PFDN1 Antibody

What is PFDN1 and what cellular functions does it perform?

PFDN1 (Prefoldin Subunit 1) is a 14 kDa protein that functions as part of the prefoldin complex, a molecular chaperone that assists in protein folding. It binds specifically to cytosolic chaperonin (c-CPN) and transfers target proteins to it. PFDN1 interacts with nascent polypeptide chains and promotes proper folding in cellular environments where multiple competing pathways exist for non-native proteins . The protein is encoded by the PFDN1 gene (Gene ID: 5201) and plays critical roles in cytoskeletal organization, particularly in the folding of actin and tubulin .

What applications are PFDN1 antibodies most commonly used for in research settings?

PFDN1 antibodies are employed across multiple research applications with validated effectiveness. The primary applications include:

Most PFDN1 antibodies require optimization for specific experimental systems to achieve optimal results .

How do monoclonal and polyclonal PFDN1 antibodies differ in research applications?

Monoclonal and polyclonal PFDN1 antibodies offer distinct advantages depending on research objectives:

Monoclonal PFDN1 Antibodies (e.g., EPR8547 clone):

• Provide high specificity to single epitopes

• Show excellent batch-to-batch consistency

• Optimal for applications requiring precise epitope recognition

• Example: ab151708 (recombinant rabbit monoclonal) demonstrates consistent reactivity across human, mouse, and rat samples

Polyclonal PFDN1 Antibodies (e.g., 11033-2-AP):

• Recognize multiple epitopes on the PFDN1 protein

• Often provide stronger signals in applications like IHC

• Better for detecting proteins in denatured states

• Example: Cloud-Clone's polyclonal antibody raised against recombinant PFDN1 (Met1~Gln122) shows flexibility across multiple applications

The choice between monoclonal and polyclonal depends on experimental requirements for specificity versus sensitivity and the specific experimental technique employed.

What are the optimal conditions for PFDN1 antibody use in Western blotting?

For optimal Western blotting results with PFDN1 antibodies, researchers should consider the following protocol parameters:

Sample Preparation:

• Human samples: Brain tissue, colon tissue, HepG2, HT1080, and other cell lines have shown reliable PFDN1 detection

• Loading amount: Typically 10-20 μg of protein lysate yields good results

• Expected molecular weight: 14 kDa (calculated and observed)

Protocol Specifications:

• Recommended dilution range: 1:500-1:1000 for most PFDN1 antibodies

• Secondary antibody: Anti-rabbit HRP at 1:2000 dilution when using rabbit-derived PFDN1 primary antibodies

• Blocking solution: Standard BSA or non-fat milk-based blocking solutions are effective

• Detection system: Both enhanced chemiluminescent (ECL) and 3,3'-Diaminobenzidine (DAB) systems work effectively, with 5μL per well for ECL and 10μL per well for DAB systems

Validation studies have demonstrated specific detection of the 14 kDa PFDN1 protein in multiple human cell lines and tissues, confirming antibody specificity and performance .

What are the critical factors for successful immunohistochemical detection of PFDN1?

Successful immunohistochemical detection of PFDN1 requires attention to several critical parameters:

Antigen Retrieval:

• Recommended method: Heat-mediated antigen retrieval before IHC staining

• Buffer options: Either TE buffer pH 9.0 (preferred) or citrate buffer pH 6.0 (alternative)

Antibody Dilutions:

• For paraffin sections (IHC-P): 1:10-1:200 depending on the antibody used

• For frozen sections (IHC-F): 1:50-1:500

Tissue Considerations:

• Validated human tissues: Colon cancer tissue, hepatocellular carcinoma, and brain tissues have shown reliable PFDN1 staining

• Positive control recommendation: Human hepatocellular carcinoma tissue has shown clear PFDN1 expression and can serve as a positive control

Signal Development:

• Both DAB and fluorescent detection systems are compatible with PFDN1 antibodies

• Background reduction: Thorough blocking and appropriate antibody dilution are essential to minimize non-specific staining

For optimal results, researchers should perform a dilution series to determine optimal antibody concentration for their specific tissue samples and detection systems .

How should researchers design experiments to investigate PFDN1 expression in cancer progression?

