IL22RA1 Antibody

What cellular distribution pattern should be expected when using IL22RA1 antibodies?

IL22RA1 expression is restricted to non-hematopoietic cells, primarily epithelial and stromal cells. Unlike many immune receptors, IL22RA1 is not expressed on hematopoietic cells . When using antibodies against IL22RA1, researchers should expect positive staining in epithelial cells from different organs and hepatocytes, with subcellular localization primarily at the cell surface and in the cytoplasm . In immunocytochemistry/immunofluorescence studies, IL22RA1 shows specific staining patterns in certain cell types, such as positive expression in HT-29 human colon adenocarcinoma cells but negative expression in HepG2 hepatocellular carcinoma cells . This tissue-specific expression pattern is important to consider when designing experiments and validating antibody specificity.

What are the recommended positive and negative control cell lines for validating IL22RA1 antibodies?

Based on experimental validation data, several cell lines are recommended as reliable controls for IL22RA1 antibody validation. For positive controls, COLO 205 (human colorectal adenocarcinoma) and HT-29 (human colon adenocarcinoma) cell lines consistently show detectable IL22RA1 expression . These cell lines demonstrate clear membrane and cytoplasmic staining with anti-IL22RA1 antibodies. For negative controls, HepG2 (human hepatocellular carcinoma) cells have been experimentally verified to lack IL22RA1 expression . When establishing experimental protocols, researchers should include both these positive and negative control cell lines to ensure antibody specificity and optimize staining conditions. Additionally, using isotype control antibodies (such as MAB002) alongside the specific anti-IL22RA1 antibody provides essential validation of staining specificity in flow cytometry applications .

What applications are IL22RA1 antibodies validated for?

IL22RA1 antibodies have been validated for multiple research applications. Flow cytometry has been extensively validated with proven detection in cell lines such as COLO 205 and HT-29 . Immunocytochemistry/immunofluorescence applications show specific membrane and cytoplasmic staining patterns in positive cell lines . ELISA applications have been developed, though sensitivity may vary depending on sample type . When selecting an antibody, researchers should confirm validation data for their specific application, as performance can vary between manufacturers and clones. For optimal results, established protocols recommend using 8-10 μg/mL concentrations for immunocytochemistry and following manufacturer-specific protocols for flow cytometry applications .

How can researchers effectively quantify IL22RA1 expression in patient samples?

Quantifying IL22RA1 expression in patient samples requires careful selection of methodologies based on sample type and research question. For protein-level detection, in-house ELISA systems can be developed using capture and detection antibody pairs (such as MAB2770 combined with polyclonal antibodies) against IL22RA1, though sensitivity limitations exist for serum and plasma samples . Flow cytometry presents a more reliable quantification method for cellular samples, using fluorophore-conjugated secondary antibodies against anti-IL22RA1 primary antibodies . For transcriptional analysis, RNA sequencing or qPCR methodologies have demonstrated utility across multiple cancer types, as evidenced by TCGA database analyses showing differential IL22RA1 expression between normal and tumor tissues . When comparing expression levels between studies, researchers should standardize their quantification methods and use appropriate reference genes or proteins to account for technical variability.

What is the relationship between IL22RA1 expression and immune cell infiltration in tumors?

The relationship between IL22RA1 expression and immune cell infiltration is complex and cancer type-dependent. Research demonstrates that IL22RA1 expression correlates with immune cell infiltration levels across multiple cancer types, though the direction and strength of these correlations vary significantly . For instance, in bladder urothelial carcinoma (BLCA), IL22RA1 expression positively correlates with CD8+ T cells (rho = 0.38), while in stomach adenocarcinoma (STAD), this correlation is negative . Similarly, IL22RA1 shows positive correlations with CD4+ Th2 cells in BLCA (rho = 0.24), regulatory T cells in cervical cancer (rho = 0.25), and myeloid dendritic cells in various cancer types . These findings suggest that IL22RA1 may play distinct roles in immune regulation across different tumor microenvironments. Researchers investigating IL22RA1 and immune infiltration should employ multiple analytical approaches, including algorithmic estimations (CIBERSORT, XCELL) and direct immunohistochemical validation, to comprehensively assess these relationships .

