DYNC1LI1 Antibody, FITC conjugated

Basic Research Questions

• What is DYNC1LI1 and what cellular processes does it regulate?

DYNC1LI1 is a light intermediate chain subunit of the dynein motor complex essential for minus-end directed transport along microtubules. It plays critical roles in fundamental cellular processes including cell division, migration, and other basic cellular functions . Within the cytoskeletal framework, DYNC1LI1 facilitates dynein transport along microtubules toward the minus end, which is crucial for maintaining cellular integrity and function .

Beyond its mechanical role in transport, DYNC1LI1 also interacts with various cargo adaptors to regulate Rab-mediated endosomal recycling and lysosomal degradation pathways . This regulatory capacity positions DYNC1LI1 as an important mediator of intracellular trafficking events that influence cell behavior and response to environmental cues.

Recent research has revealed DYNC1LI1's involvement in the regulation of mucosal barrier integrity, suggesting broader implications beyond basic cellular transport . This multifunctional protein represents an important target for research across multiple disciplines, from basic cell biology to cancer research.

• How does DYNC1LI1 expression vary across different cancer types?

DYNC1LI1 expression has been documented across several cancer types with notable variations:

In colorectal cancer (CRC), DYNC1LI1 expression levels are significantly elevated in metastatic samples compared to non-metastatic tissues . Specifically, cDNA array analyses of colorectal tissues covering four CRC stages demonstrated that DYNC1LI1 expression is significantly higher in samples from patients with metastasis (n=10) compared to patients without metastasis (n=30) .

Among CRC cell lines, LS 174T cells (AJCC stage II) exhibit significantly higher DYNC1LI1 expression compared to other CRC cell lines such as HCT 116 and SW480 . This differential expression pattern suggests potential cell line-specific regulatory mechanisms governing DYNC1LI1 levels.

Beyond colorectal cancer, DYNC1LI1 expression has been detected in the urine of patients with pancreatic ductal adenocarcinoma, indicating its potential utility as a biomarker . Additionally, increased DYNC1LI1 phosphorylation has been observed in prostate cancer, suggesting post-translational modifications may influence its function in oncogenic contexts .

The consistent association of DYNC1LI1 with multiple cancer types points to its potential role in tumorigenesis, making it a valuable research target for cancer studies.

• What experimental techniques are most effective for visualizing DYNC1LI1 localization in fixed cells?

For effective visualization of DYNC1LI1 localization in fixed cells, researchers should consider the following methodological approach:

Fixation Protocol: Paraformaldehyde (4%) fixation for 15-20 minutes at room temperature preserves both cell morphology and DYNC1LI1 epitopes. For better preservation of microtubule structures, methanol fixation at -20°C for 10 minutes can be used as an alternative.

Permeabilization: Use 0.1-0.2% Triton X-100 for 5-10 minutes to ensure antibody access to intracellular DYNC1LI1 without disrupting its association with cytoskeletal structures.

Blocking: Implement a 30-minute blocking step with 5% normal serum (from the species in which the secondary antibody was raised) to minimize non-specific binding.

Antibody Incubation: Apply FITC-conjugated DYNC1LI1 antibody at an optimized dilution (typically 1:50 to 1:200) in blocking buffer. Overnight incubation at 4°C generally provides optimal results.

Co-staining: For spatial context, co-stain with markers for dynein/dynactin complex components such as IC1, IC2, or p62, as these proteins have been shown to interact with DYNC1LI1 in BioID experiments . This approach provides valuable information about DYNC1LI1's position within the larger motor complex.

Counterstaining: Include nuclear staining (DAPI) and potentially microtubule staining (anti-α-tubulin) to establish subcellular localization in relation to these structures.

Microscopy: Confocal microscopy with appropriate filter sets for FITC (excitation ~495nm, emission ~519nm) provides the optimal resolution for visualizing DYNC1LI1 localization patterns.

This comprehensive approach allows for precise localization of DYNC1LI1 within the cellular context, particularly in relation to its role in the dynein/dynactin complex.

