MFF Antibody

What is MFF and why are antibodies against it important for research?

Mitochondrial fission factor (MFF) is a human outer membrane protein encoded by the MFF gene (also known as EMPF2 and C2orf33). It plays a critical role in mitochondrial fission and peroxisome morphology . Structurally, the protein is approximately 38.5 kilodaltons in mass, though multiple isoforms exist due to alternative splicing .

MFF antibodies are essential research tools because:

• They enable visualization and quantification of MFF protein in various experimental contexts

• They facilitate investigation of mitochondrial dynamics, which is implicated in numerous diseases

• They allow researchers to study the interaction between MFF and other proteins such as Drp1 (Dynamin-related protein 1)

• They help elucidate the role of MFF in normal cellular function and pathological conditions

MFF has emerged as a particularly important target in cancer research, with studies showing that MFF isoforms are overexpressed in non-small cell lung cancer and form complexes with voltage-dependent anion channel-1 (VDAC1) .

What are the common applications for MFF antibodies in research?

MFF antibodies are utilized across several laboratory techniques, each offering distinct advantages for specific research questions:

When designing experiments, researchers should consider that MFF antibodies typically detect multiple isoforms, which may appear at different molecular weights on Western blots .

How should I validate an MFF antibody before using it in my experiments?

Antibody validation is critical for ensuring reliable research results. For MFF antibodies, follow these methodological steps:

• Genetic validation: Test the antibody in MFF knockout or knockdown models to confirm specificity . This is considered the gold standard for antibody validation.

• Multiple antibody approach: Compare results using at least two different antibodies targeting distinct epitopes of MFF .

• Western blot analysis: Verify that the antibody detects bands at the expected molecular weights (typically 25-29 kDa and 35-38 kDa depending on the isoform) .

• Cross-reactivity testing: If working with non-human samples, test the antibody against the species of interest, as reactivity can vary .

• Application-specific validation: Even if an antibody works well for Western blot, it may not be suitable for immunohistochemistry or other applications .

Remember that antibody characterization should document: (i) binding to the target protein; (ii) binding to the target protein in complex mixtures; (iii) absence of binding to non-target proteins; and (iv) performance under your specific experimental conditions .

How can I detect specific MFF isoforms when multiple splice variants exist?

Detecting specific MFF isoforms requires careful antibody selection and experimental design:

• Epitope mapping: Choose antibodies raised against epitopes unique to your isoform of interest. Review the immunogen sequence information provided by manufacturers .

• Recombinant isoform controls: Express recombinant MFF isoforms as positive controls to identify the molecular weight of each variant .

• RT-PCR verification: Complement protein detection with isoform-specific RT-PCR to confirm expression patterns at the mRNA level .

• Isoform-specific knockdown: Design siRNAs targeting unique exons of specific isoforms. For example, siRNA targeting exon 8 will affect only isoforms containing this exon .

Research has demonstrated that MFF has several splice isoforms with functional differences. One significant finding is that the major phosphoacceptor site of MFF (Ser172 of human isoform 1/Ser146 of human isoforms 2-5) lies within an AMPK phosphorylation motif that spans differentially spliced exons, suggesting that MFF splice isoforms are phosphorylated by AMPK to varying degrees .

What are the optimal conditions for studying MFF-Drp1 interactions using antibody-based approaches?

Studying MFF-Drp1 interactions requires careful experimental design:

• Co-immunoprecipitation protocol:

  • Use mild lysis conditions (e.g., 1% Triton X-100, no SDS) to preserve protein-protein interactions

  • Pre-clear lysates with protein A/G beads to reduce non-specific binding

  • Immunoprecipitate with anti-MFF antibody (e.g., Proteintech 17090-1-AP at 1:2000 dilution)

  • Western blot for Drp1 using specific antibodies (e.g., Cell Signaling D6C7 at 1:1000)

• Proximity ligation assay (PLA):

  • Provides in situ detection of protein interactions with high specificity

  • Use validated anti-MFF and anti-Drp1 antibodies from different species

  • Optimize fixation conditions (typically 3.7% formaldehyde for 10 minutes)

• Mitochondrial fractionation:

  • Isolate mitochondria before immunoprecipitation to enrich for relevant interactions

  • Verify fraction purity using markers (e.g., Tom20 for outer mitochondrial membrane)

Research has shown that MFF promotes the recruitment and association of Drp1 to the mitochondrial surface, playing a crucial role in mitochondrial fission . The AMPK-dependent phosphorylation of MFF enhances Drp1 recruitment to mitochondria, particularly in response to mitochondrial stress .

