RBM18 Human

What is RBM18 and what is its genomic location in humans?

RBM18 (RNA binding motif protein 18) is a probable RNA-binding protein encoded by the RBM18 gene in humans. The gene is located on chromosome 9 according to reference genome assemblies (GRCh38.p14 and GRCh37.p13) . Methodologically, to study this protein's genomic context, researchers typically utilize genome browsers and databases like NCBI Gene, which provide accurate and updated information about the gene's chromosomal location, neighboring genes, and genomic structure. When investigating RBM18's genomic features, it's advisable to cross-reference multiple databases including UCSC Genome Browser and Ensembl to verify consistency of genomic coordinates and gene architecture.

What is the tissue expression pattern of RBM18 in humans?

The Human Protein Atlas provides tissue expression data for RBM18 across various human tissues . When analyzing RBM18 expression patterns, researchers should employ multiple complementary approaches:

• Examine RNA-seq data from tissue panels

• Validate with quantitative PCR in tissues of interest

• Perform immunohistochemistry with validated antibodies

• Compare expression levels across developmental stages
  Methodologically, it's important to note that expression data should be normalized appropriately, and analyses should include both positive and negative controls for each tissue type. Discrepancies between RNA and protein expression levels should be investigated as they may indicate post-transcriptional regulation mechanisms.

What post-translational modifications (PTMs) are known to occur on RBM18?

RBM18 undergoes several documented post-translational modifications that may regulate its function. According to iPTMnet data, these include:

• Mass spectrometry-based proteomic approaches with enrichment for specific modifications

• Site-directed mutagenesis to create phospho-mimetic or phospho-dead variants

• In vitro kinase assays to identify enzymes responsible for specific phosphorylation events

• Functional assays comparing wild-type and mutant forms of the protein
  The temporal dynamics of these modifications should also be investigated in response to cellular signaling events.

How should researchers design experiments to identify the RNA targets of RBM18?

Identifying RNA targets of RBM18 requires a multi-faceted approach combining high-throughput and validation methods:

• CLIP-seq approaches: Cross-linking immunoprecipitation followed by sequencing (CLIP-seq) remains the gold standard for identifying direct RNA-protein interactions. For RBM18 specifically, consider:

  • iCLIP or eCLIP for single-nucleotide resolution binding sites

  • PAR-CLIP if photoactivatable ribonucleoside analogs can be incorporated

  • Formaldehyde RNA immunoprecipitation (fRIP-seq) as an alternative approach

• RNA interactome capture: Methods such as enhanced interactome capture (eRIC), chemistry-assisted interactome capture (CARIC), and total RNA-associated protein purification (TRAPP) can be used to identify RNA-protein interactions on a transcriptome-wide scale .

• Validation approaches:

  • RNA electrophoretic mobility shift assays (EMSAs)

  • Luciferase reporter assays with wild-type and mutant binding sites

  • RNA pull-down assays followed by western blotting for RBM18

• Computational analysis:

  • Motif discovery in bound sequences

  • Secondary structure analysis of binding regions

  • Integration with transcriptomic data to correlate binding with functional outcomes
    Given the recent development of techniques like Capture RIC-seq (CRIC-seq), researchers can also explore spatial interaction maps of RBM18 with RNA targets in a cellular context .

What are the experimental challenges in studying RBM18's role in alternative splicing regulation?

Studying RBM18's role in alternative splicing regulation presents several methodological challenges:

• Functional redundancy: Like many RBPs, RBM18 may have functional redundancy with other splicing factors. Addressing this requires:

  • Combined knockdown/knockout approaches of multiple RBPs

  • Domain-specific functional studies to identify unique activities

  • Careful analysis of compensatory mechanisms

• Splicing complexity detection:

  • RNA-seq with sufficient depth (>50 million reads) and appropriate read length (>100bp paired-end)

  • PCR-based validation of specific splicing events with isoform-specific primers

  • Minigene splicing assays for mechanistic studies of individual splicing events

• Temporal dynamics:

  • Time-course experiments following RBM18 perturbation

  • Inducible depletion or overexpression systems

  • Single-cell approaches to capture heterogeneity in splicing responses

• Distinguishing direct vs. indirect effects:

  • Integration of binding data (CLIP-seq) with splicing outcomes

  • Motif enrichment analyses near differentially spliced exons

  • In vitro splicing assays with purified components

• Context-dependency:

  • Cell type-specific splicing patterns must be considered

  • Tissue-specific cofactors may modify RBM18 activity

  • Environmental conditions may alter RBM18 function
    Given that RBPs can induce exon inclusion or exclusion, or alternative use of 5′ or 3′ splice sites by binding to pre-mRNA exons or flanking introns , researchers should design experiments that can detect all possible splicing modalities.

How can researchers resolve contradictory data regarding RBM18 function across different cell types?

