lrfn1l Antibody

What is LRFN1/SALM2 and why is it significant for neuroscience research?

LRFN1, also known as SALM2 (Synaptic Adhesion-like Molecule 2), is a 105 kDa type I transmembrane glycoprotein belonging to the LRFN family. The mature human SALM2 protein contains a 740 amino acid sequence with a 505 aa extracellular domain that includes seven leucine-rich repeats (LRR), an IgC2-like domain, and a fibronectin type-III domain . Human SALM2 shares 99% amino acid identity with mouse and rat SALM2 .

SALM2/LRFN1 is primarily significant for neuroscience research because:

• It co-localizes with both pre- and post-synaptic proteins at excitatory synapses in mature neurons

• It promotes neurite outgrowth, suggesting roles in neuronal development

• It exhibits strong expression in the adult brain, particularly in the cerebellum

• Northern blot analysis shows it is also present in adult testis in rats

These characteristics make LRFN1/SALM2 an important target for studying synaptic development, function, and plasticity, potentially providing insights into neurological disorders involving synaptic dysfunction.

What types of LRFN1 antibodies are available and how do they differ?

Several types of LRFN1/SALM2 antibodies are available for research applications, each with distinct characteristics:

Monoclonal Antibodies:

• Mouse monoclonal antibodies (e.g., Clone # 610712)

• Applications: Validated for immunohistochemistry on paraffin-embedded tissue sections

• Advantages: High specificity and consistency between batches

• Target region: Recognizes human SALM2/LRFN1 (Gln32-Gly534)

Polyclonal Antibodies:

• Sheep polyclonal IgG (antigen affinity-purified)

• Applications: Validated for Western blot with minimal cross-reactivity

• Target: Typically generated against CHO-derived recombinant human SALM2/LRFN1

• Cross-reactivity: Less than 5% cross-reactivity with recombinant human SALM3 and SALM4

• Rabbit polyclonal antibodies

• Applications: Validated for ELISA, immunofluorescence, immunohistochemistry, and Western blot

• Advantages: Potentially recognize multiple epitopes, providing stronger signals

The choice between these antibody types depends on the specific research application, required specificity, and experimental conditions. Monoclonal antibodies offer higher specificity but may be more sensitive to epitope modifications, while polyclonal antibodies recognize multiple epitopes but may have higher batch-to-batch variation.

What are the validated applications for LRFN1 antibodies with protocol parameters?

LRFN1/SALM2 antibodies have been validated for several experimental applications, each with specific protocol parameters:

Western Blot:

• Sample type: Human brain cortex and hippocampus lysates (negative controls: lung and liver tissue)

• Concentration: 1 μg/mL recommended working dilution

• Detection: PVDF membrane with HRP-conjugated secondary antibody

• Expected result: Specific bands at approximately 90-110 kDa under reducing conditions

Immunohistochemistry (IHC):

• Sample preparation: Immersion fixed paraffin-embedded sections

• Epitope retrieval: Heat-induced epitope retrieval using Antigen Retrieval Reagent-Basic

• Antibody concentration: 15 μg/mL

• Incubation conditions: Overnight at 4°C

• Detection system: Anti-Mouse HRP-DAB Cell & Tissue Staining Kit with hematoxylin counterstain

• Positive control: Human cerebellum

ELISA:

• Direct ELISA format validated

• Cross-reactivity testing shows less than 5% cross-reactivity with recombinant human SALM3 and SALM4

Immunofluorescence (IF):

• Validated for detection of LRFN1 in various samples

• Often used for co-localization studies with other synaptic markers

Selection of the appropriate technique should be based on the research question (protein quantification, localization, interaction studies) and available sample types.

How should LRFN1 antibodies be stored and handled to maintain optimal activity?

