DLD Antibody

What is DLD and what cellular functions does it regulate?

Dihydrolipoamide dehydrogenase (DLD) is a mitochondrial flavoprotein that functions as the E3 component of several multienzyme complexes. It is a 54.2 kilodalton protein that may also be known by alternative names including Lipoamide Dehydrogenase, DLDD, DLDH, GCSL, and dihydrolipoyl dehydrogenase, mitochondrial . DLD plays a crucial role in cellular energy metabolism by catalyzing the NAD+-dependent reoxidation of dihydrolipoyl groups attached to the E2 component of multiple enzyme complexes, including pyruvate dehydrogenase and α-ketoglutarate dehydrogenase. This enzymatic activity is essential for proper mitochondrial function and cellular respiration, with DLD dysfunction being implicated in various pathological conditions. Its involvement in the breast cancer tumor microenvironment suggests additional roles beyond energy metabolism, potentially in immune regulation and cancer progression .

What types of DLD antibodies are available for research and what applications are they suited for?

A comprehensive range of DLD antibodies are available from various suppliers, with over 435 DLD antibody products across 32 suppliers documented in research databases . These antibodies vary in their properties and applications:

Researchers should select antibodies based on their specific application requirements, with Western blotting (WB), immunohistochemistry (IHC), and immunofluorescence (IF) being the most commonly validated applications for DLD antibodies . For investigating DLD in tumor microenvironments, multiplexed immunostaining techniques may require specialized antibody formats that maintain specificity in complex multi-antibody panels .

How does DLD expression vary across normal and pathological tissues?

DLD expression demonstrates significant variation between normal and pathological tissues, particularly in cancer. Analysis of TCGA and GTEx databases reveals that DLD expression is significantly upregulated in breast cancer compared to normal tissue . This differential expression extends beyond breast cancer, as DLD exhibits varying expression patterns across different immune and molecular subtypes of human cancers . The upregulation of DLD in tumor tissues compared to adjacent normal tissues makes it a potential diagnostic marker, particularly in breast cancer where elevated DLD levels correlate with poor prognosis . Researchers investigating DLD should be aware of this tissue-specific expression pattern when designing experiments and interpreting results. Additionally, within the tumor microenvironment, DLD expression correlates with immune cell infiltration patterns, showing relationships with CD68+ macrophages and specific T cell populations, suggesting functional implications in immune regulation within the tumor stroma .

How does DLD influence the tumor immune microenvironment in breast cancer?

DLD has significant associations with the tumor immune microenvironment (TIME) in breast cancer, as evidenced by multiple correlation analyses. Research demonstrates that DLD expression displays negative correlations with both the estimate score (r = −0.11; P = 1.7e-4) and immune score (r = 0.14; P = 2.5e-6) in breast cancer datasets, suggesting its potential role in modulating the TIME . More specifically, DLD exhibits a negative correlation with the immune checkpoint PD-1 (r = −0.178; P = 2.96e-9), indicating a potential immunosuppressive mechanism . The TIMER database analysis reveals significant associations between DLD expression and various immune-infiltrating cells, including neutrophils (r = 0.217; P < 0.001), CD4+ T cells (r = −0.081; P = 0.0107), CD8+ T cells (r = 0.22; P = 9.7E-14), and macrophages (r = 0.28; P = 2.6e-21) .

Importantly, multiplexed immunohistochemistry studies have demonstrated that tumor regions with high DLD expression are enriched in PD-L1 and macrophages, while stromal regions with high DLD expression contain CD4+ T cells and macrophages . These spatial relationships suggest DLD may contribute to immune cell recruitment or retention within specific tumor compartments. The mechanisms by which DLD modulates these immune interactions remain an active area of investigation, potentially involving metabolic reprogramming of immune cells or direct signaling effects on immune checkpoint pathways.

What experimental approaches are optimal for investigating DLD's role in cancer cell proliferation and metastasis?

Investigating DLD's role in cancer cell proliferation and metastasis requires a multifaceted experimental approach:

• DLD Knockdown Studies: Knockdown experiments in breast cancer cell lines (such as MDA-MB-468 and SK-BR-3) using siRNA or shRNA techniques can effectively suppress DLD expression. Knockdown efficiency should be validated via western blot before proceeding with functional assays .

