DCHS1 Antibody

What is DCHS1 and why is it significant for research?

DCHS1 (Dachsous cadherin related 1) is a calcium-dependent cell-adhesion membrane protein encoded by the DCHS1 gene. In humans, the canonical protein has 3298 amino acid residues with a mass of 346.2 kDa and is localized in the cell membrane. It's significant because it plays critical roles in neuroprogenitor cell proliferation and differentiation, proper morphogenesis of the mitral valve, and is involved in cell adhesion mechanisms . DCHS1 is also known by several synonyms including CDH25, CDHR6, FIB1, MMVP2, MVP2, PCDH16, VMLDS1, and CDH19 . Research interest in DCHS1 has grown due to its association with diseases like Van Maldergem syndrome and mitral valve prolapse .

What are the most effective applications for DCHS1 antibodies in research?

DCHS1 antibodies are most effectively utilized in:

• Western Blot (1:500-1000 dilution): For detecting protein expression levels and molecular weight confirmation

• ELISA: For quantitative measurement of DCHS1 in samples

• Immunofluorescence (IF): For cellular localization studies (1:50-100 dilution)

• Immunohistochemistry (IHC): For tissue expression pattern analysis

• Immunocytochemistry (ICC): For subcellular localization (1:50-100 dilution)

The applications should be selected based on your specific research question, with ELISA being preferred for quantification and immunohistochemistry for spatial expression patterns .

How do I choose the right DCHS1 antibody for my specific research application?

Selection of the appropriate DCHS1 antibody should be based on:

• Target epitope: Antibodies targeting different regions (e.g., AA 2964-2981) may have different specificities

• Species reactivity: Ensure the antibody reacts with your species of interest (human, mouse, rat, etc.)

• Clonality: Polyclonal antibodies offer broader epitope recognition, while monoclonal antibodies provide higher specificity

• Conjugation requirements: Select based on detection method - unconjugated for standard IHC/WB, or specific conjugates (FITC, Biotin, HRP) for specialized applications

• Validation data: Review data showing antibody performance in your intended application

For applications requiring high sensitivity in detecting native protein conformation, antibodies validated for immunofluorescence are recommended .

What is the normal tissue expression pattern of DCHS1 and how can it be accurately assessed?

DCHS1 shows variable expression across normal human tissues:

• High expression: Endometrium, Cervix, Colon, and Urinary bladder tissues

• Moderate expression: Fibroblasts

• Low/Absent expression: Melanocytes and keratinocytes

For accurate assessment of DCHS1 expression:

• RNA-level analysis: qPCR with validated primers or RNA-seq analysis compared to reference genes

• Protein-level analysis: Immunohistochemistry with properly validated antibodies at 1:100 dilution

• Subcellular localization: Immunofluorescence microscopy focusing on membrane localization

When comparing expression between normal and pathological states, researchers should use consistent methodology and include proper controls to account for tissue-specific baseline expression levels .

How does DCHS1 expression differ across cancer types and what techniques best demonstrate these differences?

DCHS1 expression varies significantly across cancer types:

Upregulated in:

• Glioblastoma (GBM)

• Head and neck squamous cell carcinoma (HNSC)

• Kidney renal clear cell carcinoma (KIRC)

• Pheochromocytoma and Paraganglioma (PCPG)

• Cholangiocarcinoma (CHOL)

Downregulated in:

• Cervical squamous cell carcinoma (CESC)

• Lung adenocarcinoma (LUAD)

• Breast invasive carcinoma (BRCA)

• Kidney renal papillary cell carcinoma (KIRP)

• Prostate adenocarcinoma (PRAD)

• Uterine Corpus Endometrial Carcinoma (UCEC)

Recommended techniques for assessment:

• RNA-seq analysis: Comparing tumor vs. normal tissue from TCGA and GTEx databases

• Immunohistochemistry: Using tissue microarrays with scoring systems for intensity (0-3+)

• Western blot: For quantitative protein expression analysis

• ROC curve analysis: To evaluate diagnostic potential (AUC values ranging from 0.717-0.980 across cancer types)

The combination of these techniques provides comprehensive evidence of DCHS1's differential expression and potential as a diagnostic biomarker in specific cancers .

