LRR1 Antibody, FITC conjugated

What is LRR1 and what are its primary cellular functions?

LRR1 (Leucine-Rich Repeat protein 1) functions as a substrate recognition subunit of the CRL2 E3 ubiquitin ligase complex (CRL2LRR1) that plays a critical role in cell cycle regulation. This protein is essential for human cell division through its involvement in two primary cellular processes: replisome disassembly during DNA replication and cell cycle control through CDK inhibitor regulation. During S phase, LRR1 mediates the unloading of CMG (CDC45-MCM-GINS) helicases from chromatin after DNA replication completion, which is crucial for recycling essential replisome components . LRR1 knockout results in failure to disassemble replisomes, leading to accumulation of chromatin-bound replisome components and ultimately reducing the rate of DNA replication . Additionally, LRR1 participates in targeting CDK inhibitors, particularly CKI-1 in C. elegans and p21 in human cells, for proteasomal degradation to permit cell cycle progression .

How does FITC conjugation of antibodies work, and what are the optimal conjugation ratios?

FITC (fluorescein isothiocyanate) conjugation involves the covalent attachment of fluorescein molecules to proteins, typically via primary amines such as lysine residues. The optimal FITC-to-antibody ratio typically ranges between 3 and 6 FITC molecules per antibody molecule. This range provides sufficient fluorescence signal while avoiding potential issues associated with higher conjugation ratios, including solubility problems and internal quenching effects that can reduce brightness . When developing FITC-conjugated antibodies for research applications, it is recommended to prepare several parallel conjugation reactions with different FITC-to-antibody ratios, followed by comparative analysis of brightness and background staining to determine the optimal conjugation conditions for each specific antibody .

What are the excitation and emission characteristics of FITC-conjugated antibodies?

FITC-conjugated antibodies are optimally excited at approximately 495 nm, typically using the 488 nm line of an argon laser, and emit fluorescence with a maximum at approximately 524 nm, with standard emission collection at around 530 nm . This spectral profile makes FITC-conjugated antibodies compatible with standard flow cytometry instrumentation and fluorescence microscopy setups equipped with appropriate filter sets. When designing multicolor experiments, it's important to consider potential spectral overlap with other fluorophores, particularly those with similar emission profiles such as GFP or other green fluorescent dyes. The specific FITC Plus fluorescent dye conjugated to commercial antibodies maintains these spectral characteristics with maximal excitation at 495 nm and emission at 524 nm .

How can LRR1 antibodies be utilized to study cell cycle regulation and cancer biology?

LRR1 antibodies can be employed in multiple experimental approaches to investigate cell cycle regulation and potential cancer applications:

• Immunofluorescence after detergent pre-extraction: This technique allows quantification of chromatin-bound LRR1 and replisome components (CDC45, GINS2, POLE1) in single cells across different cell cycle phases. The protocol involves careful extraction of non-chromatin bound proteins while preserving chromatin-bound fractions, followed by immunostaining with FITC-conjugated LRR1 antibodies and other replisome markers .

• Co-immunoprecipitation assays: FITC-conjugated LRR1 antibodies can be used to investigate physical interactions between LRR1 and cell cycle regulators such as CDK inhibitors (p21, p27) or replisome components. These experiments can reveal the substrate recognition mechanisms of the CRL2LRR1 complex and how they may be altered in cancer cells .

• Subcellular fractionation combined with immunoblotting: This approach separates soluble and chromatin-bound protein fractions to study how LRR1 depletion affects the distribution of replisome components, providing insights into the mechanisms underlying replication defects in cancer cells with altered LRR1 expression .

• Cell cycle synchronization experiments: Combined with flow cytometry using FITC-conjugated LRR1 antibodies to track changes in LRR1 expression and localization throughout different cell cycle phases and in response to DNA damage or replication stress .

Given that LRR1 is essential for human cell division, these approaches can provide valuable insights into its potential as a cancer therapeutic target .

What are the recommended protocols for flow cytometric analysis using FITC-conjugated LRR1 antibodies?

