immt-1 Antibody

What is IMMT and why is it significant in cancer research?

IMMT (mitofilin) is an inner mitochondrial membrane protein that plays a crucial role in maintaining mitochondrial cristae morphology. Recent research has confirmed that IMMT expression is significantly higher in breast cancer tissues compared to normal tissues, serving as an independent prognostic factor for breast cancer patients. Analysis of The Cancer Genome Atlas (TCGA) database has revealed correlations between high IMMT expression and larger tumor size (>2 cm), increased Ki-67 expression (>15%), and HER-2 status, highlighting its potential as both a prognostic marker and therapeutic target in breast cancer treatment . IMMT is preferentially expressed in heart tissue but has been found to play significant roles in various cancer types, making it a valuable target for oncological investigations .

What are the primary applications for IMMT-1 antibody in experimental research?

IMMT-1 antibody has been validated for multiple experimental applications, including Western blotting (WB), immunohistochemistry (IHC), immunofluorescence/immunocytochemistry (IF/ICC), flow cytometry (FC), immunoprecipitation (IP), and enzyme-linked immunosorbent assay (ELISA). The antibody demonstrates species specificity for human, mouse, and rat samples, with documented citations for use across these species . In breast cancer research specifically, IMMT-1 antibody has been employed for immunohistochemical analysis of paraffin-embedded tissues to assess expression levels and correlate with clinicopathological parameters, as well as for protein detection in Western blot validation of knockdown experiments .

What is the recommended workflow for IMMT-1 antibody validation prior to experimental use?

A systematic validation workflow should begin with Western blot analysis to confirm specificity, using appropriate positive control cell lines (such as SK-BR-3 or MDA-MB-436 for breast cancer studies) and negative controls. For IMMT-1 antibody, detection of a protein band at 80-90 kDa (observed MW) should be anticipated despite the calculated MW of 90 kDa . Validation should continue with application-specific tests: for IHC, include known positive tissue samples alongside negative controls where primary antibody is substituted with buffer; for IF/ICC, verify subcellular localization pattern consistent with mitochondrial membrane localization; for IP experiments, confirm pull-down specificity with subsequent Western blot detection. Cross-validation with alternative IMMT antibody clones or IMMT knockdown samples provides additional confidence in antibody specificity .

How should IMMT-1 antibody be optimized for immunohistochemistry in clinical breast cancer specimens?

For optimal IHC staining of breast cancer specimens with IMMT-1 antibody, the following methodology has proven effective: First, fix tumor specimens in formalin overnight followed by paraffin embedding and sectioning at 4 mm thickness. After dewaxing with xylene and rehydrating through graded ethanol solutions, perform antigen retrieval using Tris-EDTA buffer (pH 9.0) with heat-mediated methods. Block endogenous peroxidase activity with 3% hydrogen peroxide for 20 minutes, then wash with PBS (3 times, 5 minutes each). Block with 10% goat serum for 30 minutes at 37°C before incubating with anti-IMMT antibody at optimal dilution (1:50 to 1:500, with 1:200 showing good results in breast cancer tissues) overnight at 4°C. Apply appropriate secondary antibody (e.g., from Servicabio, China) for 1 hour at room temperature, then visualize with DAB solution. Scoring should be performed independently by two pathologists, calculating IHC scores by multiplying the percentage of stained cells (scored 1-4) by staining intensity (scored 0-3) . This method has successfully demonstrated differential IMMT expression between cancerous and normal breast tissues.

What protocols are most effective for IMMT knockdown experiments in breast cancer cell lines?

For effective IMMT knockdown experiments in breast cancer cell lines, siRNA transfection has been successfully implemented using the following approach: Design siRNAs specifically targeting IMMT mRNA (example sequence: 5′-CCGGGAAAGUGUAGAGAAATT-3′) along with appropriate control siRNAs (example: 5′-UUCUCCGAACGUGUCACGUTT-3′). Seed breast cancer cells (such as SK-BR-3, MDA-MB-436, or MDA-MB-231) in 6-well plates and transfect with 20 nM of the siRNA using appropriate transfection reagents like GP-transfect-MATE or Lipofectamine RNAiMAX. Verify knockdown efficiency by Western blot 72-96 hours post-transfection, using IMMT-1 antibody (dilution 1:5000-1:50000) . Successfully transfected cells can then be used for downstream functional assays such as proliferation assays, mitochondrial morphology assessment, and metabolic pathway analysis. This approach has demonstrated that IMMT suppression results in reduced cell proliferation and alterations in mitochondrial cristae structure in breast cancer cells .

How should Western blotting be optimized for IMMT detection in cancer studies?