What experimental approaches can determine DCHS1's role in cell adhesion and migration?

To investigate DCHS1's role in cell adhesion and migration, researchers can employ:

• Gene silencing/overexpression studies:

  • siRNA or shRNA knockdown of DCHS1

  • CRISPR-Cas9 gene editing to generate DCHS1-knockout cell lines

  • Overexpression of wild-type or mutant DCHS1 constructs

• Functional assays:

  • Cell adhesion assays using matrix-coated plates

  • Wound healing/scratch assays to measure migration rates

  • Transwell migration and invasion assays

  • Live-cell imaging with fluorescently tagged DCHS1

• Protein stability and dynamics:

  • Cycloheximide chase assays to measure protein half-life (wild-type DCHS1: 5.8 hours vs. mutant DCHS1: 1.6 hours)

  • Co-immunoprecipitation to identify interacting partners

  • Calcium dependency tests by EGTA treatment

• In vivo models:

  • Dchs1 mutant mouse analysis focusing on tissue integrity and morphogenesis

  • Embryonic development assessment at key timepoints (E11.5-E17.5)

These approaches should be combined to establish comprehensive understanding of DCHS1's functional role in cellular processes .

How does DCHS1 contribute to epithelial-mesenchymal transition (EMT) and what markers should be assessed?

DCHS1 has been implicated in epithelial-mesenchymal transition (EMT), a process critical in development and cancer progression. To investigate this relationship:

• EMT marker profiling following DCHS1 modulation:

  • Epithelial markers: E-cadherin, ZO-1, claudins, occludin

  • Mesenchymal markers: N-cadherin, vimentin, fibronectin, α-SMA

  • EMT transcription factors: Snail, Slug, ZEB1/2, Twist

• Functional assays to assess EMT phenotype:

  • Cell morphology changes (epithelial to spindle-shaped)

  • Migration and invasion capacity in Transwell chambers

  • Resistance to anoikis (suspension culture survival)

  • Colony formation in 3D matrices

• Signaling pathway analysis:

  • Wnt signaling pathway activation (implicated by GSEA analysis)

  • Focal adhesion pathway components

  • TGF-β pathway activation status

• In vitro validation:

  • Cell proliferation assessment using CCK8 assay

  • Migration capacity via Transwell assay

  • EMT marker expression via Western blot

Gene Set Enrichment Analysis (GSEA) has revealed significant association between DCHS1 expression and EMT pathways across multiple cancer types, suggesting a mechanistic link that warrants further investigation .

What is the evidence linking DCHS1 mutations to mitral valve prolapse and how was this discovered?

The connection between DCHS1 mutations and mitral valve prolapse (MVP) has been established through complementary genetic and functional approaches:

• Genetic evidence:

  • Identification of DCHS1 mutations (p.P197L and p.R2513H) in families with inherited non-syndromic MVP

  • The p.R2513H variant showed particularly strong association with pathogenicity

• Protein stability studies:

  • Mutant DCHS1 protein showed ~60% reduced expression compared to wild-type

  • No significant change in mRNA levels, suggesting post-transcriptional effects

  • Cycloheximide treatment revealed dramatically reduced protein half-life (wild-type: 5.8 hours vs. mutant: 1.6 hours)

• Animal model validation:

  • Dchs1+/- and Dchs1-/- mice displayed abnormal mitral valve morphology

  • Changes in valve shape were observed at E15.5-E17.5 developmental stages

  • Three-dimensional reconstructions of valve leaflets confirmed consistent phenotypes

• Expression analysis:

  • In situ hybridization and immunohistochemistry demonstrated Dchs1 expression in endocardial and mesenchymal cells of atrioventricular valve leaflets

This multi-modal evidence establishes DCHS1 as a critical factor in mitral valve development, with mutations leading to MVP through mechanisms involving protein stability and altered developmental processes .

How does DCHS1 expression correlate with immune infiltration in the tumor microenvironment?

