AIM1 Antibody

What is AIM1 and why is it significant in cancer research?

AIM1 (absent in melanoma 1) is an actin-binding protein that plays a critical role in suppressing cell migration and invasion in normal epithelial cells. AIM1 directly interacts with β-actin, influencing cytoskeletal dynamics that affect cell motility and invasion . Research has demonstrated that AIM1 depletion leads to increased cytoskeletal remodeling and enhanced invasive properties in cells, particularly in prostate epithelium .

AIM1 is significant in cancer research because:

• It functions as a metastasis suppressor that becomes dysregulated in primary and metastatic cancers

• Its loss or mislocalization correlates with cancer progression and invasive phenotypes

• AIM1 alterations may serve as biomarkers for aggressive disease

The protein strongly associates with the actin cytoskeleton in normal prostate epithelial cells but shows disrupted localization patterns in cancer tissues, making it a valuable target for studying cancer progression mechanisms .

What applications are recommended for AIM1 antibodies in experimental research?

Based on published research methodologies, AIM1 antibodies are effective in the following applications:

When designing experiments, researchers should consider that different fixation methods may affect epitope accessibility, particularly for cytoskeletal-associated proteins like AIM1 .

How should AIM1 antibodies be validated before experimental use?

Rigorous validation of AIM1 antibodies is essential to ensure experimental reproducibility and data reliability. Based on established research practices, the following validation strategy is recommended:

• Genetic controls: Test antibody specificity using:

  • AIM1 knockdown cells (shRNA or siRNA-mediated)

  • AIM1 knockout models (if available)

  • AIM1 overexpression systems

• Western blot validation:

  • Confirm single band of expected molecular weight (~150 kDa)

  • Compare signal intensity across samples with known AIM1 expression levels

  • Include positive and negative control cell lines

• Immunohistochemistry optimization:

  • Test multiple antibody dilutions on formalin-fixed paraffin-embedded (FFPE) tissues

  • Include normal prostate epithelium as positive control (shows strong membranous staining)

  • Use metastatic tissues as comparative controls (typically show reduced expression)

• Cross-validation:

  • Compare results from multiple antibodies targeting different AIM1 epitopes

  • Confirm consistency between protein detection methods (Western blot vs. IHC)

Research has shown that custom-made AIM1 antibodies have been successfully validated using these approaches, demonstrating specificity in genetically controlled cell line models .

What techniques can detect changes in AIM1 expression and localization in cancer progression?

To effectively monitor changes in AIM1 expression and subcellular localization during cancer progression, researchers should employ a multi-modal approach:

• Quantitative immunohistochemistry:

  • Implement the H-score system to separately evaluate membranous and cytoplasmic AIM1 staining

  • Score both distribution (percentage of positive cells) and intensity of staining

  • Normal prostate epithelium typically shows high membranous and low cytoplasmic staining

  • Adenocarcinoma exhibits decreased membranous staining with increased cytoplasmic accumulation

• Confocal immunofluorescence microscopy:

  • Co-stain tissues with AIM1 and β-actin antibodies

  • Calculate co-localization coefficients between AIM1 and β-actin

  • Normal tissues show high co-localization (coefficient ~0.82)

  • Cancer tissues show disrupted co-localization (coefficient ~0.38)

• Biochemical fractionation:

  • Separate cellular components into membrane, cytoskeletal, and cytosolic fractions

  • Analyze AIM1 distribution across fractions by Western blotting

  • Quantify relative distribution changes between normal and cancer samples

This comprehensive approach can reveal both quantitative changes in expression and qualitative shifts in subcellular distribution that correlate with disease progression .

What are the key considerations for using AIM1 antibodies in tissue microarrays?

When implementing AIM1 antibodies in tissue microarray (TMA) studies, researchers should address the following critical factors:

• Tissue selection and controls:

  • Include matched normal tissue controls within each TMA

  • Represent various disease stages and Gleason grades for prostate cancer studies

  • Include lymph node metastases when available to assess expression in metastatic settings

• Staining protocol optimization:

  • Standardize antigen retrieval methods across all TMA sections

  • Determine optimal antibody concentration using titration experiments

  • Include isotype controls to assess non-specific binding

• Scoring methodology:

  • Implement a consistent scoring system (e.g., H-score) that evaluates:

    • Membranous staining intensity and distribution

    • Cytoplasmic staining intensity and distribution

  • Use digital pathology tools for quantitative assessment when possible

  • Employ multiple independent scorers to ensure reliability

• Data analysis considerations:

  • Correlate AIM1 staining patterns with clinicopathologic parameters

  • Analyze membranous versus cytoplasmic staining separately

  • Consider both protein levels and subcellular localization in statistical analyses

Research has demonstrated correlations between Gleason grade and membranous AIM1 staining, with lower-grade lesions retaining more membranous staining compared to higher-grade lesions .