Advanced Research Questions

• How does DYNC1LI1 knockdown affect cellular functions in cancer models?

DYNC1LI1 knockdown in cancer models produces several significant functional alterations:

Chemosensitivity Changes: In LS 174T colorectal cancer cells, DYNC1LI1 knockdown using shRNA significantly increases sensitivity to the chemotherapeutic agent 5-fluorouracil (5-FU) . This enhanced sensitivity represents a potential therapeutic advantage, suggesting DYNC1LI1 as a target for combination therapy approaches in colorectal cancer.

Altered Mucin Expression Patterns: Following DYNC1LI1 knockdown, LS 174T cells display a distinctive shift in mucin expression profiles. Specifically, MUC1 expression decreases by approximately 0.06-fold, while MUC2, MUC4, and MUC5AC expression levels increase by 2.70, 4.17, and 2.75-fold, respectively . This mucin expression pattern (low MUC1, high MUC2/MUC4/MUC5AC) correlates with improved response to 5-FU treatment, establishing a molecular link between DYNC1LI1, mucin expression, and chemotherapeutic efficacy.

Angiogenic Response: In human endothelial cells, siRNA-mediated DYNC1LI1 depletion results in significantly enhanced sprouting compared to control treatments . This effect was demonstrated in 3D in vitro collagen type 1 gel assays with HUVECs, where siDYNC1LI1-treated cells exhibited markedly increased sprouting behavior .

Cell Surface Receptor Dynamics: LIC1 (DYNC1LI1)-deficient human endothelial cells display increased cell surface levels of pro-angiogenic receptors such as VEGFR2 . This alteration in receptor presentation likely contributes to the enhanced angiogenic phenotype observed in DYNC1LI1-depleted cells.

These diverse functional consequences highlight DYNC1LI1's multifaceted role in cancer cell biology and suggest potential therapeutic strategies targeting this protein.

• What methodologies enable successful knockdown of DYNC1LI1 for functional studies?

For effective DYNC1LI1 knockdown in functional studies, researchers should consider these methodological approaches:

Lentiviral shRNA Approach:
The lentiviral construct pLKO_TRC005-DYNC1LI1 (clone ID: TRCN0000299843) containing shRNA targeting DYNC1LI1 has been successfully used to achieve stable knockdown in colorectal cancer cell lines . This system typically employs:

• Cell seeding at 1.25×10^5 cells/well in 6-well plates

• Lentiviral infection at multiplicity of infection (MOI) = 3

• Selection with 2 mg/ml puromycin to establish stable knockdown cell lines

• Verification of knockdown efficiency via RT-qPCR and western blotting

siRNA Approach for Transient Knockdown:
For shorter-term studies, siRNA targeting DYNC1LI1 (siDYNC1LI1) has proven effective, particularly in human endothelial cells . This approach offers flexibility for acute interventions and typically requires:

• Transfection with lipid-based reagents

• Confirmation of knockdown via western blotting

• Functional assays conducted 48-72 hours post-transfection

Controls and Validation:
Regardless of the knockdown method, proper controls are essential:

• For lentiviral approaches, control vectors targeting luciferase (e.g., pLKO_TRC005-luciferase, clone ID: TRCN0000231719) serve as appropriate negative controls

• Expression analysis should employ validated primers and probes for accurate quantification

• Protein-level verification should accompany mRNA-level assessment to confirm functional knockdown

Functional Assessment:
Post-knockdown, multifaceted functional analyses can be performed:

• Chemosensitivity assays using MTT to evaluate drug response (e.g., 5-FU or oxaliplatin)

• RT-qPCR to assess consequent changes in gene expression patterns (e.g., mucin expression)

• 3D in vitro sprouting assays to evaluate angiogenic potential

• Flow cytometry to quantify cell surface receptor levels

These methodological approaches provide a comprehensive framework for investigating DYNC1LI1 function through targeted knockdown strategies.

• How does DYNC1LI1 interact with the dynein/dynactin complex, and what experimental approaches can characterize these interactions?

