in T-Cell Differentiation