MYL1 Antibody

What is MYL1 protein and why is it significant in research?

MYL1, also known as Myosin Light Chain 1 (Alkali, Skeletal, Fast), functions as a crucial component of the hexameric ATPase motor protein complex in muscle tissue. It plays an essential role in regulating myosin activity, particularly in fast skeletal muscle contraction. MYL1 consists of two heavy chains combined with two nonphosphorylatable alkali light chains and two phosphorylatable regulatory light chains . Research significance lies in its involvement in muscle development, differentiation, and function, acting as an early marker of rapid muscle cell differentiation . Studies have also revealed its potential role as a prognostic biomarker in certain cancers, making it valuable for both basic muscle physiology and pathological investigations .

What are the key considerations when selecting an MYL1 antibody for research?

When selecting an MYL1 antibody, researchers should consider several critical factors: (1) Epitope specificity - different antibodies target different amino acid sequences (e.g., AA 1-150, AA 1-80, AA 101-135) , which affects recognition of specific isoforms or conformational states; (2) Host species - available options include rabbit polyclonal and mouse monoclonal antibodies, which must be compatible with your secondary detection systems and sample species to avoid cross-reactivity ; (3) Clonality - polyclonal antibodies offer broader epitope recognition while monoclonal antibodies (e.g., L-13 clone) provide higher specificity ; (4) Validated applications - confirm the antibody has been validated for your specific application (WB, IHC, IF, IP, ELISA) ; and (5) Reactivity spectrum - verify cross-reactivity with your species of interest (human, mouse, rat) .

How does MYL1 expression differ across tissue types and developmental stages?

MYL1 exhibits a distinctive tissue-specific expression pattern, predominantly in fast skeletal muscle fibers. During embryonic development, MYL1 serves as an early marker of muscle cell differentiation, particularly evident in zebrafish muscle fiber development . Expression levels vary significantly between different muscle fiber types, with highest expression in fast-twitch muscle fibers. Developmental regulation involves temporal control, with specific expression patterns during myogenesis and muscle regeneration. In pathological conditions, aberrant expression has been documented in rhabdomyosarcoma tumor tissues (significantly upregulated compared to healthy skeletal muscle) and paradoxically downregulated in head and neck squamous cell carcinoma (HNSCC) compared to normal tissues . These tissue-specific and developmental expression patterns necessitate careful selection of positive and negative controls when using MYL1 antibodies in experimental designs.

What are the optimal protocols for using MYL1 antibodies in immunohistochemistry (IHC)?

For optimal IHC with MYL1 antibodies, begin with proper tissue preparation: fix tissues in 10% neutral buffered formalin for 24-48 hours, followed by paraffin embedding and sectioning at 4-6 μm thickness. Antigen retrieval is critical - use citrate buffer (pH 6.0) with heat-induced epitope retrieval at 95-98°C for 15-20 minutes. For blocking, apply 3-5% normal serum (from the same species as the secondary antibody) with 1% BSA for 1 hour at room temperature. Antibody dilution requires optimization, typically 1:100-1:500 for commercial MYL1 antibodies . Incubate primary antibody overnight at 4°C in a humidified chamber. For detection, use appropriate HRP-conjugated secondary antibody systems followed by DAB or AEC chromogen development. Counterstain with hematoxylin for 30-60 seconds. Include positive controls (skeletal muscle tissue) and negative controls (antibody diluent without primary antibody) in each experiment. For multiplexed imaging with other markers, consider fluorescent-based detection systems with appropriate spectral separation .

How can researchers optimize Western blot protocols for detecting MYL1 protein?

For optimal Western blot detection of MYL1 (molecular weight ~21 kDa), prepare protein samples from tissues or cells using RIPA buffer supplemented with protease inhibitors. Load 20-40 μg of total protein per lane on 12-15% SDS-PAGE gels (higher percentage recommended due to MYL1's low molecular weight). For transfer, use PVDF membranes (0.2 μm pore size) with a semi-dry or wet transfer system (25V for 1.5 hours or 30V overnight at 4°C). Block membranes with 5% non-fat dry milk in TBST for 1 hour at room temperature. Dilute primary MYL1 antibodies at 1:500-1:2000 in 5% BSA/TBST and incubate overnight at 4°C with gentle rocking . After washing (3×10 min with TBST), apply HRP-conjugated secondary antibody (1:5000-1:10000) for 1 hour at room temperature. For enhanced sensitivity, especially when detecting low expression levels as seen in certain cancer samples, use ECL Plus or SuperSignal West Femto substrate . Validate specificity with recombinant MYL1 protein as a positive control. For normalization, reprobe with housekeeping proteins such as GAPDH or β-actin after membrane stripping.

