EPSTI1 Antibody, FITC conjugated

What is EPSTI1 and what cellular processes is it involved in?

EPSTI1 (Epithelial-stromal interaction 1) is a protein first identified through its increased expression in breast cancer epithelial cells when cocultured with stromal fibroblasts. It functions as an interferon-responsive gene involved in immune responses and inflammation. Research has established that EPSTI1 plays significant roles in:

• Monocyte adhesion to endothelial cells, which is crucial in atherosclerosis development

• Inflammatory responses in macrophages

• Modulation of immune responses in chronic inflammatory diseases

• Regulation of cell apoptosis and inflammatory cytokine production

• Immune infiltration in cancer microenvironments

EPSTI1 expression is not restricted to breast tissue but is present in various normal tissues and is dramatically upregulated in certain pathological conditions such as atherosclerotic plaques .

How does FITC conjugation of antibodies work?

FITC (Fluorescein Isothiocyanate) conjugation involves chemically linking this fluorescent dye to antibodies through the free amino groups of the antibody protein. The conjugation process typically uses established protocols where FITC fluorophore is crosslinked with the primary antibody .

The reaction occurs as follows:

• FITC reacts with primary amines (mainly lysine residues) on the antibody

• This forms a stable thiourea bond

• The resulting conjugate retains antibody specificity while gaining fluorescent properties

The F/P (fluorophore-to-protein) molar ratio varies between proteins and must be determined empirically for optimal signal-to-noise ratio . Modern approaches like Lightning-Link technology enable rapid conjugation (under 20 minutes) with minimal hands-on time and 100% antibody recovery .

What are the main applications of EPSTI1 antibody, FITC conjugated?

EPSTI1 antibody, FITC conjugated is primarily used in:

• Immunofluorescence microscopy: For detection and localization of EPSTI1 in tissue sections or cultured cells, particularly in examining endothelial cells of atherosclerotic plaques versus normal arteries

• Flow cytometry: For quantitative analysis of EPSTI1 expression across different cell populations, especially in immune infiltration studies

• Cell adhesion assays: For studying the role of EPSTI1 in monocyte-endothelial cell adhesion, which is critical in atherosclerosis pathogenesis

• Expression analysis in disease models: For examining EPSTI1 expression in various pathological conditions including atherosclerosis, pneumonia, and cancer

• Single-cell studies: For validation of single-cell RNA sequencing findings related to EPSTI1 expression patterns

What cell types typically express EPSTI1?

Based on comprehensive research findings, EPSTI1 expression has been observed in:

• Endothelial cells: Significantly higher expression in endothelial cells of atherosclerotic plaques compared to normal arteries

• Inflammatory cells: Macrophages (particularly when stimulated with LPS or IFN-γ)

• Cancer cells: Originally identified in breast cancer epithelial cells; also relevant in colon cancer where expression correlates with immune infiltration

• Lung cells: Expressed in WI-38 lung fibroblasts and upregulated upon LPS treatment

• Muscle cells: Identified in muscle stem cells (MuSCs) during the inflammatory phase of muscle regeneration

Notably, immunofluorescence studies have demonstrated that EPSTI1 is predominantly expressed in endothelial cells of normal aortas, with significantly elevated expression in endothelial cells of atherosclerotic plaques .

What are appropriate controls when using EPSTI1 antibody, FITC conjugated?

When using EPSTI1 antibody, FITC conjugated, the following controls are essential:

For flow cytometry applications, Fluorescence-Minus-One (FMO) controls are particularly valuable for accurate gating of EPSTI1-positive populations.

How can I optimize immunofluorescence staining protocols for EPSTI1 detection in atherosclerotic plaque samples?

