lrfn1 Antibody

What is LRFN1 and what are its key structural domains?

LRFN1 (Leucine Rich Repeat And Fibronectin Type III Domain Containing 1), also known as SALM2, is a 105 kDa type I transmembrane glycoprotein from the LRFN family. Its structure consists of a 31 amino acid signal sequence, followed by an extracellular domain containing seven leucine-rich repeats (LRR), an IgC2-like domain, and a fibronectin type-III domain. LRFN1 also contains a transmembrane region and a cytoplasmic region with a PDZ binding domain that is conserved among SALMs 1-3 but absent in SALMs 4 and 5 .

How does LRFN1 function in neuronal systems?

LRFN1 plays several key roles in neuronal systems: it promotes neurite outgrowth in hippocampal neurons, regulates the maintenance of excitatory synapses, and induces clustering of excitatory postsynaptic proteins including DLG4, DLGAP1, GRIA1, and GRIN1. LRFN1 co-localizes with both pre- and post-synaptic proteins at excitatory synapses in mature neurons. Experimental procedures to examine these functions typically involve transfecting primary neuronal cultures with LRFN1 constructs and analyzing neurite length, synapse density, and colocalization with synaptic markers using immunofluorescence techniques .

What experimental techniques are commonly used to detect LRFN1 expression?

LRFN1 expression can be detected using several techniques:

• Western blotting (typically using 1 μg/mL antibody concentration)

• Immunohistochemistry (IHC) on paraffin-embedded or frozen sections

• Immunofluorescence (IF) on cultured cells or tissue sections

• ELISA

• Immunocytochemistry (ICC)

For IHC applications, heat-induced epitope retrieval using basic antigen retrieval reagents is recommended. Optimal antibody dilutions should be determined by each laboratory for each specific application .

How does the miR-187-3p/LRFN1 axis modulate tumor progression in clear cell renal cell carcinoma (ccRCC)?

The miR-187-3p/LRFN1 axis plays a significant role in ccRCC progression through multiple mechanisms. Research indicates that miR-187-3p acts as a tumor suppressor by directly targeting LRFN1-3'-UTR and negatively modulating LRFN1 expression. This can be verified using luciferase reporter assays with wild-type and mutant 3'-UTR of LRFN1.

In ccRCC, increased LRFN1 expression significantly correlates with high tumor grade and advanced clinical cancer stage (P < 0.001). LRFN1 overexpression promotes:

• Cellular proliferation and invasion

• Tumor growth in subcutaneous xenograft models

• Intratumoral heterogeneity

• Altered immune-infiltrating microenvironment characterized by elevated M2 macrophage infiltration, CD8+ T cell activity, and PD-L1 expression

To study this axis, researchers typically use a combination of:

• miR-187-3p mimics transfection in ccRCC cell lines

• Luciferase reporter assays to confirm direct targeting

• Proliferation, migration, and apoptosis assays

• Deep-sequencing technology and bioinformatics analyses

• In vivo xenograft models

• CIBERSORT algorithm for immune infiltration analysis .

How does LRFN1 interact with SNX27 and what are the functional consequences of this interaction?

LRFN1 interacts with SNX27 (sorting nexin-27) through its Type I PDZ binding motif located in its cytosolic C-terminal tail. This interaction can be validated through GFP-nanotrap immunoisolation and quantitative western blotting. When LRFN1's PDZ binding motif is deleted (by removing the last three amino acids), the association with SNX27 is abolished.

Functionally, this interaction regulates LRFN1 trafficking:

• SNX27 mediates the retrieval of LRFN1 from lysosomal degradation

• SNX27 is required for recycling LRFN1 back to the cell surface

• In SNX27-suppressed cells, LRFN1 shows increased colocalization with lysosomal marker LAMP2

To study this interaction, researchers use:

• Transient transfection of GFP-tagged LRFN1 constructs (wild-type and PDZ-binding motif deletion mutants)

• Immunoprecipitation assays

• Colocalization studies with lysosomal markers

• Surface expression analysis in control vs. SNX27-suppressed cells .