Resolving contradictory data regarding RBM18 function across different cell types requires systematic approaches:

• Standardization of experimental systems:

  • Use identical RBM18 perturbation methods across cell types

  • Ensure comparable expression levels in overexpression studies

  • Apply the same analytical pipelines to all datasets

• Cell type-specific cofactor analysis:

  • Immunoprecipitation followed by mass spectrometry to identify cell type-specific interaction partners

  • Co-expression correlation analyses across cell types

  • Targeted validation of key interactions in multiple cell types

• Context-dependent regulation:

  • Investigate post-translational modifications of RBM18 across cell types

  • Examine subcellular localization patterns

  • Analyze chromatin context of target genes in different cell types

• Integrative data analysis:

  • Meta-analysis of multiple datasets with proper batch correction

  • Statistical modeling of cell type as a variable

  • Bayesian approaches to integrate disparate data types

• Validation in isogenic backgrounds:

  • Use isogenic cell lines differentiated into different cell types

  • Apply CRISPR-Cas9 engineering to create consistent genetic backgrounds

  • Derive multiple cell types from the same donor for primary cells
    This methodological framework mirrors approaches used to resolve contradictory findings for other RBPs, such as the debate surrounding PTBP1's role in glial-neuronal trans-differentiation .

What disease-associated variants of RBM18 have been identified and how should they be studied?

Several disease-associated variants of RBM18 have been identified, particularly in cancer contexts:

• Structural and functional characterization:

  • Generate structural models of wild-type and variant proteins

  • Perform in vitro RNA binding assays to assess impact on target recognition

  • Create cell lines expressing variant forms using CRISPR knock-in approaches

• Splicing impact assessment:

  • RNA-seq analysis comparing wild-type and variant-expressing cells

  • RT-PCR validation of key splicing events

  • Minigene assays to directly test splicing regulation

• Phenotypic consequences:

  • Cell proliferation, migration, and invasion assays

  • Xenograft models for cancer-associated variants

  • Patient-derived organoids or primary cells when available

• Clinical correlation:

  • Analyze survival data stratified by variant status

  • Examine treatment response correlations

  • Develop biomarker potential through liquid biopsy approaches

• Mechanistic studies:

  • Protein-protein interaction changes resulting from variants

  • Altered PTM patterns in variant forms

  • Changes in subcellular localization or stability
    These approaches should be tailored to the specific variant being studied and the disease context in which it appears.

How does RBM18 contribute to cancer progression and what methodologies best capture this relationship?

While specific information about RBM18's role in cancer is limited in the search results, we can outline methodological approaches based on what is known about RBPs in cancer generally:

• Expression analysis across cancer types:

  • Mining TCGA and other cancer genomics databases for RBM18 expression

  • Immunohistochemistry of tumor tissue microarrays

  • Single-cell RNA-seq of tumor samples to identify cell type-specific expression

• Functional genomics approaches:

  • CRISPR screens in cancer cell lines to determine dependency

  • shRNA or siRNA knockdown followed by phenotypic assays

  • Overexpression studies in pre-malignant cell models

• Alternative splicing impact:

  • RNA-seq of tumors with high versus low RBM18 expression

  • Identification of cancer-specific splicing events regulated by RBM18

  • Mechanistic studies of how these splicing events promote hallmarks of cancer

• Clinical correlation studies:

  • Survival analyses stratified by RBM18 expression levels

  • Association with treatment resistance mechanisms

  • Development of prognostic signatures incorporating RBM18-regulated splicing events

• In vivo models:

  • Genetically engineered mouse models with altered RBM18 expression

  • PDX models treated with RBM18-targeting approaches

  • Orthotopic tumor models to study metastasis in context of RBM18 manipulation
    RNA binding proteins have been implicated in various aspects of cancer progression through regulation of alternative splicing events that impact cell growth, development, differentiation, migration, and apoptosis .

What are the most effective antibodies and validation methods for studying RBM18?

The Human Protein Atlas indicates there are antibodies available for RBM18 , but effective antibody selection and validation require:

• Antibody selection criteria:

  • Target unique epitopes within RBM18

  • Validate specificity through multiple methods

  • Consider monoclonal antibodies for reproducibility

  • Ensure recognition of native protein conformations when necessary

• Essential validation methods:

  • Western blotting with appropriate positive and negative controls

  • Immunoprecipitation followed by mass spectrometry

  • siRNA/shRNA knockdown to confirm specificity

  • CRISPR knockout cell lines as negative controls

  • Immunofluorescence with appropriate controls

• Application-specific validation:

  • For ChIP applications: validate with spike-in controls

  • For flow cytometry: titration experiments and isotype controls

  • For immunohistochemistry: tissue-specific expression patterns matching RNA data

  • For CLIP experiments: size-shifted protein-RNA complexes

• Recombinant protein controls:

  • Generate recombinant RBM18 for positive controls

  • Use as standards for quantitative assessments

  • Develop peptide competition assays for specificity testing

• Cross-reactivity testing:

  • Test against closely related RNA-binding proteins

  • Validate in multiple cell types to ensure consistent specificity

  • Check for non-specific binding to common contaminants
    Document all validation experiments thoroughly according to the guidelines established by the International Working Group for Antibody Validation.