How can AIM1 antibodies be optimized for studying the interaction between AIM1 and the actin cytoskeleton?

Studying the dynamic interaction between AIM1 and the actin cytoskeleton requires specialized approaches beyond standard immunostaining techniques. Based on published methodologies, researchers should consider:

• Proximity ligation assays (PLA):

  • Use paired antibodies against AIM1 and β-actin

  • Optimize antibody concentrations to minimize background

  • Include appropriate controls (single antibody, isotype controls)

  • Quantify PLA signals to measure interaction strength in different cellular contexts

  • Compare normal versus cancer tissues to assess interaction disruption

• Co-immunoprecipitation optimization:

  • Use gentle lysis conditions to preserve cytoskeletal interactions

  • Perform reciprocal co-IPs (β-actin pull-down with AIM1 detection and vice versa)

  • Include crosslinking steps to stabilize transient interactions

  • Compare different cellular fractions to determine interaction localization

  • Use both overexpression systems and endogenous protein detection

• Live-cell imaging approaches:

  • Generate fluorescently tagged AIM1 constructs for live-cell visualization

  • Combine with actin probes (LifeAct, SiR-Actin) for co-localization studies

  • Perform FRAP (Fluorescence Recovery After Photobleaching) to assess dynamics

  • Implement FRET-based approaches to measure direct interactions

• Biochemical fractionation with F/G-actin assessment:

  • Separate F-actin and G-actin by ultracentrifugation

  • Analyze AIM1 distribution between fractions

  • Compare F-actin/G-actin ratios in AIM1-depleted versus control cells

These approaches can provide high-resolution insights into how AIM1 interacts with the actin cytoskeleton in different cellular contexts and how this interaction is disrupted in cancer.

What methodologies can resolve conflicting data when using different AIM1 antibodies?

When researchers encounter conflicting results using different AIM1 antibodies, a systematic troubleshooting approach is necessary:

• Comprehensive epitope mapping:

  • Determine the specific epitopes recognized by each antibody

  • Assess whether epitopes might be masked by protein-protein interactions

  • Generate domain-specific antibodies targeting different regions of AIM1

  • Test antibodies against AIM1 deletion mutants (e.g., Δ859 mutant lacking βγ-crystallin domains)

• Validation in multiple model systems:

  • Compare antibody performance across:

    • Cell lines from different tissue origins

    • Primary cell cultures versus established cell lines

    • Normal versus cancer-derived models

    • Patient-derived tissues with known AIM1 expression levels

  • Use genetic controls (knockdown, knockout, overexpression) in each model

• Cross-platform validation protocol:

  • Implement a sequential validation workflow:

    • Western blot to confirm specific band detection

    • Immunofluorescence to assess subcellular localization patterns

    • Mass spectrometry validation of immunoprecipitated proteins

    • RNA expression correlation (qPCR or RNA-seq) with protein detection

• Experimental condition optimization matrix:

  • Test multiple fixation methods (formaldehyde, methanol, acetone)

  • Evaluate different antigen retrieval protocols

  • Compare blocking reagents to minimize non-specific binding

  • Assess antibody performance across a dilution series

This systematic approach can identify the source of conflicting results and determine which antibody performs optimally for specific applications and experimental contexts.

How can AIM1 antibodies be used to study AIM1's role in cytoskeletal dynamics and cell invasion?

To comprehensively investigate AIM1's functional impact on cytoskeletal dynamics and cell invasion, researchers should implement a multi-faceted experimental strategy:

• Cytoskeletal remodeling assessment:

  • Combine AIM1 immunostaining with specialized cytoskeletal probes

  • Quantify F-actin and G-actin distribution using:

    • Phalloidin staining for F-actin

    • G-actin-specific antibodies for monomeric actin

  • Measure cytoskeletal dynamics in AIM1-depleted versus control cells

  • Analyze trailing edge G-actin accumulation in migration assays

• Advanced biophysical measurements:

  • Implement magnetic twisting cytometry to measure cytoskeletal remodeling rates

  • Use traction force microscopy to quantify cellular traction forces

  • Compare mechanical properties in cells with normal versus altered AIM1 expression

