ACAT1 Antibody

What is the optimal dilution for ACAT1 antibody in Western blot applications?

The optimal dilution varies by manufacturer and specific antibody clone. Current recommendations based on commercially available antibodies include:

• Cell Signaling Technology's ACAT1 Antibody #44276: 1:1000 for Western blotting

• Proteintech's ACAT1 antibody (16215-1-AP): 1:500-1:2000 for Western blotting

• Assay Genie's ACAT1 Rabbit Polyclonal Antibody (CAB13273): 1:500-1:1000 for Western blotting

It is strongly recommended to perform an antibody titration experiment with your specific samples to determine the optimal concentration for the best signal-to-noise ratio. Begin with the manufacturer's recommended dilution and adjust based on your experimental conditions, sample type, and detection method.

Which species does ACAT1 antibody typically show reactivity with?

The species reactivity profile varies between antibody products:

When planning experiments with model organisms, select an antibody with validated reactivity for your species of interest. While cross-reactivity may occur due to sequence homology between species, proper validation is essential for reliable results.

What is the expected molecular weight of ACAT1 in Western blot analysis?

Based on current product information:

• Cell Signaling Technology reports a molecular weight of 42 kDa

• Abcam notes a predicted molecular weight of 45 kDa

• Proteintech indicates a calculated molecular weight of 45 kDa with observed weights ranging from 38-45 kDa

The variations in observed molecular weight may result from post-translational modifications, splice variants, or differences in electrophoresis conditions. When analyzing Western blot results, a band within the 38-45 kDa range would be consistent with ACAT1 detection. Always include appropriate positive controls and molecular weight markers for accurate interpretation.

What cellular localization pattern should be expected when using ACAT1 antibody for immunohistochemistry?

ACAT1 exhibits specific subcellular localization patterns that vary by tissue type. In human cerebellum, ACAT1 immunoreactivity is most intense in neuronal cell bodies, particularly in the large Purkinje cells, with a distinctive subcellular localization pattern whose functional significance remains under investigation .

ACAT1 is primarily associated with mitochondria and endoplasmic reticulum membranes, consistent with its role in cholesterol metabolism. When performing immunohistochemistry or immunofluorescence, expect staining patterns corresponding to these subcellular locations, especially in cell types with high metabolic activity or lipid processing requirements.

Which tissues and cell lines serve as effective positive controls for ACAT1 antibody validation?

Multiple tissues and cell lines have been successfully used to detect ACAT1:

When validating a new ACAT1 antibody, these tissues and cell types serve as reliable positive controls. For negative controls, consider ACAT1 knockout tissues/cells if available, or tissues known to express minimal ACAT1 levels.

How can ACAT1 antibodies be used to investigate cholesterol metabolism in neurodegenerative disease models?

ACAT1 antibodies provide valuable tools for exploring the relationship between cholesterol metabolism and neurodegenerative diseases, particularly Alzheimer's disease (AD). Research strategies include:

• Expression profiling: Use ACAT1 antibodies for Western blot or immunohistochemistry to compare expression levels between healthy and diseased brain tissues, establishing correlations between ACAT1 expression and disease progression.

• Co-localization analysis: Combine ACAT1 antibodies with antibodies against disease-specific markers (such as amyloid-β) for immunofluorescence microscopy to examine potential spatial relationships.

• Therapeutic intervention assessment: When testing ACAT1-targeting therapies, such as AAV-mediated Acat1 gene knockdown, antibodies confirm knockdown efficiency and correlate with changes in disease biomarkers.

• Mechanistic investigations: ACAT1 antibodies can be used alongside lipid analyses to understand how changes in ACAT1 levels affect cellular cholesterol homeostasis and subsequently influence neurodegenerative pathology.

Research has demonstrated that AAV-mediated Acat1 knockdown in AD mice decreased both brain amyloid-β and full-length human amyloid precursor protein (hAPP), suggesting a mechanistic link between ACAT1 activity and AD pathology that warrants further investigation .

What methodological considerations are essential for using ACAT1 antibody in co-immunoprecipitation studies?

For successful co-immunoprecipitation (Co-IP) studies with ACAT1 antibodies:

• Antibody selection: Use antibodies validated for immunoprecipitation applications. Cell Signaling Technology recommends a 1:50 dilution , while Proteintech recommends 0.5-4.0 μg per 1.0-3.0 mg of total protein lysate .

• Lysis conditions: Since ACAT1 associates with membranes, use non-denaturing lysis buffers containing 1% NP-40 or 1% Triton X-100 with protease and phosphatase inhibitors to preserve protein-protein interactions while effectively solubilizing membrane proteins.

• Pre-clearing: Pre-clear lysates with appropriate control beads to minimize non-specific binding.