How can I address potential cross-reactivity issues when using MFF antibodies in tissue samples?

Cross-reactivity is a significant concern when using antibodies in complex tissue samples. For MFF antibodies, implement these methodological approaches:

• Comprehensive controls:

  • Positive controls: Tissues known to express high levels of MFF (heart, brain, muscle)

  • Negative controls: MFF knockout tissues or tissues treated with MFF-targeting siRNA

  • Peptide competition assays: Pre-incubate antibody with immunizing peptide to block specific binding

• Orthogonal validation techniques:

  • Combine antibody detection with mRNA localization (in situ hybridization)

  • Compare results from multiple antibodies targeting different epitopes

  • Use mass spectrometry to confirm protein identity in immunoprecipitated samples

• Tissue-specific optimization:

  • Adjust antigen retrieval methods (TE buffer pH 9.0 is recommended for MFF)

  • Optimize antibody concentration for each tissue type

  • Consider tissue-specific blocking agents to reduce background

Research has shown that MFF expression varies significantly between tissues, with highest expression in tissues with high energy demands including heart, brain, and muscles . This tissue-specific expression pattern can serve as an internal control for antibody specificity.

What strategies can overcome challenges in detecting phosphorylated forms of MFF?

Detecting phosphorylated MFF presents unique challenges due to low abundance and dynamic regulation. Implement these methodological approaches:

• Phospho-specific antibodies:

  • Use antibodies specifically targeting phosphorylated residues (e.g., Ser172/Ser146)

  • Verify specificity with dephosphorylation controls (treat samples with phosphatase)

  • Include positive controls (e.g., cells treated with AMPK activators like AICAR)

• Phosphatase inhibitors:

  • Always include phosphatase inhibitors in lysis buffers (e.g., sodium fluoride, sodium orthovanadate)

  • Process samples quickly and keep them cold to minimize dephosphorylation

• Phospho-enrichment:

  • Use phospho-protein enrichment kits before Western blotting

  • Consider Phos-tag™ SDS-PAGE to separate phosphorylated from non-phosphorylated forms

• Mass spectrometry:

  • For unbiased phosphosite identification, immunoprecipitate MFF and analyze by LC-MS/MS

  • Use targeted mass spectrometry (MRM/PRM) for quantitative analysis of specific phosphosites

Research has demonstrated that AMPK directly phosphorylates MFF at two sites to enhance recruitment of Drp1 to mitochondria, controlling the ability of MFF to drive acute mitochondrial fission in response to mitochondrial stress .

How can I optimize MFF antibody staining for co-localization studies with mitochondrial markers?

Co-localization studies require careful optimization to generate reliable data:

• Sample preparation protocol:

  • Fix cells with 3.7% formaldehyde for 10 minutes at room temperature

  • For mitochondrial visualization, add 20 nM MitoTracker Red 30 minutes before fixation

  • Permeabilize with 0.2% Triton X-100 for 5 minutes

  • Block with 1% BSA in PBS for 15-30 minutes

• Antibody optimization:

  • Test several dilutions of primary antibody (typically 1:50-1:500 for IF)

  • Use fluorophore-conjugated secondary antibodies with non-overlapping emission spectra

  • Include single-staining controls to assess bleed-through

• Imaging considerations:

  • Use confocal microscopy for optimal resolution of mitochondrial structures

  • Acquire z-stacks to capture the full volume of mitochondrial networks

  • Apply deconvolution to improve signal-to-noise ratio

• Quantitative analysis:

  • Use specialized co-localization software (e.g., JACoP plugin for ImageJ)

  • Calculate Pearson's or Mander's coefficients for quantitative assessment

  • Compare co-localization metrics across different experimental conditions

Research has shown that MFF localizes to both mitochondria and peroxisomes, necessitating careful discrimination between these organelles in co-localization studies .

