BTF3 Antibody Pair

What is BTF3 and why is it significant in research?

BTF3 (Basic Transcription Factor 3) is a general transcription factor that forms a stable complex with RNA polymerase II and is required for transcriptional initiation . It serves two primary functions:

• Transcriptional regulation: BTF3 is essential for the initiation of transcription .

• Protein targeting: When associated with NACA (Nascent polypeptide-associated complex Alpha), BTF3 prevents inappropriate targeting of non-secretory polypeptides to the endoplasmic reticulum .

BTF3 has gained significant research interest due to its upregulation in multiple cancer types, including prostate cancer , hepatocellular carcinoma , and colorectal cancer . High BTF3 expression correlates with poor prognosis in cancer patients, making it a potential biomarker and therapeutic target .

What types of BTF3 antibodies are available for research?

Several BTF3 antibodies are available for research, varying in:

Host species:

• Rabbit polyclonal

• Mouse monoclonal

• Goat polyclonal

Epitope regions:

• N-terminal (AA 1-30)

• Middle region (AA 48-206, AA 50-150)

• C-terminal

• Full-length

Applications:

• Western blot (WB)

• Immunohistochemistry (IHC)

• Immunofluorescence (IF)

• ELISA

• Flow cytometry

• Immunoprecipitation (IP)

Reactivity:

• Human-specific

• Multi-species (Human, Mouse, Rat)

• Broad-spectrum (including Dog, Zebrafish, Cow, Guinea Pig, Horse, etc.)

How should I select the optimal BTF3 antibody for my specific research application?

Selection should be guided by:

• Experimental application: Different antibodies perform optimally in specific applications. For example:

  • For Western blot: Consider antibodies validated specifically for WB with clear band detection at ~18-22 kDa

  • For IHC: Choose antibodies specifically validated for tissue sections with optimized antigen retrieval protocols

• Species cross-reactivity: Match the antibody's reactivity to your experimental model:

  • Human cancer studies: Human-reactive antibodies

  • Animal models: Antibodies with validated cross-reactivity to your model organism

• Epitope region: Consider the functional domain you wish to study:

  • N-terminal antibodies may detect different isoforms (BTF3a/BTF3b)

  • Middle region antibodies typically detect both major isoforms

• Antibody format: Consider:

  • Conjugated vs. unconjugated based on your detection system

  • Monoclonal for specificity or polyclonal for broader epitope recognition

• Validation evidence: Prioritize antibodies with:

  • Published validation in peer-reviewed literature

  • Multiple validation methods (Western blot, IHC, knockdown controls)

What are the recommended protocols for BTF3 antibody optimization in immunohistochemistry?

Optimal IHC protocols for BTF3 detection include:

Antigen retrieval:

• Heat-mediated antigen retrieval with Tris/EDTA buffer pH 9.0 has shown effective results

• For formalin-fixed paraffin-embedded tissues, basic antigen retrieval reagents perform better than acidic solutions

Antibody dilution:

• Start with 1:250 dilution for most commercial BTF3 antibodies

• Adjust based on signal-to-noise ratio in your specific tissue type

Detection systems:

• For DAB-based detection: Use appropriate species-specific HRP-conjugated secondary antibodies (typically at 1:500 dilution)

• For fluorescence: Alexa Fluor-conjugated secondary antibodies (1:1000) provide optimal signal

Controls:

• Positive controls: Human cervix carcinoma, pancreatic cancer tissues, or liver tissues

• Negative controls: Omit primary antibody but include all other steps

Quantification:

• For unbiased quantification, use automated image analysis protocols such as those implemented in ImageJ software

How can BTF3 antibodies be used to investigate cancer progression mechanisms?

