ADAMTS9 Antibody

What is ADAMTS9 and why is it significant in research?

ADAMTS9 belongs to the ADAMTS (A Disintegrin And Metalloproteinase with ThromboSpondin motifs) family of secreted proteases. It contains a prometalloprotease domain of the reprolysin type and is notable for its 15 potentially anti-angiogenic thrombospondin repeats (TSRs) . ADAMTS9 functions as a cell-autonomous anti-angiogenic factor and tumor suppressor gene in esophageal and nasopharyngeal cancers . Its significance stems from its involvement in critical biological processes including angiogenesis inhibition, connective tissue organization, inflammation, and cell migration . Understanding ADAMTS9 is crucial for investigating cancer biology, vascular development, and extracellular matrix remodeling.

What are the structural features of ADAMTS9 that antibodies typically target?

ADAMTS9 contains several structural domains that antibodies may target. It has a catalytic metalloprotease domain essential for its proteolytic function, followed by a disintegrin domain . After these domains, ADAMTS9 contains 15 thrombospondin-like (TS) domains arranged in specific patterns with intervening spacer and linker domains . Commercial antibodies often target specific peptide sequences, such as PA1-1760 which targets a synthetic peptide corresponding to residues CFDGKHFNINGLLPN of human ADAMTS9 . For research applications, antibodies targeting the catalytic domain are particularly valuable when investigating the protein's enzymatic activity, while those targeting the TS domains may be useful for studying ECM interactions.

How is ADAMTS9 expression regulated in normal tissues?

ADAMTS9 is expressed in various tissues including ovary, testis, heart, placenta, lung, skeletal tissue, and pancreas . During embryonic development, ADAMTS9 expression has been detected in capillaries through in situ hybridization . In adult tissues, regulation appears tissue-specific, with particularly notable expression in reproductive organs, suggesting specialized functions there . In the central nervous system, ADAMTS9 expression is associated with the cell body and elongated processes of parenchymal astrocytes . Additionally, ADAMTS9 expression increases during wound healing processes, indicating its regulation is responsive to tissue injury and repair mechanisms .

What are the recommended protocols for detecting ADAMTS9 expression in tissue samples?

For detecting ADAMTS9 in tissue samples, immunohistochemistry on cryostat sections has proven effective. Based on published protocols, tissue sections should be mounted on polylysine-coated glass slides and fixed in ice-cold acetone for 10 minutes . For immunofluorescence, sections can be incubated with polyclonal ADAMTS9 antibody (1:100 dilution) overnight at 4°C, followed by detection using Alexa 488 or Alexa 568-conjugated secondary antibodies (1:1000 dilution) . For co-localization studies, dual-label immunofluorescence can be performed by incubating with ADAMTS9 antibody followed by cell-specific markers such as GFAP (for astrocytes), HLA-DR (for microglia/macrophages), NF-L (for neurons), or vWF (for endothelial cells) . Images should be captured using confocal microscopy for optimal resolution of cellular localization.

What experimental controls should be included when using ADAMTS9 antibodies?

When using ADAMTS9 antibodies, several controls are essential for result validation. Negative controls should include omission of primary antibody and use of isotype-matched control antibodies to assess non-specific binding . Positive controls should utilize tissues known to express ADAMTS9, such as placenta or reproductive organs . For antibody specificity validation, pre-absorption of the antibody with the immunizing peptide can confirm specificity . When performing knockdown experiments, appropriate control siRNAs are crucial to distinguish specific from non-specific effects . Additionally, multiple commercially available antibodies should be tested to ensure consistency of results and avoid cross-reactions with other proteins, as demonstrated in studies examining ADAMTS9 expression in astrocytes using three different anti-GFAP antibodies .

How can ADAMTS9 function be assessed in cellular models?

ADAMTS9 function can be assessed through several complementary approaches. siRNA-mediated knockdown in human microvascular endothelial cells (HBMECs) allows evaluation of ADAMTS9's effects on cell adhesion, migration, and tube formation on Matrigel . Conversely, overexpression of wild-type or catalytically inactive mutants (e.g., E427A mutant) helps distinguish between proteolytic and non-proteolytic functions . Cell migration can be quantified using monolayer wounding assays, where scratch closures are monitored via time-lapse microscopy . Angiogenic potential can be evaluated through tube formation assays on Matrigel, comparing ADAMTS9-deficient cells to controls . For proteolytic activity assessment, co-transfection experiments with potential substrates (such as thrombospondins) followed by Western blotting for cleavage products provides insights into substrate specificity .

How does ADAMTS9 differ functionally from other ADAMTS family members in angiogenesis regulation?

ADAMTS9 exhibits distinct anti-angiogenic mechanisms compared to other family members, particularly ADAMTS1. While both inhibit angiogenesis, ADAMTS9 operates through cell-autonomous effects in endothelial cells requiring its proteolytic activity, as demonstrated by the ineffectiveness of catalytically inactive mutants . Unlike ADAMTS1, which inhibits angiogenesis by cleaving thrombospondins 1 and 2 and sequestering vascular endothelial growth factor 165 (VEGF165), ADAMTS9 neither cleaves thrombospondins nor binds VEGF165 . This suggests ADAMTS9 employs alternative molecular mechanisms to achieve its anti-angiogenic effects. Additionally, ADAMTS9 works cooperatively with ADAMTS20 in melanoblast development , indicating potential functional overlap with some family members but distinct mechanisms in vascular contexts.

