IFITM3 Antibody

What is IFITM3 and why are specific antibodies important for studying it?

IFITM3 is a viral restriction factor that has been shown to inhibit replication of approximately seventeen primarily enveloped RNA viruses, including influenza A virus (IAV), HIV-1, Ebola, SARS coronavirus, and Dengue virus. The protein's ability to restrict such a broad range of viruses with different cellular tropisms makes it a significant target for immunological research . Specific antibodies are critical for studying IFITM3 because most commercially available antibodies cross-react with the closely related homolog IFITM2, which can lead to confounded experimental results and data misinterpretation . Custom-generated IFITM3-specific antibodies with minimal cross-reactivity to IFITM2 have enabled researchers to accurately determine expression patterns across cell types and investigate IFITM3's role in viral restriction mechanisms .

How do IFITM3 expression patterns differ across immune cell types?

IFITM3 expression shows consistent patterns across immune cell populations, with higher expression in myeloid compared to lymphoid cells. This pattern has been observed in adult blood samples, lung para-tumor tissue, and cord blood samples . Within the myeloid compartment, CD16+ monocytes from adult blood exhibit the highest IFITM3 expression, while granulocyte populations show highest expression in lung tissue . In cord blood samples, hematopoietic stem cells (HSCs) display high IFITM3 expression, though CD16+ monocytes in these samples also show substantial expression, indicating that the relationship between differentiation and expression is complex rather than linear . This expression pattern differs from other interferon-stimulated genes (ISGs) involved in antiviral responses, such as STAT1 or BST2, though these proteins also show higher expression in myeloid compared to lymphoid cells .

How is IFITM3 expression regulated by different types of interferons?

IFITM3, as an interferon-stimulated gene, shows differential responses to various types of interferons. Human cell lines (HEK293 and A549) stimulated with 0-10,000 U/mL IFN for 24 hours demonstrate strong upregulation of IFITM3 in response to type I IFNs, but considerably weaker responses to type II and III IFNs, with some variation between cell lines . In primary immune cells, IFITM3 induction patterns vary significantly by cell type. For instance, CD14+ monocytes show strong IFITM3 induction following type I IFN stimulation, while CD16+ monocytes, which already have high basal expression, show minimal additional induction . Notably, lymphoid cells (NK, B, and T cells) exhibit minimal IFITM3 induction following IFN stimulation, with T cell populations showing no increase at all . The exception among lymphoid cells is plasmacytoid dendritic cells, which respond with approximately threefold induction following type III IFN stimulation due to their expression of the IFN-λ receptor, IFNLR1 .

What experimental approaches can detect IFITM3 incorporation into viral particles?

Detection of IFITM3 incorporation into viral particles requires specialized techniques that maintain viral structural integrity while enabling protein visualization. Immunoelectron microscopy following immunostaining for IFITM3 is an effective approach for directly visualizing IFITM3 within viral particles . This technique should be applied to both wild-type viruses and those grown in the presence of IFITM3-expressing cells to establish incorporation patterns. For quantitative analysis, researchers can perform western blot analysis on purified viral particles, comparing the ratio of IFITM3 to viral proteins (such as hemagglutinin for influenza viruses) between different experimental conditions . Additionally, super-resolution microscopy techniques like STORM or PALM can be employed to visualize the co-localization of viral proteins and IFITM3 at the plasma membrane during viral budding . These methods have revealed that IFITM3 can be incorporated into influenza A virus particles, where it competes with viral hemagglutinin for incorporation, potentially affecting the virus's susceptibility to antibody-mediated neutralization .

How should researchers optimize IFITM3 antibody concentrations for different applications?

Optimization of IFITM3 antibody concentrations requires systematic titration experiments specific to each application and cell type. For flow cytometry applications, researchers should perform serial dilutions (typically 1:50 to 1:1000) of the antibody and analyze signal-to-noise ratios to determine the optimal concentration that maximizes specific staining while minimizing background . For Western blot applications, a similar titration approach should be used, typically starting with the manufacturer's recommended dilution (e.g., 0.1-1.0 μg/mL) and adjusting based on signal intensity and background levels . For immunofluorescence microscopy, researchers should test various antibody concentrations while simultaneously varying fixation methods (paraformaldehyde, methanol, or acetone) to identify conditions that preserve IFITM3 epitopes while maintaining cellular architecture . Additionally, for all applications, optimization should include multiple negative controls, including isotype controls and IFITM3-knockout samples, to accurately assess non-specific binding. The optimization process should be repeated for each new lot of antibody, as variation in antibody performance between lots is common.

How can researchers investigate the role of IFITM3 in antibody responses to vaccination?

