SLC16A11 Antibody, FITC conjugated

What is SLC16A11 and why is it significant in metabolic research?

SLC16A11 (solute carrier family 16 member 11) functions as a proton-linked monocarboxylic acid transporter, playing a crucial role in cellular metabolism. Its significance in metabolic research stems from its involvement in hepatic lipid metabolism and association with type 2 diabetes (T2D) risk, particularly in Mexican populations . SLC16A11 likely catalyzes the transport of monocarboxylates across the plasma membrane and significantly impacts lipid profiles .

Research has established that a risk haplotype in SLC16A11 is characterized by alterations in fatty acid metabolism, with carriers showing distinctive metabolomic profiles. This gene has been associated with early-onset T2D, decreased insulin action, higher acute insulin secretory response to glucose, and elevated alanine aminotransferase concentrations . The 48 kDa protein (471 amino acids) therefore represents an important target for metabolic disease investigations.

What applications can SLC16A11 Antibody, FITC conjugated be used for in research protocols?

SLC16A11 Antibody, FITC conjugated can be effectively employed in multiple research applications with varying protocols:

Researchers should note that while these applications have been validated, optimal conditions may be sample-dependent. The antibody shows reactivity with both human and mouse samples, making it suitable for comparative studies across these species .

What is the recommended protocol for SLC16A11 Antibody, FITC detection using flow cytometry?

For optimal flow cytometric detection of SLC16A11 using FITC-conjugated antibody, researchers should follow this methodological approach:

• Cell Preparation:

  • Harvest cells in exponential growth phase

  • Wash cells twice with PBS

  • Fix cells using 4% paraformaldehyde for 15 minutes at room temperature

  • Permeabilize with 0.1% Triton X-100 in PBS for 5 minutes

• Antibody Staining:

  • Block with 1% BSA for 30 minutes

  • Use 0.25 μg of SLC16A11 Antibody, FITC per 10^6 cells in 100 μl suspension

  • Incubate for 30-45 minutes at room temperature in the dark

  • Wash twice with PBS containing 0.1% BSA

• Analysis:

  • Analyze using appropriate flow cytometry settings for FITC detection (excitation ~495 nm, emission ~520 nm)

  • Include proper compensation controls if multiplexing with other fluorophores

  • Use isotype controls to determine background fluorescence levels

This protocol has been validated specifically with A549 cells , but can be optimized for other cell types with appropriate controls.

What is the specificity profile of SLC16A11 Antibody, FITC conjugated in various experimental systems?

The specificity profile of SLC16A11 Antibody, FITC conjugated has been characterized across multiple experimental systems:

The antibody targets a specific epitope within amino acids 428-471 of the human SLC16A11 protein . Western blot analysis confirms recognition of the correct molecular weight target (48 kDa) . The polyclonal nature of this antibody, raised in rabbits, contributes to its robust recognition profile across different experimental systems.

This specificity makes it suitable for studying SLC16A11 in both human and mouse models, facilitating translational research in metabolic conditions.

What are the optimal storage and handling conditions for maintaining SLC16A11 Antibody, FITC conjugated activity?

To maintain optimal activity of SLC16A11 Antibody, FITC conjugated, researchers should adhere to the following storage and handling guidelines:

• Storage Temperature:

  • Store at -20°C for long-term stability

  • Aliquoting is unnecessary for -20°C storage

  • Avoid repeated freeze-thaw cycles

• Buffer Composition:

  • The antibody is suspended in PBS with 0.02% sodium azide and 50% glycerol at pH 7.3

  • Some formulations may contain 0.03% Proclin 300 as a preservative

• Stability Information:

  • Stable for one year after shipment when stored properly

  • 20μl sizes contain 0.1% BSA for additional stability

• Light Protection:

  • Critical for FITC-conjugated antibodies

  • Store in amber tubes or wrapped in foil

  • Minimize exposure to light during handling and experiments

• Working Solution Preparation:

  • Dilute only the amount needed for immediate use

  • Use sterile buffers when preparing working solutions

  • Return stock solution to -20°C immediately after use

Following these guidelines will help ensure consistent experimental results and maximize the usable lifespan of the antibody.