  • Correlate biophysical measurements with invasion capacity

• Functional migration and invasion assays:

  • Scratch wound healing assays to assess cell motility

  • Boyden chamber invasion assays with different matrix barriers:

    • Collagen

    • Laminin

    • Matrigel

  • 3D spheroid invasion models

  • Time-lapse imaging with quantitative motion analysis

• Rescue experiments with domain mutants:

  • Express wild-type AIM1 or domain mutants (e.g., Δ859) in AIM1-depleted cells

  • Assess rescue of cytoskeletal architecture and invasion phenotypes

  • Quantify differential effects of mutants on F/G-actin ratios

  • Correlate functional rescue with restoration of actin binding

These approaches can provide mechanistic insights into how AIM1 regulates cytoskeletal dynamics and how its loss promotes invasive phenotypes in cancer cells.

What considerations are important when using AIM1 antibodies to study patient-derived tissues?

Working with patient-derived tissues introduces additional complexities that researchers must address when using AIM1 antibodies:

• Tissue preservation and processing factors:

  • Assess impact of fixation time on epitope availability

  • Compare frozen versus FFPE tissue performance

  • Optimize antigen retrieval protocols specifically for AIM1 in clinical samples

  • Consider ischemia time effects on protein degradation

• Heterogeneity considerations:

  • Implement laser capture microdissection to isolate specific cell populations

  • Account for stromal contamination in expression analyses

  • Compare different regions within the same tumor

  • Analyze multiple cores from each patient sample to account for tumor heterogeneity

• Clinicopathological correlation approach:

  • Develop a standardized scoring system for:

    • Membranous AIM1 expression (intensity and percentage)

    • Cytoplasmic AIM1 expression (intensity and percentage)

    • Nuclear AIM1 expression (if detected)

  • Correlate with clinical parameters:

    • Gleason grade in prostate cancer

    • Disease stage

    • Treatment response

    • Metastatic status

• Comparative analysis framework:

  • Include matched normal adjacent tissue as controls

  • Compare primary tumor versus matched metastatic lesions

  • Analyze progressive stages of disease (e.g., PIN, localized cancer, metastatic disease)

  • Correlate AIM1 localization with actin cytoskeleton organization

Research has demonstrated that AIM1 expression patterns correlate with Gleason grade, with lower-grade lesions (Gleason ≤6) retaining more membranous staining compared to higher-grade lesions .

How can AIM1 antibodies be used to evaluate metastatic potential in experimental cancer models?

To investigate the relationship between AIM1 expression and metastatic potential, researchers should implement a comprehensive experimental approach:

• In vivo metastasis quantification methodology:

  • Generate stable cell lines with AIM1-targeting shRNAs or control constructs

  • Establish xenograft models using these engineered cells

  • At experimental endpoint, harvest multiple target organs (lung, liver, spleen)

  • Quantify micrometastatic burden using human-specific Alu DNA element qPCR

  • Calculate fold changes in metastatic burden between AIM1-depleted and control groups

• Correlation analysis framework:

  • Document AIM1 protein levels by immunohistochemistry in primary tumors

  • Assess actin cytoskeleton organization in tumor sections

  • Measure local invasion at primary tumor site

  • Correlate AIM1 levels with metastatic burden across experimental groups

• Molecular signature assessment:

  • Perform RNA-seq or qPCR panels focusing on:

    • Epithelial-mesenchymal transition markers

    • Cytoskeletal regulators

    • Cell adhesion molecules

    • Matrix metalloproteinases

  • Correlate molecular signatures with AIM1 status and metastatic potential

• Therapeutic intervention studies:

  • Test whether restoring AIM1 expression can reduce metastatic potential

  • Evaluate compounds targeting cytoskeletal dynamics in AIM1-deficient models

  • Assess combination approaches targeting both AIM1 pathways and standard therapies

Research has demonstrated that AIM1 depletion significantly increases micrometastatic burden in xenograft models, with observed increases of 194-fold (range 6–8378-fold) for VCaP cells and 426-fold (range 0.59–10,415-fold) for PC3 cells .

What are common technical issues when using AIM1 antibodies in Western blotting?

Researchers frequently encounter technical challenges when using AIM1 antibodies for Western blotting. The following troubleshooting guide addresses common issues:

When optimizing Western blot protocols for AIM1 detection, researchers should consider that fractionation experiments separating F-actin and G-actin require special attention to preserve cytoskeletal integrity during sample preparation .

How can researchers optimize AIM1 antibodies for immunofluorescence co-localization studies?

