TJP1 Antibody, FITC conjugated

What is TJP1 and why is it a significant research target?

TJP1 (Tight Junction Protein 1) is a membrane-expressed protein that plays a critical role in maintaining cell integrity. It serves as a key component of tight junctions, preventing epithelial cell separation through adhesion, and functions as the first barrier that cancer cells must overcome for metastasis . Research has identified TJP1 as a potential therapeutic target for lung cancer, with studies demonstrating its role in invasion, migration, and proliferation of cancer cells . TJP1's expression is also associated with cell motility in various cancers including breast, colorectal, and human gastrointestinal cancers .

What are the primary applications for FITC-conjugated TJP1 antibodies in cancer research?

FITC-conjugated TJP1 antibodies are valuable tools for multiple fluorescence-based applications in cancer research. They can be utilized for immunofluorescence to visualize TJP1 localization at the membrane, as demonstrated in previous studies where TJP1 was confirmed as primarily a membrane-expressed protein . Additionally, these antibodies are suitable for flow cytometry applications to quantify TJP1 expression across different cell lines, as previously done with lung cancer cell lines where varying expression levels were observed (with relative mean fluorescent intensities ranging from 2.0 to 24.9) . The direct FITC conjugation eliminates the need for secondary antibody steps, providing more efficient workflows for high-throughput screening applications.

What positive controls should be included when using FITC-conjugated TJP1 antibodies?

Based on expression profiling data, optimal positive controls for FITC-conjugated TJP1 antibody validation include the NCI-H2170 (lung squamous cell carcinoma) and NCI-H69 cell lines, which demonstrated high TJP1 expression with relative mean fluorescent intensities of 23.8 and 24.9, respectively . For moderate expression controls, SK-LU-1, NCI-H526, and NCI-H520 cell lines can be used (with relative MFIs of 14.6, 18.0, and 13.6) . Low-expressing cell lines such as PC-9 and 1G2 (with relative MFIs of 2.0 and 2.5) serve as appropriate negative or low-expression controls . Including isotype control antibodies with FITC conjugation is essential to establish background fluorescence levels.

What is the optimal antibody concentration for TJP1 detection by flow cytometry, and how should titration be performed?

While specific titration data for FITC-conjugated TJP1 antibodies isn't provided in the search results, a methodological approach would involve:

• Begin with a concentration range of 1-5 μg/mL based on the successful detection of TJP1 in previous flow cytometry studies that established relative MFI values across lung cancer cell lines .

• Perform a titration experiment using a high-expressing cell line (e.g., NCI-H2170) and a low-expressing cell line (e.g., PC-9) to determine the optimal signal-to-noise ratio.

• Analyze the separation index (SI) or stain index (SI = [MFI positive - MFI negative]/2 × standard deviation of negative) at each concentration.

• Select the concentration that provides maximum separation between positive and negative populations while minimizing non-specific background.

• Validate the chosen concentration across additional cell lines with varying TJP1 expression levels as documented in the expression profiling table below:

How can researchers optimize immunofluorescence protocols for TJP1 membrane localization studies?

To optimize immunofluorescence protocols for TJP1 membrane localization:

• Cell Preparation: Fix cells with 4% paraformaldehyde but limit permeabilization time to preserve membrane structures. For membrane proteins like TJP1, overly harsh permeabilization can disrupt the membrane localization pattern observed in previous studies .

• Blocking Optimization: Use 5% BSA in PBS for 1 hour at room temperature to reduce non-specific binding, particularly important for membrane proteins where background can obscure distinct membrane staining.

• Antibody Concentration: Start with 1:100-1:200 dilution of FITC-conjugated TJP1 antibody, as successful membrane localization was previously demonstrated with TJP1 antibodies .

• Counterstaining: Include membrane markers (e.g., WGA-Texas Red) and nuclear stains (DAPI) to provide cellular context for the membrane localization of TJP1.

• Z-stack Imaging: Collect optical sections through the entire cell to fully capture membrane distribution, as TJP1 has been confirmed to be "mainly a membrane-expressed protein" .

• Signal Verification: Compare staining patterns with previous research showing co-localization of TJP1 and specific antibodies like CL007473 which bind to TJP1 with "no background binding" .

How can FITC-conjugated TJP1 antibodies be used to investigate TJP1's role in cancer cell motility?

To investigate TJP1's role in cancer cell motility using FITC-conjugated TJP1 antibodies:

• Expression Baseline Establishment: Use flow cytometry with the FITC-conjugated TJP1 antibody to quantify baseline expression across candidate cell lines before motility studies, following methods previously used to profile TJP1 expression in 16 lung cancer cell lines .

