FLCN Antibody

What is FLCN protein and why is it important in research applications?

FLCN (folliculin) is a multi-functional protein encoded by the FLCN gene. It is a member of the Folliculin protein family with a canonical protein length of 579 amino acid residues and a mass of 64.5 kDa in humans . FLCN has significant research importance due to its:

• Role as a tumor suppressor and involvement in colorectal tumorigenesis

• Association with Birt-Hogg-Dubé syndrome (BHD), characterized by fibrofolliculomas, renal tumors, lung cysts, and pneumothorax

• Function in cellular responses to amino acid availability and regulation of glycolysis

• Subcellular localization in the nucleus, lysosomes, and cytoplasm

• Involvement in multiple signaling pathways including mTORC1, HIF1A, and TGFβ-SMAD signaling

The protein is expressed in most tissues tested, including skin, lung, kidney, heart, testis, and stomach, making it relevant for various research areas .

What are the optimal methods for validating FLCN antibody specificity?

Validating FLCN antibody specificity is crucial for generating reliable research data. The most rigorous validation approaches include:

• Knockout validation: This gold-standard method involves comparing antibody reactivity between wild-type and FLCN-knockout samples.

  • Example: Abcam FLCN antibody (ab124885) was validated in western blot using mouse knockout samples

  • Cell Signaling Technology FLCN antibody (D14G9) was validated with human FLCN-knockout samples

• Multiple detection techniques: Cross-validating with different methodologies:

  • Western blot to confirm appropriate molecular weight (observed at 64-75 kDa compared to calculated 38 kDa)

  • Immunohistochemistry with appropriate positive controls (e.g., human intrahepatic cholangiocarcinoma tissue)

  • Immunofluorescence to confirm expected subcellular localization patterns

• RNA interference controls: Comparing antibody reactivity between FLCN knockdown and control cells

• Positive and negative tissue controls: Testing antibody reactivity in tissues known to express or lack FLCN

What are the recommended conditions for Western blot detection of FLCN?

For optimal Western blot detection of FLCN, consider the following protocol parameters:

When troubleshooting weak signals:

• Increase antibody concentration

• Extend primary antibody incubation time

• Optimize protein loading (10-30 μg total protein)

• Consider using enhanced chemiluminescence detection systems

For non-specific bands, implement more stringent washing steps and optimize blocking conditions .

How should researchers optimize FLCN immunohistochemistry protocols?

Successful FLCN immunohistochemistry requires careful optimization of multiple parameters:

• Antigen retrieval methods:

  • Primary recommendation: TE buffer pH 9.0

  • Alternative method: Citrate buffer pH 6.0

• Antibody dilution ranges:

  • Starting dilution: 1:50-1:500

  • Recommended approach: Perform titration experiments to determine optimal concentration for each tissue type

• Positive control tissues:

  • Human intrahepatic cholangiocarcinoma tissue

  • Other tissues with known FLCN expression (skin, lung, kidney)

• Detection system considerations:

  • For weak signals: Consider using amplification systems (tyramide signal amplification)

  • For reduced background: Use polymer-based detection systems

  • For double staining: Select compatible chromogens based on co-localization analysis needs

• Counterstaining considerations:

  • Light hematoxylin counterstaining improves nuclear contrast while preserving FLCN signal intensity

Each testing system requires optimization to obtain reliable results, as antibody performance is sample-dependent .

How can FLCN antibodies be used to study its tumor suppressor activity?

FLCN antibodies are valuable tools for investigating the tumor suppressor function of FLCN through multiple experimental approaches:

• Expression analysis in tumor progression:

  • Compare FLCN protein levels between normal and tumor tissues using immunohistochemistry

  • Correlate FLCN expression with clinical outcomes and tumor stages

  • Example approach: Use FLCN antibodies at 1:50-1:500 dilution with appropriate antigen retrieval

• Protein-protein interaction studies:

  • Employ FLCN antibodies in co-immunoprecipitation experiments to identify binding partners

  • Cell Signaling Technology D14G9 antibody is suitable for immunoprecipitation at 1:100 dilution

• Pathway analysis after FLCN manipulation:

  • Monitor changes in downstream effectors after FLCN knockdown

  • Key pathways to examine include mTORC1 signaling, HIF1A targets, TGFβ-SMAD signaling, and WNT signaling

  • Western blot analysis can reveal alterations in cell cycle regulators like CCND1, TP53, and RB1 phosphorylation

• Subcellular localization studies:

  • Immunofluorescence with FLCN antibodies can reveal normal localization patterns

  • Changes in localization may occur during tumorigenesis or cellular stress

Long-term FLCN knockdown models show increased tumorigenicity, with larger and more heterogeneous colonies in soft agar assays, providing a model system to study FLCN's tumor suppressor functions .

What controls should be included when using FLCN antibodies in functional studies?