How does SLC16A11 expression correlate with altered metabolomic profiles in diabetes research models?

Research on the SLC16A11 risk haplotype has revealed consistent alterations in metabolomic profiles, particularly in fatty acid metabolism pathways. A 24-week longitudinal study of Mexican individuals with prediabetes demonstrated that SLC16A11 risk haplotype carriers exhibit a distinctive metabolomic signature compared to non-carriers .

These metabolomic alterations align with in vitro studies of SLC16A11 disruption in hepatocytes, which showed elevated intracellular acylcarnitines, diacylglycerols, and triacylglycerols . The accumulation of acylcarnitines suggests decreased β-oxidation of fatty acids, potentially linking SLC16A11 function to mitochondrial metabolism. This metabolomic profile may underlie the increased T2D risk in SLC16A11 risk haplotype carriers.

What experimental controls should be incorporated when validating SLC16A11 Antibody, FITC results?

When validating results obtained with SLC16A11 Antibody, FITC conjugated, researchers should implement a comprehensive set of controls to ensure data reliability:

• Positive Controls:

  • Use known SLC16A11-expressing cells (A549, HeLa)

  • Include mouse brain and stomach tissue for cross-species validation

  • Consider recombinant SLC16A11 protein as a reference standard

• Negative Controls:

  • Isotype control (Rabbit IgG, FITC-conjugated)

  • Unstained samples to establish autofluorescence baseline

  • Cells where SLC16A11 is knocked down via siRNA or CRISPR

• Specificity Controls:

  • Preabsorption with immunogen peptide (amino acids 428-471)

  • Competitive binding assays

  • Dual labeling with non-conjugated SLC16A11 antibody

• Technical Controls:

  • Secondary antibody-only controls for non-conjugated applications

  • Fluorescence-minus-one (FMO) controls for multicolor flow cytometry

  • Concentration gradient to establish optimal signal-to-noise ratio (1:2000-1:16000 for WB)

• Biological Validation:

  • Correlate detection with SLC16A11 mRNA expression

  • Compare wild-type vs. SLC16A11 risk haplotype carriers

  • Use tissues with known differential expression patterns

This multilayered validation approach ensures reliable interpretation of experimental results and minimizes the risk of false positives or artifacts.

How can researchers optimize SLC16A11 detection in multi-parameter flow cytometry experiments?

Optimizing SLC16A11 detection in multi-parameter flow cytometry requires careful consideration of several methodological aspects:

• Panel Design Considerations:

  • FITC emission spectrum (peak ~520 nm) has potential spectral overlap with PE

  • Position SLC16A11-FITC in a panel where compensation with adjacent channels is minimizable

  • Avoid bright markers in adjacent channels when possible

• Sample Preparation Optimization:

  • Fixation method impacts epitope preservation; paraformaldehyde (4%) for 15-20 minutes is optimal

  • Permeabilization strength affects intracellular accessibility (0.1% Triton X-100 recommended)

  • Pre-block with species-specific serum to reduce non-specific binding

• Instrument Setup and Validation:

  • Use single-stained controls for each fluorochrome for accurate compensation

  • Include SLC16A11-FITC titration series (starting with 0.25 μg per 10^6 cells)

  • Perform standardization with fluorescent beads to ensure day-to-day consistency

• Data Analysis Strategies:

  • Apply consistent gating strategy across experimental conditions

  • Consider median fluorescence intensity rather than percent positive for quantitative comparisons

  • Use dimensionality reduction techniques (tSNE, UMAP) for identifying SLC16A11-expressing subpopulations

• Co-expression Analysis:

  • For metabolic studies, pair with glucose transporters (GLUT1, GLUT4)

  • In diabetes research, co-stain with insulin receptor and downstream signaling markers

  • Include cell type-specific markers for identifying expression in heterogeneous populations

These methodological refinements enable accurate quantification of SLC16A11 expression across different cell populations while minimizing technical artifacts.