• Antibody incubation: Incubate cleared lysates with ACAT1 antibody overnight at 4°C with gentle rotation to maximize antigen-antibody interaction while minimizing protein degradation.

• Complex capture: Use protein A/G beads for capturing antibody-protein complexes, followed by multiple gentle washes to remove non-specifically bound proteins.

• Control experiments: Include negative controls (non-specific IgG) and, ideally, ACAT1-depleted or knockout samples.

These methodological considerations enhance the specificity and reliability of co-immunoprecipitation results when investigating ACAT1 protein interactions.

How can researchers quantitatively analyze ACAT1 expression changes in gene knockdown studies?

When quantifying ACAT1 expression changes following gene knockdown, consider these methodological approaches:

• Knockdown validation method selection: The research on AAV-mediated Acat1 gene knockdown in AD mice used an in vitro ACAT activity assay rather than Western blot, noting "we opted to assay mouse brain ACAT activity in vitro rather than perform western blot for ACAT1 to test the effectiveness of our AAV at diminishing ACAT1 activity in mouse brains" . This suggests activity assays may sometimes provide more sensitive or functionally relevant measurements than protein detection.

• Time-course design: Plan sample collection at multiple time points that account for both mRNA and protein half-lives to capture the full temporal profile of knockdown effects.

• Multi-method quantification approach:

  • Western blot with appropriate loading controls (e.g., GAPDH) and digital band intensity quantification

  • Immunofluorescence for both intensity and localization changes

  • Flow cytometry for quantitative single-cell analysis

• Statistical analysis: Apply appropriate statistical tests to determine significance of observed changes. Research on Acat1 knockdown effects showed "statistically significant difference between the group means (P = 0.03)" .

• Comprehensive controls:

  • Include scrambled/negative control constructs (e.g., AAV-NC as used in published research)

  • Test multiple independent knockdown constructs (research tested multiple miRNAs targeting different Acat1 regions)

  • Include complete knockout controls when available (AD/Acat1−/− mice were used as controls)

What approaches can resolve discrepancies between ACAT1 protein levels and enzymatic activity measurements?

Discrepancies between antibody-detected ACAT1 protein levels and enzymatic activity measurements may arise for several reasons, requiring systematic troubleshooting approaches:

• Post-translational regulation analysis:

  • Employ phospho-specific antibodies if phosphorylation is suspected

  • Use native gel electrophoresis to preserve protein complexes

  • Apply 2D gel electrophoresis to separate protein variants with different modifications

• Protein conformation assessment:

  • Test multiple antibodies targeting different epitopes

  • Compare results from native versus denatured Western blots

  • Evaluate monoclonal versus polyclonal antibody detection patterns

• Subcellular localization investigation:

  • Perform subcellular fractionation followed by parallel Western blot and activity assays

  • Use high-resolution immunofluorescence microscopy to correlate localization patterns with activity

  • Consider the distinctive subcellular localization observed in certain cell types like Purkinje cells

• Protein complex analysis:

  • Apply blue native PAGE to preserve native protein complexes

  • Conduct co-immunoprecipitation to identify potential regulatory partners

  • Perform crosslinking studies to capture transient interactions

• Technical factor evaluation:

  • Verify antibody specificity with appropriate controls

  • Compare sensitivity thresholds between protein detection and activity assays

  • Assess whether sample preparation differences affect results

Researchers studying ACAT1 in AD mice chose to measure enzyme activity rather than relying solely on Western blot detection , illustrating how complementary approaches may be necessary depending on the specific research question and experimental context.

How does ACAT1 immunostaining pattern compare between different brain regions, and what are the implications for neurological disease research?

ACAT1 immunostaining reveals distinct patterns across brain regions that may have important implications for neurological disease research:

• Regional variation: In human cerebellum, ACAT1 immunoreactivity is most intense in neuronal cell bodies, particularly in the large Purkinje cells, with a distinctive subcellular localization pattern . Systematic comparison across brain regions could reveal region-specific vulnerabilities in diseases.

• Quantitative comparison methodology:

  • Standardize staining protocols with consistent antibody dilutions (1:50-1:500 for IHC)

  • Apply digital image analysis for objective quantification of staining intensity and distribution

  • Use reference markers to identify specific cell types and subcellular compartments

• Disease-specific alterations:

  • In Alzheimer's disease, ACAT1 activity correlates with amyloid-β levels, as AAV-mediated Acat1 knockdown decreased brain amyloid-β and APP levels in mouse models

  • A dot blot analysis with A11 antibody showed that treatment of AD mice with AAV-Acat1 led to a statistically significant decrease in oligomeric Aβ compared to controls

• Functional correlation analysis:

  • Compare ACAT1 expression with cholesterol distribution using filipin staining

  • Correlate ACAT1 patterns with markers of cellular stress or neurodegeneration

  • Examine relationship between ACAT1 localization and mitochondrial function

• Therapeutic targeting implications:

  • Region-specific ACAT1 expression patterns may inform targeted delivery strategies for ACAT1-modulating therapies

  • The significant reduction in amyloid-β following Acat1 knockdown suggests potential for therapeutic applications

Understanding the significance of region-specific ACAT1 expression patterns could provide insights into selective vulnerability in neurological diseases and guide development of targeted therapeutic approaches.

