CISD1 Antibody

What is CISD1 and why is it significant in research?

CISD1 is a mitochondrial outer membrane protein containing a CDGSH iron-sulfur domain that functions as a regulator of mitochondrial metabolism. It negatively regulates ferroptosis and plays important roles in electron transport and oxidative phosphorylation . CISD1 has emerged as a significant research target due to its implications in various diseases, including breast cancer, lung adenocarcinoma, and Parkinson's disease . Recent studies have shown it's overexpressed in multiple cancer types, with high expression correlating with poor prognosis in breast cancer patients .

What should researchers consider when selecting a CISD1 antibody?

When selecting a CISD1 antibody, researchers should consider:

• Species reactivity: Ensure the antibody reacts with your target species. Many CISD1 antibodies show reactivity with human, mouse, and rat samples .

• Applications required: Verify the antibody is validated for your specific application (WB, IHC, IF/ICC, IP) .

• Clonality: Both monoclonal (e.g., [EPR29116-27], D5M4C) and polyclonal antibodies are available; monoclonals offer higher specificity while polyclonals may provide stronger signals .

• Validation methods: Look for knockout-tested antibodies for highest specificity confirmation .

• Recognition of different forms: CISD1 forms homodimers with high stringency even on reducing gels, so choose antibodies that can detect both monomeric (~12-17 kDa) and dimeric forms when needed .

What is the expected molecular weight for CISD1 in Western blots?

While the calculated molecular weight of CISD1 is approximately 12 kDa, it typically appears as a 14-17 kDa band on Western blots . This size discrepancy is attributed to posttranslational modifications and/or metal binding activity . Additionally, CISD1 can form homodimers that may be detected even under reducing conditions, appearing at approximately twice the monomer size . When performing Western blots, using CISD1 knockout cells as negative controls is highly recommended to confirm antibody specificity .

What are the optimal conditions for CISD1 immunohistochemistry staining?

For optimal CISD1 immunohistochemistry:

• Antigen retrieval: Use TE buffer pH 9.0 as the preferred method, though citrate buffer pH 6.0 can be used as an alternative .

• Section preparation: Use 3 μm paraffin-embedded sections .

• Blocking: Block with 3% hydrogen peroxide for 20 minutes at 24°C .

• Primary antibody incubation: Dilute antibody appropriately (typically 1:50-1:500 for polyclonal, 1:1000-1:4000 for certain monoclonal) and incubate at 4°C overnight (12 hours) .

• Secondary antibody: Use appropriate HRP-conjugated secondary antibody (e.g., MaxVision™ HRP-Polymer anti-Mouse/Rabbit) and incubate at 4°C for 30 minutes .

• Detection: Perform DAB staining for 1 minute followed by hematoxylin counterstaining for 1 minute .

• Interpretation: Cytoplasmic brown staining indicates CISD1 positivity .

How can researchers accurately quantify CISD1 expression in IHC samples?

To accurately quantify CISD1 expression in IHC samples:

• Scoring system: Implement a dual scoring system that evaluates both staining intensity and percentage of positive cells:

  • Intensity score: 0 (negative), 1 (weakly positive/light yellow), 2 (moderately positive/yellowish-brown), 3 (strongly positive/tan) .

  • Percentage score: 0-100 based on the percentage of positive tumor cells relative to total tumor cells .

• Total score calculation: Multiply the intensity score by the percentage score. Total scores ≥100 indicate high CISD1 expression, while scores <100 indicate low expression .

• Software analysis: Use image analysis software such as Image-Pro Plus 6.0 to objectively measure protein expression intensity .

• Validation by pathologists: Have interpretation performed by two senior pathologists to ensure consistency and accuracy .

• Controls: Include both positive controls (known CISD1-expressing tissues) and negative controls (adjacent non-cancerous tissues) .

How does CISD1 dimerization status correlate with disease pathology?

CISD1 dimerization status has emerged as a critical indicator in disease pathology:

• Parkinson's disease connection: Studies in iPSC-derived dopaminergic neurons from Parkinson's disease patients with PINK1 mutations showed significantly increased CISD1 dimer/monomer ratios compared to gene-corrected controls, suggesting that heightened CISD1 dimerization is relevant to human Parkinson's disease pathophysiology .

• Structural basis: Molecular modeling studies revealed that mutations affecting the iron-sulfur cluster binding, particularly the C83S mutation, result in larger surface-surface contact between monomers, facilitating tighter binding and increased dimerization .

• Functional implications: Iron depletion (apo form) of CISD1 promotes dimerization, while holo-CISD1 (with intact iron-sulfur clusters) tends to exist in a more balanced monomer-dimer equilibrium .

• Detection methodology: When analyzing CISD1 dimerization, use non-reducing or mildly reducing conditions in SDS-PAGE to preserve dimeric forms. Quantification of the dimer/monomer ratio provides valuable information about the functional state of CISD1 in pathological conditions .

• Therapeutic targeting: The differential dimerization properties of CISD1 in disease states present opportunities for targeted therapeutic interventions that could stabilize the protein in its functional conformation .