When designing experiments to investigate PFDN1 expression in cancer progression, researchers should consider a multi-method approach:

Tissue Sample Selection:

• Include paired tumor/adjacent normal tissues when possible

• Consider tissue microarrays for high-throughput analysis

• Include samples representing different cancer stages and grades to establish correlation with disease progression

Multi-modality Expression Analysis:

• Transcriptomic analysis:

  • Utilize qRT-PCR for mRNA expression quantification

  • Consider mining public databases (TCGA, Oncomine) for large-scale expression data

• Protein expression analysis:

  • Western blotting for semi-quantitative analysis

  • IHC for spatial distribution analysis in tissue sections

  • Consider comparing PFDN1 with other prefoldin family members (PFDN2-6)

• Clinicopathological correlation:

  • Analyze PFDN1 expression in relation to:

    • Tumor stage and grade

    • Patient survival data

    • Metastatic status

    • Other clinical parameters

Recent research has identified PFDN1 as significantly overexpressed in hepatocellular carcinoma, with elevated expression correlating with poor prognosis. This suggests PFDN1 may serve as a potential prognostic biomarker in HCC and possibly other cancer types .

How can researchers validate the specificity of PFDN1 antibody signals in their experiments?

Validating PFDN1 antibody specificity requires implementing multiple controls and verification strategies:

Positive Controls:

• Use tissues/cells known to express PFDN1: Human brain tissue, colon tissue, HepG2 cells, and HT1080 cells have demonstrated reliable PFDN1 expression

• Include recombinant PFDN1 protein as a positive control when available

Negative Controls:

• Omit primary antibody while maintaining all other experimental conditions

• Use non-expressing tissues/cells (if identified) as biological negative controls

• Consider using PFDN1 knockdown/knockout samples as definitive negative controls

Cross-Validation Strategies:

• Multiple antibody verification: Test multiple antibodies targeting different PFDN1 epitopes

• Multiple technique verification: Confirm findings using orthogonal techniques (e.g., if positive in IHC, verify with Western blot)

• Molecular weight confirmation: Ensure detected bands match the expected 14 kDa molecular weight of PFDN1

• Signal competition: Pre-incubate antibody with recombinant PFDN1 before application to demonstrate signal reduction

If inconsistencies are observed between different detection methods or antibodies, this may indicate post-translational modifications, splice variants, or potential cross-reactivity that requires further investigation.

What are common technical challenges when working with PFDN1 antibodies and their solutions?

Researchers frequently encounter several technical challenges when working with PFDN1 antibodies. Here are common issues and their solutions:

For particularly challenging applications, manufacturers recommend titrating the antibody concentration for each specific experimental system to determine optimal working conditions .

How should researchers interpret PFDN1 expression patterns in normal versus diseased tissues?

When interpreting PFDN1 expression patterns, researchers should consider:

Normal Tissue Expression Patterns:

• PFDN1 is normally expressed at moderate levels in various human tissues

• In Western blot analyses, PFDN1 appears as a discrete band at 14 kDa in normal human brain and colon tissues

• Normal expression primarily shows cytoplasmic localization with some nuclear presence

Disease-Associated Expression Changes:

• Hepatocellular Carcinoma (HCC): Recent studies demonstrate significant PFDN1 overexpression in HCC tissues compared to adjacent normal tissues, as confirmed by IHC, Western blotting, and RT-PCR

• Expression levels correlate with clinicopathological features and prognosis in HCC patients

• Similar overexpression patterns are observed for other prefoldin family members (PFDN2-4) in HCC

Interpretation Guidelines:

• Subcellular localization: Note any shifts in PFDN1 localization between cytoplasmic and nuclear compartments

• Expression intensity: Quantify relative expression levels using appropriate controls

• Expression heterogeneity: Assess cellular and regional variation within tissues

• Correlation with pathology: Analyze association with histopathological features and patient outcomes

Research indicates that elevated PFDN1 expression correlates with poor prognosis in HCC, suggesting its potential utility as a prognostic biomarker. Similar patterns may exist in other cancer types, warranting further investigation .

How can PFDN1 antibodies be utilized in cancer biomarker research?

