C1QTNF1 Antibody

What is C1QTNF1 and what cellular functions does it perform?

C1QTNF1 (also referred to as CTRP1, GIP, or ZSIG37) belongs to the C1q/tumor necrosis factor-alpha-related protein family. It is a secreted protein with a modular structure comprising an N-terminal signal peptide, a short variable region, a collagenous domain, and a C-terminal globular domain . Unlike adiponectin (which is expressed exclusively in differentiated adipocytes), C1QTNF1 is expressed in various tissues .

C1QTNF1 functions include:

• Regulation of metabolic processes, particularly glucose and lipid metabolism

• Modulation of inflammatory responses

• Enhancement of insulin sensitivity

• Promotion of fatty acid oxidation and energy expenditure in skeletal muscle

• Involvement in tumor development via platelet-related cancer signaling pathways

• Induction of aldosterone synthesis in the adrenal cortex

In disease states, C1QTNF1 levels are elevated in obesity, hypertension, and diabetes, though they can decrease in the serum of diet-induced obese mice .

What are the validated applications for C1QTNF1 antibodies in research?

Based on the literature and product information, C1QTNF1 antibodies have been validated for multiple applications:

When performing Western blot analysis, C1QTNF1 typically appears as a band of approximately 35 kDa under reducing conditions, though post-translational modifications can cause migration at positions other than the predicted size of 31.7 kDa .

How should C1QTNF1 antibodies be stored and handled for optimal performance?

For optimal performance of C1QTNF1 antibodies, follow these storage and handling recommendations:

• Use a manual defrost freezer and avoid repeated freeze-thaw cycles

• Store unopened antibody at -20°C to -70°C for up to 12 months from date of receipt

• After reconstitution, store at 2-8°C under sterile conditions for up to 1 month

• For longer storage after reconstitution, aliquot and store at -20°C to -70°C for up to 6 months under sterile conditions

• When diluting antibodies for specific applications, use the recommended buffers (e.g., PBS containing 0.02% sodium azide)

• For IHC applications, heat-induced epitope retrieval using Antigen Retrieval Reagent-Basic has been validated

How does C1QTNF1 expression correlate with patient prognosis in different cancer types?

Research has revealed significant correlations between C1QTNF1 expression and patient outcomes across multiple cancer types:

Hepatocellular Carcinoma (HCC):

Clear Cell Renal Cell Carcinoma (KIRC):

Other Cancers:

• Elevated C1QTNF1 expression was associated with worse prognosis in Bladder Urothelial Carcinoma (BLCA), Brain Lower Grade Glioma (LGG), and Uveal Melanoma (UVM)

These contrasting findings highlight the tissue-specific and context-dependent roles of C1QTNF1 in different cancer types.

What are the optimal experimental conditions for detecting C1QTNF1 in different sample types?

Optimal conditions vary by application and sample type:

Western Blot:

• Recommended antibody concentration: 1:500-1:1000 dilution or 2 μg/mL

• Use reducing conditions and appropriate immunoblot buffer groups

• When using PVDF membrane, probing with anti-C1QTNF1 antibody followed by HRP-conjugated secondary antibody provides optimal results

• For normalization, GAPDH (5174, CST, 1:1000) has been validated as an effective internal control

• Expected molecular weight: ~35 kDa, though post-translational modifications may cause variations

Immunohistochemistry:

• Recommended antibody concentration: 1-25 μg/mL

• For paraffin-embedded sections, heat-induced epitope retrieval using basic antigen retrieval reagent improves results

• Incubation time: 1 hour at room temperature, followed by incubation with appropriate HRP polymer secondary antibody

• Visualization with DAB (brown) and counterstaining with hematoxylin (blue)

• C1QTNF1 staining localizes to the sarcoplasm in cardiomyocytes in heart tissue samples

ELISA:

• Specific dilution requirements should be determined by each laboratory

• For plasma samples in clinical studies (such as AMD research), proteomic analysis methods have detected C1QTNF1 with statistically significant differences between patient groups

How can researchers validate C1QTNF1 knockdown experiments effectively?