Optimizing immunofluorescence staining for EPSTI1 in atherosclerotic plaque samples requires several methodological considerations:

Sample preparation:

• Use fresh frozen sections rather than FFPE when possible to preserve epitope integrity

• For FITC detection, avoid fixatives containing aldehydes as they can increase autofluorescence

• Use methanol fixation (10 minutes at -20°C) for optimal EPSTI1 epitope accessibility

Staining protocol:

• Permeabilize samples with 0.1% Triton X-100 if needed for intracellular access

• Block with PBS containing 10% FBS for at least 20 minutes at room temperature

• Dilute EPSTI1-FITC antibody in blocking solution (start with 1:500 dilution)

• Incubate samples with diluted antibody for 1 hour at room temperature in the dark

• Wash thoroughly with PBS (2-3 times, 5 minutes each)

• For co-staining experiments, use CD31 as an endothelial cell marker with a spectrally distinct fluorophore

Optimization strategies:

• Perform antibody titration (1:100 to 1:1000) to determine optimal signal-to-noise ratio

• Consider extending incubation to overnight at 4°C for improved signal penetration

• Implement antigen retrieval if signal is weak (citrate buffer pH 6.0)

• Include Sudan Black B treatment (0.1% in 70% ethanol) to reduce tissue autofluorescence

Research has shown that EPSTI1 is predominantly expressed in endothelial cells of atherosclerotic plaques, making co-staining with endothelial markers particularly informative .

What are the technical considerations when using EPSTI1 antibody, FITC conjugated for flow cytometry?

For optimal flow cytometric analysis of EPSTI1 expression using FITC-conjugated antibodies, consider these technical aspects:

Sample preparation:

• For intracellular EPSTI1 detection, use permeabilization reagents (0.1% saponin or commercially available kits)

• Maintain cell concentration at 1×10^6 cells/mL for consistent staining

• Include viability dyes (non-FITC) to exclude dead cells that may bind antibodies non-specifically

Instrument considerations:

• FITC signal is detected in the 515-545 nm channel (blue laser excitation)

• Perform proper compensation when using multiple fluorophores, as FITC has spectral overlap with PE

• Set PMT voltages to position negative population on scale while maximizing resolution between positive and negative populations

Protocol optimization:

• Titrate antibody concentration (typically 2-5 μg/mL as starting point)

• Include proper controls (FMO, isotype, unstained, and positive controls)

• For cells with expected low EPSTI1 expression, increase incubation time to 60 minutes

• Wash cells thoroughly after staining (2-3 times with excess buffer)

Analysis approach:

• When examining immune infiltration, use hierarchical gating with lineage markers before analyzing EPSTI1 expression

• EPSTI1 expression has been shown to correlate with specific immune populations such as M1 macrophages and myeloid dendritic cells

• Consider analysis of EPSTI1 in conjunction with markers such as CD31 for endothelial cells

How can I verify that EPSTI1 antibody, FITC conjugated is specifically detecting EPSTI1 in my experimental system?

Verifying specificity of EPSTI1 antibody requires multiple validation approaches:

Genetic validation approaches:

• siRNA knockdown validation:

  • Transfect cells with EPSTI1-specific siRNAs (research has validated siEPSTI1 #2 as particularly effective)

  • Confirm reduction in FITC signal via flow cytometry or immunofluorescence

  • Example from literature: siEPSTI1 effectively reduced EPSTI1 protein by ~75% in HUVECs

• Overexpression validation:

  • Transfect cells with EPSTI1 expression vector versus empty vector control

  • Confirm increased signal intensity in EPSTI1-overexpressing cells

  • Research demonstrated 2.5-fold increase in EPSTI1 expression upon transfection

Biological validation:

• Stimulation experiments:

  • Treat cells with LPS (100-1000 ng/mL) which has been shown to induce EPSTI1 expression in a dose-dependent manner

  • Confirm increased signal following treatment

  • Data shows LPS induces EPSTI1 at both mRNA and protein levels with peak expression at 24 hours

• Tissue comparison:

  • Compare staining patterns in tissues with known differential expression

  • Atherosclerotic plaques show significantly higher EPSTI1 expression than normal arterial tissue

Biochemical validation:

• Western blotting using non-conjugated EPSTI1 antibody to confirm molecular weight (~36.8 kDa)

• Peptide competition assay to verify epitope specificity

What approaches can be used to quantify EPSTI1 expression levels in different cell types?

