HMGCS1 Antibody

What is the optimal application range for HMGCS1 antibodies in common laboratory techniques?

Most validated HMGCS1 antibodies demonstrate utility across multiple applications with specific dilution recommendations:

• Western Blot: 1:500-1:5000 dilution (antibody-dependent)

• Immunohistochemistry (paraffin): 1:200-1:1200 dilution

• Immunofluorescence/ICC: 1:400-1:1600 dilution

• Immunoprecipitation: 0.5-4.0 μg for 1.0-3.0 mg of total protein lysate

• Flow Cytometry: Application-dependent, typically 1:50-1:200

Most commercially available HMGCS1 antibodies demonstrate reactivity with human samples, with many cross-reacting with mouse and rat tissues due to high sequence homology .

Which tissue and cell samples serve as reliable positive controls for HMGCS1 antibody validation?

Based on validation studies, the following samples consistently show detectable HMGCS1 expression:

• Cell lines: A431, A549, HepG2, HeLa, LNCaP, 293T

• Tissues: Mouse and human liver tissue, which express high levels of HMGCS1

• Cancer tissues: Human ovarian cancer tissue provides robust expression signals

For negative controls, knockout validation using CRISPR-Cas9-mediated HMGCS1 knockout cells (such as the validated AGS HMGCS1 KO #BF4 and #FE4 cell lines) provides the most stringent verification of antibody specificity .

How can researchers distinguish between cytosolic HMGCS1 and mitochondrial HMGCS2 with antibodies?

Discriminating between these homologous enzymes requires:

• Antibody selection: Choose antibodies raised against non-conserved epitopes, particularly:

  • Those targeting the C-terminal region (aa 450 to C-terminus) of HMGCS1

  • Antibodies generated against aa 290-317 from the central region of HMGCS1

• Validation strategy:

  • Perform subcellular fractionation to separate cytosolic (HMGCS1) and mitochondrial (HMGCS2) fractions

  • Include specificity controls using recombinant proteins of both isoforms

  • Confirm with knockout/knockdown cell models where one isoform is depleted

• Technical approach:

  • For immunofluorescence, use co-localization studies with mitochondrial markers

  • For Western blot, leverage the slight size difference (HMGCS1: 57 kDa vs. HMGCS2: 56.6 kDa)

What are the recommended fixation and antigen retrieval methods for optimal HMGCS1 immunohistochemistry?

Optimal protocols for HMGCS1 IHC include:

• Fixation:

  • 10% neutral buffered formalin fixation for 24-48 hours for tissue samples

  • 4% paraformaldehyde for 10-15 minutes for cultured cells

• Antigen retrieval (two effective options):

  • Heat-mediated antigen retrieval with TE buffer at pH 9.0 (primary recommendation)

  • Alternative: Citrate buffer at pH 6.0 if TE buffer produces background issues

• Blocking:

  • 5-10% normal serum (matching secondary antibody host) with 1% BSA

  • 30-60 minutes at room temperature

• Primary antibody incubation:

  • Dilutions ranging from 1:300-1:1200 (antibody-dependent)

  • Overnight at 4°C for highest sensitivity and specificity

How should researchers approach HMGCS1 protein extraction to preserve epitope integrity for immunodetection?

Optimal HMGCS1 protein extraction protocols:

• Lysis buffer composition (two validated options):

  • NETN buffer: 20 mM Tris-HCl (pH 8.0), 100 mM NaCl, 1 mM EDTA, 0.5% Nonidet P-40

  • Alternative: PBS with 0.02% sodium azide and 50% glycerol (pH 7.3)

• Extraction conditions:

  • Keep samples cold (4°C) throughout extraction

  • Include protease inhibitor cocktails (PMSF, aprotinin, leupeptin, pepstatin A)

  • For nuclear fraction enrichment, include phosphatase inhibitors (NaF, Na₃VO₄)

• Sample processing:

  • Avoid repeated freeze-thaw cycles (aliquot samples)

  • Store at -20°C for short-term or -80°C for long-term stability

  • For SDS-PAGE, 7.5% gels provide optimal separation for HMGCS1 (57 kDa)

What approaches can address non-specific binding when using HMGCS1 antibodies in complex samples?

To minimize non-specific binding and improve signal-to-noise ratio:

• Pre-absorption protocol:

  • Incubate primary antibody with 5-10× excess of immunizing peptide

  • Use control peptide-absorbed antibody alongside non-absorbed antibody

• Sample preparation modifications:

  • For fatty tissues (like liver), extend washing times by 50%

  • Consider protein precipitation methods like SAS (Saturated Ammonium Sulfate) precipitation before antibody application

• Blocking optimization:

  • Test multiple blocking agents (BSA, normal serum, commercial blockers)

  • Extend blocking time to 2 hours at room temperature

  • Include 0.1-0.3% Triton X-100 for improved penetration in ICC/IF applications

How does HMGCS1 expression correlate with prognosis across different cancer types?