For effective validation of C1QTNF1 knockdown experiments, consider this methodological approach:

• Design appropriate targeting strategies:

  • Target specific regions of C1QTNF1 mRNA using validated siRNA or shRNA sequences

  • Consider the full protein sequence (amino acids 142-281 of human C1QTNF1 is a key immunogenic region)

• Verify knockdown efficiency at multiple levels:

  • mRNA level: Use RT-PCR with primers specific to C1QTNF1

  • Protein level: Perform Western blot using validated antibodies (e.g., anti-C1QTNF1 ab25973, Abcam, 1:1000)

  • Normalize protein expression levels to GAPDH band density

• Include essential controls:

  • Non-targeting siRNA/shRNA controls

  • Wild-type cells

  • Multiple biological replicates (at least three different donors or three independent experiments)

• Assess functional consequences:

  • For cancer studies, evaluate proliferation changes (e.g., CCK-8 assay has been validated for C1QTNF1 knockdown effects)

  • Assess cell migration and invasion capabilities after knockdown

  • Consider colony formation assays in addition to proliferation assays to avoid errors due to non-dividing cells

• Statistical validation:

  • Use appropriate statistical methods such as the Wilcoxon test for comparing expression levels

  • Consider a P value < 0.05 as statistically significant

  • Repeat experiments with cells at least three times or with samples from at least three different donors

Note: Current research indicates that knockdown studies should include verification at the protein level, as some studies have noted limitations when only mRNA reduction was confirmed .

What is the relationship between C1QTNF1 and immune cell infiltration in tumor microenvironments?

Recent research has revealed complex relationships between C1QTNF1 expression and tumor immune infiltration:

Positive correlations with immune cell types:
Analysis based on ssGSEA method demonstrated that C1QTNF1 expression positively correlates with the infiltration of numerous immune cell types in KIRC, including:

• NK cells

• Plasmacytoid dendritic cells (pDC)

• Effector memory T cells (Tem)

• T helper 2 cells (Th2)

• T helper 1 cells (Th1)

• Macrophages

• Dendritic cells (DC)

• Regulatory T cells (TReg)

• Mast cells

• B cells

• γδ T cells (Tgd)

• NK CD56dim cells

• T follicular helper cells (TFH)

• Cytotoxic cells

• Activated dendritic cells (aDC)

• Immature dendritic cells (iDC)

• T cells

• CD8 T cells

• NK CD56bright cells

Negative correlations:

• Th17 cells showed negative correlation with C1QTNF1 expression

Immune checkpoint correlation:

• C1QTNF1 expression positively correlates with key immune checkpoint proteins, including PDCD1 and CTLA4

• This correlation suggests that targeting C1QTNF1 might potentially improve immunotherapy efficacy in KIRC patients

Clinical significance:

• High infiltration of Treg cells in KIRC correlates with poor patient prognosis, consistent with high C1QTNF1 expression implications

• Studies have shown that CXCL13-secreting CD8+ T cells impair the immune function of total CD8+ T cells in KIRC patients with poor prognosis

These findings suggest C1QTNF1 may play a significant role in tumor immune evasion mechanisms, making it a potential target for improving immunotherapy outcomes.

How do post-translational modifications of C1QTNF1 affect antibody detection and protein function?

