PHLDA1 Antibody

What is PHLDA1 and why is it important in cellular research?

PHLDA1 (Pleckstrin Homology-Like Domain Family A Member 1) is a multifunctional protein involved in regulating cell growth, apoptosis, energy homeostasis, and differentiation . The protein contains a pleckstrin homology (PH) domain that is split into N-terminal (β sheets 1–3) and C-terminal (β sheets 4–7 and an α helix) portions .

PHLDA1 appears to play significant roles in:

• Regulation of apoptosis, particularly detachment-mediated programmed cell death

• Neuronal development-associated apoptosis

• Modulation of anti-apoptotic effects of insulin-like growth factor-1 (IGF1)

• Translational regulation

• Immune response and inflammation

Recent research has demonstrated PHLDA1's involvement in various disease processes, including cancer progression, neurological disorders, and inflammatory conditions, making it an important target for investigation .

What applications are PHLDA1 antibodies validated for in research?

PHLDA1 antibodies have been validated for multiple research applications:

• Western Blot (WB): Detecting PHLDA1 protein expression levels in cell and tissue lysates. Most antibodies can detect the predominant short form of PHLDA1 (~38 kDa) versus the long form (~53 kDa) .

• Immunohistochemistry (IHC): Examining PHLDA1 distribution in formalin-fixed, paraffin-embedded tissue sections to evaluate expression patterns in normal versus diseased tissues .

• Immunofluorescence (IF): Visualizing PHLDA1 localization within cells using fluorescently labeled secondary antibodies .

• Immunocytochemistry (ICC): Detecting PHLDA1 in cultured cells to study subcellular localization .

• ELISA: Quantifying PHLDA1 levels in biological samples .

• Co-immunoprecipitation: Isolating PHLDA1 and its binding partners to study protein-protein interactions .

When selecting a PHLDA1 antibody, researchers should verify validation data for their specific application and consider recommended dilutions provided by manufacturers (e.g., 1:2000-1:10000 for ELISA, 1:20-1:200 for IHC) .

How is PHLDA1 expression regulated in normal versus diseased tissues?

PHLDA1 expression varies significantly between normal and diseased tissues, particularly in:

Neuroblastoma:

• High expression of PHLDA1 positively correlates with survival in MYCN-amplified neuroblastoma patients

• PHLDA1 appears to influence neuronal differentiation through interaction with the DCAF7/AUTS2 complex

Neuroinflammatory conditions:

• PHLDA1 is upregulated after subarachnoid hemorrhage (SAH), peaking at 24 hours post-injury

• Increased PHLDA1 is predominantly found in microglial cells

• PHLDA1 deficiency reduces pro-inflammatory cytokines (IL-1β, IL-6, IL-18) and increases anti-inflammatory IL-10

These expression patterns suggest PHLDA1 may function differently depending on tissue context and disease state, highlighting the importance of experimental controls when using PHLDA1 antibodies .

What are the methodological considerations when using PHLDA1 antibodies for Western blotting?

When performing Western blot analysis with PHLDA1 antibodies, researchers should consider:

Protein size variation:

• The PHLDA1 gene encodes both long and short isoforms

• Predicted molecular weights are 45 kDa (long) and 30 kDa (short)

• These often appear at 53 kDa and 38 kDa respectively on SDS-PAGE

• The short form (~38 kDa) is predominantly expressed in human cells

Sample preparation:

• Effective cell lysis is critical - consider using lysis buffers containing protease inhibitors

• For neuronal or brain tissue samples, specialized extraction methods may be needed to overcome high lipid content

Antibody selection and dilution:

• Monoclonal antibodies (e.g., EPR6674) offer high specificity and reproducibility

• Recommended dilutions vary (e.g., 1:10000 for ab133654)

• Verify cross-reactivity with target species (most PHLDA1 antibodies are validated for human samples)

Controls:

• Positive controls: U87 and U251 cells show high PHLDA1 expression

• Negative/low expression controls: HEB and LN229 cells show relatively low expression

• Consider using PHLDA1 knockdown or overexpression samples as specificity controls

Detection method:

• HRP-conjugated secondary antibodies with appropriate species reactivity (e.g., Goat anti-Rabbit HRP at 1:2000 dilution)

• Enhanced chemiluminescence (ECL) systems provide sensitive detection

These considerations help ensure specific and reproducible detection of PHLDA1 in Western blot experiments .

