KIF11 Antibody, HRP conjugated

What is KIF11 and what role does it play in cellular processes?

KIF11 (Kinesin Family Member 11) is a motor protein that plays a critical role in mitosis, specifically in the formation and maintenance of bipolar spindles. It functions as a microtubule-based motor that is essential for proper chromosome segregation during cell division . Beyond its canonical role in mitosis, KIF11 has been implicated in various cellular processes including the immune system, where it interacts with Tat protein to promote apoptosis in CD4+ cells . It is also known by several other names including Eg5, HKSP (Kinesin-like spindle protein), and TRIP-5 (Thyroid receptor-interacting protein 5) . Structurally, KIF11 is a 119.2 kilodalton protein that has been extensively studied for its potential as a therapeutic target in cancer research.

How are KIF11 antibodies, particularly HRP-conjugated variants, typically produced?

KIF11 antibodies, including HRP-conjugated variants, are typically produced using recombinant protein fragments as immunogens. For example, the KIF11 antibody described in the search results was generated using a recombinant Human Kinesin-like protein KIF11 protein fragment (amino acids 920-1038) . The production process generally involves:

• Immunogen preparation: Expression and purification of a recombinant KIF11 protein fragment

• Host immunization: Typically using rabbits for polyclonal antibodies or mice/rats for monoclonal antibodies

• Antibody purification: Usually through Protein G affinity chromatography, achieving >95% purity

• Conjugation process: Chemical coupling of horseradish peroxidase (HRP) to the purified antibody

• Quality control: Testing for specificity, sensitivity, and application performance (e.g., ELISA)

The resulting HRP-conjugated antibodies are formulated in a stabilizing buffer, typically containing preservatives like Proclin 300 and cryoprotectants like glycerol .

What is the significance of KIF11 overexpression in cancer research?

KIF11 overexpression has emerged as a significant biomarker in cancer research due to its strong correlation with poor prognosis in multiple cancer types. Research findings indicate:

In hepatocellular carcinoma specifically, KIF11 expression was found to be:

These findings suggest that KIF11 functions as an oncogene in multiple cancer types, making it a valuable research target for both diagnostic and therapeutic applications .

What are the optimal protocols for using KIF11 Antibody, HRP conjugated in immunohistochemistry (IHC)?

When using KIF11 Antibody, HRP conjugated for immunohistochemistry, researchers should follow this optimized protocol:

• Tissue Preparation and Pretreatment:

  • Fix tissues in formalin and embed in paraffin

  • Cut sections to 4 μm thickness

  • Deparaffinize in xylene and rehydrate through graded alcohols

  • Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) for 20 minutes

• Blocking and Antibody Incubation:

  • Block endogenous peroxidase activity with 3% H₂O₂ for 10 minutes

  • Block non-specific binding with 10% goat serum for 30 minutes

  • Incubate with KIF11 antibody at 1:100 dilution overnight at 4°C

  • For HRP-conjugated antibodies, secondary antibody step can be skipped

• Detection and Visualization:

  • Rinse thoroughly with PBS (3 × 5 minutes)

  • Develop signal using DAB substrate for 5 minutes

  • Counterstain with hematoxylin

  • Dehydrate, clear, and mount with permanent mounting medium

• Controls and Optimization:

  • Include positive controls (known KIF11-expressing tissues like proliferating cancer tissues)

  • Include negative controls (omission of primary antibody)

  • Optimize antibody concentration through titration experiments

Note: KIF11 protein is primarily expressed in the cytoplasm of cells, which should be considered when evaluating staining patterns .

How can KIF11 Antibody be effectively used in western blot applications to quantify KIF11 expression differences?

