NIF3L1 Antibody

What is NIF3L1 and what cellular functions does it perform?

NIF3L1, also known as ALS2CR1 (Amyotrophic lateral sclerosis 2 chromosomal region candidate gene 1 protein), is a 42 kDa protein primarily localized in the cytoplasm. It functions as a transcriptional corepressor through its interaction with COPS2, negatively regulating the expression of genes involved in neuronal differentiation . Research has demonstrated that NIF3L1 plays significant roles in early neural development pathways. Expression analysis on cell lines has revealed strong overexpression in spermatogonia-derived cell lines and teratocarcinoma cell lines, suggesting tissue-specific functionality . When fused with GFP, human NIF3L1 protein localizes exclusively to the cytoplasm, supporting its role in cytoplasmic signaling pathways rather than direct nuclear transcriptional activities .

What are the known alternative names for NIF3L1 in scientific literature?

Researchers should be aware of multiple nomenclatures when searching literature:

Understanding these alternative designations is crucial when conducting comprehensive literature searches across different databases and research periods .

What types of NIF3L1 antibodies are available and how should I select the appropriate one for my research?

NIF3L1 antibodies are available in various formulations with distinct characteristics:

Selection criteria should include:

• Research application (WB vs. IHC vs. IF)

• Target species compatibility

• Epitope specificity (full-length, C-terminal, or specific amino acid regions)

• Detection method requirements (direct visualization vs. secondary antibody systems)

For applications requiring high specificity, monoclonal antibodies like clone 5A7 offer consistent results with minimal background. For detection of specific domains, epitope-targeted antibodies against defined amino acid regions (e.g., AA 1-377 or AA 201-300) may be preferable .

What are the optimal experimental conditions for Western blot detection of NIF3L1?

For Western blot detection of NIF3L1, researchers should consider:

• Sample preparation:

  • Use fresh lysates from cells expressing NIF3L1 (Jurkat cells and human fetal kidney tissue have been verified as positive controls)

  • Include protease inhibitors to prevent degradation

• Antibody dilution:

  • For polyclonal antibodies: 1:200-1:1000 dilution range is recommended

  • For monoclonal antibodies: Follow manufacturer-specific recommendations, typically 1:500-1:2000

• Detection system:

  • Standard size confirmation: Look for a band at approximately 42 kDa

  • Consider using phosphate buffered solutions (pH 7.4) with stabilizers for antibody dilution

• Controls:

  • Include positive controls (e.g., Jurkat cell lysate)

  • Use blocking peptides to confirm specificity

Optimization through titration experiments is recommended as antibody performance can vary between lots and experimental systems .

How can I optimize immunofluorescence experiments using NIF3L1 antibodies?

Immunofluorescence detection of NIF3L1 requires specific optimization:

• Fixation method:

  • Paraformaldehyde (4%) fixation for 15-20 minutes preserves cytoplasmic structures

  • Avoid methanol fixation which can disrupt cytoplasmic protein epitopes

• Permeabilization:

  • Use 0.1-0.3% Triton X-100 for adequate penetration while maintaining structural integrity

  • Brief (5-10 minute) permeabilization is typically sufficient

• Antibody selection:

  • Choose antibodies validated for IF applications (not all WB-validated antibodies work in IF)

  • Consider using the OTI1F6 clone which has demonstrated successful IF staining in transfected COS7 cells

• Controls and validation:

  • Include transfection controls (e.g., COS7 cells transiently transfected with pCMV6-ENTRY NIF3L1)

  • Use counterstains for subcellular compartment identification to confirm cytoplasmic localization

• Signal amplification:

  • For weak signals, consider tyramide signal amplification or use directly conjugated primary antibodies (FITC-conjugated options are available)

Expect primarily cytoplasmic staining patterns based on previously published localization studies using NIF3L1/GFP fusion proteins .

What is known about the role of NIF3L1 in neuronal differentiation and how can antibodies help elucidate these mechanisms?

NIF3L1 functions as a transcriptional corepressor and influences neuronal differentiation:

• Mechanism of action:

  • NIF3L1 interacts with COPS2 to form a corepressor complex

  • This complex negatively regulates genes involved in neuronal differentiation pathways

• Experimental approaches:

  • Co-immunoprecipitation using NIF3L1 antibodies can isolate protein complexes to identify interacting partners

  • ChIP-seq with NIF3L1 antibodies can map genomic binding sites of the corepressor complex

  • Time-course immunostaining during neural differentiation can track expression changes

• Key findings:

  • Luciferase reporter assays have demonstrated that NIF3L1 acts as a transcriptional repressor

  • The protein appears to synergize with other factors in early neural development

  • Studies suggest differential expression in neural progenitor versus differentiated neuronal cells

• Research applications:

  • Knockdown/knockout validation using NIF3L1 antibodies to confirm protein depletion

  • Immunostaining to track subcellular relocalization during differentiation processes

  • Comparative analysis across neural disease models to identify pathological alterations

This research direction is particularly relevant given the gene's nomenclature connection to amyotrophic lateral sclerosis (ALS2CR1), though direct disease associations require further investigation .

