Multiplexed immunofluorescence imaging of formalin-fixed, paraffin-embedded (FFPE) specimens mounted on glass slides allow the identification of multiple cell phenotypes while retaining spatial and morphological context. Multiplex immunofluorescence protocols have already been developed and validated for mouse tissues. Immunophenotyping analysis reliably depicts the immune landscape of cancer tissues that has been demonstrated to influence cancer development and progression as well as to have an impact on therapy responsiveness and resistance. Here, we describe a method for multiplexed fluorescence image analysis, enabling analysis of mouse cancer morphology and cell phenotypes in FFPE sections.
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http://dx.doi.org/10.1016/bs.mcb.2022.07.003 | DOI Listing |
Sci Rep
December 2024
Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, 222-1 Wangsimni-ro, Seongdong-Gu, Seoul, 04763, Korea.
Limited knowledge exists regarding biomarkers that predict treatment response in Lupus nephritis (LN). We aimed to identify potential molecular biomarkers to predict treatment response in patients with LN. We enrolled 66 patients with active LN who underwent renal biopsy upon enrollment.
View Article and Find Full Text PDFJ Exp Clin Cancer Res
December 2024
Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Head and neck squamous cell carcinoma (HNSCC) is a very aggressive disease characterized by a heterogeneous tumor immune microenvironment (TIME). Tumor-associated macrophages (TAMs) constitute the major innate immune population in the TIME where they facilitate crucial regulatory processes that participate in malignant tumor progression. SPP1 + macrophages (SPP1 + Macs) are found in many cancers, but their effects on HNSCC remain unknown.
View Article and Find Full Text PDFHum Reprod
December 2024
Department of Medical BioSciences, Radboudumc, Nijmegen, The Netherlands.
Study Question: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
Summary Answer: A combination of a machine learning-based tissue analysis software for tissue segmentation and a deep learning-based algorithm for segmentation-independent cell identification shows strong performance on the automated histological analysis of endometriosis sections.
What Is Known Already: Endometriosis is characterized by the complex interplay of various cell types and exhibits great variation between patients and endometriosis subtypes.
Study Design, Size, Duration: Endometriosis tissue samples of eight patients of different subtypes were obtained during surgery.
Elife
December 2024
Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Parkinson's disease (PD) is a multifactorial disease caused by irreversible progressive loss of dopaminergic neurons (DANs). Recent studies have reported the successful conversion of astrocytes into DANs by repressing polypyrimidine tract binding protein 1 (PTBP1), which led to the rescue of motor symptoms in a chemically-induced mouse model of PD. However, follow-up studies have questioned the validity of this astrocyte-to-DAN conversion model.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Biomedical Engineering and Computational Biology Program, OHSU, Portland, OR, USA.
Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data.
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