For effective western blot quantification of KIF11 expression differences using HRP-conjugated antibodies:

• Sample Preparation:

  • Extract total protein using RIPA buffer with protease inhibitors

  • Quantify protein concentrations using BCA or Bradford assay

  • Load equal amounts (25-50 μg) of protein per lane

• Electrophoresis and Transfer:

  • Separate proteins using 8% SDS-PAGE (optimal for the 119.2 kDa KIF11 protein)

  • Transfer to PVDF membranes at 100V for 90-120 minutes in cold transfer buffer

• Antibody Incubation:

  • Block membranes with 5% non-fat milk in TBST for 1 hour at room temperature

  • For HRP-conjugated antibodies: Directly incubate with the KIF11 antibody (1:1000-1:5000 dilution) in blocking buffer overnight at 4°C

  • Wash thoroughly with TBST (3 × 10 minutes)

• Detection and Quantification:

  • Develop using ECL substrate

  • Capture images using a digital imaging system

  • Quantify band intensity using software like ImageJ

  • Normalize to loading controls (β-actin, GAPDH)

• Experimental Validation:

  • Include knockdown/overexpression controls to validate antibody specificity

  • For comparative studies, process all samples simultaneously to minimize inter-blot variations

Based on published research, this approach successfully detected significant decreases in KIF11 protein levels following shRNA-mediated knockdown in hepatoma cell lines, correlating with decreased expression of proliferation markers Ki-67 and PCNA .

What considerations are important when designing cell-based assays to study KIF11 function using KIF11 antibodies?

When designing cell-based assays to study KIF11 function using antibodies:

• Cell Line Selection:

  • Choose appropriate cell lines based on endogenous KIF11 expression levels

  • HCC cell lines like Hep3B and SNU-475 have been successfully used in KIF11 studies

  • Consider using multiple cell lines to ensure reproducibility across different cellular contexts

• Experimental Controls:

  • Generate stable knockdown cell lines using validated shRNA constructs

  • Verify knockdown efficiency at both mRNA (qPCR) and protein levels (western blot)

  • Include scrambled shRNA controls to account for non-specific effects

• Functional Assays:

  • Proliferation assays: CCK-8 assays and colony formation assays effectively demonstrate KIF11's role in cell proliferation

  • Cell cycle analysis: Flow cytometry to assess the impact of KIF11 manipulation on cell cycle progression

  • Immunofluorescence: To visualize mitotic spindle formation and chromosome segregation

• Endpoint Measurements:

  • Quantify proliferation markers (Ki-67, PCNA) by western blot or immunofluorescence

  • Measure growth curves over 5-7 days to capture temporal effects

  • Analyze colony formation capacity after 10-14 days of culture

• Data Validation:

  • Confirm in vitro findings with in vivo models when possible

  • Use rescue experiments (re-expression of KIF11 in knockdown cells) to verify specificity

Research has shown that KIF11 depletion significantly inhibits proliferation in hepatocellular carcinoma cell lines, with corresponding decreases in Ki-67 and PCNA expression, confirming its role in cancer cell growth .

How should researchers interpret conflicting KIF11 expression data between different experimental techniques?

When encountering conflicting KIF11 expression data between different experimental techniques:

• Assess Technique-Specific Limitations:

  • IHC: Provides spatial information but may be affected by antibody specificity, fixation methods, and subjective scoring

  • Western blot: Quantitative but lacks spatial information and may be affected by extraction efficiency

  • qRT-PCR: Measures mRNA but not protein levels, which may not correlate due to post-transcriptional regulation

  • Bioinformatics data (e.g., TCGA): Large sample size but potential batch effects or heterogeneity issues

• Cross-Validation Approach:

  • Implement a multi-technique validation strategy

  • For instance, researchers have successfully validated KIF11 overexpression in HCC by combining:

    • TCGA database analysis

    • IHC staining of patient specimens

    • Western blot validation

    • qRT-PCR confirmation

• Sample-Related Considerations:

  • Tissue heterogeneity: Different regions of the same tumor may show variable expression

  • Patient stratification: Analyze data according to clinical parameters (tumor stage, grade)

  • Matched samples: Prioritize paired tumor/normal tissue analyses to control for individual variation

• Statistical Analysis:

  • Apply appropriate statistical tests based on data distribution

  • Consider multivariate analysis to account for confounding factors

  • Use adequate sample sizes to ensure statistical power

• Resolution Strategies:

  • If conflicts persist, prioritize functional studies (knockdown/overexpression) to determine biological relevance

  • Consider alternative antibodies or isoform-specific detection methods

  • Report all findings transparently, acknowledging limitations

In published research, investigators successfully resolved data inconsistencies by combining TCGA database mining with direct IHC staining of clinical specimens, followed by functional validation in cellular and animal models .

What are common troubleshooting strategies for non-specific binding when using KIF11 antibodies in immunoassays?

