HSPG2 antibody
- Known as:
- HSPG2 (anti-)
- Catalog number:
- 10-1959
- Product Quantity:
- 200 ul
- Category:
- -
- Supplier:
- Fitzgerald
- Gene target:
- HSPG2 antibody
Related genes to: HSPG2 antibody
- Gene:
- HSPG2 NIH gene
- Name:
- heparan sulfate proteoglycan 2
- Previous symbol:
- SJS1
- Synonyms:
- perlecan, PRCAN
- Chromosome:
- 1p36.12
- Locus Type:
- gene with protein product
- Date approved:
- 1991-02-27
- Date modifiied:
- 2016-10-05
Related products to: HSPG2 antibody
Related articles to: HSPG2 antibody
- Plaque heterogeneity underlies the propensity of atherosclerotic lesions to rupture and trigger cardiovascular events. Most proteomic studies examine bulk changes, obscuring key spatial differences in protein abundance. We report a high-resolution spatial proteomics workflow exploring the molecular landscape of human plaques and a murine myocardium. By combining laser capture microdissection with high-sensitivity ion-mobility mass spectrometry, spatial profiling of cellular and extracellular matrix (ECM) proteomes was achieved. Over 2700 proteins were detected from 50,000 μm2 areas, revealing substantial intraplaque heterogeneity across distinct regions (lipid-rich, media, shoulder, necrotic core, intima) and distance from the artery lumen. Inverse correlations between proteases (cathepsin B) and core structural ECM proteins (perlecan, HSPG2) indicated active ECM remodeling. Analysis of media layers indicated distinct protein signatures associated with smooth muscle contraction and cell-cell communication. Blood coagulation signatures, including platelet degranulation and fibrin formation, were enriched at the intima. Inflammatory (clusters of differentiation 4/68, CD4/CD68; vascular cell adhesion molecule 1, VCAM1) and vascular damage markers (tenascin-C, TNC) were enriched in shoulder regions. The necrotic core was dominated by blood proteins, consistent with intraplaque hemorrhage. This workflow resolves proteomic changes over ∼200 μm distances, providing unprecedented insights into plaque morphology and offers a powerful tool for elucidating plaque biology. - Source: PubMed
Jokumsen Kathrine VLorentzen Lasse GYeung KarinResch Timothy AEiberg Jonas PDavies Michael JGamon Luke F - Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage destruction, yet the heterogeneity of transitional chondrocyte states and their contributions to pathogenesis remain incompletely understood. In this study, we constructed a single-cell transcriptomic atlas of human knee cartilage from 16 OA samples and 2 non-OA normal donors. High-resolution sub-clustering of the prehypertrophic chondrocytes (preHTC) compartment revealed eight transcriptionally distinct subtypes, among which CRIP1 preHTC emerged as the most significantly expanded population in OA, particularly in weight-bearing regions. Pseudotemporal trajectory inference and regulon analysis identified EGR3 as a candidate key regulator for CRIP1 preHTC. High-dimensional weighted gene coexpression network analysis (hdWGCNA) demonstrated matrix-remodeling and proteostasis-stress transcriptional programs were highly active in CRIP1 preHTC. Cell-cell communication analysis uncovered that CRIP1 preHTC acts as a central signaling hub in OA, with markedly enhanced FN1 signaling pathway. Spatial transcriptomics confirmed that CRIP1 preHTC co-localizes with prefibroblasts (preFC) in pathological niches and interacts with other cells in the OA microenvironment. Finally, a machine-learning-based 10-gene classifier (JUN, MCOLN3, RHOC, AKR7A2, HSPG2, AK4, B4GALT2, CLSTN1, LRRC41, and GADD45A) derived from CRIP1 preHTC-associated hub genes accurately discriminated OA from normal tissues in two independent bulk RNA-seq cohorts. Collectively, these findings suggest that CRIP1 preHTC may represent a pathogenic cell state in OA, provide a multi-layered molecular framework that links a specific chondrocyte transitional subset to cartilage degradation, and offer a potentially useful gene signature for OA diagnosis. - Source: PubMed
Xie ZikangWang YuLiu JinzhuLi HeBian HuweiGu Chonghao - To describe the available research on genetic determinants with special emphasis on the developmental dysplasia of the hip (DDH), in Saudi Arabia and Gulf nations, with reference to the genetic variants, family trend, and environmental factors. - Source: PubMed
Publication date: 2026/08/06
