BHLH91 Antibody

What are bHLH transcription factors and why are they important research targets?

bHLH (basic helix-loop-helix) transcription factors comprise a family of proteins characterized by their conserved bHLH domain structure. They function as master regulators of various developmental processes, including embryonic morphogenesis, cell fate determination, and tissue-specific gene expression. TWIST1, for example, functions as a critical regulator of embryonic morphogenesis . The bHLH family includes numerous members that play diverse roles in biological processes, making them important targets for research in developmental biology, cancer biology, and neuroscience. Antibodies against these proteins are essential tools for studying their expression patterns, localization, and functional roles in normal development and disease states .

What are the common applications for bHLH antibodies in research settings?

Based on the available antibody products, bHLH antibodies are primarily used in the following applications:

The BHLHB9 antibody (21019-1-AP) specifically targets BHLHB9 in ELISA applications and shows reactivity with human samples .

What is the molecular structure and function of BHLHB9?

BHLHB9 (basic helix-loop-helix domain containing, class B, 9) is a member of the bHLH transcription factor family. According to the antibody information, the full-length human BHLHB9 protein consists of 547 amino acids with a calculated molecular weight of approximately 60 kDa. The gene is referenced by GenBank accession number BC041409, NCBI gene ID 80823, and UniProt ID Q6PI77 . While the specific biological functions are not detailed in the search results, as a member of the bHLH family, it likely plays a role in transcriptional regulation of specific target genes during developmental processes or in response to certain cellular signals.

What are the recommended protocols for detecting low-abundance bHLH transcription factors?

Detecting low-abundance bHLH transcription factors requires specialized techniques due to their often transient expression and low protein levels. Based on research with other bHLH factors:

• High-sensitivity imaging systems: Use confocal microscopy equipped with highly sensitive detectors for fluorescent protein-tagged bHLH factors .

• Spectral imaging techniques: This approach is particularly effective when the fluorescent signal is weaker than cellular autofluorescence. Spectral imaging and linear unmixing can discriminate between genuine signals and autofluorescence .

• Signal optimization: When working with reporter-bHLH fusion proteins, it's important to note their short half-lives (approximately 20 minutes for many bHLH proteins). Therefore, continuous monitoring with minimal phototoxicity is essential .

• Background reduction strategies: For antibody-based detection, optimizing blocking conditions and using highly specific antibodies with minimal cross-reactivity is crucial.

For BHLHB9 antibody specifically, the antigen affinity purification method helps ensure specificity for detecting the target protein in human samples .

How should researchers design experiments to study the dynamic expression patterns of bHLH transcription factors?

Research on bHLH factors has revealed that their expression often follows oscillatory patterns, particularly in stem cells. To effectively study these dynamics:

• Real-time imaging approaches: Utilize fluorescent protein-bHLH fusion reporter systems. This can be achieved through:

  • BAC transgene constructs expressing reporter-bHLH fusion proteins under original regulatory elements

  • Knock-in reporter lines where fluorescent proteins are fused to endogenous bHLH factors

• Time-lapse imaging parameters:

  • Imaging intervals of 15-30 minutes to capture oscillations

  • Low-phototoxicity microscopy settings to enable long-term imaging

  • Temperature and CO₂ control to maintain physiological conditions

• Quantification methods:

  • Single-cell tracking over time

  • Fluorescence intensity measurements normalized to control markers

  • Mathematical modeling of oscillatory patterns

• Validation approaches:

  • Pharmacological blocking experiments (e.g., using cycloheximide to block protein synthesis)

  • Comparison of reporter activity with endogenous protein levels through immunostaining

What are the best preservation and storage conditions for maintaining antibody reactivity?

For optimal preservation of antibody reactivity, storage conditions are critical. The BHLHB9 antibody requires specific storage conditions:

• Store at -20°C in PBS with 0.02% sodium azide and 50% glycerol at pH 7.3

• The antibody remains stable for one year after shipment when properly stored

• Aliquoting is unnecessary for -20°C storage

• Small volume formats (20μl) contain 0.1% BSA for added stability

These storage recommendations apply to the BHLHB9 antibody but represent general best practices for many research antibodies. Avoiding repeated freeze-thaw cycles and maintaining the appropriate buffer conditions are crucial for preserving antibody functionality.

How can researchers use bHLH antibodies to investigate transcription factor dynamics during cell fate determination?

Studies of bHLH transcription factors have revealed crucial insights into the mechanisms of cell fate determination, particularly in neural stem cells (NSCs). Advanced research approaches include:

• Oscillatory vs. sustained expression analysis: Research has shown that the oscillatory expression of multiple bHLH transcription factors (like Ascl1/Mash1, Hes1, and Olig2) correlates with the multipotent and self-renewable state of NSCs, whereas sustained expression of a selected bHLH transcription factor regulates fate determination .