PFDN1 antibodies can be strategically employed in cancer biomarker research through various approaches:

Prognostic Biomarker Development:

• Utilize IHC with validated PFDN1 antibodies on tissue microarrays to correlate expression with patient outcomes

• Recent research demonstrates that elevated PFDN1 expression significantly correlates with poor prognosis in hepatocellular carcinoma patients

• Combine with other prefoldin family members (PFDN2-4) analysis for improved prognostic value

Diagnostic Application Development:

• Apply PFDN1 antibodies in multiplex IHC panels alongside established diagnostic markers

• Evaluate sensitivity and specificity in distinguishing cancer from non-cancer tissues

• Develop scoring systems for PFDN1 expression in different tumor types

Predictive Biomarker Exploration:

• Investigate PFDN1 expression in pre- and post-treatment samples to assess therapy response

• Correlate expression patterns with sensitivity to specific therapeutic approaches

Research Approaches:

• Retrospective tissue analysis: Apply PFDN1 IHC to archived patient samples with known outcomes

• Prospective biomarker validation: Include PFDN1 testing in prospective clinical trials

• Liquid biopsy development: Explore potential for detecting PFDN1 in circulating tumor cells

Comprehensive bioinformatics analysis from the TCGA database has revealed that PFDN1, along with PFDN2/3/4, shows significant correlation with advanced clinicopathologic features in HCC, positioning these proteins as promising biomarker candidates .

What role does PFDN1 play in the molecular mechanisms of cancer progression?

Research into PFDN1's role in cancer progression reveals several potential mechanisms:

Cytoskeletal Regulation:

• PFDN1 assists in the folding of actin and tubulin proteins, which are critical for cell migration and invasion

• Dysregulation may contribute to altered cytoskeletal dynamics in cancer cells, promoting metastatic potential

Protein Homeostasis:

• As a molecular chaperone component, PFDN1 helps maintain protein folding homeostasis

• Cancer cells often experience proteotoxic stress; PFDN1 upregulation may represent an adaptive response

Signaling Pathway Interactions:

• Bioinformatic analyses suggest PFDN1 expression correlates with genes involved in cell cycle progression and proliferation

• May function through direct or indirect interaction with key signaling pathways driving oncogenesis

Immune System Modulation:

• PFDN1 expression levels show correlation with immune cell infiltration patterns

• Specifically, associations with T follicular helper (Tfh) and T helper 2 (Th2) cell infiltration have been observed

Research Evidence:
Recent studies demonstrate that PFDN1, along with other prefoldin family members, is significantly overexpressed in hepatocellular carcinoma tissues. This overexpression correlates with advanced clinicopathological features and poor prognosis, suggesting active involvement in cancer progression mechanisms .

What emerging technologies and methods are being developed for studying PFDN1 in complex experimental systems?

Several cutting-edge technologies are being applied or developed for more sophisticated analysis of PFDN1:

Advanced Imaging Techniques:

• Super-resolution microscopy: Enables visualization of PFDN1 interactions with cytoskeletal components at nanoscale resolution

• Multiplexed immunofluorescence: Allows simultaneous detection of PFDN1 with multiple protein markers to study pathway interactions

• Live-cell imaging: Combined with fluorescently-tagged PFDN1 to track dynamic cellular localization changes

Integrated Multi-omics Approaches:

• Proteogenomic analysis: Correlating PFDN1 protein expression with genomic alterations and transcriptomic profiles

• Spatial transcriptomics: Mapping PFDN1 expression patterns within tissue microenvironments to understand regional variation

Functional Genomics Tools:

• CRISPR-Cas9 screens: Systematic investigation of PFDN1 interaction networks through targeted gene editing

• Patient-derived organoids: Testing PFDN1 function in 3D tumor models that better recapitulate in vivo conditions

Computational Methods:

• Single-cell analysis: Examining PFDN1 expression at single-cell resolution to identify cell-type specific functions

• Advanced bioinformatics: Using methods such as single-sample gene set enrichment analysis (ssGSEA) to explore PFDN1's relationship with immune cell infiltration and tumor microenvironment

These emerging technologies are enabling researchers to conduct more sophisticated analyses of PFDN1's functions in cancer and other complex biological systems, potentially revealing new therapeutic targets and diagnostic applications.

What factors should researchers consider when selecting between different PFDN1 antibody options?

