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See detailGigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets
Kratochvil, Miroslav UL; Hunewald, Oliver; Heirendt, Laurent UL et al

in GigaScience (2020), 9(11),

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data ... [more ▼]

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. [less ▲]

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See detailGlutathione Restricts Serine Metabolism to Preserve Regulatory T Cell Function
Kurniawan, Henry; Franchina, Davide G.; Guerra, Luana UL et al

in Cell Metabolism (2020), 31(5), 920--9367

Regulatory T cells (Tregs) maintain immune homeostasis and prevent autoimmunity. Serine stimulates glutathione (GSH) synthesis and feeds into the one-carbon metabolic network (1CMet) essential for ... [more ▼]

Regulatory T cells (Tregs) maintain immune homeostasis and prevent autoimmunity. Serine stimulates glutathione (GSH) synthesis and feeds into the one-carbon metabolic network (1CMet) essential for effector T cell (Teff) responses. However, serine’s functions, linkage to GSH, and role in stress responses in Tregs are unknown. Here, we show, using mice with Treg-specific ablation of the catalytic subunit of glutamate cysteine ligase ( Gclc), that GSH loss in Tregs alters serine import and synthesis and that the integrity of this feedback loop is critical for Treg suppressive capacity. Although Gclc ablation does not impair Treg differentiation, mutant mice exhibit severe autoimmunity and enhanced anti-tumor responses. Gclc-deficient Tregs show increased serine metabolism, mTOR activation, and proliferation but downregulated FoxP3. Limitation of cellular serine in vitro and in vivo restores FoxP3 expression and suppressive capacity of Gclc-deficient Tregs. Our work reveals an unexpected role for GSH in restricting serine availability to preserve Treg functionality. [less ▲]

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