What approaches should be used for quantitative analysis of MYL1 expression in tissue samples?

Quantitative analysis of MYL1 expression requires a multi-method approach for robust results. For RT-qPCR analysis, design primers specific to MYL1 transcript variants, using reference genes appropriate for the tissue type (e.g., GAPDH, β-actin, or 18S rRNA). Calculate relative expression using the 2^-ΔΔCt method with appropriate normalization controls . For protein quantification, densitometric analysis of Western blot bands using ImageJ or similar software provides semi-quantitative data when normalized to loading controls. For tissue samples, quantitative immunohistochemistry using digital image analysis software (QuPath, ImageJ, or HALO) allows measurement of staining intensity, percentage of positive cells, and H-score calculations . In high-throughput studies, consider utilizing tissue microarrays for standardized comparison across multiple samples. For absolute quantification, develop ELISA-based methods using recombinant MYL1 protein standards. Multi-method correlation analysis comparing mRNA and protein levels provides more comprehensive understanding of MYL1 regulation, as post-transcriptional mechanisms may lead to discrepancies between transcript and protein levels, as observed in HNSCC tissues .

How is MYL1 implicated in cancer progression, and what methodologies best capture its role?

To investigate MYL1's role in cancer, researchers should employ a multi-modal approach: (1) Utilize immunohistochemistry with carefully validated antibodies to assess spatial distribution in tumor tissues; (2) Conduct dual immunofluorescence studies to correlate MYL1 expression with proliferation markers (Ki-67) or invasion markers (MMPs); (3) Perform in vitro functional assays following MYL1 overexpression or knockdown to assess effects on proliferation, migration (as demonstrated in Fadu cells where MYL1 overexpression promoted migration) , and invasion; (4) Analyze signaling pathway interactions, particularly with EGF/EGFR, which showed increased expression with MYL1 overexpression ; (5) Conduct co-immunoprecipitation studies to identify MYL1 protein interaction partners in cancer cells; and (6) Correlate MYL1 expression with immune cell infiltration profiles, as positive correlations have been observed with various immune cell types in HNSCC .

What are the most reliable methods to evaluate MYL1 as a prognostic biomarker in clinical samples?

To evaluate MYL1 as a prognostic biomarker, implement a systematic approach combining multiple techniques and rigorous statistical analysis. Begin with immunohistochemical staining of tissue microarrays containing adequate sample sizes (n>30) with appropriate clinicopathological data and follow-up information . Use standardized scoring systems (H-score or Allred) with multiple independent pathologists for evaluation. Verify antibody specificity through Western blot analysis of tissue lysates.

For clinical implementation, develop standardized cut-off values based on ROC curve analysis to stratify patients into high and low expression groups. Validate findings in independent cohorts using the same methodology. Consider developing more accessible assays for clinical application, such as multiplex immunoassays that include MYL1 alongside other established biomarkers.

How does MYL1 expression correlate with immune cell infiltration in tumors, and what techniques best demonstrate this relationship?

MYL1 expression shows significant correlations with immune cell infiltration in tumor microenvironments, particularly in HNSCC. Positive correlations have been observed between MYL1 expression and infiltration of CD4+ T cells (r>0.1, p<0.01), and various other immune cell populations including Tcm CD8 cells, Tcm CD4+ cells, NK cells, mast cells, NKT cells, Tfh cells, and Treg cells .

To investigate these relationships, researchers should employ: (1) Multiplex immunofluorescence staining to simultaneously visualize MYL1 expression and immune cell markers within the spatial context of tumor architecture; (2) Flow cytometry analysis of dissociated tumor samples to quantify immune cell subpopulations and correlate with MYL1 expression levels; (3) Single-cell RNA sequencing to explore cell-specific expression patterns and potential paracrine signaling mechanisms; (4) Computational deconvolution of bulk transcriptome data using algorithms such as CIBERSORT, xCell, or MCP-counter to estimate immune cell proportions; (5) In vitro co-culture systems combining MYL1-expressing tumor cells with immune cell populations to assess functional interactions; and (6) Cytokine/chemokine profiling of MYL1-high versus MYL1-low tumors to identify potential immunomodulatory mechanisms .

