MACC1 Antibody, FITC conjugated

What is MACC1 and what is its significance in cancer research?

MACC1 (Metastasis Associated in Colon Cancer 1) is a protein-coding gene that functions as a transcription activator for MET and a key regulator of the HGF-MET signaling pathway. This pathway is critical for cellular growth, epithelial-mesenchymal transition, angiogenesis, cell motility, invasiveness, and metastasis . MACC1 has significant research importance because it promotes cell motility, proliferation, and hepatocyte growth factor (HGF)-dependent scattering in vitro, as well as tumor growth and metastasis in vivo . Studies have demonstrated that MACC1 overexpression accelerates proliferation and facilitates metastasis in colon cancer cell lines, making it an important biomarker for cancer progression .

What are the key characteristics of MACC1 Antibody with FITC conjugation?

MACC1 Antibody with FITC (Fluorescein Isothiocyanate) conjugation is typically a rabbit polyclonal antibody designed to target specific amino acid sequences of the MACC1 protein . These antibodies generally target regions such as AA 371-514 of the human MACC1 protein . The FITC conjugation provides green fluorescence with excitation/emission spectra of approximately 499/515 nm and is compatible with the 488 nm laser line in fluorescence microscopy and flow cytometry applications . The antibodies undergo protein G purification to achieve >95% purity and are typically stored in buffer solutions containing PBS, preservatives like Proclin-300, and glycerol to maintain stability .

How does MACC1 regulate colorectal cancer progression through the HGF/c-MET axis?

MACC1 functions as a key regulator in the MACC1/HGF/c-MET signaling axis, which plays a crucial role in colorectal cancer progression. Research has established that MACC1 expression is significantly higher in colon cancer tissues compared to surrounding normal tissues . There is a positive correlation between MACC1 expression and the proliferation and migration capabilities of colon cancer cells . Mechanistically, MACC1 acts as a transcriptional activator for c-MET, and the expression of c-MET in colon cancer cells varies with changes in MACC1 levels . When MACC1 is knocked out, it inhibits the expression of proliferation markers like Ki67 and MCM, as well as EMT-related proteins such as N-cadherin, thereby suppressing cell migration and invasion . Additionally, the pathway can be stimulated by HGF, which enhances MACC1 expression, creating a feedback loop that promotes tumor development .

What are the optimal protocols for using MACC1-FITC antibody in immunofluorescence applications?

For optimal immunofluorescence applications using MACC1-FITC antibody, follow these methodological steps:

• Sample Preparation:

  • For paraffin-embedded sections: Deparaffinize, rehydrate, and perform antigen retrieval (typically citrate buffer pH 6.0 or EDTA buffer pH 9.0)

  • For frozen sections: Fix with 4% paraformaldehyde and permeabilize with 0.1-0.5% Triton X-100

  • For cultured cells: Fix with 4% paraformaldehyde for 15 minutes and permeabilize with 0.1% Triton X-100 for 5 minutes

• Blocking and Antibody Incubation:

  • Block with 5-10% normal serum in PBS for 1 hour at room temperature

  • Dilute MACC1-FITC antibody at 1:50-1:200 in blocking buffer as recommended

  • Incubate samples with diluted antibody overnight at 4°C in a humidified chamber

  • Protect from light during and after antibody incubation due to the FITC conjugation

• Nuclear Counterstaining and Mounting:

  • Counterstain nuclei with DAPI (1:1000) for 5 minutes

  • Mount using anti-fade mounting medium

• Imaging Considerations:

  • Use appropriate filter sets for FITC (excitation 499 nm, emission 515 nm)

  • To minimize photobleaching, limit exposure time and use anti-fade reagents

How can researchers validate the specificity of MACC1-FITC antibody in their experiments?

Researchers should implement multiple validation strategies to ensure MACC1-FITC antibody specificity:

• Positive and Negative Controls:

  • Positive controls: Use cell lines with confirmed high MACC1 expression (e.g., metastatic colorectal cancer cell lines)

  • Negative controls: Include samples without primary antibody incubation

  • Competitive blocking: Pre-incubate antibody with recombinant MACC1 protein (371-514AA) before staining

• Genetic Validation:

  • Compare staining in wild-type cells versus MACC1 knockout cells

  • Use MACC1 siRNA/shRNA knockdown followed by immunofluorescence to confirm signal reduction

• Multi-method Confirmation:

  • Correlate immunofluorescence results with other detection methods such as Western blotting

  • Compare staining patterns with antibodies targeting different MACC1 epitopes

• Cross-reactivity Assessment:

  • Test antibody on samples from different species to confirm the declared reactivity

  • Examine staining in tissues known to have minimal MACC1 expression

What are the recommended procedures for MACC1 protein detection in Western blot experiments?