Post-translational modifications significantly impact C1QTNF1 detection and function:

Effect on molecular weight and detection:

• The calculated molecular weight of C1QTNF1 is approximately 31.7 kDa, but it typically appears at approximately 35 kDa in Western blot analyses

• In some instances, C1QTNF1 has been observed at approximately 68 kDa, suggesting dimerization or extensive post-translational modification

• Product documentation explicitly notes: "These proteins are often highly modified post-translationally and migrate in SDS-PAGE at positions other than their predicted size"

Oligomerization:

• C1QTNF1 forms trimeric structures that can further assemble into hexameric and higher-order molecular forms

• This oligomerization is mediated by cysteine residues, as noted in studies examining "cysteine-mediated oligomerizations"

• The structural arrangement resembles that of adiponectin, with trimeric structures forming higher-order assemblies

Functional implications:

• Different oligomeric forms may have distinct functional properties

• Modifications in the collagenous domain versus the globular domain may affect different protein interactions

• When designing experiments, researchers should consider using conditions that can detect various oligomeric states (reducing vs. non-reducing conditions)

Methodological considerations:

• When validating antibodies, confirm which epitope/region is being targeted

• Consider using multiple antibodies targeting different regions of the protein

• For Western blot applications, compare reducing and non-reducing conditions to understand the native state of the protein

• Be aware that tissue-specific post-translational modifications may exist, as C1QTNF1 expression varies across tissues

What controls should be included when evaluating C1QTNF1 expression in experimental studies?

For robust experimental design when studying C1QTNF1, include these essential controls:

Positive controls:

• Human heart (atrium) tissue has been validated as a positive control for C1QTNF1 expression in Western blot applications

• Mouse placenta has been identified as a positive sample for C1QTNF1 detection

• For cancer studies, include known high-expressing cell lines or tissues based on database information from TCGA and GTEx

Loading and normalization controls:

• GAPDH (5174, CST, 1:1000) has been validated as an effective internal control for Western blot normalization

• For protein expression analysis, normalize C1QTNF1 band density to GAPDH band density

Negative controls:

• Include tissues known to have low C1QTNF1 expression based on tissue expression databases

• For antibody validation, include secondary antibody-only controls

• In immunohistochemistry, include isotype-matched control antibodies

Statistical validation:

• Perform experiments with at least three biological replicates

• For clinical samples, appropriate statistical methods include Wilcoxon rank sum test for comparing expression between groups

• For survival analysis, use Kaplan-Meier curves with log-rank test and univariate Cox proportional hazards regression

• Consider P < 0.05 as statistically significant

Application-specific controls:

• For RNA-seq data analysis, apply appropriate normalization methods (TPM format followed by log2 transformation has been validated)

• When analyzing differential gene expression between high and low C1QTNF1 expression groups, use volcano plots to visualize significantly upregulated and downregulated genes

How can researchers troubleshoot inconsistent results when detecting C1QTNF1?

When facing inconsistent results in C1QTNF1 detection, consider these methodological approaches:

Western Blot troubleshooting:

• Molecular weight variations:

  • Expected molecular weight is approximately 35 kDa, but post-translational modifications may cause migration at different positions

  • Try both reducing and non-reducing conditions to observe different oligomeric states

  • Consider using gradient gels to better resolve potential oligomeric forms

• Antibody selection:

  • Confirm antibody specificity for the target species (human, mouse, etc.)

  • Different antibodies target different epitopes - for human C1QTNF1, some target amino acids 142-281 region , while others target amino acids 80-130

  • For difficult samples, try multiple antibodies targeting different regions

• Protocol optimization:

  • Adjust antibody concentration (validated ranges: 1:500-1:1000 or 2 μg/mL )

  • Optimize blocking conditions to reduce background

  • For membrane proteins, ensure adequate lysis conditions

Immunohistochemistry troubleshooting:

• Epitope retrieval:

  • Heat-induced epitope retrieval using Antigen Retrieval Reagent-Basic has been validated

  • Adjust retrieval time based on tissue type and fixation conditions

• Signal detection:

  • Optimize antibody concentration (1-25 μg/mL range has been validated)

  • Incubate at room temperature for 1 hour followed by appropriate HRP detection system

  • For tissues with high background, consider amplification systems or longer washing steps

Sample-specific considerations:

• Cancer tissue heterogeneity:

  • In cancer studies, C1QTNF1 expression varies significantly between cancer types

  • HCC shows positive prognostic correlation , while KIRC shows negative correlation

  • Consider microdissection for heterogeneous tumors

• Expression level verification:

  • Compare protein detection with mRNA expression

  • Inconsistencies may reflect post-transcriptional regulation or protein stability differences

  • For clinical samples, consider normalizing to appropriate reference genes based on tissue type

What are the key differences between polyclonal and monoclonal C1QTNF1 antibodies for different applications?