What is the relationship between LRFN1 and AMPA receptors in neuronal systems?

LRFN1 associates with AMPA receptor subunits GluA1 and GluA2 primarily through its extracellular LRR and Ig domains, as demonstrated by co-immunoprecipitation experiments with truncation mutants. This interaction has functional significance:

• LRFN1 and AMPA receptors show overlapping distributions on the cell surface of dendrites

• LRFN1 suppression results in:

  • Significant reduction in GluA2 surface expression (approximately 40%)

  • No significant effect on GluA1 surface expression

This differential effect suggests that LRFN1 specifically helps maintain surface expression of GluA1-lacking AMPA receptors, while other pathways (possibly involving other LRFN family members) maintain GluA1-containing receptors.

Experimental approaches to study this relationship include:

• Co-transfection of tagged LRFN1 and AMPA receptor subunits

• Immunoisolation and western analysis

• Creation of deletion mutants to map interaction domains

• Surface labeling of AMPA receptor subunits in neurons with suppressed LRFN1 expression

• Quantitative immunofluorescence analysis .

What are the optimal conditions for using LRFN1 antibodies in immunohistochemistry?

For optimal IHC conditions when using LRFN1 antibodies:

• Tissue preparation:

  • For paraffin-embedded sections: Use immersion fixation followed by heat-induced epitope retrieval with basic antigen retrieval reagents (e.g., Catalog # CTS013 as used in protocols)

  • For frozen sections: Standard fixation with 4% paraformaldehyde is typically sufficient

• Antibody concentration:

  • Starting concentration: 15 μg/mL for monoclonal antibodies

  • Incubation: Overnight at 4°C

• Detection system:

  • For chromogenic detection: HRP-DAB systems (e.g., Anti-Mouse HRP-DAB Cell & Tissue Staining Kit)

  • Counterstaining: Hematoxylin for nuclear visualization

• Validation controls:

  • Positive control: Human cerebellum shows good LRFN1/SALM2 expression

  • Negative controls: Primary antibody omission and isotype controls

Each laboratory should optimize antibody dilutions and incubation conditions for their specific samples and detection systems through titration experiments .

How can I optimize western blotting protocols for detecting LRFN1?

For optimal western blotting of LRFN1:

• Sample preparation:

  • Lyse cells in RIPA buffer supplemented with protease inhibitors

  • For membrane proteins like LRFN1, include 1% NP-40 or Triton X-100 in lysis buffer

  • Heat samples at 70°C rather than 95-100°C to prevent aggregation of membrane proteins

• Gel electrophoresis and transfer:

  • Use 8-10% SDS-PAGE gels (LRFN1 is approximately 105 kDa)

  • Transfer to PVDF membranes (preferred over nitrocellulose for hydrophobic proteins)

  • Use transfer buffer containing 10-20% methanol

• Antibody incubation:

  • Blocking: 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature

  • Primary antibody: Start with 1 μg/mL concentration in blocking buffer

  • Incubation: Overnight at 4°C with gentle rocking

• Detection and troubleshooting:

  • If background is high: Increase washing steps and dilute antibody further

  • If signal is weak: Enrich membrane fractions during sample preparation

  • Verify specificity: Use recombinant LRFN1 protein as a positive control

• Expected results:

  • LRFN1 should appear as a band at approximately 105 kDa

  • Multiple bands may indicate glycosylation variants .

What are the key considerations for using LRFN1 antibodies in co-immunoprecipitation experiments?

