ACC1 Antibody

What is ACC1 and why is it significant in metabolic research?

ACC1 (Acetyl-CoA carboxylase 1), also known as ACACA, belongs to the biotin-containing enzyme family and catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, a rate-limiting step in fatty acid synthesis . This enzyme plays a pivotal role in regulating fatty acid metabolism and energy production. ACC1 is particularly significant in research because it sits at a crucial metabolic junction, converting acetyl-CoA (the end product of glycolysis) into malonyl-CoA (the building block for fatty acid synthesis) . The enzyme is regulated through multiple mechanisms including transcriptional control, translational regulation, and short-term phosphorylation/dephosphorylation events that respond to cellular energy states . Its expression pattern varies across tissues, with high expression in brain, placenta, skeletal muscle, renal, pancreatic, and adipose tissues, but low expression in pulmonary tissue and limited detection in liver . This tissue-specific expression pattern makes ACC1 particularly interesting for studies focusing on metabolic disorders and tissue-specific energy regulation.

What applications can ACC1 antibodies be reliably used for?

ACC1 antibodies have been validated for multiple research applications, with specific dilution requirements for optimal results. The primary applications include:

The literature shows extensive validation in published research, with 139 publications using these antibodies for Western blotting, 13 for IHC, 7 for IF, and smaller numbers for IP and RIP applications . When selecting an application, researchers should consider the specific biological question, sample type, and required sensitivity level. For challenging applications, optimization experiments with positive controls are strongly recommended.

How should researchers determine the optimal working dilution for ACC1 antibodies?

Determining the optimal working dilution for ACC1 antibodies requires systematic titration rather than relying solely on manufacturer recommendations. While manufacturers provide dilution ranges (e.g., 1:1000-1:8000 for WB, 1:50-1:500 for IHC and IF/ICC) , these should be considered starting points. The optimal approach involves:

• Perform an initial titration experiment using 3-4 different dilutions within the recommended range

• Include positive control samples with known ACC1 expression levels (e.g., HepG2 cells, HeLa cells)

• Evaluate signal-to-noise ratio, not just signal intensity alone

• Consider sample-specific factors that might affect optimal dilution:

  • Fixation method and duration for IHC/IF samples

  • Protein loading amount for Western blots

  • Buffer composition for immunoprecipitation

As noted in the technical information, "It is recommended that this reagent should be titrated in each testing system to obtain optimal results" and results can be "Sample-dependent" . Researchers should document optimization results to ensure reproducibility across experiments and to facilitate troubleshooting if unexpected results occur.

What is the molecular weight of ACC1 protein and how does this inform antibody validation?

ACC1 has a calculated molecular weight of approximately 265-266 kDa based on its amino acid sequence (2383 amino acids), but the observed molecular weight in experimental systems is typically around 250-277 kDa . This discrepancy between calculated and observed molecular weights is critical for antibody validation and can be attributed to:

• Post-translational modifications affecting protein migration

• Alternative splicing resulting in different isoforms

• The biotin moiety essential for ACC1's catalytic function

Researchers should use this molecular weight information for proper antibody validation by:

• Confirming band position in Western blots corresponds to the expected 250-277 kDa size

• Being aware that ACC1 has four reported isoforms produced by alternative promoter usage, with molecular weights between 260-270 kDa

• Including positive control lysates from tissues/cells known to express ACC1 (e.g., HepG2, HeLa, brain tissue)

• Using ACC1 knockout/knockdown samples as negative controls where possible

This rigorous validation approach ensures that experimental observations genuinely reflect ACC1 biology rather than antibody artifacts.

How does ACC1 function in pathogenic T cell populations and what antibody considerations apply?