For optimal flow cytometric analysis using FITC-conjugated LRR1 antibodies, researchers should follow these methodological steps:

• Sample preparation:

  • For cell suspensions: Harvest 1×10^6 cells, wash with PBS containing 2% FBS, and fix if necessary (4% paraformaldehyde for 15 minutes at room temperature)

  • For permeabilization (if studying intracellular LRR1): Use 0.1% Triton X-100 in PBS for 5-10 minutes

• Antibody staining:

  • Use 5 μl of pre-titrated FITC-conjugated LRR1 antibody per 10^6 cells in a 100 μl cell suspension

  • For whole blood applications, use 5 μl per 100 μl of whole blood

  • Incubate for 30-45 minutes at 4°C in the dark

  • Wash twice with 2 ml of PBS containing 2% FBS

• Analysis parameters:

  • Use appropriate compensation controls if performing multicolor analysis

  • Set excitation at 488 nm laser line and collect emission at approximately 530 nm

  • Include appropriate isotype controls (mouse IgG2b-FITC for commercial antibodies) to determine background fluorescence

• Data interpretation:

  • For cell cycle analysis, correlate LRR1 expression with DNA content by counterstaining with propidium iodide or DAPI

  • When studying replisome dynamics, consider co-staining with antibodies against CDC45 or other replisome components

Storage of the antibody at 2-8°C while avoiding light exposure is critical for maintaining stability, with most commercial preparations remaining stable for one year after shipment .

How can subcellular fractionation be optimized to study LRR1's role in replisome disassembly?

Optimizing subcellular fractionation for studying LRR1's role in replisome disassembly requires careful experimental design:

• Cell synchronization strategy:

  • Synchronize cells at the G1/S boundary using DNA polymerase inhibitors such as aphidicolin

  • Release cells from the block and harvest at specific timepoints throughout S phase (typically 0, 2, 5, and 8 hours post-release) to capture the dynamics of replisome assembly and disassembly

• Fractionation protocol:

  • Lyse cells in buffer containing 0.1% Triton X-100, 10 mM HEPES (pH 7.9), 10 mM KCl, 1.5 mM MgCl₂, 0.34 M sucrose, 10% glycerol, 1 mM DTT, and protease inhibitors

  • Centrifuge at 1,500 × g for 5 minutes at 4°C to separate nuclei (pellet) from cytoplasm (supernatant)

  • Wash nuclei once and lyse with buffer containing 3 mM EDTA, 0.2 mM EGTA, 1 mM DTT, and protease inhibitors

  • Centrifuge at 1,700 × g for 5 minutes to separate soluble nuclear proteins from chromatin

• Validation and quality control:

  • Confirm proper fractionation by immunoblotting for compartment-specific markers:

    • GAPDH for soluble cytoplasmic fraction

    • Histone H4 for chromatin-bound fraction

• Quantification methods:

  • Use LI-COR-based quantitative chemiluminescence detection or fluorescence-based detection systems for precise quantification of protein levels

  • Normalize chromatin-bound replisome components to histone H4 levels

  • Compare wild-type cells with LRR1 knockout or knockdown cells to assess the impact on replisome component distribution

This approach allows researchers to accurately measure how LRR1 depletion affects the chromatin association of key replisome components like CDC45, GINS2, POLE1, and Timeless throughout S phase progression.

How can researchers effectively distinguish between different LRR-family proteins in immunological experiments?

Distinguishing between LRR-family proteins (such as LRR1, LRP1, and LRIG1) requires careful antibody selection and experimental design due to potential cross-reactivity issues:

• Antibody validation strategies:

  • Perform Western blot analysis comparing wild-type cells with specific knockout or knockdown cells for each LRR-family protein

  • Test antibody specificity using overexpression systems with tagged versions of different LRR-family proteins

  • Conduct peptide competition assays using the immunogen peptide sequence to confirm binding specificity

• Epitope selection considerations:

  • Select antibodies targeting unique regions with minimal sequence homology between family members

  • For LRR1 specifically, antibodies raised against the substrate recognition domain rather than the leucine-rich repeat regions may offer better specificity

  • Consider using antibodies recognizing different epitopes of the same protein for validation

• Advanced detection strategies:

  • Implement dual-labeling approaches combining FITC-conjugated antibodies with antibodies conjugated to spectrally distinct fluorophores

  • Use proximity ligation assays (PLA) to verify protein interactions with known binding partners specific to each LRR-family protein

  • Consider mass spectrometry-based approaches for definitive protein identification

• Controls for specificity:

  • Include samples expressing only one family member at a time through genetic manipulation

  • Perform careful titration of antibody concentrations to minimize off-target binding

  • Include appropriate isotype controls matched to the specific antibody class and host species

These approaches help ensure that experimental findings are accurately attributed to the specific LRR-family protein under investigation rather than related family members.