Proper storage and handling of LRFN1/SALM2 antibodies is critical for maintaining their activity and ensuring consistent experimental results:

Storage conditions:

• Lyophilized antibody: -20 to -70°C as supplied (stable for 12 months from receipt)

• After reconstitution:

  • Short-term (≤1 month): 2 to 8°C under sterile conditions

  • Long-term (≤6 months): -20 to -70°C under sterile conditions

Handling guidelines:

• Reconstitution: Reconstitute at 0.2 mg/mL in sterile PBS

• Freeze-thaw sensitivity: Use a manual defrost freezer and avoid repeated freeze-thaw cycles

• Working solution: When diluted in buffers containing carrier proteins (BSA, serum), prepare fresh working solutions daily

Critical considerations:

• For liquid antibody preparations, refer to the Certificate of Analysis for concentration

• Do not use antibody solutions that appear cloudy or have visible precipitates

• Prepare small working aliquots to minimize freeze-thaw cycles that can reduce antibody activity

• Return to appropriate storage conditions promptly after use

Adhering to these storage and handling guidelines will help maintain antibody specificity and sensitivity, ensuring reliable and reproducible experimental results.

What controls should be included when working with LRFN1 antibodies?

Implementing appropriate controls when working with LRFN1/SALM2 antibodies is essential for experimental validity:

Tissue controls:

• Positive controls:

  • Human cerebellum (high LRFN1 expression)

  • Human brain cortex and hippocampus

  • These should consistently show strong staining

• Negative controls:

  • Human lung tissue (does not express LRFN1)

  • Human liver tissue (does not express LRFN1)

  • Should show no specific staining

Procedural controls:

• Secondary antibody only (omit primary antibody)

• Isotype control (irrelevant primary antibody of same isotype)

• These identify non-specific binding of secondary antibody

Specificity controls:

• Peptide competition: Pre-incubate antibody with immunizing peptide/protein

• Should abolish specific staining

• Validates epitope-specific binding

Multi-method validation:

• Compare results across different detection methods (IHC, WB, IF)

• Concordant results across techniques increase confidence in specificity

Advanced controls (if available):

• Samples with LRFN1 overexpression

• Samples with LRFN1 knockdown

• Should show corresponding increase or decrease in signal

Systematic implementation of these controls ensures the specificity and reliability of results obtained with LRFN1 antibodies and helps differentiate true signals from artifacts.

How can I optimize detection of LRFN1 in Western blot applications?

For optimal LRFN1/SALM2 detection in Western blot applications, follow these detailed methodological guidelines:

Sample preparation:

• Tissue selection: Brain tissue (cortex, hippocampus) shows high expression

• Lysis buffer: RIPA buffer with protease inhibitor cocktail

• Protein determination: BCA or Bradford assay

• Loading: 20-50 μg total protein per lane

Gel electrophoresis:

• Gel percentage: 7.5-10% SDS-PAGE for optimal separation of 90-110 kDa LRFN1 protein

• Running conditions: 100V constant voltage until dye front reaches bottom

Transfer parameters:

• Membrane: PVDF membrane recommended over nitrocellulose

• Transfer conditions: 100V for 60-90 minutes or 30V overnight at 4°C

• Verification: Ponceau S staining to confirm transfer efficiency

Blocking and antibody incubation:

• Blocking: 5% non-fat dry milk in TBST (TBS + 0.1% Tween-20) for 1 hour at room temperature

• Primary antibody: 1 μg/mL in blocking buffer, overnight at 4°C

• Washing: 3 × 10 minutes with TBST

• Secondary antibody: HRP-conjugated anti-sheep IgG (1:2000-1:5000), 1 hour at room temperature

• Final wash: 3 × 10 minutes with TBST

Detection and analysis:

• Detection method: Enhanced chemiluminescence (ECL)

• Expected result: Specific bands at 90-110 kDa

• Size verification: Use appropriate molecular weight markers

• Analysis: Quantify band intensity using image analysis software

Troubleshooting common issues:

• High background: Increase washing duration/frequency or decrease antibody concentration

• Weak signal: Increase protein loading, antibody concentration, or exposure time

• Multiple bands: Increase stringency of washing, optimize antibody concentration

Following this protocol should produce clear, specific detection of LRFN1/SALM2 with minimal background.

What are the challenges in detecting LRFN1 in different sample types?