Multiple complementary approaches can be used to quantify EPSTI1 expression:

Flow cytometry-based quantification:

• Measure Mean Fluorescence Intensity (MFI) of EPSTI1-FITC staining

• Use calibration beads to convert arbitrary fluorescence units to Molecules of Equivalent Soluble Fluorochrome (MESF)

• Compare EPSTI1 expression across different cell populations using multiparameter analysis

• Research has used this approach to compare EPSTI1 levels in different immune cell populations

Imaging-based quantification:

• Quantitative immunofluorescence:

  • Capture images using standardized exposure settings

  • Measure integrated density or mean gray value of FITC signal using ImageJ

  • Subtract background fluorescence from region of interest

  • Normalize to cell number or area

• High-content imaging:

  • Automated multi-well imaging of cells labeled with EPSTI1-FITC

  • Segmentation of cells based on nuclear counterstain

  • Measurement of FITC intensity within defined cellular compartments

  • Statistical analysis of population-level expression patterns

Molecular quantification:

• qRT-PCR for mRNA quantification (correlate with protein levels)

• Western blot with densitometry for bulk protein analysis

• Single-cell RNA sequencing to examine cell-specific expression patterns as used in muscle regeneration studies

Implementation table for different research questions:

How does EPSTI1 expression in inflammatory conditions compare to cancer models?

EPSTI1 expression shows distinct patterns in inflammatory conditions versus cancer models:

Inflammatory conditions:

Cancer models:

Comparative analysis:

• EPSTI1 appears to have context-dependent roles in different disease settings

• In inflammatory conditions, EPSTI1 generally promotes pro-inflammatory responses through NF-κB signaling

• In colon cancer, higher EPSTI1 expression correlates with favorable immune infiltration patterns and better clinical outcomes

• In both contexts, EPSTI1 modulates immune cell behavior, particularly macrophage responses

Research findings suggest that while EPSTI1 may exacerbate inflammatory conditions by promoting pro-inflammatory pathways, its presence in certain cancers may enhance anti-tumor immunity through increased immune cell infiltration .

What are the methodological considerations when studying EPSTI1's role in monocyte-endothelial cell adhesion?

Studying EPSTI1's role in monocyte-endothelial cell adhesion requires careful experimental design:

Cell models and preparation:

• Endothelial cells: HUVECs are commonly used as demonstrated in research

• Monocytes: THP-1 cells provide a reliable model system

• Cell labeling: Label THP-1 cells with Dil (red) and HUVECs with Hoechst 33342 (blue) for visualization

Experimental approaches:

• Static adhesion assay protocol:

  • Transfect HUVECs with EPSTI1 expression vector or siRNA

  • Verify EPSTI1 expression/knockdown by Western blot

  • Label THP-1 cells with Dil fluorescent dye

  • Label HUVEC nuclei with Hoechst 33342

  • Co-incubate labeled THP-1 cells with HUVECs

  • Wash to remove non-adherent cells

  • Quantify adherent THP-1 cells by fluorescence microscopy

• EPSTI1 manipulation strategies:

  • Overexpression: Transfect HUVECs with EPSTI1 vector (produces ~2.5-fold increase in expression)

  • Knockdown: Use siEPSTI1 #2 for most effective silencing

  • Downstream targets: Simultaneously manipulate VCAM-1/ICAM-1 using siVCAM-1 #1 and siICAM-1 #2

Molecular mechanism analysis:

• Measure VCAM-1 and ICAM-1 expression by qPCR and Western blot

• Assess NF-κB pathway activation by measuring p65 nuclear translocation

• Research has shown that EPSTI1 overexpression increases both mRNA and protein levels of VCAM-1 and ICAM-1 in HUVECs

Data from literature:

• EPSTI1 overexpression significantly increased THP-1 cell adhesion to HUVECs

• EPSTI1 knockdown decreased THP-1 cell adhesion to HUVECs

• Simultaneous knockdown of VCAM-1 and ICAM-1 completely blocked EPSTI1-promoted adhesion

How does EPSTI1 interact with NF-κB signaling pathway in different experimental models?