When encountering non-specific binding with KIF11 antibodies in immunoassays:

• Antibody Validation and Optimization:

  • Titrate antibody concentration to determine optimal working dilution (typically starting at 1:100 for IHC and 1:1000 for western blotting)

  • Validate antibody specificity using positive controls (KIF11-expressing tissues/cells) and negative controls (KIF11-depleted samples)

  • Consider testing multiple antibodies targeting different epitopes of KIF11

• Blocking Optimization:

  • Extend blocking time (1-2 hours at room temperature)

  • Test different blocking agents (BSA, normal serum, commercial blockers)

  • For western blots, increase blocking buffer concentration (3-5% BSA or milk)

  • Use blocking buffer that matches the host species of the antibody

• Protocol Adjustments:

  • For IHC: Optimize antigen retrieval conditions (citrate vs. EDTA buffer, pH, duration)

  • For western blot: Increase wash duration and frequency (4-5 washes, 10 minutes each)

  • For ELISA: Implement more stringent wash procedures and consider adding detergents to wash buffers

• Sample-Specific Considerations:

  • Ensure proper fixation and processing of tissues for IHC

  • For cell lysates, use protease inhibitors to prevent degradation

  • Pre-clear lysates by centrifugation to remove debris that may cause non-specific binding

• Advanced Solutions:

  • Perform peptide competition assays to confirm specific binding

  • Use knockout/knockdown samples as definitive negative controls

  • Consider alternative detection systems if HRP conjugation is causing background issues

Based on published protocols, researchers have achieved specific KIF11 staining in IHC by employing a 1:100 antibody dilution, overnight incubation at 4°C, and thorough PBS washing steps .

How do researchers accurately quantify and normalize KIF11 expression data from complex tissue samples?

Accurate quantification and normalization of KIF11 expression from complex tissue samples requires:

• Sample Preparation Considerations:

  • Ensure consistent tissue processing and preservation methods

  • For heterogeneous tissues, consider laser capture microdissection to isolate specific cell populations

  • Process all experimental and control samples simultaneously to minimize batch effects

• Protein Quantification Methods:

  • For western blot:

    • Use gradient gels to better resolve the 119.2 kDa KIF11 protein

    • Implement digital densitometry (ImageJ/Image Lab) for band quantification

    • Normalize to multiple housekeeping proteins (β-actin, GAPDH, tubulin) to account for loading variability

  • For IHC quantification:

    • Use digital image analysis software for objective scoring

    • Quantify based on both staining intensity and percentage of positive cells

    • Implement H-score or Allred scoring systems for semi-quantitative analysis

• RNA Quantification Considerations:

  • For qRT-PCR:

    • Use multiple reference genes selected for stability in the tissue type

    • Apply 2^-ΔΔCt method for relative quantification

    • Validate primer efficiency (90-110%) for accurate quantification

• Data Normalization Strategies:

  • Internal normalization: Express KIF11 levels relative to housekeeping genes/proteins

  • External normalization: Include calibration samples across experiments

  • For IHC, normalize to tissue-specific positive controls run in parallel

• Statistical Analysis:

  • Apply appropriate statistical tests based on data distribution

  • For clinical samples, stratify by relevant parameters (tumor stage, grade)

  • Consider power analysis to determine adequate sample sizes

In published research on KIF11 in HCC, investigators successfully quantified expression differences using a combination of TCGA database analysis (for large-scale screening) and laboratory validation through western blot and IHC, with normalization to established housekeeping proteins .

How can KIF11 antibodies be applied in studying the relationship between KIF11 expression and cancer progression?

KIF11 antibodies can be strategically applied to investigate KIF11's role in cancer progression through:

Research has demonstrated that knockdown of KIF11 in HCC cells significantly reduced tumor growth in mouse models, with tumor volumes reaching only approximately 110 mm³ compared to 200 mm³ in control tumors after two weeks , providing strong evidence for KIF11's role in cancer progression.

What are the methodological considerations for using KIF11 antibodies in multiplex immunofluorescence studies?