• Live Cell Imaging: Implement time-lapse fluorescence microscopy using sub-lethal concentrations of the FITC-conjugated antibody to track TJP1 redistribution during cell migration.

• siRNA Knockdown Correlation: Perform TJP1 knockdown experiments using siRNA (similar to the siRNA-5274 which achieved 25-36% knockdown efficiency in NCI-H2170 cells and 16-44% in SK-LU-1 cells) , then use the FITC-conjugated antibody to confirm reduced TJP1 expression via flow cytometry.

• Motility Assays: Correlate FITC signal intensity with functional wound healing or transwell migration assays, building upon previous findings that "after knockdown of TJP1 in lung cancer cell lines... cell migration ability, cell invasion ability, and cell proliferation ability were significantly reduced" .

• Data Analysis: Generate quantitative correlations between TJP1 expression levels (as measured by FITC signal intensity) and migration metrics, establishing whether higher TJP1 expression correlates with enhanced motility across different cancer types.

What are the advantages and limitations of using FITC-conjugated antibodies for TJP1 detection compared to unconjugated primary antibodies?

Advantages:

• Simplified Workflow: Direct conjugation eliminates the secondary antibody incubation step, reducing experiment time by approximately 1-2 hours compared to protocols using unconjugated antibodies like those described for TJP1 detection .

• Multiplexing Capability: FITC-conjugated TJP1 antibodies can be combined with other directly conjugated antibodies with different fluorophores for simultaneous detection of multiple targets, particularly valuable for co-localization studies of TJP1 with other tight junction proteins.

• Reduced Cross-Reactivity: Eliminates potential cross-reactivity issues from secondary antibodies, particularly important when studying TJP1 in complex tissue samples as was done in the tissue microarray analysis .

Limitations:

• Signal Amplification: Unlike unconjugated antibodies where multiple secondary antibodies can bind to each primary antibody, FITC-conjugated antibodies provide no signal amplification, potentially reducing sensitivity for detecting low TJP1 expression as observed in cell lines like PC-9 (relative MFI of 2.0) .

• Photobleaching: FITC is more susceptible to photobleaching compared to other fluorophores, potentially limiting extended imaging sessions for TJP1 localization studies.

• Limited Flexibility: Conjugated antibodies cannot be repurposed for non-fluorescent applications such as chromogenic IHC or Western blotting, which were successfully used for TJP1 detection in previous research .

How can researchers address potential cross-reactivity when using TJP1 antibodies in multi-tissue studies?

To address cross-reactivity concerns in multi-tissue TJP1 studies:

• Validation Across Tissues: Verify antibody specificity across different tissue types using Western blotting, similar to how TJP1 expression was characterized across multiple cancer types including "colon cancer, pancreatic cancer, liver cancer, brain cancer, prostate cancer, and ovarian cancer" .

• Absorption Controls: Perform pre-absorption controls using the specific TJP1 peptide sequence used for immunization. According to available information, certain TJP1 antibodies were "prepared from whole rabbit serum produced by repeated immunizations with a synthetic peptide corresponding to an internal portion of human ZO-1 (TJP1) conjugated to Keyhole Limpet Hemocyanin (KLH)" .

• Knockout/Knockdown Validation: Include TJP1 knockdown samples as negative controls, building on previous research where siRNA-5274 successfully reduced TJP1 expression .

• Tissue-Specific Controls: Include appropriate tissue-specific controls based on the TJP1 expression profile established through tissue microarray analysis, which revealed varied expression patterns across cancer types as shown in this partial table:

• Orthogonal Validation: Confirm fluorescence microscopy findings with complementary techniques like qRT-PCR to correlate protein detection with mRNA expression, similar to the multi-method approach used in previous TJP1 studies .

What are the most common artifacts when using FITC-conjugated antibodies for TJP1 detection, and how can they be mitigated?

Common artifacts and mitigation strategies for FITC-conjugated TJP1 antibody use:

• Autofluorescence Interference:

  • Artifact: Tissue autofluorescence in the FITC channel can mask true TJP1 signal, particularly in lung tissues.

  • Mitigation: Incorporate an autofluorescence quenching step using Sudan Black B (0.1-0.3%) or implement spectral unmixing during image acquisition to distinguish FITC signal from autofluorescence.

• Fixation-Induced Artifacts:

  • Artifact: Overfixation can mask TJP1 membrane epitopes, reducing detection efficiency.

  • Mitigation: Optimize fixation protocols (4% paraformaldehyde for 10-15 minutes) and perform antigen retrieval if necessary, guided by methods that successfully visualized TJP1 as "mainly a membrane-expressed protein" .