For optimal Western blot detection of IMMT in cancer studies, harvest cells using RIPA lysis buffer and quantify protein concentration. Load 30 μg of protein per lane and separate by SDS-PAGE. After transfer to PVDF membrane, block with appropriate blocking buffer and incubate with IMMT-1 antibody at a dilution of 1:5000-1:50000 (with 1:15000 showing excellent results in multiple cell lysates) . Use β-actin (anti-β-actin, A5441, Sigma-Aldrich) as loading control. Following secondary antibody incubation, visualize using enhanced chemiluminescence. When analyzing results, expect to observe IMMT at 80-90 kDa despite its calculated molecular weight of 90 kDa. For quantification, use software such as GelQuant.NET to measure signal intensity, normalizing to the loading control. This protocol has been validated across multiple cancer cell lines and provides reliable detection of IMMT expression levels for comparative analyses between control and experimental conditions .

How can IMMT-1 antibody be used to investigate mitochondrial dynamics in cancer progression?

IMMT-1 antibody serves as a powerful tool for investigating mitochondrial structural changes during cancer progression through multi-modal imaging approaches. Researchers should implement dual immunofluorescence staining, pairing IMMT-1 antibody (1:50-1:500 dilution) with other mitochondrial markers to analyze cristae remodeling in cancer cells. For optimal results, fix cells with 4% PFA, permeabilize with appropriate buffer, and co-stain with CL594-phalloidin for cytoskeletal context . Confocal microscopy with Z-stack acquisition allows three-dimensional reconstruction of mitochondrial networks. Electron microscopy of IMMT-immunogold labeled samples provides ultrastructural analysis of cristae morphology, particularly important when examining the consequences of IMMT knockdown on mitochondrial integrity. Combined with functional assays measuring cytochrome c release (as observed in BC cells after IMMT suppression), this approach has revealed that IMMT knockdown alters cristae structure and promotes cytochrome c release without triggering complete mitochondrial apoptosis . Time-lapse imaging of live cells transfected with fluorescent IMMT constructs further elucidates dynamic changes in mitochondrial morphology during cancer cell responses to metabolic stress.

What methodologies can link IMMT expression to metabolic reprogramming in cancer cells?

Investigating IMMT's role in metabolic reprogramming requires an integrated multi-omics approach. Begin with IMMT-1 antibody-based protein expression analysis in patient cohorts and cell lines, establishing correlation with metabolic phenotypes. Following IMMT knockdown in breast cancer cell lines (SK-BR-3, MDA-MB-436), implement comprehensive metabolic profiling through these sequential methods: (1) Measure glycolytic function using extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) via Seahorse XF analyzer; (2) Quantify glycolytic intermediates and TCA cycle metabolites using targeted metabolomics; (3) Assess expression changes in metabolic enzymes through quantitative proteomics; (4) Perform Gene Set Enrichment Analysis (GSEA) using the "Clusterprofile" package on TCGA database, setting FDR q<0.1 and P<0.05 to identify significantly enriched KEGG metabolic pathways . This approach has revealed that IMMT potentially modulates breast cancer advancement through interaction with 16 metabolic-related genes, with validated changes in glycolysis-related pathways following IMMT inhibition . Complementary in silico analysis using STRING can predict IMMT binding to metabolic proteins, generating testable hypotheses about protein-protein interactions driving metabolic effects.

What are common challenges in IMMT-1 antibody immunostaining and how can they be addressed?

Several technical challenges may arise during IMMT-1 antibody immunostaining. High background staining often results from insufficient blocking or excessive antibody concentration; address this by optimizing blocking conditions (extending 10% goat serum blocking to 45-60 minutes) and titrating antibody dilutions (starting with 1:200 and adjusting as needed) . Weak or absent staining may indicate inadequate antigen retrieval; optimize using heat-mediated retrieval with Tris-EDTA buffer (pH 9.0) rather than citrate buffer . For formalin-fixed paraffin-embedded tissues showing inconsistent results, extend antigen retrieval time and ensure proper fixation protocols (overnight formalin fixation has shown good results) . Non-specific nuclear staining can indicate over-permeabilization; reduce detergent concentration or incubation time. When comparing results across multiple specimens, standardize all protocol steps including fixation duration, section thickness (4 mm recommended), antibody incubation times, and DAB development . For multi-color immunofluorescence, spectral overlap between fluorophores can cause false positives; employ proper controls and sequential staining approaches with intermediate blocking steps between antibodies.

How should researchers interpret contradictory results between IMMT protein detection and functional outcomes?

When faced with contradictory results between IMMT protein detection and functional outcomes, implement a systematic analytical approach. First, verify antibody specificity through multiple controls: (1) include tissue/cells with known IMMT expression levels; (2) perform parallel experiments with alternative IMMT antibody clones; (3) validate with siRNA knockdown samples showing corresponding reduction in signal . For functional discrepancies, consider that IMMT's effects may be context-dependent due to: (a) compensation by related mitochondrial proteins (examine levels of other MICOS complex components); (b) cell-type specific roles (compare results across multiple cell lines); (c) incomplete knockdown efficiency (quantify protein reduction by Western blot densitometry); and (d) timing effects (perform time-course experiments from 24-96 hours post-knockdown). Recent research demonstrated that while IMMT suppression reduced cell proliferation and altered mitochondrial cristae with cytochrome c release, it did not trigger complete mitochondrial apoptosis – a seemingly contradictory result explained by activation of alternative survival pathways. Examine broader cellular responses through transcriptomic and proteomic analyses to identify compensatory mechanisms. Finally, consider post-translational modifications of IMMT that might affect antibody binding but not protein function, and vice versa.