How does IL22RA1 signaling interact with JAK/STAT pathway in cancer progression?

IL22RA1 signaling primarily functions through the JAK/STAT pathway, particularly STAT3 activation. When IL22 binds to the IL22RA1/IL10RB receptor complex, it activates intracellular JAK kinases, which subsequently phosphorylate and activate STAT3 . This activation leads to STAT3 translocation to the nucleus where it regulates genes involved in cell proliferation, survival, and immune response modulation . In cancer contexts, upregulated IL22RA1 expression correlates with increased expression of multiple JAK/STAT pathway components including TYK2, STAT1, and STAT3, forming a signaling network that promotes tumor progression . The pan-cancer analysis reveals that IL22RA1 expression correlates with at least 30 genes involved in JAK/STAT signaling, including IL17D, IL22RA2, IL20RB, IL10RA, IL10RB, and TSLP . Methodologically, researchers can investigate this signaling axis using phospho-specific antibodies to detect activated STAT3 (pSTAT3) following IL22 stimulation in IL22RA1-expressing cells, or through inhibitor studies targeting various components of the pathway to determine functional relationships .

How should researchers approach IL22RA1 antibody validation for novel cell types or tissues?

Validating IL22RA1 antibodies for novel cell types or tissues requires a systematic approach to ensure specificity and reliability. First, researchers should perform antibody titration experiments to determine optimal concentrations for the specific application and cell type, typically starting with manufacturer-recommended ranges (8-10 μg/mL for immunocytochemistry) . Blocking peptide competition assays, where the antibody is pre-incubated with purified IL22RA1 protein, can confirm binding specificity. RNA interference or CRISPR-mediated knockdown of IL22RA1 in positive cell lines provides powerful validation by demonstrating decreased antibody staining following target reduction. For novel tissues, researchers should compare expression patterns with publicly available RNA-seq data from resources like the Human Protein Atlas or TCGA . Multiple antibody validation is also recommended, where possible using antibodies targeting different epitopes of IL22RA1. Finally, correlation between protein detection methods (such as Western blot, immunohistochemistry, and flow cytometry) strengthens confidence in antibody specificity for the novel application.

What are the key considerations when investigating IL22RA1 mutations in cancer samples?

Investigating IL22RA1 mutations in cancer samples requires careful consideration of multiple factors. Mutation frequency varies substantially across cancer types, with higher frequencies observed in uterine cancer and melanoma compared to other cancers . Researchers should employ targeted sequencing approaches with sufficient depth to detect potentially rare mutations. Functional classification of mutations is essential, as different mutations may lead to distinct phenotypic outcomes - some IL22RA1 mutations in uterine cancer upregulate DNA damage/repair genes, while others in melanoma affect the HIF signaling pathway and metabolic processes . When analyzing mutation data, researchers should distinguish between potentially activating and inactivating mutations, correlating findings with expression levels of downstream targets. Comparing transcriptional profiles between mutated and wild-type samples can reveal altered pathways, as demonstrated by the upregulation of SERPINE1 and PFKP in IL22RA1-mutated melanoma samples . Additionally, researchers should consider the impact of co-occurring mutations, particularly in genes within the same signaling networks, which may have synergistic or compensatory effects.

Why might IL22RA1 detection vary between different cancer cell lines?

Variability in IL22RA1 detection between cancer cell lines stems from multiple biological and technical factors. At the biological level, tissue-specific expression patterns are well-documented, with IL22RA1 naturally expressed at higher levels in certain tissues (pancreas, rectum, small intestine) than others . Cancer-specific alterations to gene expression programs further modify these baseline differences. Epigenetic regulation, including promoter methylation and histone modifications, can silence IL22RA1 expression in some cell lines while permitting expression in others, even from the same tissue origin. At the technical level, detection sensitivity varies between methodologies - flow cytometry may detect low-level expression missed by immunohistochemistry. Additionally, antibody epitope accessibility can differ between cell lines due to differential protein glycosylation, conformation, or complex formation. Cell fixation and permeabilization protocols significantly impact epitope exposure, with certain methods better preserving IL22RA1 antigenicity than others. To address this variability, researchers should employ multiple detection methods, optimize protocols for each cell line, and consider both surface and intracellular staining approaches, as IL22RA1 has been detected in both membrane and cytoplasmic compartments .

