CD3e antibody (Azide Free)
- Known as:
- CD3e (anti-) (Azide Free)
- Catalog number:
- 10r-cd3egmle
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Fitzgerald industries international
- Gene target:
- CD3e antibody (Azide Free)
Related genes to: CD3e antibody (Azide Free)
- Gene:
- CD3E NIH gene
- Name:
- CD3e molecule
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 11q23.3
- Locus Type:
- gene with protein product
- Date approved:
- 1986-01-01
- Date modifiied:
- 2019-04-23
Related products to: CD3e antibody (Azide Free)
Related articles to: CD3e antibody (Azide Free)
- This study aimed to identify key molecular signatures and therapeutic targets in early sepsis through integrated bioinformatics analysis. - Source: PubMed
Publication date: 2026/03/23
Gai XiaoweiLi YaqingWang YananGao DanWu ShanshanGeng YananZhang JiaminYao MinghuiYao GaiqiWang Qiuyan - T-cell lymphomas (TCLs) account for a relatively small fraction of lymphoid malignancies and are characterized by highly aggressive course often refractory to current available therapies. We previously reported potent in vitro and in vivo antitumor activity of a Bispecific T-Cell Engager (UMG1/CD3ε-BTCE) directed against UMG1, a unique CD43 epitope that is abundantly expressed on T-cell acute lymphoblastic leukemia (T-ALL) and diffuse large B-cell lymphoma (DLBCL) cells, while absent in most normal tissues, except thymocytes and a small fraction of peripheral blood T lymphocytes (< 5%). Here, we investigated the in vitro efficacy of UMG1/CD3ε-BTCE against TCLs. IHC analysis of Tissue Micro Arrays (TMAs) revealed high UMG1 expression in 62.3% of TCL samples, including peripheral T-cell lymphoma-not otherwise specified (PTCL-NOS) and ALK-negative anaplastic large cell lymphoma (ALCL). Notably, all T-PLL primary specimens (27/27) were positive, and 3 of 4 TCL cell lines also expressed UMG1 by flow cytometry. The asymmetric UMG1/CD3ε-BTCE induced robust redirected cytotoxicity against UMG1-expressing TCL cells. Moreover, this activity was strengthened by cell exposure to the HDAC inhibitor SAHA. We observed a dose-dependent engaged T-cell-mediated cytotoxicity and inflammatory cytokine release, resulting in lysis of UMG1-expressing cells, with no significant effect on UMG1-not expressing cells. Our findings suggest that the UMG1/CD3ε-BTCE selectively exerts potent anti-tumor activity against a relevant subset of TCLs. These findings support the development of a precision immunotherapy approach for patients with UMG1-expressing aggressive hematologic malignancies. - Source: PubMed
Caracciolo DanieleGentile CarloSquillacioti SaraSignorelli StefaniaRiillo CaterinaFaviana PinucciaConforti FrancescoDe Ieso KatiaProcopio ElisabettaAltomare EmanuelaPolerà NicolettaGaetano MariaBalducci EstelleBeganovic OmerTuccillo Franca MariaBonelli PatriziaGrillone KatiaLhermitte LudovicTagliaferri PierosandroTassone Pierfrancesco - Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tissue architecture. On the other hand, multiplex immunofluorescence (mIF) enables more precise cell type identification via proteomic markers, but has yet to achieve widespread clinical adoption due to cost and logistical constraints. To bridge this gap, we introduce MIPHEI (Multiplex Immunofluorescence Prediction from H&E Images), a U-Net-inspired architecture that leverages a ViT pathology foundation model as an encoder to predict mIF signals from H&E images using rich pretrained representations. MIPHEI targets a comprehensive panel of markers spanning nuclear content, immune lineages (T cells, B cells, myeloid), epithelium, stroma, vasculature, and proliferation. We train our model using the publicly available OrionCRC dataset of restained H&E and mIF images from colorectal cancer tissue, and validate it on five independent datasets: HEMIT, PathoCell, IMMUcan, Lizard and PanNuke. On OrionCRC test set, MIPHEI achieves accurate cell-type classification from H&E alone, with F1 scores of 0.93 for Pan-CK, 0.83 for α-SMA, 0.68 for CD3e, 0.36 for CD20, and 0.28 for CD68, substantially outperforming both a state-of-the-art baseline and a random classifier for most markers. Our results indicate that, for some molecular markers, our model captures the complex relationships between nuclear morphologies in their tissue context, as visible in H&E images and molecular markers defining specific cell types. MIPHEI offers a promising step toward enabling cell-type-aware analysis of large-scale H&E datasets, in view of uncovering relationships between spatial cellular organization and patient outcomes. - Source: PubMed
Publication date: 2026/03/07
Balezo GuillaumeTrullo RogerPla Planas AlbertDecencière EtienneWalter Thomas - A comprehensive understanding of cancer progression requires integrating tissue morphology with spatial molecular profiles. We present SHEST, a multi-task profiling framework that leverages haematoxylin and eosin morphology to predict cellular composition and reconstruct spatial gene expression at single-cell resolution. SHEST employs a quadruple-tile input capturing nuclear and contextual information, combined with a neighbourhood-informed clustering algorithm to filter ambiguous cellular signals. It comprises a shared morphological encoder with two task-specific heads: a classifier for cell-type prediction and a reconstructor for gene expression. Multi-task optimization uses cross-entropy and zero-inflated negative binomial losses, specifically addressing the sparsity of spatial transcriptomic data. Evaluation on human lung adenocarcinoma datasets demonstrated high accuracy for the principal reciprocal constituents of the tumour-immune axis ($F_{1}$: 0.97 for tumour cells and 0.91 for lymphocytes). External validation confirmed its generalizability, revealing alveolar cells and their early neoplastic transitions. Reconstructed gene expression reproduced spatially resolved, cell-type-specific marker patterns-EPCAM in tumour cells, LTBP2 in fibroblasts, and CD3E in lymphocytes-recovering biologically coherent transcriptional architecture. SHEST also preserved distance-dependent spatial relationships and gene-level autocorrelation, reflecting the multicellular niche structure of the tumour microenvironment. By unifying cell-type identification, gene expression reconstruction, and spatial mapping within a single interpretable framework, SHEST provides a synergistic and cost-efficient bridge between histopathology and spatial transcriptomics. This approach facilitates comprehensive tissue characterization and forms a foundation for precision oncology through spatially informed, cell-level insights into tumour-immune ecosystems. - Source: PubMed
Jeong HoyeonOh JunghanLee DonggeonKang Jae HwanChoi Yoon-La - Ovarian cancer is a rare cancer, it has the worst prognosis and the highest mortality rate, especially in high-grade serous ovarian cancer (HGSOC). High-throughput data generation is developed and provides an opportunity to investigate molecular pathways involved in cancer progression. The purpose of this study is to explore the role of main genes linked to the immune system and immune microenvironment in HGSOC using bioinformatics approaches to introduce promising biomarkers. - Source: PubMed
Publication date: 2025/11/28
Fatehi RaziehTabatabaiefar MohammadAminBehnamfar FaribaKhanahmad Hossein