Implementing proper controls is essential for generating reliable and interpretable data with FLCN antibodies:

• Positive controls:

  • Cell lines with confirmed FLCN expression: A431, HeLa, HEK-293, NCCIT cells

  • Tissue samples with validated FLCN expression

  • Recombinant FLCN protein standards for quantitative analyses

• Negative controls:

  • FLCN knockout or knockdown samples

  • Isotype controls matching the host species and immunoglobulin class

  • Secondary antibody-only controls to assess non-specific binding

• Validation controls:

  • Multiple FLCN antibodies targeting different epitopes to confirm specificity

  • Peptide competition assays to verify epitope-specific binding

  • Correlation with mRNA expression data

• Experimental design controls:

  • Include wild-type and FLCN-manipulated samples in parallel

  • For time-course experiments, include multiple time points to capture dynamic changes

  • For drug treatment studies, include vehicle-only controls

These comprehensive controls help distinguish true FLCN-specific signals from artifacts and non-specific interactions, particularly important when investigating complex signaling pathways affected by FLCN .

How do FLCN knockout/knockdown models reveal the protein's role in cellular signaling networks?

FLCN knockout/knockdown models have provided crucial insights into its role in multiple signaling pathways:

• Cell cycle regulation:

  • Long-term FLCN knockdown alters expression of cell cycle regulators including CCND1, TP53, PPARGC1A, and RBL1

  • Flow cytometry analysis reveals changes in G0/G1 and G2/M populations

  • Protein level changes in CCND1, RB1 phosphorylation, TP53, and CDKN1A (p21/WAF1)

• mTORC1 signaling:

  • FLCN loss leads to dramatic reduction (93%) of DEPTOR, a negative regulator of mTORC1

  • FLCN regulates mTORC1 by modulating leucine signaling in lysosomes

• Metabolic alterations:

  • Enhanced AMPK and Acetyl-CoA carboxylase (ACC) phosphorylation after long-term FLCN knockdown

  • Increased SQSTM1 (p62) expression at both mRNA and protein levels

  • Upregulation of PPARGC1A and its target genes: NR1H3, SOX9, and HMOX1

• Additional affected pathways:

  • HIF1A upregulation affecting targets SLC2A2 (11.2-fold increase) and DDIT4 (1.6-fold increase)

  • Enhanced TGFβ (2.1-fold increase) and SMAD3 (1.8-fold increase) expression

  • Elevated β-catenin expression indicating increased WNT signaling

These findings from FLCN knockout/knockdown models highlight the protein's role as a multifunctional regulator of diverse cellular processes relevant to both normal physiology and disease states.

What are the considerations for choosing between different FLCN antibody clones for specific research applications?

Selecting the appropriate FLCN antibody clone requires careful consideration of multiple factors:

Application-specific considerations:

• For Western blotting:

  • Consider observed molecular weight discrepancies (calculated 38 kDa vs. observed 64-75 kDa)

  • Validated antibodies include Cell Signaling D14G9 (1:1000-1:3000 dilution)

• For immunoprecipitation:

  • Cell Signaling D14G9 clone has been validated at 1:100 dilution

  • Santa Cruz D-4 mouse monoclonal also works for IP applications

• For immunohistochemistry:

  • Consider epitope accessibility in fixed tissues

  • Proteintech 11236-2-AP works at 1:50-1:500 with appropriate antigen retrieval

• For immunofluorescence:

  • Consider subcellular localization pattern (nuclear, lysosomal, cytoplasmic)

  • Several antibodies from different vendors are validated for IF applications

• For cross-species studies:

  • Verify reactivity with target species (human, mouse, rat)

  • Proteintech 11236-2-AP shows reactivity with human, mouse, and rat samples

When possible, validate any newly purchased antibody against known positive and negative controls before proceeding with critical experiments .

How can researchers address discrepancies between calculated and observed molecular weights of FLCN?

The significant discrepancy between FLCN's calculated molecular weight (38 kDa) and its observed weight in Western blots (64-75 kDa) represents a common challenge. Here's how to address and understand this phenomenon:

• Potential causes of molecular weight discrepancies:

  • Post-translational modifications: FLCN undergoes phosphorylation and potentially other modifications that increase apparent molecular weight

  • Alternative splicing: Three different isoforms have been reported for FLCN

  • Protein structure effects: Structural features can affect protein migration in SDS-PAGE

  • Technical factors: Gel percentage, buffer systems, and marker types can influence apparent weights

• Verification approaches:

  • Multiple antibody validation: Use antibodies recognizing different epitopes to confirm band identity

  • FLCN knockdown/knockout controls: Observe band disappearance in knockout samples

  • Recombinant protein standards: Compare migration patterns with tagged recombinant FLCN

  • Mass spectrometry validation: Confirm protein identity in excised gel bands

• Experimental considerations:

  • Always include molecular weight markers appropriate for the expected range

  • Document the observed molecular weight in your specific experimental system

  • Consider using gradient gels (4-20%) for better resolution of the target weight range

  • When reporting results, cite both calculated and observed molecular weights with appropriate references

This discrepancy is well-documented in the literature, and the 64-75 kDa band represents the full-length FLCN protein despite the lower calculated weight .