What approaches are effective for PHLDA1 knockdown in experimental models?

Researchers have successfully employed several strategies for PHLDA1 knockdown:

RNA interference (RNAi):

• siRNA-mediated knockdown:

  • Direct transfection of PHLDA1-specific siRNA sequences

  • Effective for short-term experiments (usually 48-72 hours)

  • Used successfully in neuroblastoma and subarachnoid hemorrhage studies

• shRNA-mediated stable knockdown:

  • Lentiviral or plasmid vectors expressing PHLDA1-targeted shRNA

  • Allows for stable, long-term knockdown

  • Example protocol: Transfection of 3 μg shRNA plasmid using JetPRIME reagent, followed by puromycin selection (0.25-0.5 μg/mL)

  • Established cell lines with stable PHLDA1 knockdown (e.g., S2 and S4 clones) compared to Mock (control shRNA) and WT (non-transduced) cells

Transfection considerations:

• Cell density: 1-3 × 10^6 cells per well in 6-well plates

• Transfection reagents: JetPRIME, Lipofectamine, or similar reagents

• Selection markers: Puromycin resistance for stable selection

• Controls: Non-targeting shRNA and GFP-expressing control plasmids to assess transfection efficiency

Validation methods:

• Western blot analysis to confirm protein reduction

• qRT-PCR to verify mRNA knockdown

• Functional assays to demonstrate phenotypic effects

Observed effects of PHLDA1 knockdown:

• In glioblastoma: Slower cell growth, reduced colony formation

• In neuroblastoma: Enhanced cellular ATP levels, increased mitochondrial membrane potential, decreased susceptibility to apoptosis

• In neuroinflammatory models: Reduced pro-inflammatory cytokines, increased anti-inflammatory IL-10, shifted microglial polarization from M1 to M2 phenotype

These approaches allow researchers to investigate PHLDA1's functions in different cellular contexts and disease models .

How does PHLDA1 influence tumor cell growth and what are the implications for cancer research?

PHLDA1 exhibits context-dependent effects on tumor cell growth, with evidence supporting both oncogenic and tumor-suppressive roles:

Tumor-suppressive functions in neuroblastoma:

• High PHLDA1 expression positively correlates with survival in MYCN-amplified neuroblastoma patients

• PHLDA1 silencing in IMR-32 neuroblastoma cells leads to:

  • Enhanced cellular ATP levels

  • Increased mitochondrial membrane potential

  • Reduced susceptibility to apoptosis

  • Increased aurora A kinase expression and its activating phosphorylation

  • Decreased CDKN1A (p21) expression

  • Increased TRKB expression (a marker of poor prognosis)

Experimental approaches to study PHLDA1 in cancer:

• Patient tissue analysis using immunohistochemistry with PHLDA1-specific antibodies

• Survival analysis correlating PHLDA1 expression with patient outcomes

• Functional studies using gene knockdown and overexpression

• Protein interaction studies to identify PHLDA1 binding partners and affected pathways

• Phosphoproteome analysis to identify downstream signaling effects

These findings suggest PHLDA1 may function differently depending on tumor type and cellular context, highlighting the need for cancer-specific investigations when targeting PHLDA1 therapeutically .

How can researchers effectively use PHLDA1 antibodies for immunohistochemistry in different tissue types?

