DGAT1 Antibody

What is DGAT1 and why is it significant for research?

DGAT1 (Diacylglycerol O-Acyltransferase 1) is a key enzyme in triglyceride synthesis, catalyzing the final step in the glycerol phosphate pathway by joining diacylglycerol with fatty acyl-CoA . The enzyme plays a critical role in lipid metabolism, with localization primarily in the endoplasmic reticulum membrane as a multi-pass membrane protein . DGAT1's significance extends beyond basic lipid metabolism to multiple disease contexts, including obesity, diabetes, and cancer research. Recent studies highlight its role in prostate cancer progression and as a potential therapeutic target . Understanding DGAT1 function is essential for research into metabolic disorders, lipid storage diseases, and the development of targeted therapies.

Which applications are validated for DGAT1 antibodies?

DGAT1 antibodies have been successfully validated across multiple experimental techniques:

For research requiring high specificity, antibodies like NB110-41487 and 11561-1-AP have demonstrated consistent reactivity across human, mouse, and rat samples . When designing experiments, researchers should consider that some DGAT1 antibodies may not be applicable for paraffin-embedded IHC sections, necessitating optimization for specific applications .

What molecular weight should I expect for DGAT1 in Western blots?

• Post-translational modifications

• Protein cleavage events

• Relative charges affecting migration

• Experimental conditions and buffer systems

It's important to note that these variations are normal and expected. To confirm specificity, researchers should include positive control lysates from cells known to express DGAT1 (such as HepG2 cells) . Additionally, using appropriate molecular weight markers and validating results with knockdown/knockout controls will help ensure accurate identification of DGAT1 bands.

What are the recommended sample preparation methods for DGAT1 antibody applications?

Optimal sample preparation varies by application:

For Western Blot:

• Harvest cells at 70-80% confluence

• Lyse cells in RIPA buffer supplemented with protease inhibitors

• Sonicate briefly to shear DNA and reduce viscosity

• Centrifuge at 14,000g for 15 minutes at 4°C

• Collect supernatant and determine protein concentration

• Denature samples at 95°C for 5 minutes in reducing sample buffer

For Immunocytochemistry/Immunofluorescence:

• Fix cells in 10% formalin for 10 minutes at room temperature

• Permeabilize with PBS containing 0.05% Triton X-100 for 5 minutes

• Block with 1-5% BSA or normal serum for 30-60 minutes

• Incubate with primary antibody overnight at 4°C

• Detect with fluorescently-labeled secondary antibodies

For Immunohistochemistry:

• Use antigen retrieval with TE buffer pH 9.0 or alternatively citrate buffer pH 6.0

• Block endogenous peroxidase activity with hydrogen peroxide

• Apply primary antibody at 1:50-1:500 dilution

Proper sample preparation is crucial for maintaining DGAT1's native structure and epitope accessibility.

How can I optimize DGAT1 antibody detection in tissues with variable expression levels?

Optimizing DGAT1 detection in tissues with variable expression requires careful consideration of multiple factors:

• Signal Amplification Systems:

  • For low-expressing tissues, employ tyramide signal amplification (TSA) to enhance detection sensitivity by 10-100 fold

  • Consider polymer-based detection systems for IHC applications to minimize background while maximizing signal

• Antigen Retrieval Optimization:

  • Conduct systematic comparison of heat-induced epitope retrieval (HIER) methods:

    • Test both citrate buffer (pH 6.0) and TE buffer (pH 9.0)

    • Optimize retrieval time (10-30 minutes) and temperature (95-121°C)

  • For particularly challenging samples, employ enzyme-based retrieval with proteinase K as a complementary approach

• Antibody Concentration Titration:

  • Perform serial dilutions ranging from 1:20 to 1:500

  • For each new tissue type, establish a tissue-specific titration curve

  • Determine optimal signal-to-noise ratio for each specimen type

• Dual Labeling Strategies:

  • Co-stain with organelle markers (e.g., KDEL for ER localization) to confirm subcellular localization

  • Use multiple antibodies targeting different DGAT1 epitopes to validate expression patterns

These optimization strategies should be systematically documented and validated with appropriate positive and negative controls, including tissues from DGAT1 knockout models when available.