What strategies can improve detection sensitivity when working with low FLCN expression levels?

When faced with challenges detecting low-abundance FLCN protein, researchers can implement several optimization strategies:

• Sample preparation optimization:

  • Enrichment methods: Use subcellular fractionation to concentrate FLCN from relevant compartments (nuclear, lysosomal, cytoplasmic)

  • Protein extraction buffers: Test different lysis buffers to improve FLCN solubility and recovery

  • Protease inhibitors: Use comprehensive inhibitor cocktails to prevent degradation during extraction

  • Sample concentration: Consider TCA precipitation or other concentration methods for dilute samples

• Western blot sensitivity enhancement:

  • Antibody selection: High-affinity antibodies like Cell Signaling D14G9 show good sensitivity for endogenous FLCN

  • Signal amplification: Use high-sensitivity ECL substrates or fluorescent detection systems

  • Blocking optimization: Test different blocking agents (BSA vs. milk) and concentrations

  • Extended exposure times: For chemiluminescence detection, try longer exposures with low background membranes

• Immunohistochemistry/Immunofluorescence enhancement:

  • Antigen retrieval optimization: Compare TE buffer pH 9.0 with citrate buffer pH 6.0

  • Signal amplification systems: Consider tyramide signal amplification (TSA) or other amplification methods

  • Detection systems: Use polymer-based detection rather than traditional ABC methods

  • Reducing background: Optimize blocking and washing steps to improve signal-to-noise ratio

• Alternative detection approaches:

  • Proximity ligation assay (PLA): For detecting protein-protein interactions with higher sensitivity

  • Immunoprecipitation before Western blot: Concentrate FLCN from larger sample volumes

  • Mass spectrometry: For detecting FLCN in complex samples with targeted approaches

Each of these strategies should be systematically tested and optimized for the specific experimental system and biological context being studied.

How does FLCN research connect to broader questions in cancer biology and metabolism?

FLCN research has significant implications across multiple fields in biomedical science:

• Cancer biology connections:

  • Tumor suppressor mechanisms: FLCN loss promotes tumorigenesis through altered cell cycle control

  • Renal cancer insights: FLCN mutations are associated with an uncommon form of kidney cancer

  • Pathway dysregulation: FLCN loss affects multiple cancer-relevant pathways including mTORC1, HIF1A, and WNT signaling

  • Biomarker potential: FLCN expression levels or localization changes may serve as diagnostic or prognostic markers

• Metabolic regulation:

  • Amino acid sensing: FLCN regulates mTORC1 by modulating leucine signaling in lysosomes

  • Glycolysis regulation: FLCN is involved in regulating glycolytic pathways

  • AMPK signaling: FLCN loss enhances AMPK and ACC phosphorylation, indicating roles in energy homeostasis

  • PGC1α-mediated metabolism: Increased PPARGC1A expression and activity after FLCN loss suggests roles in mitochondrial biogenesis and function

• Cellular stress responses:

  • Lysosomal function: FLCN's lysosomal localization suggests roles in autophagy regulation

  • Nutrient sensing: FLCN links nutrient availability to cellular growth control

  • Oxidative stress: HMOX1 upregulation after FLCN loss connects to redox regulation

• Translational implications:

  • Therapeutic targeting: Understanding FLCN-regulated pathways may reveal vulnerabilities in BHD-associated tumors

  • Disease mechanisms: FLCN research provides insights into pathogenesis of fibrofolliculomas, renal tumors, and lung cysts

  • Model systems: FLCN knockdown models offer platforms for testing targeted therapies

These connections highlight why FLCN antibodies are essential tools for investigating fundamental biological processes with relevance to both basic science and clinical applications.

What methodological considerations apply when studying FLCN across different model systems?

Investigating FLCN across various model systems requires careful methodological adjustments:

• Species-specific considerations:

  • Antibody cross-reactivity: Verify antibody reactivity with the target species (human, mouse, rat)

  • Isoform variations: Be aware of species-specific FLCN isoforms and their expression patterns

  • Fixation sensitivity: Optimize fixation protocols for each species and tissue type

  • Background concerns: Some antibodies may show higher background in certain species

• Cell line models:

  • Endogenous expression levels: A431, HeLa, HEK-293, and NCCIT cells express detectable FLCN

  • Knockdown/knockout approaches: Both short-term and long-term FLCN knockdown models reveal different aspects of FLCN function

  • 3D culture systems: Consider using organoid or spheroid cultures to better model in vivo architecture

