DLL4 Mouse

What is DLL4 and what is its role in vascular development?

DLL4 (Delta-like protein 4) is a type I membrane protein belonging to the Delta/Serrate/Lag2 (DSL) family of Notch ligands that plays crucial roles in vascular development. DLL4 is selectively expressed in arterial endothelial cells and angiogenic tip cells during development, serving as a key regulator of arterial specification and vascular branching morphogenesis. Research has demonstrated that DLL4 promotes arterial differentiation while simultaneously restricting excessive vessel branching. This dual function positions DLL4 as a critical molecular switch that balances vascular expansion and arterial differentiation during embryonic development and in adult tissues. In the Notch signaling pathway, DLL4 acts by modulating VEGF receptor expression in sprouting vessels and decreasing VEGF responsiveness, a mechanism believed to contribute to its restriction of angiogenic sprouting .

What phenotypes are observed in DLL4 knockout or heterozygous mice?

DLL4 heterozygous (Dll4+/-) mice exhibit distinct vascular phenotypes that demonstrate the gene's importance in vascular development and function. The most notable phenotype is approximately twofold more pial collateral connections compared to wild-type littermates, observed from early development (P1) and maintained into adulthood. While collateral pruning during postnatal development proceeds at similar rates in both Dll4+/- and wild-type mice, the adult Dll4+/- mice retain approximately twice as many collaterals .

Complete DLL4 knockout (Dll4-/-) mice show more severe phenotypes, including reduced aorta size and ectopic expression of venous markers in the aorta, consistent with impaired arterial specification. Conversely, conditional overexpression of DLL4 results in an enlarged aorta and reduced vascular branching during embryogenesis .

Despite the increased collateral vessel numbers in Dll4+/- mice, these animals paradoxically show poor blood flow recovery upon arterial occlusion, indicating that vessel functionality rather than mere quantity determines tissue perfusion outcomes after ischemic events .

What are the common methods to detect DLL4 expression in mouse tissues?

Several validated methods can be employed to detect DLL4 expression in mouse tissues:

• Western Blot Analysis: Using specific anti-DLL4 antibodies, such as the Goat Anti-Mouse DLL4 Antigen Affinity-purified Polyclonal Antibody. For example, DLL4 can be detected in lysates of bEnd.3 mouse endothelioma cell line as a specific band at approximately 90 kDa under reducing conditions .

• Immunofluorescence/Immunohistochemistry: DLL4 can be detected in fixed tissues using specific antibodies. For instance, in bEnd.3 mouse endothelioma cells, DLL4 staining is localized to the cytoplasm using the appropriate antibody and visualization systems .

• Reporter Systems: Genetic approaches combining DLL4 with reporter genes like β-galactosidase (lacZ) or GFP allow visualization of DLL4 expression patterns. The Dll4+/- mice carrying a beta-galactosidase reporter, when intercrossed with transgenic mice expressing EGFP driven by the arterial Gja5 (connexin 40) promoter, enable simultaneous detection of arteries and pial arteriolar-arteriolar collateral anastomoses .

• In situ Hybridization: This technique can be used to detect DLL4 mRNA expression in tissue sections, providing spatial information about gene expression patterns.

What are the optimal timepoints for studying DLL4's role in vascular development?

Based on developmental studies of collateral vessel formation, several key timepoints are critical for studying DLL4's role in vascular development:

• Embryonic Development: This period is crucial for studying initial arterial specification and branching morphogenesis influenced by DLL4-Notch signaling.

• P1 (Postnatal Day 1): Research indicates that wild-type mice show highest numbers of pial collaterals at P1, making this an essential timepoint for studying native collateral formation. The difference between Dll4+/- and wild-type mice is already evident at this stage, with Dll4+/- mice exhibiting approximately twice as many pial collateral connections .

• P0-P9: This period has been used successfully for conditional gain-of-function studies, where doxycycline induction of DLL4 overexpression between P0-P9 produced measurable effects on collateral vessel formation, assessable at P9 .

• P21 onwards: By P21, collateral numbers reach adult levels after the natural pruning process. Studies investigating adult phenotypes should focus on this timepoint or later .

• 8 weeks: This age has been used for functional studies such as middle cerebral artery occlusion to assess the functional consequences of altered collateral networks in adult mice .

How can I design genetic loss- and gain-of-function approaches to study DLL4 in mice?

