FASTK antibodies target the FASTK protein (UniProt ID: Q14296), a serine/threonine kinase involved in mitochondrial gene expression and Fas-mediated apoptosis . These antibodies are widely used in ELISA, Western blot (WB), and immunofluorescence assays to study FASTK's role in cellular processes .
Apoptosis Regulation: Phosphorylates TIA1, promoting pro-apoptotic FAS receptor mRNA splicing .
Mitochondrial RNA Processing: Stabilizes ND6 mRNA and ensures proper mitochondrial transcript processing .
Oxidative Phosphorylation: Maintains electron transport chain efficiency by regulating mitochondrial mRNA maturation .
FASTK Knockout Models:
Western Blot: Detects FASTK at 61 kDa in PC-12, NIH-3T3, and U251 cell lysates .
Immunofluorescence: Localizes FASTK to cytoplasmic granules in HeLa cells .
siRNA Interference: Reduces FASTK protein/mRNA levels in U251 glioblastoma cells .
FASTK (Fas activated serine/threonine kinase) is a 549 amino acid protein with a mass of 61.1 kDa that localizes primarily to mitochondria in humans. It belongs to the FAST protein kinase family and plays significant roles in apoptotic pathways and protein phosphorylation. Its importance stems from its wide expression pattern across vital tissues including heart, brain, placenta, lung, liver, skeletal muscle, kidney, and pancreas. The protein undergoes post-translational modifications, particularly phosphorylation, making it a valuable research target for understanding cellular signaling and disease mechanisms .
FASTK antibodies have demonstrated highest utility in three primary applications: Western Blot (WB), Enzyme-Linked Immunosorbent Assay (ELISA), and Immunohistochemistry (IHC). Western Blot remains the most widely used application, allowing researchers to detect and quantify FASTK protein in tissue lysates, while immunohistochemistry provides crucial spatial information about protein localization within tissues. When designing experiments, researchers should consider these validated applications for optimal results rather than attempting less established methods .
When investigating FASTK's relationship with immune infiltration, researchers should employ a multi-omics approach. Based on recent studies of FASTK in kidney renal clear cell carcinoma (KIRC), experimental designs should incorporate:
RNA-seq data analysis using fragments per kilobase per million fragments mapped (FPKM) or transcripts per million (TPM) methods
Single-cell analysis platforms such as CancerSEA for functional status assessment
Correlation analysis with established immune cell markers
Validation across multiple databases (TCGA, GEO datasets, etc.)
The experimental workflow should follow a pattern similar to that used in recent KIRC studies, moving from expression analysis to correlation with immune infiltration markers .
The FASTK family includes multiple members (FASTK, FASTKD1, FASTKD2, FASTKD3, TBRG4, FASTKD5) with distinct but potentially overlapping functions. When designing antibody-based experiments:
Validate antibody specificity against each family member using knockout/knockdown controls
Consider epitope locations to ensure discrimination between family members
Employ antibodies targeting different regions (N-terminal vs. C-terminal) for confirmation
Use orthogonal detection methods (mRNA quantification) to support protein-level findings
When encountering contradictory FASTK expression data across cancer types:
Compare normalization methods used in different studies (FPKM vs. TPM)
Examine cohort demographics and tumor characteristics
Consider tumor heterogeneity and the presence of distinct molecular subtypes
Analyze correlation with patient outcomes rather than absolute expression levels
Perform immune cell deconvolution to account for infiltrating immune component contributions
Recent studies in KIRC demonstrate the importance of correlating FASTK expression with tumor immune microenvironment features rather than focusing solely on expression levels .
For optimal FASTK detection:
Use RIPA buffer with protease and phosphatase inhibitors
Include reducing agents (β-mercaptoethanol) in sample buffer
Heat samples at 95°C for 5 minutes before loading
Transfer using semi-dry methods at 15V for 1 hour for optimal protein transfer
Use 10% neutral buffered formalin fixation (24 hours)
Perform antigen retrieval using citrate buffer (pH 6.0)
Block with 5% normal serum corresponding to secondary antibody species
Optimize primary antibody dilution (typically 1:100-1:500)
Extract protein using mild detergent buffers
Dilute samples to fall within standard curve range
Follow specific manufacturer recommendations for coating buffers
These protocols should be optimized based on tissue type and experimental conditions .
A comprehensive FASTK antibody validation approach should include:
Positive and negative control tissues (based on known expression patterns)
FASTK knockdown/knockout controls when possible
Peptide competition assays to confirm epitope specificity
Detection of expected molecular weight (61.1 kDa) in Western blot
Correlation of protein data with mRNA expression
Comparison of results using antibodies against different epitopes
Testing in multiple applications (WB, IHC, etc.) to confirm consistent results
Proper validation is particularly important given the multiple FASTK family members and potential splice variants .