What controls are essential for validating DGAT1 antibody specificity?

Rigorous validation of DGAT1 antibody specificity requires a comprehensive set of controls:

Positive Controls:

• Cell lines with confirmed DGAT1 expression (e.g., HepG2, as demonstrated in validated scientific data images)

• Tissues with known high DGAT1 expression (liver, adipose tissue)

• Recombinant DGAT1 protein as a reference standard

Negative Controls:

• DGAT1 knockout/knockdown samples (essential for definitive validation)

• Primary antibody omission controls

• Isotype controls to assess non-specific binding

• Tissues known to have minimal DGAT1 expression

Peptide Competition Assay:

• Pre-incubate the DGAT1 antibody with excess immunizing peptide

• Compare staining between blocked and unblocked antibody

• Specific binding should be significantly reduced or eliminated

Orthogonal Validation:

• Correlate protein detection with mRNA expression data

• Confirm findings using alternative antibodies targeting different DGAT1 epitopes

• Validate subcellular localization using fractionation followed by Western blot

Implementation of these controls ensures that experimental observations reflect genuine DGAT1 expression rather than technical artifacts or cross-reactivity.

How do I design experiments to study DGAT1's role in lipid metabolism disorders?

Designing robust experiments to investigate DGAT1's role in lipid metabolism disorders requires a multifaceted approach:

In Vitro Models:

• Cell Selection:

  • Hepatocytes (e.g., HepG2, primary hepatocytes) for studying hepatic steatosis

  • Adipocytes (e.g., 3T3-L1) for adipose tissue dysfunction

  • Myocytes (e.g., C2C12) for investigating muscle lipid accumulation

• Lipid Challenge Protocols:

  • Treat cells with different fatty acid compositions:

    • Palmitate (PALM) for saturated fat exposure

    • Normal physiological mixture (NORM)

    • High saturated fatty acid condition (HSFA)

  • Monitor DGAT1 expression changes via Western blot

  • Quantify lipid droplet formation using fluorescent dyes (BODIPY, Nile Red)

• Genetic Manipulation:

  • Employ CRISPR/Cas9 for DGAT1 knockout

  • Use siRNA for transient knockdown

  • Develop stable overexpression models to study gain-of-function effects

In Vivo Approaches:

• Animal Models:

  • High-fat diet-induced obesity models

  • Genetic models (ob/ob, db/db mice)

  • Conditional tissue-specific DGAT1 knockout models

• Intervention Studies:

  • DGAT1 inhibitor treatment (shown to reduce proliferation rate by ~50% in LNCaP cells and ~20% in PC-3 cells)

  • Time-course experiments to establish temporal relationships

  • Dose-response studies to determine threshold effects

• Analytical Methods:

  • Lipidomics to profile triglyceride species

  • Metabolic flux analysis using isotope tracers

  • Combination of imaging, biochemical, and molecular techniques

This experimental framework enables systematic investigation of DGAT1's mechanistic contributions to lipid metabolism disorders while providing multiple lines of evidence.

What are the methodological considerations when studying DGAT1 interactions with microtubule-organizing centers (MTOCs)?

Recent research has uncovered an unexpected relationship between DGAT1, lipid droplets, and microtubule-organizing centers (MTOCs) . When investigating this novel connection, researchers should consider these methodological approaches:

• Co-localization Studies:

  • Perform triple immunofluorescence staining for:

    • DGAT1 (using validated antibodies at 1:40-1:100 dilution)

    • Lipid droplets (BODIPY or perilipin markers)

    • MTOC components (γ-tubulin, GM130, CLASP2)

  • Employ super-resolution microscopy (STED, SIM) for detailed spatial relationships

  • Quantify co-localization using Pearson's correlation coefficient or Manders' overlap

• Functional Interaction Analysis:

  • Apply DGAT1 inhibitors and monitor effects on:

    • MTOC number and distribution

    • Microtubule stability and organization

    • Cell migration and proliferation

  • Conduct time-lapse imaging to capture dynamic relationships

  • Perform proximity ligation assays to detect protein-protein interactions in situ

• Biochemical Verification:

  • Immunoprecipitate DGAT1 and probe for MTOC components

  • Fractionate cells to isolate ER, lipid droplets, and MTOCs

  • Analyze composition using mass spectrometry

• Disruption Strategies:

  • Investigate whether GM130 depletion affects DGAT1 expression/activity

  • Test if DGAT1 inhibition reduces GM130 levels (as suggested by research showing reduced proliferation and migration after DGAT1 inhibition)

  • Examine the negative feedback loop between DGAT1, PEDF, and GM130

This methodological framework will help elucidate the mechanistic basis of the newly discovered signaling communication system between lipid droplets and non-centrosomal MTOCs in various cellular contexts.

What are common issues in Western blot analysis of DGAT1 and how can they be resolved?

When performing Western blots for DGAT1, researchers may encounter several technical challenges:

For optimal results, researchers should note that the theoretical molecular weight of DGAT1 is 55 kDa, but the observed weight may vary between 50-57 kDa due to post-translational modifications and experimental conditions .

How should I optimize immunofluorescence protocols for DGAT1 subcellular localization studies?

For precise subcellular localization of DGAT1, optimize your immunofluorescence protocol with these considerations:

• Fixation Method Comparison:

  • Test paraformaldehyde (10% formalin, 10 minutes) versus methanol fixation (-20°C, 10 minutes)

  • Evaluate glutaraldehyde (0.1-0.5%) for enhanced membrane protein preservation

  • Note: Published protocols show successful results with 10% formalin for 10 minutes

• Permeabilization Optimization:

  • Titrate Triton X-100 concentration (0.01-0.5%)

  • Compare with alternative detergents (saponin 0.1-0.5%, digitonin 0.001-0.01%)

  • Adjust permeabilization time (2-10 minutes) based on cell type

  • Validated protocol uses PBS with 0.05% Triton X-100 for 5 minutes

• Co-localization Markers:

  • Pair DGAT1 staining with:

    • ER markers (calnexin, PDI, KDEL)

    • Lipid droplet markers (BODIPY, PLIN2/3)

    • Golgi markers for transitioning DGAT1

  • Use spectrally distinct fluorophores with minimal overlap

• Signal Enhancement Techniques:

  • Apply tyramide signal amplification for low-abundance detection

  • Use high-sensitivity cameras with extended exposure times

  • Implement deconvolution algorithms for improved signal-to-noise ratio

• Quantification Methods:

  • Establish objective parameters for co-localization analysis

  • Perform line scans across cellular structures

  • Apply Pearson's or Manders' coefficients for statistical analysis

For reliable subcellular localization, use the recommended antibody dilution of 1:40-1:100 and include appropriate controls to confirm specificity of the observed patterns .

When should different DGAT1 antibody clones be used in research applications?

Selection of the appropriate DGAT1 antibody clone should be guided by specific experimental requirements:

• Application-Based Selection:

  • Western Blot: Both polyclonal antibodies (NB110-41487, 11561-1-AP) show strong performance

  • Immunocytochemistry: NB110-41487 has validated protocols for HepG2 cells

  • Flow Cytometry: Choose antibodies cited for this application in literature

  • Simple Western: NB110-41487 at 1:50 dilution is recommended

• Epitope Considerations:

  • For studying specific domains: Select antibodies targeting relevant regions

    • NB110-41487 targets an internal region (residues 200-300) of human DGAT1

  • For detecting potential isoforms: Use antibodies targeting different regions

  • For conformational studies: Consider antibodies raised against native protein

• Cross-Species Research:

  • Both featured antibodies (NB110-41487, 11561-1-AP) demonstrate reactivity with human, mouse, and rat samples

  • For evolutionary studies: Verify cross-reactivity with target species

  • Note the high conservation of DGAT1 across primates (100% predicted reactivity)

• Technical Requirements:

  • BSA-free formulations (like NB110-41487) for sensitive applications

  • Consider host species to avoid cross-reactivity in multi-labeling experiments

  • Select appropriate concentrations (1.0 mg/ml for NB110-41487)

• Experimental Validation:

  • Use multiple antibodies in parallel for confirmation

  • Validate with genetic controls (siRNA, CRISPR-Cas9)

  • Consult published applications (PMID:32326330 for rat applications)

The choice between different clones should be documented and justified based on the specific research objectives and technical requirements of each experimental system.

