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Negative binomial mixture model for identification of noise in antibody-antigen specificity predictions from single-cell data.
Bioinform Adv
December 2024
Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, TN, 37232, United States.
Motivation: LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in single-cell B cell receptor sequencing data, such as LIBRA-seq, is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies.
Results: In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model.
Discovery of epigenetic modulators targeting HDACs and EZH2 simultaneously for the treatment of hematological malignancies.
Bioorg Chem
December 2024
Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Ji'nan, Shandong 250012, PR China. Electronic address:
Epigenetic-targeted therapy has been applied in the treatment of several types of cancer. Herein, based on the synergistic antitumor effects of co-targeting HDACs and EZH2 in some hematological malignancies, a novel series of tazemetostat-based HDACs/EZH2 dual inhibitors were rationally designed, synthesized, and biologically evaluated. Satisfyingly, compounds 22a and 22b were identified as potent HDACs/EZH2 dual inhibitors with robust antiproliferative activities against one diffuse large-cell B cell lymphomas (DLBCL) cell line harboring EZH2 mutation and multiple acute myeloid leukemia (AML) cell lines.
View Article and Find Full Text PDFscImmOmics: a manually curated resource of single-cell multi-omics immune data.
Nucleic Acids Res
January 2025
The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Article Synopsis
- Single-cell sequencing technology has advanced the understanding of immune cell subpopulations and their functions, which is vital for studying immune responses in health and disease.
- The newly developed scImmOmics is a curated multi-omics immune database that houses over 2.9 million labeled immune cells from various sequencing technologies, covering 131 immune cell types across 47 tissues and 4 species.
- It provides standardized nomenclature and detailed immune regulatory information, making it a crucial resource for exploring the diversity of immune cells and understanding their regulatory mechanisms at the single-cell level.
Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments.
Commun Biol
September 2024
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
Article Synopsis
- The tumor microenvironment (TME) significantly influences tumor growth and treatment response, and new imaging technologies are emerging to study it.
- This study introduces a computational method called CISA, which automatically analyzes and quantifies T-cell interactions with other cells in the TME using multiplex imaging data.
- CISA has been validated with melanoma and breast cancer data, revealing critical correlations between immune synapse formation and patient outcomes, emphasizing its importance in understanding cancer biology and improving treatment strategies.
Causal role of immune cells in aplastic anemia: Mendelian randomization (MR) study.
Sci Rep
August 2024
Department of Hematology, The First Hospital of Jilin University, Changchun, China.
Prior research has identified associations between immune cells and aplastic anaemia (AA); however, the causal relationships between them have not been conclusively established. A two-sample Mendelian randomisation analysis was conducted to investigate the causal link between 731 immune cell signatures and AA risk using publicly available genetic data. Four types of immune signatures, including relative cell, absolute cell (AC), median fluorescence intensities and morphological parameters, were considered sensitivity analyses were also performed to verify the robustness of the results and assess potential issues such as heterogeneity and horizontal pleiotropy.
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