CFLAR Antibody

What is CFLAR and why is it important in research?

CFLAR (CASP8 and FADD-like apoptosis regulator), also known as c-FLIP, is a 480-amino acid protein belonging to the Peptidase C14A family . It functions as a key regulator in the apoptosis pathway by interfering with death receptor signaling. CFLAR is particularly important in research because it plays critical roles in cell survival, apoptosis resistance, and immune response modulation. Recent studies have identified CFLAR as having significant diagnostic and prognostic value in various cancers, especially soft tissue sarcomas . The protein's ability to influence immune cell infiltration in tumor microenvironments makes it a valuable target for both basic research and potential therapeutic applications in cancer immunology.

What are the key applications for CFLAR antibodies in experimental research?

CFLAR antibodies serve multiple crucial functions in experimental research. They are commonly employed in Western blotting (WB) to evaluate protein expression levels, in immunohistochemistry (IHC-P) to visualize protein localization in tissue samples, and in immunofluorescence (IF) to examine subcellular distribution . Flow cytometry applications allow researchers to quantify CFLAR expression in different cell populations, while immunoprecipitation techniques help identify protein-protein interactions involving CFLAR . Additionally, ELISA methods enable quantitative measurement of CFLAR levels in biological samples . In cancer research, CFLAR antibodies are particularly valuable for investigating apoptosis resistance mechanisms, assessing immune infiltration patterns, and evaluating potential correlations between CFLAR expression and clinical outcomes in various malignancies.

How do I select the appropriate CFLAR antibody for my specific research application?

When selecting a CFLAR antibody for your research, consider several critical factors beyond basic reactivity. First, determine which CFLAR isoform your research targets, as antibodies may have differential specificity for the long (FLIPL) versus short (FLIPS) isoforms . Second, match the antibody's validated applications (WB, IHC, IF, etc.) with your experimental needs; some antibodies perform exceptionally in Western blot but poorly in immunohistochemistry . Third, verify species reactivity—while many CFLAR antibodies cross-react with human, mouse, and rat samples, specific epitope recognition varies . For quantitative studies, choose antibodies with demonstrated linear signal response. For localization studies, select antibodies validated for clean subcellular staining. Review published literature using specific antibody catalog numbers to evaluate performance in contexts similar to your experimental design, as this provides reliable evidence of antibody functionality in real research settings.

What are the optimal protocols for using CFLAR antibodies in Western blotting?

For optimal Western blotting results with CFLAR antibodies, precise protocol adjustments are essential. Begin with sample preparation: use RIPA buffer supplemented with protease inhibitors and phosphatase inhibitors when studying phosphorylation status of CFLAR. During protein separation, employ 10-12% SDS-PAGE gels as CFLAR isoforms range from 25-55 kDa. For transfer, PVDF membranes are preferable to nitrocellulose due to stronger protein retention. Critical for CFLAR detection is the blocking step—use 5% non-fat milk in TBST for 2 hours at room temperature to prevent non-specific binding. For primary CFLAR antibody incubation, dilutions between 1:500-1:1000 in 5% BSA typically yield optimal results, and overnight incubation at 4°C is recommended . Secondary antibody incubation should be performed at room temperature for 1 hour. Include positive controls from cell lines known to express CFLAR (such as Jurkat cells) and negative controls using siRNA knockdown samples to validate specificity. For quantitative analysis, normalize CFLAR expression to housekeeping proteins like GAPDH or β-actin to account for loading variations across samples.

How should I optimize immunohistochemistry protocols when using CFLAR antibodies?

Optimizing immunohistochemistry protocols for CFLAR antibodies requires careful attention to tissue preparation and antigen retrieval methods. For formalin-fixed, paraffin-embedded (FFPE) tissues, heat-induced epitope retrieval using citrate buffer (pH 6.0) typically yields superior results for CFLAR detection . The protocol should include heating at medium heat for 8 minutes, followed by no heat for 8 minutes, and medium-low heat for 7 minutes . Endogenous peroxidase blocking with 3% hydrogen peroxide for 25 minutes is essential to reduce background, followed by protein blocking with bovine serum albumin (BSA) for 30 minutes at room temperature . Primary antibody dilutions for CFLAR typically range from 1:100 to 1:1000, with overnight incubation at 4°C producing optimal staining . For visualization, HRP-conjugated secondary antibodies with appropriate fluorescent labels (such as CY3 or Alexa Fluor 488) enable detection of CFLAR in relation to other markers in multiplex immunofluorescence staining . Always include positive control tissues (lymphoid tissues typically express CFLAR) and negative controls (primary antibody omitted) to validate staining specificity and troubleshoot protocol adjustments.

What cell types and tissue samples are most appropriate for studying CFLAR expression?

Several cell types and tissue samples are particularly valuable for studying CFLAR expression due to their physiological relevance and expression patterns. Immune cells, especially T lymphocytes and macrophages, prominently express CFLAR and are excellent models for studying its role in immune regulation and apoptosis resistance . Cancer cell lines derived from soft tissue sarcomas, lymphomas, and carcinomas frequently show aberrant CFLAR expression, making them suitable for investigating its role in tumorigenesis . For tissue samples, lymphoid tissues (tonsil, lymph nodes, spleen) serve as positive controls due to constitutive CFLAR expression, while leiomyosarcoma and fibrosarcoma tumor samples can reveal clinically relevant expression patterns . Single-cell sequencing data from datasets such as GSE131309 have demonstrated heterogeneous CFLAR expression across different cell populations within tumors, suggesting the value of studying complex tissue samples rather than homogeneous cell cultures alone . When selecting experimental systems, consider that CFLAR expression is often inducible by cytokines, death receptor ligands, and various stress stimuli, allowing for dynamic expression studies in appropriate cellular models.

How can CFLAR antibodies be used to investigate the tumor microenvironment in cancer research?

CFLAR antibodies have become essential tools for investigating the complex tumor microenvironment (TME) in cancer research, particularly through multiplex immunofluorescence (mIF) approaches. These techniques allow simultaneous visualization of CFLAR expression alongside immune cell markers like CD8 (for cytotoxic T cells) and iNOS (for M1 macrophages) . Recent studies have demonstrated significant correlations between CFLAR expression and immune cell infiltration in soft tissue sarcomas, with high CFLAR expression associated with enhanced CD8+ T cell and M1 macrophage presence . To implement this methodology effectively, researchers should perform multiplex staining with carefully optimized antibody panels (using CFLAR mouse antibody at 1:1000 dilution, CD8 rabbit antibody at 1:200, and iNOS mouse antibody at 1:200) . Quantitative spatial analysis of these staining patterns can reveal whether CFLAR-expressing tumor cells colocalize with specific immune populations, providing insights into potential immunomodulatory mechanisms. Beyond visual assessment, researchers can complement these findings with computational approaches like CIBERSORTx analysis to calculate immune cell infiltration scores and correlate them with CFLAR expression levels across tumor samples .

What is the relationship between CFLAR expression and immune checkpoint inhibitor efficacy?

The relationship between CFLAR expression and immune checkpoint inhibitor (ICI) efficacy represents an emerging area of cancer immunotherapy research. Studies using TCGA-SARC datasets have demonstrated significant correlations between CFLAR expression and established ICI targets . High CFLAR expression appears to positively influence the tumor immune microenvironment by enhancing CD8+ T cell infiltration, which is typically associated with better responses to checkpoint inhibition therapy . Research methodologies to investigate this relationship include: (1) correlation analysis between CFLAR expression and known ICI markers (PD-1, PD-L1, CTLA-4) using Spearman's rank correlation coefficient on transcriptomic data; (2) stratification of patient cohorts into high and low CFLAR expression groups followed by comparison of immunotherapy response rates; and (3) multiplex immunofluorescence to visualize spatial relationships between CFLAR-expressing cells and immune checkpoint molecule-expressing cells within the tumor microenvironment . Researchers should particularly focus on whether CFLAR influences T cell exhaustion markers, as these could significantly impact ICI efficacy. This research direction may ultimately help identify CFLAR as a potential biomarker for patient selection in immunotherapy trials.

How can single-cell analysis technologies be integrated with CFLAR antibody-based detection methods?

Integrating single-cell analysis technologies with CFLAR antibody-based detection methods enables unprecedented resolution of cellular heterogeneity within complex tissues. This integration requires careful methodological considerations spanning multiple platforms. For single-cell RNA sequencing integration, researchers should first perform quality control filtering (50-9,000 genes per cell, <5% mitochondrial genes) followed by normalization and identification of hypervariable genes (typically 1,500) . Dimensional reduction techniques like uniform manifold approximation and projection (UMAP) can then visualize CFLAR expression distribution across cell clusters . Complementary protein-level detection using CFLAR antibodies in flow cytometry provides validation of transcriptomic findings and enables sorting of specific CFLAR-expressing populations for further functional studies. Most advanced is the integration with CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing), where CFLAR antibodies conjugated to oligonucleotide barcodes allow simultaneous detection of surface protein expression and transcriptome in the same cells. For spatial context preservation, researchers can employ imaging mass cytometry or multiplex immunofluorescence with CFLAR antibodies to correlate single-cell transcriptomic profiles with spatial distribution in tissue architecture . Together, these approaches provide multi-dimensional insights into CFLAR biology impossible with any single technique.

What are the common challenges in CFLAR antibody specificity and how can they be addressed?

Several specificity challenges commonly arise when working with CFLAR antibodies, each requiring targeted troubleshooting approaches. First, cross-reactivity between CFLAR isoforms (FLIPL, FLIPS, FLIPR) can complicate interpretation of Western blot bands appearing at different molecular weights (55kDa, 27kDa, and 25kDa respectively) . To address this, researchers should select antibodies with documented epitope specificity and validate with recombinant protein standards for each isoform. Second, CFLAR shares structural homology with caspase-8, potentially leading to false positive signals . This issue can be mitigated by performing parallel detection with caspase-8 specific antibodies and CFLAR knockdown controls to distinguish genuine signals. Third, CFLAR expression can be dynamically regulated by experimental conditions, causing inconsistent detection. Standardize sample handling procedures and document treatment conditions that may alter expression levels. Finally, fixation-sensitive epitopes in CFLAR may be destroyed during tissue processing for IHC/IF applications. To overcome this, compare multiple fixation protocols (paraformaldehyde, methanol, acetone) and antigen retrieval methods (citrate, EDTA, enzymatic) to identify optimal conditions for epitope preservation . Always validate antibody specificity through multiple complementary approaches, including genetic knockdown/knockout controls, detection with alternative antibodies targeting different epitopes, and pre-absorption controls.

How should researchers interpret conflicting CFLAR expression data between different detection methods?

Interpreting conflicting CFLAR expression data between different detection methods requires systematic evaluation of methodological variables and biological context. When mRNA levels (qPCR, RNA-seq) disagree with protein detection (Western blot, IHC), consider post-transcriptional regulation mechanisms that may cause discrepancies—CFLAR is subject to microRNA regulation and variable protein stability . For contradictions between different antibody-based methods, evaluate epitope accessibility differences: conformational epitopes may be preserved in native conditions (IF, flow cytometry) but denatured in others (Western blot). Sample preparation variables significantly impact results; for instance, the fixation method dramatically affects epitope preservation in IHC/IF (paraformaldehyde versus ethanol fixation) . When single-cell analyses reveal heterogeneity not captured in bulk tissue assessments, the apparent contradiction likely reflects biological reality rather than methodological error. To systematically resolve discrepancies: (1) verify antibody specificity through knockout/knockdown controls across all methods; (2) compare protein extraction efficiencies using multiple lysis buffers; (3) assess isoform-specific detection capabilities of each method; and (4) evaluate subcellular localization, as cytoplasmic versus nuclear CFLAR may yield different signals. Ultimately, triangulation of multiple orthogonal methods provides the most robust interpretation of CFLAR expression patterns.

What controls should be included when validating CFLAR antibody specificity in experimental settings?

A comprehensive validation strategy for CFLAR antibody specificity requires multiple layers of controls to ensure reliable experimental results. Positive controls should include samples with confirmed high CFLAR expression, such as stimulated T lymphocytes or cell lines transfected with CFLAR expression constructs . Negative controls are equally important and should incorporate CFLAR knockout or knockdown models (using CRISPR-Cas9 or siRNA) to verify signal absence when the target protein is depleted . Isoform-specific controls are essential when studying particular CFLAR variants; cells transfected with individual CFLAR isoforms (FLIPL, FLIPS, FLIPR) allow discrimination between antibodies that recognize specific or multiple isoforms . For immunohistochemistry applications, include tissue-specific controls such as lymphoid tissues (tonsil, spleen) which naturally express CFLAR at detectable levels . Peptide competition assays, where the antibody is pre-incubated with excess immunizing peptide, should show signal reduction if binding is specific. Technical controls must include secondary antibody-only staining to assess background levels. Additionally, cross-validation with multiple antibodies targeting different CFLAR epitopes provides stronger evidence of specificity than results from a single antibody. When publishing, document all validation controls performed, as this significantly enhances data reliability and reproducibility.