For successful co-immunoprecipitation (co-IP) of LRFN1 and its interacting partners:

• Buffer optimization:

  • Use mild lysis buffers (e.g., 1% NP-40, 150 mM NaCl, 50 mM Tris pH 7.4)

  • Include protease inhibitors and phosphatase inhibitors if studying phosphorylation events

  • For membrane protein complexes, consider using 1% digitonin or CHAPS which better preserve protein-protein interactions

• Antibody selection and controls:

  • Choose antibodies validated for immunoprecipitation

  • Perform reciprocal IPs (e.g., IP with anti-LRFN1 and probe for interacting partners, then IP with antibody against interacting partner and probe for LRFN1)

  • Include IgG control to identify non-specific binding

• Experimental design for LRFN1 interactions:

  • For PDZ domain interactions (e.g., with SNX27): Use GFP-nanotrap immunoisolation with GFP-tagged LRFN1

  • For receptor interactions (e.g., with AMPA receptors): Consider using crosslinking prior to lysis

  • Create deletion mutants (e.g., ΔPDZ binding motif) as negative controls

• Detection strategies:

  • For weak interactions: Consider crosslinking before lysis or use proximity labeling techniques

  • Western blot analysis should include input, unbound, and IP fractions

  • Validate results with reverse co-IP experiments .

How can LRFN1 expression be assessed as a biomarker in cancer research?

Assessment of LRFN1 as a cancer biomarker involves multiple methodological approaches:

In ccRCC studies, LRFN1 has demonstrated potential as an independent prognostic biomarker across multiple independent cohorts, with elevated expression correlating with higher tumor grade and advanced clinical stage .

What approaches can be used to study the role of LRFN1 in neurological disorders?

Investigating LRFN1 in neurological disorders involves multiple experimental approaches:

• Genetic analysis:

  • Sequencing LRFN1 in patient cohorts with conditions like Epilepsy, Familial Temporal Lobe, 1

  • Genotype-phenotype correlation studies

  • Analysis of variants in functional domains (e.g., LRR, PDZ binding motif)

• Functional studies in neuronal models:

  • Primary neuronal cultures expressing wild-type vs. mutant LRFN1

  • Analysis of:

    • Dendritic spine morphology

    • Synaptic protein clustering

    • Synaptic transmission (electrophysiology)

    • AMPA receptor trafficking and surface expression

• Animal models:

  • LRFN1 knockout or knockin mice

  • Behavioral phenotyping (learning, memory, seizure susceptibility)

  • Electrophysiological recordings (LTP, LTD, basal synaptic transmission)

  • Histological analysis of brain development and synapse formation

• Therapeutic targeting strategies:

  • Antibodies targeting extracellular domains to modulate function

  • Small molecules disrupting specific protein-protein interactions

  • Peptide mimetics of the PDZ binding domain to compete with endogenous interactions

When using antibodies for these studies, it's crucial to validate specificity against other LRFN family members due to their structural similarity .

How can LRFN1 antibodies be designed for improved specificity and functionality?

Designing improved LRFN1 antibodies requires sophisticated approaches:

• Epitope selection strategies:

  • Target unique regions of LRFN1 not conserved in other LRFN family members

  • Consider using the extracellular domain (ECD) between amino acids Gln32-Gly534 as immunogen

  • For functional modulation, target specific domains:

    • LRR domain (protein-protein interactions)

    • IgC2-like domain (cell adhesion)

    • Fibronectin type-III domain (receptor binding)

• Advanced antibody development approaches:

  Approach	Methodology	Advantages
  Phage display	Selection against various ligand combinations	Allows identification of specific binding modes
  NGS-based screening	High-throughput sequencing of selected antibodies	Enables computational prediction of binding properties
  Biophysics-informed modeling	Identifying distinct binding modes for different ligands	Allows design of antibodies with customized specificity profiles

• Specificity validation framework:

  • Cross-reactivity testing against all LRFN family members

  • Epitope mapping using deletion mutants

  • Functional validation in knockout/knockdown systems

  • Binding kinetics analysis (SPR/BLI) to quantify specificity

• Antibody engineering considerations:

  • Format selection (full IgG, Fab, scFv) based on application

  • Fc engineering for desired effector functions

  • Humanization for therapeutic applications

  • Adding detection tags for specific applications while preserving antigen binding

Recent advances in computational methods allow for the design of antibodies with customized specificity profiles that can either target LRFN1 specifically or demonstrate cross-specificity with other LRFN family members as desired for particular experimental applications .

How can multi-omics approaches be integrated to study LRFN1 biology?