What are the critical factors affecting FITC stability and how can signal loss be prevented in long-term experiments?

Several critical factors affect FITC stability in conjugated antibodies, and researchers can implement specific strategies to prevent signal loss:

• pH sensitivity management:

  • FITC fluorescence intensity decreases significantly at pH < 7.0

  • Maintain buffers at pH 8.0-8.5 for optimal fluorescence

  • Add 25 mM HEPES buffer to imaging media to stabilize pH during long-term experiments

• Photobleaching mitigation:

  • Reduce exposure time and light intensity during imaging

  • Add anti-fade agents such as ProLong Gold or SlowFade Diamond to mounting media

  • Consider using oxygen scavenger systems (e.g., glucose oxidase/catalase) for live-cell imaging

  • Store slides in the dark at 4°C between imaging sessions

• Storage optimization:

  • Store FITC-conjugated antibodies at 2-8°C protected from light

  • Avoid repeated freeze-thaw cycles that accelerate degradation

  • Add protein stabilizers (1% BSA) and preservatives (0.09% sodium azide) to storage buffers

  • Aliquot stock solutions to minimize exposure to environmental factors

• Alternative approaches for long-term experiments:

  • Consider more photostable fluorophores (Alexa Fluor 488) for extended imaging

  • Implement spectral unmixing techniques to distinguish between autofluorescence and specific signal

  • Use time-lapse microscopy with minimal illumination or resonant scanning confocal microscopy to reduce photobleaching

By implementing these strategies, researchers can maintain FITC signal integrity throughout long-term experimental procedures, ensuring consistent and reliable data collection.

How can researchers troubleshoot non-specific binding when using FITC-conjugated LRR1 antibodies?

When encountering non-specific binding with FITC-conjugated LRR1 antibodies, researchers should implement the following troubleshooting approaches:

• Optimizing blocking conditions:

  • Extend blocking time to 1-2 hours at room temperature

  • Test different blocking agents: 5-10% normal serum (matched to secondary antibody species), 3-5% BSA, commercial blocking buffers, or 0.1-0.3% gelatin

  • Include 0.1-0.3% Triton X-100 or 0.05-0.1% Tween-20 in blocking solutions to reduce hydrophobic interactions

• Antibody titration and validation:

  • Perform careful titration experiments (typically testing 1:50, 1:100, 1:200, 1:500, and 1:1000 dilutions)

  • Validate specificity using positive and negative control samples (e.g., LRR1 knockout cells)

  • Compare staining patterns with different antibody clones targeting distinct epitopes of LRR1

• Washing protocol optimization:

  • Increase wash duration and number of washes (5-6 washes of 5-10 minutes each)

  • Use buffers containing 0.05-0.1% Tween-20 or 0.1% Triton X-100

  • Include 150-300 mM NaCl in wash buffers to reduce ionic interactions

• Advanced approaches for persistent issues:

  • Pre-adsorb antibodies with cellular extracts from relevant negative control tissues

  • Implement Fc receptor blocking when working with cells expressing high levels of Fc receptors

  • Consider using F(ab) or F(ab')₂ fragments instead of complete IgG molecules

  • For flow cytometry applications, implement fluorescence-minus-one (FMO) controls to determine gating boundaries accurately

These methodological adjustments help distinguish between specific LRR1 signals and background fluorescence, particularly important when studying low-abundance proteins or when analyzing tissues with high autofluorescence.

How should researchers quantify and analyze LRR1 expression patterns during cell cycle progression?

Quantifying and analyzing LRR1 expression patterns during cell cycle progression requires robust methodological approaches:

• Synchronized cell population analysis:

  • Synchronize cells using established methods (thymidine block, nocodazole arrest, or aphidicolin treatment)

  • Collect samples at defined intervals (typically every 2 hours) following synchronization release

  • Perform flow cytometry with FITC-conjugated LRR1 antibodies combined with DNA content staining (propidium iodide or DAPI)

  • Generate bivariate plots of LRR1 expression versus DNA content to associate expression levels with specific cell cycle phases

• Quantitative image analysis framework:

  • For immunofluorescence data, implement detergent-based pre-extraction to retain only chromatin-bound proteins