When selecting PFDN1 antibodies for research applications, consider these critical factors:

Antibody Format and Source:

• Host species: Rabbit-derived antibodies are common for PFDN1 detection, available as both polyclonal and monoclonal formats

• Clonality: Choose monoclonal for consistent epitope recognition or polyclonal for enhanced signal detection across multiple epitopes

• Production method: Recombinant antibodies (e.g., EPR8547) offer better batch-to-batch consistency than hybridoma-derived antibodies

Application-Specific Performance:

• For Western blotting: Confirm detection of the expected 14 kDa band in relevant samples

• For IHC: Review validated antigen retrieval methods and staining patterns in tissues of interest

• For multiplexed applications: Consider host species compatibility with other antibodies in the panel

Validation Evidence:

• Review published applications citing the specific antibody

• Examine validation data in tissues/cells relevant to your research question

• Check species cross-reactivity if working with non-human models

Technical Specifications:

• Immunogen design: Some antibodies are raised against full-length proteins while others target specific peptide sequences

• Formulation: Consider buffer compatibility with your experimental system

• Storage requirements: Most PFDN1 antibodies require -20°C storage for optimal stability

The selection process should prioritize antibodies with validation data in experimental systems similar to your research application, with appropriate controls and documented specificity.

How can researchers optimize immunoprecipitation protocols for studying PFDN1 protein interactions?

Optimizing immunoprecipitation (IP) protocols for PFDN1 requires careful consideration of several key parameters:

Lysis Conditions:

• Use mild, non-denaturing lysis buffers to preserve protein-protein interactions

• Include protease inhibitors to prevent degradation of PFDN1 and its binding partners

• Consider phosphatase inhibitors if studying phosphorylation-dependent interactions

Antibody Selection and Application:

• Choose antibodies validated for IP applications or those that recognize native protein conformations

• Typical antibody amounts range from 1-5 μg per IP reaction

• Pre-clear lysates with appropriate control IgG to reduce non-specific binding

Optimization Strategies:

• Cross-linking consideration: For transient or weak interactions, consider using reversible cross-linking agents

• Bead selection: Protein A/G beads work well for rabbit-derived PFDN1 antibodies

• Washing stringency: Balance between maintaining specific interactions and reducing background

• Elution conditions: Use either acidic elution or SDS-based methods depending on downstream applications

Expected Interacting Partners:

• PFDN1 is expected to co-precipitate with other prefoldin complex components (PFDN2-6)

• Cytoskeletal proteins, particularly nascent actin and tubulin, are likely binding partners

• Chaperonin-containing TCP-1 (CCT) complex components may also co-precipitate with PFDN1

Validation Approaches:

• Confirm successful IP by immunoblotting a small fraction of the IP sample for PFDN1

• Identify novel interactions through mass spectrometry analysis of co-immunoprecipitated proteins

• Validate key interactions through reverse IP or proximity ligation assays

For studying specific PFDN1 functions in rabies virus infection, researchers should consider targeted IP approaches focusing on viral protein interactions, as PFDN1 has been reported to show subcellular redistribution during viral infection .

What is the significance of PFDN1 expression in hepatocellular carcinoma research?

PFDN1 has emerged as a significant factor in hepatocellular carcinoma (HCC) research with multiple lines of evidence supporting its relevance:

Expression Pattern and Prognostic Value:

• PFDN1 shows significantly elevated expression in HCC tissues compared to adjacent normal tissues

• High PFDN1 expression correlates with poor prognosis in HCC patients based on TCGA database analysis

• This pattern extends to other prefoldin family members (PFDN2-4), suggesting broader involvement of the prefoldin complex in HCC pathogenesis

Clinicopathological Correlations:

• PFDN1 overexpression associates with advanced clinicopathological features in HCC

• Expression analysis through multiple methods (IHC, Western blot, RT-PCR) consistently demonstrates PFDN1 upregulation in HCC cell lines and tissues

Research Applications:

• Biomarker development: PFDN1 shows potential as a prognostic biomarker for HCC patient outcomes

• Therapeutic target exploration: The consistent overexpression pattern suggests PFDN1 as a potential therapeutic target

• Biological mechanism studies: Investigating PFDN1's role in HCC may reveal insights into fundamental aspects of liver cancer progression

Analytical Methods:
Researchers investigating PFDN1 in HCC should employ multiple detection methods:

• IHC with optimized protocols for liver tissue analysis

• Western blotting to confirm protein expression levels

• Quantitative RT-PCR for mRNA expression analysis

• Bioinformatic analysis of public databases to correlate expression with clinical parameters

These findings position PFDN1 as a promising research target in HCC, with potential applications in patient stratification, prognostic assessment, and therapeutic development.