• Non-Specific Binding:

  • Artifact: FITC-conjugated antibodies can bind non-specifically to highly charged cellular components.

  • Mitigation: Include additional blocking steps with normal serum matching the host species of the cells being examined and ensure thorough washing steps with PBST (PBS containing Tween-20) as mentioned in previous protocols .

• Photobleaching:

  • Artifact: FITC signal fading during extended imaging sessions.

  • Mitigation: Use anti-fade mounting media, minimize exposure times, and consider acquiring the FITC channel first in multi-channel imaging experiments.

• Concentration-Dependent Aggregation:

  • Artifact: High concentrations of FITC-conjugated antibodies can form aggregates appearing as punctate artifacts.

  • Mitigation: Centrifuge antibody solution before use (10,000g, 5 minutes) and maintain optimal antibody dilutions based on titration experiments.

How can researchers design multiplex assays incorporating FITC-conjugated TJP1 antibodies to study tight junction dynamics?

To design effective multiplex assays for tight junction dynamics:

• Compatible Fluorophore Selection: Pair FITC-conjugated TJP1 antibodies with fluorophores having minimal spectral overlap, such as:

  • Claudin antibodies conjugated to Texas Red (emission peak ~615nm)

  • Occludin antibodies conjugated to APC (emission peak ~660nm)

  • F-actin staining with far-red dyes (emission >650nm)

• Sequential Antibody Application: Apply antibodies sequentially when using multiple primary antibodies from the same host species to prevent cross-reactivity, a potential concern when studying multiple tight junction components.

• Live Cell Imaging Protocol:

  • Use sub-lethal concentrations of FITC-conjugated TJP1 antibody (determined through viability assays)

  • Incorporate membrane-permeable DNA dyes for nuclear reference

  • Implement time-lapse confocal microscopy with environmental control to monitor tight junction dynamics during cell division or migration

• Quantification Framework:

  • Develop co-localization analyses for TJP1 with other tight junction proteins

  • Measure fluorescence intensity at cell-cell interfaces

  • Calculate Pearson's or Mander's correlation coefficients to quantify spatial relationships between TJP1 and other targets

• Functional Correlation:

  • Correlate fluorescence patterns with barrier function measurements (e.g., transepithelial electrical resistance)

  • Apply mechanical or chemical stress to investigate tight junction remodeling

  • Incorporate findings from previous research showing that "TJP1 is responsible for the protein network between actin and global tight junction proteins, such as Occludin and Claudin, which maintain cell integrity"

How can researchers interpret TJP1 expression patterns in the context of cancer progression and prognosis?

To interpret TJP1 expression patterns in cancer progression and prognosis:

What are the key considerations for using FITC-conjugated TJP1 antibodies in patient-derived xenograft (PDX) models?

Key considerations for TJP1 analysis in PDX models:

• Species Cross-Reactivity Analysis: Verify that the FITC-conjugated TJP1 antibody specifically recognizes human TJP1 but not murine TJP1 to ensure selective detection of tumor-derived TJP1. This is particularly important as the available information indicates human-specific reactivity for some TJP1 antibodies .

• Background Mitigation: Implement additional blocking steps to reduce mouse tissue background:

  • Block with mouse serum (5-10%) prior to antibody application

  • Include mouse IgG blocking reagents if the primary antibody was raised in mouse

  • Use specific blocking peptides corresponding to the immunization antigen

• Validation Controls:

  • Include human tumor tissue positive controls with known TJP1 expression patterns based on the tissue microarray data

  • Include mouse tissue negative controls

  • Use dual-staining with human-specific markers to confirm human origin of TJP1-expressing cells

• Sampling Strategy:

  • Analyze multiple regions within PDX tumors to account for heterogeneity

  • Compare TJP1 expression patterns between the original patient tumor and derived xenografts across multiple passages

  • Sample both tumor core and invasive front to assess TJP1 expression in different microenvironments

• Quantification Approach:

  • Develop multichannel analysis algorithms to distinguish human tumor cells from mouse stromal components

  • Implement digital pathology approaches for whole-slide quantification

  • Correlate FITC signal intensity with tumor growth characteristics and response to therapies

How can FITC-conjugated TJP1 antibodies be incorporated into high-content screening assays to identify modulators of tight junction function?