Optimizing PHLDA1 immunohistochemistry (IHC) across different tissue types requires careful attention to several methodological aspects:

Tissue preparation and fixation:

• Formalin-fixed, paraffin-embedded (FFPE) tissues are commonly used

• Optimal fixation time is critical: overfixation can mask epitopes

• Consider antigen retrieval methods appropriate for the specific PHLDA1 epitope

Antibody selection:

• Choose antibodies validated specifically for IHC applications

• Consider using monoclonal antibodies for higher specificity

• Verify species reactivity (many PHLDA1 antibodies are human-specific)

Protocol optimization by tissue type:

• Brain tissue:

  • May require extended antigen retrieval due to lipid content

  • Background staining can be problematic - optimize blocking steps

  • Used successfully to study PHLDA1 in glioma tissues with clear differentiation between normal brain and glioma tissue

• Liver tissue:

  • Demonstrated successful staining with PACO55230 antibody at 1:100 dilution

  • Higher background is common due to endogenous peroxidases - additional blocking may be required

• Tumor tissues:

  • Often show heterogeneous expression - consider analyzing multiple fields

  • Compare with adjacent normal tissue when possible

Quantification approaches:

• Histological score (Hscore) can be used to quantify PHLDA1 levels

• Define clear scoring criteria (e.g., median PHLDA1 Hscore as cut-off value for high vs. low expression)

• Use digital image analysis software for unbiased quantification

• Consider double immunostaining to study co-localization with other markers (e.g., PHLDA1 with microglial markers)

Controls and validation:

• Include positive and negative control tissues with known PHLDA1 expression

• For brain tissue, normal brain samples serve as low expression controls

• Use isotype control antibodies to assess non-specific binding

• Consider PHLDA1 knockdown tissues as negative controls

These approaches allow for reliable detection and quantification of PHLDA1 across different tissue types in research and potential diagnostic applications .

What is known about PHLDA1's interaction with the NLRP3 inflammasome and how can these interactions be studied?

PHLDA1 has emerged as an important modulator of the NLRP3 inflammasome pathway, particularly in neuroinflammatory conditions:

Functional relationship between PHLDA1 and NLRP3 inflammasome:

• PHLDA1 blockade inhibits NLRP3 inflammasome signaling in neurological disorders including:

  • Subarachnoid hemorrhage (SAH)

  • Cerebral ischemia/reperfusion injury

  • Parkinson's disease models

• PHLDA1 deficiency reduces inflammatory cytokines (IL-1β, IL-6, IL-18) that are downstream products of inflammasome activation

• NLRP3 inflammasome activator (nigericin) reverses the beneficial effects of PHLDA1 blockade, confirming a functional relationship

• PHLDA1 appears to regulate microglial polarization through NLRP3 inflammasome signaling

Methodological approaches to study this interaction:

• Protein expression analysis:

  • Western blot analysis of NLRP3, ASC, pro-caspase-1, and cleaved caspase-1

  • ELISA measurement of inflammasome products (IL-1β, IL-18)

  • Immunofluorescence co-localization studies of PHLDA1 with inflammasome components

• Functional manipulation:

  • PHLDA1 knockdown using siRNA or shRNA approaches

  • Pharmacological manipulation of NLRP3 using:

    • Activators (nigericin)

    • Inhibitors (MCC950, CY-09, etc.)

  • Combined approaches to establish pathway relationships

• Microglial polarization assessment:

  • Double immunostaining for:

    • Iba1+/CD16/32+ cells (M1 phenotype)

    • Iba1+/CD206+ cells (M2 phenotype)

  • Quantification of M1/M2 ratios after PHLDA1 or NLRP3 manipulation

• Cytokine profiling:

  • Measure inflammatory cytokines (IL-1β, IL-6, IL-18) and anti-inflammatory cytokines (IL-10)

  • Use ELISA, cytometric bead array, or multiplex assays for comprehensive profiling

• In vivo models:

  • SAH model with PHLDA1 siRNA treatment

  • Assessment of neurological function and brain injury

  • Histological and biochemical analysis of inflammasome activation

These research approaches have revealed that PHLDA1 blockade ameliorates neuroinflammation by balancing microglial M1/M2 polarization via suppression of NLRP3 inflammasome signaling, suggesting potential therapeutic targets for neuroinflammatory conditions .