Understanding the differences between polyclonal and monoclonal C1QTNF1 antibodies is critical for experimental design:

Polyclonal C1QTNF1 Antibodies:

Monoclonal C1QTNF1 Antibodies:

Application-specific recommendations:

• Western Blot:

  • Both antibody types work well

  • Monoclonal antibodies (e.g., 2 μg/mL Mouse Anti-Human CTRP1/C1qTNF1 Monoclonal Antibody) have shown excellent specificity for a 35 kDa band in human heart tissue

  • Polyclonal antibodies may detect multiple forms of the protein

• Immunohistochemistry:

  • Monoclonal antibodies have been extensively validated for IHC applications

  • For paraffin-embedded sections, monoclonal antibodies at 1 μg/mL have demonstrated specific staining of C1QTNF1 in cardiomyocyte sarcoplasm

  • Heat-induced epitope retrieval is essential for optimal results

• ELISA:

  • Both antibody types are suitable

  • Consider using monoclonal antibodies as capture antibodies and polyclonal for detection

• Research context considerations:

  • For discovery research (identifying new forms or modifications), polyclonal antibodies may be advantageous

  • For specific quantification or localization studies, monoclonal antibodies offer more consistent results

  • Consider using both types to validate findings in critical experiments

How does C1QTNF1 integrate lipid metabolism and inflammatory responses in disease models?

C1QTNF1 functions as a key mediator between metabolic pathways and inflammatory processes:

Lipid metabolism regulation:

• In skeletal muscle, C1QTNF1 promotes fatty acid oxidation and energy expenditure

• C1QTNF1 enhances insulin sensitivity and increases glucose uptake and glycolysis

• Studies have shown that C1QTNF1 expression increases after exposure to oxidized low-density lipoprotein (oxLDL)

• OxLDL is a major component of drusen observed in age-related macular degeneration (AMD) and a key player in atherosclerotic plaque formation

Inflammatory pathway interactions:

• C1QTNF1 belongs to a family of proteins that evolved from a common ancestral innate immunity gene, creating a structural and evolutionary link between TNF and C1q-containing proteins

• In inflammation models, C1QTNF1 expression is upregulated in atherosclerotic plaques or adipose tissue when exposed to oxidized LDL or inflammatory cytokines

• C1QTNF1 can induce the expression of inflammatory cytokines and upregulate adhesion proteins on vascular endothelial cells

• Paradoxically, systemically administered C1QTNF1 can limit tissue damage following myocardial infarction

Disease model insights:

• Cancer microenvironment:

  • In KIRC, C1QTNF1 expression positively correlates with immune infiltration of multiple cell types

  • High C1QTNF1 expression correlates with increased levels of immune checkpoint proteins PDCD1 and CTLA4, suggesting potential involvement in immune evasion mechanisms

• Age-related macular degeneration (AMD):

  • Patients with AMD and glucose disturbances showed significantly higher C1QTNF1 concentrations compared to control groups

  • C1QTNF1 may serve as a link between oxLDL accumulation and local inflammation in AMD development

• Metabolic disorders:

  • Circulating levels of C1QTNF1 are elevated in obesity, hypertension, and diabetes

  • In obese (ob/ob) mice, mCTRP1 transcripts are substantially higher in adipose tissues compared to normal mice

These findings suggest C1QTNF1 functions at the intersection of metabolic regulation and inflammatory responses, with context-dependent effects across different disease models.