When implementing multiplex immunofluorescence studies with KIF11 antibodies:

• Antibody Selection and Validation:

  • Choose primary antibodies from different host species to prevent cross-reactivity

  • For KIF11, rabbit polyclonal antibodies are commonly available

  • Validate each antibody individually before multiplexing

  • Perform single-color controls to confirm specific staining patterns

• Panel Design Considerations:

  • Combine KIF11 with functionally relevant markers:

    • Proliferation markers: Ki-67, PCNA

    • Cell cycle regulators: Cyclins, CDKs

    • Mitotic markers: Phospho-histone H3, Aurora kinases

    • Cell type-specific markers to assess expression in different cell populations

• Technical Implementation:

  • Sequential staining approaches:

    • Apply stripping protocols between antibody applications

    • Validate complete stripping before subsequent antibody application

  • Simultaneous staining approaches:

    • Carefully select fluorophores with minimal spectral overlap

    • Implement appropriate blocking between antibody applications

• Imaging and Analysis Considerations:

  • Use spectral imaging systems to separate overlapping fluorophores

  • Implement automated image analysis for quantification:

    • Cell segmentation algorithms

    • Colocalization analysis

    • Expression intensity quantification across cell populations

• Controls and Validation:

  • Include single-color controls for spectral unmixing

  • Use biological controls (KIF11 knockdown samples) to validate specificity

  • Implement appropriate negative controls for each antibody in the panel

This approach allows researchers to simultaneously evaluate KIF11 expression and its correlation with proliferation markers or other cancer-related proteins at the single-cell level, providing deeper insights into heterogeneity within tumor samples and the functional relationships between KIF11 and other cancer-related proteins.

How can researchers effectively combine KIF11 antibody-based detection with gene expression analysis for comprehensive pathway studies?

For integrating KIF11 antibody-based detection with gene expression analysis:

This integrated approach has been successfully applied in HCC research, where KIF11 overexpression was initially identified through TCGA database analysis, validated at the protein level through IHC and western blot, and functionally characterized through knockdown experiments that demonstrated its role in cell proliferation and tumor growth .

What are the latest methodological advances in using KIF11 antibodies for high-throughput screening of potential anti-cancer compounds?

Recent methodological advances in using KIF11 antibodies for high-throughput screening include:

• Cell-Based Screening Platforms:

  • Automated immunofluorescence microscopy:

    • High-content imaging of KIF11 localization during mitosis

    • Quantification of mitotic spindle defects following compound treatment

    • Multiplexed with cell cycle markers for comprehensive analysis

  • Cell viability assays in KIF11-dependent cell lines:

    • Differential cytotoxicity screening between KIF11-high vs. KIF11-low cells

    • Correlation of efficacy with KIF11 expression levels

• Biochemical Assay Innovations:

  • ATPase activity assays:

    • Measuring inhibition of KIF11's ATPase activity by candidate compounds

    • Correlation with anti-proliferative effects in cellular models

  • Protein-protein interaction disruption screens:

    • Identification of compounds that interfere with KIF11's interaction with binding partners

    • ELISA-based methods using KIF11 antibodies to detect complex formation

• Target Engagement Confirmation:

  • Cellular thermal shift assays (CETSA):

    • Using KIF11 antibodies to detect compound-induced thermal stabilization of KIF11

    • Western blot or AlphaLISA readouts for high-throughput application

  • In-cell target engagement assays:

    • Proximity-based assays to confirm compound binding to KIF11 in intact cells

    • BRET/FRET assays using tagged KIF11 constructs

• Translational Research Applications:

  • Patient-derived organoid screening:

    • KIF11 antibody-based assessment of compound effects in 3D patient-derived models

    • Correlation with patient-specific KIF11 expression patterns

  • Ex vivo tissue slice cultures:

    • Evaluation of KIF11 inhibitors in intact tissue architecture

    • IHC-based readouts for cell proliferation and mitotic defects

• Computational Integration:

  • Structure-based virtual screening paired with experimental validation:

    • Docking studies to identify potential KIF11 inhibitors

    • Validation of hits using antibody-based detection methods

  • Machine learning models to predict compound efficacy based on:

    • Chemical structure features

    • Cell line KIF11 expression profiles

    • Genetic background factors

These advanced screening methodologies have contributed to the development of several KIF11 inhibitors that are currently undergoing clinical trials, highlighting KIF11's potential as a therapeutic target in cancer .

How might emerging technologies enhance the utility of KIF11 antibodies in personalized medicine approaches?