DCHS1 expression shows significant correlation with immune cell infiltration in the tumor microenvironment:

• Key immune cell correlations:

  • Cancer-associated fibroblasts (CAFs): Strong positive correlation across multiple cancer types

  • Endothelial cells (ECs): Significant positive correlation

  • Hematopoietic stem cells: Notable positive association

• Cancer type specificity:

  • Correlation patterns vary across cancer types

  • Strongest associations observed in cancers where DCHS1 shows differential expression

• Methodological approaches:

  • Single-cell RNA sequencing for cell type-specific analysis

  • Deconvolution algorithms (e.g., CIBERSORT, xCell) for bulk RNA-seq data

  • Multiplex immunohistochemistry for spatial context

  • Digital spatial profiling for high-dimensional analysis

• Therapeutic implications:

  • Potential indicator of immunotherapy response

  • Association with sensitivity to various antitumor drugs

  • Possible target for modulating the tumor immune microenvironment

These correlations suggest DCHS1 may influence tumor progression through modulation of the immune microenvironment, particularly through interactions with stromal components like CAFs and ECs .

What are the optimal conditions for using DCHS1 antibodies in challenging applications like chromatin immunoprecipitation (ChIP)?

While standard applications for DCHS1 antibodies are well-established, more challenging techniques like ChIP require optimization:

• Antibody selection considerations:

  • High specificity and affinity antibodies (validated for IP applications)

  • Polyclonal antibodies targeting multiple epitopes often perform better for ChIP

  • Confirm antibody recognizes native protein conformation

• Protocol optimization:

  • Crosslinking: Start with 1% formaldehyde for 10 minutes at room temperature

  • Sonication: Optimize cycles to achieve 200-500bp DNA fragments

  • Antibody concentration: Test range from 2-10μg per ChIP reaction

  • Incubation time: Extend to overnight at 4°C for maximum binding

  • Washing stringency: Balance between reducing background and maintaining signal

• Controls:

  • IgG negative control: Crucial for determining background signal

  • Input DNA: Use 5-10% of starting material

  • Positive control loci: Include known DCHS1 binding regions if available

  • Sequential ChIP: Consider for protein complex studies

• Validation approaches:

  • qPCR of target regions vs. non-binding regions

  • Western blot of immunoprecipitated material

  • Mass spectrometry verification of pulled-down proteins

Given DCHS1's role as a membrane protein, membrane extraction and solubilization steps may require particular attention to ensure efficient immunoprecipitation while maintaining protein-DNA interactions.

How can DCHS1 antibodies be integrated into advanced imaging approaches such as super-resolution microscopy?

Integrating DCHS1 antibodies into super-resolution microscopy requires specialized optimization:

• Antibody preparation for super-resolution techniques:

  • Select high-affinity antibodies with minimal background

  • Consider directly conjugated antibodies to reduce localization errors

  • For STORM/PALM: Use photoswitchable fluorophore conjugates

  • For STED: Select fluorophores with appropriate depletion properties

  • Antibody concentration: Typically lower than conventional microscopy (1:200-1:500)

• Sample preparation considerations:

  • Fixation: 4% PFA with mild permeabilization to preserve membrane structures

  • Buffer optimization: Oxygen scavenging systems for STORM

  • Mounting media: Specialized for each super-resolution technique

  • Cell culture on specific coverslips (high precision thickness)

• Validation approaches:

  • Correlative microscopy with conventional techniques

  • Dual-color imaging with known markers of cell membrane or cadherin family

  • Antibody clustering analysis

  • Quantitative assessment of labeling density and specificity

• Analysis strategies:

  • Cluster analysis of DCHS1 distribution

  • Co-localization with interacting partners

  • Temporal dynamics using live-cell super-resolution

  • 3D reconstruction of membrane distribution patterns

When designing super-resolution experiments, researchers should consider DCHS1's membrane localization and potential clustering properties, which may be particularly well-suited for techniques like PALM or STORM that can resolve nanoscale protein organization .

How can antibody-based approaches be combined with genetic tools to study DCHS1 function in developmental processes?