For high-content screening with FITC-conjugated TJP1 antibodies:

• Assay Development:

  • Establish epithelial cell monolayers in 96- or 384-well imaging plates

  • Optimize cell density to achieve consistent tight junction formation (typically 1-2×10^5 cells/cm²)

  • Standardize fixation and staining protocols for automated liquid handling systems

• Primary Readouts:

  • TJP1 localization (membrane vs. cytoplasmic distribution)

  • Quantitative FITC intensity at cell-cell junctions

  • Junction continuity metrics (percent of cell perimeter with continuous TJP1 staining)

• Secondary Markers:

  • Include additional membrane markers for cell boundary definition

  • Add nuclear stains for cell counting and normalization

  • Consider dual staining with other tight junction proteins to assess co-regulation

• Positive Controls:

  • EGTA (calcium chelator) to disrupt tight junctions

  • Cytochalasin D to disrupt actin cytoskeleton

  • TJP1 siRNA (e.g., siRNA-5274 which showed effective knockdown)

• Image Analysis Parameters:

  • Develop algorithms to quantify TJP1 junctional continuity

  • Measure intensity ratios between membrane and cytoplasmic compartments

  • Implement machine learning approaches to classify junction morphology patterns

• Biological Validation:

  • Confirm hits with functional assays (permeability, TEER measurements)

  • Validate effects on different cell lines with varying TJP1 expression levels as established in the expression profiling table

  • Correlate TJP1 modulation with changes in cancer cell phenotypes, building on findings that TJP1 knockdown "inhibit[s] the invasion and migration of lung cancer cells and inhibit[s] the proliferation of cancer cells"

How should researchers normalize and compare TJP1 expression data across different experimental platforms and cancer types?

For normalizing and comparing TJP1 expression data:

• Platform-Specific Normalization:

  • Flow Cytometry: Report TJP1 expression as relative MFI (ratio to isotype control) as done in previous profiling studies , or as molecules of equivalent soluble fluorochrome (MESF) using calibration beads

  • Western Blotting: Normalize to housekeeping proteins and include common reference cell lines across blots

  • qRT-PCR: Use validated reference genes and implement ΔΔCt method for relative quantification

  • Immunofluorescence: Include fluorescence calibration standards in each imaging session

• Cross-Platform Data Integration:

  • Establish conversion factors between platforms using a panel of reference cell lines with known TJP1 expression levels

  • Generate correlation plots between protein detection methods (flow cytometry, Western blot) and mRNA measurements

  • Develop rank-based metrics that preserve relative expression ordering across platforms

• Cancer Type Considerations:

  • Account for tissue-specific background levels as observed in the tissue microarray data

  • Create tissue-specific reference ranges based on normal adjacent tissues

  • Consider subcellular localization differences between cancer types

• Statistical Approaches:

  • Apply z-score normalization within each cancer type before cross-cancer comparisons

  • Implement quartile normalization for robust non-parametric comparisons

  • Use mixed-effect models to account for batch effects and technical variations

• Visualization Strategies:

  • Generate heat maps grouped by cancer type with hierarchical clustering

  • Create waterfall plots showing expression distribution within and across cancer types

  • Develop radar charts to compare expression patterns across multiple parameters

What statistical methods are most appropriate for analyzing correlations between TJP1 expression and cancer cell phenotypes?

Appropriate statistical methods for TJP1 expression correlation analyses:

• Correlation Analyses:

  • Pearson correlation: For linear relationships between TJP1 expression levels (as measured by FITC signal intensity) and continuous variables like migration distance

  • Spearman rank correlation: For non-parametric assessment of monotonic relationships between TJP1 expression and cell phenotypes

  • Point-biserial correlation: When correlating TJP1 expression with binary outcomes like metastatic status

• Regression Models:

  • Multiple linear regression: To assess TJP1 contribution to phenotypes while controlling for covariates

  • Cox proportional hazards regression: For survival analyses relating TJP1 expression to patient outcomes, similar to the approaches used in previous TCGA data analysis

  • Logistic regression: For binary outcomes such as treatment response

• Categorical Analyses:

  • Stratify TJP1 expression into high/medium/low categories based on the established classification system (H-M-L) used in previous profiling studies

  • Use ANOVA with post-hoc tests for comparing phenotypic differences across expression groups

  • Apply chi-square tests for association between TJP1 expression categories and clinical parameters

• Multivariate Approaches:

  • Principal component analysis: To identify patterns in multiparametric data including TJP1 and other biomarkers

  • Hierarchical clustering: To identify patient subgroups based on TJP1 and related protein expression patterns

  • Random forest models: For identifying the relative importance of TJP1 among multiple predictors of cancer phenotypes

• Time-Series Analyses:

  • Mixed-effects models: For longitudinal studies tracking TJP1 expression changes during disease progression

  • Survival analysis with time-dependent covariates: When TJP1 expression is measured at multiple timepoints during treatment