How can DGAT1 antibodies be used to investigate cancer metabolism?

Recent research has revealed DGAT1 as a promising target in cancer metabolism studies, particularly in prostate cancer research . Researchers can employ DGAT1 antibodies in several specialized applications:

• Expression Profiling Across Cancer Types:

  • Perform systematic immunohistochemical analysis of DGAT1 expression in tumor tissue microarrays

  • Compare DGAT1 levels between normal epithelium and cancer cells

  • Research has shown a stepwise increase in DGAT1 protein when comparing normal prostate epithelium to prostate cancer cell lines (LNCaP and PC-3)

• Correlation with Metabolic Phenotypes:

  • Combine DGAT1 immunostaining with:

    • Lipid droplet quantification (Oil Red O or BODIPY staining)

    • Proliferation markers (PCNA, Ki-67)

    • Microtubule-organizing center (MTOC) markers (γ-tubulin, GM130)

  • Quantify relationships between DGAT1 expression, lipid storage, and proliferative capacity

• Mechanistic Studies:

  • Investigate DGAT1 inhibition effects on:

    • Tumor cell proliferation (reduced by ~50% in LNCaP and ~20% in PC-3 cells)

    • Migration and invasion capacity

    • Lipid droplet density (reduced by 69.5% in LNCaP and 64.7% in PC-3 cells)

    • MTOC number (reduced to 1-2 per cell after treatment)

• Translational Applications:

  • Develop DGAT1 as a potential biomarker for aggressive cancer phenotypes

  • Evaluate DGAT1 inhibition as a therapeutic strategy

  • Investigate combinations with conventional therapies

This methodological framework leverages DGAT1 antibodies to explore the link between lipid metabolism, cytoskeletal organization, and cancer progression, potentially leading to novel therapeutic approaches.

What methodological considerations are important when using DGAT1 antibodies in metabolic disease models?

When studying metabolic diseases using DGAT1 antibodies, researchers should consider these methodological aspects:

• Model Selection and Characterization:

  • High-fat diet models: Document diet composition, feeding duration, and metabolic parameters

  • Genetic models: Verify genotype and characterize metabolic baseline

  • Cell culture models: Select appropriate cells (hepatocytes, adipocytes, myocytes) and validate metabolic responses

  • Consider C2C12 muscle cells for lipid storage studies as demonstrated in published research

• Experimental Conditions:

  • Compare multiple lipid conditions:

    • Palmitate (PALM) - representing saturated fatty acids

    • Normal physiological mixture (NORM)

    • High saturated fatty acid (HSFA) exposure

  • Document exact concentrations, exposure times, and culture conditions

  • Validate metabolic responses with appropriate markers

• Analytical Approaches:

  • Quantitative Western blot: Express DGAT1 relative to appropriate housekeeping proteins

  • Tissue immunohistochemistry: Compare DGAT1 expression across metabolic states

  • Immunofluorescence: Co-localize DGAT1 with lipid droplets and metabolic organelles

  • Consider dynamic measures (pulse-chase, metabolic flux) over static measurements

• Data Interpretation:

  • Account for tissue-specific regulation of DGAT1

  • Consider post-translational regulation that may not reflect mRNA levels

  • Integrate findings with other lipogenic/lipolytic pathway components

  • Document statistical approaches for analyzing DGAT1 protein abundance differences

• Validation Approaches:

  • Confirm antibody specificity in disease models where protein expression may be altered

  • Include biological replicates across different metabolic states

  • Validate key findings with complementary techniques (activity assays, genetic modulation)

These methodological considerations ensure robust and reproducible findings when investigating DGAT1's role in metabolic disease pathophysiology.