• Cell sorting strategies: Researchers can sort cells based on bHLH expression levels to study differentiation preferences. For example:

  • Hes1-high NSCs preferentially differentiate into astrocytes

  • Ascl1-high NSCs preferentially differentiate into neurons

  • Olig2-high NSCs preferentially differentiate into oligodendrocytes

• Temporal expression pattern analysis: Combining antibody detection with time-course experiments can reveal how transient down-regulation of one factor (e.g., Hes1) and concomitant up-regulation of another (e.g., Ascl1) before cell division can bias stem cells toward specific fates .

• Optogenetic manipulation: Advanced studies have employed optogenetic methods (photo-activatable Gal4/UAS system) to artificially manipulate the expression patterns of bHLH transcription factors using blue light illumination, confirming that oscillatory expression activates proliferation while sustained expression induces differentiation .

What approaches can overcome challenges in detecting nuclear bHLH transcription factors?

Nuclear transcription factors present specific detection challenges due to nuclear membrane barriers, chromatin interactions, and often low abundance. Advanced approaches include:

• Optimized nuclear permeabilization:

  • Use of detergents like Triton X-100 in carefully optimized concentrations

  • Two-step fixation protocols with formaldehyde followed by alcohol permeabilization

  • Antigen retrieval techniques for formaldehyde-fixed samples

• Signal amplification systems:

  • Tyramide signal amplification (TSA) for immunofluorescence

  • Polymer-based detection systems for immunohistochemistry

  • Proximity ligation assays (PLA) for detecting protein-protein interactions

• Chromatin state considerations:

  • Chromatin decompaction treatments for improved epitope accessibility

  • Combined chromatin immunoprecipitation (ChIP) and immunofluorescence approaches

• Subcellular fractionation:

  • Nuclear extraction protocols prior to Western blotting

  • Density gradient ultracentrifugation for nuclear protein enrichment

For flow cytometry applications specifically, specialized intracellular staining protocols have been developed, as demonstrated with the Human Twist-1 antibody for detection in A549 human lung carcinoma cell lines .

How can researchers validate the specificity of bHLH antibodies in complex tissues?

Validating antibody specificity is crucial for accurate research outcomes, particularly with structurally similar bHLH family members:

• Genetic validation approaches:

  • Testing in knockout/knockdown models where the target protein is absent

  • Comparison between heterozygous and wild-type samples for gene dosage effects

  • Ectopic expression systems with tagged versions of the protein

• Cross-reactivity assessment:

  • Testing against closely related family members

  • Peptide competition assays using the immunizing peptide

  • Immunoprecipitation followed by mass spectrometry to identify all bound proteins

• Signal correlation methods:

  • Comparison of antibody signals with fluorescent reporter fusion proteins

  • Dual-staining with antibodies against different epitopes of the same protein

  • Correlation of protein detection with mRNA expression (e.g., combining immunofluorescence with in situ hybridization)

For example, in studies of Venus-Hes1, Venus-Ascl1, and mCherry-Olig2 fusion reporter mice, researchers validated the reporter system by confirming that expression levels of fusion proteins highly correlated with endogenous bHLH proteins in cultured NSCs .

What are common artifacts in bHLH antibody staining and how can they be avoided?

Researchers frequently encounter several artifacts when working with bHLH antibodies:

• Autofluorescence interference:

  • Issue: Cellular autofluorescence may mask genuine signals, particularly with dim fluorescent protein-bHLH fusion proteins

  • Solution: Use spectral imaging and linear unmixing techniques to discriminate between fluorescent signals with overlapping spectral characteristics. For example, while autofluorescence appears at approximately 560 nm when illuminated with a 514-nm argon laser, Venus-Hes1 or Venus-Ascl1 fusion protein signals appear at approximately 530 nm .

• Non-specific nuclear staining:

  • Issue: Antibodies may bind non-specifically to condensed chromatin

  • Solution: Optimize blocking conditions, increase washing stringency, and validate with appropriate controls including isotype control antibodies as demonstrated in flow cytometry applications .

• Fixation-induced epitope masking:

  • Issue: Formaldehyde fixation can mask epitopes

  • Solution: Test different fixation methods or incorporate antigen retrieval steps

• Cross-reactivity with related bHLH proteins:

  • Issue: Antibodies may detect related family members

  • Solution: Validate specificity using knockout controls or peptide competition assays

How should researchers interpret contradictory results between antibody-based detection and reporter models?