A multi-modal approach combining antibody detection with genetic manipulation provides powerful insights into DCHS1 function:

• Spatiotemporal expression mapping:

  • Immunohistochemistry with DCHS1 antibodies at key developmental stages

  • In situ hybridization to correlate protein with mRNA expression

  • Lineage tracing combined with DCHS1 immunostaining

• Genetic manipulation approaches:

  • CRISPR/Cas9 genome editing to introduce specific mutations

  • Conditional knockout models (Cre-loxP) for tissue-specific deletion

  • BAC transgenic reporter lines for live imaging

  • Inducible expression systems for temporal control

• Combinatorial analysis techniques:

  • Single-cell RNA-seq with antibody-based cell sorting

  • ATAC-seq combined with DCHS1 ChIP to assess chromatin accessibility

  • Proximity labeling (BioID, APEX) with DCHS1 antibody validation

  • Optogenetic control of DCHS1 with antibody-based readouts

• In vivo developmental analysis:

  • Analysis of Dchs1+/- and Dchs1-/- mouse models at critical timepoints

  • Whole-mount immunostaining for morphological assessment

  • 3D reconstruction of developing organs (e.g., heart valves)

Studies with Dchs1 mutant mice have revealed that while no morphological defects are observed in Dchs1+/- mice during early embryonic development (E11.5–E13.5), significant changes in mitral-valve shape become apparent at later timepoints (E15.5–E17.5), with more severe phenotypes in Dchs1-/- animals .

How can artificial intelligence and machine learning improve DCHS1 antibody design and validation?

AI and machine learning approaches are revolutionizing antibody design and validation processes for targets like DCHS1:

• AI-driven antibody design approaches:

  • Sequence-based antibody design models like DyAb can predict affinity improvements based on limited training data

  • Deep learning models can incorporate protein structural information to optimize binding sites

  • Machine learning algorithms can select combinations of mutations to enhance antibody properties

• Performance metrics and validation:

  • Correlation between predicted and measured improvements in affinity (ΔpKD)

  • Models have achieved Pearson correlation coefficients of r=0.84 and Spearman coefficients of ρ=0.84 for antibody variant prediction

  • High expression and binding rates (>85%) for AI-designed antibodies compared to traditional methods

• Implementation strategies:

  • Using genetic algorithms (GA) to generate and score antibody sequence combinations

  • Limiting edit distance (ED) to maintain "natural" sequences and avoid design failures

  • Structural analysis of top designs to understand binding mechanisms

  • In silico prediction followed by experimental validation

• Applications specific to DCHS1:

  • Epitope optimization targeting crucial functional domains

  • Cross-reactivity prediction across species for comparative studies

  • Affinity maturation for improved sensitivity in low-expression contexts

Recent advancements have shown that AI-designed antibodies maintain high expression and binding rates comparable to single point mutants while significantly improving target affinity, suggesting promising applications for DCHS1 research .

What emerging single-cell techniques can be applied with DCHS1 antibodies to understand heterogeneity in expression and function?

Emerging single-cell technologies offer unprecedented insights when combined with DCHS1 antibodies:

• Single-cell protein analysis techniques:

  • Mass cytometry (CyTOF) with DCHS1 antibodies conjugated to metal isotopes

  • CITE-seq combining antibody detection with transcriptomics

  • Single-cell Western blotting for protein expression heterogeneity

  • Microfluidic antibody capture for quantitative single-cell surface protein analysis

• Spatial profiling approaches:

  • Imaging mass cytometry for tissue section analysis with DCHS1 antibodies

  • Co-detection by indexing (CODEX) for highly multiplexed tissue imaging

  • Digital spatial profiling combining region selection with molecular quantification

  • 4i (iterative indirect immunofluorescence imaging) for sequential antibody staining

• Functional single-cell applications:

  • Live-cell antibody imaging with microfluidic cell capture

  • Single-cell secretion assays combined with DCHS1 surface detection

  • Clonal tracking with DCHS1 expression correlation

  • Lineage tracing combined with antibody-based detection

• Data integration strategies:

  • Multi-omics integration of DCHS1 protein data with transcriptomics

  • Trajectory analysis correlating DCHS1 expression with cell state transitions

  • Cell-cell interaction mapping based on DCHS1 and partner protein expression

  • Spatial statistics for tissue organization analysis

These techniques are particularly valuable for understanding DCHS1's heterogeneous expression in cancer and its correlation with immune cell infiltration in the tumor microenvironment, potentially revealing new therapeutic targets and prognostic indicators .