BTF3 antibodies are valuable tools for investigating cancer mechanisms:

Expression analysis:

• Quantitative immunohistochemistry using BTF3 antibodies in tissue microarrays can identify correlation between BTF3 expression and clinical outcomes

• Research shows 2-2.5 fold increased BTF3 expression in malignant vs. non-malignant prostate tissue (p<0.0001)

Functional studies:

• Use BTF3 antibodies to validate knockdown efficiency in siRNA or shRNA experiments studying:

  • Cell proliferation effects (shown to decrease in HCC and prostate cancer cells upon BTF3 knockdown)

  • Apoptosis induction (BTF3 knockdown increases apoptosis in hepatocellular carcinoma cells)

  • Cell cycle regulation (BTF3 knockdown causes G2/M arrest)

Mechanism investigation:

• ChIP assays using BTF3 antibodies can identify direct transcriptional targets:

  • BTF3 directly regulates PDCD2L transcription in HCC

  • BTF3 binds to promoters of RFC genes in prostate cancer

  • BTF3 regulates GLUT1 expression via FOXM1 in HCC, affecting glycolysis

Multi-marker panels:

• Combined analysis with other markers improves diagnostic power:

  • BTF3 + ODC1 provided 93% correct identification of malignant cases

  • BTF3 + HINT1 + NDRG1 in triple-labeled immunofluorescence showed significant changes in co-localization patterns between relapse vs. non-relapse prostate cancer

What approaches are recommended for studying BTF3's protein-protein interactions?

To investigate BTF3's interactions with other proteins:

Co-immunoprecipitation (Co-IP):

• Use anti-BTF3 antibodies for immunoprecipitation followed by immunoblotting for suspected interacting partners

• Specific protocol: Use 1mg of cell lysate with BTF3 antibody (1:70 dilution), followed by appropriate secondary antibody capture

• Control experiments should include isotype control antibodies

Proximity ligation assay (PLA):

• Combine BTF3 antibody with antibodies against suspected interaction partners

• This allows visualization of protein interactions in situ with subcellular resolution

Chromatin immunoprecipitation (ChIP):

• BTF3 antibodies have been successfully used in ChIP to identify direct transcriptional targets:

  • Validated for binding to PDCD2L promoter in HCC cells

  • Used to demonstrate binding to RFC gene promoters in prostate cancer cells

Multi-labeled immunofluorescence:

• Triple-labeled immunofluorescence with BTF3, HINT1, and NDRG1 antibodies revealed co-localization patterns that differentiate biochemical relapse vs. non-relapse in prostate cancer (Pearson coefficients: 0.73 ± 0.02 vs. 0.60 ± 0.07, p<0.02)

How can contradictory findings about BTF3 expression across different cancer types be resolved?

Researchers investigating contradictory BTF3 findings should consider:

Methodological standardization:

• Use unbiased, quantitative methods for protein expression analysis (automated image analysis protocols in ImageJ software)

• Apply consistent cutoff values for defining "high" vs. "low" expression

• Standardize tissue processing and antibody dilutions across studies

Isoform specificity:

• BTF3 exists in multiple isoforms (BTF3a, BTF3b) with potentially different functions:

  • BTF3b, but not BTF3a, regulates transcription of RFC genes

  • BTF3b, but not BTF3a, is associated with cisplatin sensitivity

• Use isoform-specific antibodies or complementary techniques (RT-PCR, RNA-seq) to distinguish isoform expression

Context-dependent function:

• Consider cellular context and interaction partners:

  • BTF3 functions differently when associated with NACA

  • Different downstream effectors in different cancers (PDCD2L in HCC , RFC genes in prostate cancer )

Integrated analysis:

• Combine multiple datasets and techniques:

  • Correlate findings from TCGA, ICGC with laboratory results

  • Integrate RNA-seq and ChIP-seq data

  • Combine IP-MS with E3 ubiquitin ligase analysis

What are the best approaches for quantitative analysis of BTF3 expression in tissue microarrays?

For robust quantitative analysis:

Image acquisition standardization:

• Use consistent microscope settings (exposure, gain, resolution)

• Include calibration standards in each batch

• Capture multiple fields per core (≥3) to account for heterogeneity

Automated analysis protocol:

• Implement an automated analysis protocol in ImageJ software as demonstrated in published research :

  • Color deconvolution to separate DAB (BTF3) from hematoxylin

  • Thresholding to identify positive areas

  • Measurement of staining intensity and area

  • Normalization to total tissue area

Multi-parameter quantification:

• Measure both intensity and distribution of staining

• Quantify nuclear vs. cytoplasmic localization

• Calculate H-scores (intensity × percentage of positive cells)

Statistical validation:

• Perform intra- and inter-observer variability assessments

• Calculate intraclass correlation coefficients

• Conduct ROC analysis to determine optimal cutoff values:

  • Research shows sensitivity in the range of 0.68 to 0.74 for individual markers

  • Combination of markers in logistic regression models demonstrated improved diagnostic power (93-97% correct identification for certain combinations)

What experimental designs can effectively demonstrate BTF3's role in transcriptional regulation?

To investigate BTF3's transcriptional regulatory functions:

ChIP-sequencing:

• Use anti-BTF3 antibodies for chromatin immunoprecipitation followed by next-generation sequencing

• This approach identified 103 genes related to BTF3 in colorectal cancer

• Compare ChIP-seq data with transcriptome analysis to identify genes positively correlated with BTF3 expression

  • Example: BTF3 and PDCD2L showed correlation coefficient of 0.5883 in HCC

Luciferase reporter assays:

• Design luciferase reporter constructs containing promoter regions of suspected target genes

• Compare reporter activity in cells with BTF3 overexpression, knockdown, and controls

• Example: Dual luciferase reporter assay showed BTF3 overexpression significantly enhanced pGL3-PDCD2L activity in HCC cells

CRISPR-based approaches:

• Use CRISPR activation or interference to modulate BTF3 expression

• Combine with RNA-seq to identify transcriptome-wide effects

• Follow up with ChIP-qPCR to validate direct binding to specific promoters

Integrated multi-omics:

• Combine RNA-seq data from BTF3 knockdown/overexpression experiments with ChIP-seq data

• Integrate with protein expression data (proteomics or Western blot)

• Analyze both total and nascent RNA to distinguish direct from indirect effects

How might BTF3 antibodies be used to develop predictive biomarkers for cancer therapy response?

BTF3 shows potential as a predictive biomarker:

Cisplatin sensitivity prediction:

• Research demonstrates BTF3 overexpression correlates with cisplatin sensitivity in prostate cancer:

  • BTF3b overexpression rendered prostate cancer cells more sensitive to cisplatin treatment in vitro and in vivo

  • BTF3 expression induces substantial accumulation of cisplatin-DNA adducts

  • This suggests BTF3 expression levels may serve as a biomarker to predict cisplatin treatment response

Development approach:

What methodological approaches can reveal the dual role of BTF3 in transcription and protein targeting?

To investigate BTF3's dual functionality:

Subcellular fractionation combined with immunoblotting:

• Separate nuclear, cytoplasmic, and ribosome-associated fractions

• Use BTF3 antibodies to detect distribution across compartments

• Compare distribution patterns in different cellular contexts and stress conditions

Proximity-dependent biotinylation (BioID or TurboID):

• Create BTF3 fusion constructs with biotin ligase

• Identify proximity partners in different cellular compartments

• Distinguish transcription-related vs. nascent polypeptide-associated interactions

Live-cell imaging with fluorescently-tagged antibody fragments:

• Track BTF3 localization and dynamics in real-time

• Correlate with transcriptional activity and ribosome association

Ribosome profiling combined with BTF3 knockdown/overexpression:

• Assess global changes in translation efficiency

• Identify mRNAs most affected by BTF3 manipulation

• Compare with transcriptome changes to distinguish translational from transcriptional effects

Table 1: Performance Characteristics of BTF3 Antibodies in Different Applications

Table 2: Diagnostic Performance of BTF3 Combined with Other Markers