  • Inducible systems: Employ doxycycline-inducible shRNA or CRISPR systems for temporal control

• Tissue sample considerations:

  • Antigen retrieval optimization: Different tissues may require specific retrieval methods (TE buffer pH 9.0 or citrate buffer pH 6.0)

  • Autofluorescence management: For IF studies, consider autofluorescence quenching methods for tissues like kidney

  • Tissue-specific protocols: Optimize antibody dilutions based on endogenous expression levels in target tissues

  • Perfusion fixation: For animal models, consider perfusion fixation for optimal antigen preservation

• In vivo studies:

  • Conditional knockout models: Target FLCN deletion to specific tissues to avoid embryonic lethality

  • Xenograft approaches: Use established FLCN knockdown cell lines for tumorigenicity studies

  • Imaging considerations: For whole-animal imaging, consider antibody formats compatible with in vivo applications

  • Genetic background effects: Control for background strain when using mouse models

These methodological considerations ensure robust and reproducible FLCN detection across diverse experimental systems, facilitating comparative studies and translational applications.

How should researchers interpret contradictory results from different FLCN antibodies?

When faced with contradictory results from different FLCN antibodies, systematic analysis is essential:

• Common sources of discrepancies:

  • Epitope differences: Antibodies targeting different regions may yield different results, especially if:

    • Post-translational modifications mask certain epitopes

    • Protein interactions conceal specific domains

    • Proteolytic processing creates truncated forms

  • Specificity variations: Some antibodies may detect cross-reactive proteins

  • Sensitivity differences: Detection thresholds vary between antibody clones

  • Application-specific performance: An antibody working well in WB might perform poorly in IHC

• Systematic investigation approach:

  • Knockout validation: Test antibodies against FLCN knockout samples to confirm specificity

  • Multiple detection methods: Compare results across WB, IHC, IF, and IP

  • Epitope mapping: Determine which domain each antibody recognizes

  • Literature cross-reference: Compare your results with published findings using the same antibodies

  • Technical replication: Repeat experiments with standardized protocols

• Synthesis and interpretation strategies:

  • Antibody consensus approach: Greater confidence in results confirmed by multiple antibodies

  • Complementary techniques: Validate antibody-based findings with non-antibody methods (mRNA analysis, mass spectrometry)

  • Functional validation: Connect observed changes to functional outcomes through knockdown/rescue experiments

  • Isoform consideration: Different antibodies may preferentially detect specific FLCN isoforms

• Reporting recommendations:

  • Clearly document which antibody was used for each experiment

  • Specify exact catalog numbers and dilutions

  • Report observed molecular weights for Western blot bands

  • Include representative images of control experiments

  • Acknowledge limitations of specific antibodies in discussion sections

By systematically analyzing discrepancies, researchers can extract valuable insights rather than simply discarding contradictory results.

What quantitative approaches are recommended for analyzing FLCN expression levels in different experimental contexts?

Accurate quantification of FLCN expression requires rigorous methodological approaches:

• Western blot quantification:

  • Normalization strategy: Use multiple housekeeping proteins (β-actin, GAPDH, α-tubulin) to ensure reliable normalization

  • Linear range validation: Establish the linear detection range for both FLCN and reference proteins

  • Technical replication: Include technical replicates on the same blot

  • Statistical analysis: Apply appropriate statistical tests for comparing expression levels

  • Software tools: Use dedicated image analysis software with background subtraction capabilities

• Immunohistochemistry quantification:

  • Digital pathology approaches: Use whole slide scanning and automated analysis software

  • Scoring systems: Develop clear scoring criteria (e.g., H-score, 0-300 scale combining intensity and percentage)

  • Region of interest selection: Establish consistent rules for selecting analysis regions

  • Blinded assessment: Have multiple investigators score samples independently

  • Benchmark controls: Include control samples with known FLCN levels in each batch

• Immunofluorescence quantification:

  • Z-stack imaging: Collect multiple focal planes to capture total signal

  • Subcellular distribution analysis: Quantify nuclear vs. cytoplasmic vs. lysosomal FLCN

  • Colocalization metrics: Use Pearson's correlation coefficient or Manders' overlap coefficient

  • Single-cell analysis: Quantify cell-to-cell variation in expression levels

  • Background correction: Implement consistent background subtraction methods

• Multi-omics integration:

  • Correlation with mRNA levels: Compare protein levels with RNA-seq or qPCR data

  • Phosphorylation analysis: Quantify both total FLCN and phosphorylated forms

  • Interactome quantification: Measure associations with known binding partners

  • Functional readouts: Correlate FLCN levels with downstream pathway activation

• Reporting standards:

  • Report both relative and absolute quantification when possible

  • Clearly state normalization methods and reference standards

  • Include representative images alongside quantitative data

  • Provide raw data and analysis scripts when publishing