DYNC1LI1 occupies a specific position within the dynein/dynactin complex, with distinct interaction patterns that can be experimentally characterized:

Structural Position:
DYNC1LI1 functions as a core subunit of the dynein motor complex, with spatial positioning that influences its interaction capabilities . BioID proximity labeling studies have revealed that LIC1 (DYNC1LI1) is positioned further away from dynactin compared to the intermediate chains (IC1 and IC2) . This spatial arrangement has functional implications for cargo binding and motor activity.

Experimental Approaches to Characterize Interactions:

Functional Significance:
DYNC1LI1 serves specific functions within the dynein/dynactin complex:

• It interacts with various cargo adaptors to regulate Rab-mediated endosomal recycling and lysosomal degradation

• BioID experiments have demonstrated that the LIC1 (DYNC1LI1) subunit can identify dynein activators

• The spatial positioning of DYNC1LI1 relative to other complex components influences cargo specificity

These experimental approaches provide complementary information about DYNC1LI1's integration within the larger dynein/dynactin machinery, offering insights into both structural arrangement and functional significance.

Methodology and Technical Questions

• What optimization steps are necessary for using DYNC1LI1 Antibody, FITC conjugated in flow cytometry?

Optimizing DYNC1LI1 Antibody, FITC conjugated for flow cytometry requires systematic protocol refinement:

Antibody Titration:
Perform a titration series (typically 1:25, 1:50, 1:100, 1:200, 1:400) to determine the optimal antibody concentration that maximizes the signal-to-noise ratio. For intracellular targets like DYNC1LI1, higher concentrations may be required compared to surface markers.

Fixation and Permeabilization:
Since DYNC1LI1 is an intracellular protein, effective permeabilization is critical:

• Test multiple fixation methods (4% paraformaldehyde, 70% ethanol, methanol)

• Compare permeabilization reagents (0.1% Triton X-100, 0.1% saponin, commercial permeabilization buffers)

• Optimize incubation times to balance epitope preservation and antibody accessibility

Controls:
Implement comprehensive controls for accurate interpretation:

• Unstained cells to establish autofluorescence baseline

• FMO (Fluorescence Minus One) controls to set proper gates

• Isotype-FITC control to assess non-specific binding

• Positive control (cells with known DYNC1LI1 expression, e.g., LS 174T cells which have high DYNC1LI1 expression)

• Negative control (cells with DYNC1LI1 knockdown, e.g., shDYNC1LI1-LS 174T cells)

Instrument Settings:

• Calibrate the flow cytometer using FITC calibration beads

• Adjust voltage settings to position the negative population appropriately on scale

• Collect sufficient events (typically 10,000-50,000) for statistical validity

Sample Processing Guidelines:

• Maintain consistent cell concentrations (1-5×10^6 cells/ml)

• Keep samples protected from light to prevent FITC photobleaching

• Process samples at 4°C when possible to minimize internalization and degradation

Data Analysis:

• Implement hierarchical gating strategies (FSC/SSC → single cells → target population)

• Consider MFI (Mean Fluorescence Intensity) for quantitative comparisons

• For experiments involving multiple cell types or treatments, normalize to internal controls

These optimization steps ensure reliable and reproducible flow cytometry results when detecting DYNC1LI1 with FITC-conjugated antibodies.

• What experimental design is optimal for investigating DYNC1LI1's role in angiogenesis using FITC-conjugated antibodies?

To comprehensively investigate DYNC1LI1's role in angiogenesis using FITC-conjugated antibodies, a multi-faceted experimental design is recommended:

In Vitro Sprouting Assays:
3D collagen type 1 gel assays with human umbilical vein endothelial cells (HUVECs) provide a controlled system for angiogenesis assessment :

• Prepare HUVECs with either siDYNC1LI1 or control siRNA treatment

• Embed cells in collagen matrix and stimulate with angiogenic factors

• Quantify sprouting cells at defined timepoints (typically 24, 48, and 72 hours)

• Use FITC-conjugated DYNC1LI1 antibody to visualize protein localization during sprouting