How can researchers investigate PFDN1's relationship with immune cell infiltration in cancer tissues?

Investigating PFDN1's relationship with immune cell infiltration requires integrated approaches combining tissue analysis, computational methods, and functional validation:

Methodological Approaches:

• Computational Analysis:

  • Single-sample gene set enrichment analysis (ssGSEA) can quantify relative infiltration of immune cell types in PFDN1-high vs. PFDN1-low tumor samples

  • Correlation analysis between PFDN1 expression and immune cell markers using tools from the "GSVA" R package

  • Differential expression analysis comparing immune-related genes between PFDN1-high and PFDN1-low tumors

• Tissue-Based Analysis:

  • Multiplex immunofluorescence to simultaneously visualize PFDN1 and immune cell markers

  • Spatial analysis of immune cell distribution in relation to PFDN1-expressing tumor regions

  • Quantitative image analysis to measure immune cell densities in PFDN1-high vs. PFDN1-low areas

• Functional Validation:

  • In vitro co-culture systems with PFDN1-manipulated tumor cells and immune cells

  • PFDN1 knockdown/overexpression studies to assess impact on immune cell recruitment and activation

  • Cytokine profiling to identify immunomodulatory mechanisms

Research Findings:
Recent studies have identified significant associations between PFDN1 expression and specific immune cell populations in HCC:

• Correlations between PFDN1-4 expression and infiltration of T follicular helper (Tfh) cells

• Associations with T helper 2 (Th2) cell infiltration patterns

• These findings suggest PFDN1 may influence the tumor immune microenvironment, potentially affecting immunotherapy responses

Researchers should employ Spearman's correlation and Wilcoxon rank sum tests to statistically evaluate the relationships between PFDN1 expression and immune cell infiltration patterns .

What is the potential role of PFDN1 as a therapeutic target, and how might researchers evaluate its druggability?

Evaluating PFDN1 as a potential therapeutic target requires systematic assessment of several key parameters:

Target Validation Approaches:

• Expression and Essentiality Assessment:

  • Confirm overexpression in disease tissues compared to normal counterparts

  • Perform CRISPR/shRNA knockout/knockdown studies to determine cancer cell dependency on PFDN1

  • Evaluate differential requirement between normal and cancer cells to establish therapeutic window

• Structural and Functional Analysis:

  • Characterize PFDN1 protein structure to identify potential drug-binding pockets

  • Analyze functional domains critical for protein-protein interactions within the prefoldin complex

  • Identify key residues required for interaction with cytoskeletal proteins or chaperonins

• Druggability Evaluation:

  • Assess amenability to small molecule inhibition through in silico docking studies

  • Consider alternative approaches such as proteolysis-targeting chimeras (PROTACs) or peptide inhibitors

  • Evaluate feasibility of targeting PFDN1-partner protein interactions rather than PFDN1 directly

Therapeutic Potential Evidence:

Recent research provides support for PFDN1's therapeutic potential:

• Significant correlation between PFDN1 overexpression and poor prognosis in HCC suggests functional relevance

• PFDN1's role in cytoskeletal protein folding indicates potential impact on cancer cell migration and invasion

• Association with multiple prefoldin family members (PFDN2-4) suggests potential for pathway-level intervention

Research and Development Strategies:

• Target Screening Approaches:

  • Develop cell-based assays measuring PFDN1 function (protein folding, cytoskeletal organization)

  • Screen compound libraries for molecules disrupting PFDN1-partner interactions

  • Validate hits through biochemical and structural studies

• Therapeutic Impact Assessment:

  • Evaluate effects on cancer cell viability, migration, and invasion

  • Assess impact on tumor growth in relevant animal models

  • Investigate potential synergies with established therapies

• Biomarker Development:

  • Develop companion diagnostics to identify patients most likely to benefit from PFDN1-targeted therapy

  • Utilize IHC protocols optimized for PFDN1 detection in clinical samples