Emerging technologies are poised to significantly enhance the utility of KIF11 antibodies in personalized medicine through:

• Single-Cell Multi-omics Integration:

  • Combining KIF11 antibody-based protein detection with single-cell:

    • Transcriptomics (scRNA-seq)

    • Epigenomics (scATAC-seq)

    • Spatial transcriptomics

  • This integration will enable identification of patient-specific cellular subpopulations that might respond to KIF11-targeted therapies

• Advanced Imaging Technologies:

  • Super-resolution microscopy:

    • Nanoscale visualization of KIF11's interaction with microtubules and mitotic apparatus

    • Correlation with drug sensitivity in patient-derived samples

  • Intravital imaging:

    • Real-time monitoring of KIF11 inhibitor effects in tumor models

    • Assessment of tumor microenvironment influences on treatment response

• Liquid Biopsy Approaches:

  • Development of highly sensitive assays for circulating tumor cells (CTCs):

    • Using KIF11 antibodies to identify and isolate CTCs with high KIF11 expression

    • Correlation with disease progression and treatment response

  • Extracellular vesicle analysis:

    • Detection of KIF11 in tumor-derived exosomes as potential biomarkers

• Companion Diagnostic Development:

  • IHC-based companion diagnostics:

    • Standardized KIF11 antibody-based assays to identify patients likely to respond to KIF11 inhibitors

    • Integration with digital pathology and AI-based image analysis for consistent scoring

  • Multiplexed biomarker panels:

    • Combining KIF11 with other relevant markers to create predictive signatures

    • Implementation on automated platforms for clinical application

• Therapeutic Antibody Derivatives:

  • KIF11-targeted antibody-drug conjugates (ADCs):

    • Delivering cytotoxic payloads specifically to KIF11-overexpressing cancer cells

    • Reducing off-target effects of traditional KIF11 inhibitors

  • Bispecific antibodies:

    • Engaging immune effector cells with KIF11-expressing tumor cells

    • Enhancing tumor-specific immune responses

These technological advances build upon the established role of KIF11 as a cancer biomarker and therapeutic target, as evidenced by its overexpression in hepatocellular carcinoma and other tumors, and its correlation with poor clinical outcomes .

What are the current limitations and future prospects for KIF11 antibody-based research in cancer therapeutics?

Current limitations and future prospects for KIF11 antibody-based research in cancer therapeutics include:

• Current Limitations:

  • Specificity Challenges:

    • Cross-reactivity with related kinesin family members

    • Difficulty in distinguishing post-translational modifications

    • Limited validation across diverse tumor types

  • Technical Constraints:

    • Variability in antibody performance across detection methods

    • Challenges in quantitative standardization across laboratories

    • Limited antibody penetration in 3D tissue models

  • Translational Barriers:

    • Incomplete understanding of KIF11's non-mitotic functions

    • Limited correlation studies between KIF11 expression and clinical response to KIF11 inhibitors

    • Need for standardized cutoffs for "high" vs. "low" expression in clinical contexts

• Future Prospects:

  • Advanced Antibody Engineering:

    • Development of isoform-specific and modification-specific antibodies

    • Creation of recombinant antibody fragments with enhanced tissue penetration

    • Engineering of bispecific antibodies targeting KIF11 and immune checkpoints

  • Expanded Functional Characterization:

    • Investigation of KIF11's role beyond mitosis in cancer cells

    • Analysis of KIF11's interaction with the tumor microenvironment

    • Exploration of synthetic lethal interactions with KIF11 inhibition

  • Therapeutic Innovations:

    • Development of KIF11-targeted antibody-drug conjugates

    • Engineering of proteolysis-targeting chimeras (PROTACs) for KIF11 degradation

    • Combination strategies with immunotherapies or targeted agents

  • Clinical Translation:

    • Implementation of standardized KIF11 testing in clinical trials

    • Development of companion diagnostics for KIF11 inhibitors

    • Identification of biomarkers predictive of response to KIF11-targeted therapies

• Research Priorities:

  • Conduct large-scale validation studies across multiple tumor types

  • Establish correlations between KIF11 expression and response to KIF11 inhibitors

  • Develop more selective tools to study KIF11 function in complex cellular contexts

  • Investigate resistance mechanisms to KIF11-targeted therapies