Designing effective genetic approaches to study DLL4 requires careful consideration of several factors:

Loss-of-Function Approaches:

• Heterozygous Models: Utilize Dll4+/- heterozygous mice, as described in previous studies. These mice are viable and exhibit clear vascular phenotypes, including increased collateral vessel numbers .

• Conditional Knockout Models: For tissue-specific or time-specific deletion, use Cre-loxP systems such as Cdh5-Cre:Notch1flox/flox mice to target endothelial cells specifically .

• Reporter Integration: Incorporate reporter genes like β-galactosidase or EGFP to facilitate visualization of DLL4 expression and vascular structures. For example, crossing Dll4+/- mice carrying a beta-galactosidase reporter with Gja5+/eGFP arterial reporter mice enables visualization of arterial structures and collateral vessels .

Gain-of-Function Approaches:

• Inducible Overexpression Systems: Implement tetracycline-inducible systems like the one described using transgenic mice expressing DLL4 under control of a tetracycline-inducible endothelial-specific Tie2 promoter. This allows temporal control by administering doxycycline in drinking water .

• Validation of Overexpression: Confirm successful induction through functional assays, such as examining retinal vessel development for predicted changes (e.g., reduction in tip cell numbers and sprouting angiogenesis) .

For both approaches, appropriate controls are essential, including wild-type littermates for genetic models and vehicle-treated controls for inducible systems. Additionally, backcrossing onto well-characterized genetic backgrounds (e.g., CD1 as used in the cited studies) ensures consistency and reproducibility .

How do I quantify changes in collateral vessel formation in DLL4-modified mice?

Quantifying collateral vessel formation in DLL4-modified mice requires systematic approaches combining multiple techniques:

• Whole-mount Staining and Visualization:

  • X-gal staining for β-galactosidase reporter expression

  • Fluorescence detection of EGFP in arterial reporter strains (e.g., Gja5+/eGFP)

  • Immunostaining with antibodies against α-smooth muscle actin (αSMA) to identify arterioles

  • Isolectin B4 staining to visualize general vasculature

• Quantification Parameters:

  • Count pial collateral connections between major cerebral arteries (e.g., between middle cerebral artery and anterior cerebral artery)

  • Measure vessel density on tissue sections

  • Evaluate cortex area to ensure changes are independent of brain size

• Temporal Assessment:

  • Analyze collateral numbers at multiple timepoints (e.g., P1, P9, P21, adulthood) to distinguish between effects on initial formation versus subsequent pruning or remodeling

• Functional Correlates:

  • Perform middle cerebral artery occlusion to assess stroke volume using TTC staining

  • Measure infarction volumes using imaging software (e.g., ImageJ)

  • Compare right and left hemisphere measurements to account for bilateral differences

• Statistical Analysis:

  • Use appropriate statistical tests comparing experimental groups and their controls

  • Account for biological replication and technical variation

  • Consider potential confounding factors such as strain background differences

This multi-parameter approach ensures comprehensive characterization of vascular phenotypes beyond simple vessel counts, addressing both structural and functional aspects of collateral networks.

What models are most appropriate for studying DLL4's role in post-ischemic recovery?

Several established models are appropriate for studying DLL4's role in post-ischemic recovery, each offering unique insights:

• Middle Cerebral Artery Occlusion (MCAO) Model:

  • Procedure: Occlude the left middle cerebral artery in 8-week-old mice

  • Assessment: Sacrifice mice 48 hours post-occlusion and collect brain tissue

  • Analysis: Create coronal slices of forebrain using a vibratome and stain with 2% 2,3,5-triphenyltetrazolium chloride (TTC)

  • Quantification: Measure right and left hemispheres and infarction volumes using ImageJ software

  • Advantages: Directly assesses functional relevance of collateral networks in cerebral ischemia

• Hind Limb Ischemia Model:

  • Procedure: Perform femoral artery occlusion in adult mice

  • Assessment: Monitor blood flow recovery over time using techniques like laser Doppler imaging

  • Analysis: Evaluate tissue ischemia severity, angiogenic response, and functional recovery

  • Advantages: Allows longitudinal assessment in the same animal; mimics peripheral arterial disease

• Arterial Function Assessment:

  • Approach: Examine excitation-contraction coupling in arterial smooth muscle in response to vasopressor agents

  • Parameters: Measure arterial vessel wall adaptation in response to increases in blood flow

  • Analysis: Evaluate flow reserve and blood flow conductance

  • Advantages: Provides mechanistic insight into how DLL4 affects both vessel structure and function

The choice of model should be guided by the specific research question. For investigating collateral vessel formation and its impact on stroke outcomes, the MCAO model is preferable. For studying peripheral arterial disease and the interplay between angiogenesis and arteriogenesis, the hind limb ischemia model is more appropriate.