• Proliferation Assays: The Cell Counting Kit-8 (CCK-8) assay and clone formation assay provide complementary data on cell proliferation following DLD knockdown. These should be conducted at multiple timepoints (24h, 48h, 72h) to capture dynamic effects .

• Migration and Invasion Assays: Transwell migration assays and invasion assays with Matrigel-coated inserts can quantify the metastatic potential of cancer cells with modified DLD expression. Image analysis software should be used to ensure objective quantification .

• In vivo Metastasis Models: Orthotopic implantation of DLD-knockdown cancer cells in immunocompetent mice allows for assessment of metastasis formation in the context of an intact immune system.

• Multi-omics Analysis: Integration of transcriptomic, proteomic, and metabolomic data can reveal downstream pathways affected by DLD manipulation, potentially identifying novel therapeutic targets .

Evidence from breast cancer studies demonstrates that DLD knockdown effectively inhibits cancer cell migration, invasion, and proliferation, confirming its pro-oncogenic functions . These findings suggest DLD may serve as both a biomarker and potential therapeutic target in cancer treatment strategies.

What are the technical challenges in using DLD antibodies for multiplexed immunostaining?

Multiplexed immunostaining with DLD antibodies presents several technical challenges that require careful optimization:

• Antibody Cross-Reactivity: When performing multiplexed immunostaining, the potential for cross-reactivity between primary and secondary antibodies increases exponentially with each additional marker. Researchers must validate each antibody combination empirically using appropriate controls, including single-stained and fluorescence-minus-one (FMO) controls .

• Epitope Masking: Sequential staining protocols can lead to epitope masking when multiple antibodies are applied to the same tissue section. This is particularly relevant for DLD antibodies targeting conformational epitopes that may be sensitive to antigen retrieval conditions .

• Signal Separation: Spectral overlap between fluorophores can confound interpretation of multiplexed images. Advanced spectral unmixing algorithms may be necessary, particularly when analyzing co-localization of DLD with immune cell markers .

• Tissue Autofluorescence: Breast cancer tissues often exhibit significant autofluorescence, which can interfere with detection of specific antibody signals. Autofluorescence quenching protocols must be optimized to maximize signal-to-noise ratios .

• Antigen Retrieval Compatibility: The optimal antigen retrieval conditions for DLD detection (typically heat-induced epitope retrieval with Tris-EDTA buffer, pH 9) may not be compatible with all co-staining markers, necessitating compromise or sequential staining approaches .

Successful multiplexed immunostaining protocols for DLD require thorough optimization of antibody dilutions, incubation times, and washing steps. Researchers should consider using automated staining platforms to ensure consistency across experiments, particularly for clinical samples where quantitative comparisons are important .

What controls are essential when using DLD antibodies in experimental protocols?

Implementing appropriate controls is crucial for ensuring reliable results when using DLD antibodies:

• Positive and Negative Tissue Controls: Include tissues known to express high levels of DLD (e.g., liver, kidney) as positive controls and tissues with minimal DLD expression as negative controls. For cancer studies, paired tumor and adjacent normal tissues provide valuable internal controls .

• Knockdown/Knockout Validation: Cell lines with confirmed DLD knockdown or knockout serve as critical specificity controls. Western blot validation of knockdown efficiency should precede other experimental applications to ensure antibody specificity .

• Isotype Controls: Include isotype-matched control antibodies from the same species to identify potential non-specific binding, particularly important for flow cytometry and immunohistochemistry applications.

• Peptide Competition: Pre-incubation of the DLD antibody with its immunizing peptide should abolish specific staining in immunohistochemistry or Western blot, confirming antibody specificity.

• Loading Controls: For Western blotting, appropriate loading controls (β-actin for cytoplasmic fractions, VDAC or COX IV for mitochondrial fractions) should be used to normalize DLD expression levels.

• Secondary Antibody-Only Controls: For immunofluorescence studies, secondary antibody-only controls help identify non-specific binding of secondary antibodies.