For effective detection of MACC1 protein using Western blotting, follow this methodological approach:

• Sample Preparation:

  • Extract proteins using RIPA buffer supplemented with protease inhibitors

  • Determine protein concentration using Bradford or BCA assay

  • Prepare 20-50 μg of protein per lane in sample buffer containing reducing agent

• Electrophoresis and Transfer:

  • Separate proteins using 8-10% SDS-PAGE (MACC1 is approximately 97 kDa)

  • Transfer proteins to PVDF membranes at 100V for 60-90 minutes or 30V overnight at 4°C

• Blocking and Antibody Incubation:

  • Block membranes with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature

  • Incubate with unconjugated MACC1 primary antibody (1:1000 dilution) overnight at 4°C

  • Wash membranes 3-5 times with TBST

  • Incubate with HRP-conjugated secondary antibody (1:1000-1:5000) for 1 hour at room temperature

• Detection and Analysis:

  • Develop using enhanced chemiluminescence (ECL) substrate

  • Use GAPDH (1:2000) as loading control

  • Quantify band intensity using image analysis software

  • Normalize MACC1 expression to loading control

What are common issues when using FITC-conjugated antibodies and how can they be addressed?

When working with FITC-conjugated MACC1 antibodies, researchers may encounter several technical challenges:

How should MACC1-FITC antibody be stored and handled to maintain optimal performance?

Proper storage and handling of MACC1-FITC antibody is crucial for maintaining its performance:

• Storage Recommendations:

  • Store at -20°C in the dark to prevent photobleaching of the FITC fluorophore

  • Upon receipt, aliquot the antibody in volumes suitable for single experiments to avoid repeated freeze-thaw cycles

  • Ensure storage solutions contain 50% glycerol as a cryoprotectant

• Handling Guidelines:

  • Thaw aliquots completely before use and mix gently by pipetting or flicking (avoid vortexing)

  • Keep on ice and protect from light during experimental procedures

  • Return unused portions to -20°C immediately after use

  • Limit exposure to room temperature to less than 4 hours per experiment

• Stability Considerations:

  • Check for visible precipitation before use; clear by centrifugation if present

  • Avoid more than 5 freeze-thaw cycles, which can cause antibody degradation

  • Monitor performance over time using consistent positive controls

  • Record lot numbers and dates to track potential performance changes

How can MACC1-FITC antibody be utilized in investigating epithelial-mesenchymal transition in cancer cells?

MACC1-FITC antibody serves as a powerful tool for investigating epithelial-mesenchymal transition (EMT) in cancer research through these advanced approaches:

• Co-localization Studies:

  • Perform dual immunofluorescence with MACC1-FITC and EMT markers such as E-cadherin, N-cadherin, and vimentin

  • Analyze spatial relationships between MACC1 and these markers using confocal microscopy

  • Quantify co-localization using Pearson's or Mander's coefficients

• Live-Cell Imaging:

  • Engineer cells with inducible MACC1 expression systems

  • Monitor real-time changes in MACC1 localization during EMT induction using FITC-labeled antibodies in live-cell compatible formats

  • Correlate MACC1 dynamics with morphological changes characteristic of EMT

• Functional Assays:

  • Track changes in MACC1 expression and localization during wound healing assays

  • Combine with migration/invasion assays to correlate MACC1 patterns with metastatic potential

  • Assess the impact of EMT-inducing factors (TGF-β, HGF) on MACC1 expression and localization

• Three-dimensional Models:

  • Employ MACC1-FITC antibody in 3D organoid or spheroid models

  • Map MACC1 distribution in relation to invasion fronts and EMT markers

  • Compare expression patterns between primary tumor models and metastatic sites

What insights can flow cytometry with MACC1-FITC antibody provide about tumor heterogeneity?

Flow cytometry using MACC1-FITC antibody offers valuable insights into tumor heterogeneity through these methodological approaches:

• Subpopulation Identification:

  • Identify distinct MACC1-expressing subpopulations within heterogeneous tumor samples

  • Establish gating strategies based on MACC1-FITC signal intensity to distinguish high, intermediate, and low expression populations

  • Correlate MACC1 expression levels with stemness markers (CD44, CD133) to identify potential cancer stem cell populations

• Multi-parameter Analysis:

  • Combine MACC1-FITC with antibodies against c-MET and cell cycle markers

  • Assess correlation between MACC1 expression and proliferation markers (Ki-67, PCNA)

  • Analyze co-expression of MACC1 with drug resistance markers to identify therapy-resistant subpopulations

• Cell Sorting Applications:

  • Sort cells based on MACC1-FITC signal intensity for subsequent functional assays

  • Perform transcriptomic or proteomic analysis on sorted populations to identify molecular signatures associated with different MACC1 expression levels

  • Assess tumorigenic potential of sorted populations through in vitro and in vivo assays

• Longitudinal Studies:

  • Monitor changes in MACC1 expression patterns following treatment

  • Track clonal evolution by analyzing MACC1 expression in patient-derived xenografts across passages

  • Correlate shifts in MACC1-expressing subpopulations with disease progression or treatment resistance

How can MACC1-FITC antibody contribute to the development of immunotherapy response biomarkers?