EPSTI1's interaction with the NF-κB signaling pathway shows consistent patterns across experimental models:

Molecular mechanisms:

• EPSTI1 triggers NF-κB signaling by interacting with valosin-containing protein (VCP) and inducing IκBα degradation

• This interaction promotes p65 nuclear translocation and activation of downstream genes

• LPS induces EPSTI1 expression through p65, creating a potential positive feedback loop

Model-specific findings:

Experimental approaches to study this interaction:

• Biochemical methods:

  • Western blotting to measure p-p65/p65 and p-IκBα/IκBα ratios

  • Nuclear/cytoplasmic fractionation to assess p65 translocation

  • Co-immunoprecipitation to detect EPSTI1-VCP interaction

• Imaging approaches:

  • Immunofluorescence to visualize p65 nuclear translocation

  • EPSTI1-FITC combined with differently labeled p65 antibodies for co-localization studies

• Functional validation:

  • NF-κB inhibitors (e.g., BAY 11-7082) to block EPSTI1 effects

  • Mutation of EPSTI1 domains to identify regions essential for NF-κB activation

Research has demonstrated that knockdown of EPSTI1 significantly inhibits LPS-induced monocyte-endothelial cell adhesion by downregulating VCAM-1 and ICAM-1 expression through NF-κB pathway inhibition .

What are the best approaches for studying EPSTI1's function in macrophage polarization?

Studying EPSTI1's function in macrophage polarization requires specialized methodologies:

Macrophage model selection:

• THP-1 cells differentiated with PMA (100 nM, 48 hours)

• RAW264.7 mouse macrophage cell line

• Primary human monocyte-derived macrophages

• Bone marrow-derived macrophages from Epsti1-/- mice

Polarization protocols:

EPSTI1 manipulation strategies:

• Knockdown approaches:

  • siRNA transfection (validated sequences available from research)

  • Lentiviral shRNA for stable knockdown

• Overexpression systems:

  • Transient transfection (challenging in macrophages)

  • Lentiviral expression systems

• Timing considerations:

  • Manipulate EPSTI1 before polarization to study its role in polarization initiation

  • Manipulate during established polarization to study maintenance/plasticity

Analysis methods:

• Flow cytometry panel for polarization markers:

  • Surface markers: CD80, CD86, MHC-II (M1); CD163, CD206 (M2)

  • Use EPSTI1-FITC in combination with differently labeled polarization markers

• Secreted factor analysis:

  • ELISA or multiplex cytokine assays for TNF-α, IL-1β, IL-6, IL-10

  • qPCR for cytokine gene expression

• Functional assays:

  • Phagocytosis capacity

  • Migration assays

  • Co-culture with T cells to assess stimulatory capacity

Mechanistic investigation:

• Analyze NF-κB pathway components as EPSTI1 has been shown to modulate this pathway

• Assess IFN signaling as EPSTI1 is an interferon response gene

• Evaluate polarization in the context of atherosclerosis models where EPSTI1 is upregulated

Research indicates that EPSTI1 expression is increased in LPS or IFN-γ treated macrophages, while knockdown of EPSTI1 promotes M2-type macrophage differentiation and inhibits M1 polarization through suppression of Stat1 and p65 activation .

What is the optimal protocol for conjugating unconjugated EPSTI1 antibodies with FITC?

For laboratories needing to conjugate their own EPSTI1 antibodies with FITC, several approaches are available:

Traditional conjugation protocol:

• Prepare antibody solution (1-2 mg/mL) in carbonate-bicarbonate buffer (0.1M, pH 9.0)

• Dissolve FITC in anhydrous DMSO at 1 mg/mL

• Slowly add FITC solution to antibody while stirring (typically 10-20 μg FITC per mg antibody)

• Incubate for 8 hours at 4°C in the dark with gentle stirring

• Purify using gel filtration (e.g., Sephadex G-25) to remove unbound FITC

• Measure F/P ratio spectrophotometrically

Commercial kit approach:
Several commercial kits offer simplified conjugation protocols:

• Lightning-Link® FITC Conjugation Kit allows conjugation in <20 minutes with minimal hands-on time

• FluoroTag™ FITC Conjugation Kit provides standardized protocols for reproducible conjugation

Quality control:

• Determine the F/P (fluorophore-to-protein) molar ratio spectrophotometrically

• Optimal F/P ratio is typically 3-8 FITC molecules per antibody

• Test antibody activity after conjugation using a known positive sample

• Compare signal-to-noise ratio with pre-conjugated commercial antibodies

Storage considerations:

• Store FITC-conjugated antibodies at 2-8°C in the dark

• For long-term storage, add stabilizers (e.g., 1% BSA) and preservatives (0.01% sodium azide)

• Aliquot to avoid freeze-thaw cycles

• Typical shelf-life is 6-12 months when properly stored

How can I troubleshoot weak or non-specific signals when using EPSTI1 antibody, FITC conjugated?