Investigating IFITM3's role in antibody responses to vaccination requires multi-modal approaches spanning genetic, cellular, and serological analyses. Human studies can examine correlations between IFITM3 polymorphisms (particularly the rs12252-C SNP) and vaccine responses by genotyping subjects and measuring post-vaccination seroconversion rates and hemagglutination inhibition (HI) titers . Research has shown that carriers of the IFITM3 rs12252-C/C genotype demonstrate lower seroconversion rates for H1N1, H3N2, and B viruses compared to C/T and T/T donors . For mechanistic investigations, IFITM3-knockout mouse models (Ifitm3-/-) can be used to comprehensively analyze the cellular and molecular components of vaccine-induced immunity. Following immunization with trivalent inactivated vaccine (TIV), researchers should analyze splenic germinal center (GC) B cells, plasma cells, vaccine-specific IgG+ antibody-secreting cells, and T follicular helper cells, all of which have been shown to be significantly reduced in Ifitm3-/- mice . Additionally, transcriptional network analysis of GC B cells and plasma cells can reveal abnormalities in differentiation pathways in the absence of IFITM3 . Multiple vaccination schedules with periodic serological analysis can determine if IFITM3's impact persists across repeated antigen exposures, as Ifitm3-/- mice maintain lower HI levels even after a third vaccination .

What structural features of IFITM3 are critical for its antiviral function?

IFITM3's antiviral function depends on specific structural domains that can be investigated using structure-function studies with appropriate antibodies. The protein contains two transmembrane regions, including a conserved hydrophobic domain (HD) referred to as the amphipathic helix, which is critical for its antiviral activity . Bioinformatic analyses have identified a highly conserved, short amphipathic helix within this hydrophobic region that is required for IFITM3-dependent inhibition of multiple viruses, including influenza virus, Zika virus, vesicular stomatitis virus, Ebola virus, and HIV . Researchers investigating these structural features should employ site-directed mutagenesis to create IFITM3 variants with modifications to the amphipathic helix, followed by viral inhibition assays to assess functional consequences. Domain-specific antibodies can be used to confirm protein expression and localization of these variants. Additionally, post-translational modifications, particularly S-palmitoylation of the amphipathic helices, play important roles in IFITM3 function and can be studied using metabolic labeling with palmitate analogs followed by click chemistry-based detection . These structural studies provide critical insights into the mechanistic basis of IFITM3's broad-spectrum antiviral activity.

Why does IFITM3 expression differ between myeloid and lymphoid cells?

The differential expression of IFITM3 between myeloid and lymphoid cells reflects distinct antiviral strategies and cellular functions within the immune system. Myeloid cells, as primary responders to pathogens, maintain higher basal IFITM3 expression to provide immediate protection against viral infection before adaptive responses develop . This is particularly important given that IFITM3 induction by IFN stimulation takes at least 24 hours to reach maximum levels, highlighting the significance of pre-existing expression during early infection stages . The consistently higher expression in CD16+ monocytes suggests these cells are primed for antiviral defense, possibly due to their patrolling function and increased likelihood of encountering viruses . In contrast, lymphoid cells (T and B cells) show both lower basal expression and minimal induction following IFN stimulation, indicating that these cell types may rely on other antiviral mechanisms or that their functions are less dependent on immediate viral restriction . This expression pattern is not shared by all interferon-stimulated genes involved in antiviral responses, suggesting that the distribution of IFITM3 has evolved to specifically protect cell types most vulnerable to or critical in controlling viral infections .

How should researchers interpret differences in IFITM3 induction between cell types?

Differences in IFITM3 induction between cell types represent important biological variations in interferon responsiveness that require careful interpretation. When analyzing IFITM3 induction data, researchers should first consider basal expression levels, as cells with high constitutive IFITM3 expression (such as CD16+ monocytes) may show limited additional induction due to already expressing near-maximal levels . The kinetics of induction should also be considered—IFITM3 typically requires at least 24 hours of IFN stimulation to reach peak expression, so measurements at earlier timepoints may underestimate induction potential . Cell type-specific differences in IFN receptor expression are another critical factor; for example, plasmacytoid dendritic cells respond strongly to type III IFNs due to their expression of the IFNLR1 receptor, while other cells lacking this receptor show minimal response to type III IFNs . Additionally, researchers should examine the expression and activity of transcription factors involved in IFITM3 regulation, including IRF3, IRF7, and STAT proteins, which may vary between cell types. These considerations are essential for accurate interpretation of IFITM3 induction data and for understanding the cell type-specific roles of IFITM3 in antiviral immunity .

What controls should be included when analyzing IFITM3 expression in tissue samples?