The positive correlation between MYL1 and CD4+ T cell activation suggests potential involvement in immune regulation, which may contribute to its prognostic significance in cancers such as HNSCC.

How can researchers apply MYL1 antibodies in single-cell analysis techniques?

For advanced single-cell analysis using MYL1 antibodies, researchers should consider several cutting-edge approaches: (1) Single-cell mass cytometry (CyTOF) - conjugate MYL1 antibodies with rare earth metals for high-dimensional phenotyping alongside other markers (optimize metal selection to avoid signal overlap); (2) Imaging mass cytometry - apply metal-conjugated MYL1 antibodies to tissue sections for spatial single-cell analysis with subcellular resolution; (3) Single-cell Western blotting - adapt microfluidic platforms to detect MYL1 in individual cells, allowing correlation with other protein markers; (4) Proximity ligation assay (PLA) - combine MYL1 antibodies with antibodies against potential interaction partners to visualize protein complexes at single-molecule resolution within individual cells; (5) CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) - use oligonucleotide-tagged MYL1 antibodies to simultaneously analyze protein expression and transcriptome in the same cell .

For optimal results, validate antibody specificity in single-cell applications using known positive controls (skeletal muscle cells) and negative controls (cell types not expressing MYL1). Consider custom conjugation of high-specificity monoclonal antibodies like the L-13 clone to fluorophores, metals, or oligonucleotides optimized for your specific single-cell platform .

What are the current approaches for studying MYL1 protein-protein interactions in muscle and non-muscle contexts?

To investigate MYL1 protein-protein interactions, researchers should implement complementary approaches for comprehensive analysis: (1) Co-immunoprecipitation (Co-IP) with highly specific MYL1 antibodies followed by mass spectrometry to identify interaction partners - use physiologically relevant buffers to maintain native interactions; (2) Proximity-dependent biotin identification (BioID) by fusing MYL1 to a biotin ligase to label proximal proteins in living cells; (3) FRET (Förster Resonance Energy Transfer) or BRET (Bioluminescence Resonance Energy Transfer) assays using fluorophore-conjugated MYL1 antibodies or fusion proteins to detect direct interactions in real-time; (4) Yeast two-hybrid screening to identify binary interactions, especially useful for mapping interaction domains; (5) Protein complementation assays such as split-luciferase reconstitution to visualize interactions in living cells .

For muscle contexts, focus on interactions with myosin heavy chains and regulatory proteins of the contractile apparatus. In non-muscle contexts like cancer cells, investigate interactions with cytoskeletal components, signaling molecules (particularly EGF/EGFR pathway components), and potential immunomodulatory factors . Computational analysis using tools like GeneMANIA can predict functional associations based on existing datasets, guiding experimental design. Validate key interactions using multiple methodologies and assess their functional relevance through domain mapping and site-directed mutagenesis studies.

How can CRISPR-Cas9 genome editing be combined with MYL1 antibody-based detection for functional genomics studies?

Integrating CRISPR-Cas9 genome editing with MYL1 antibody-based detection creates powerful approaches for functional genomics: (1) Generate MYL1 knockout cell lines using CRISPR-Cas9 and validate deletion efficiency using Western blot with MYL1 antibodies; (2) Create knock-in cell lines expressing tagged MYL1 variants (FLAG, HA, or fluorescent proteins) for antibody-based tracking of modified protein; (3) Implement CRISPR interference (CRISPRi) or activation (CRISPRa) systems targeting MYL1 promoter/enhancer regions, then quantify expression changes via immunodetection; (4) Perform CRISPR screens targeting genes potentially regulating MYL1, using immunofluorescence or flow cytometry with MYL1 antibodies as readouts; (5) Generate cellular models with CRISPR-edited versions of MYL1 containing patient-specific mutations, then analyze protein expression, localization, and function using validated antibodies .