When investigating FASTK's role in tumor immune microenvironments:
Use single-cell RNA sequencing to distinguish cell type-specific expression
Apply multiplex immunofluorescence to simultaneously visualize FASTK and immune markers
Employ immune deconvolution algorithms (CIBERSORT, xCell, etc.) for bulk RNA-seq data
Consider spatial transcriptomics to map FASTK expression relative to immune niches
Correlate FASTK expression with specific immune cell markers using methods like:
| Immune Cell Type | Recommended Markers for Co-analysis with FASTK |
|---|---|
| T cells | CD3D, CD3E, CD2, CD8A, CD8B |
| B cells | CD19, CD79A |
| Monocytes | CD86, CD115 (CSF1R) |
| TAMs | CCL2, CD68, IL10 |
| M1 Macrophages | INOS (NOS2), IRF5, COX2 (PTGS2) |
| M2 Macrophages | CD163, VSIG4, MS4A4A |
| Neutrophils | CD66b, CD11b (ITGAM), CCR7 |
| NK cells | KIR2DL1, KIR3DL1, KIR3DL2 |
| Dendritic cells | HLA-DPB1, HLA-DQB1, HLA-DRA, CD11c |
The above markers have been validated in KIRC studies and should be considered for comprehensive immune profiling .
When facing discrepancies between protein and mRNA levels:
Consider post-transcriptional regulation mechanisms, particularly miRNAs targeting FASTK
Evaluate protein stability and half-life factors
Assess technical limitations (antibody sensitivity vs. RNA-seq depth)
Examine potential alternative splicing affecting epitope availability
Consider cell type heterogeneity in bulk samples
A combined approach analyzing both protein (Western blot/IHC) and mRNA (qPCR/RNA-seq) is recommended to resolve contradictions. Researchers should also calculate correlation coefficients between protein and mRNA measurements across samples to quantify the extent of discordance .
For robust statistical analysis of FASTK and immune infiltration correlations:
Use Spearman correlation for non-parametric assessment of monotonic relationships
Apply Wilcoxon signed-rank sum test for comparing high vs. low FASTK expression groups
Employ Single-sample Gene Set Enrichment Analysis (ssGSEA) to quantify immune infiltration
Consider multivariate regression to account for confounding clinical variables
Validate findings through multiple computational tools (TIMER, TISIDB, etc.)
Set significance threshold at p < 0.05 with appropriate multiple testing corrections
Visualize correlations using heatmaps and scatter plots with regression lines
These statistical approaches have been successfully applied in KIRC studies and provide a framework for other cancer types .
To distinguish direct from indirect FASTK effects on immune function:
Perform conditional knockout experiments targeting FASTK in specific cell populations
Use co-culture systems with FASTK-modulated cells and immune components
Analyze protein-protein interaction networks using STRING database
Examine gene-gene interaction networks with GeneMANIA
Apply causal inference statistical methods (e.g., mediation analysis)
Consider temporal dynamics through time-course experiments
Evaluate signaling pathway activation downstream of FASTK
Understanding these relationships requires integrating both computational predictions and experimental validation to establish causality rather than mere correlation .
Cutting-edge technologies that complement antibody-based FASTK research include:
CRISPR-Cas9 gene editing for creating precise FASTK mutants
Proximity labeling methods (BioID, APEX) to identify novel FASTK interactors
Mass spectrometry-based phosphoproteomics to characterize FASTK substrates
Single-cell multi-omics to correlate FASTK expression with cell states
Spatial transcriptomics to map FASTK expression in tissue microenvironments
Cryo-EM for structural studies of FASTK complexes
Live-cell imaging with FASTK-fluorescent protein fusions
These technologies address limitations of antibody-based approaches and provide complementary data for comprehensive FASTK characterization .
For comparative oncology studies of FASTK family members:
This systematic approach allows researchers to identify both common and unique roles of FASTK family members across cancer types .
For mitochondrial FASTK research:
Use appropriate subcellular fractionation techniques to isolate pure mitochondria
Employ mitochondria-specific markers (TOMM20, COX4) as controls in co-localization studies
Consider mitochondrial dynamics (fusion/fission) when interpreting FASTK localization patterns
Assess mitochondrial function parameters (membrane potential, respiration, ROS production)
Analyze mitochondrial morphology changes upon FASTK modulation
Study mtDNA-encoded gene expression as potential FASTK targets
Consider cell type-specific variations in mitochondrial content and function
Given FASTK's mitochondrial localization, careful attention to mitochondrial isolation quality and appropriate controls is essential for accurate interpretation of results .