Achieving high-quality co-localization data for AIM1 and cytoskeletal components requires careful optimization:

• Sample preparation optimization:

  • Test multiple fixation methods:

    • 4% paraformaldehyde (10-15 minutes) preserves cytoskeletal structure

    • Methanol fixation may better preserve certain epitopes

    • Consider dual fixation protocols for multi-antibody staining

  • Optimize permeabilization:

    • 0.1-0.5% Triton X-100 for general permeabilization

    • Saponin for more gentle membrane permeabilization

    • Digitonin for selective plasma membrane permeabilization

• Antibody selection and validation:

  • Test antibodies raised in different species to enable multi-color staining

  • Validate antibody specificity using knockdown controls

  • Titrate antibody concentrations to optimize signal-to-noise ratio

  • Consider directly conjugated antibodies to reduce protocol complexity

• Imaging parameters for co-localization:

  • Use confocal microscopy with appropriate channel separation

  • Implement sequential scanning to prevent bleed-through

  • Collect Z-stacks to analyze co-localization in three dimensions

  • Apply deconvolution to improve spatial resolution

• Quantification methodology:

  • Calculate co-localization coefficients (Pearson's, Manders', etc.)

  • Compare co-localization in different cellular regions

  • Use line scan analysis across cellular structures

  • Apply threshold controls to distinguish specific from non-specific signal

Research has demonstrated that in normal prostate epithelium, AIM1 shows high co-localization with β-actin (mean co-localization coefficient = 0.82, SD = 0.11), while this co-localization is disrupted in prostatic adenocarcinoma (mean co-localization coefficient = 0.38, SD = 0.08) .

What controls are essential when using AIM1 antibodies in mechanistic studies?

Implementing appropriate controls is crucial for ensuring data reliability in mechanistic studies involving AIM1:

• Genetic controls hierarchy:

  • Primary validation controls:

    • AIM1 knockdown cells (multiple shRNA or siRNA constructs)

    • AIM1 knockout models (CRISPR/Cas9-mediated)

    • AIM1 overexpression systems (wild-type and mutant constructs)

  • Secondary validation controls:

    • Domain deletion mutants (e.g., Δ859 lacking βγ-crystallin domains)

    • Point mutants affecting specific functions

    • Rescue experiments with wild-type and mutant constructs

• Experimental technique controls:

  • Western blotting:

    • Loading controls (avoiding β-actin due to potential interaction bias)

    • Molecular weight markers

    • Positive and negative control cell lines

  • Immunoprecipitation:

    • IgG control pull-downs

    • Input samples

    • Reverse co-IP (pull-down partner protein)

  • Immunofluorescence:

    • Secondary antibody-only controls

    • Isotype controls

    • Peptide competition controls

• Biological context controls:

  • Cell density controls (AIM1 localization may vary with confluence)

  • Cell cycle synchronization (if cytoskeletal dynamics change with cell cycle)

  • Matrix composition controls for invasion assays

  • Matched normal/tumor tissue pairs for translational studies

• Functional validation controls:

  • Compare multiple functional readouts (migration, invasion, traction force)

  • Use pharmacological inhibitors of cytoskeletal dynamics as controls

  • Include rescued cell lines in all functional experiments

  • Verify phenotypes in multiple cell models

Implementing this comprehensive control strategy ensures that observations related to AIM1 function are specific and reproducible across experimental systems.

How can AIM1 antibodies be utilized in high-throughput screening applications?

AIM1 antibodies can be adapted for high-throughput screening applications to identify modulators of AIM1 expression, localization, or function:

• Automated immunofluorescence screening platform:

  • Develop high-content imaging protocols for:

    • AIM1 expression levels

    • AIM1 subcellular localization

    • Co-localization with β-actin

    • Morphological parameters (cell spreading, protrusion formation)

  • Implement machine learning-based image analysis for:

    • Quantifying membranous versus cytoplasmic staining

    • Measuring co-localization coefficients

    • Correlating AIM1 patterns with cellular phenotypes

• Compound screening methodology:

  • Screen for compounds that:

    • Restore AIM1 expression in deficient cells

    • Promote AIM1 membrane localization

    • Enhance AIM1-actin interaction

    • Reverse invasive phenotypes in AIM1-depleted cells

  • Use cellular models with stable reporters for:

    • AIM1 promoter activity

    • AIM1-fluorescent protein fusions

    • Invasion/migration readouts

• siRNA/CRISPR library screening approach:

  • Identify genetic modifiers of:

    • AIM1 expression

    • AIM1 localization

    • AIM1-dependent phenotypes

  • Implement multiplex immunofluorescence to simultaneously measure:

    • Knockdown efficiency

    • AIM1 status

    • Cell invasion capacity

    • Cytoskeletal organization

• Patient-derived models for personalized screening:

  • Develop protocols for primary cultures from patient samples

  • Assess correlation between AIM1 status and drug sensitivity

  • Test restoration of AIM1 function as a therapeutic strategy

These approaches can identify novel regulators of AIM1 biology and potential therapeutic strategies for cancers with AIM1 dysregulation.