  • Acquire images under identical exposure conditions using appropriate controls

  • Define nuclear regions of interest (ROIs) based on DAPI staining

  • Measure mean fluorescence intensity of LRR1 staining within nuclear ROIs

  • Normalize to appropriate reference proteins (e.g., histone H4)

• Statistical analysis approaches:

  • Compare LRR1 levels across cell cycle phases using appropriate statistical tests (ANOVA with post-hoc tests)

  • For cell populations, analyze at least 1000-5000 cells per condition

  • For high-resolution imaging, analyze 50-100 cells per cell cycle phase

  • Present data as both population-level distributions (histograms or box plots) and single-cell measurements to capture heterogeneity

• Correlation with functional markers:

  • Co-stain with markers of specific cell cycle phases (cyclin E for G1/S, cyclin A for S, cyclin B for G2/M)

  • Assess correlation between LRR1 expression and replisome components (CDC45, GINS2, MCM2) to evaluate functional relevance

  • Consider pulse-chase experiments with EdU or BrdU to correlate LRR1 dynamics with DNA synthesis rates

These approaches provide comprehensive analysis of how LRR1 expression and localization change throughout the cell cycle, critical for understanding its function in replisome regulation.

How can researchers distinguish between direct and indirect effects when studying LRR1 knockout phenotypes?

Distinguishing between direct and indirect effects in LRR1 knockout studies requires a multilayered experimental approach:

• Temporal analysis using inducible systems:

  • Implement doxycycline-inducible CRISPR-resistant LRR1 expression systems in LRR1 knockout backgrounds

  • Monitor phenotypic changes at short intervals (2-4 hours) after LRR1 depletion to identify primary effects

  • Compare with long-term depletion consequences (24-72 hours) to distinguish secondary effects

  • Perform time-course experiments measuring multiple parameters simultaneously

• Rescue experiments with domain-specific mutants:

  • Design structure-function studies using LRR1 mutants affecting specific domains:

    • Substrate-binding domain mutants

    • CUL2-interaction domain mutants

    • Localization signal mutants

  • Express these mutants in LRR1 knockout cells to determine which functions are essential for particular phenotypes

  • Quantify rescue efficiency for different phenotypic endpoints (replisome disassembly, DNA replication rate, cell cycle progression)

• Substrate identification and validation:

  • Perform immunoprecipitation followed by mass spectrometry to identify all potential LRR1 substrates

  • Validate direct substrates through:

    • In vitro binding assays with purified components

    • Ubiquitination assays demonstrating direct modification

    • Half-life measurements upon LRR1 manipulation

  • Determine whether knockout phenotypes can be recapitulated by overexpressing non-degradable versions of identified substrates

• Combinatorial genetic approaches:

  • Conduct epistasis experiments by creating double knockouts of LRR1 and its putative substrates

  • If the phenotype of the double mutant matches that of the substrate knockout alone, this suggests the substrate acts downstream of LRR1

  • Implement CRISPR screens to identify genetic suppressors of LRR1 knockout phenotypes

This systematic approach helps distinguish primary molecular functions of LRR1 from secondary consequences of its depletion in complex cellular systems.

What are the most appropriate quantitative methods for measuring replisome dynamics using FITC-conjugated antibodies?

For measuring replisome dynamics using FITC-conjugated antibodies, researchers should employ these quantitative methods:

• High-content imaging analysis:

  • Implement detergent-based pre-extraction to retain only chromatin-bound proteins

  • Acquire standardized images of thousands of individual cells using automated microscopy platforms

  • Measure parameters including:

    • Mean nuclear intensity of replisome components

    • Number and intensity of individual replisome foci

    • Colocalization coefficients between different replisome components

  • Apply machine learning algorithms to classify cells based on replisome patterns

• Flow cytometry-based quantification:

  • Combine FITC-conjugated antibodies against replisome components with DNA content staining

  • Analyze correlation between replisome component levels and cell cycle phase

  • Implement multiparameter analysis to simultaneously measure multiple replisome components

  • Present data as:

  Cell Cycle Phase	Mean CDC45-FITC	Mean GINS2	Mean POLE1	Colocalization Index
  Early S	124.5 ± 12.3	86.2 ± 9.1	65.3 ± 7.4	0.72 ± 0.08
  Mid S	156.7 ± 18.5	102.3 ± 11.8	83.9 ± 9.2	0.68 ± 0.07
  Late S	187.3 ± 22.7	128.5 ± 14.3	96.7 ± 10.5	0.63 ± 0.09
  G2	42.8 ± 8.5	36.4 ± 7.2	31.8 ± 6.4	0.35 ± 0.11

• Pulse-chase approaches:

  • Label nascent DNA with EdU or BrdU pulses of defined duration

  • Perform immunofluorescence with FITC-conjugated antibodies against replisome components

  • Measure colocalization between labeled DNA and replisome components

  • Calculate replisome assembly/disassembly rates based on appearance/disappearance of colocalization over time

• Quantitative biochemical fractionation:

  • Separate chromatin-bound and soluble protein fractions

  • Quantify replisome components in each fraction using quantitative immunoblotting

  • Calculate the chromatin-bound fraction as percentage of total protein

  • Compare wild-type and LRR1-deficient cells across S-phase progression

  • Present quantified data as in this example:

  Protein	Condition	% Chromatin-Bound (Early S)	% Chromatin-Bound (Mid S)	% Chromatin-Bound (Late S)
  CDC45	Wild-type	58.3 ± 6.2	63.7 ± 7.1	35.2 ± 4.8
  CDC45	LRR1 knockout	62.1 ± 5.9	72.4 ± 8.3	69.5 ± 7.6
  GINS2	Wild-type	51.6 ± 5.4	57.2 ± 6.5	32.8 ± 4.1
  GINS2	LRR1 knockout	55.3 ± 6.1	68.9 ± 7.5	64.2 ± 6.8
  Timeless	Wild-type	42.7 ± 4.9	48.5 ± 5.7	28.3 ± 3.9
  Timeless	LRR1 knockout	46.2 ± 5.3	56.8 ± 6.4	53.9 ± 5.5

These quantitative approaches provide robust metrics for assessing how LRR1 affects replisome dynamics throughout DNA replication .

How can LRR1 antibodies be utilized in cancer research and potential therapeutic development?

LRR1 antibodies can be strategically employed in cancer research and therapeutic development through several advanced applications:

• Biomarker development and patient stratification:

  • Perform immunohistochemistry with LRR1 antibodies on tumor microarrays across different cancer types

  • Correlate LRR1 expression levels with clinical outcomes, treatment response, and other molecular markers

  • Develop standardized scoring systems for LRR1 expression to stratify patients for clinical trials

  • Evaluate LRR1 as a companion diagnostic marker for drugs targeting cell cycle regulation

• Target validation approaches:

  • Use FITC-conjugated LRR1 antibodies in high-content imaging to screen for small molecule modulators of LRR1 localization or stability

  • Develop cell-based reporter assays using LRR1 substrates (p21) tagged with fluorescent proteins to monitor degradation dynamics

  • Implement CRISPR-based synthetic lethality screens to identify genetic contexts where LRR1 inhibition would be most effective

  • Test combination treatments targeting LRR1 and complementary pathways

• Therapeutic antibody development:

  • Design function-blocking antibodies targeting the substrate recognition domain of LRR1

  • Develop antibody-drug conjugates using LRR1 antibodies to deliver cytotoxic agents specifically to cells with high LRR1 expression

  • Create bispecific antibodies linking LRR1 recognition with immune cell engagement

  • Implement intrabody approaches to inhibit LRR1 function in specific cellular compartments

• Mechanistic understanding in cancer progression:

  • Investigate how LRR1 expression and function change during cancer evolution and metastasis

  • Determine if LRR1 contributes to replication stress tolerance in cancer cells

  • Study the relationship between LRR1 and genomic instability across cancer types

  • Examine how LRR1-mediated regulation of p21 contributes to therapy resistance mechanisms

These approaches leverage our understanding of LRR1's essential role in cell division to develop novel cancer diagnostics and therapeutics targeting cell cycle regulation pathways .

What advanced imaging techniques can enhance the study of LRR1 dynamics throughout the cell cycle?