What approaches can resolve technical challenges when detecting SLC16A11 in complex tissue samples?

Detecting SLC16A11 in complex tissue samples presents several technical challenges that can be addressed through specific methodological approaches:

• Tissue Processing Considerations:

  • Fresh-frozen tissue preserves epitopes better than formalin-fixed paraffin-embedded samples

  • For fixed tissues, antigen retrieval using citrate buffer (pH 6.0) improves detection

  • Section thickness affects antibody penetration (optimal: 5-8 μm)

• Background Reduction Strategies:

  • Pre-block with 5-10% serum from the same species as secondary antibody

  • Include 0.1-0.3% Triton X-100 for permeabilization

  • Use specific blocking peptides to identify non-specific binding

• Signal Amplification Methods:

  • Consider tyramide signal amplification for low-abundance detection

  • Use biotin-streptavidin systems for enhanced sensitivity

  • Implement longer primary antibody incubation (overnight at 4°C)

• Multi-labeling Optimization:

  • Sequential rather than simultaneous staining reduces cross-reactivity

  • Include tissue-specific markers to identify SLC16A11-expressing cell types

  • Use nuclear counterstains (DAPI) for cellular context

• Validation in Tissue Context:

  • Correlate immunostaining with mRNA expression by in situ hybridization

  • Compare staining patterns in tissues from multiple species (human and mouse)

  • Include tissues with known expression patterns (brain, stomach) as positive controls

These approaches help overcome common technical challenges when working with complex tissue samples, enabling reliable detection of SLC16A11 in its native biological context.

How does SLC16A11 genetic variation affect experimental approaches and interpretation in diabetes research?

The SLC16A11 risk haplotype significantly impacts experimental approaches and data interpretation in diabetes research, requiring specific methodological considerations:

• Genotyping Requirements:

  • Researchers should genotype study participants/samples for the five-SNP haplotype associated with T2D risk

  • Consider ancestry-informed analysis, as frequency varies across populations (highest in Latin American populations)

  • Include adequate sample sizes of both carriers and non-carriers for statistical power

• Metabolomic Analysis Considerations:

  • Risk haplotype carriers show distinct metabolomic profiles before and after interventions

  • Requires longitudinal sampling to detect treatment-responsive metabolites

  • Analysis should adjust for covariates including age, sex, BMI, and genetic ancestry principal components

• Intervention Study Design:

  • Stratify analysis by SLC16A11 haplotype status

  • Consider differential responses to lifestyle interventions between carriers and non-carriers

  • Monitor specific metabolites that differ between genotypes (e.g., methylmalonylcarnitine, betaine)

• Integration with Cellular Studies:

  • In vitro models should consider differences in fatty acid metabolism

  • Overexpression models show increases in triacylglycerol levels and intracellular diacylglycerols

  • Decreases in lysophosphatidylcholine, cholesterol ester, and sphingomyelin lipids are observed

• Clinical Translation Considerations:

  • SLC16A11 status may predict treatment response to lifestyle interventions

  • Genotype information could inform personalized nutrition approaches

  • Monitor carnitine-related metabolites as potential biomarkers of intervention efficacy

This genotype-informed approach enhances the precision of diabetes research and may contribute to developing more effective, personalized interventions for individuals with or at risk for T2D.

What methodological approaches can be used to study SLC16A11 function in cellular lipid metabolism?