For muscle development studies, combine CRISPR-edited myoblast models with time-course immunofluorescence analysis during differentiation. For cancer research, edit MYL1 in cell lines like Fadu (HNSCC) or rhabdomyosarcoma models, then assess phenotypic changes in migration, proliferation, and immune interactions . Design carefully controlled experiments with appropriate isotype controls for antibody specificity verification, especially when analyzing subtle phenotypic changes following genome editing.

How should researchers address discrepancies between mRNA and protein expression levels of MYL1?

When encountering discrepancies between MYL1 mRNA and protein levels, as observed in HNSCC tissues , implement a systematic troubleshooting approach: (1) Verify technical accuracy by using multiple primer pairs targeting different MYL1 exons for RT-qPCR and different antibodies recognizing distinct epitopes (e.g., antibodies targeting AA 1-150 versus AA 101-135) ; (2) Assess post-transcriptional regulation by examining microRNA expression that might target MYL1 transcripts using prediction algorithms (TargetScan, miRDB) followed by luciferase reporter assays; (3) Evaluate protein stability and turnover using cycloheximide chase assays combined with Western blotting to determine MYL1 half-life in different cellular contexts; (4) Investigate potential alternative splicing using RT-PCR with exon-spanning primers followed by sequencing to identify tissue-specific isoforms that might be differentially recognized by antibodies; (5) Examine post-translational modifications using phospho-specific antibodies or mass spectrometry to identify modifications affecting protein detection or stability .

The biological significance of these discrepancies may reveal important regulatory mechanisms - for instance, in HNSCC, post-transcriptional suppression might represent an attempted compensatory mechanism within the tumor microenvironment. Document methodological details meticulously to distinguish biological phenomena from technical artifacts.

What are the most common sources of false positives/negatives when using MYL1 antibodies, and how can they be mitigated?

Common sources of false results with MYL1 antibodies include: (1) Cross-reactivity with other myosin light chain family members - MYL1 shares sequence homology with MYL3, MYL4, and other isoforms, which can cause cross-reactivity. Mitigate by using highly specific monoclonal antibodies like L-13 clone and validate with recombinant protein controls; (2) Epitope masking due to protein-protein interactions or conformational changes - optimize antigen retrieval methods for IHC and sample preparation for Western blotting to ensure epitope accessibility; (3) Background signals in skeletal muscle-adjacent tissues - implement stringent blocking protocols (5% BSA with 5% normal serum) and titrate antibody concentration carefully; (4) Variation in fixation effects - standardize fixation protocols (10% neutral buffered formalin for 24 hours) and validate antibody performance across different fixation conditions; (5) Detection system limitations - use high-sensitivity detection methods for low-expressing samples, particularly in cancer tissues with variable MYL1 expression .

Implement rigorous controls including positive controls (skeletal muscle), negative controls (tissues known not to express MYL1), absorption controls (pre-incubating antibody with recombinant MYL1), and isotype controls (matched immunoglobulin at equivalent concentration). When possible, validate key findings using orthogonal detection methods or multiple antibodies targeting different epitopes .

How can researchers interpret contradictory findings regarding MYL1's role in different cancer types?

The contradictory findings regarding MYL1's role across cancer types (upregulated in rhabdomyosarcoma but downregulated in HNSCC protein levels) require careful analytical approaches: (1) Conduct comprehensive meta-analysis of MYL1 expression across multiple cancer types using TCGA, GEO, and other public databases, stratifying by cancer subtype, stage, and molecular classification; (2) Perform cell-type specific analysis within tumor samples using single-cell RNA-seq or spatial transcriptomics to determine if expression patterns differ between malignant cells, stromal components, and infiltrating immune cells; (3) Investigate context-dependent functions through comparative pathway analysis using phosphoproteomics and transcriptional profiling in MYL1-modulated cell models of different cancer types; (4) Examine tissue-of-origin effects by comparing MYL1's normal physiological role in tissues from which different cancers derive; (5) Analyze mutational status and epigenetic modifications of MYL1 and its regulatory elements across cancer types to identify potential mechanisms for differential expression patterns .

When interpreting seeming contradictions, consider MYL1's dual functionality: its structural role in muscle contraction versus potential non-canonical functions in cell migration, signaling pathway modification (e.g., EGF/EGFR pathway), and immune cell interactions . The apparent paradox of decreased expression but negative prognostic impact in HNSCC suggests complex regulatory mechanisms that may involve compensatory pathways or altered protein functionality rather than simple expression levels.