What considerations are important when using MFF antibodies in studies of neurodegenerative diseases?

Using MFF antibodies in neurodegenerative disease research requires special considerations:

• Tissue handling:

  • Post-mortem interval significantly affects mitochondrial protein integrity

  • Document and control for PMI across samples

  • Consider using perfusion fixation for animal models to preserve mitochondrial morphology

• Disease-specific modifications:

  • Oxidative stress in neurodegenerative diseases may alter MFF epitopes

  • Test antibody performance in disease models before clinical samples

  • Consider extracting samples with reducing agents to preserve epitope recognition

• Brain region specificity:

  • MFF expression and function may vary across brain regions

  • Use neuroanatomical markers to identify specific brain regions

  • Compare MFF staining patterns between affected and unaffected regions

• Cell-type specificity:

  • Co-stain with neuronal, glial, and vascular markers to determine cell-type specific changes

  • Consider laser capture microdissection for cell-type specific analysis

  • Use neuron-specific or glia-specific promoters in genetic manipulation studies

NeuroMab offers mouse monoclonal antibodies optimized for use in mammalian brain studies, with emphasis on antibodies useful in immunohistochemistry and Western blots . Their validation approach includes screening approximately 1,000 clones in parallel ELISAs against both the immunogen and heterologous cells expressing the antigen that have been fixed and permeabilized to mimic brain tissue preparation .

How can MFF antibodies be employed to investigate mitochondrial dynamics in cancer research?

MFF has emerged as a significant target in cancer research, with specialized methodological approaches:

• Cancer tissue analysis:

  • Compare MFF expression between tumor and adjacent normal tissue

  • Correlate MFF levels with clinical outcomes and tumor characteristics

  • Examine MFF-VDAC1 complex formation in clinical samples

• Therapeutic targeting strategies:

  • Use MFF antibodies to validate knockdown efficiency in functional studies

  • Monitor MFF-protein interactions (particularly with VDAC1) in response to treatments

  • Employ MFF peptide inhibitors that disrupt MFF-VDAC1 interaction

• Mitochondrial dynamics assessment:

  • Quantify mitochondrial morphology parameters (length, interconnectivity, circularity)

  • Monitor mitochondrial membrane potential in response to MFF manipulation

  • Assess mitochondrial-dependent cell death pathways

Research has shown that MFF isoforms (MFF1 and MFF2) are overexpressed in patients with non-small cell lung cancer and form complexes with voltage-dependent anion channel-1 (VDAC1), a key regulator of mitochondrial outer membrane permeability . A cell-permeable MFF Ser223-Leu243 d-enantiomeric peptidomimetic has been developed that disrupts the MFF-VDAC1 complex, triggering cell death in various tumor types but having no effect on normal cells .

What are the best practices for multiplexing MFF antibodies with other mitochondrial protein markers?

Multiplexing MFF antibodies requires careful selection of compatible antibodies and detection systems:

• Antibody compatibility planning:

  • Select primary antibodies from different host species (e.g., rabbit anti-MFF with mouse anti-Tom20)

  • Alternatively, use directly conjugated primary antibodies

  • Consider using MFF antibodies of non-IgG1 subclasses to facilitate multiplex labeling with subclass-specific secondaries

• Sequential staining protocol:

  • If antibodies are from the same species, use sequential staining with intermediate blocking

  • Apply first primary antibody, detect with secondary, then block with excess unconjugated secondary

  • Apply second primary and detect with differently labeled secondary

• Spectral considerations:

  • Choose fluorophores with minimal spectral overlap

  • Include single-stained controls for spectral unmixing

  • Consider brightness differences when selecting fluorophores

• Advanced multiplexing techniques:

  • Tyramide signal amplification allows use of same-species antibodies

  • Mass cytometry (CyTOF) enables high-dimensional analysis using metal-conjugated antibodies

  • Cyclic immunofluorescence permits sequential staining and imaging rounds

When designing multiplex experiments, researchers should consider that NeuroMab gives "special attention given to candidates with less common non-IgG1 IgG subclasses that can facilitate simultaneous multiplex labeling with subclass-specific secondary antibodies" .