How does PHLDA1 regulate microglial polarization in neuroinflammatory disorders and what are the best methods to investigate this mechanism?

PHLDA1 has emerged as a critical regulator of microglial polarization in various neuroinflammatory disorders. Understanding this mechanism requires sophisticated experimental approaches:

Current understanding of PHLDA1's role in microglial polarization:

• PHLDA1 expression increases in activated microglia following neurological injuries such as subarachnoid hemorrhage (SAH) and ischemic stroke

• PHLDA1 knockdown shifts microglial polarization from pro-inflammatory M1 phenotype toward anti-inflammatory M2 phenotype

• This polarization shift is associated with:

  • Decreased pro-inflammatory cytokines (IL-1β, IL-6, IL-18)

  • Increased anti-inflammatory cytokines (IL-10)

  • Improved neurological outcomes in various disease models

• The regulatory effect appears to be mediated through NLRP3 inflammasome signaling

Methodological approaches to study PHLDA1-mediated microglial polarization:

• Microglial phenotype characterization:

  • Double immunofluorescence staining for:

    • M1 markers: Iba1+/CD16/32+ cells

    • M2 markers: Iba1+/CD206+ cells

  • Quantitative analysis of marker expression and cellular morphology

  • Flow cytometry analysis of isolated microglia using multiple M1/M2 markers

  • Transcriptomic profiling of M1/M2 signature genes

• In vitro modeling:

  • Primary microglial cultures with PHLDA1 knockdown/overexpression

  • Microglial cell lines (BV2, HAPI) for mechanistic studies

  • Co-culture systems with neurons to assess neuroprotective effects

  • Live-cell imaging to track phenotypic transitions in real-time

• Pathway analysis:

  • PHLDA1 silencing combined with NLRP3 activators (e.g., nigericin)

  • Phosphoproteomic analysis to identify signaling pathways affected by PHLDA1 manipulation

  • Protein-protein interaction studies using co-immunoprecipitation and mass spectrometry

  • Chromatin immunoprecipitation (ChIP) to identify transcriptional targets

• Advanced in vivo approaches:

  • Conditional PHLDA1 knockout specific to microglial cells

  • Inducible systems to control timing of PHLDA1 manipulation

  • Intravital microscopy to observe microglial dynamics in living brain tissue

  • Single-cell RNA sequencing of microglia from different brain regions

• Translational relevance assessment:

  • Correlation of PHLDA1 levels with microglial polarization markers in human brain samples

  • Testing therapeutic potential of PHLDA1 inhibition in combination with other treatments

  • Long-term outcomes assessment in animal models

These approaches reveal that PHLDA1 blockade ameliorates neuroinflammation by balancing microglial M1/M2 polarization via NLRP3 inflammasome suppression, suggesting potential therapeutic targets for conditions including stroke, Parkinson's disease, and subarachnoid hemorrhage .

What are the methodological considerations for studying PHLDA1 protein interactions using co-immunoprecipitation followed by mass spectrometry?

Investigating PHLDA1 protein interactions through co-immunoprecipitation (co-IP) and mass spectrometry (MS) requires careful experimental design and execution:

Optimization of PHLDA1 co-immunoprecipitation:

• Antibody selection:

  • Validate antibody specificity for PHLDA1 by Western blot before co-IP

  • Consider monoclonal antibodies for higher specificity (e.g., mouse anti-PHLDA1 SC-23866)

  • Include isotype control antibodies (e.g., mouse IgG2a) for identifying non-specific interactions

• Lysis buffer considerations:

  • Use buffers that maintain protein-protein interactions (e.g., RIPA or NP-40-based buffers)

  • Include protease and phosphatase inhibitors to preserve phosphorylation status

  • Pre-clear lysates with Protein G magnetic beads to reduce background

• Pull-down protocol optimization:

  • Antibody immobilization on Protein G magnetic beads (e.g., Dynabeads™)