How can I differentiate between DLL4's effects on angiogenesis versus arteriogenesis?

Differentiating between DLL4's effects on angiogenesis versus arteriogenesis requires specific analytical approaches that target the distinct characteristics of each process:

• Marker-Based Discrimination:

  • Angiogenesis: Use general endothelial markers like CD31 or isolectin B4 to identify all vessels

  • Arteriogenesis: Employ arterial-specific markers such as:

    • α-smooth muscle actin (αSMA) for smooth muscle cell coverage

    • Connexin 40 (Gja5) for arterial endothelial cells

    • Ephrin B2 for arterial specification

• Morphological Analysis:

  • Angiogenesis: Quantify capillary density, branching points, and tip cell formation

  • Arteriogenesis: Measure arterial diameter, wall thickness, and collateral connections between existing arterial networks

• Temporal Assessment:

  • Angiogenesis: Generally more prominent in acute response to hypoxia (early phase)

  • Arteriogenesis: Involves remodeling of pre-existing collaterals and takes longer to develop (later phase)

• Functional Assessments:

  • Angiogenesis: Evaluate tissue hypoxia markers (e.g., HIF-1α, VEGF expression)

  • Arteriogenesis: Assess blood flow conductance and pressure gradient measurements across collateral circuits

• Genetic Reporter Systems:

  • Utilize dual reporter systems like Dll4+/- mice carrying a beta-galactosidase reporter crossed with Gja5+/eGFP arterial reporter mice

  • This enables simultaneous visualization of general vasculature (X-gal staining) and arterial structures (EGFP fluorescence)

• Response to Intervention:

  • Angiogenesis: More sensitive to hypoxia-driven signals and VEGF

  • Arteriogenesis: More responsive to fluid shear stress and inflammatory mediators

By employing these complementary approaches, researchers can dissect the specific contributions of DLL4 to each process and understand how they collectively impact vascular development and adaptation to ischemic conditions.

How should recombinant mouse DLL4 proteins be reconstituted and stored?

Proper reconstitution and storage of recombinant mouse DLL4 proteins are critical for maintaining their biological activity. The protocols differ slightly depending on the specific protein formulation:

For DLL4 Fc Chimera Protein (CF):

• Formulation: Lyophilized from a 0.2 μm filtered solution in PBS

• Reconstitution: Reconstitute at 500 μg/mL in PBS

• Storage:

  • 12 months from date of receipt at -20°C to -70°C as supplied

  • 1 month at 2-8°C under sterile conditions after reconstitution

  • 3 months at -20°C to -70°C under sterile conditions after reconstitution

• Special considerations: Use a manual defrost freezer and avoid repeated freeze-thaw cycles

For DLL4 His-tag Protein with BSA carrier:

• Formulation: Lyophilized from a 0.2 μm filtered solution in Tris-HCl, NaCl and PEG3350 with BSA as a carrier protein

• Reconstitution: Reconstitute at 200 μg/mL in sterile PBS

• Storage:

  • 12 months from date of receipt at -20°C to -70°C as supplied

  • 1 month at 2-8°C under sterile conditions after reconstitution

  • 3 months at -20°C to -70°C under sterile conditions after reconstitution

• Special considerations: Use a manual defrost freezer and avoid repeated freeze-thaw cycles

For DLL4 His-tag Protein (Carrier Free):

• Formulation: Lyophilized from a 0.2 μm filtered solution in Tris-HCl, NaCl and PEG3350

• Reconstitution: Reconstitute at 200 μg/mL in sterile PBS

• Storage: Same as for the version with BSA carrier

For all recombinant proteins, it is advisable to aliquot the reconstituted protein to minimize freeze-thaw cycles. Additionally, the shipping conditions (ambient temperature) are suitable for these proteins, but upon receipt, they should immediately be stored at the recommended temperature .

What are the differences between DLL4 Fc chimera and DLL4 His-tag proteins in experimental applications?

Recombinant mouse DLL4 proteins with different tags offer distinct advantages for specific experimental applications:

For applications where carrier protein might interfere, carrier-free versions of both protein types are available. The choice between these proteins should be guided by the specific experimental requirements, including immobilization needs, detection methods, and potential interference with binding partners or downstream assays .

What antibodies are recommended for detecting mouse DLL4 in different applications?