What are the considerations for developing AIM1 as a biomarker in clinical applications?

Developing AIM1 as a clinically relevant biomarker requires addressing several critical considerations:

Research has demonstrated that AIM1 shows consistent mislocalization patterns in prostate cancer, with reduced membranous staining correlating with higher Gleason grades, suggesting potential utility as a prognostic biomarker .

How can researchers investigate the interaction between AIM1 and other cytoskeletal regulatory proteins?

Investigating AIM1's interactions with the broader cytoskeletal regulatory network requires specialized experimental approaches:

• Comprehensive protein interaction mapping:

  • Implement proximity-dependent biotinylation (BioID or TurboID) with AIM1 as bait

  • Perform mass spectrometry analysis of AIM1 immunoprecipitates under different conditions

  • Use domain-specific constructs to map interaction regions

  • Compare interaction landscapes in normal versus cancer cells

  • Validate key interactions with reciprocal co-immunoprecipitation

• Network analysis methodology:

  • Construct protein-protein interaction networks centered on AIM1

  • Identify key network hubs that may regulate AIM1 function

  • Compare interaction networks in:

    • Different cell types

    • Normal versus transformed cells

    • Different subcellular compartments

• Functional interaction assessment:

  • Perform co-depletion experiments targeting AIM1 and potential partners

  • Assess synthetic phenotypes suggesting pathway interactions

  • Use live-cell imaging to track co-dynamics of AIM1 and interaction partners

  • Implement FRET-based approaches to measure direct interactions

• Cytoskeletal regulator screening panel:

  • Test AIM1 interactions with known regulators of:

    • Actin polymerization (Arp2/3, formins)

    • Filament stabilization (tropomyosins)

    • Contractility (myosins)

    • Crosslinking (filamins, spectrins)

  • Compare interaction strength under different cellular conditions

The unbiased proteomic interaction screen identified 79 AIM1-interacting proteins, with strong enrichment for components of the actin cytoskeleton, including β-actin, non-muscle myosin 9, and filamin A, providing a foundation for further interaction studies .

What approaches can determine if post-translational modifications affect AIM1 function?

Post-translational modifications (PTMs) often regulate protein function, localization, and interactions. To investigate PTMs of AIM1:

• PTM identification strategy:

  • Perform immunoprecipitation of AIM1 followed by:

    • Mass spectrometry analysis optimized for PTM detection

    • Western blotting with PTM-specific antibodies (phospho, ubiquitin, etc.)

  • Compare PTM profiles between:

    • Normal versus cancer cells

    • Different cellular compartments

    • Various cellular states (migration, stress, etc.)

• Site-directed mutagenesis approach:

  • Generate AIM1 mutants at identified or predicted PTM sites

  • Create phosphomimetic and non-phosphorylatable mutants

  • Test mutant effects on:

    • Actin binding

    • Subcellular localization

    • Suppression of invasion phenotypes

    • Protein stability

• PTM modulation experiments:

  • Treat cells with:

    • Kinase inhibitors

    • Phosphatase inhibitors

    • Proteasome inhibitors

    • Deacetylase inhibitors

  • Assess effects on AIM1 function, localization, and stability

  • Use specific pathway activators/inhibitors to identify regulatory inputs

• PTM-specific antibody development:

  • Generate antibodies against key AIM1 PTM sites

  • Validate specificity using mutant constructs

  • Apply to tissue samples to correlate PTM status with disease progression

This comprehensive approach can reveal how PTMs regulate AIM1 function and potentially identify therapeutic targets for restoring normal AIM1 function in cancer cells.

How can AIM1 antibodies be used to study mechanotransduction pathways?