What high-throughput methods are most suitable for studying RBM18's role in transcriptome regulation?

Based on advances in RBP research, several high-throughput methods are particularly suitable for studying RBM18's role in transcriptome regulation:

• RNA-protein interaction methods:

  • CLIP-seq variants (eCLIP, iCLIP, PAR-CLIP)

  • RNA interactome capture techniques like eRIC, CARIC, and TRAPP

  • RBDmap for identifying RNA-binding domains

  • CRIC-seq to analyze in-situ RNA-RNA interaction sites mediated by RBM18

• Alternative splicing analysis:

  • RNA-seq with specialized computational pipelines (rMATS, MAJIQ, VAST-TOOLS)

  • Long-read sequencing (PacBio, Oxford Nanopore) for full isoform detection

  • JunctionSeq for specific analysis of splicing junctions

  • THISTLE method for identifying genetic regulatory sites associated with RNA alternative splicing

• Functional impact assessment:

  • Ribosome profiling to assess translational consequences

  • Proteomics to identify protein isoform changes

  • CRISPR screens with splicing reporters

  • Massively parallel reporter assays for regulatory element testing

• Integrative approaches:

  • Multi-omics integration frameworks

  • Network analyses of splicing regulation

  • Machine learning for predicting RBM18 binding and functional impacts

  • Systems biology approaches to place RBM18 within regulatory networks

• Spatial transcriptomics:

  • In situ sequencing technologies to examine spatial regulation

  • Single-cell RNA-seq with spatial information

  • Imaging-based approaches for visualizing RBM18-RNA interactions
    The specific choice of methods should be guided by the research question, available resources, and technical expertise, with appropriate controls and validation approaches for each method.

How does RBM18 function compare to other members of the RNA-binding protein family?

Comparing RBM18 to other RNA-binding proteins requires systematic approaches:

• Sequence and structural comparisons:

  • Domain architecture analysis using protein family databases

  • Structural modeling and comparison with crystallized RBPs

  • Evolutionary conservation studies across species

  • Phylogenetic analysis within the RNA-binding protein superfamily

• Binding specificity comparison:

  • Motif analysis from CLIP-seq data

  • RNA structure preferences around binding sites

  • Competition assays between RBM18 and other RBPs

  • In vitro binding assays with synthetic RNA substrates

• Functional redundancy assessment:

  • Co-depletion experiments with related RBPs

  • Rescue experiments with domain-swapped proteins

  • Analysis of correlated expression patterns

  • Identification of shared versus unique targets

• Regulatory network positioning:

  • Network analysis of protein-protein interactions

  • Co-expression patterns across tissues and conditions

  • Identification of cooperative or antagonistic relationships

  • Perturbation studies examining combined effects

• Disease relevance comparison:

  • Mutation patterns in human diseases

  • Expression alterations in pathological conditions

  • Phenotypic consequences of perturbation

  • Therapeutic targeting potential
    This comparative approach should consider that RBPs like PTBP1, HNRNPA1, and SRSF1 have been studied extensively , providing reference points for understanding RBM18's position within the broader RBP family.

What are the most appropriate model systems for studying RBM18 function in development and disease?

Selecting appropriate model systems for RBM18 studies should be guided by the specific research questions:

• Cell line models:

  • Human cell lines representing tissues where RBM18 is highly expressed

  • Isogenic modified cell lines (CRISPR knockout, knockdown, overexpression)

  • Inducible expression systems for temporal control

  • Differentiation models to study developmental transitions

• Organoid systems:

  • Brain organoids for neural development studies

  • Cancer organoids for disease modeling

  • Co-culture systems to study cell-cell interactions

  • Patient-derived organoids for personalized disease modeling

• Animal models:

  • Conditional knockout mice for tissue-specific studies

  • Developmental studies in zebrafish for rapid phenotyping

  • Xenograft models for cancer studies

  • Drosophila for basic mechanistic conservation studies

• Patient-derived samples:

  • Primary cells from tissues of interest

  • Patient-derived xenografts

  • Fresh frozen tissue for molecular analyses

  • FFPE samples for retrospective studies

• iPSC-based models:

  • Differentiation into relevant cell types

  • Disease modeling with patient-derived iPSCs

  • Genome-edited iPSCs for isogenic comparisons

  • 3D culture systems for tissue-like organization
    The choice should consider that studies in neural systems have shown the importance of RBPs in development , suggesting neural models may be particularly relevant for RBM18 functional studies.