DHAR3 Antibody

What is DHRS3 and what is its primary biochemical function?

DHRS3 functions as a critical enzyme in retinoid metabolism, specifically catalyzing the reduction of all-trans-retinal to all-trans-retinol in the presence of NADPH . Also known by alternative names including RDH17, SDR16C1, and retSDR1, this enzyme belongs to the short-chain dehydrogenase/reductase family. The protein has a predicted molecular weight of approximately 34 kDa and plays important roles in retinoid homeostasis, which affects numerous biological processes including cellular differentiation and embryonic development.

Which experimental applications are DHRS3 antibodies validated for?

Based on available research data, commercial DHRS3 antibodies such as ab198005 are validated for Western Blot (WB) at 1/550 dilution and Immunohistochemistry on paraffin-embedded tissues (IHC-P) at 1/30 dilution . This particular antibody is a rabbit polyclonal that reacts with both mouse and human samples, with confirmed reactivity against mouse liver tissue lysate and A375 melanoma cell lysate for WB applications, and human breast cancer tissue for IHC-P applications .

How can I confirm antibody specificity when studying DHRS3?

Confirming DHRS3 antibody specificity requires multiple validation approaches:

• Verify the presence of a single band at the expected molecular weight of 34 kDa in Western blot analysis

• Include positive controls where DHRS3 expression is established (mouse liver tissue, A375 cells)

• Implement negative controls (tissues known not to express DHRS3, primary antibody omission)

• Perform knockdown/knockout validation experiments using siRNA or CRISPR-Cas9

• Compare results using antibodies targeting different DHRS3 epitopes

• Correlate protein detection with mRNA expression data from RT-PCR

What are the optimal sample preparation protocols for DHRS3 Western blot analysis?

For optimal DHRS3 detection by Western blot:

Table 1: Western Blot Protocol Optimization for DHRS3 Detection

How should I optimize immunohistochemistry protocols for DHRS3 detection in tissue samples?

Successful IHC detection of DHRS3 requires careful protocol optimization:

• Fixation: Use 10% neutral buffered formalin with standardized fixation times (12-24 hours)

• Antigen retrieval: Heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

• Blocking: 5-10% normal serum (from same species as secondary antibody) for 1 hour at room temperature

• Primary antibody: Apply at 1/30 dilution as validated for human breast cancer tissue

• Detection system: Polymer-based detection systems offer high sensitivity with low background

• Counterstaining: Light hematoxylin counterstain to visualize tissue architecture without obscuring DAB signal

• Controls: Include positive control (breast cancer tissue) and negative controls (primary antibody omission)

What factors affect reproducibility in DHRS3 antibody experiments?

Several critical factors influence experimental reproducibility:

• Antibody lot variation: Different lots may show subtle variations in specificity and sensitivity

• Sample preparation consistency: Standardize lysis buffers, protein quantification methods, and handling procedures

• Protocol standardization: Document precise conditions for key steps (blocking times, antibody dilutions, wash steps)

• Positive/negative controls: Include consistent controls across experiments to normalize results

• Quantification methods: Standardize image acquisition settings and analysis parameters

• Reagent quality: Use high-quality, consistently sourced reagents

How can I implement multiplex immunofluorescence to study DHRS3 in relation to other proteins?

Implementing multiplex immunofluorescence for DHRS3 studies requires:

• Antibody panel design: Since DHRS3 antibody ab198005 is a rabbit polyclonal , pair with antibodies from different host species (mouse, goat) targeting interacting proteins

• Sequential staining approach:

  • Begin with the lowest concentration antibody

  • Use tyramide signal amplification for signal enhancement if needed

  • Implement heat or chemical stripping between antibody applications

• Spectral unmixing: Select fluorophores with minimal spectral overlap or employ spectral imaging with computational unmixing

• Controls:

  • Single-stain controls for spectral compensation

  • FMO (fluorescence minus one) controls to set proper thresholds

• Quantitative analysis: Use specialized software for colocalization analysis and expression quantification

What approaches can be used to study DHRS3 protein-protein interactions in research contexts?

To investigate DHRS3 protein interactions:

• Co-immunoprecipitation (Co-IP):

  • Use DHRS3 antibody for pull-down experiments

  • Follow with Western blot analysis for potential binding partners

  • Verify with reciprocal Co-IP using antibodies against suspected interacting proteins

• Proximity ligation assay (PLA):

  • Combine DHRS3 antibody with antibodies against potential interacting partners

  • Signals indicate protein proximity (<40 nm)

  • Quantify interaction frequency in different cellular contexts

• FRET/BRET analysis:

  • Generate fluorescent/bioluminescent protein fusions with DHRS3

  • Measure energy transfer as indication of protein proximity

  • Analyze in live cells to capture dynamic interactions

• Mass spectrometry-based approaches:

  • Immunoprecipitate DHRS3 using validated antibodies

  • Perform LC-MS/MS to identify interaction partners

  • Validate key interactions with orthogonal methods

How can DHRS3 antibodies be applied in high-content screening approaches?