HMGCS1 demonstrates significant prognostic value across multiple cancers:

This pattern suggests HMGCS1 as a promising biomarker for stratifying patients and potentially guiding treatment decisions across multiple cancer types .

What methodological approaches are optimal for studying HMGCS1's role in cancer stem cell biology?

Based on published protocols, the following methodologies yield reproducible results when investigating HMGCS1 in cancer stem cells:

• Tumorsphere formation assay:

  • Plate cells at low density (1000-5000 cells/well) in ultra-low attachment plates

  • Use serum-free medium supplemented with EGF, bFGF, and B27

  • Assess HMGCS1 expression in tumorspheres vs. parental cells using both mRNA (qRT-PCR) and protein (Western blot) analyses

• Gene manipulation strategies:

  • For overexpression: Transfection with HMGCS1-expressing construct followed by selection

  • For knockdown: siRNA, shRNA, or CRISPR-Cas9-mediated knockout

  • Critical to validate expression changes at both mRNA and protein levels

• In vivo models:

  • Subcutaneous xenograft model: 1×10^6 HMGCS1-manipulated cells injected into immunocompromised mice

  • Metastasis model: Intravenous tail vein injection of HMGCS1-modified cells

  • Tumors should be analyzed for both HMGCS1 and stemness markers (Oct4, SOX-2)

• Correlative analysis with stemness markers:

  • Single-cell qPCR for co-expression analysis of HMGCS1 with stemness markers

  • Flow cytometry to identify HMGCS1 expression in CSC-enriched populations (CD44+/CD24- for breast cancer)

How can HMGCS1 inhibition be leveraged as a therapeutic strategy in cancer treatment?

Multiple approaches to HMGCS1 inhibition show therapeutic potential:

• Small molecule inhibition:

  • Hymeglusin (specific HMGCS1 inhibitor) shows efficacy in AML models

  • Demonstrated synergistic effects with conventional chemotherapeutics (adriamycin, cytarabine)

  • More potent than standard statin treatment in inhibiting cancer stem cell activities

• RNA interference strategies:

  • siRNA/shRNA knockdown reduces tumor growth in multiple cancer models

  • Increases chemosensitivity in AML cells

  • Attenuates metastatic potential in gastric cancer models

• Combination therapy approaches:

  • Co-targeting HMGCS1 with chemotherapy enhances treatment efficacy

  • Potential for combining with immunotherapies based on correlation with immune cell infiltration

  • Sensitizes resistant cancer cells to standard therapies

• Biomarker-guided patient selection:

  • Higher HMGCS1 expression correlates with poorer prognosis

  • Could identify patients likely to benefit from mevalonate pathway inhibition

  • Particularly relevant for relapsed/refractory patients

What is the optimal protocol for generating and validating CRISPR-Cas9-mediated HMGCS1 knockout cell models?

Based on successful published protocols, the following approach yields reliable HMGCS1 knockout models:

• sgRNA design and cloning:

  • Design sgRNAs targeting HMGCS1 using established tools (e.g., http://crispr.mit.edu/)

  • Clone annealed oligonucleotides into lentiCRISPRv2 vector after BsmBI digestion

  • Validate recombinant plasmid by sequencing

• Lentiviral production:

  • Co-transfect HEK 293T cells with:

    • Recombinant lentiCRISPRv2 plasmid containing sgRNA

    • Packaging plasmids pCMVdeltaR8.91 and pMD.G

  • Harvest virus-containing supernatant at 48-72 hours post-transfection

• Cell transduction and selection:

  • Infect target cells with appropriate MOI

  • Apply puromycin selection (1 μg/mL) 48-72 hours post-infection

  • Isolate single-cell clones through serial dilution

• Validation (multi-level approach):

  • Genomic level: PCR amplification of the targeted region, T&A vector cloning, and sequencing to confirm indels

  • Protein level: Western blot analysis using validated anti-HMGCS1 antibodies

  • Functional level: Assess phenotypic changes (proliferation, stemness, metabolism)

How does circular RNA HMGCS1 (circHMGCS1) function as a microRNA sponge, and what methods best characterize this interaction?

CircHMGCS1 functions as a miRNA sponge with significant implications for disease progression:

• Molecular mechanism:

  • CircHMGCS1 competitively binds miR-4521, preventing its regulatory function

  • This relieves miR-4521-mediated suppression of ARG1 (arginase 1)

  • Consequently promotes type 2 diabetes-induced vascular endothelial dysfunction

• Experimental approaches to characterize circRNA-miRNA interactions:

  • RNA immunoprecipitation (RIP) to confirm physical interaction between circHMGCS1 and miR-4521

  • Luciferase reporter assays with wild-type and mutated miRNA binding sites on circHMGCS1

  • RNA pull-down assays using biotinylated probes specific to circHMGCS1 or miR-4521

• Functional validation methods:

  • Overexpression of circHMGCS1 to observe increased ARG1 expression

  • Co-expression of miR-4521 to demonstrate rescue of the phenotype

  • Knockdown of circHMGCS1 to show reduced ARG1 expression and increased miR-4521 availability

  • In vivo studies using circHMGCS1 overexpression models with miR-4521 agomir treatment

What approaches can characterize HMGCS1's protein-protein interactions within the mevalonate pathway?