How can researchers contribute to improving MFF antibody validation standards?

Researchers can play a crucial role in enhancing antibody validation standards through these methodological approaches:

• Comprehensive reporting:

  • Document detailed antibody information (catalog number, lot number, dilution, incubation conditions)

  • Report both positive and negative results from antibody validation experiments

  • Share validation protocols through repositories or protocol-sharing platforms

• Independent validation:

  • Validate antibodies using at least two different methods (e.g., Western blot and immunofluorescence)

  • Include genetic controls (knockout/knockdown) whenever possible

  • Compare results across multiple antibodies targeting different epitopes

• Community engagement:

  • Submit data to antibody validation initiatives such as the Antibody Registry

  • Participate in collaborative validation efforts like the Human Protein Atlas

  • Provide feedback to manufacturers about antibody performance

• Advanced validation approaches:

  • Consider implementing the "five pillars" of antibody validation: genetic strategies, orthogonal strategies, independent antibody strategies, recombinant expression, and immunocapture MS

  • Contribute to open science initiatives that promote antibody validation data sharing

The scientific community has recognized the "antibody characterization crisis," with estimates that approximately 50% of commercial antibodies fail to meet basic standards for characterization, resulting in financial losses of $0.4-1.8 billion per year in the United States alone .

What emerging technologies are improving the specificity and reproducibility of MFF antibody-based research?

Several technological advances are enhancing antibody-based research reliability:

• Recombinant antibody technology:

  • Recombinant MFF antibodies offer greater reproducibility than polyclonal antibodies

  • Sequence-defined antibodies eliminate lot-to-lot variation

  • Enable precise epitope targeting through antibody engineering

• CRISPR-based validation:

  • CRISPR/Cas9 knockout cell lines serve as definitive negative controls

  • Epitope tagging of endogenous MFF allows validation against tag-specific antibodies

  • Homology-directed repair can introduce specific mutations to test epitope requirements

• Advanced imaging technologies:

  • Super-resolution microscopy provides nanoscale visualization of MFF localization

  • Expansion microscopy physically enlarges specimens for improved resolution

  • Cryo-electron tomography enables structural studies of MFF in its native environment

• AI and computational approaches:

  • Machine learning algorithms help predict antibody specificity and cross-reactivity

  • Computational epitope mapping improves antibody design

  • Deep learning models like AlphaFold aid in predicting antibody-antigen complexes

Representatives from various companies at recent workshops have presented recombinant antibody generation technologies, with demonstrations showing that "recombinant antibodies were more effective than polyclonal antibodies, and far more reproducible" .

How will advancements in protein structure prediction impact MFF antibody development and application?

Protein structure prediction is revolutionizing antibody research through several methodological approaches:

• Structure-guided epitope selection:

  • AlphaFold and other prediction tools enable visualization of MFF's three-dimensional structure

  • Allows identification of surface-exposed, unique epitopes for antibody generation

  • Facilitates prediction of post-translational modifications that might affect antibody binding

• Antibody-antigen interaction modeling:

  • Predicts binding interface between MFF and antibodies

  • Helps optimize antibody affinity through rational design

  • Enables identification of potential cross-reactive epitopes

• Isoform-specific targeting:

  • Structural differences between MFF isoforms can be leveraged for isoform-specific antibodies

  • Predicts conformational changes that may expose or hide epitopes

  • Identifies conserved regions across species for cross-reactive antibodies

• Application-specific optimization:

  • Predicts how fixation or denaturation affects epitope accessibility

  • Guides selection of antibodies suitable for specific applications

  • Helps design recombinant antibody fragments with optimal properties

Deep learning models like AlphaFold will likely play increasingly important roles "in the future development and optimization of antibodies" by enabling "predictions of the antibody-antigen complex, aid in the identification of the epitope targeted, and help determine if folding, post-translational modifications or other issues may influence the output from use of the antibody" .