MACC1-FITC antibody can significantly contribute to immunotherapy biomarker development through these advanced research approaches:

• Immune Infiltrate Characterization:

  • Analyze the spatial relationship between MACC1-expressing tumor cells and tumor-infiltrating lymphocytes

  • Correlate MACC1 expression patterns with infiltration levels of various immune cell populations

  • Research indicates MACC1 expression negatively correlates with immune infiltration levels, suggesting potential immune evasion mechanisms

• Checkpoint Expression Analysis:

  • Perform multiplex immunofluorescence combining MACC1-FITC with antibodies against immune checkpoint molecules (PD-1, PD-L1, CTLA-4)

  • Investigate correlations between MACC1 expression and checkpoint molecule levels

  • Evidence suggests MACC1 expression correlates with several immune checkpoint biomarkers, potentially influencing immunotherapy response

• Predictive Biomarker Development:

  • Stratify patient samples based on MACC1 expression levels and correlate with immunotherapy response data

  • Calculate immunophenoscores (IPS) in relation to MACC1 expression

  • Research indicates high MACC1 expression correlates with lower response rates to immune checkpoint inhibitors (ICIs) in colorectal adenocarcinoma

• Therapeutic Target Identification:

  • Use MACC1-FITC to monitor changes in MACC1 expression following immunotherapy

  • Investigate combination approaches targeting both MACC1 and immune checkpoints

  • Explore the mechanistic basis of MACC1's influence on the tumor immune microenvironment using TISIDB and similar analytical tools

What emerging technologies could enhance the utility of MACC1-FITC antibody in cancer research?

Several emerging technologies show promise for expanding MACC1-FITC antibody applications:

• Spatial Transcriptomics Integration:

  • Combine MACC1-FITC immunofluorescence with spatial transcriptomics to correlate protein expression with transcriptional profiles in preserved tissue architecture

  • Map MACC1 protein distribution alongside related gene expression patterns

  • Identify spatial relationships between MACC1-expressing cells and cells with specific transcriptional signatures

• Advanced Microscopy Techniques:

  • Apply super-resolution microscopy (STED, STORM, PALM) to visualize MACC1 subcellular localization beyond diffraction limits

  • Utilize light-sheet microscopy for rapid 3D imaging of MACC1 distribution in large tissue volumes

  • Implement FRET techniques with MACC1-FITC and acceptor-labeled interaction partners to study molecular proximities

• Microfluidics and Single-Cell Analysis:

  • Integrate MACC1-FITC antibody into microfluidic platforms for high-throughput single-cell analysis

  • Develop CyTOF/mass cytometry compatible MACC1 antibodies for highly multiplexed analysis

  • Combine with single-cell RNA sequencing to correlate protein and transcript levels at individual cell resolution

• In Vivo Imaging Applications:

  • Develop MACC1 antibody derivatives suitable for intravital microscopy

  • Create near-infrared fluorescent versions for deeper tissue penetration in animal models

  • Explore antibody fragments (Fab, scFv) with improved tissue penetration for in vivo applications

How might systems biology approaches incorporate MACC1-FITC antibody data to model cancer progression?

Systems biology approaches can leverage MACC1-FITC antibody data through these methodological frameworks:

• Network Analysis Integration:

  • Incorporate MACC1 protein expression data from FITC antibody studies into protein-protein interaction networks

  • Integrate with phosphoproteomic data to map MACC1's role in signaling cascades

  • Identify network perturbations associated with MACC1 dysregulation

• Multi-omics Data Fusion:

  • Correlate MACC1 protein levels from immunofluorescence with genomic, transcriptomic, and metabolomic data

  • Build predictive models of tumor progression incorporating MACC1 as a key node

  • Identify potential synthetic lethal interactions with MACC1 overexpression

• Dynamic Modeling:

  • Utilize time-series data of MACC1 expression during disease progression

  • Develop ordinary differential equation models of the MACC1/HGF/c-MET axis

  • Simulate perturbations to predict therapeutic vulnerabilities

• Patient Stratification Models:

  • Develop multivariate models incorporating MACC1 expression data to predict patient outcomes

  • Identify patient subgroups based on MACC1 and related biomarker patterns

  • Create decision trees for potential therapeutic strategies based on MACC1 status