Investigating SLC16A11 function in cellular lipid metabolism requires specialized methodologies that can detect subtle alterations in lipid profiles and metabolic pathways:

• Genetic Manipulation Approaches:

  • CRISPR/Cas9 gene editing to create loss-of-function or risk haplotype models

  • Inducible expression systems to study dose-dependent effects

  • siRNA knockdown to study acute effects of reduced SLC16A11 expression

• Metabolomic Profiling Methods:

  • Liquid chromatography/mass spectrometry to quantify specific metabolites

  • Focus on acylcarnitines, diacylglycerols, and triacylglycerols

  • Include ceramides and phospholipids in targeted panels

• Lipid Trafficking Visualization:

  • Fluorescently labeled fatty acids to track cellular uptake and metabolism

  • Time-lapse imaging with SLC16A11-fluorescent protein fusions

  • Co-localization studies with organelle markers (mitochondria, ER, lipid droplets)

• Functional Transport Assays:

  • Radiolabeled substrate uptake studies

  • pH-sensitive fluorescent probes to measure proton coupling

  • Membrane vesicle preparations for isolated transport assessment

• Integration with Insulin Signaling Assessments:

  • Phospho-specific antibodies for insulin receptor and AKT

  • Glucose uptake assays using fluorescent glucose analogs

  • Lipid-induced insulin resistance models with SLC16A11 modulation

These approaches provide complementary data on how SLC16A11 influences cellular lipid metabolism, potentially identifying mechanisms by which the risk haplotype contributes to metabolic dysfunction and diabetes risk.

How can researchers assess the impact of SLC16A11 on metabolic pathways in primary human tissues?

Investigating SLC16A11's role in metabolic pathways within primary human tissues requires specialized approaches that bridge genetic variation with functional outcomes:

• Tissue-Specific Expression Analysis:

  • Quantitative PCR to measure SLC16A11 expression across tissues

  • Western blot analysis using validated SLC16A11 antibody (1:2000-1:16000 dilution)

  • Single-cell RNA sequencing to identify cell type-specific expression patterns

• Ex Vivo Tissue Metabolism Studies:

  • Fresh tissue explants cultured with isotope-labeled metabolic substrates

  • Measurement of substrate utilization rates in genotyped samples

  • Comparison between risk haplotype carriers and non-carriers

• Integrative Multi-Omics Approaches:

  • Parallel assessment of transcriptomics, proteomics, and metabolomics

  • Pathway enrichment analysis focused on lipid metabolism networks

  • Integration with genotype data to identify haplotype-specific signatures

• Imaging-Based Metabolic Assessment:

  • Immunofluorescence using SLC16A11 Antibody, FITC conjugated

  • Co-staining with metabolic organelle markers

  • Lipid droplet quantification in relation to SLC16A11 expression

• Functional Metabolic Testing:

  • Extracellular flux analysis on primary cells from genotyped donors

  • Substrate preference testing (glucose vs. fatty acids)

  • Response to metabolic stressors based on SLC16A11 genotype

These methodological approaches provide comprehensive insights into how SLC16A11 and its genetic variants influence metabolic pathways in physiologically relevant human tissues, advancing our understanding of its role in diabetes pathophysiology.

What experimental approaches can researchers use to investigate the relationship between SLC16A11 and treatment responses in diabetes?

Investigating SLC16A11's influence on treatment responses in diabetes requires systematic experimental approaches that integrate genotyping with intervention outcomes:

• Genotype-Stratified Intervention Studies:

  • Randomize participants to interventions after SLC16A11 genotyping

  • Compare metabolomic changes between carriers and non-carriers

  • Implement lifestyle interventions with standardized protocols for 24+ weeks

• Biomarker Monitoring Framework:

  • Track specific metabolites associated with SLC16A11 status:

    • Methylmalonylcarnitine (responds differently based on genotype)

    • Betaine (shows differential changes during intervention)

    • Hippurate and cinnamoylglycine (persistently elevated in carriers)

• Nutrigenomic Analysis Methods:

  • Assess interaction between SLC16A11 genotype and dietary components

  • Focus on polyunsaturated fatty acid intake, which associates with methylmalonylcarnitine levels

  • Implement controlled feeding studies with crossover design

• Statistical Approach for Treatment Effect Modification:

  • Use linear mixed-effect models to analyze longitudinal data

  • Include interaction terms between SLC16A11 genotype and intervention

  • Adjust for covariates: age, sex, BMI, and genetic ancestry principal components

• Translational Validation in Cellular Models:

  • Primary cells from donors of known SLC16A11 genotype

  • Expose to therapeutic compounds used in diabetes treatment

  • Measure metabolic responses (glucose uptake, lipid metabolism, insulin signaling)

This comprehensive experimental framework enables identification of genotype-specific treatment responses, potentially leading to personalized intervention strategies for individuals with different SLC16A11 genetic backgrounds.