Detecting LRFN1/SALM2 across different sample types presents several technical challenges that require specific methodological solutions:

Brain tissue samples:

• Challenge: High lipid content and complex matrix

• Solution: Optimize extraction buffers with appropriate detergents

• Method: Use heat-induced epitope retrieval with basic pH buffers for IHC

• Data: SALM2 detection in human cerebellum requires antigen retrieval before primary antibody incubation

Fixed versus fresh tissues:

• Challenge: Fixation can mask epitopes

• Solution: Optimal fixation conditions are critical

• Method: For IHC, heat-induced epitope retrieval is essential with basic pH buffers

• Evidence: Successful detection in paraffin-embedded sections after proper retrieval

Species cross-reactivity:

• Challenge: Antibodies may have variable cross-reactivity across species

• Solution: Validate antibodies for each species separately

• Data: Human SALM2 shares 99% amino acid identity with mouse and rat SALM2

Specificity versus related proteins:

• Challenge: Cross-reactivity with related SALM family proteins

• Solution: Select antibodies with validated specificity

• Data: Less than 5% cross-reactivity with recombinant human SALM3 and SALM4 in direct ELISA

Detection in non-neural tissues:

• Challenge: LRFN1 expression is primarily neuronal

• Solution: Use appropriate positive controls (brain) and negative controls (lung, liver)

• Method: Higher antibody concentrations or more sensitive detection systems may be needed for low-expressing tissues

Post-translational modifications:

• Challenge: Modifications may affect antibody binding

• Solution: Use antibodies targeting different epitopes for validation

• Method: Compare results from monoclonal and polyclonal antibodies

Understanding these challenges and implementing the suggested methodological approaches will optimize LRFN1/SALM2 detection across different experimental systems.

How can immunohistochemistry protocols be optimized for LRFN1 detection?

Optimal immunohistochemistry (IHC) detection of LRFN1/SALM2 requires careful protocol optimization:

Sample preparation parameters:

• Fixation: Immersion fixation in 4% paraformaldehyde is recommended

• Embedding: Paraffin embedding preserves tissue morphology

• Sectioning: 4-6 μm sections provide optimal resolution

Critical epitope retrieval optimization:

• Method: Heat-induced epitope retrieval is essential

• Buffer: Basic antigen retrieval reagent provides optimal results

• Conditions: Validated protocol uses Antigen Retrieval Reagent-Basic (Catalog # CTS013)

• Duration: Typically 15-20 minutes at 95-100°C

Antibody parameters:

• Concentration: 15 μg/mL has been validated for paraffin sections

• Incubation: Overnight at 4°C for optimal binding

• Dilution buffer: PBS with 1-2% normal serum from secondary antibody species

Detection system optimization:

• Method: For mouse monoclonal antibodies, HRP-DAB system works well

• Validated approach: Anti-Mouse HRP-DAB Cell & Tissue Staining Kit

• Counterstaining: Hematoxylin provides good nuclear contrast

Signal-to-noise optimization:

• Background reduction: Additional blocking steps with 0.3% H₂O₂ to quench endogenous peroxidase

• Non-specific binding: Block with serum from the secondary antibody host species

• Washing: Extended washing steps (3 × 10 minutes) between each major step

Validation approach:

• Positive control: Human cerebellum shows strong LRFN1 expression

• Negative controls: Omit primary antibody; use non-expressing tissues (lung, liver)

• Signal specificity: Peptide competition assay if blocking peptide is available

Following this optimized IHC protocol should provide specific staining of LRFN1/SALM2 with minimal background in neural tissues.

How can LRFN1 antibodies be used to study synaptic proteins and function?

LRFN1/SALM2 antibodies can be powerful tools for investigating synaptic biology through several methodological approaches:

Co-localization studies with synaptic markers:

• Method: Multiplex immunofluorescence with synaptic proteins

• Markers: Combine LRFN1 antibodies with:

  • Presynaptic markers: synaptophysin, bassoon

  • Postsynaptic markers: PSD-95, Homer

• Analysis: Quantify co-localization coefficients (Pearson's, Mander's)

• Significance: LRFN1 co-localizes with both pre- and post-synaptic proteins at excitatory synapses

Developmental expression profiling:

• Method: Western blot or IHC across developmental timepoints

• Analysis: Quantify expression changes during synaptogenesis

• Rationale: LRFN1 promotes neurite outgrowth, suggesting developmental roles

Synaptic fractionation studies:

• Method: Biochemical isolation of synaptic compartments

• Fractions: Compare LRFN1 distribution in:

  • Presynaptic membranes

  • Postsynaptic density

  • Synaptic vesicles

• Western blot: Quantify relative enrichment in different fractions

Activity-dependent regulation:

• Method: Stimulate neurons before analysis

• Approaches:

  • Chemical stimulation (KCl, glutamate)

  • Electrical stimulation

  • Behavioral paradigms in vivo

• Analysis: Measure changes in LRFN1 localization or expression levels

Protein interaction studies:

• Method: Co-immunoprecipitation with LRFN1 antibodies

• Analysis: Mass spectrometry to identify binding partners

• Controls: Reverse IP and IgG controls to confirm specificity

Functional interference:

• Method: Antibody-mediated blocking in live preparations

• Analysis: Measure effects on:

  • Synapse formation

  • Structural plasticity

  • Electrophysiological properties

These methodological approaches leverage LRFN1 antibodies to elucidate the protein's role in synaptic development, maintenance, and plasticity, contributing to our understanding of neural circuit function.

How does experimental design for flow cytometry applications differ when using LRFN1 antibodies?

While specific flow cytometry protocols for LRFN1/SALM2 are not detailed in the search results, a systematic approach based on flow cytometry principles can be tailored for LRFN1 detection:

Panel design considerations:

• Expression level assessment: LRFN1 is strongly expressed in neuronal tissues , requiring careful fluorochrome selection

• Marker combinations: Consider co-staining with neural markers (NCAM, βIII-tubulin) for population identification

• Fluorochrome selection based on expression:

  Expression Level	Recommended Fluorochromes
  High (LRFN1 in neurons)	PE-Cy5, APC-Cy7
  Medium	PE, APC
  Low	FITC, PE-Cy7

Sample preparation optimization:

• Cell source considerations:

  • Primary neurons require gentle dissociation

  • Neural cell lines may have variable expression

• Surface vs. intracellular detection:

  • LRFN1 is a transmembrane protein, but optimal epitope exposure may require permeabilization

  • Test both surface staining and permeabilized protocols

Critical controls:

• Fluorescence Minus One (FMO) controls

• Isotype controls matched to primary antibody

• Unstained controls for autofluorescence assessment

• Positive control: Neuronal populations

• Negative control: Non-neural cell types (e.g., fibroblasts)

Protocol development workflow:

• Titrate antibody concentrations (typically 0.1-10 μg/mL)

• Optimize fixation/permeabilization conditions

• Compare different blocking agents

• Test various staining buffers

• Establish appropriate instrument settings

Following the experimental design principles outlined in the Maastricht University flow cytometry guidelines will help establish reliable protocols for LRFN1 detection in various neural cell populations.

What approaches can be used to validate the specificity of LRFN1 antibodies?

Validating LRFN1/SALM2 antibody specificity requires a systematic multi-method approach:

Western blot validation:

• Expected result: Single band at 90-110 kDa in brain tissue lysates

• Tissue specificity: Strong signal in brain (cortex, hippocampus); absent in lung and liver

• Reducing vs. non-reducing: Compare migration pattern under different conditions

Immunohistochemical validation:

• Expected pattern: Cell surface and synaptic localization in neurons

• Tissue distribution: Strong in cerebellum, present in cortex and hippocampus, absent in non-neural tissues

• Subcellular localization: Co-localization with synaptic markers

Cross-reactivity testing:

• Direct ELISA: Less than 5% cross-reactivity with related proteins (SALM3, SALM4)

• Comparative analysis: Test against recombinant proteins from the SALM family

• Species reactivity: Confirm cross-reactivity with mouse/rat SALM2 (99% amino acid identity)

Peptide competition assays:

• Method: Pre-incubate antibody with immunizing peptide/protein

• Analysis: Should abolish specific staining in all applications

• Concentration series: Titrate blocking peptide to demonstrate specificity

Genetic validation approaches:

• Knockout/knockdown: Test antibody in LRFN1-deficient samples

• Overexpression: Test in samples with confirmed LRFN1 overexpression

• Expected results: Signal intensity should correlate with expression level

Multi-antibody validation:

• Compare antibodies targeting different epitopes

• Example protocol: Compare Mouse monoclonal (Clone #610712) versus Sheep polyclonal

• Analysis: Concordant results increase confidence in specificity

Application-specific validation:

• For each application (WB, IHC, IF, ELISA), perform separate validation

• Document optimal conditions for each application

• Maintain validation records for reproducibility

This comprehensive validation approach ensures that experimental findings with LRFN1 antibodies are reliable and specific across different applications and experimental conditions.