What protocol modifications are necessary when using ACAT1 antibodies across different experimental platforms?

When adapting ACAT1 antibody use across different experimental platforms, consider these protocol modifications:

Platform-specific optimizations improve detection sensitivity and specificity while maintaining consistent results across techniques.

How can researchers validate knockdown efficiency in ACAT1-targeting therapeutic approaches?

Validation of ACAT1 knockdown efficiency requires a multi-faceted approach:

• RNA-level verification:

  • RT-qPCR to quantify ACAT1 mRNA reduction

  • Northern blot for qualitative assessment of transcript changes

• Protein-level confirmation:

  • Western blot with optimized antibody dilutions (1:500-1:2000)

  • Immunohistochemistry to assess spatial distribution of knockdown

  • Flow cytometry for single-cell quantification

• Functional assessment:

  • Enzymatic activity assays measuring ACAT activity in mixed micelles (as used in AAV-mediated Acat1 knockdown research)

  • Cellular cholesterol esterification rate measurement

  • Filipin staining to assess free cholesterol levels

• Downstream effect analysis:

  • Quantification of amyloid-β levels after ACAT1 knockdown (dot blot analysis with A11 antibody)

  • Assessment of disease-relevant phenotypes

Research on AAV-mediated Acat1 knockdown demonstrated that functional activity assays can sometimes be more informative than protein detection alone for assessing intervention efficacy .

What are the critical factors for reproducible quantification of ACAT1 in tissue microarrays?

For reproducible ACAT1 quantification in tissue microarrays:

• Sample preparation standardization:

  • Consistent fixation protocols to preserve epitope accessibility

  • Standardized antigen retrieval methods (TE buffer pH 9.0 or citrate buffer pH 6.0)

  • Uniform tissue core diameter and thickness

• Antibody validation:

  • Test multiple ACAT1 antibodies to identify the most specific and sensitive

  • Determine optimal dilution through titration experiments (typically 1:50-1:500 for IHC)

  • Include positive controls (human colon, human liver tissue)

• Staining protocol optimization:

  • Automated staining systems to minimize batch variation

  • Standardized incubation times and temperatures

  • Consistent detection chemistry (DAB, fluorescence)

• Quantification methodology:

  • Digital image analysis with calibrated acquisition settings

  • Standardized algorithms for cellular/subcellular compartment segmentation

  • Reference standards on each array for inter-array normalization

• Quality control measures:

  • Include positive and negative control tissues on each array

  • Apply statistical methods to identify and address batch effects

  • Implement blinded scoring when manual assessment is used

These factors enhance the reliability and reproducibility of ACAT1 quantification in large-scale tissue microarray studies.

How can researchers distinguish between ACAT1 and ACAT2 when studying cholesterol metabolism?

Distinguishing between the two ACAT isoforms requires specific methodological approaches:

• Antibody selection specificity:

  • Choose antibodies raised against unique epitopes in ACAT1 not present in ACAT2

  • Validate antibody specificity using tissues with known differential expression (ACAT1 is widely expressed, while ACAT2 is primarily expressed in intestine and liver)

  • Consider using ACAT1 antibodies generated against the specific immunogen sequences noted in product information (e.g., "amino acids 1-145 of human ACAT1")

• Expression pattern analysis:

  • ACAT1 is widely expressed across tissues including brain, heart, and macrophages

  • ACAT1 shows distinctive subcellular localization in neuronal populations like Purkinje cells

  • ACAT1 typically shows mitochondrial localization, while ACAT2 localizes to ER

• Functional discrimination:

  • ACAT1 influences cellular cholesterol homeostasis and is implicated in diseases like atherosclerosis and neurodegenerative disorders

  • ACAT1 knockdown in brain reduces amyloid-β levels in AD mouse models

  • Selective inhibitor studies can differentiate isoform-specific activities

• Genetic approaches:

  • Isoform-specific siRNA or shRNA targeting

  • CRISPR-Cas9 targeting of specific isoforms

  • Use of isoform-specific knockout models

• Subcellular localization:

  • Co-staining with organelle markers (mitochondria for ACAT1, ER for ACAT2)

  • Subcellular fractionation followed by Western blotting with isoform-specific antibodies

These approaches allow researchers to distinguish the specific roles of ACAT1 versus ACAT2 in cholesterol metabolism across different tissues and disease states.