Alrashdi Naif ZAlmansour Ahmed MJamal Azfar - In brief: Accurate assessment of oocyte competence remains challenging in assisted reproduction. Artificial intelligence morphology analysis is a promising noninvasive tool. This study demonstrates that oocyte morphology scores are associated with distinct molecular signatures in follicular fluid from young oocyte donors, supporting a biological basis for computational oocyte assessment. Abstract: Oocyte quality is a key determinant of reproductive success, yet its assessment in assisted reproduction largely relies on subjective morphological criteria. Artificial intelligence-based image analysis has introduced greater objectivity into oocyte evaluation; however, the biological features captured by artificial intelligence-derived morphological scores remain incompletely defined. In this study, we integrated artificial intelligence-based oocyte morphology with molecular profiling of follicular fluid (FF) to identify biological correlates of oocyte competence. Reproductive outcomes were analysed in 49 young oocyte donors (20-33 years), while FF samples pooled per woman from a subset of 25 donors were analysed for metabolic (glucose, total cholesterol, triglycerides, high-density lipoprotein [HDL], low-density lipoprotein [LDL], apolipoprotein A1 [APOA1]), extracellular matrix-related (heparan sulfate proteoglycan 2 [HSPG2]/Perlecan), signaling-related (Gremlin-1), and fertility-related (anti-Müllerian hormone [AMH], LH, FSH) biomarkers. Artificial intelligence-derived oocyte quality scores were positively associated with specific intrafollicular markers, including glucose, total cholesterol, HDL, AMH, and HSPG2, while no associations were observed with triglycerides, LDL, Gremlin-1, LH, or FSH. APOA1 showed a positive trend with the artificial intelligence score. These findings provide a biological context for artificial intelligence-based oocyte morphological assessment by linking digital image-derived scores with metabolic and structural features of the follicular microenvironment. The integration of artificial intelligence-driven morphology with donor FF biomarker profiling may contribute to the development of more objective and biologically informed approaches for oocyte quality evaluation in assisted reproduction. - Source: PubMed
Herrero YamilaVelazquez CandelaNeira MelanieCriscione RominaLavolpe MarianoAbramovich DalhiaParborell Fernanda - : Bone metastasis is a frequent and debilitating complication of advanced cancer, particularly in breast and prostate cancer, and is driven by complex interactions among tumor cells, bone-resident cells, immune populations, vascular components, and the extracellular matrix. Within this specialized microenvironment, proteoglycans have emerged as key molecular regulators of tumor-bone crosstalk, matrix remodeling, metastatic niche formation, dormancy, and therapeutic resistance. : We conducted a narrative review using targeted searches of PubMed and Google Scholar for studies published through 31 May 2026. Search terms included combinations of proteoglycan- and glycosaminoglycan-related concepts, including "proteoglycans," "glycosaminoglycans," "heparan sulfate proteoglycans," "hyaluronan," "heparanase," "syndecans," "glypicans," "perlecan/HSPG2," "versican," and "decorin," with disease- and process-related terms such as "bone metastasis," "extracellular matrix," "tumor-bone crosstalk," "breast cancer," "prostate cancer," "metastatic niche," "osteolytic metastasis," "osteoblastic metastasis," "dormancy," "reactivation," "immune regulation," and "therapy resistance." Original studies, reviews, and translational reports were selected according to their relevance to cell-surface, pericellular, and extracellular proteoglycans in bone metastatic progression. : Proteoglycans and associated GAG/ECM axes are implicated in multiple processes involved in skeletal metastasis, including growth factor availability, extracellular matrix organization, osteolytic and osteoblastic niche formation, angiogenesis, immune evasion, metastatic dormancy, reactivation, and therapy resistance. These functions are highly context-dependent and are influenced by proteoglycan localization, core protein structure, glycosaminoglycan composition, sulfation patterns, proteolytic processing, and cellular source. : Proteoglycans represent critical molecular nodes in the bone metastatic microenvironment and hold potential as biomarkers, therapeutic targets, and tools for stratifying metastatic niche heterogeneity. Their clinical translation will require validation in human bone metastasis samples, improved models that reproduce the mineralized and immune-rich bone niche, and a clearer distinction between causal mechanisms and correlative associations. Future studies should integrate matrisome profiling, spatial proteomics, single-cell and spatial transcriptomics, glycosaminoglycan omics, degradomics, and three-dimensional bone niche models to define actionable proteoglycan-dependent mechanisms and improve therapeutic targeting of metastatic bone disease. - Source: PubMed
Publication date: 2026/07/16
Mora Guzmán ZoilaSalvador Ibarra Ibzan JahzeelJuárez PatriciaBorrás Enríquez Anahí JobethDíaz García Edmar de JésúsCabrera-Fuentes Hector AlejandroHernández-Huerta María Teresa