Receptor Trafficking Analysis:
Since DYNC1LI1-deficient endothelial cells show increased cell surface levels of VEGFR2 :

• Implement flow cytometry with dual staining (surface VEGFR2 and intracellular DYNC1LI1-FITC)

• Conduct time-course internalization assays after VEGF stimulation

• Perform live-cell imaging with FITC-DYNC1LI1 and fluorescently-tagged VEGFR2 to track dynamic interactions

Comparative Inhibitor Studies:
Include parallel treatments to dissect the endocytic trafficking pathway components :

• Dynasore (endocytosis inhibitor) to compare with DYNC1LI1 knockdown phenotypes

• Targeted inhibitors of Rab-mediated trafficking

• Document DYNC1LI1-FITC localization patterns under each condition

In Vivo Models:
Zebrafish models offer advantages for angiogenesis visualization :

• Compare wild-type to dync1li1 mutant zebrafish (e.g., with premature stop codon at exon 12/13 splice acceptor site)

• Inject FITC-conjugated DYNC1LI1 antibody for in vivo imaging

• Quantify vascular parameters including vessel branching, length, and diameter

• Correlate phenotypes with molecular alterations in endocytic trafficking

Microscopy Analysis:
Implement advanced imaging approaches:

• Confocal microscopy for high-resolution localization of DYNC1LI1-FITC

• Time-lapse imaging to capture dynamic trafficking events

• Super-resolution techniques for nanoscale visualization of DYNC1LI1 association with endocytic vesicles

This comprehensive experimental design allows for detailed investigation of DYNC1LI1's role in angiogenesis, particularly through its regulation of receptor trafficking and endocytic processes.

• How can contradictory data about DYNC1LI1 function across different cell types be reconciled experimentally?

When faced with contradictory data regarding DYNC1LI1 function across different cell types, a systematic experimental approach can help reconcile these discrepancies:

Standardized Expression Analysis:
Implement consistent methodologies across cell types:

• Use identical RT-qPCR protocols with validated primers and probes for DYNC1LI1 quantification

• Apply western blotting with standardized loading controls

• Develop a reference panel of cell lines with well-characterized DYNC1LI1 expression levels, including LS 174T cells (high expression) , HCT 116 and SW480 (lower expression)

Isoform-Specific Investigation:
Determine whether cell type-specific contradictions stem from isoform variations:

• Design primers to distinguish potential DYNC1LI1 splice variants

• Compare expression patterns of different isoforms across cell types

• Generate isoform-specific constructs for functional rescue experiments

Interactome Comparison:
Apply BioID proximity labeling technology across multiple cell types:

• Create consistent DYNC1LI1-BioID fusion constructs

• Express in diverse cell types of interest

• Compare resulting interactome profiles to identify cell type-specific interaction partners

• Focus particularly on interactions with known dynein activators like BICD2, HOOK3, or the newly identified ninein (NIN) and ninein-like (NINL) proteins

Functional Domain Analysis:
Implement domain-specific mutations to identify critical regions:

• Generate a series of DYNC1LI1 constructs with mutations in key functional domains

• Express these constructs in DYNC1LI1-depleted cells of different origins

• Assess rescue capability for diverse phenotypes (e.g., chemosensitivity in cancer cells , angiogenic sprouting in endothelial cells )

• Correlate functional outcomes with cell type-specific binding partners

Pathway Dependency Mapping:
Systematically inhibit potential DYNC1LI1-associated pathways:

• Target Rab-mediated endosomal recycling components

• Disrupt cytoskeletal structures using specific inhibitors

• Modulate mucin expression patterns in relevant cell types

• Determine if pathway dependencies explain cell type-specific functions

Multi-Dimensional Data Integration:
Develop computational models that integrate:

• Expression data across cell types

• Interaction networks

• Functional outcomes from knockdown/mutation studies

• Cell type-specific parameters (e.g., differentiation state, metabolic profile)

This systematic approach provides a framework for reconciling apparently contradictory data about DYNC1LI1 function, ultimately yielding a more nuanced understanding of its context-dependent roles.