When encountering weak or non-specific signals with EPSTI1-FITC antibodies, consider these troubleshooting approaches:

Weak signal troubleshooting:

Non-specific signal troubleshooting:

Flow cytometry-specific troubleshooting:

• Ensure proper compensation if using multiple fluorophores

• Use viability dyes to exclude dead cells that may bind antibodies non-specifically

• Include FMO controls for accurate gating

Immunofluorescence-specific troubleshooting:

• Include DAPI nuclear counterstain to verify cellular integrity

• Consider tyramide signal amplification for weak signals

• Use confocal microscopy for improved signal-to-noise ratio

Research shows that using optimal blocking (PBS + 10% FBS) and appropriate antibody dilution (1:500) in proper incubation conditions (1 hour at room temperature in the dark) yields specific EPSTI1 staining .

How do I design multicolor flow cytometry panels incorporating EPSTI1-FITC for immune cell characterization?

Designing effective multicolor flow cytometry panels incorporating EPSTI1-FITC requires careful consideration of spectral compatibility, marker selection, and panel optimization:

Spectral considerations:

• FITC excites at 488 nm and emits at ~519 nm

• Avoid fluorophores with significant spectral overlap (e.g., GFP, BB515)

• Compatible fluorophores include PE (yellow-orange), APC (red), and BV421 (violet)

Panel design strategy for immune cell characterization:

Optimization steps:

• Single-color controls: Run each antibody individually to establish compensation matrix

• Titration: Determine optimal concentration of each antibody, especially EPSTI1-FITC

• FMO controls: Include Fluorescence-Minus-One controls for accurate gating

• Biological controls: Include samples with known EPSTI1 expression patterns (e.g., LPS-stimulated cells)

Analysis considerations:

• Use hierarchical gating strategy (viability → lineage → subset markers → EPSTI1)

• Consider EPSTI1 expression across different immune populations

• Research shows EPSTI1 expression correlates positively with M1 macrophages and myeloid dendritic cells, but negatively with M2 macrophages and resting NK cells

Example applications:

• Characterizing immune infiltration in colon cancer specimens where EPSTI1 serves as a prognostic marker

• Examining monocyte/macrophage polarization in atherosclerosis models where EPSTI1 is upregulated

• Analyzing inflammatory responses in various tissue contexts

What experimental designs best demonstrate the functional significance of EPSTI1 in disease models?

To demonstrate EPSTI1's functional significance in disease models, consider these experimental approaches:

In vitro models:

• Atherosclerosis models:

  • Monocyte-endothelial adhesion assay:

    • Manipulate EPSTI1 expression in HUVECs through overexpression or siRNA knockdown

    • Measure adhesion of labeled monocytes (THP-1 cells)

    • Quantify VCAM-1/ICAM-1 expression as downstream mediators

  • Endothelial inflammation model:

    • Treat endothelial cells with LPS to induce EPSTI1 expression

    • Knockdown EPSTI1 and measure inflammatory markers and adhesion molecule expression

• Inflammatory disease models:

  • Macrophage polarization assay:

    • Generate M1/M2 macrophages with appropriate stimuli

    • Manipulate EPSTI1 expression and assess polarization marker changes

    • Measure inflammatory cytokine production (IL-1β, IL-6, TNF-α)

  • Lung inflammation model:

    • Treat WI-38 lung fibroblasts with LPS to induce inflammatory injury

    • Knockdown EPSTI1 and assess cell viability, apoptosis, and inflammatory cytokine production

• Cancer models:

  • Immune infiltration assays:

    • Co-culture cancer cells with immune cells under different EPSTI1 expression conditions

    • Assess changes in immune cell recruitment and activation

    • Particularly focus on M1 macrophages and myeloid dendritic cells that show correlation with EPSTI1

In vivo approaches:

• Genetic models:

  • Use Epsti1-/- (knockout) mice for comparative studies

  • Create tissue-specific conditional knockouts to study context-dependent functions

• Disease-specific models:

  • Atherosclerosis model:

    • ApoE-/- mice fed high-fat diet with EPSTI1 manipulation

    • Assess plaque formation, immune infiltration, and adhesion molecule expression

  • Cancer model:

    • Tumor xenografts with EPSTI1 manipulation

    • Analyze tumor growth, immune infiltration, and survival outcomes

• Therapeutic intervention studies:

  • Develop EPSTI1-targeting approaches (e.g., blocking antibodies, siRNA delivery)

  • Test in appropriate disease models

  • Assess changes in disease progression and inflammatory markers

Readout parameters:

Research has demonstrated that knockdown of EPSTI1 significantly inhibits LPS-induced monocyte-endothelial cell adhesion and inflammatory cytokine production, suggesting its potential as a therapeutic target in atherosclerosis and inflammatory diseases .