How can computational approaches enhance antibody specificity prediction for LRFN1?

Computational modeling can significantly improve LRFN1/SALM2 antibody development and characterization:

Epitope prediction and analysis:

• Method: Algorithms identify potential antigenic regions on LRFN1

• Application: Compare predicted epitopes with related proteins (SALM3, SALM4)

• Benefit: Identifies unique regions with minimal cross-reactivity potential

• Data: This approach helps explain the <5% cross-reactivity observed in experimental validation

Structure-based antibody modeling:

• Method: Molecular dynamics simulations of antibody-LRFN1 binding

• Application: Calculate binding energies for specific interactions

• Output: Predict binding affinity and potential off-target interactions

Machine learning approaches:

• Method: Train models on existing antibody binding data

• Application: As demonstrated in research, biophysics-informed models trained on experimentally selected antibodies can predict specificity profiles

• Data: The model successfully disentangles binding modes even for chemically similar ligands

Computational design of specificity:

• Method: Generate and evaluate antibody variants in silico

• Application: Models can design antibodies with customized specificity profiles

• Data: Research demonstrates computational design of antibodies with either specific high affinity for particular targets or cross-specificity for multiple targets

Experimental-computational integration:

• Method: Combine phage display experimental data with computational predictions

• Application: This integrated approach allows identification of binding modes associated with specific ligands

Validation metrics:

These computational approaches can significantly enhance the development and characterization of LRFN1 antibodies, reducing experimental iterations and improving specificity outcomes.

How can LRFN1 antibodies be used in studying neurological disorders?

LRFN1/SALM2 antibodies offer valuable tools for investigating neurological disorders where synaptic dysfunction plays a key role:

Neurodevelopmental disorder applications:

• Method: Compare LRFN1 expression/localization in patient-derived samples versus controls

• Rationale: LRFN1's role in neurite outgrowth suggests potential implications in neurodevelopmental conditions

• Approach: Quantitative IHC or Western blot analysis of postmortem tissue or patient-derived neurons

Neurodegenerative disease investigations:

• Method: Analyze LRFN1 distribution in affected brain regions

• Techniques:

  • Multiplex immunofluorescence with markers of neurodegeneration

  • Quantitative Western blot to measure expression changes

  • Co-immunoprecipitation to detect altered protein interactions

• Analysis: Correlate changes with disease progression markers

Synaptic pathology assessment:

• Method: Quantify synaptic LRFN1 as a marker of excitatory synapse integrity

• Application: Monitor synaptic changes in disease models

• Analysis: Compare with other synaptic markers to distinguish specific from general synaptic pathology

• Rationale: LRFN1/SALM2 co-localizes with both pre- and post-synaptic proteins at excitatory synapses

Experimental therapeutic evaluation:

• Method: Monitor LRFN1 expression/localization following treatment

• Application: Use as a biomarker for synaptic restoration

• Analysis: Quantify changes in expression/localization as measure of treatment efficacy

Patient-derived cellular models:

• Method: Generate neurons from patient iPSCs

• Application: Compare LRFN1 dynamics in patient versus control neurons

• Analysis: Correlate with functional electrophysiological measurements

Brain organoid applications:

• Method: 3D brain organoid immunostaining

• Approach: Track LRFN1 expression during organoid development

• Analysis: Compare developmental trajectories between control and disease organoids

These research applications leverage LRFN1 antibodies to provide insights into neurological disorders, particularly those involving synaptic dysfunction, potentially contributing to diagnostic or therapeutic advancements.