These controls collectively ensure that the observed signals are specific to DLD and not artifacts of the experimental protocol. Particularly for multiplexed immunostaining techniques, comprehensive controls must be implemented to validate co-localization findings that may inform understanding of DLD's role in the tumor microenvironment .

How should researchers optimize Western blotting protocols for DLD detection?

Optimizing Western blotting for DLD detection requires attention to several key parameters:

• Sample Preparation: Since DLD is primarily localized to mitochondria, efficient extraction of mitochondrial proteins is essential. For optimal results, use specialized mitochondrial isolation buffers containing protease inhibitors and perform mechanical homogenization followed by differential centrifugation.

• Protein Denaturation: DLD protein (54.2 kDa) may form dimers or interact with other mitochondrial proteins. Sample denaturation at 95°C for 5 minutes in reducing buffer (containing β-mercaptoethanol or DTT) is recommended to ensure complete denaturation and accurate molecular weight determination .

• Gel Percentage and Transfer Conditions: Use 10-12% polyacrylamide gels for optimal resolution of DLD. For transfer, semi-dry transfer systems with PVDF membranes often provide better results than nitrocellulose for mitochondrial proteins.

• Blocking Conditions: 5% non-fat dry milk in TBST is generally effective, but for phospho-specific DLD antibodies, 5% BSA blocking is preferred to avoid phosphatase activity in milk proteins.

• Antibody Selection and Dilution: Select antibodies validated specifically for Western blotting applications. Starting dilutions of 1:1000 for primary antibodies are typical, but optimization may be necessary based on the specific antibody and expected expression levels .

• Signal Detection: Enhanced chemiluminescence (ECL) detection systems provide good sensitivity for DLD detection. For quantitative analysis, consider fluorescent secondary antibodies and imaging systems that provide a broader linear detection range.

• Stripping and Reprobing: If sequential probing for DLD and loading controls is necessary, mild stripping buffers are preferred to preserve epitope integrity for subsequent detection.

By following these optimization steps, researchers can achieve consistent and reliable detection of DLD protein in various sample types, facilitating accurate quantification for comparative studies .

What methods are recommended for quantitative assessment of DLD expression in tissue samples?

Quantitative assessment of DLD expression in tissue samples requires rigorous methodological approaches:

• Real-time Quantitative PCR (RT-qPCR): For mRNA expression analysis, RT-qPCR using validated DLD-specific primers provides sensitive quantification. Normalization to multiple reference genes (GAPDH, ACTB, and 18S rRNA) is recommended for accurate results . The 2^-ΔΔCt method can be used to calculate relative expression levels between tumor and normal tissues.

• Multiplexed Immunostaining Quantification: This technique allows for spatial analysis of DLD expression within different tissue compartments. For breast cancer studies, the following approach has proven effective:

  • Use of 5-μm-thick paraffin-embedded sections mounted on tissue microarray slides

  • Microwave heating in 10× Tris-EDTA buffer solution (pH 9) for antigen retrieval

  • Signal amplification via secondary antibodies conjugated with horseradish peroxidase

  • Counterstaining with DAPI for nuclear visualization

  • Digital image analysis using specialized software for objective quantification

• Western Blot Densitometry: For protein-level quantification, densitometric analysis of Western blots provides relative expression levels. Multiple biological and technical replicates (minimum n=3) are essential for statistical validity.

• Mass Spectrometry-Based Proteomics: For absolute quantification, targeted proteomics approaches using isotope-labeled peptide standards can provide precise DLD protein levels across multiple samples.

• Digital Spatial Profiling: This emerging technology combines antibody-based protein detection with spatial resolution, allowing quantification of DLD expression in specific regions of interest within heterogeneous tissue samples.

Each method has specific advantages and limitations, with multiplexed immunostaining providing the unique benefit of preserving spatial information relevant to understanding DLD's role in the tumor microenvironment . For comprehensive analysis, combining multiple quantification approaches is recommended to validate findings across different biological levels (mRNA, protein, functional activity).

How can researchers troubleshoot non-specific binding issues with DLD antibodies?

