DSCC1 Antibody

What is DSCC1 and why is it significant in cancer research?

DSCC1 is a component of an alternative replication factor C complex that loads proliferating cell nuclear antigen onto DNA during the S phase of the cell cycle. This protein plays crucial roles in sister chromatid cohesion (SCC), DNA replication, and DNA damage response mechanisms. Recent pan-cancer analyses have revealed that DSCC1 is frequently overexpressed across diverse tumor tissues compared to their normal counterparts, making it a significant biomarker in cancer research . Notably, DSCC1 amplification at chromosome 8q24 is common in hepatocellular carcinoma (HCC) and has been associated with poor prognosis . The protein's involvement in fundamental cellular processes and its aberrant expression in multiple cancer types highlight its importance as a research target for understanding cancer biology and developing potential therapeutic strategies.

What types of DSCC1 antibodies are currently available for research?

DSCC1 antibodies are available in various configurations to suit different experimental needs:

By Host Species:

• Mouse-derived polyclonal antibodies

• Rabbit-derived polyclonal antibodies (including specific clones like RB17803)

By Reactivity:

• Primarily human-reactive antibodies

• Some antibodies may cross-react with other species, though human reactivity is most commonly validated

By Application Validation:

• Western Blot (WB)

• Immunofluorescence (IF), both cellular and paraffin

• Enzyme-Linked Immunosorbent Assay (ELISA)

• Immunohistochemistry (IHC), both paraffin-embedded and frozen sections

• Immunocytochemistry (ICC)

By Clonality:

• Polyclonal antibodies are most commonly available

• Custom-produced antibodies, such as those generated from mice immunized with purified recombinant DSCC1 C-terminal protein

How should researchers validate DSCC1 antibodies before experimental use?

Proper validation of DSCC1 antibodies is critical for ensuring experimental reproducibility and accuracy:

• Positive and negative controls: Include known DSCC1-expressing tissues/cells (such as cancer cell lines, particularly HCC) alongside tissues with low expression (normal counterparts) .

• Antibody specificity verification: This can be achieved through:

  • Western blot analysis confirming a single band of appropriate molecular weight

  • Comparison with DSCC1 knockdown/knockout cells to confirm signal reduction

  • When developing custom antibodies, validation through recombinant protein recognition

• Cross-application validation: If using the antibody for multiple applications (e.g., both WB and IHC), verify consistent results across techniques .

• Titration experiments: Determine optimal antibody concentration for each application to maximize signal-to-noise ratio. For example, studies have used 1:500 dilution for IHC applications .

• Blocking experiments: Preincubation with the immunizing peptide should abolish specific staining if the antibody is truly specific.

What are the optimal protocols for DSCC1 antibody use in immunohistochemistry?

For effective immunohistochemical detection of DSCC1, researchers should follow these methodological steps:

• Tissue preparation:

  • Fix tissues in formalin buffer and embed in paraffin wax

  • Section tissues to approximately 4-μm thickness

  • Deparaffinize sections thoroughly

• Antigen retrieval:

  • Perform heat-induced epitope retrieval using citrate buffer

  • This critical step helps unmask antigens that may have been cross-linked during fixation

• Blocking steps:

  • Quench endogenous peroxidase activity with 3% hydrogen peroxide in methanol

  • Block nonspecific binding using 1% BSA for optimal signal-to-noise ratio

• Primary antibody incubation:

  • Dilute DSCC1 antibody appropriately (e.g., 1:500 as reported)

  • Incubate for 60 minutes at room temperature in a humidified chamber

  • For mouse-derived antibodies targeting human samples, be aware of potential background issues

• Detection and visualization:

  • Use biotinylated secondary antibody appropriate to the primary antibody host species

  • Counterstain with Mayer's hematoxylin for proper visualization of tissue architecture

• Evaluation metrics:

  • Assess both staining intensity (high vs. low) and pattern (nuclear vs. cytoplasmic)

  • Compare tumor samples with matched normal tissues to quantify expression differences

This protocol has successfully demonstrated elevated DSCC1 protein expression in KIRP, LIHC, and LUAD samples compared to corresponding normal tissues .

How can DSCC1 antibodies be optimized for Western blot applications?

