SLC16A3 Antibody

What is SLC16A3 and why is it important in research?

SLC16A3, also known as MCT4 or MCT-4, is a member of the monocarboxylate transporter family responsible for the transport of lactate and other monocarboxylates like pyruvate. It is a proton-dependent transporter that plays a predominant role in L-lactate efflux from highly glycolytic cells . The protein is approximately 49.5 kilodaltons in mass and is encoded by the SLC16A3 gene in humans . Its importance in research stems from its critical role in cancer metabolism, particularly in maintaining glycolytic activity in tumors, making it a potential therapeutic target and biomarker in various cancers .

How does SLC16A3 differ from other monocarboxylate transporters like SLC16A1?

While both SLC16A1 (MCT1) and SLC16A3 (MCT4) transport monocarboxylates, they have distinct functional differences:

What is the synthetic lethality relationship between SLC16A1 and SLC16A3?

Synthetic lethality between SLC16A1 and SLC16A3 occurs when the loss or inhibition of both transporters leads to cell death, while the loss of either one alone is tolerated. This relationship has been leveraged in a screening strategy called paralog-dependent isogenic cell assay (PARADISO) to develop specific inhibitors of SLC16A3 . The system involves isogenic cell lines engineered to be dependent on various paralog genes for survival/fitness. When SLC16A1 is knocked out, cells become dependent on SLC16A3 function, creating a model system for identifying selective SLC16A3 inhibitors .

What applications are SLC16A3 antibodies most commonly used for?

SLC16A3 antibodies are predominantly used in:

• Western Blotting (WB): For detecting SLC16A3 protein in cell or tissue lysates

• Immunohistochemistry (IHC): For visualizing SLC16A3 expression in tissue sections

• Immunocytochemistry/Immunofluorescence (ICC/IF): For cellular localization studies

• ELISA: For quantitative detection in solution

According to the search results, commercially available antibodies are validated for these applications with varying species reactivity, primarily human, mouse, and rat samples . When selecting an antibody, researchers should consider the specific application needs and validated reactivity with their species of interest.

What factors should be considered when choosing an SLC16A3 antibody for specific applications?

When selecting an SLC16A3 antibody, researchers should consider:

• Epitope location: Some antibodies target specific regions (e.g., N-terminal vs. C-terminal). The ab244385 antibody targets a recombinant fragment within human SLC16A3 aa 400 to C-terminus .

• Species reactivity: Verify cross-reactivity with your experimental model. Many antibodies react with human, mouse, and rat SLC16A3 .

• Clonality: Polyclonal antibodies offer broader epitope recognition but may have batch-to-batch variability; monoclonal antibodies provide consistency but may be more sensitive to epitope changes.

• Validated applications: Ensure the antibody has been validated for your specific application (WB, IHC, IF, etc.).

• Published references: Check if the antibody has been cited in publications similar to your research context .

How should sample preparation be optimized for detecting SLC16A3 in different applications?

For optimal SLC16A3 detection:

• Western Blotting:

  • Use appropriate lysis buffers containing detergents to solubilize membrane proteins

  • Include protease inhibitors to prevent degradation

  • Consider reducing agents to break disulfide bonds

  • Avoid excessive heating which may cause membrane protein aggregation

• Immunohistochemistry:

  • Fixation: PFA fixation has been successful for SLC16A3 detection

  • Antigen retrieval: May be necessary to expose epitopes after formalin fixation

  • Blocking: Use appropriate blocking to reduce background signal

• Immunofluorescence:

  • Permeabilization: Triton X-100 permeabilization has been successful with U-251 MG cells

  • Fixation: PFA fixation works well for SLC16A3 detection

  • Signal amplification: May be necessary for low expression levels

What are common issues when detecting SLC16A3 and how can they be resolved?

Common issues include:

• Weak or no signal:

  • Increase antibody concentration

  • Extend incubation time

  • Optimize antigen retrieval methods

  • Use signal amplification systems

  • Verify expression levels in your sample with reference data

• Non-specific binding:

  • Increase blocking time/concentration

  • Optimize antibody dilution

  • Use more stringent washing conditions

  • Verify antibody specificity with positive and negative controls

• Variable results between experiments:

  • Standardize protocols

  • Use consistent antibody lots

  • Include internal controls

  • Normalize data appropriately

How can researchers validate SLC16A3 antibody specificity?

Validation approaches include:

• Genetic approaches:

  • Use SLC16A3 knockout cells/tissues as negative controls

  • Use SLC16A3 overexpression systems as positive controls

  • Compare with siRNA/shRNA knockdown samples

• Analytical approaches:

  • Perform peptide competition assays

  • Compare results with multiple antibodies targeting different epitopes

  • Confirm observed molecular weight matches the expected size (49.5 kDa)

  • Correlate protein detection with mRNA expression data

• Cross-species validation:

  • Test antibody in species with known sequence homology

  • Perform BLAST analysis between the immunogen sequence and target species

How should researchers approach cross-species reactivity testing for SLC16A3 antibodies?

When testing cross-species reactivity:

• Sequence analysis:

  • Perform BLAST analysis between the immunogen sequence and the target species

  • Evaluate homology percentage in the epitope region

• Pilot testing:

  • Start with standard protocols optimized for validated species

  • Use positive control samples from the validated species alongside test samples

  • Consider using gradient dilutions of antibody to find optimal concentration

  • Include appropriate negative controls

• Validation confirmation:

  • Verify band size matches predicted molecular weight for that species

  • Confirm specificity through knockdown/knockout controls if available

  • Document experimental conditions thoroughly for future reference

How can SLC16A3 antibodies be used to study cancer metabolism?