  • Overnight incubation at 4°C on a rotating platform for maximal binding

  • Stringent washing steps balanced with maintaining specific interactions

  • Proper elution methods to release complexes without antibody contamination

• Controls and validation:

  • Input samples to confirm PHLDA1 expression

  • Isotype control antibodies to identify non-specific binding

  • Reverse co-IP to confirm interactions (pull-down with antibodies against identified partners)

  • Western blot validation of selected interactions prior to MS analysis

Mass spectrometry workflow for PHLDA1 interactome analysis:

• Sample preparation:

  • In-gel or in-solution digestion of immunoprecipitated proteins

  • Peptide fractionation to increase proteome coverage

  • Consider crosslinking approaches for transient interactions

• MS data acquisition strategies:

  • Data-dependent acquisition for discovery-based approaches

  • Targeted methods for validation of specific interactions

  • Quantitative approaches (label-free or isotope labeling) to compare interactomes under different conditions

• Data analysis and interpretation:

  • Filtering against isotype control to remove non-specific binders

  • Network analysis using platforms like STRING to identify protein complexes

  • Pathway enrichment analysis using tools like Reactome to identify biological processes

  • In one study, this approach identified 111 potential PHLDA1-binding partners in neuroblastoma cells

Case study findings from neuroblastoma research:

• Co-IP-MS identified different PHLDA1 interactors in control vs. antibody-treated neuroblastoma cells

• 56 proteins were found in both conditions, while 43 new proteins appeared after antibody treatment

• Pathway analysis revealed enrichment of specific signaling processes:

  • Antibody-specific interactors: antimicrobial response and Rho GTPase signaling

  • Control-specific interactors: glutamate and glutamine metabolism

  • Shared interactors: protein and RNA metabolism

• PHLDA1 interaction with DCAF7 and AUTS2 was confirmed by follow-up Western blot analysis

These methodological considerations enable researchers to reliably identify and characterize PHLDA1 protein interactions, providing insights into its diverse cellular functions and involvement in disease processes .

How can researchers reconcile conflicting findings about PHLDA1's role in cell survival versus apoptosis across different tissue types?

The literature reveals seemingly contradictory roles for PHLDA1 in cell survival and apoptosis, requiring sophisticated approaches to reconcile these differences:

Contrasting roles of PHLDA1 in different contexts:

• Pro-survival/oncogenic functions:

  • In glioblastoma:

    • PHLDA1 promotes cell growth and colony formation

    • High PHLDA1 expression correlates with poor patient survival

    • PHLDA1 knockdown reduces proliferation in U87 and U251 cells

    • PHLDA1 overexpression enhances growth in HEB and LN229 cells

• Pro-apoptotic/tumor-suppressive functions:

  • In neuroblastoma:

    • PHLDA1 silencing decreases susceptibility to apoptosis

    • High PHLDA1 expression correlates with better survival in MYCN-amplified patients

    • PHLDA1 is suggested to function as a pro-apoptotic protein

  • Historical evidence shows PHLDA1 (TDAG51) was initially identified as a pro-apoptotic protein in T-cell death

Methodological approaches to reconcile these differences:

• Context-specific expression analysis:

  • Compare PHLDA1 isoform expression patterns across tissue types

  • Examine post-translational modifications in different contexts

  • Study subcellular localization differences using fractionation and immunofluorescence

  • Analyze PHLDA1 expression in relation to cell cycle phase

• Interactome characterization:

  • Perform tissue-specific co-IP-MS to identify different binding partners

  • Compare PHLDA1 interactomes between:

    • Normal vs. cancer cells

    • Different cancer types

    • Various developmental stages

  • Functional validation of key interaction partners

• Signaling pathway analysis:

  • Phosphoproteomic analysis following PHLDA1 manipulation

  • Receptor tyrosine kinase (RTK) profiling in PHLDA1-silenced cells

  • Test effects of pathway-specific inhibitors on PHLDA1 function

  • One study found PHLDA1 silencing upregulates EGFR pathway in neuroblastoma

• Genetic approaches:

  • Domain-specific mutations to identify functional regions

  • CRISPR-Cas9 genetic screens to identify synthetic lethality partners

  • Inducible expression systems to study temporal effects of PHLDA1

• Integrated analysis of clinical data:

  • Multi-cancer type analysis of PHLDA1 expression and patient outcomes

  • Consideration of confounding variables (gender differences were noted in glioma)

  • Integration with genomic alterations and molecular subtypes

Working hypotheses to explain contradictory findings:

• Isoform-specific effects: The long (53 kDa) and short (38 kDa) PHLDA1 isoforms may have different or opposing functions

• Cellular context dependence: PHLDA1 may interact with tissue-specific factors that determine whether it promotes survival or apoptosis

• Pathway interaction model: PHLDA1's role may depend on the status of other signaling pathways (e.g., EGFR, NLRP3 inflammasome)

• Threshold effect hypothesis: PHLDA1 may promote survival at moderate levels but trigger apoptosis at very high levels

• Temporal dynamics: PHLDA1's function may change depending on exposure time and cellular state

These approaches can help researchers design experiments that account for context-specific functions of PHLDA1 and resolve apparent contradictions in the literature .

What are the challenges in developing specific antibodies against different PHLDA1 isoforms and how can they be addressed?

Developing isoform-specific antibodies for PHLDA1 presents several technical challenges that require sophisticated approaches:

Structural and sequence challenges for PHLDA1 isoform discrimination:

• Isoform characteristics:

  • PHLDA1 gene encodes long (~45 kDa, appears at ~53 kDa) and short (~30 kDa, appears at ~38 kDa) isoforms

  • The short form is predominantly expressed in human cells

  • Both isoforms share the majority of their sequence, making specific targeting difficult

• Domain structure considerations:

  • PHLDA1 contains a split PH domain with N-terminal (β sheets 1–3) and C-terminal (β sheets 4–7 and α helix) portions

  • Isoform-specific regions may have limited immunogenicity or accessibility

  • Post-translational modifications may differ between isoforms, affecting epitope recognition

Technical approaches to develop isoform-specific antibodies:

• Epitope selection strategies:

  • Identify unique peptide sequences present in only one isoform

  • Target splice junction regions where sequences diverge

  • Consider the three-dimensional structure to identify accessible regions

  • Use computational epitope prediction tools to identify antigenic regions

• Immunization approaches:

  • Use synthetic peptides representing isoform-unique regions

  • Develop recombinant protein fragments with isoform-specific regions

  • Consider multiple host species to overcome tolerance issues

  • Implement novel immunization protocols (DNA immunization, virus-like particles)

• Screening and validation methods:

  • Multi-step screening against both isoforms to identify differential reactivity

  • Overexpression systems for each isoform as positive controls

  • PHLDA1 knockout cells as negative controls

  • Competitive binding assays with isoform-specific peptides

  • Validation across multiple applications (WB, IHC, IP) to ensure specificity

• Advanced antibody engineering:

  • Recombinant antibody approaches with affinity maturation

  • Phage display screening against specific isoforms

  • Single-cell B cell cloning from immunized animals

  • Structural biology approaches to guide antibody optimization

Current antibody limitations and solutions:

• Cross-reactivity issues:

  • Most available antibodies (e.g., PACO55230, EPR6674) detect both isoforms

  • Western blot can separate isoforms by molecular weight, but IHC/IF applications cannot

  • Solution: Use panel approaches with multiple antibodies targeting different epitopes

• Application-specific considerations:

  • Some antibodies work well for WB but poorly for IHC or IP

  • Solution: Validate each antibody for specific applications and optimize conditions

  • Example: PACO55230 is recommended at different dilutions for different applications (ELISA 1:2000-1:10000, IHC 1:20-1:200, IF 1:50-1:200)

• Reproducibility challenges:

  • Polyclonal antibodies show batch-to-batch variation

  • Solution: Monoclonal antibodies like EPR6674 (ab133654) provide greater consistency

  • Recombinant antibody technology ensures reproducible reagent production

These approaches can help researchers develop and validate antibodies capable of distinguishing between PHLDA1 isoforms, enabling more precise investigation of their potentially distinct functions in various cellular contexts .