Based on the search results, the following antibody has been validated for detecting mouse DLL4 across multiple applications:

Goat Anti-Mouse DLL4 Antigen Affinity-purified Polyclonal Antibody (Catalog # AF1389):

This antibody has been specifically tested and validated for detecting endogenous mouse DLL4 in multiple cell and tissue contexts, including bEnd.3 mouse endothelioma cell line and mouse embryonic tissues, making it a versatile tool for DLL4 research across various experimental paradigms .

How can I validate the specificity of anti-DLL4 antibodies in mouse samples?

Validating the specificity of anti-DLL4 antibodies in mouse samples is critical for ensuring reliable experimental results. Based on best practices and the information from the search results, a comprehensive validation approach should include:

• Positive and Negative Controls:

  • Positive controls: Use tissues or cell lines known to express DLL4, such as bEnd.3 mouse endothelioma cell line

  • Negative controls: Include samples from DLL4 knockout mice or cell lines where DLL4 has been silenced

  • Peptide blocking: Pre-incubate the antibody with the immunizing peptide to confirm signal elimination

• Multiple Detection Methods:

  • Compare results across different applications (Western blot, immunohistochemistry, ELISA) to confirm consistent detection patterns

  • For Western blot, verify that the detected band appears at the expected molecular weight (~90 kDa for mouse DLL4)

  • For immunostaining, confirm the expected subcellular localization (cytoplasmic staining for DLL4)

• Cross-Validation with Alternative Approaches:

  • Compare protein detection with mRNA expression (RT-PCR or in situ hybridization)

  • Use reporter mouse models (e.g., Dll4+/- mice with β-galactosidase reporter) to correlate reporter activity with antibody staining

  • Employ multiple antibodies targeting different epitopes of DLL4

• Specificity Checks:

  • Test for cross-reactivity with related proteins (other DSL family members)

  • Document known cross-reactivity (e.g., ~50% cross-reactivity with human DLL4 in direct ELISAs)

  • Use appropriate blocking reagents to minimize background

• Technical Controls:

  • Include secondary antibody-only controls to assess background

  • Use isotype controls to evaluate non-specific binding

  • For fluorescent detection, include autofluorescence controls

By implementing this comprehensive validation strategy, researchers can ensure that their anti-DLL4 antibody is specific and reliable for the intended applications in mouse samples, thereby increasing confidence in experimental results and interpretations.

How do I resolve contradictory findings between DLL4's effects on vessel number versus functionality?

The paradoxical finding that Dll4+/- mice have increased collateral vessel numbers yet show poorer blood flow recovery after arterial occlusion highlights a fundamental principle: vessel quantity does not necessarily correlate with functional efficacy. To resolve such contradictions, researchers should implement a multi-faceted analytical approach:

• Distinguish Structure from Function:

  • Quantify not only vessel numbers but also structural parameters such as vessel diameter, wall thickness, and smooth muscle coverage

  • Assess functional parameters including blood flow conductance, pressure gradients, and tissue perfusion using techniques like laser Doppler imaging

  • Remember that Dll4+/- mice show adverse effects on excitation-contraction coupling in arterial smooth muscle and impaired arterial vessel wall adaptation to increased blood flow

• Analyze Vascular Hierarchy and Network Architecture:

  • Evaluate the distribution of vessels across different caliber classes (capillaries, arterioles, small arteries)

  • Assess network connectivity and efficiency using computational modeling approaches

  • Consider that excessive branching may actually reduce flow efficiency by increasing resistance

• Examine Molecular and Cellular Phenotypes:

  • Investigate endothelial cell phenotypes and arterial specification markers

  • Assess smooth muscle cell coverage and contractility

  • Analyze expression of key functional proteins (ion channels, contractile proteins, gap junctions)

• Implement Temporal Analysis:

  • Study acute versus chronic responses to ischemia

  • Distinguish between developmental effects and adaptive responses

  • Consider that initial vessel formation and subsequent remodeling may be differentially affected by DLL4 signaling

• Context-Dependent Analysis:

  • Compare results across different tissues and vascular beds

  • Consider potential compensatory mechanisms that may differ between genetic backgrounds

  • Analyze how environmental factors (inflammation, comorbidities) might influence outcomes

By integrating these approaches, researchers can develop a more nuanced understanding of how DLL4 simultaneously affects vessel formation and function, potentially reconciling apparently contradictory findings through a more comprehensive mechanistic framework .

What statistical approaches are recommended for analyzing collateral vessel density in DLL4 studies?