For optimal Western blot detection of DSCC1, researchers should consider these methodological approaches:

• Sample preparation:

  • Lyse cells with radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitor cocktail

  • Maintain samples on ice during processing to prevent protein degradation

  • Load approximately 30 μg of protein per lane for standard detection

• Electrophoresis conditions:

  • Use 10-14% SDS-PAGE gels for effective separation

  • Include positive control samples from known DSCC1-expressing cell lines

• Transfer optimization:

  • Employ rapid transfer systems (e.g., Transblot Turbo) for efficient protein transfer

  • Verify transfer efficiency with reversible staining before blocking

• Blocking and antibody incubation:

  • Block membranes with 5% skim milk in PBS

  • Optimize primary antibody concentration through titration experiments

  • Incubate with HRP-conjugated secondary antibodies at room temperature

• Detection method:

  • Use enhanced chemiluminescence detection reagents for visualization

  • Employ digital imaging systems (e.g., Ez-Capture MG) for quantitative analysis

• Controls and validation:

  • Include DSCC1 knockdown samples as negative controls

  • For studies investigating cancer tissues, compare tumor and adjacent normal tissue samples

What approaches are recommended for generating and using DSCC1 knockdown models?

DSCC1 knockdown models are valuable for studying protein function and validating antibody specificity:

• shRNA-mediated knockdown:

  • Use commercially available short hairpin RNA (shRNA) targeting DSCC1

  • Transfect packaging cells (e.g., Lenti-X 293T) with lentiviral packaging mix

  • Transduce target cell lines (e.g., HCT116, SW480, HCC cell lines) with virus-enriched media

  • Select stable knockdown cells using puromycin (4 μg/ml)

• Validation of knockdown efficiency:

  • Verify DSCC1 knockdown at both mRNA level (qPCR) and protein level (Western blot)

  • Use validated DSCC1 antibodies to confirm protein level reduction

• Functional assays following knockdown:

  • Colony-forming assays to assess clonogenic capacity

  • Cell cycle analysis to detect cell cycle alterations (typically G0-G1 arrest)

  • Cell proliferation assays to measure growth inhibition

  • DNA fiber assays to assess DNA replication fork speed

  • Immunofluorescence for γ-H2AX to quantify DNA damage

• Expected phenotypes:

  • In HCC cell lines: significant inhibition of clonogenic capacity, G0-G1 cell cycle arrest (increase from 60% to >80%), and inhibited cell proliferation

  • In other systems: sister chromatid cohesion defects, reduced DNA replication fork speed, increased spontaneous DNA damage, modest G2/M accumulation, and moderate growth rate reduction

How can DSCC1 antibodies be used to investigate drug sensitivity correlations?

DSCC1 expression has been correlated with sensitivity to numerous anticancer drugs, making it a potential predictive biomarker:

• Experimental approach:

  • Establish cell lines with varied DSCC1 expression levels (including knockdown/knockout models)

  • Treat with candidate drugs at various concentrations

  • Assess cell viability/proliferation using methods like CTB (CellTiter-Blue) assay

  • Compare drug sensitivity between DSCC1-normal and DSCC1-altered models

• Drug categories to test:

  • ATR inhibitors (e.g., AZD6738)

  • Topoisomerase I inhibitors (e.g., camptothecin)

  • PARP inhibitors (e.g., talazoparib)

  • G-quadruplex stabilizers (e.g., quarfloxin)

  • Transcription/replication-blocking agents (e.g., illudin S)

• Data analysis approaches:

  • Generate dose-response curves to calculate IC50 values

  • Compare relative proliferation between wild-type and DSCC1-altered cells

  • Correlate DSCC1 expression levels with drug sensitivity metrics

• Validation in patient samples:

  • Use DSCC1 antibodies for IHC staining of tumor samples

  • Correlate DSCC1 expression with clinical response to related therapies

  • Analyze public databases (e.g., GSCA) to identify additional DSCC1-drug sensitivity correlations

Research has identified both positive associations (e.g., with 17-AAG, Bleomycin, FTI-277, RDEA119, Trametinib, Selumetinib) and negative correlations (with 23 other drugs including AR-42, AT-7519, BMS345541) between DSCC1 expression and drug sensitivity .

What methodological considerations are important when using DSCC1 antibodies to study DNA replication and sister chromatid cohesion?