Research Applications

• What are the implications of DYNC1LI1 expression for cancer chemosensitivity, and how can this be investigated experimentally?

The relationship between DYNC1LI1 expression and cancer chemosensitivity represents a promising research direction with significant therapeutic implications:

Established Correlations:
Research has demonstrated that DYNC1LI1 knockdown in LS 174T colorectal cancer cells significantly enhances sensitivity to 5-fluorouracil (5-FU) . Specifically, LS 174T cells with decreased DYNC1LI1 expression were more sensitive to 5-FU compared to cells with endogenous DYNC1LI1 expression levels . This chemosensitivity effect appears to be drug-specific, warranting detailed mechanistic investigation.

Experimental Investigation Protocol:

1. Cell Model Selection:

• Choose multiple cancer cell lines with varying baseline DYNC1LI1 expression

• Include LS 174T cells as a reference model with established DYNC1LI1-dependent chemosensitivity

• Consider patient-derived organoids to enhance clinical relevance

2. DYNC1LI1 Modulation:

• Implement shRNA-mediated stable knockdown using validated constructs (e.g., pLKO_TRC005-DYNC1LI1, clone ID: TRCN0000299843)

• Generate DYNC1LI1-overexpressing lines for comparison

• Create rescue cell lines expressing DYNC1LI1 variants with specific mutations

3. Chemosensitivity Assessment:

• Conduct MTT assays with multiple chemotherapeutic agents:

  • 5-Fluorouracil (5-FU)

  • Oxaliplatin

  • Combination regimens (e.g., FOLFOX4)

• Generate dose-response curves to determine IC50 values

• Perform time-course analyses to assess resistance development

4. Mechanistic Studies:

• Analyze mucin expression patterns (MUC1, MUC2, MUC4, MUC5AC) via RT-qPCR

• Assess drug uptake, retention, and efflux

• Evaluate DNA damage response pathways

• Monitor apoptotic markers before and after drug treatment

5. Pathway Integration:

• Investigate endocytic trafficking of drug transporters

• Examine lysosomal degradation of chemotherapeutic agents

• Assess whether dynein motor function directly influences drug compartmentalization

6. Clinical Correlation:

• Analyze patient tumor samples for DYNC1LI1 expression

• Correlate expression levels with treatment response data

• Stratify analysis based on cancer stage and molecular subtypes

This comprehensive experimental approach will elucidate the mechanisms underlying DYNC1LI1's influence on chemosensitivity, potentially identifying new strategies to enhance treatment efficacy in cancer patients.

• How can researchers effectively combine DYNC1LI1-FITC antibody staining with other markers for multi-parameter analyses?

For effective multi-parameter analyses combining DYNC1LI1-FITC antibody with other markers, researchers should implement the following methodological approach:

Spectral Compatibility Planning:
Carefully select additional fluorophores to minimize spectral overlap with FITC (excitation ~495nm, emission ~519nm):

• Consider far-red fluorophores (e.g., Alexa Fluor 647) for maximum separation

• Use fluorophores with narrow emission spectra when possible

• Plan sequential scanning protocols for confocal microscopy if spectra cannot be adequately separated

Validated Marker Combinations:

• Dynein Complex Components:

  • Pair DYNC1LI1-FITC with antibodies against IC1, IC2, or p62 (dynactin) using spectrally distinct fluorophores

  • Include microtubule staining (e.g., α-tubulin with Alexa Fluor 647)

  • These combinations provide structural context for DYNC1LI1 within the motor complex

• Cancer Research Applications:

  • Combine DYNC1LI1-FITC with mucin markers (MUC1, MUC2, MUC4, MUC5AC)

  • Include proliferation markers (Ki-67) and apoptosis indicators (cleaved caspase-3)

  • Add chemotherapy response markers relevant to 5-FU mechanism of action

• Angiogenesis Research:

  • Pair with endothelial markers (CD31) and pro-angiogenic receptors (VEGFR2)