How can immunotherapy approaches be combined with IMMT-targeted strategies in cancer treatment?

Emerging research suggests promising synergies between immunotherapy and IMMT-targeted approaches. Investigators should design studies evaluating combination therapies through a systematic framework: First, establish baseline IMMT expression in tumor tissues and tumor-infiltrating lymphocytes using IMMT-1 antibody immunohistochemistry (1:50-1:500 dilution) . Then implement co-culture systems combining IMMT-knockdown cancer cells with immune effectors (T cells, NK cells) to assess direct effects on immune recognition and killing. Evidence from related immunotherapeutic approaches such as ImmTAC (immune-mobilizing monoclonal TCR against cancer) shows that induced regulatory T (iTreg) cells can significantly impair redirected T-cell responses, but this suppression can be overcome by combining with anti-PD-1 antibodies . This suggests IMMT-targeted therapies should be evaluated in the context of regulatory T cell function, particularly since lung cancer patients failing to respond to immunotherapy showed significantly higher fractions of Treg cells in peripheral blood . Research protocols should include flow cytometric analysis of immune checkpoint expression (PD-1, CTLA-4) on T cells following exposure to IMMT-knockdown cancer cells, and assessment of T cell activation markers (CD25, CD107a, Granzyme B, Perforin) in response to combined IMMT-targeted and immune checkpoint blockade strategies . This approach builds on findings that immunosuppressive pathways may limit the efficacy of targeted therapies, pointing toward rational combination strategies.

What advanced imaging approaches can elucidate IMMT's role in mitochondrial network dynamics?

Advanced imaging approaches for studying IMMT's role in mitochondrial network dynamics should integrate multiple cutting-edge technologies. Implement structured illumination microscopy (SIM) or stimulated emission depletion (STED) super-resolution imaging using IMMT-1 antibody (1:50-1:500 dilution) co-stained with outer mitochondrial membrane markers to resolve cristae ultrastructure at 50-100 nm resolution. For dynamic studies, utilize live-cell lattice light-sheet microscopy with genetically encoded fluorescent IMMT constructs to capture real-time changes in mitochondrial morphology with minimal phototoxicity. Correlative light and electron microscopy (CLEM) provides powerful contextual insights by first imaging IMMT-immunolabeled cells using confocal microscopy, followed by transmission electron microscopy of the same cells to visualize cristae at nanometer resolution. For functional correlation, combine these approaches with mitochondrial membrane potential indicators like TMRM or JC-1, alongside measurements of mitochondrial calcium using targeted genetically encoded calcium indicators. Implement automated image analysis workflows using machine learning algorithms to quantify parameters including cristae density, junction points, and network connectivity. This multi-modal approach has revealed that IMMT suppression in breast cancer cells results in significant alterations to mitochondrial cristae morphology, concurrent with cytochrome c release but without triggering complete mitochondrial apoptosis – findings that challenge conventional understanding of mitochondrial structural-functional relationships in cancer.

How can multi-omics approaches integrate IMMT status with metabolic signatures in precision oncology?

A comprehensive multi-omics framework for integrating IMMT status with metabolic signatures should proceed through sequential analytical layers. Begin with parallel assessment of IMMT protein expression using IMMT-1 antibody in patient-derived samples via immunohistochemistry (1:50-1:500 dilution) and Western blotting (1:5000-1:50000 dilution) . Complement this with RNA-seq to capture transcriptional networks associated with different IMMT expression levels. Implement metabolomics profiling using liquid chromatography-mass spectrometry to identify metabolite signatures, with particular focus on glycolytic intermediates identified through GSEA analysis of TCGA data . Integrate proteomic data focusing on the 16 metabolic-related genes previously identified as potential IMMT interaction partners . For computational integration, employ multi-block partial least squares discriminant analysis to identify correlated patterns across omics layers. This approach should be applied to both clinical cohorts and experimental models where IMMT has been knocked down using validated siRNA approaches . To translate findings toward therapeutic applications, utilize the CRISPR-screen data from the Genomics of Drug Sensitivity in Cancer (GDSC) repository to identify potential drug candidates whose efficacy correlates with IMMT expression levels . Final validation should involve testing predicted therapeutic vulnerabilities in patient-derived organoids stratified by IMMT expression levels, creating a translational pipeline from multi-omics signatures to precision treatment strategies.