How can researchers differentiate between IL22RA1 and related receptors like IL20RA in experimental systems?

Differentiating between IL22RA1 and related receptors of the class II cytokine receptor family requires strategic approaches to ensure specificity. The class II receptor family shares structural similarities, with IL22RA1 showing 22-25% amino acid sequence identity with other members like IL-10R, IL-20R, and IL-28R in their extracellular domains . For antibody-based detection, researchers should select antibodies targeting unique epitopes verified not to cross-react with related receptors. Clone 305405 (MAB2770) has been experimentally validated for IL22RA1 specificity . At the transcript level, primer design should target regions of low sequence homology between related receptors, with subsequent validation using specific positive and negative control tissues. Functional discrimination can be achieved through receptor-specific ligand stimulation experiments - IL22 specifically activates cells expressing IL22RA1/IL10RB complexes but not other receptor combinations. Competition assays using recombinant IL22BP (IL22 binding protein), which specifically interferes with IL22-IL22RA1 interactions, can further confirm signal specificity . Finally, genetic approaches using siRNA knockdown targeting specific receptors can definitively determine which receptor is responsible for observed cellular responses to cytokine stimulation.

What are the emerging techniques for studying IL22RA1 in the tumor microenvironment?

Emerging techniques for studying IL22RA1 in the tumor microenvironment leverage advanced technologies to provide higher resolution insights. Spatial transcriptomics methods now allow simultaneous visualization of IL22RA1 expression and immune cell markers within intact tissue sections, providing crucial spatial context to expression data . Single-cell RNA sequencing enables identification of specific cell populations expressing IL22RA1 within heterogeneous tumors, revealing previously unappreciated cellular sources. Mass cytometry (CyTOF) permits the simultaneous detection of IL22RA1 alongside dozens of other proteins at single-cell resolution, facilitating comprehensive phenotyping of IL22RA1-expressing cells. For functional studies, CRISPR-Cas9 genome editing enables precise modification of IL22RA1 or pathway components in relevant cell types. Organoid models derived from patient tumors provide physiologically relevant systems to study IL22RA1 signaling within three-dimensional tissue architectures that better recapitulate in vivo conditions. Live cell imaging using fluorescently tagged IL22RA1 allows real-time visualization of receptor trafficking and signaling dynamics. Finally, multi-omics integration approaches combining transcriptomics, proteomics, and epigenomics data provide comprehensive views of how IL22RA1 functions within broader regulatory networks, as demonstrated by studies correlating IL22RA1 expression with multiple signaling pathways across cancer types .

How does the IL22RA1/IL22 axis interact with other inflammatory pathways in disease progression?

The IL22RA1/IL22 axis intersects with multiple inflammatory pathways, creating complex regulatory networks in disease progression. IL22RA1 signaling interacts with other cytokine pathways, particularly those involving IL10 family members, as evidenced by correlations between IL22RA1 and IL10RA/IL10RB expression in pan-cancer analyses . The JAK/STAT pathway serves as a central node for this cross-regulation, with IL22RA1 activation leading to STAT3 phosphorylation, which then influences numerous downstream targets involved in inflammation, cell survival, and proliferation . In cancer contexts, IL22RA1 signaling can synergize with oncogenic mutations, as demonstrated by the interaction between IL22 signaling and mutant KRAS in promoting poor prognosis in colorectal cancer . IL22RA1 expression is further modulated by inflammatory mediators in the tumor microenvironment, creating feedback loops that can perpetuate inflammatory states. The pathway also influences tissue remodeling through regulation of extracellular matrix proteins and proteases, contributing to invasion and metastasis . Methodologically, investigating these complex interactions requires integrated approaches combining in vitro stimulation experiments, signaling pathway inhibitors, and in vivo models with genetic manipulation of pathway components. Researchers should employ time-course experiments to capture the dynamic nature of these interactions and consider the tissue-specific context, as the consequences of IL22RA1 activation vary significantly between different organ systems .