What emerging applications are being developed for C1QTNF1 antibodies in clinical and translational research?

Several promising directions are emerging for C1QTNF1 antibodies in translational research:

Cancer prognostic biomarker development:

• C1QTNF1 has demonstrated prognostic value in multiple cancers with contrasting effects:

  • Positive prognostic indicator in HCC

  • Negative prognostic indicator in KIRC, BLCA, LGG, and UVM

• Time-dependent ROC curve analysis showed AUC values >0.5 for predicting 1-, 3-, and 5-year survival rates in KIRC patients

• Nomogram models incorporating C1QTNF1 expression have demonstrated good predictive value for patient survival

Therapeutic target validation:

• Knockdown studies have demonstrated that reducing C1QTNF1 inhibits tumor cell proliferation, migration, and invasion in KIRC

• In HCC, overexpression of C1QTNF1 before the critical tipping point effectively prevented cancer occurrence

• These contrasting effects suggest tissue-specific therapeutic approaches may be required

Immuno-oncology applications:

• The strong correlation between C1QTNF1 expression and immune checkpoint proteins (PDCD1 and CTLA4) suggests potential applications in immunotherapy response prediction

• As immune checkpoint inhibitor therapy becomes more prevalent, C1QTNF1 may serve as a biomarker for patient selection or combination therapy approaches

Non-coding RNA regulation:

• Recent research has identified potential regulatory mechanisms of C1QTNF1 expression involving ncRNAs:

  • CYTOR and the AC040970.1/hsa-miR-27b-3p axis were identified as likely upstream regulators of C1QTNF1 in KIRC

  • This opens avenues for exploring RNA-based therapeutic approaches targeting C1QTNF1 expression

Metabolic and inflammatory disease biomarkers:

• C1QTNF1's elevated expression in obesity, hypertension, and diabetes suggests potential applications as a biomarker for metabolic disorders

• In AMD patients with glucose disturbances, C1QTNF1 showed significantly higher concentrations, suggesting utility as a biomarker for inflammatory eye diseases

What technical limitations currently exist in C1QTNF1 research and how might they be addressed?

Several technical challenges continue to affect C1QTNF1 research, with potential solutions emerging:

Protein structure complexity:

• C1QTNF1 forms complex oligomeric structures (trimers, hexamers, and higher-order assemblies)

• Current detection methods may not distinguish between different oligomeric forms

• Solution approach: Develop antibodies specifically recognizing different oligomeric states or use native PAGE techniques to separate forms before immunoblotting

Post-translational modification heterogeneity:

• C1QTNF1 is "highly modified post-translationally" causing migration variations in SDS-PAGE

• The nature and functional impact of these modifications remain poorly characterized

• Solution approach: Apply mass spectrometry techniques to identify specific modifications and develop modification-specific antibodies

Tissue-specific expression patterns:

• Unlike adiponectin (expressed only in adipocytes), C1QTNF1 is expressed in various tissues with potentially different functions

• Current approaches may not capture this tissue-specific complexity

• Solution approach: Develop tissue-specific conditional knockout models and single-cell analysis techniques to better characterize tissue-specific roles

Contradictory prognostic implications:

• C1QTNF1 shows opposite prognostic correlations in different cancers (positive in HCC, negative in KIRC)

• The mechanisms underlying these contradictory effects remain unclear

• Solution approach: Conduct comparative studies across cancer types with mechanistic exploration of signaling pathway differences

Technical standardization issues:

• Different studies use varied antibodies, detection methods, and cutoff values

• Normalization approaches differ between laboratories

• Solution approach: Develop standardized protocols and reference materials for C1QTNF1 detection across applications

Causality vs. correlation:

• Many studies establish correlations between C1QTNF1 levels and disease states, but causality remains unproven

• Solution approach: Expand functional studies using gene editing technologies (CRISPR/Cas9) in relevant model systems to establish causal relationships