Dissecting cell-type-specific metabolism in pancreatic ductal adenocarcinoma

Lau, Allison N.; Li, Zhaoqi; Danai, Laura V.; Westermark, Anna M.; Darnell, Alicia M.; Ferreira, Raphael; Gocheva, Vasilena; Sivanand, Sharanya; Lien, Evan C.; Sapp, Kiera M.; Mayers, Jared R.; Biffi, Giulia; Chin, Christopher R.; Davidson, Shawn M.; Tuveson, David A.; Jacks, Tyler; Matheson, Nicholas J; Yılmaz, Ömer H.; Heiden, Matthew G. Vander

doi:10.7554/elife.56782

Cited by 74 publications

(74 citation statements)

References 99 publications

(189 reference statements)

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“…This work reveals that diverse cell populations can preferentially uptake different metabolites from a common pool in the TME in line with recent publications 38,39 . Previously, direct comparisons of in vivo glucose utilization between cancer cells and immune cells have not been reliably quantified 9,[40][41][42] .…”

Section: Selective Nutrient Partitioningsupporting

confidence: 89%

Cell Programmed Nutrient Partitioning in the Tumor Microenvironment

Reinfeld

Madden

Wolf

et al. 2020

Preprint

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The tumor microenvironment (TME) includes transformed cancer and infiltrating immune cells. Cancer cells can consume large quantities of glucose through Warburg metabolism that can be visualized with positron emission tomography (PET). While infiltrating immune cells also rely on glucose, disruptions to metabolism can contribute to tumor immunological evasion. How immune cell metabolism is programmed or restrained by competition with cancer cells for nutrients, remains uncertain. Here we used PET tracers to measure the accessibility of glucose and glutamine to cell subsets in the TME. Surprisingly, myeloid cells including macrophages were the greatest consumers of intra-tumoral glucose, followed by T cells and cancer cells. Cancer cells, in contrast, had the highest glutamine uptake. This distinct nutrient partitioning was programmed through selective mTORC1 signaling and glucose or glutamine-related gene expression. Inhibition of glutamine uptake enhanced glucose uptake across tumor resident cell types and shifted macrophage phenotype, demonstrating glucose is not limiting in the TME. Thus, cancer cells are not the only cells in tumors which exhibit high glucose uptake in vivo and instead preferentially utilize glutamine over other cell types. We observe that intrinsic cellular programs can play a major role in the use of some nutrients. Together, these data argue cell selective partitioning of glucose and glutamine can be exploited to develop therapies and imaging strategies to alter the metabolic programs of specific cell populations in the TME.

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Section: Selective Nutrient Partitioningsupporting

confidence: 89%

Cell Programmed Nutrient Partitioning in the Tumor Microenvironment

Reinfeld

Madden

Wolf

et al. 2020

Preprint

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“…Cells can undergo metabolic changes upon removal from their in vivo environment ( Lau et al, 2020 ). This is a particular problem when cells are enzymatically dissociated as they exchange metabolites with the dissociation medium, or when cells are sorted into buffers that require additional processing steps before cell lysis and metabolite extraction ( Lau et al, 2020 ; Binek et al, 2019 ; Llufrio et al, 2018 ). To avoid changes in metabolites during cell processing, we worked quickly and kept the cells cold from the time they left the animal until they were sorted into acetonitrile.…”

Section: Discussionmentioning

confidence: 99%

“…This limitation is particularly apparent when considering rare cells, such as stem cells or circulating cancer cells, that may be metabolically different from other cells. The difficulty in performing metabolomics on small numbers of these cells is compounded by the need to purify them from tissues, introducing additional technical challenges for metabolic analysis ( Binek et al, 2019 ; Llufrio et al, 2018 ; Lau et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues

DeVilbiss

Zhao

Martin-Sandoval

et al. 2021

eLife

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Little is known about the metabolic regulation of rare cell populations because most metabolites are hard to detect in small numbers of cells. We previously described a method for metabolomic profiling of flow cytometrically isolated hematopoietic stem cells (HSCs) that detects 60 metabolites in 10,000 cells (Agathocleous et al., 2017). Here we describe a new method involving hydrophilic liquid interaction chromatography and high-sensitivity orbitrap mass spectrometry that detected 160 metabolites in 10,000 HSCs, including many more glycolytic and lipid intermediates. We improved chromatographic separation, increased mass resolution, minimized ion suppression, and eliminated sample drying. Most metabolite levels did not significantly change during cell isolation. Mouse HSCs exhibited increased glycerophospholipids relative to bone marrow cells and methotrexate treatment altered purine biosynthesis. Circulating human melanoma cells were depleted for purine intermediates relative to subcutaneous tumors, suggesting decreased purine synthesis during metastasis. These methods facilitate the routine metabolomic analysis of rare cells from tissues.

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“…Mass spectrometry-based approaches are complicated, given that only limited CD8 + T cell numbers can be obtained from a tumor and extensive processing and sorting procedures are required for their isolation, which are bound to impact metabolite pool sizes. Recently, several groups have made progress in this area, for example using in vivo infusion of isotopically labeled glucose to assess T cell metabolism ( Ma et al, 2019 ) or tracing isotopically labeled nutrients into macromolecules with a slow turnover rate in distinct intratumoral cell populations, specifically cancer cells and fibroblasts ( Lau et al, 2020 ). Others have developed advanced flow cytometric strategies for reading out metabolic characteristics at a single-cell level ( Ahl et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

The effects of age and systemic metabolism on anti-tumor T cell responses

Drijvers

Sharpe

Haigis

2020

eLife

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Average age and obesity prevalence are increasing globally. Both aging and obesity are characterized by profound systemic metabolic and immunologic changes and are cancer risk factors. The mechanisms linking age and body weight to cancer are incompletely understood, but recent studies have provided evidence that the anti-tumor immune response is reduced in both conditions, while responsiveness to immune checkpoint blockade, a form of cancer immunotherapy, is paradoxically intact. Dietary restriction, which promotes health and lifespan, may enhance cancer immunity. These findings illustrate that the systemic context can impact anti-tumor immunity and immunotherapy responsiveness. Here, we review the current knowledge of how age and systemic metabolic state affect the anti-tumor immune response, with an emphasis on CD8+ T cells, which are key players in anti-tumor immunity. A better understanding of the underlying mechanisms may lead to novel therapies enhancing anti-tumor immunity in the context of aging or metabolic dysfunction.

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Dissecting cell-type-specific metabolism in pancreatic ductal adenocarcinoma

Cited by 74 publications

References 99 publications

Cell Programmed Nutrient Partitioning in the Tumor Microenvironment

Cell Programmed Nutrient Partitioning in the Tumor Microenvironment

Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues

The effects of age and systemic metabolism on anti-tumor T cell responses

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