How are machine learning approaches being applied to predict antibody-antigen binding for proteins like NIF3L1?

Recent advances in machine learning are transforming antibody research:

• Library-on-library approaches:

  • Many-to-many relationship analysis between antibodies and antigens enables identification of specific interacting pairs

  • Machine learning models can analyze these relationships to predict binding properties

• Challenges in prediction:

  • Out-of-distribution prediction difficulties arise when test antibodies and antigens aren't represented in training data

  • Limited availability of comprehensive datasets due to the high cost of generating experimental binding data

• Active learning solutions:

  • Starting with small labeled subsets of data and iteratively expanding the labeled dataset

  • Novel active learning strategies have been developed specifically for antibody-antigen binding prediction

  • The best algorithms have reduced required antigen mutant variants by up to 35%

  • Learning process acceleration of 28 steps compared to random baseline approaches has been demonstrated

• Research implications:

  • These approaches can significantly reduce experimental costs

  • Improved prediction accuracy for novel antibody-antigen pairs

  • Accelerated development timelines for research applications

• Simulation frameworks:

  • The Absolut! simulation framework has been utilized to evaluate out-of-distribution performance of these prediction models

These computational approaches represent the cutting edge of antibody research methodology, with direct applications to studying complex proteins like NIF3L1 .

How can I validate NIF3L1 antibody specificity in my experimental system?

Comprehensive validation is essential before conducting extensive experiments:

• Genetic validation approaches:

  • CRISPR/Cas9 knockout of NIF3L1 should eliminate antibody signal

  • siRNA knockdown should proportionally reduce signal intensity

  • Overexpression systems should demonstrate increased signal in Western blots and immunostaining

• Epitope blocking:

  • Pre-incubation with immunizing peptide should abolish specific binding

  • For antibodies targeting specific domains, competing peptides can confirm epitope specificity

• Cross-reactivity assessment:

  • Test in multiple species when using antibodies claiming multi-species reactivity

  • Verify size differences across species match predicted protein molecular weights

• Application-specific controls:

  • For Western blotting: Include recombinant NIF3L1 protein standards

  • For IHC/IF: Include known positive control tissues (based on verified samples in Jurkat cells and human fetal kidney)

  • For IP: Confirm pulled-down protein by mass spectrometry

• Cross-antibody verification:

  • Compare results using antibodies targeting different epitopes within NIF3L1

  • Monoclonal (e.g., clone 5A7 or OTI1F6) and polyclonal antibody comparison can provide complementary validation

Proper validation reduces experimental artifacts and ensures reproducibility of NIF3L1-focused research findings across laboratories.

What are the optimal storage conditions for maintaining NIF3L1 antibody activity?

Proper storage significantly impacts antibody performance and longevity:

• Temperature considerations:

  • Store at -20°C for long-term preservation

  • Avoid repeated freeze/thaw cycles which can degrade antibody structure

• Buffer composition:

  • Most commercial NIF3L1 antibodies are supplied in phosphate buffered solution (pH 7.4)

  • Formulations typically contain 0.05% stabilizer and 50% glycerol to prevent ice crystal formation

• Aliquoting recommendations:

  • Upon receipt, divide into small working aliquots before freezing

  • Single-use aliquots eliminate freeze/thaw degradation

• Reconstitution protocols:

  • For lyophilized antibodies, reconstitute to approximately 1 mg/mL using distilled water

  • For carrier-free preparations intended for conjugation, perform additional desalting steps using appropriate columns (e.g., Zeba Spin Desalting Columns, 7KMWCO)

• Shipping considerations:

  • Products typically ship with ice packs

  • Upon receipt, immediately transfer to recommended storage conditions

Most commercial NIF3L1 antibodies maintain activity for at least 12 months when stored properly, though activity testing before critical experiments is always recommended, especially for older antibody stocks .

What emerging research areas might benefit from improved NIF3L1 antibody technologies?

Several promising research directions could advance through optimized antibody technologies:

• Neurodegenerative disease connections:

  • Given the ALS2CR1 alternative name, exploring NIF3L1's potential role in motor neuron diseases

  • Investigating expression patterns in ALS and other neurodegenerative disease models

• Developmental neurobiology:

  • Tracking expression during critical periods of neural development

  • Examining spatial and temporal regulation in neurogenesis and differentiation

• Transcriptional regulation mechanisms:

  • Further characterizing the NIF3L1-COPS2 corepressor complex

  • Identifying target genes regulated by this complex during neural differentiation

• Advanced imaging applications:

  • Super-resolution microscopy to visualize subcellular localization with greater precision

  • Live-cell imaging using newer tag-based antibody visualization systems

• Therapeutic target potential:

  • Exploring whether manipulation of NIF3L1 pathways could influence neural regeneration

  • Developing more specific antagonists or agonists of NIF3L1-mediated pathways