How does PHLDA1 influence phosphoproteome networks and what technologies are best suited to study these effects?

PHLDA1 appears to significantly impact cellular phosphoproteome networks, with important implications for understanding its diverse functions across different biological contexts:

Current evidence of PHLDA1's impact on phosphoproteome:

• Neuroblastoma findings:

  • PHLDA1 silencing leads to global phosphoproteome changes

  • PHLDA1 knockdown modulates the balance of two AUTS2 isoforms

  • PHLDA1 silencing upregulates EGFR pathway activation

  • Phospho-RTK array analysis revealed altered receptor tyrosine kinase phosphorylation patterns after PHLDA1 knockdown

• Glioblastoma research:

  • PHLDA1 knockdown affects aurora A kinase expression and its activating phosphorylation at Thr288

  • PHLDA1 interacts with proteins involved in cell cycle regulation

  • Correlation between PHLDA1 and proliferation markers suggests involvement in growth signaling pathways

• Neuroinflammation studies:

  • PHLDA1 appears to regulate NLRP3 inflammasome activation

  • PHLDA1 may influence phosphorylation events in microglial polarization pathways

Advanced technologies for studying PHLDA1-mediated phosphoproteome changes:

• Phosphoproteomic mass spectrometry approaches:

  • Phosphopeptide enrichment strategies:

    • Titanium dioxide (TiO2) chromatography

    • Immobilized metal affinity chromatography (IMAC)

    • Phosphotyrosine-specific antibody enrichment

  • Quantitative methods:

    • Label-free quantification

    • SILAC (Stable Isotope Labeling with Amino acids in Cell culture)

    • TMT (Tandem Mass Tag) labeling for multiplexed analysis

  • Data acquisition strategies:

    • Data-dependent acquisition (DDA)

    • Data-independent acquisition (DIA)

    • Parallel reaction monitoring (PRM) for targeted analysis

• Kinase activity profiling:

  • Kinase substrate peptide arrays

  • Phospho-specific antibody arrays (e.g., RTK arrays used in PHLDA1 studies)

  • Activity-based protein profiling with ATP probes

  • Live-cell kinase activity reporters

• Computational phosphoproteomics:

  • Kinase-substrate prediction algorithms

  • Pathway enrichment analysis

  • Phosphorylation motif analysis

  • Integration with protein-protein interaction networks

  • Temporal modeling of phosphorylation dynamics

• Functional validation approaches:

  • Site-directed mutagenesis of key phosphorylation sites

  • Pharmacological inhibition of identified kinases

  • CRISPR-based deletion of phosphorylation sites

  • Phosphomimetic and phospho-dead mutations to assess functional relevance

Research design considerations for studying PHLDA1 phosphoproteome effects:

• Experimental model selection:

  • Choose cell types where PHLDA1 has established functions (e.g., glioblastoma, neuroblastoma cells)

  • Consider both PHLDA1 knockdown and overexpression approaches

  • Include appropriate controls (scrambled siRNA, empty vectors)

• Temporal dynamics assessment:

  • Analyze phosphoproteome changes at multiple time points after PHLDA1 manipulation

  • Capture both immediate and delayed effects on signaling networks

  • Consider inducible systems for precise temporal control

• Context-dependent analyses:

  • Compare phosphoproteome effects under different conditions:

    • Growth factor stimulation

    • Stress conditions (oxidative stress, nutrient deprivation)

    • Differentiation signals

  • Integrate with transcriptomic and proteomic data for systems-level understanding

• Translational relevance:

  • Correlate identified phosphorylation events with clinical outcomes

  • Test combined inhibition of PHLDA1 and identified kinase pathways

  • In neuroblastoma, PHLDA1 silencing sensitizes cells to EGFR inhibitors like gefitinib, suggesting potential for combination therapy