Non-specific binding is a common challenge when working with DLD antibodies. The following troubleshooting strategies can help improve specificity:

• Antibody Validation Strategy:

  • Verify antibody specificity using multiple techniques (Western blot, IHC, IF)

  • Compare results with RNA expression data from the same samples

  • Use DLD knockdown/knockout samples as negative controls

  • Consider multiple antibodies targeting different DLD epitopes

• Optimization of Blocking Conditions:

  • Test different blocking agents (BSA, normal serum, commercial blockers)

  • Increase blocking time (1-2 hours at room temperature)

  • Include 0.1-0.3% Triton X-100 in blocking buffer for better penetration

  • Add 10% serum from the secondary antibody host species to reduce background

• Antigen Retrieval Modifications:

  • Compare heat-induced epitope retrieval methods (microwave, pressure cooker)

  • Test different pH buffers (citrate pH 6.0 vs. Tris-EDTA pH 9.0)

  • Optimize retrieval duration (10-30 minutes)

• Antibody Dilution and Incubation:

  • Prepare a dilution series to determine optimal concentration

  • Test longer incubation times at 4°C (overnight) vs. shorter at room temperature

  • Add 0.05% Tween-20 to antibody diluent to reduce non-specific binding

• Washing Protocol Enhancement:

  • Increase number and duration of washes

  • Use higher salt concentration in wash buffers (up to 0.5M NaCl)

  • Add 0.1% Tween-20 to wash buffers

For persistent non-specific binding issues, consider alternative detection methods or antibody formats. For applications requiring high specificity such as multiplexed immunostaining, affinity-purified antibodies generally perform better than crude antisera . Additionally, pre-adsorption of antibodies against tissues known to cause cross-reactivity can improve specificity for certain applications.

How are DLD antibodies being used to develop prognostic models for cancer?

DLD antibodies are playing a crucial role in developing prognostic models for cancer, particularly breast cancer, through several innovative approaches:

These approaches collectively underscore DLD's significant predictive value in cancer prognosis. As antibody technologies continue to advance, particularly in multiplexed detection platforms, the integration of DLD expression data with other biomarkers promises to further refine prognostic models and potentially inform personalized treatment strategies .

What emerging technologies are enhancing DLD antibody development and applications?

Several cutting-edge technologies are revolutionizing DLD antibody development and applications:

• Deep Learning-Based Antibody Design: Machine learning approaches are being employed to generate novel antibody sequences with optimized properties. Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN+GP) models have successfully generated antibody variable region sequences with high "medicine-likeness" and humanness, which could be applied to creating improved DLD antibodies with enhanced specificity and developability .

• Single-Cell Antibody Technologies: Single-cell sequencing combined with antibody repertoire analysis allows for identification of highly specific DLD antibodies from immunized animals or human donors, potentially leading to antibodies with superior binding characteristics.

• Spatially Resolved Antibody Profiling: Technologies such as Digital Spatial Profiling and Imaging Mass Cytometry enable simultaneous visualization of multiple proteins, including DLD, with precise spatial resolution in tissue samples. This is particularly valuable for understanding DLD's role in heterogeneous tumor microenvironments .

• Recombinant Antibody Engineering: Structure-guided antibody engineering approaches allow for rational design of DLD antibodies with improved specificity, affinity, and stability. Computational screening of antibody libraries against structural models of DLD can identify optimal binding candidates before experimental validation .

• Antibody-Drug Conjugates (ADCs): For cases where DLD is overexpressed in cancer tissues, DLD antibodies are being explored as targeting moieties for ADCs, combining the specificity of antibody recognition with the cytotoxic effects of conjugated drugs.

These technological advancements are not only improving the quality and applications of DLD antibodies but also expanding their utility beyond traditional research applications into potential diagnostic and therapeutic realms. The integration of computational approaches with experimental validation represents a particularly promising direction for accelerating antibody development while maintaining high quality standards .

What are the key considerations for researchers designing DLD antibody-based experiments?

When designing DLD antibody-based experiments, researchers should consider several critical factors to ensure reliable and interpretable results: