Single-cell temporal analysis of natural dengue infection reveals skin-homing lymphocyte expansion one day before defervescence

Arora, Jantarika Kumar; Opasawatchai, Anunya; Poonpanichakul, Tiraput; Jiravejchakul, Natnicha; Sungnak, Waradon; Matangkasombut, Oranart; Teichmann, Sarah A.; Matangkasombut, Ponpan; Charoensawan, Varodom; Sakuntabhai, Anavaj; Singhasivanon, Pratap; Suraamornkul, Swangjit; Yingtaweesak, Tawatchai; Manopwisedjaroen, Khajohnpong; Pitabut, Nada

doi:10.1016/j.isci.2022.104034

Cited by 11 publications

(32 citation statements)

References 85 publications

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“…As an example of real-world implementation, we use SCLSC to project cell labels from PBMC dataset from Zheng et al 19 onto PBMC from a patient with dengue fever (DF) and a patient with dengue hemorrhagic fever (DHF) 20 . The PBMC samples of dengue dataset were gathered on specific days: at defervescence (Def) day, two days before defervescence (Day-2), one day before defervescence (Day-1)—collectively known as the febrile illness period, and two weeks after defervescence (Wk2), which represents the convalescence or follow-up phase.…”

Section: Resultsmentioning

confidence: 99%

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Predicting cell types with supervised contrastive learning on cells and their types

Heryanto,

Zhang,

Imoto

2024

Sci Rep

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Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges related to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to five state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

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Section: Resultsmentioning

confidence: 99%

“…These data originate from PBMC cells taken from patients with acute dengue virus infection 20 . Because this dataset lacks labels, we utilized a PBMC dataset as a reference to annotate the cells in the dengue dataset for real-world application experiments.…”

Section: Methodsmentioning

confidence: 99%

Predicting cell types with supervised contrastive learning on cells and their types

Heryanto,

Zhang,

Imoto

2024

Sci Rep

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“…58 Skin dengue infection Response to Dengue. 59 Vitiligo Response to autoantigens. 60 Abbreviation: CLA, cutaneous lymphocyte-associated antigen.…”

Section: Papuloerythrodermamentioning

confidence: 99%

“…in dengue, CLA is upregulated in virus-specific effector CD4 + and CD8 + T cell populations the day before defervescence 59 ; in herpes simplex, CLA is selectively expressed on circulating virus-specific…”

Section: Salt 44 Properties Cla + T Cells Propertiesmentioning

confidence: 99%

“…In this sense, circulating CLA + T cells respond to antigens, allergens, viruses, bacterial superantigens and drugs, and their phenotype has been reported to correlate with the clinical activity and response to treatment of cutaneous diseases (Table 2). 26 For instance, in cutaneous T‐cell lymphoma, CLA is expressed on tumorigenic CCR4 + CD4 + T cells 48 ; in dengue, CLA is upregulated in virus‐specific effector CD4 + and CD8 + T cell populations the day before defervescence 59 ; in herpes simplex, CLA is selectively expressed on circulating virus‐specific CD8 + T cells, which are involved in viral clearance in the skin 51 ; in papuloerythroderma, circulating CD4 + and CD8 + T cells preferentially express the CLA marker and produce IL‐4, IL‐13, IL‐22 and IL‐31, that decrease after clinical remission 55 ; and in plaque psoriasis, the response to Streptococcus pyogenes is confined to CLA + T cells with an IL‐17 response that correlates with the clinical status of the patients 56 …”

Section: Cla Expression On Human T Cells and Skinmentioning

confidence: 99%

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CLA⁺ memory T cells in atopic dermatitis

Nicolàs

Czarnowicki

Akdiş

et al. 2023

Allergy

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Circulating skin‐homing cutaneous lymphocyte‐associated antigen (CLA)+ T cells constitute a small subset of human memory T cells involved in several aspects of atopic dermatitis: Staphylococcus aureus related mechanisms, the abnormal Th2 immune response, biomarkers, clinical aspects of the patients, pruritus, and the mechanism of action of targeted therapies. Superantigens, IL‐13, IL‐31, pruritus, CCL17 and early effects on dupilumab‐treated patients have in common that they are associated with the CLA+ T cell mechanisms in atopic dermatitis patients. The function of CLA+ T cells corresponds with the role of T cells belonging to the skin‐associated lymphoid tissue and could be a reason why they reflect different mechanisms of atopic dermatitis and many other T cell mediated skin diseases. The goal of this review is to gather all this translational information of atopic dermatitis pathology.

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The dynamic molecular characteristics of neutrophils are associated with disease progression in dengue patients

Ou,

Liu,

Zou

et al. 2024

Journal of Medical Virology

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Dengue, the most prevalent mosquito‐borne disease worldwide, poses a significant health burden. This study integrates clinical data and transcriptomic datasets from different phases of dengue to investigate distinctive and shared cellular and molecular features. Clinical data from 29 dengue patients were collected and analyzed alongside a public transcriptomic data set (GSE28405) to perform differential gene expression analysis, functional enrichment, immune landscape assessment, and development of machine learning model. Neutropenia was observed in 54.79% of dengue patients, particularly during the defervescence phase (65.79%) in clinical cohorts. Bioinformatics analyses corroborated a significant reduction in neutrophil immune infiltration in dengue patients. Receiver operating characteristic curve analysis demonstrated that dynamic changes in neutrophil infiltration levels could predict disease progression, especially during the defervescence phase, with the area under the curve of 0.96. Three neutrophil‐associated biomarkers—DHRS12, Transforming growth factor alpha, and ZDHHC19—were identified as promising for diagnosing and predicting dengue progression. In addition, the activation of neutrophil extracellular traps was significantly enhanced and linked to FcγR‐mediated signaling pathways and Toll‐like receptor signaling pathways. Neutrophil activation and depletion play a critical role in dengue's immune response. The identified biomarkers and their associated pathways offer potential for improved diagnosis and understanding of dengue pathogenesis and progression.

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Single-cell temporal analysis of natural dengue infection reveals skin-homing lymphocyte expansion one day before defervescence

Cited by 11 publications

References 85 publications

Predicting cell types with supervised contrastive learning on cells and their types

Predicting cell types with supervised contrastive learning on cells and their types

CLA⁺ memory T cells in atopic dermatitis

The dynamic molecular characteristics of neutrophils are associated with disease progression in dengue patients

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Single-cell temporal analysis of natural dengue infection reveals skin-homing lymphocyte expansion one day before defervescence

Cited by 11 publications

References 85 publications

Predicting cell types with supervised contrastive learning on cells and their types

Predicting cell types with supervised contrastive learning on cells and their types

CLA+ memory T cells in atopic dermatitis

The dynamic molecular characteristics of neutrophils are associated with disease progression in dengue patients

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Product

Resources

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CLA⁺ memory T cells in atopic dermatitis