How is EPSTI1 emerging as a biomarker in cancer immunotherapy research?

EPSTI1 is gaining attention as a potential biomarker in cancer immunotherapy research, particularly in the context of the tumor immune microenvironment:

Prognostic significance:

• Research on stage III colon cancer has revealed EPSTI1 as a protective factor (HR = 0.79, 95% CI, 0.65-0.96) associated with better relapse-free survival

• Meta-analysis of 547 patients confirmed EPSTI1's association with improved outcomes

• Higher EPSTI1 expression correlates with favorable TME immune scores

Immune infiltration associations:

• EPSTI1 expression positively correlates with beneficial immune infiltrates:

  • M1 macrophages (anti-tumor phenotype) show strong correlation with EPSTI1 (R = 0.561 in GSE39582; R = 0.394 in TCGA-COAD)

  • CD8+ T cells and follicular helper T cells (important for anti-tumor responses)

  • Myeloid dendritic cells (critical for antigen presentation)

• Negative correlation with immunosuppressive cell types:

  • M2 tumor-associated macrophages (pro-tumor phenotype)

  • Myeloid-derived suppressor cells (MDSCs)

  • Resting NK cells

Potential applications in immunotherapy:

• Patient stratification: Identifying patients likely to respond to immunotherapies based on EPSTI1 expression levels

• Combination therapy design: Targeting pathways that enhance EPSTI1 expression in combination with existing immunotherapies

• Response monitoring: Tracking changes in EPSTI1 expression during treatment as a potential indicator of immune activation

Future research directions:

• Validation in additional cancer types beyond colon cancer

• Integration with other immune biomarkers for improved predictive power

• Development of EPSTI1-targeted approaches to enhance anti-tumor immunity

• Investigation of EPSTI1's role in response to specific immunotherapy modalities (checkpoint inhibitors, CAR-T, cancer vaccines)

What novel methodologies are being developed for studying EPSTI1 in single-cell resolution contexts?

Emerging technologies are enabling more sophisticated analysis of EPSTI1 at single-cell resolution:

Single-cell RNA sequencing applications:

• Analysis of EPSTI1 expression during muscle regeneration has utilized scRNA-seq data to track expression in muscle stem cells at different time points after injury

• Integration of EPSTI1 expression with cell clustering algorithms to identify specific cell populations with differential expression

• Trajectory analysis to map EPSTI1 expression changes during cell differentiation or activation processes

Spatial transcriptomics approaches:

• Combining in situ hybridization for EPSTI1 mRNA with immunofluorescence for protein markers

• Visium or Slide-seq technologies to map EPSTI1 expression in tissue contexts with spatial resolution

• Particularly valuable for heterogeneous tissues like atherosclerotic plaques or tumor microenvironments

Multi-omics integration:

• Correlating EPSTI1 expression with epigenetic modifications at single-cell level

• Linking transcriptional states with protein expression and signaling pathway activation

• Computational approaches to integrate multiple data types for comprehensive understanding

Advanced imaging methodologies:

• Imaging mass cytometry combining EPSTI1-FITC with dozens of other markers for comprehensive cellular phenotyping

• Super-resolution microscopy to examine subcellular localization of EPSTI1 protein

• Live-cell imaging of EPSTI1-GFP fusion proteins to track dynamics in real-time

Single-cell functional assays:

• High-throughput CRISPR screening at single-cell resolution to identify regulators of EPSTI1

• Droplet-based assays to correlate EPSTI1 expression with cellular functions

• Microfluidic systems to analyze individual cell behaviors in controlled environments

These advanced methodologies will provide unprecedented insights into EPSTI1's cell type-specific functions and regulation in complex biological systems such as the tumor microenvironment and inflammatory conditions.