What is the relationship between CISD1 expression and immune cell infiltration in cancers?

CISD1 expression shows significant correlations with immune cell infiltration in cancers:

• Negative correlation with lymphocytes: In lung adenocarcinoma (LUAD), high CISD1 expression negatively correlates with CD4+ T cell and B cell infiltration, as demonstrated by multiple immune scoring methods (TIMER, EPIC, CIBERSORT, and TIP scores) .

• Quantitative assessment: Immunohistochemical analysis of 60 LUAD cases confirmed that high CISD1 expression was associated with significantly lower numbers of CD4+ cells (p<0.001; Z=-6.575) and CD20+ B cells (p<0.001; Z=-5.970) .

• Breast cancer implications: Single-sample gene set enrichment analysis (ssGSEA) has been used to assess the relationship between CISD1 expression and immune cell infiltration in breast cancer, revealing potential mechanisms by which CISD1 influences the tumor immune microenvironment .

• Macrophage polarization: Inhibiting CISD1 expression in macrophages promotes polarization toward the M1 phenotype while inhibiting M2 phenotype polarization, with corresponding changes in cytokine production (TNF-α and IL-6) .

• Methodological approach: To investigate these relationships, researchers should combine transcriptomic analyses, immunohistochemistry with specific immune cell markers, and functional assays to comprehensively evaluate the impact of CISD1 on tumor immunity .

How can researchers effectively study CISD1 iron-sulfur cluster transfer in experimental settings?

To effectively study CISD1 iron-sulfur cluster transfer:

• Split nanoluciferase complementation assay: This technique allows for real-time monitoring of CISD1 dimerization, which is closely related to iron-sulfur cluster status. Use LgBit and SmBit fragments cloned N-terminally to human CISD1, with various point mutations introduced by site-directed mutagenesis .

• Iron chelation experiments: Use iron chelators like deferiprone (1 mM) to induce iron depletion from CISD1. This approach helps distinguish between holo-CISD1 (iron-sulfur cluster intact) and apo-CISD1 (iron-sulfur cluster depleted) .

• Spectroscopic methods: Monitor the characteristic absorbance of [2Fe-2S] clusters (~400-500 nm range) to track cluster transfer or loss under different experimental conditions .

• Coupled protein systems: Design experiments with potential [2Fe-2S] acceptor proteins to study the transfer kinetics and specificity of cluster donation from CISD1 .

• Oxidation state manipulation: Since CISD1 can transfer its [2Fe-2S] cluster only when in the oxidized state, researchers should control redox conditions to study how oxidative stress regulates cluster transfer capabilities .

How can researchers differentiate between CISD1 and CISD2 when using antibodies that recognize both proteins?

Differentiating between CISD1 and CISD2 requires careful experimental design:

• Subcellular fractionation: CISD1 is localized to the mitochondrial outer membrane, while CISD2 is found in the endoplasmic reticulum. Proper subcellular fractionation can help separate these proteins based on their distinct localizations .

• Specific knockdown controls: Perform siRNA-mediated knockdown of either CISD1 or CISD2 separately to identify which bands correspond to which protein on Western blots .

• Knockout cell lines: Use CISD1 knockout cell lines as definitive negative controls to identify CISD1-specific bands. The search results mention CISD1 KO MEFs that can serve this purpose .

• Molecular weight discrimination: Although both proteins have similar calculated molecular weights, they may show slight differences in apparent molecular weight on SDS-PAGE. CISD1 typically appears at 14-17 kDa, while CISD2 may show a slightly different migration pattern .

• Double immunolabeling: For microscopy applications, combine the antibody recognizing both CISD1 and CISD2 with organelle-specific markers (mitochondrial markers for CISD1; ER markers for CISD2) to distinguish their localization patterns .

What approaches can resolve conflicting CISD1 expression data between different experimental methods?

When facing conflicting CISD1 expression data:

• Multi-method validation: Combine multiple detection methods (e.g., qPCR, Western blot, immunohistochemistry) to cross-validate expression levels, as demonstrated in studies of CISD1 in breast cancer .

• Post-translational modifications: Consider that discrepancies might arise from post-translational modifications or different CISD1 conformational states. The calculated molecular weight (12 kDa) differs from the observed weight (14-17 kDa) due to modifications and metal binding .

• Dimerization status: CISD1 forms homodimers that persist even under reducing conditions, which can complicate quantification. Ensure your analysis accounts for both monomeric and dimeric forms .

• Epitope accessibility: Different antibodies target different epitopes that may be variably accessible depending on CISD1's conformation, particularly regarding its iron-sulfur cluster status (holo vs. apo form) .

• Technical standardization: Standardize sample preparation methods across experiments, particularly regarding redox conditions which can affect CISD1's iron-sulfur cluster status and consequently antibody binding .

• Statistical approaches: When analyzing high-throughput data such as TCGA datasets, use appropriate statistical methods like Wilcoxon rank-sum tests and chi-square tests to evaluate CISD1 expression differences between groups .

