AIMC Topic: Single-Cell Analysis

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Comprehensive integration of single-cell and bulk transcriptome to reveal plasma cell heterogeneity and a prognosis signature in head and neck squamous cell carcinoma.

Oral oncology
Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy with a low five-year survival rate, emphasising the urgent need for effective prognostic biomarkers to guide patient stratification and personalised treatment. Plasma cells (PCs)...

Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes.

Journal of proteome research
Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this st...

Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets.

BMC bioinformatics
As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-mod...

Reflection-Enhanced Raman Identification of Single Bacterial Cells Patterned Using Capillary Assembly.

ACS sensors
Raman spectroscopy is an enticing tool for the rapid identification of pathogenic bacteria and has the potential to meet the demand for early diagnosis and timely treatment of patients. However, it remains a challenge to devise a reliable Raman detec...

Identification of prognostic genes related to T cell proliferation in papillary thyroid cancer based on single-cell RNA sequencing and bulk RNA sequencing data.

Clinical and experimental medicine
Papillary thyroid carcinoma (PTC) is the main pathological subtype of thyroid cancer. Given the strong association between T cells and PTC, this study focused on the prognostic value and potential molecular mechanisms of T cell proliferation-related ...

CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

BMC biology
BACKGROUND: The advent of single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights into cancer cellular diversity, enabling a comprehensive understanding of cancer at the single-cell level. However, identifying cancer cells remains ...

CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

BMC bioinformatics
BACKGROUND: Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Single-cell image-based screens identify host regulators of Ebola virus infection dynamics.

Nature microbiology
Filoviruses such as Ebola virus (EBOV) give rise to frequent epidemics with high case fatality rates while therapeutic options remain limited. Earlier genetic screens aimed to identify potential drug targets for EBOV relied on systems that may not fu...

A multi-omic single-cell landscape reveals transcription and epigenetic regulatory features of t(8;21) AML.

Journal of translational medicine
BACKGROUND: Comprehensive analysis of single-cell transcriptome and chromatin accessibility will contribute to interpret the heterogeneity of acute myeloid leukemia (AML). We hypothesize that integrating single-cell transcriptomic and chromatin acces...