Statistical and machine learning methods to study human CD4+ T cell proteome profiles

Suomi, Tomi; Elo, Laura L.

doi:10.1016/j.imlet.2022.03.006

Cited by 3 publications

(3 citation statements)

References 149 publications

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“…Machine learning (Cadow et al, 2021; Suomi & Elo, 2022) aims to produce a predictive model based on the extraction and identification of features from defined classes in the input data set. The selection of appropriate features is an iterative process of running and rerunning the machine learning algorithm while determining how well the given set of features classifies the input data.…”

Section: Single‐cell and Single‐organelle Data Visualization And Anal...mentioning

confidence: 99%

Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements

Castro

Chan-Andersen

Romanova

et al. 2023

Mass Spectrometry Reviews

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Exploring the chemical content of individual cells not only reveals underlying cell‐to‐cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single‐cell and single‐organelle chemical characterization. As the chemical coverage and throughput of single‐cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix‐assisted laser desorption/ionization MS approaches for single‐cell and single‐organelle characterization, which is followed by advances in mass spectral data visualization and analysis.

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Section: Single‐cell and Single‐organelle Data Visualization And Anal...mentioning

confidence: 99%

Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements

Castro

Chan-Andersen

Romanova

et al. 2023

Mass Spectrometry Reviews

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“…Initial genomic and transcriptomic studies laid the baseline for understanding the T cell reprogramming following TCR stimulation but only captured a partial snapshot of this complex process, also involving several protein-protein interactions and protein phosphorylation 13 , 14 . Our knowledge of changes in the proteome of activated T cells has widened with the application of high throughput proteomic technologies in the field of immunology 8 , 15 – 17 . Based on proteomics analysis of mouse primary cells, the competitive proliferative advantage of activated CD8 over CD4 T cells was found to be associated with differences in their intrinsic nutrient transport and biosynthetic capacity 18 .…”

Section: Introductionmentioning

confidence: 99%

Integrative temporal multi-omics reveals uncoupling of transcriptome and proteome during human T cell activation

Weerakoon,

Mohamed,

Wong

et al. 2024

npj Syst Biol Appl

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Engagement of the T cell receptor (TCR) triggers molecular reprogramming leading to the acquisition of specialized effector functions by CD4 helper and CD8 cytotoxic T cells. While transcription factors, chemokines, and cytokines are known drivers in this process, the temporal proteomic and transcriptomic changes that regulate different stages of human primary T cell activation remain to be elucidated. Here, we report an integrative temporal proteomic and transcriptomic analysis of primary human CD4 and CD8 T cells following ex vivo stimulation with anti-CD3/CD28 beads, which revealed major transcriptome-proteome uncoupling. The early activation phase in both CD4 and CD8 T cells was associated with transient downregulation of the mRNA transcripts and protein of the central glucose transport GLUT1. In the proliferation phase, CD4 and CD8 T cells became transcriptionally more divergent while their proteome became more similar. In addition to the kinetics of proteome-transcriptome correlation, this study unveils selective transcriptional and translational metabolic reprogramming governing CD4 and CD8 T cell responses to TCR stimulation. This temporal transcriptome/proteome map of human T cell activation provides a reference map exploitable for future discovery of biomarkers and candidates targeting T cell responses.

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“…Initial genomic and transcriptomic studies laid the baseline for understanding the T-cell reprogramming following TCR stimulation but only captured a partial snapshot of this complex process, also involving several protein-protein interactions and protein phosphorylation (Soskic et al , 2022; Shifrut et al , 2018). Our knowledge on changes in the proteome of activated T-cells has widened with the application of high throughput proteomic technologies in the field of immunology (Weerakoon et al , 2020; Subbannayya et al , 2021; Suomi & Elo, 2022; Papale, 2021). Based on proteomics analysis of mouse primary cells, the competitive proliferative advantage of activated CD8 + over CD4 + T-cells was found to be associated with differences in their intrinsic nutrient transport and biosynthetic capacity (Howden et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrative proteomics and transcriptomics of human T-cells reveals temporal metabolic reprogramming following TCR-induced activation

Weerakoon

Mohamed

Wong

et al. 2023

Preprint

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T-cells are critical components of the adaptive immune system. Upon activation, they acquire effector functions through a complex interplay between mRNA transcripts and proteins, the landscape of which remains to be fully elucidated. In this resource article, we present an integrative temporal proteomic and transcriptomic analysis of primary human CD4+ and CD8+ T-cells following ex vivo activation with anti-CD3/CD28 Dynabeads. Our data reveal a time-dependent dissociation between the T-cell transcriptome and proteome during activation. A transient downregulation of GLUT1, the central glucose transporter in T-cells, marked the onset of reprogramming in both CD4+ and CD8+ T-cells. At late activation, CD4+ T-cells upregulated enzymes associated with degradation of fatty acids while CD8+ T-cells preferentially upregulated enzymes in the metabolism of cofactors and vitamins. Surprisingly, we found that activated CD4+ and CD8+ T-cells became transcriptionally more divergent at the same time their proteome became more similar. In addition to the metabolic reprogramming highlighted in our analysis, this dataset provides a public resource for understanding temporal molecular changes governing the acquisition of effector functions by T-cells.

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Statistical and machine learning methods to study human CD4+ T cell proteome profiles

Cited by 3 publications

References 149 publications

Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements

Probe‐based mass spectrometry approaches for single‐cell and single‐organelle measurements

Integrative temporal multi-omics reveals uncoupling of transcriptome and proteome during human T cell activation

Integrative proteomics and transcriptomics of human T-cells reveals temporal metabolic reprogramming following TCR-induced activation

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