When investigating DSCC1's role in DNA replication and sister chromatid cohesion, consider these specialized approaches:

• Sister chromatid cohesion analysis:

  • Prepare metaphase spreads from DSCC1-normal and DSCC1-altered cells

  • Use DSCC1 antibodies in combination with cohesion markers

  • Score at least 50 metaphases per condition across multiple independent experiments

  • Categorize and quantify observed cohesion defects

• DNA replication fork studies:

  • Perform DNA fiber assays to measure replication fork speed

  • Label ongoing replication with nucleoside analogs

  • Score at least 65 fibers per experiment

  • Compare fork progression rates between DSCC1-normal and -altered cells

• DNA damage assessment:

  • Use immunofluorescence with DSCC1 antibodies alongside γ-H2AX antibodies

  • Score cells with more than five γ-H2AX foci (minimum 47 cells per condition)

  • Calculate the percentage of cells with DNA damage markers

• Cell cycle analysis:

  • Monitor cell cycle distribution following DSCC1 manipulation

  • Look for specific accumulation patterns (G2/M in some systems, G0-G1 in others)

  • Correlate with other functional phenotypes

• Protein complex analysis:

  • Use co-immunoprecipitation with DSCC1 antibodies to identify interacting partners

  • Consider alternative RFC complex components and related factors

  • Investigate both established interactions and novel candidates

Research indicates that DSCC1-RFC facilitates the de novo cohesin loading pathway, whereas other factors like DDX11 and MMS22L-TONSL contribute to the cohesin conversion pathway .

How should researchers interpret contradictory findings in DSCC1 expression across different cancer types?

When facing discrepancies in DSCC1 expression patterns across cancer types, researchers should implement these analytical approaches:

• Standardized scoring system:

  • Develop quantitative metrics for DSCC1 protein expression by IHC

  • Score both intensity (low/medium/high) and percentage of positive cells

  • Generate H-scores or similar composite measures for comparison

  • Apply consistent thresholds across different cancer types

• Multi-omics integration:

  • Correlate protein expression (antibody-based) with mRNA expression data

  • Assess DSCC1 copy number variations alongside expression

  • Evaluate epigenetic regulation that might explain tissue-specific differences

• Contextual analysis:

  • Consider tissue-specific roles of DSCC1

  • Analyze pathway activation status in different cancers

  • Interpret DSCC1 expression in the context of cell proliferation rates

  • Evaluate p53 status, as TP53-deficiency may alter DSCC1 effects

• Validation across multiple cohorts:

  • Confirm findings in independent patient cohorts

  • Use multiple antibodies targeting different DSCC1 epitopes

  • Compare results from different detection methods (IHC, Western blot, mass spectrometry)

• Functional validation:

  • Test DSCC1 knockdown effects in cell lines from different cancer types

  • Assess cancer-specific phenotypes following DSCC1 modulation

  • Determine if DSCC1 operates through different mechanisms in various tissues

Research demonstrates elevated DSCC1 protein expression in KIRP, LIHC, and LUAD samples compared to normal tissues, with particularly strong associations with poor prognosis in HCC .

How can researchers overcome challenges in detecting low-abundance DSCC1 in normal tissues?

Detecting low DSCC1 expression in normal tissues requires specialized approaches:

• Signal amplification methods:

  • Employ tyramide signal amplification for IHC/IF

  • Use high-sensitivity ECL substrates for Western blot

  • Consider polymer-based detection systems rather than conventional ABC methods

• Sample enrichment strategies:

  • Perform subcellular fractionation to concentrate nuclear proteins

  • Use immunoprecipitation to enrich DSCC1 before detection

  • Consider tissue-specific lysis buffers optimized for nuclear proteins

• Antibody selection considerations:

  • Choose antibodies with demonstrated sensitivity in detecting low abundance targets

  • Consider antibodies targeting different epitopes if certain domains are masked

  • Polyclonal antibodies may offer improved sensitivity for low-abundance detection

• Protocol modifications:

  • Extend primary antibody incubation times (overnight at 4°C)

  • Optimize antigen retrieval conditions specifically for DSCC1

  • Reduce washing stringency while maintaining specificity

  • Adjust blocking conditions to minimize background while preserving specific signals

• Imaging and quantification:

  • Use high-sensitivity digital imaging systems

  • Implement advanced image analysis software for detecting subtle differences

  • Consider longer exposure times with appropriate controls for background

What are the best approaches for multiplex detection of DSCC1 with other DNA replication and cohesion markers?