How can I design experiments to study DGAT1 inhibition effects using antibody-based detection methods?

To effectively study DGAT1 inhibition using antibody-based detection, researchers should implement this comprehensive experimental design:

• Inhibitor Selection and Characterization:

  • Choose between commercially available or novel DGAT1 inhibitors

  • Determine IC50 values in relevant cell models

  • Verify target engagement using enzymatic activity assays

  • Document inhibitor specificity (e.g., DGAT1 vs. DGAT2 selectivity)

• Treatment Protocol Design:

  • Establish dose-response relationships (typically 4-6 concentrations spanning 2 logs)

  • Determine time-course effects (acute vs. chronic inhibition)

  • Include appropriate vehicle controls

  • Consider washout experiments to assess reversibility

• Multi-parameter Assessment:

  • DGAT1 protein levels: Western blot using validated antibodies (2 μg/ml)

  • Cellular phenotypes: Proliferation, migration, lipid accumulation

  • Organelle effects: MTOC number, lipid droplet density

  • Signaling pathway changes: Related proteins (PEDF, GM130)

• Quantification Methods:

  • For Western blot: Densitometric analysis normalized to loading controls

  • For microscopy:

    • Lipid droplet quantification (mean of 17.5 LDs/cell in LNCaP and 39.4 LDs/cell in PC-3 after inhibition)

    • MTOC counting (reduced to 1-2 per cell after DGAT1 inhibition)

    • Proliferation assessment (PCNA-positive cell percentage)

• Validation Approaches:

  • Complement pharmacological inhibition with genetic approaches

  • Test multiple inhibitors with different chemical scaffolds

  • Perform rescue experiments with DGAT1 overexpression

  • Include positive controls (e.g., cells with known sensitivity to DGAT1 inhibition)

This experimental framework enables comprehensive characterization of DGAT1 inhibition effects while minimizing potential artifacts and ensuring robust, reproducible results.

What are the recommendations for multiplex immunofluorescence using DGAT1 antibodies?

Multiplex immunofluorescence with DGAT1 antibodies requires careful planning to obtain reliable co-localization data:

By following these recommendations, researchers can generate reliable multiplexed immunofluorescence data revealing DGAT1's relationships with other cellular components in normal and disease states.

How is DGAT1 antibody being used to investigate the relationship between obesity and cancer?

Recent research employing DGAT1 antibodies has revealed important connections between obesity, lipid metabolism, and cancer progression:

• Mechanistic Studies:

  • DGAT1 antibodies have enabled researchers to discover that increased DGAT1 expression in prostate cancer cells is associated with:

    • Enhanced lipid droplet formation

    • Microtubule-organizing center (MTOC) amplification

    • Increased proliferative and migratory capacity

  • Immunofluorescence studies have revealed a previously unknown signaling communication system between lipid droplets and non-centrosomal MTOCs

• Therapeutic Target Validation:

  • Western blot analysis with DGAT1 antibodies has demonstrated that DGAT1 inhibition results in:

    • Reduced proliferation (50% decrease in LNCaP cells, 20% in PC-3 cells)

    • Decreased lipid droplet density (69.5% reduction in LNCaP, 64.7% in PC-3)

    • Normalized MTOC numbers (reduced to 1-2 per cell)

  • These findings position DGAT1 as a multifunctional target with potential for suppressing both tumor growth and progression

• Translational Research Applications:

  • DGAT1 antibodies enable screening of patient-derived samples to:

    • Assess DGAT1 expression as a potential biomarker

    • Correlate expression with clinical outcomes

    • Identify patient subsets likely to benefit from DGAT1-targeted therapies

• Emerging Research Directions:

  • Investigation of DGAT1 in additional cancer types

  • Exploration of combinatorial approaches targeting both DGAT1 and related metabolic enzymes

  • Development of antibody-drug conjugates targeting DGAT1-expressing cells

  • Study of DGAT1's role in tumor microenvironment and immune cell function

These research applications highlight how DGAT1 antibodies are instrumental in uncovering the complex relationships between lipid metabolism alterations in obesity and their impact on cancer development and progression.

What are the current methodological challenges in DGAT1 research and how can they be addressed?

Despite significant progress, several methodological challenges persist in DGAT1 research:

• Antibody Specificity Issues:

  • Challenge: Distinguishing between DGAT1 and related acyltransferases (especially DGAT2)

  • Solution:

    • Validate antibodies using DGAT1 knockout/knockdown controls

    • Perform peptide competition assays to confirm specificity

    • Use multiple antibodies targeting different epitopes

    • Document observed molecular weight (expected 55 kDa but may vary between 50-57 kDa)

• Complex Subcellular Localization:

  • Challenge: DGAT1 distributes between ER membrane, lipid droplets, and potentially other compartments

  • Solution:

    • Implement super-resolution microscopy techniques

    • Perform subcellular fractionation followed by Western blot

    • Use organelle-specific markers for co-localization studies

    • Apply live-cell imaging with fluorescently tagged DGAT1

• Post-translational Regulation:

  • Challenge: Antibodies detect protein presence but not activity state

  • Solution:

    • Complement antibody detection with activity assays

    • Develop phospho-specific antibodies for regulatory modifications

    • Correlate protein levels with enzymatic activity

    • Integrate proteomics data to identify modification sites

• Tissue Heterogeneity:

  • Challenge: Variable DGAT1 expression across cell types within tissues

  • Solution:

    • Apply single-cell analysis techniques

    • Use laser capture microdissection prior to analysis

    • Implement spatial transcriptomics alongside protein detection

    • Develop computational tools for heterogeneity assessment

• Technical Limitations in Inhibitor Studies:

  • Challenge: Differentiating direct inhibition from secondary effects

  • Solution:

    • Design time-course experiments to distinguish primary from secondary effects

    • Employ target engagement assays alongside antibody detection

    • Use genetic complementation to validate inhibitor specificity

    • Implement systems biology approaches to map pathway perturbations

Addressing these methodological challenges requires integration of multiple techniques and careful experimental design to ensure robust and reproducible findings in DGAT1 research.

How can DGAT1 antibodies be used to study the newly discovered connection between lipid metabolism and microtubule organization?

The recently uncovered relationship between DGAT1, lipid droplets, and microtubule-organizing centers represents an exciting research frontier . To investigate this connection, researchers can employ these specialized approaches:

• Multi-parameter Imaging Analysis:

  • Triple Immunofluorescence Protocol:

    • DGAT1 antibody (1:40-1:100 dilution)

    • γ-tubulin for MTOCs

    • BODIPY 493/503 for lipid droplets

    • DAPI for nuclear counterstain

  • Quantitative Parameters:

    • MTOC number per cell (baseline vs. after DGAT1 inhibition)

    • Lipid droplet density (reduced by 69.5% in LNCaP and 64.7% in PC-3 after DGAT1 inhibition)

    • Spatial relationships between these structures

• Mechanistic Dissection Studies:

  • Perturbation Experiments:

    • DGAT1 inhibition (reduces MTOC number to 1-2 per cell)

    • GM130 depletion (reduces proliferation and migration)

    • Microtubule disruption (nocodazole treatment)

  • Protein Interaction Analysis:

    • Co-immunoprecipitation of DGAT1 with MTOC components

    • Proximity ligation assays for in situ interaction detection

    • Investigation of the negative feedback loop between DGAT1, PEDF, and GM130

• Dynamic Process Investigation:

  • Live-cell Imaging Applications:

    • Fluorescently tagged DGAT1 combined with ER and MTOC markers

    • Time-lapse microscopy during lipid loading/depletion

    • FRAP (Fluorescence Recovery After Photobleaching) to assess protein dynamics

  • Cell Cycle Analysis:

    • Synchronize cells and assess DGAT1-MTOC relationships across cell cycle phases

    • Correlate with proliferation markers (PCNA)

• Translational Extensions:

  • Tissue-based Analysis:

    • Examine DGAT1-MTOC relationships in normal versus tumor tissues

    • Correlate patterns with clinical outcomes

    • Assess effects of metabolic interventions (diet, exercise, drugs)

  • Therapeutic Targeting Strategies:

    • Combination approaches targeting both DGAT1 and MTOC components

    • Time-dependent intervention strategies

    • Biomarker development for patient stratification

This research framework enables systematic investigation of this newly discovered "biosensor function" of MTOCs in assessing intracellular lipid content , potentially revealing novel therapeutic targets at the intersection of metabolism and cell structure.

What are the key considerations for selecting the optimal DGAT1 antibody for specific research applications?

When selecting a DGAT1 antibody for your research, consider these critical factors to ensure optimal results:

• Application Compatibility:

  • For Western blot: Both NB110-41487 and 11561-1-AP antibodies show strong performance

  • For immunofluorescence: NB110-41487 has validated protocols at 1:40-1:100 dilution

  • For Simple Western: NB110-41487 at 1:50 dilution is recommended

  • For IHC: 11561-1-AP at 1:50-1:500 dilution with appropriate antigen retrieval

• Species Reactivity:

  • Both featured antibodies demonstrate reactivity with human, mouse, and rat samples

  • Consider predicted reactivity for other species (e.g., primate reactivity is predicted at 100% for NB110-41487)

  • Validate antibody performance in your specific model system

• Epitope Characteristics:

  • NB110-41487 targets an internal region (residues 200-300) of human DGAT1

  • Consider epitope accessibility in your application (native vs. denatured protein)

  • Evaluate whether post-translational modifications might affect epitope recognition

• Technical Specifications:

  • Concentration: Both antibodies are supplied at workable concentrations (1.0 mg/ml for NB110-41487)

  • Format: Consider BSA-free formulations for sensitive applications

  • Storage requirements: -20°C with avoidance of freeze-thaw cycles

• Validation Evidence:

  • Review scientific data images provided by manufacturers

  • Assess published applications (e.g., PMID:32326330 for rat applications)

  • Consider antibodies with multiple validated applications for flexible experimental design

By carefully evaluating these factors against your specific research objectives, you can select the optimal DGAT1 antibody to ensure robust, reproducible results across your experimental program.

How might advances in DGAT1 antibody technology impact future metabolic and cancer research?

Emerging advances in DGAT1 antibody technology are poised to transform both metabolic disorder and cancer research:

• Next-Generation Antibody Formats:

  • Recombinant antibodies with enhanced specificity and batch-to-batch consistency

  • Single-domain antibodies (nanobodies) for improved access to conformational epitopes

  • Bispecific antibodies simultaneously targeting DGAT1 and related pathway components

  • These technologies will enable more precise characterization of DGAT1's complex roles in lipid metabolism and cancer progression

• Multiplex Detection Systems:

  • Advanced multiplexing technologies allowing simultaneous detection of DGAT1 with 10+ other proteins

  • Spatial proteomics approaches for tissue-level analysis of DGAT1 distribution

  • Single-cell Western blot technologies for heterogeneity assessment

  • These approaches will reveal DGAT1's context-dependent functions within complex tissue environments

• Therapeutic Applications:

  • Development of antibody-drug conjugates targeting DGAT1-expressing cells in tumors

  • Intrabodies directed against DGAT1 for cell-type specific modulation

  • Combination therapies targeting DGAT1 alongside other metabolic enzymes

  • Such approaches may exploit the newly discovered connections between DGAT1, lipid droplets, and MTOCs

• Diagnostic and Predictive Applications:

  • DGAT1 antibody-based companion diagnostics for patient stratification

  • Liquid biopsy approaches for monitoring treatment response

  • Automated image analysis platforms for quantitative DGAT1 assessment

  • These applications may help identify patients most likely to benefit from DGAT1-targeted therapies

• Mechanistic Discovery Platforms:

  • Antibody-enabled proteomics to map DGAT1 interaction networks

  • DGAT1 proximity labeling for identifying novel partners

  • CRISPR screening combined with antibody-based detection for genetic modifier discovery

  • Such approaches will accelerate our understanding of DGAT1's role in the newly discovered signaling system between lipid droplets and non-centrosomal MTOCs