DHRS3 antibodies can enable sophisticated high-content screening by:

• Developing cellular assays:

  • Track DHRS3 expression, localization, and post-translational modifications

  • Correlate with cellular phenotypes (morphology, viability, differentiation)

  • Screen for compounds affecting DHRS3 function or expression

• Automated image analysis:

  • Quantify DHRS3 subcellular localization patterns

  • Measure co-localization with organelle markers

  • Apply machine learning algorithms for pattern recognition

• Multi-parametric analysis:

  • Combine DHRS3 staining with markers for cell cycle, apoptosis, or differentiation

  • Develop custom analysis pipelines for phenotypic profiling

  • Integrate with transcriptomic or metabolomic data

• Validation strategies:

  • Confirm hits with orthogonal assays

  • Utilize DHRS3 knockout/knockdown controls

  • Correlate screening results with enzymatic activity measurements

How should I troubleshoot weak or absent signals when using DHRS3 antibodies?

When facing detection issues with DHRS3 antibodies:

Table 2: Troubleshooting Guide for DHRS3 Antibody Applications

How can I accurately quantify DHRS3 expression levels across different experimental conditions?

For accurate quantification of DHRS3 expression:

• Western blot quantification:

  • Use appropriate loading controls (β-actin, GAPDH)

  • Ensure signal is within linear detection range

  • Normalize DHRS3 band intensity to loading control

  • Use at least three biological replicates

  • Apply statistical analysis to validate significance

• IHC quantification:

  • Standardize staining conditions across all samples

  • Use digital image analysis software with validated algorithms

  • Quantify staining intensity and percentage of positive cells

  • Develop scoring systems (H-score, Allred) appropriate for DHRS3 expression pattern

  • Ensure blinded evaluation by multiple researchers

• Flow cytometry quantification:

  • Use calibration beads to standardize fluorescence measurements

  • Include isotype controls for background determination

  • Report data as median fluorescence intensity (MFI)

  • Apply compensation for spectral overlap if performing multicolor analysis

What patterns of DHRS3 expression are reported in different tissues and disease states?

DHRS3 expression patterns vary across tissues and disease contexts:

• Normal tissues:

  • Expressed in liver tissue with cytoplasmic localization

  • Present in various epithelial cell types

  • Expression often correlates with tissues involved in retinoid metabolism

• Cancer tissues:

  • Detected in human breast cancer samples

  • Expression may be altered compared to normal tissue counterparts

  • Subcellular localization might change in malignant transformation

• Data interpretation considerations:

  • Assess both staining intensity and percentage of positive cells

  • Note subcellular localization (cytoplasmic, membranous, nuclear)

  • Consider heterogeneity of expression within tissue samples

  • Correlate expression with clinical parameters when available

How are advanced computational approaches enhancing antibody-based DHRS3 research?

Computational approaches are revolutionizing DHRS3 antibody research:

• AI-based image analysis:

  • Deep learning algorithms can automatically quantify DHRS3 staining patterns

  • Machine learning classifiers can identify correlations between DHRS3 expression and histopathological features

  • Computer vision techniques enhance detection of subtle expression differences

• Predictive modeling:

  • Predict functional consequences of DHRS3 expression patterns

  • Model DHRS3 interactions in retinoid metabolism pathways

  • Simulate effects of DHRS3 modulation on cellular functions

• Antibody design:

  • AI approaches like those used for other antibodies can inform DHRS3 antibody optimization

  • Pre-trained antibody language models may predict optimal CDRH3 sequences for DHRS3 targeting

  • Active learning approaches can improve antibody-antigen binding prediction

What are the considerations for developing novel DHRS3 antibodies using AI-based approaches?

Developing next-generation DHRS3 antibodies using AI technologies involves:

• Epitope selection strategy:

  • Identify conserved regions unique to DHRS3

  • Utilize structural prediction algorithms to identify surface-exposed regions

  • Apply AI models like those described for other targets to optimize epitope selection

• Antibody design approaches:

  • Use pre-trained antibody language models (like PALM-H3) to generate optimized CDRH3 sequences

  • Leverage generative AI to design antibodies with improved specificity and affinity

  • Apply computational screening before experimental validation

• Validation pipeline:

  • Implement binding prediction algorithms to evaluate candidates in silico

  • Design assays to compare AI-generated antibodies with traditional antibodies

  • Test across multiple applications (Western blot, IHC, flow cytometry)

• Considerations for research use:

  • Compare performance metrics between AI-designed and conventional antibodies

  • Evaluate cross-reactivity profiles against related short-chain dehydrogenase/reductase family members

  • Document epitope information for reproducible research

How might DHRS3 antibody research intersect with therapeutic antibody development methodologies?

Intersection between DHRS3 research antibodies and therapeutic approaches:

• Antibody-drug conjugate (ADC) methodology applications:

  • DHRS3 antibodies could potentially be evaluated using similar frameworks to other targets like HER3-DXd

  • Drug-to-antibody ratio (DAR) analysis methods using UV-visible spectrophotometry could be applied

  • Safety and efficacy assessment protocols developed for therapeutic antibodies could inform research approaches

• Translational research considerations:

  • Evaluate DHRS3 expression patterns across normal vs. diseased tissues

  • Assess antibody internalization properties critical for therapeutic applications

  • Investigate potential for targeting DHRS3 in specific disease contexts

• Research applications of therapeutic antibody technologies:

  • Apply insights from antibody humanization to develop better research reagents

  • Utilize affinity maturation techniques to enhance DHRS3 antibody performance

  • Implement quality control methods from therapeutic antibody production to improve research antibody consistency