Recent research has revealed a critical role for ACC1 in controlling the inflammatory function of pathogenic CD4+ T cell populations, particularly in promoting type 2 inflammation in lung and skin . When studying ACC1 in immune contexts, researchers should consider:

• ACC1 expression is significantly elevated in lung and skin pathogenic CD4+ T cells compared to other T cell populations

• ACC1-dependent fatty acid biosynthesis induces pathogenic cytokine production of CD4+ T cells through:

  • Metabolic reprogramming

  • Modulation of acetyl-CoA availability for epigenetic regulation

For immunological research applications:

• When staining for ACC1 in T cell subsets, use validated T cell markers in co-staining experiments

• Consider fixation and permeabilization protocols optimized for intracellular enzymes

• Include appropriate blocking steps to minimize non-specific binding

• When studying ACC1 in T cell pathology models, genetic deletion approaches have demonstrated that ACC1 controls:

  • IL-5 production in lung Tpath2 cells

  • IL-3 production by skin CD4+ T cells

  • ST2 expression in both T cells and ILC2s

These findings suggest ACC1 antibodies can be valuable tools for investigating metabolic regulation of inflammatory responses in allergic and immune-mediated diseases.

What methodological approaches are recommended for studying ACC1 in lipid metabolism research?

When investigating ACC1's role in lipid metabolism regulation, researchers should employ multiple complementary approaches:

• Pharmacological inhibition coupled with antibody detection:

  • Use specific ACC1 inhibitors to block enzymatic activity

  • Monitor changes in ACC1 phosphorylation status using phospho-specific antibodies

  • Track downstream metabolic consequences via lipidomics approaches

• Genetic manipulation strategies:

  • Conditional knockout models show that genetic deletion of CD4+ T cell-intrinsic ACC1 dampens eosinophilic and basophilic inflammation by constraining specific cytokine production

  • Use inducible systems to distinguish between developmental vs. functional roles

• Metabolic flux analysis:

  • Integrate ACC1 antibody-based quantification with isotope tracing to measure de novo fatty acid synthesis rates

  • Correlate ACC1 expression/activity levels with metabolic pathway dynamics

• Context-specific considerations:

  • ACC1 activation status responds to cellular energy state, requiring careful experimental design

  • ACC1 is activated by dephosphorylation and deactivated by phosphorylation

  • Different tissues show variable ACC1 expression patterns, necessitating tissue-specific validation

When using ACC1 antibodies in metabolism studies, researchers should consider the dynamic nature of ACC1 regulation and incorporate appropriate activity assays alongside expression analyses to fully characterize ACC1 biology in their experimental system.

How can researchers effectively validate ACC1 antibody specificity for their experimental system?

Rigorous validation of ACC1 antibody specificity is essential for generating reliable research data. A comprehensive validation approach should include:

• Multiple detection methods:

  • Compare results across different applications (WB, IHC, IF) using the same antibody

  • Use antibodies targeting different epitopes within ACC1

  • Confirm similar results with monoclonal and polyclonal antibodies where possible

• Genetic controls:

  • Use ACC1 knockout/knockdown samples as negative controls

  • Rescue experiments with ACC1 overexpression to confirm specificity

  • The search results mention multiple publications using ACC1 antibodies in KD/KO experiments that can serve as methodology references

• Peptide competition assays:

  • Pre-incubate antibody with immunizing peptide before application

  • Gradual loss of signal with increasing peptide concentration confirms specificity

  • Especially valuable when genetic controls are unavailable

• Cross-reactivity assessment:

  • Test antibody against related proteins (e.g., ACC2)

  • Consider species cross-reactivity - available ACC1 antibodies show reactivity with human, mouse, and rat samples, with predicted reactivity extending to other species like pig, zebrafish, bovine, horse, sheep, rabbit, dog, and chicken

• Application-specific validation:

  • For IHC: Compare antigen retrieval methods (TE buffer pH 9.0 vs. citrate buffer pH 6.0)

  • For IP: Verify pulled-down protein by mass spectrometry

  • For WB: Confirm band size matches predicted molecular weight (265-277 kDa)

Comprehensive validation not only ensures experimental reliability but also enables troubleshooting when unexpected results occur.

What are the recommended protocol modifications for detecting ACC1 in difficult tissue samples?

Detecting ACC1 in challenging tissue samples may require protocol optimizations beyond standard procedures:

• Antigen retrieval optimization for IHC/IF:

  • The primary recommendation is TE buffer at pH 9.0

  • Alternative approach using citrate buffer at pH 6.0 may be necessary for certain tissues

  • Extended retrieval times (15-30 minutes) may improve detection in fibrous tissues

• Signal amplification strategies:

  • Use tyramide signal amplification for low abundance detection

  • Consider polymer-based detection systems for improved sensitivity

  • Optimize blocking conditions using tissue-specific blockers to reduce background

• Sample preparation considerations:

  • For adipose tissue: Minimize lipid interference through appropriate fixation and processing

  • For muscle tissue: Additional permeabilization steps may be needed

  • For brain tissue: Perfusion fixation improves antibody penetration and consistent staining

• Antibody incubation parameters:

  • Extended primary antibody incubation (overnight at 4°C)

  • Optimize antibody concentration specifically for each tissue type

  • Consider the use of penetration enhancers for thick sections

These optimizations should be systematically evaluated and documented to establish tissue-specific protocols that yield consistent results while maintaining specificity.

How should researchers troubleshoot weak or non-specific ACC1 antibody signals?

When encountering weak or non-specific signals with ACC1 antibodies, researchers should implement a systematic troubleshooting approach:

• For weak signals:

  • Increase antibody concentration (within validated range)

  • Extend incubation time (e.g., overnight at 4°C)

  • Optimize antigen retrieval (for IHC/IF)

  • Increase protein loading (for WB)

  • Ensure sample freshness and proper storage

  • Verify ACC1 expression in your sample type through literature or database review

• For non-specific signals:

  • Increase blocking duration and concentration

  • Use alternative blocking agents (BSA, serum, commercial blockers)

  • Increase washing stringency (duration, detergent concentration)

  • Validate secondary antibody separately

  • Reduce primary antibody concentration

  • Consider alternative antibody clones targeting different epitopes

• Application-specific troubleshooting:

  • WB: Optimize transfer conditions for high molecular weight proteins (250+ kDa)

  • IHC: Test multiple antigen retrieval methods (heat vs. enzymatic, different buffers)

  • IF: Adjust fixation conditions (duration, fixative type)

  • IP: Modify lysis conditions to preserve protein-protein interactions

• Controls to include:

  • Positive control: HEK-293 cells, HeLa cells, mouse brain tissue, HepG2 cells

  • Secondary-only control to assess background

  • Isotype control to identify non-specific binding

Systematic documentation of troubleshooting steps facilitates protocol optimization and ensures experimental reproducibility.

What are essential experimental controls when using ACC1 antibodies?

Robust experimental design with ACC1 antibodies requires appropriate controls to ensure data reliability:

• Positive controls:

  • Samples known to express ACC1: HEK-293 cells, HeLa cells, mouse brain tissue, HepG2 cells

  • Recombinant ACC1 protein (for antibody validation)

  • Tissues with documented ACC1 expression: brain, placenta, skeletal muscle, renal, pancreatic, and adipose tissues

• Negative controls:

  • Secondary antibody only (omit primary antibody)

  • Isotype control antibody (same species and isotype as ACC1 antibody)

  • ACC1 knockdown/knockout samples when available

  • Tissues with minimal ACC1 expression: liver samples show limited detection

• Treatment controls:

  • Include samples with known ACC1 modulation:

    • Insulin treatment (increases ACC1 activity)

    • AMPK activators like AICAR (decrease ACC1 activity through phosphorylation)

    • Metabolic pathway inhibitors relevant to research question

• Technical controls:

  • Loading controls for WB (housekeeping proteins appropriate for tissue/cell type)

  • Peptide competition controls for antibody specificity

  • Multiple antibody clones targeting different epitopes

  • Antigen retrieval controls for IHC (comparing different methods)

• Validation controls:

  • Correlation of protein detection with mRNA expression

  • Functional readouts of ACC1 activity (fatty acid synthesis assays)

  • Phosphorylation status assessment for activity correlation

Implementing these controls ensures data validity and facilitates accurate interpretation of ACC1-related findings in complex biological systems.

How can ACC1 antibodies be leveraged to study metabolic reprogramming in immune cells?

Recent research reveals ACC1's crucial role in immune cell function, particularly in pathogenic T helper cells facilitating allergic inflammation . When applying ACC1 antibodies to immunometabolism research:

• Integration with functional assays:

  • Combine ACC1 antibody detection with cytokine profiling (IL-5, IL-3)

  • Correlate ACC1 expression with ST2 levels in type 2 immune cells

  • Link ACC1 detection to glycolysis measurements, as ACC1-mediated fatty acid biosynthesis controls maximal glycolytic activation

• Cell population-specific analysis:

  • Use flow cytometry with ACC1 antibodies to analyze expression across immune cell subsets

  • Consider multiparameter approaches combining ACC1 with:

    • Surface markers (CD4, ST2)

    • Intracellular cytokines (IL-5, IL-13, IL-3)

    • Other metabolic enzymes

• Intervention studies:

  • Monitor ACC1 expression changes following:

    • Cytokine stimulation

    • TCR activation

    • Metabolic pathway inhibitors

    • Environmental challenges (allergen exposure)

• Tissue-specific considerations:

  • Optimize protocols for different tissue environments:

    • Lung (bronchoalveolar lavage samples, lung tissue)

    • Skin (epidermal T cells)

    • Secondary lymphoid organs

Research has demonstrated that "ACC1 controls the inflammatory function of the pathogenic CD4+ T cell population to promote type 2 inflammation in the lung and skin" , making ACC1 antibodies valuable tools for investigating metabolic control of immune responses in allergic diseases.

What methodological considerations apply when using ACC1 antibodies in co-immunoprecipitation experiments?

Co-immunoprecipitation (co-IP) with ACC1 antibodies requires special considerations due to ACC1's large size (265-277 kDa) and complex regulatory interactions:

• Optimization of lysis conditions:

  • Use gentle lysis buffers to preserve protein-protein interactions

  • Consider membrane solubilization methods appropriate for metabolic enzymes

  • Adjust detergent type and concentration based on interaction strength

  • Include protease and phosphatase inhibitors to preserve modification states

• Antibody selection and application:

  • Use 0.5-4.0 μg antibody for 1.0-3.0 mg of total protein lysate

  • Consider antibody orientation (ACC1 antibody for IP vs. interactor antibody)

  • Pre-clearing lysates reduces non-specific binding

  • Verify antibody compatibility with IP buffer components

• Controls specific for ACC1 co-IP:

  • IgG control from same species as ACC1 antibody

  • Input controls (5-10% of lysate used for IP)

  • Reciprocal IP (using antibody against suspected interactor)

  • Competition with immunizing peptide

  • Validation in knockdown/knockout systems

• Analysis considerations:

  • Western blot detection of high molecular weight ACC1 requires:

    • Extended transfer times

    • Lower percentage gels (6-8%)

    • Gradient gels to resolve ACC1 and interacting proteins

  • Mass spectrometry validation for novel interactions

  • Functional validation of identified interactions

• Biological variables affecting ACC1 interactions:

  • Phosphorylation status alters ACC1 protein interactions

  • Metabolic state of cells affects complex formation

  • Consider insulin/glucagon pre-treatment to modulate ACC1 activity

Published research has successfully applied IP techniques with ACC1 antibodies , providing precedent for exploring ACC1's interactome in various biological contexts.

How can researchers effectively study ACC1 phosphorylation status?

ACC1 activity is regulated through phosphorylation/dephosphorylation mechanisms that respond to cellular energy states . Studying these modifications requires specific methodological approaches:

• Phosphorylation-specific antibody selection:

  • Use phospho-specific antibodies targeting key regulatory sites

  • Combine with total ACC1 antibodies to normalize phosphorylation levels

  • Include controls with known phosphorylation status (AMPK activator/inhibitor treated samples)

• Sample preparation considerations:

  • Rapid sample collection and processing prevents phosphorylation artifacts

  • Include phosphatase inhibitors in all buffers

  • Standardize handling conditions across experimental groups

  • Consider cell synchronization to control for cell cycle effects

• Analytical approaches:

  • Western blotting with phospho-specific antibodies

  • Phos-tag gels for separation of phosphorylated species

  • Mass spectrometry for comprehensive phosphorylation mapping

  • Functional correlation through enzymatic activity assays

• Biological manipulations to study regulation:

  • Metabolic interventions that alter ACC1 phosphorylation:

    • Insulin treatment (promotes dephosphorylation/activation)

    • AMPK activators (promote phosphorylation/inactivation)

    • Glucagon exposure (affects phosphorylation status)

  • Genetic approaches:

    • Phospho-mimetic or phospho-deficient mutants

    • Kinase/phosphatase knockdown or inhibition

• Data interpretation considerations:

  • Correlation between phosphorylation status and enzymatic activity

  • Tissue-specific regulation patterns

  • Temporal dynamics of phosphorylation/dephosphorylation

  • Integration with metabolic pathway analysis

Understanding ACC1 phosphorylation dynamics provides crucial insights into metabolic regulation and potential therapeutic targeting strategies in diseases with dysregulated lipid metabolism.

How are ACC1 antibodies being used to investigate metabolic targeting in disease models?

ACC1 antibodies are increasingly valuable for investigating metabolic targeting strategies in various disease models, particularly in cancer and inflammatory conditions:

• Cancer metabolism research:

  • ACC1 promotes glucose-mediated fatty acid synthesis enhancing survival of hepatocellular carcinoma

  • Antibodies enable monitoring of ACC1 expression in response to metabolic inhibitors

  • Correlation of ACC1 levels with tumor aggressiveness and patient outcomes

  • Validation of target engagement by ACC1-targeting therapeutics

• Inflammatory disease models:

  • ACC1 controls pathogenic T cell functions in allergic inflammation

  • Antibodies facilitate assessment of:

    • Cell type-specific ACC1 expression in inflammatory tissues

    • Changes in ACC1 expression during disease progression

    • Effects of anti-inflammatory treatments on ACC1 levels

• Methodological approaches:

  • Tissue microarrays with ACC1 antibodies for high-throughput screening

  • Single-cell analysis of ACC1 expression in heterogeneous disease tissues

  • Correlation of ACC1 expression with disease biomarkers

  • Monitoring ACC1 in preclinical drug screening platforms

• Translational applications:

  • Patient-derived xenograft models assessed for ACC1 expression

  • Ex vivo tissue culture systems for therapeutic testing

  • Correlation of ACC1 expression with treatment response

  • Monitoring ACC1 as a potential biomarker for metabolic-targeting therapies

The literature reports that ACC1 is critical for processes like stem cell pluripotency, hepatocellular carcinoma survival, and liver cancer metastasis , highlighting the importance of ACC1 antibodies in diverse disease research contexts.

What considerations apply when using ACC1 antibodies in multi-parameter flow cytometry?

Multi-parameter flow cytometry with ACC1 antibodies enables detailed analysis of metabolic heterogeneity within complex cell populations, with several important considerations:

• Antibody panel design:

  • Combine ACC1 with lineage markers, activation markers, and functional readouts

  • Consider fluorophore brightness relative to ACC1 expression level

  • Avoid fluorophore combinations with significant spillover

  • Include other metabolic enzymes for comprehensive metabolic profiling

• Optimization for intracellular ACC1 detection:

  • Compare fixation protocols (paraformaldehyde, methanol, combination approaches)

  • Evaluate permeabilization reagents compatible with ACC1 epitope preservation

  • Titrate antibody concentrations specifically for flow cytometry

  • Validate staining with positive control cells (HeLa, HepG2)

• Controls critical for ACC1 flow cytometry:

  • Fluorescence minus one (FMO) controls

  • Isotype controls from same host species

  • Phosphorylation-specific controls (if using phospho-ACC1 antibodies)

  • Biological controls with manipulated ACC1 expression/activity

• Analysis considerations:

  • Gating strategies to identify ACC1high vs. ACC1low populations

  • Correlation with functional parameters (cytokine production, activation markers)

  • Quantification approaches (median fluorescence intensity, population percentages)

  • Visualization methods for high-dimensional data (tSNE, UMAP)

• Application to specific research questions:

  • Identifying metabolically distinct immune cell subsets in allergic inflammation

  • Tracking ACC1 expression changes during cellular activation

  • Measuring effects of metabolic inhibitors on ACC1+ populations

  • Correlating ACC1 expression with disease severity markers

This approach enables researchers to connect ACC1-mediated metabolic programs with cellular function at the single-cell level, providing insights impossible with bulk analysis methods.

How can researchers stay updated with evolving ACC1 antibody applications?

Maintaining current knowledge about ACC1 antibody applications requires systematic approaches to literature monitoring and community engagement:

• Literature monitoring strategies:

  • Set up citation alerts for key ACC1 antibody publications

  • Create search alerts for new ACC1 research across multiple databases

  • Monitor journals focused on metabolism, immunology, and cancer research

  • Follow research groups with established ACC1 expertise

• Resource utilization:

  • Antibody validation databases (Antibodypedia, CiteAb)

  • Metabolic pathway databases (KEGG, Reactome)

  • Proteomics repositories for ACC1 interaction data

  • Tissue expression databases for context-specific information

• Community engagement:

  • Participate in metabolism-focused conferences and workshops

  • Join research consortia focused on metabolic regulation

  • Engage with technical forums for antibody methodology discussions

  • Contribute to collaborative validation initiatives

• Emerging applications to monitor:

  • Single-cell metabolic profiling with ACC1 antibodies

  • Spatial metabolomics integrating ACC1 localization data

  • Therapeutic targeting approaches monitoring ACC1 as a biomarker

  • Multi-omics approaches correlating ACC1 protein levels with metabolite profiles

By systematically tracking methodological advances and biological discoveries related to ACC1, researchers can continually refine their experimental approaches and contribute to expanding knowledge of this critical metabolic regulator.

What validation standards should researchers apply to ensure reproducible ACC1 antibody results?

Ensuring reproducible results with ACC1 antibodies requires adherence to rigorous validation standards:

• Comprehensive antibody validation:

  • Confirm reactivity in relevant species and cell types

  • Verify antibody specificity through genetic approaches (KO/KD)

  • Test multiple antibody lots for consistent performance

  • Document validation results thoroughly in laboratory records and publications

• Standardized experimental protocols:

  • Develop detailed SOPs for each application (WB, IHC, IF, IP)

  • Specify critical parameters:

    • Antibody dilutions (e.g., 1:1000-1:8000 for WB)

    • Incubation conditions (time, temperature)

    • Buffer compositions

    • Sample preparation methods

  • Include trouble-shooting decision trees

• Reporting standards:

  • Document complete antibody information in publications:

    • Catalog number

    • Clone designation

    • Lot number

    • Host species

    • Epitope information if available

  • Provide detailed methodological descriptions

  • Share raw data when possible

• Replication practices:

  • Independent biological replicates (minimum n=3)

  • Technical replicates to assess methodological variation

  • Cross-validation with orthogonal methods

  • Blinded analysis when feasible

• Data analysis transparency:

  • Pre-specified analysis plans

  • Complete reporting of statistical approaches

  • Sharing of analysis code when appropriate

  • Documentation of exclusion criteria