Contradictory results between antibody detection and reporter systems are common challenges in bHLH research:

• Evaluate reporter design limitations:

  • Reporter-bHLH fusion proteins may affect protein stability or function

  • BAC transgene constructs with one-copy expression should be preferred for physiological relevance

  • Consider that reporter gene insertions might disrupt endogenous gene regulation

• Assess antibody limitations:

  • Antibody epitopes may be masked in certain protein complexes

  • Post-translational modifications might affect antibody recognition

  • Some antibodies may recognize degradation products or specific protein isoforms

• Resolution strategies:

  • Use multiple detection methods including Western blotting, immunofluorescence, and reporter systems

  • Perform careful time-course experiments to identify temporal discrepancies

  • Evaluate protein turnover rates with cycloheximide chase experiments

  • Consider subcellular localization differences that might explain discrepancies

• Validation in multiple systems:

  • Test in different cell types and model systems

  • Use genetic manipulation to alter protein levels and confirm detection sensitivity

What factors influence the oscillatory expression patterns of bHLH transcription factors?

Research on bHLH factors has revealed complex oscillatory expression dynamics, particularly in stem cells. Key factors influencing these patterns include:

• Negative feedback loops:

  • Many bHLH factors regulate their own expression through negative feedback

  • Protein and mRNA instability (short half-lives of approximately 20 minutes) enables rapid oscillations

• Cross-regulatory interactions:

  • Mutual inhibition between different bHLH factors affects oscillation patterns

  • For example, interactions between Hes1, Ascl1, and Olig2 in neural stem cells

• Cell state influences:

  • Oscillatory expression correlates with the multipotent state

  • Transition to sustained expression occurs during cell fate determination

  • Different expression levels bias cell fate choices even before commitment

• Technical considerations for measurement:

  • Imaging frequency must be sufficient to capture oscillations (typically 15-30 minute intervals)

  • Population-level measurements may mask oscillations that are asynchronous between cells

  • Single-cell analysis is essential for accurate characterization of oscillation patterns

Understanding these oscillatory dynamics has led to important insights into the mechanisms of stem cell maintenance and differentiation, as demonstrated in neural stem cell studies .

How might CRISPR-based approaches enhance studies of bHLH transcription factors?

CRISPR/Cas9 technology offers promising new approaches for studying bHLH transcription factors:

• Endogenous tagging strategies:

  • CRISPR knock-in of fluorescent reporters at endogenous loci to maintain native regulation

  • Creation of split-fluorescent protein tags to study protein-protein interactions

  • Development of degradation-resistant variants to study protein turnover dynamics

• Functional genomic screens:

  • CRISPR activation (CRISPRa) or interference (CRISPRi) screens to identify regulators of bHLH expression

  • Screens for factors that modulate oscillatory vs. sustained expression patterns

  • Identification of transcriptional targets through CRISPRa/i approaches

• Spatiotemporal control:

  • Optogenetic CRISPR systems for light-controlled gene expression

  • Chemically inducible CRISPR systems for temporal control of gene editing

  • Tissue-specific Cas9 expression for in vivo studies

• Structural studies:

  • CRISPR-mediated insertion of proximity labeling tags to identify interaction partners

  • Creation of domain-specific mutations to study structure-function relationships

What emerging techniques might improve detection of dynamic bHLH protein interactions?

Emerging technologies are enhancing our ability to study the dynamic interactions of bHLH proteins:

• Advanced imaging approaches:

  • Super-resolution microscopy for visualizing nuclear organization of transcription factors

  • Light-sheet microscopy for reduced phototoxicity in long-term imaging

  • Adaptive optics for improved deep tissue imaging in developing organisms

• Protein interaction detection systems:

  • FRET/FLIM (Fluorescence Resonance Energy Transfer/Fluorescence Lifetime Imaging) for quantifying protein interactions

  • Split protein complementation assays for visualizing interactions in living cells

  • Proximity labeling techniques (BioID, APEX) for identifying transient interaction partners

• Single-molecule tracking:

  • High-speed tracking of individual molecules to determine binding kinetics

  • Multi-color imaging to simultaneously track multiple bHLH factors

  • Correlation with chromatin dynamics through simultaneous DNA labeling

• Integrative approaches:

  • Combined single-cell transcriptomics and proteomics

  • Multi-omics approaches linking transcription factor binding to epigenetic changes

  • Mathematical modeling of gene regulatory networks

How might artificial intelligence enhance the analysis of bHLH expression patterns?

Artificial intelligence approaches are revolutionizing the analysis of complex biological data, with particular promise for bHLH expression studies:

• Automated image analysis:

  • Deep learning for cell segmentation and tracking in time-lapse microscopy

  • Automated identification of oscillatory patterns across thousands of single cells

  • Classification of cell states based on expression patterns of multiple factors

• Predictive modeling:

  • Machine learning models to predict cell fate decisions based on bHLH expression dynamics

  • Neural networks to identify patterns in regulatory element usage

  • Systems biology models of transcription factor networks

• Multi-dimensional data integration:

  • Integration of imaging, transcriptomic, and proteomic data

  • Pattern recognition across diverse experimental conditions

  • Identification of previously unrecognized correlations between different bHLH factors

• Experimental design optimization:

  • Reinforcement learning approaches to optimize experimental conditions

  • Adaptive sampling strategies for time-lapse imaging

  • Automated feedback between data analysis and experimental protocols