What are the considerations for multiplexing LRFN1 antibodies with other synaptic markers?

Implementing LRFN1/SALM2 antibodies in multiplex staining with other synaptic markers requires careful technical consideration:

Antibody compatibility planning:

• Host species selection: Choose primary antibodies from different species to avoid cross-reactivity

• Example multiplex panel:

  Target	Host Species	Application	Concentration
  LRFN1	Mouse monoclonal	IHC/IF	15 μg/mL
  PSD-95	Rabbit	IF	Optimized per antibody
  Synaptophysin	Goat	IF	Optimized per antibody
  MAP2	Chicken	IF	Optimized per antibody

Epitope retrieval optimization:

• Method: Test whether all targets are retrievable under the same conditions

• LRFN1 requirement: Heat-induced epitope retrieval with basic pH buffer

• Validation: Confirm each marker's signal integrity under shared retrieval conditions

Detection system selection:

• Indirect immunofluorescence: Use species-specific secondary antibodies

• Direct conjugation: Consider directly labeled antibodies to avoid species limitations

• Spectral considerations: Choose fluorophores with minimal overlap

• Signal amplification: Consider tyramide signal amplification for low-abundance targets

Sequential staining protocol:

• Primary antibody cocktail (if compatible) or sequential application

• Washing (3 × 10 minutes PBS)

• Secondary antibody cocktail

• Washing (3 × 10 minutes PBS)

• Nuclear counterstain

• Mounting in anti-fade medium

Critical controls:

• Single-stain controls for each antibody

• Fluorescence minus one (FMO) controls

• Secondary-only controls for background assessment

Image acquisition parameters:

• Sequential scanning to minimize bleed-through

• Channel optimization to balance signal intensity across markers

• Z-stack acquisition for comprehensive synaptic analysis

• Consistent acquisition settings between samples

Analysis considerations:

• Colocalization analysis methods:

  • Pixel-based (Pearson's, Mander's coefficients)

  • Object-based (distance measurements between centroids)

• 3D analysis for volumetric assessment of synaptic structures

• Batch processing using consistent thresholding parameters

Following these methodological guidelines will enable successful multiplex imaging of LRFN1 with other synaptic markers, providing comprehensive spatial information about synaptic organization.

How can LRFN1 antibodies be used in functional interference studies?

LRFN1/SALM2 antibodies can be leveraged for functional interference studies to elucidate the protein's role in neuronal development and synaptic function:

Antibody-mediated blocking approach:

• Principle: Function-blocking antibodies bind LRFN1 and prevent its normal interactions

• Selection criteria: Choose antibodies targeting functional domains (e.g., LRR domain or fibronectin type-III domain)

• Application: Add antibodies to live neuronal cultures or brain slices

• Controls: Non-blocking antibodies or isotype controls

Experimental readouts for functional assessment:

• Morphological parameters:

  • Neurite outgrowth (LRFN1 is known to promote neurite extension)

  • Dendritic spine density and morphology

  • Synapse formation

• Functional parameters:

  • Calcium imaging to assess neuronal activity

  • Electrophysiological recordings (mEPSCs, evoked responses)

  • Synaptic vesicle recycling (using FM dyes or pHluorin)

Ex vivo application protocol:

• Prepare acute brain slices from appropriate region (e.g., hippocampus)

• Incubate with function-blocking LRFN1 antibody (10-50 μg/mL)

• Wash to remove unbound antibody

• Perform electrophysiology to assess synaptic transmission

• Compare with control antibody-treated slices

In vitro neuronal culture protocol:

• Culture primary neurons or neural cell lines

• Apply function-blocking LRFN1 antibody at DIV7-14

• Assess impact on:

  • Synapse formation (immunostaining for synaptic markers)

  • Neurite outgrowth (morphometric analysis)

  • Functional connectivity (calcium imaging)

• Compare with control conditions

Comparison with genetic approaches:

• Advantages over genetic knockdown:

  • Acute intervention without developmental compensation

  • Domain-specific blocking depending on antibody epitope

  • Reversible effect

• Limitations:

  • Incomplete blocking compared to genetic knockout

  • Potential off-target effects

  • Limited brain penetration for in vivo applications