Integrating multi-omics approaches to study LRFN1 requires systematic methodology:

• Data generation and integration:

  • Transcriptomics: RNA-seq to identify LRFN1 expression patterns and correlations

  • Proteomics: Mass spectrometry to identify LRFN1 interactome and post-translational modifications

  • Epigenomics: ChIP-seq to identify transcriptional regulators of LRFN1

  • Single-cell approaches: scRNA-seq to identify cell-type specific expression

• Computational analysis framework:

  • Network analysis to identify LRFN1-centered protein interaction networks

  • Pathway enrichment to identify biological processes involving LRFN1

  • Integration of multiple data types using methods like:

    • Multi-omics factor analysis (MOFA)

    • Similarity network fusion

    • Joint non-negative matrix factorization

• Validation of computational predictions:

  • CRISPR/Cas9-mediated knockout or knockin

  • Proximity labeling techniques (BioID, APEX) to validate protein interactions

  • In vivo models to confirm pathway predictions

In ccRCC studies, this approach has already revealed LRFN1's role in reshaping the tumor immune microenvironment, demonstrating the power of integrating multiple data types to understand complex biological functions .

What are the challenges and solutions in developing conformation-specific LRFN1 antibodies?

Developing conformation-specific LRFN1 antibodies presents several challenges:

• Key challenges:

  • LRFN1 undergoes conformational changes upon binding partners

  • Maintaining native protein conformation during immunization

  • Distinguishing between active/inactive states

  • Cross-reactivity with other LRFN family members

• Advanced solution strategies:

  Challenge	Technical Solution	Implementation Approach
  Maintaining native conformation	Structure-guided epitope selection	Use computational modeling to identify accessible epitopes in native state
  Distinguishing active/inactive states	Conformation-locking techniques	Chemical crosslinking or use of binding partners to stabilize specific conformations
  Selectivity	Negative selection strategies	Deplete antibody libraries of cross-reactors using related proteins
  Validation	Functional assays	Test antibodies in systems where LRFN1 activity can be measured

• Innovative screening approaches:

  • Phage display with alternating positive/negative selection rounds

  • High-throughput sequencing to identify enriched clones

  • Computational analysis to disentangle binding modes

  • Testing predicted antibodies that weren't present in the initial library

• Validation methodology:

  • Surface plasmon resonance to quantify binding to different conformational states

  • Cellular assays to confirm recognition of native protein

  • Molecular dynamics simulations to predict epitope accessibility

  • Crystal structures of antibody-antigen complexes to confirm binding mechanism

These approaches have been successfully applied to other challenging targets and represent promising directions for developing conformation-specific LRFN1 antibodies .

How might LRFN1-targeting antibodies be developed as therapeutic agents?

Developing LRFN1-targeting antibodies as therapeutic agents requires consideration of several factors:

• Therapeutic rationale development:

  • For cancer: Target LRFN1 to modulate tumor immune microenvironment

  • For neurological disorders: Modulate synaptic maintenance and AMPA receptor trafficking

  • Validate therapeutic hypothesis in relevant disease models

• Antibody design considerations:

  • Format selection:

    • Full IgG for effector functions (ADCC, CDC) in cancer applications

    • Fab or scFv fragments for better CNS penetration in neurological applications

  • Epitope selection:

    • Blocking epitopes to prevent protein-protein interactions

    • Non-blocking epitopes for targeted degradation approaches

• Developability assessment:

  • Expression and purification optimization

  • Stability testing under various conditions

  • Immunogenicity prediction and mitigation

  • Cross-reactivity profiling against human tissues

• Preclinical development pathway:

  • In vitro efficacy in disease-relevant cell models

  • In vivo pharmacokinetics and biodistribution

  • Efficacy studies in animal models

  • Toxicology studies to evaluate safety profile

• Innovative therapeutic approaches:

  • Bispecific antibodies targeting LRFN1 and immune cells for cancer applications

  • Antibody-drug conjugates for targeted delivery to LRFN1-expressing cells

  • Intrabodies for intracellular targeting of LRFN1 signaling pathways