Advanced imaging techniques can significantly enhance the study of LRR1 dynamics throughout the cell cycle:

• Super-resolution microscopy approaches:

  • Stimulated Emission Depletion (STED) microscopy: Achieves 30-80 nm resolution to visualize individual replisome complexes

  • Stochastic Optical Reconstruction Microscopy (STORM): Enables precise localization of LRR1 relative to other replisome components at 20-30 nm resolution

  • Structured Illumination Microscopy (SIM): Provides 100-120 nm resolution with less photodamage than other super-resolution techniques

  • Implementation strategy:

    • Use directly conjugated FITC-LRR1 antibodies optimized for super-resolution imaging

    • Combine with appropriate DNA stains and cell cycle markers

    • Perform multi-color imaging to map spatial relationships between LRR1 and its interaction partners

• Live-cell imaging technologies:

  • CRISPR-mediated endogenous tagging of LRR1 with fluorescent proteins (preferably mNeonGreen due to brightness and photostability)

  • Fluorescence Recovery After Photobleaching (FRAP) to measure LRR1 binding kinetics at replication foci

  • Fluorescence Correlation Spectroscopy (FCS) to determine LRR1 diffusion rates in different cellular compartments

  • Fluorescence Resonance Energy Transfer (FRET) to monitor real-time interactions between LRR1 and substrate proteins

• Correlative light and electron microscopy (CLEM):

  • Combine fluorescence imaging of FITC-labeled LRR1 with electron microscopy

  • Identify specific replication structures where LRR1 localizes

  • Visualize the ultrastructural context of LRR1-mediated replisome disassembly

  • Implementation requires:

    • Specialized sample preparation with electron-dense markers

    • Registration protocols to align fluorescence and EM images

    • Quantitative analysis workflows to correlate structure and function

• Lattice light-sheet microscopy:

  • Enables long-term 3D imaging with minimal phototoxicity

  • Achieves subsecond temporal resolution to capture rapid dynamics of LRR1 recruitment and dissociation

  • Allows simultaneous visualization of multiple labeled components of the LRR1-CRL2 complex

  • Particularly valuable for tracking LRR1 dynamics at individual replication forks throughout S phase

These advanced imaging approaches provide unprecedented spatial and temporal resolution for understanding LRR1's dynamic behavior during DNA replication and cell cycle progression.

How can multi-omics approaches be integrated with LRR1 antibody-based studies to gain comprehensive insights?

Integrating multi-omics approaches with LRR1 antibody-based studies creates a powerful framework for comprehensive understanding:

• ChIP-seq integration:

  • Perform chromatin immunoprecipitation with FITC-conjugated LRR1 antibodies followed by next-generation sequencing

  • Map LRR1 chromatin association patterns genome-wide

  • Correlate with replication timing data and origins of replication

  • Compare wild-type patterns with cells expressing mutant LRR1 variants

  • Integration workflow:

    • Optimize crosslinking conditions for transient LRR1-chromatin interactions

    • Validate ChIP efficiency using known binding sites

    • Apply computational approaches to identify enrichment patterns across the genome

• Proteomics-based interaction mapping:

  • Implement BioID or APEX proximity labeling with LRR1 as the bait protein

  • Perform quantitative proteomics on immunoprecipitated LRR1 complexes across cell cycle stages

  • Apply SILAC or TMT labeling to compare interactome changes upon replication stress

  • Validate key interactions using co-immunoprecipitation with FITC-conjugated LRR1 antibodies

  • Example interaction dataset:

  Protein	Wild-type Interaction Score	LRR1 Mutant Interaction Score	Cell Cycle Phase Specificity
  CDC45	0.89 ± 0.07	0.36 ± 0.09	S phase
  p21	0.76 ± 0.06	0.12 ± 0.05	G1/S transition
  CUL2	0.92 ± 0.05	0.88 ± 0.06	Pan cell cycle
  GINS2	0.65 ± 0.08	0.29 ± 0.07	S phase
  MCM2	0.58 ± 0.09	0.22 ± 0.08	S phase

• Transcriptomics correlation:

  • Perform RNA-seq on LRR1 knockout or knockdown cells

  • Compare with ChIP-seq data to identify direct transcriptional impacts

  • Analyze temporal gene expression changes following acute LRR1 depletion

  • Implement pathway analysis to identify cellular processes affected by LRR1 dysfunction

• Integrative bioinformatics framework:

  • Develop computational pipelines correlating:

    • LRR1 chromatin association (ChIP-seq)

    • Protein interaction networks (IP-MS)

    • Transcriptional consequences (RNA-seq)

    • Replication dynamics (Repli-seq)

    • Chromatin accessibility (ATAC-seq)

  • Apply machine learning approaches to predict cell cycle-specific functions

  • Create network models of LRR1-dependent regulation

This multi-omics integration provides a systems-level understanding of LRR1 function, revealing both direct mechanisms and broader cellular consequences of LRR1 activity across different physiological and pathological contexts.