What are the implications of ADAMTS9 expression patterns in CNS pathologies like multiple sclerosis?

In multiple sclerosis (MS) tissue samples, ADAMTS9 expression shows distinctive patterns relevant to disease pathology. Significantly increased ADAMTS9 expression is observed in active MS lesions compared to normal-appearing white matter (NAWM) and normal control white matter . Dual-label immunofluorescence studies reveal ADAMTS9 co-localization with cerebral vascular endothelium, foamy macrophages, activated microglia, and particularly with astrocytes in MS active lesions . This expression pattern suggests ADAMTS9 may participate in extracellular matrix remodeling during MS pathogenesis, potentially contributing to blood-brain barrier dysfunction, inflammatory cell infiltration, or tissue repair processes. The association with multiple cell types implies complex regulatory roles that may represent therapeutic targets or biomarkers for MS progression.

What are the optimal methods for quantifying ADAMTS9 expression levels in research samples?

For accurate quantification of ADAMTS9 expression, multiple complementary techniques should be employed. At the mRNA level, reverse transcription-polymerase chain reaction (RT-PCR) using specific primers (e.g., forward 5′-CGGTTTGTAGAAGTCTTG-3′ and reverse 5′-CAGGTTCGTTAAGCAAAC-3′) generates a 622 bp amplicon that can be quantified . For protein quantification, Western blotting using validated antibodies such as PA1-1760 provides relative expression levels . In tissue sections, immunofluorescence intensity can be quantified using image analysis software such as ImageJ, comparing signal intensities across different experimental conditions . For accurate statistical analysis of expression differences between experimental groups, non-parametric tests like the Kruskal-Wallis test are recommended when data may not follow normal distribution . In all cases, appropriate housekeeping genes or proteins (such as GAPDH) should be used as internal controls.

How can researchers differentiate between active and inactive forms of ADAMTS9 in experimental systems?

Differentiating between active and inactive ADAMTS9 requires multiple approaches. Site-directed mutagenesis of the catalytic domain (such as the E427A mutation) creates proteolytically inactive controls that can distinguish between enzymatic and structural functions . Furin processing significantly affects ADAMTS9 activity, so using the Arg 74/209/287Ala mutant (which prevents furin cleavage) can enhance proteolytic activity compared to wild-type constructs . Functional assays measuring effects on endothelial tube formation provide indirect evidence of activity status . For direct assessment of proteolytic activity, researchers can use synthetic fluorogenic peptide substrates or monitor cleavage of potential natural substrates via Western blotting. Additionally, antibodies specifically recognizing the processed forms of ADAMTS9 can help distinguish between zymogen and active enzyme, though such antibodies may require custom development.

How should researchers interpret ADAMTS9 co-localization with different cell markers in tissue sections?

When interpreting ADAMTS9 co-localization with cellular markers, researchers should apply rigorous analytical approaches. True co-localization should be verified using specialized software that analyzes individual pixels across different fluorescence channels, as demonstrated in MS tissue studies using Zeiss 510 confocal microscopy software . Co-localization appears as yellow pixels in composite images when both channels show signal above threshold in the same location . In brain tissue, ADAMTS9 co-localizes with various cell types including astrocytes (GFAP+), endothelial cells (vWF+), and activated microglia/macrophages (HLA-DR+) . Quantification of co-localization should include statistical analysis comparing different tissue regions or disease states. Importantly, apparent co-localization at low magnification should be confirmed at higher resolution to distinguish between true cellular co-expression and close proximity of different cell types expressing the protein separately.

What statistical approaches are most appropriate for analyzing ADAMTS9 expression differences between experimental conditions?

When analyzing ADAMTS9 expression differences between experimental conditions, statistical approach selection should be guided by data characteristics. For immunofluorescence quantification, non-parametric tests like the Kruskal-Wallis test are often appropriate since intensity data may not follow normal distribution . Significance thresholds should be clearly defined (e.g., *p<0.01 and *p<0.001) . For cell-based assays comparing ADAMTS9 knockdown versus control conditions, paired t-tests can evaluate differences in migration rates, tube formation, or proliferation . When comparing multiple groups (e.g., wild-type, heterozygous, and homozygous models), ANOVA followed by appropriate post-hoc tests should be employed. For all analyses, sample sizes should be sufficient to achieve statistical power, with biological replicates (n≥3) preferred over technical replicates. Visualization methods should include box plots or scatter plots that display individual data points rather than bar graphs showing only means.

How can researchers reconcile contradictory findings about ADAMTS9 function across different model systems?

Reconciling contradictory findings about ADAMTS9 across model systems requires systematic analysis of experimental variables. First, researchers should consider species differences, as ADAMTS9 functions may vary between human, mouse, and other models despite sequence conservation . Second, cell type-specific effects should be evaluated, as ADAMTS9 demonstrates different functions in endothelial cells versus other cell types . Third, the microenvironment significantly influences ADAMTS9 activity - findings from 2D cell culture may differ from 3D matrices or in vivo contexts . Fourth, genetic background effects are important, as demonstrated by the impact of congenic breeding into C57Bl/6 strain on ADAMTS9 knockout phenotypes . Finally, technical variables including antibody specificity, knockdown efficiency, and overexpression levels can dramatically affect experimental outcomes. Meta-analysis approaches that systematically compare methodologies across contradictory studies can help identify the source of discrepancies and guide experimental design to resolve these contradictions.