Comprehensive analysis of IFITM3 expression in tissue samples requires multiple controls to ensure accurate interpretation. First, researchers must include isotype controls matched to the IFITM3 antibody to distinguish specific staining from Fc receptor binding or other non-specific interactions, particularly important in myeloid cell-rich tissues . Second, inclusion of IFITM2 expression analysis is crucial given the high sequence homology between IFITM2 and IFITM3, allowing researchers to verify antibody specificity and compare expression patterns of these related proteins . Third, tissue samples from IFITM3-knockout models or, for human samples, CRISPR-edited cell lines lacking IFITM3 expression serve as negative controls to establish background staining levels . Fourth, parallel analysis of other interferon-stimulated genes (such as STAT1 or BST2) allows researchers to determine whether IFITM3 expression patterns reflect general interferon responsiveness or IFITM3-specific regulation . Finally, inclusion of multiple cell identification markers is essential for accurately characterizing IFITM3 expression across different cell populations within heterogeneous tissue samples, particularly when examining complex tissues like lung para-tumor samples that contain multiple cell types with varying IFITM3 expression levels .

How can researchers address cross-reactivity issues with IFITM3 antibodies?

Cross-reactivity issues with IFITM3 antibodies can be addressed through multiple complementary strategies that enhance experimental specificity. First, researchers should perform pre-adsorption of antibodies with recombinant IFITM2 protein to remove cross-reactive antibodies before use in experimental applications . Second, including IFITM2 knockout controls alongside IFITM3 knockout controls allows researchers to distinguish signals resulting from specific IFITM3 binding versus cross-reactivity with IFITM2 . Third, epitope mapping can identify regions unique to IFITM3 that are not present in IFITM2, guiding the development or selection of antibodies targeting these distinct epitopes . Fourth, competitive binding assays using excessive unlabeled antibody can confirm binding specificity. Fifth, researchers can implement sequential immunoprecipitation strategies, first depleting samples of IFITM2 using specific antibodies before analyzing IFITM3 with potentially cross-reactive antibodies. Finally, validation across multiple detection platforms (western blot, flow cytometry, and immunofluorescence) with consistent results increases confidence in antibody specificity. These approaches are critical because experiments investigating IFITM3 biology have been historically hindered by antibody cross-reactivity issues, leading to potentially confounded results in the scientific literature .

What strategies can optimize detection of IFITM3 in virus-infected samples?

Optimizing detection of IFITM3 in virus-infected samples requires specialized techniques that account for infection-induced changes in protein expression and localization. First, researchers should implement time-course experiments to capture the dynamic changes in IFITM3 expression following infection, as levels may fluctuate significantly depending on viral immune evasion mechanisms and interferon responses . Second, cell fractionation prior to analysis can enhance detection by separating membrane-associated IFITM3 from cytosolic components, improving signal-to-noise ratios in western blot or immunofluorescence applications . Third, co-immunostaining for viral proteins alongside IFITM3 enables visualization of potential co-localization patterns and can reveal infection-specific changes in IFITM3 distribution . Fourth, researchers should optimize fixation and permeabilization protocols specifically for virus-infected samples, as standard protocols may not adequately preserve both viral and host protein epitopes. Fifth, signal amplification techniques such as tyramide signal amplification can enhance detection sensitivity for low-abundance IFITM3 without increasing background. Finally, comparing infected samples with interferon-stimulated uninfected controls can help distinguish infection-specific effects from general interferon-induced upregulation of IFITM3 .

How can researchers quantitatively compare IFITM3 expression across different experimental conditions?

Quantitative comparison of IFITM3 expression across experimental conditions requires standardized approaches that minimize technical variability and maximize biological signal. For western blot analysis, researchers should implement loading controls specifically matched to IFITM3's subcellular localization—membrane protein controls such as Na+/K+ ATPase rather than cytosolic proteins like GAPDH—to accurately normalize for loading variations . Additionally, inclusion of recombinant IFITM3 protein standards at known concentrations allows creation of standard curves for absolute quantification . For flow cytometry applications, researchers should use antibody-binding capacity (ABC) beads to convert fluorescence intensity values to absolute numbers of antibody binding sites, enabling direct comparison between different instruments and experiments . For immunofluorescence approaches, co-staining with reference proteins that remain stable across experimental conditions provides internal normalization standards. When conducting qRT-PCR analysis of IFITM3 transcript levels, multiple reference genes should be validated for stability under the specific experimental conditions before being used for normalization . Finally, researchers should evaluate both protein and transcript levels when possible, as post-transcriptional regulation may lead to discrepancies between mRNA and protein expression patterns, particularly following cytokine stimulation where Ifitm3 transcript levels can be significantly upregulated by IFN alpha 2 and IFN gamma .