SLC16A3 antibodies can be utilized to:

What are the recommended approaches for studying SLC16A3 in relation to tumor progression and metastasis?

For studying SLC16A3 in tumor progression:

• Expression analysis across cancer stages:

  • Use tissue microarrays to evaluate expression in primary vs. metastatic lesions

  • Correlate expression with clinical parameters like tumor stage, grade, and patient outcomes

  • SLC16A3 shows significant increase in both tumor and metastatic tissues compared to normal ovarian tissues

• Functional studies:

  • Use genetic manipulation (knockdown/overexpression) combined with antibody detection

  • Evaluate effects on invasion, migration, and extracellular matrix organization

  • SLC16A3-associated genes are enriched in pathways related to extracellular matrix organization, leukocyte trans-endothelial migration, and regulation of actin cytoskeleton

• Mechanistic investigations:

  • Study co-localization with other glycolytic enzymes

  • Evaluate metabolite profiles in relation to SLC16A3 expression

  • Investigate lactate dynamics in the tumor microenvironment

How should researchers approach the development of combination studies targeting both SLC16A1 and SLC16A3?

When designing combination studies:

• Expression profiling:

  • Characterize baseline expression of both transporters in your model system

  • Consider tissue-specific expression patterns (SLC16A1 and SLC16A3 show different expression patterns and prognostic implications in ovarian cancer)

• Synthetic lethality approaches:

  • Design isogenic cell models with controlled expression of both transporters

  • Consider the PARADISO screening approach to identify selective inhibitors

  • Engineer cellular dependency on specific paralogs for functional studies

• Inhibitor studies:

  • Use selective inhibitors like AZD3965 (SLC16A1 inhibitor) and slCeMM1 (SLC16A3 inhibitor)

  • Evaluate synergistic effects of combined inhibition

  • Consider genetic approaches (siRNA, CRISPR) alongside pharmacological inhibition

How does SLC16A3 expression vary across cancer types and what are the clinical implications?

SLC16A3 expression patterns across cancers:

This expression data suggests SLC16A3 could be a valuable prognostic marker across multiple cancer types, with particularly strong evidence in prostate and ovarian cancers.

What is the relationship between SLC16A3 genomic alterations and protein expression in cancer?

The relationship between genomic alterations and protein expression is complex:

This indicates researchers should not rely solely on genomic data when studying SLC16A3 and should incorporate transcriptomic and proteomic approaches.

How can SLC16A3 be utilized as a target for developing novel cancer therapeutics?

Approaches for targeting SLC16A3 in cancer therapy:

• Direct inhibition strategies:

  • Development of selective SLC16A3 inhibitors like slCeMM1

  • Structure-based drug design informed by functional residues (V239F mutation affects function)

• Synthetic lethality approaches:

  • Exploiting the synthetic lethality between SLC16A1 and SLC16A3

  • Using PARADISO screening strategy to identify compounds selective for SLC16A3

  • Combining SLC16A3 inhibitors with glycolysis inhibitors

• Biomarker-guided therapy:

  • Stratifying patients based on SLC16A3 expression levels

  • Correlating expression with response to metabolism-targeting drugs

  • Developing companion diagnostics using validated antibodies

These strategies highlight the potential of SLC16A3 as both a therapeutic target and a prognostic/predictive biomarker in personalized cancer medicine.

What controls should be included when using SLC16A3 antibodies in research?

Essential controls include:

• Positive controls:

  • Cell lines with known high SLC16A3 expression (glycolytic cancer cell lines)

  • Tissues with established expression (skeletal muscle has shown positive staining)

  • Recombinant SLC16A3 protein or overexpression systems

• Negative controls:

  • SLC16A3 knockout cells/tissues

  • Primary antibody omission controls

  • Isotype controls matching the primary antibody species/class

  • Tissues known to have low/no expression

• Specificity controls:

  • Peptide competition assays

  • siRNA knockdown samples

  • Multiple antibodies targeting different epitopes

Including these controls ensures reliable interpretation of SLC16A3 antibody results across different experimental systems.

How should researchers approach quantitative analysis of SLC16A3 expression in tissues?

For quantitative tissue analysis:

• Immunohistochemistry quantification:

  • Use digital pathology platforms for objective scoring

  • Establish clear scoring criteria (intensity, proportion of positive cells)

  • Consider automated image analysis software

  • Include appropriate normalization controls

• Western blot quantification:

  • Use housekeeping proteins appropriate for your tissue/condition

  • Consider membrane protein-specific loading controls

  • Perform densitometry with linear range validation

  • Include concentration standards if possible

• Statistical considerations:

  • Use appropriate statistical tests for your data distribution

  • Consider paired analyses for tumor/normal comparisons

  • Account for clinical variables in multivariate analyses

  • Report effect sizes along with p-values

What considerations are important when studying SLC16A3 in relation to other SLC16 family members?

When studying multiple SLC16 family members:

• Antibody specificity:

  • Ensure antibodies do not cross-react between family members

  • Validate specificity in systems with differential expression

  • Consider epitope mapping to confirm targeted regions

• Functional redundancy:

  • Account for compensatory mechanisms between transporters

  • Design experiments that can distinguish individual contributions

  • Consider the PARADISO approach to isolate paralog-specific effects

• Co-expression analysis:

  • Evaluate patterns of co-expression across tissues and conditions

  • Consider the divergent prognostic implications (SLC16A1 vs. SLC16A3 in ovarian cancer)

  • Integrate with metabolomic data to understand functional relationships

These considerations help researchers accurately interpret complex relationships between SLC16 family members in biological systems.