How can CFLAR antibodies be utilized in studying the role of anoikis resistance in cancer metastasis?

CFLAR antibodies provide powerful tools for investigating anoikis resistance—survival of cells detached from extracellular matrix—a critical mechanism in cancer metastasis. Experimental approaches should begin with comparative analysis of CFLAR expression in adherent versus suspension cultures of cancer cell lines using quantitative Western blotting with CFLAR antibodies (typically at 1:500-1:1000 dilution) . Flow cytometry with CFLAR antibodies can simultaneously measure cell death markers (Annexin V, propidium iodide) and CFLAR expression in single cells, revealing correlations between expression levels and anoikis resistance . For mechanistic studies, researchers should evaluate CFLAR's interaction with death receptors during matrix detachment through co-immunoprecipitation using CFLAR antibodies followed by Western blotting for binding partners like FADD and caspase-8 . In vivo relevance can be assessed through multiplex immunofluorescence of circulating tumor cells using CFLAR antibodies (1:1000 dilution) compared with primary tumor sections . Importantly, CFLAR expression should be manipulated through overexpression and silencing approaches to establish causality in anoikis resistance, with resulting phenotypes monitored via CFLAR antibody-based detection methods. This multifaceted approach reveals whether CFLAR serves as a biomarker or therapeutic target in preventing metastasis through the inhibition of anoikis resistance mechanisms.

What are the latest techniques for studying CFLAR post-translational modifications using specific antibodies?

Advanced techniques for studying CFLAR post-translational modifications (PTMs) using specific antibodies have significantly expanded our understanding of its regulation. Phosphorylation-specific CFLAR antibodies enable detection of key regulatory sites through phosphoproteomics workflows. Researchers should employ immunoprecipitation with pan-CFLAR antibodies followed by phospho-specific Western blotting, or direct immunoprecipitation with phospho-specific antibodies to enrich modified forms . For ubiquitination analysis, tandem ubiquitin binding entity (TUBE) pull-downs followed by CFLAR antibody detection identify poly-ubiquitinated CFLAR species. Mass spectrometry-based approaches coupled with CFLAR immunoprecipitation provide comprehensive PTM mapping, revealing novel modification sites beyond established phosphorylation and ubiquitination events. Proximity ligation assays (PLA) using CFLAR antibodies paired with PTM-specific antibodies (phospho, ubiquitin, SUMO) enable in situ visualization of modified CFLAR with subcellular resolution. For temporal dynamics, researchers can employ pulse-chase experiments with metabolic labeling followed by CFLAR immunoprecipitation to track modification turnover rates. Most cutting-edge is the application of "modification-specific interaction resin" technology, where engineered binding domains specific for particular PTMs are used alongside CFLAR antibodies in sequential immunoprecipitation to isolate precisely modified subpopulations. These techniques collectively provide mechanistic insights into how PTMs regulate CFLAR's stability, localization, and anti-apoptotic functions in both normal and disease states.

How can machine learning approaches enhance CFLAR antibody-based imaging analysis in cancer diagnostics?

Machine learning approaches are revolutionizing CFLAR antibody-based imaging analysis for cancer diagnostics through multiple sophisticated methodologies. Image segmentation algorithms can now automatically identify tissue compartments (tumor nests, stroma, infiltrating lymphocytes) in multiplexed immunofluorescence images containing CFLAR antibody staining, eliminating subjective manual annotation . Convolutional neural networks (CNNs) trained on CFLAR expression patterns can classify tumors with greater accuracy than traditional pathologist scoring, especially when integrated with clinical outcome data . For quantitative analysis, advanced algorithms measure not just CFLAR intensity but spatial relationships between CFLAR-positive cells and key immune populations (CD8+ T cells, M1 macrophages), revealing prognostically significant interaction patterns invisible to conventional analysis . Implementation requires: (1) annotated training datasets of multiplexed immunofluorescence images with validated CFLAR antibody staining; (2) preprocessing pipelines for normalization across different staining batches; (3) feature extraction algorithms specific to CFLAR subcellular localization patterns; and (4) integration with other biomarker data through ensemble learning approaches. Recent studies employing LASSO, Support Vector Machine, and Random Forest algorithms identified CFLAR as having high importance scores for diagnosing soft tissue sarcomas, demonstrating the power of combining machine learning with antibody-based detection . These computational approaches significantly enhance the diagnostic and prognostic value of CFLAR antibody staining beyond traditional visual assessment.