How can researchers integrate SLC16A11 antibody data with systems biology approaches to understand metabolic disease mechanisms?

Integrating SLC16A11 antibody data with systems biology approaches creates a powerful framework for understanding complex metabolic disease mechanisms:

• Multi-Scale Data Integration Protocol:

  • Flow cytometry data on SLC16A11 protein expression (using FITC conjugated antibody)

  • Transcriptomic data on SLC16A11 and related metabolic genes

  • Metabolomic profiles with focus on fatty acid metabolism intermediates

  • Clinical parameters (lipid profiles, glucose homeostasis markers)

• Network Analysis Methodology:

  • Protein-protein interaction networks centered on SLC16A11

  • Metabolic pathway enrichment analysis

  • Identification of regulatory nodes connecting SLC16A11 to insulin signaling

• Computational Modeling Approaches:

  • Constraint-based metabolic models incorporating SLC16A11 function

  • Dynamic simulations of lipid metabolism with variable SLC16A11 activity

  • Prediction of metabolic flux distributions based on genotype

• Multi-Tissue Integration Framework:

  • Compare SLC16A11 expression and function across relevant tissues (liver, muscle, adipose)

  • Identify tissue-specific consequences of SLC16A11 variation

  • Model inter-tissue metabolic crosstalk influenced by SLC16A11

• Translational Application Method:

  • Identify potential therapeutic targets within SLC16A11-influenced networks

  • Predict genotype-specific responses to metabolic interventions

  • Develop biomarker panels for monitoring treatment responses in carriers

This integrated systems biology approach transforms protein-level data generated using SLC16A11 antibodies into comprehensive mechanistic insights about metabolic disease pathways, potentially identifying novel intervention points for precision medicine approaches.

What methodological considerations are important when studying SLC16A11 in the context of lipotoxicity and insulin resistance?

Investigating SLC16A11 in the context of lipotoxicity and insulin resistance requires specialized methodological approaches that connect molecular function to pathophysiological outcomes:

• Cellular Model Selection and Validation:

  • Hepatocyte models (primary or HepG2) reflect SLC16A11's role in liver metabolism

  • Confirm SLC16A11 expression by Western blot (1:2000-1:16000 dilution)

  • Verify subcellular localization using SLC16A11 Antibody, FITC conjugated by confocal microscopy

• Lipotoxicity Induction Protocols:

  • Palmitate treatment (250-500 μM) to induce lipotoxic conditions

  • Time-course analysis (6-48 hours) to capture dynamic responses

  • Measure cell viability, lipid accumulation, and ER stress markers

• Insulin Signaling Assessment Methods:

  • Insulin stimulation dose-response (1-100 nM)

  • Quantify phosphorylation of insulin receptor and downstream targets (IRS1, AKT)

  • Measure PKCε activation, which mediates DAG-induced insulin resistance

• Lipid Species Analysis:

  • Quantify specific lipid species altered by SLC16A11 status:

    • Diacylglycerols (DAGs): key mediators of insulin resistance

    • Triacylglycerols (TAGs): increased with SLC16A11 overexpression

    • Lysophosphatidylcholine, cholesterol esters, sphingomyelins: decreased with SLC16A11 overexpression

• Metabolic Flux Analysis:

  • Isotope-labeled substrate tracing to determine metabolic pathway activities

  • Focus on fatty acid oxidation (β-oxidation) which may be impaired

  • Measure acylcarnitine accumulation as marker of incomplete fatty acid oxidation

These methodological considerations enable researchers to establish mechanistic links between SLC16A11 function, lipotoxicity development, and insulin resistance pathways, potentially revealing therapeutic intervention points for metabolic diseases.