For effective multiplex detection of DSCC1 alongside other markers:

• Antibody compatibility assessment:

  • Select DSCC1 antibodies from different host species than other target antibodies

  • Verify absence of cross-reactivity between detection systems

  • Test antibodies individually before combining in multiplex assays

• Multiplex immunofluorescence strategy:

  • Use spectrally distinct fluorophores for each target protein

  • Consider sequential staining protocols for challenging combinations

  • Include appropriate controls for autofluorescence and spectral overlap

• Multiplexed IHC approaches:

  • Implement chromogenic multiplex IHC with different substrates

  • Consider tyramide-based sequential multiplex protocols

  • Use multispectral imaging systems for analysis

• Co-localization analysis:

  • Employ high-resolution confocal microscopy

  • Use quantitative co-localization metrics (Pearson's coefficient, Manders' overlap)

  • Analyze cell cycle-dependent changes in co-localization patterns

• Recommended marker combinations:

  • DSCC1 with other RFC complex components

  • DSCC1 with cohesion markers (e.g., cohesin subunits)

  • DSCC1 with DNA damage response proteins (e.g., γ-H2AX)

  • DSCC1 with cell cycle markers to assess phase-specific expression

What controls are essential when using DSCC1 antibodies to evaluate synthetic lethality relationships?

When using DSCC1 antibodies in synthetic lethality studies:

• Genetic control panel:

  • Include isogenic cell line pairs (DSCC1-WT vs. DSCC1-KO)

  • Prepare complemented cell lines (DSCC1-KO with re-expressed DSCC1)

  • Generate cells with varying DSCC1 expression levels for dose-response analysis

• Technical validation controls:

  • Confirm DSCC1 knockout/knockdown by both Western blot and immunofluorescence

  • Verify that phenotypes can be rescued by wildtype but not mutant DSCC1

  • Include multiple DSCC1-targeting reagents (different shRNAs/sgRNAs) to rule out off-target effects

• Biological context controls:

  • Test synthetic interactions in multiple cell backgrounds

  • Assess dependency in both p53-proficient and -deficient contexts

  • Evaluate the relationship in different phases of the cell cycle

• Drug treatment controls:

  • Include concentration gradients for all compounds

  • Monitor drug efficacy with appropriate cellular markers

  • Compare results between DSCC1-dependent and -independent cell lines

• Data analysis considerations:

  • Calculate and compare synthetic lethality scores

  • Apply statistical methods appropriate for synthetic lethality assessment

  • Validate key hits with orthogonal approaches

Research has identified synthetic lethality between DSCC1 loss and DNA helicases, the POLE3-4 heterodimer, and cohesion establishment genes .

How can DSCC1 antibodies be used to develop prognostic tools for cancer patients?

DSCC1 antibodies offer valuable tools for developing cancer prognostic markers:

What methodological approaches are recommended for studying DSCC1's role in immune cell infiltration?

To investigate DSCC1's associations with tumor immune microenvironment:

• Multiplex immunophenotyping:

  • Combine DSCC1 antibodies with immune cell markers (CD8, CD4, B cell markers)

  • Implement multiplexed immunofluorescence or multiplex IHC

  • Quantify spatial relationships between DSCC1-expressing cells and immune infiltrates

• Correlation analysis with immune infiltration:

  • Use computational tools like TIMER2 database to analyze associations

  • Assess correlations between DSCC1 expression and specific immune cell populations

  • Focus particularly on CD8+ T cells, CD4+ T cells, and B cells

• Functional immune assays:

  • Compare immune cell activity in DSCC1-high vs. DSCC1-low models

  • Assess how DSCC1 manipulation affects immune response

  • Investigate potential mechanisms connecting DSCC1 to immune regulation

• Integration with immunotherapy response:

  • Correlate DSCC1 expression with response to immune checkpoint inhibitors

  • Investigate whether DSCC1 status modifies immunotherapy efficacy

  • Develop predictive models incorporating DSCC1 and immune parameters

• Mechanistic studies:

  • Investigate whether DSCC1-mediated DNA damage responses influence immune recognition

  • Assess potential connections to inflammatory signaling pathways

  • Evaluate DSCC1's impact on antigen presentation machinery

Research has revealed robust positive correlations between DSCC1 expression and the presence of immune cell types including CD8+ T cells, CD4+ T cells, and B cells in certain cancer contexts .