How can ACAT1 antibodies contribute to understanding the metabolic reprogramming in cancer cells?

ACAT1 antibodies offer valuable tools for investigating metabolic adaptations in cancer:

• Expression profiling across cancer types:

  • Immunohistochemical analysis of ACAT1 in tissue microarrays from multiple cancer types

  • Correlation with clinical outcomes and metabolic phenotypes

  • Use of validated ACAT1 antibodies at appropriate dilutions (1:50-1:500 for IHC)

• Metabolic pathway interaction analysis:

  • Co-immunoprecipitation to identify cancer-specific ACAT1 interaction partners

  • Proximity ligation assays to visualize protein-protein interactions in situ

  • Western blot analysis of ACAT1 expression in response to metabolic stress conditions

• Therapeutic response monitoring:

  • Quantification of ACAT1 expression changes following metabolic intervention

  • Correlation between ACAT1 levels and sensitivity to metabolism-targeting drugs

  • Assessment of ACAT1 as a potential biomarker for therapeutic response

• Subcellular dynamics investigation:

  • High-resolution imaging of ACAT1 localization in cancer versus normal cells

  • Analysis of mitochondrial function in relation to ACAT1 expression

  • Evaluation of cholesterol metabolism alterations in cancer progression

• Gene-metabolite correlation studies:

  • Integration of ACAT1 expression data with metabolomic profiling

  • Analysis of cholesterol ester accumulation in relation to ACAT1 levels

  • Investigation of lipid droplet formation and composition

ACAT1 antibodies enable comprehensive analysis of this enzyme's role in cancer metabolic reprogramming, potentially revealing new therapeutic vulnerabilities.

What are the most effective protocols for multiplexed imaging of ACAT1 with other metabolic enzymes?

For effective multiplexed imaging of ACAT1 with other metabolic enzymes:

• Antibody panel design:

  • Select ACAT1 antibodies from different host species than other target antibodies

  • Validate each antibody individually before multiplexing

  • Consider using rabbit polyclonal ACAT1 antibodies paired with mouse monoclonal antibodies for other targets

• Sequential staining approach:

  • Apply tyramide signal amplification for sequential labeling with antibodies from the same species

  • Use heat or chemical stripping between rounds of staining

  • Implement rigorous controls to ensure complete stripping of previous antibodies

• Multi-spectral imaging optimization:

  • Use fluorophores with minimal spectral overlap

  • Apply spectral unmixing algorithms to separate overlapping signals

  • Include single-stained controls for accurate spectral separation

• Sample preparation refinement:

  • Optimize fixation to preserve multiple epitopes simultaneously

  • Test different antigen retrieval methods compatible with all target proteins

  • Consider tissue clearing techniques for thick section imaging

• Analysis pipeline development:

  • Implement cell segmentation algorithms for quantitative single-cell analysis

  • Develop colocalization metrics for assessing enzyme interactions

  • Apply machine learning for pattern recognition in complex datasets

These protocols enable simultaneous visualization of ACAT1 alongside other metabolic enzymes, providing insights into metabolic network organization within cells.

How can researchers integrate ACAT1 antibody staining with functional metabolic assays?

Integration of ACAT1 antibody staining with functional metabolic assays provides comprehensive insights:

• Sequential analysis workflow:

  • Perform live-cell metabolic assays (e.g., oxygen consumption, extracellular acidification)

  • Fix cells and conduct ACAT1 immunostaining using validated antibody dilutions (1:50-1:500)

  • Correlate single-cell metabolic profiles with ACAT1 expression patterns

• Metabolic tracer studies integration:

  • Incubate cells with labeled metabolic substrates (e.g., 13C-glucose, 13C-acetate)

  • Fix and immunostain for ACAT1

  • Perform mass spectrometry imaging or single-cell metabolomics on the same samples

• Combined imaging approaches:

  • Use fluorescent cholesterol analogs to track cholesterol trafficking

  • Apply FRET-based sensors for real-time metabolic activity monitoring

  • Follow with ACAT1 immunofluorescence and digital image coregistration

• Multi-modal tissue analysis:

  • Perform metabolic activity mapping on tissue sections

  • Apply ACAT1 immunohistochemistry to serial sections

  • Use computational approaches to align and integrate data

• Functional manipulation-immunodetection protocols:

  • Modify ACAT1 activity through pharmacological or genetic approaches

  • Assess metabolic consequences through functional assays

  • Confirm intervention specificity through ACAT1 immunostaining

These integrated approaches connect ACAT1 protein expression patterns with dynamic metabolic functions, providing deeper mechanistic insights than either approach alone.