Analyzing collateral vessel density in DLL4 studies requires robust statistical approaches that account for biological variation and experimental design. Based on best practices in vascular biology research:

By implementing these statistical approaches, researchers can ensure robust analysis of collateral vessel density data, facilitating meaningful comparisons between experimental groups and reliable interpretation of DLL4's effects on vascular development and remodeling.

How should I interpret changes in flow recovery after arterial occlusion in DLL4-modified mice?

Interpreting changes in flow recovery after arterial occlusion in DLL4-modified mice requires careful consideration of multiple factors beyond simple blood flow measurements:

• Integrate Structural and Functional Data:

  • Correlate blood flow measurements with collateral vessel morphology

  • Consider that Dll4+/- mice showed reduced blood flow conductance after femoral artery occlusion despite increased angiogenesis, indicating functional impairment

  • Evaluate tissue ischemia severity alongside blood flow measurements

• Dissect Vascular Functions:

  • Assess excitation-contraction coupling in arterial smooth muscle

  • Evaluate arterial vessel wall adaptation to increased blood flow

  • Measure flow reserve capacity, which indicates the ability to increase perfusion in response to demand

  • Remember that loss of Dll4 adversely affected these arterial functions in experimental models

• Consider Temporal Dynamics:

  • Distinguish between acute (hours), intermediate (days), and chronic (weeks) responses

  • Evaluate the rate of recovery rather than just endpoints

  • Assess stability of recovered flow over time

• Account for Compensatory Mechanisms:

  • Analyze inflammatory responses and their correlation with flow recovery

  • Consider angiogenic responses secondary to ischemia

  • Evaluate potential systemic adaptations (e.g., blood pressure changes)

• Contextualize with Tissue Outcomes:

  • Correlate flow recovery with tissue damage markers

  • In stroke models, relate flow recovery to infarct volume measured by TTC staining

  • In hindlimb ischemia, correlate with tissue necrosis or functional recovery

  • Note that despite increased collateral numbers, Dll4+/- mice did not show reduced stroke volume after middle cerebral artery occlusion

• Control for Confounding Factors:

  • Account for variations in surgical technique

  • Consider age and sex differences in vascular responses

  • Adjust for physiological parameters like blood pressure and heart rate

This comprehensive interpretive framework allows researchers to move beyond simplistic correlations between gene modification and flow recovery, enabling mechanistic insights into how DLL4 influences the quality and functionality of the vasculature rather than merely affecting vessel numbers .

What are the key considerations when comparing DLL4 expression across different mouse strains?

When comparing DLL4 expression or DLL4-dependent phenotypes across different mouse strains, several critical factors must be considered to ensure valid comparisons and interpretations:

• Genetic Background Effects:

  • Recognize that different inbred strains have inherent differences in vascular development and collateral formation

  • Document the specific strain background (e.g., CD1 as used in the referenced studies)

  • For genetically modified mice, note the number of backcrosses to achieve congenic status

  • Consider using F1 hybrids to minimize strain-specific effects if crossing different backgrounds

• Expression Analysis Standardization:

  • Use consistent methods for quantifying DLL4 expression (qPCR, Western blot, immunostaining)

  • Include appropriate housekeeping genes or loading controls specific to each strain

  • Perform relative quantification within strains before comparing across strains

  • Consider absolute quantification methods for more direct comparisons

• Developmental Timing:

  • Account for potential differences in developmental pace between strains

  • Compare animals at equivalent developmental stages rather than strictly by chronological age

  • Note that collateral pruning rates might differ between strains, affecting adult phenotypes

• Anatomical Variations:

  • Standardize anatomical regions for analysis

  • Verify that metrics like cortex area are comparable between strains before comparing vessel densities

  • Document strain-specific anatomical variations that might affect vascular patterns

• Environmental Controls:

  • House different strains under identical conditions

  • Control for environmental factors known to affect vascular development (maternal diet, litter size, stress)

  • Consider cohort effects and implement appropriate randomization

• Experimental Design Considerations:

  • Include wild-type littermates as controls whenever possible

  • For conditional models, verify similar expression levels of Cre recombinase across strains

  • Consider using multiple independent lines or founder animals to confirm phenotypes

• Reporting and Interpretation:

  • Explicitly state the strain background in methods sections

  • Discuss strain-specific effects as potential confounding factors

  • Be cautious about generalizing findings from one strain to another without validation

By addressing these considerations, researchers can make more valid comparisons of DLL4 expression and function across mouse strains, enhancing the reproducibility and translational relevance of their findings.