Several complementary approaches provide insights into HMGCS1's interaction network:

• Co-immunoprecipitation strategies:

  • Standard protocol: Anti-HMGCS1 antibody (e.g., sc-166763) conjugated to protein A-Sepharose

  • Whole-cell extracts incubated with antibody-conjugated beads overnight at 4°C

  • Western blot analysis of immunoprecipitated complexes with antibodies against potential interactors (e.g., PERK)

• Proximity labeling methods:

  • BioID or TurboID fusion with HMGCS1 for proximity-dependent biotinylation

  • APEX2-based proximity labeling

  • Mass spectrometry analysis of biotinylated proteins to identify proximal interactors

• Fluorescence-based interaction assays:

  • Fluorescence resonance energy transfer (FRET) between HMGCS1 and putative interactors

  • Bimolecular fluorescence complementation (BiFC) for in vivo visualization of interactions

  • Live-cell imaging to monitor dynamic interactions during metabolic changes

• Network analysis approaches:

  • Integration of STRING database information showing HMGCS1 interactions with ACAT2 and MVD

  • Pathway enrichment analysis focusing on steroid biosynthesis pathways

  • Correlation analysis of expression patterns across multiple datasets

How can researchers resolve conflicting results when using different HMGCS1 antibodies?

When encountering discrepancies between antibodies, implement this systematic resolution approach:

• Epitope mapping analysis:

  • Compare the immunogen information for each antibody:

    • Central region (aa 290-317) antibodies

    • C-terminal region (aa 442-470) antibodies

    • Full-length or fusion protein-derived antibodies

  • Target region differences may explain variation in detection patterns

• Validation hierarchy implementation:

  • Prioritize antibodies with comprehensive validation:

    • Evidence of specificity in knockout/knockdown models

    • Consistent performance across multiple applications

    • Publication record in peer-reviewed literature

• Application-specific optimization:

  • For Western blot: Test different extraction methods and blotting conditions

  • For IHC/IF: Compare fixation protocols and antigen retrieval methods

  • Consider tissue-specific modifications to standard protocols

• Multi-antibody consensus approach:

  • Use at least two antibodies recognizing different epitopes

  • Compare results and focus on consistent observations

  • For critical findings, validate with orthogonal techniques (mRNA analysis, mass spectrometry)

What factors contribute to variability in HMGCS1 expression measurements across different experimental systems?

Several factors influence HMGCS1 expression variability:

• Biological regulation mechanisms:

  • Sterol-dependent transcriptional regulation through SREBP pathway

  • miRNA-mediated post-transcriptional regulation (e.g., miR-223)

  • Feedback responses to cholesterol levels

  • Cell cycle-dependent expression patterns

• Experimental parameters:

  • Cell culture conditions: Serum concentration affects sterol availability

  • Confluency: HMGCS1 expression varies with cell density

  • Passage number: Expression may drift in extended culture

  • Media composition: Lipid content influences mevalonate pathway regulation

• Technical considerations:

  • RNA extraction methods affect mRNA stability and recovery

  • Protein extraction efficiency varies with buffer composition

  • Antibody lot-to-lot variability impacts detection sensitivity

  • Normalization strategy choice significantly affects quantitative outcomes

• Sample-specific variables:

  • Tissue heterogeneity in complex samples

  • Variable expression across different regions of the same tumor

  • Patient-specific factors (treatment history, genetic background)

  • Sampling method and preservation protocol variations

What normalization strategies are most appropriate for quantitative analysis of HMGCS1 expression?

Optimal normalization approaches for HMGCS1 quantification:

• RNA-level normalization:

  • Multiple reference gene approach using:

    • GAPDH, β-actin, and 18S rRNA for general studies

    • Tissue-specific stable reference genes for specialized applications

  • Use geometric mean of multiple reference genes following GeNorm or NormFinder validation

  • For qRT-PCR, primers such as: 5′-CCC CTT CAC AAA TGA CCA CA-3′ (forward) and 5′-GAC AGC TGA TTC AGA TTC G-3′ (reverse)

• Protein-level normalization:

  • Total protein normalization using stain-free technology or Ponceau S

  • Housekeeping proteins with demonstrated stability in the specific model system

  • Avoid single housekeeping protein approach due to potential regulation

  • Consider loading controls appropriate for subcellular fraction being analyzed

• Immunohistochemistry normalization:

  • Internal tissue controls within the same section

  • Standardized automated image analysis algorithms

  • H-score or Allred score calculations for semi-quantitative assessment

  • Include batch controls across multiple staining runs

• Cross-platform validation:

  • Correlate protein levels (Western blot) with mRNA expression (qRT-PCR)

  • Confirm IHC patterns with IF observations

  • Validate findings with orthogonal techniques when possible