How can CISD1 antibodies be used to investigate the protein's role in ferroptosis regulation?

To investigate CISD1's role in ferroptosis:

• Knockout/knockdown validation: Use CISD1 antibodies to confirm successful CISD1 depletion in knockdown/knockout models before assessing ferroptosis sensitivity. For example, studies using si-CISD1 transfection verified knockdown efficiency via Western blotting before analyzing downstream effects .

• Subcellular localization during ferroptosis: Track CISD1's subcellular redistribution during ferroptosis induction using immunofluorescence with co-localization markers for mitochondria and other organelles .

• Dimerization status: Monitor changes in CISD1 dimerization (dimer/monomer ratio) during ferroptotic conditions, as CISD1's conformational state correlates with its function in redox regulation .

• Oxidation state assessment: Combine CISD1 immunoprecipitation with mass spectrometry to identify post-translational modifications associated with oxidative stress during ferroptosis .

• Iron-sulfur cluster integrity: Correlate CISD1 antibody detection patterns with the protein's iron-sulfur cluster status, which changes during ferroptosis. Iron chelators like deferiprone can be used to manipulate cluster integrity experimentally .

• Partner protein interactions: Use co-immunoprecipitation with CISD1 antibodies to identify interaction partners that change during ferroptotic conditions, providing insights into the mechanistic pathways involved .

What methodologies combine CISD1 antibodies with other techniques to study its role in mitochondrial dynamics?

Advanced methodologies combining CISD1 antibodies with other techniques include:

• Proximity labeling: Combine CISD1 antibodies with proximity labeling techniques (BioID or APEX) to identify proteins in close proximity to CISD1 under different physiological conditions, revealing dynamic interaction networks .

• Live-cell imaging: Use fluorescently labeled CISD1 antibody fragments (Fabs) for live-cell imaging in combination with mitochondrial dynamics markers to track CISD1's role in real-time .

• Electron transport chain assays: Pair CISD1 immunodetection with spectrophotometric assays of electron transport chain complexes I, III, IV, and V activities to correlate CISD1 levels with mitochondrial function .

  For example, complex I activity can be measured by NADH oxidation at 340 nm, complex III by cytochrome c reduction at 550 nm, complex IV by cytochrome c oxidation at 550 nm, and ATP synthase activity using a coupled assay with pyruvate kinase .

• Super-resolution microscopy: Combine immunofluorescence using CISD1 antibodies with super-resolution microscopy techniques to precisely locate CISD1 within the mitochondrial membrane architecture .

• Iron sensing assays: Integrate CISD1 antibody detection with iron-specific probes or sensors to correlate CISD1's conformational state with mitochondrial iron levels and transport activities .

• Mitochondrial isolation quality control: Use CISD1 antibodies as markers of mitochondrial outer membrane to verify the quality of mitochondrial isolations, ensuring intact outer membranes before functional studies .

How can researchers integrate CISD1 antibody-based findings with clinical data for translational research?

For effective integration of CISD1 antibody-based findings with clinical data:

• Standardized scoring systems: Implement consistent scoring systems for CISD1 immunohistochemistry across patient cohorts. For example, use combined intensity and area scoring:

  • Intensity: 0 (negative), 1 (weak), 2 (moderate), 3 (strong)

  • Percentage area: 0-100%

  • Total score: Product of intensity and percentage

• Correlation with clinical parameters: Systematically correlate CISD1 expression patterns with clinical parameters using appropriate statistical methods:

  As demonstrated in breast cancer research, CISD1 expression levels correlate with:

  • N stage (p=0.012)

  • M stage (p=0.047)

  • Age (p=0.022)

  • Race (p<0.001)

  • PR status (p<0.001)

  • ER status (p<0.001)

• Survival analysis stratification: Perform Kaplan-Meier survival and Cox regression analyses stratified by CISD1 expression levels, with additional subgroup analyses based on clinical parameters:

  In lung cancer, patients with high CISD1 expression show significantly worse survival rates across different stages:

  • T1 stage: 67.6% (high) vs. 79.6% (low)

  • T4 stage: 16.7% (high) vs. 71.4% (low)

  • N0 stage: 69.3% (high) vs. 76.4% (low)

  • M0 stage: 56.6% (high) vs. 67.3% (low)

  • M1 stage: 31.3% (high) vs. 55.6% (low)

• Multi-omics integration: Combine antibody-based protein detection with methylation analysis and transcriptomics. For example, studies have shown that CISD1 methylation status correlates with prognosis, with patients having low CISD1 methylation levels showing shorter survival times .

• Immune correlation analyses: Integrate CISD1 expression data with immune cell infiltration analyses using multiple scoring systems (e.g., TIMER, EPIC, CIBERSORT, TIP scores) to understand the immune microenvironment implications .

• Drug sensitivity correlations: Use connectivity mapping (CMap) to identify potential therapeutic compounds that might reverse the CISD1-associated gene expression signature in cancer patients .