Given AIM1's role in cytoskeletal dynamics and traction forces, investigating its involvement in mechanotransduction requires specialized approaches:

• Mechanical stimulation experimental design:

  • Apply defined mechanical stimuli using:

    • Substrate stretching

    • Fluid shear stress

    • Atomic force microscopy indentation

    • Varying substrate stiffness

  • Monitor AIM1 localization and protein complex formation in response to mechanical cues

  • Compare responses in control versus AIM1-depleted cells

• Force measurement integration:

  • Combine traction force microscopy with AIM1 immunofluorescence

  • Correlate cellular forces with AIM1 localization patterns

  • Measure nanoscale tracer motions to assess cytoskeletal remodeling rates

  • Compare mechanical properties between:

    • AIM1-expressing and AIM1-depleted cells

    • Cells with wild-type versus mutant AIM1

• Signaling pathway analysis:

  • Investigate AIM1's relationship with known mechanosensing pathways:

    • YAP/TAZ signaling

    • MRTF-SRF axis

    • Integrin-FAK-Src pathway

    • RhoA-ROCK signaling

  • Assess whether AIM1 depletion affects mechanically induced signaling

  • Test whether mechanical stimulation alters AIM1-actin interaction

• Tissue-level mechanobiology:

  • Examine AIM1 distribution in tissues under different mechanical loads

  • Compare AIM1 patterns at tissue boundaries with varying stiffness

  • Correlate AIM1 localization with mechanical properties in tumor microenvironment

Research has shown that AIM1 depletion increases cellular traction forces and cytoskeletal remodeling rates, suggesting it may function as a mechanosensitive regulator of cell behavior .

What emerging technologies could enhance AIM1 antibody applications in research?

Several cutting-edge technologies show promise for advancing AIM1 research beyond traditional antibody applications:

• Super-resolution microscopy integration:

  • Apply techniques such as:

    • STORM/PALM for nanoscale localization

    • SIM for enhanced resolution of cytoskeletal structures

    • Expansion microscopy for physical magnification

  • Develop optimized protocols for AIM1 antibody compatibility with these methods

  • Visualize nanoscale organization of AIM1-actin complexes

  • Map precise spatial relationships between AIM1 and other cytoskeletal components

• Live-cell antibody fragment applications:

  • Generate Fab fragments or nanobodies against AIM1

  • Conjugate with cell-permeable tags for live imaging

  • Track AIM1 dynamics during:

    • Cell migration

    • Division

    • Response to mechanical stimuli

  • Pair with optogenetic tools to manipulate AIM1 function

• Single-cell proteomics approach:

  • Develop methods to quantify AIM1 expression at single-cell level

  • Correlate with cytoskeletal protein abundance

  • Map heterogeneity within tumor populations

  • Link protein expression patterns to cellular phenotypes

• Spatial transcriptomics integration:

  • Combine AIM1 immunofluorescence with spatial transcriptomics

  • Correlate protein localization with local gene expression patterns

  • Map tumor microenvironment influences on AIM1 biology

  • Identify spatial markers that co-occur with AIM1 alterations

These emerging technologies can provide unprecedented insights into AIM1 biology and its role in cancer progression.

How might single-cell approaches reveal heterogeneity in AIM1 expression and function?

Single-cell technologies offer powerful tools to investigate heterogeneity in AIM1 biology that may be masked in bulk analyses:

• Single-cell multi-omics integration protocol:

  • Combine technologies to simultaneously assess:

    • AIM1 protein expression (antibody-based detection)

    • AIM1 transcript levels (RNA-seq)

    • Chromatin accessibility at AIM1 locus (ATAC-seq)

    • Cell identity markers

  • Correlate AIM1 status with cellular phenotypes at single-cell resolution

  • Identify rare cell populations with distinct AIM1 expression patterns

• Spatial single-cell analysis framework:

  • Implement multiplexed immunofluorescence with cyclic staining or mass cytometry

  • Map AIM1 expression in tissue contexts with spatial information preserved

  • Analyze AIM1 patterns at invasion fronts versus tumor cores

  • Correlate with microenvironmental features and neighboring cell types

• Lineage tracing methodology:

  • Track cells with different AIM1 expression levels over time

  • Assess competitive advantages of AIM1-low versus AIM1-high cells

  • Determine whether AIM1 status influences cell fate decisions

  • Investigate clonal evolution of AIM1 expression during cancer progression

• Single-cell functional heterogeneity assessment:

  • Combine AIM1 immunostaining with functional readouts at single-cell level:

    • Migration tracking

    • Traction force measurements

    • Invasion capacity

    • Drug sensitivity

  • Identify correlations between AIM1 status and cellular behaviors

These approaches can reveal how heterogeneity in AIM1 expression contributes to tumor progression and treatment response.