  • Include markers for endocytic vesicles to track trafficking

  • Incorporate proliferation markers specific to endothelial cells

Protocol Optimization:

• Sequential Staining Approach:

  • Begin with DYNC1LI1-FITC antibody staining

  • Fix secondary antibodies if used

  • Proceed with additional marker staining

  • This minimizes potential cross-reactivity between antibodies

• Blocking Strategy:

  • Implement robust blocking with 5-10% normal serum

  • Add species-specific F(ab) fragments when using multiple antibodies from the same species

  • Consider avidin/biotin blocking for protocols involving biotinylated reagents

• Controls:

  • Include single-stained samples for each fluorophore

  • Prepare fluorescence-minus-one (FMO) controls

  • Use DYNC1LI1 knockdown samples as biological negative controls

Analysis Approaches:

• Co-localization Analysis:

  • Calculate Pearson's correlation coefficient and Mander's overlap coefficient

  • Implement intensity correlation analysis (ICA)

  • Use line profile analysis across structures of interest

• Hierarchical Classification:

  • Develop gating strategies for flow cytometry data

  • Create decision trees for image cytometry

  • Implement supervised machine learning for complex pattern recognition

This methodological framework enables researchers to effectively combine DYNC1LI1-FITC antibody with other markers for comprehensive multi-parameter analyses across various research applications.

• What are the most sensitive detection methods for quantifying small changes in DYNC1LI1 expression in patient samples?

For detecting subtle changes in DYNC1LI1 expression in patient samples, a hierarchical approach employing multiple complementary techniques offers the greatest sensitivity:

Primary Detection Techniques:

• Digital Droplet PCR (ddPCR):

  • Provides absolute quantification with higher sensitivity than traditional qPCR

  • Allows detection of 1.2-fold changes in DYNC1LI1 expression

  • Requires minimal sample input, suitable for limited patient material

  • Employs TaqMan probes similar to those used in published DYNC1LI1 studies

• Nanostring nCounter Technology:

  • Offers direct digital counting of DYNC1LI1 mRNA molecules

  • Eliminates amplification bias

  • Suitable for degraded samples, including FFPE material

  • Enables simultaneous detection of DYNC1LI1 along with associated genes (mucins, dynein complex components)

• Targeted Mass Spectrometry:

  • Provides absolute protein quantification using isotope-labeled peptide standards

  • Focuses on unique DYNC1LI1 peptides

  • Offers superior specificity compared to antibody-based methods

  • Can detect post-translational modifications relevant to DYNC1LI1 function

Complementary Approaches:

• Proximity Ligation Assay (PLA):

  • Detects DYNC1LI1 protein with enhanced specificity

  • Signal amplification enables detection of low-abundance protein

  • Provides spatial information on DYNC1LI1 interactions

  • Can be multiplexed with other targets for comprehensive analysis

• Single-Cell RNA Sequencing:

  • Reveals cell-specific variations in DYNC1LI1 expression

  • Identifies rare cell populations with altered DYNC1LI1 levels

  • Enables correlation with global transcriptional programs

  • Particularly valuable for heterogeneous samples like tumors

Sample Processing Guidelines:

• Standardize collection procedures to minimize pre-analytical variables

• Process samples immediately or use RNA/protein stabilizing reagents

• Include quality control metrics (RNA integrity, protein degradation indices)

• Implement batch correction for multi-center studies

Statistical Considerations:

• Utilize paired sample analysis when appropriate

• Implement bootstrapping for robust confidence intervals

• Apply machine learning algorithms to identify subtle expression patterns

• Calculate minimal detectable differences based on technical variability

Validation Strategy:

• Confirm findings using orthogonal methods

• Include technical and biological replicates

• Compare results against established DYNC1LI1 expression baselines in reference tissues

• Correlate expression with functional outcomes (e.g., chemosensitivity, angiogenic potential)

This comprehensive approach maximizes sensitivity for detecting small but biologically significant changes in DYNC1LI1 expression in patient samples, enabling more precise stratification and potentially informing personalized treatment decisions.