Integrated Transcriptomics to Identify Novel TET2-CHIP Inflammatory Targets

Problem mapping

Objective

Perform bulk and single-cell RNA-seq on TET2-deficient mouse and human models to identify and validate novel inflammatory mediators beyond known chemokines, followed by orthogonal validation.

Readouts

• Primary: Differentially expressed genes (DEGs) in TET2-deficient vs WT macrophages with FDR<0.05 and |log2FC|>1.5, filtered against known CHIP markers
• Secondary: Cell-type-specific expression patterns from scRNA-seq identifying myeloid subpopulations driving novel gene expression
• Secondary: qPCR and Western blot validation of top 10 novel candidates with >2-fold expression change and correlation R²>0.7 with RNA-seq

Experimental design

Overview: Generate TET2-knockout and wild-type bone marrow-derived macrophages (BMDMs), perform bulk RNA-seq and scRNA-seq under basal and inflammatory conditions (LPS stimulation), conduct bioinformatic filtering to exclude known markers, then validate top novel candidates by qPCR, Western blot, and ELISA in mouse and human TET2-CHIP samples.

• WP1: Generate Tet2-/- and WT mouse BMDMs, culture under basal and LPS conditions (10 ng/ml, 6h), isolate RNA and prepare samples for bulk RNA-seq (n=4/group) and scRNA-seq (n=3/group)
• WP2: Perform sequencing (150bp paired-end bulk RNA-seq at 40M reads/sample; 10X Chromium scRNA-seq targeting 5000 cells/sample), conduct quality control, alignment, and differential expression analysis with DESeq2 and Seurat
• WP3: Bioinformatic prioritization - exclude genes matching literature search for CHIP/TET2/cardiovascular inflammation, prioritize secreted proteins and surface markers with drug target potential using Gene Ontology and druggability databases
• WP4: Validate top 10 candidates by qPCR in independent BMDM samples (n=6/group), Western blot for protein expression (n=4/group), and ELISA for secreted factors in culture supernatants and plasma from Tet2-/- atherosclerosis mice

Controls:

• Positive control: CXCL2, CXCL3, and IL8 expression must show expected upregulation (>3-fold) in TET2-deficient conditions, validating the model
• Negative control: Wild-type BMDMs under identical culture and stimulation conditions to establish baseline expression
• Technical control: ERCC spike-in controls for bulk RNA-seq; housekeeping genes (ACTB, GAPDH, 18S) for qPCR normalization; isotype controls for flow cytometry if surface markers identified

Sample size plan: Bulk RNA-seq: n=4 biological replicates per condition (WT basal, WT+LPS, Tet2-/- basal, Tet2-/-+LPS). scRNA-seq: n=3 pooled samples per condition targeting 5000 cells each. qPCR validation: n=6 independent biological replicates. Western blot: n=4. ELISA: n=8 mouse plasma samples per genotype.

Success criteria:

• Identification of ≥50 novel DEGs (FDR<0.05, |log2FC|>1.5) in TET2-deficient conditions that are absent from curated list of 200+ known CHIP-associated inflammatory markers
• ≥5 of top 10 prioritized candidates validated by qPCR with concordant direction and >2-fold change (p<0.05), and ≥3 confirmed at protein level by Western or ELISA

Estimated timeline: 16 weeks

Materials

Direct cost estimate: $28,000 – $42,000 (Includes RNA-seq (bulk and single-cell) library prep and sequencing, qPCR reagents and custom assays, Western blot supplies, ELISA/multiplex assays, and molecular biology reagents. Excludes mouse housing costs, personnel, bioinformatics compute infrastructure, and institutional core facility access fees.)

References

• Fuster et al. (2017) Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice. Science 355(6327):842-847 — Established protocols for generating TET2-CHIP mouse models and characterizing inflammatory phenotypes in cardiovascular disease context
• Jaiswal et al. (2017) Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease. N Engl J Med 377:111-121 — Reference for known CHIP-associated markers to create exclusion list for novel target identification
• Zheng et al. (2017) Massively parallel digital transcriptional profiling of single cells. Nature Communications 8:14049 — 10X Genomics scRNA-seq methodology and quality control parameters for immune cell profiling
• Love et al. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550 — Statistical framework for differential expression analysis with appropriate FDR correction
• Bustin et al. (2009) The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry 55(4):611-622 — Best practices for qPCR validation including primer design, normalization strategies, and statistical analysis

Lab handoff checklist

• Curated exclusion list of 200+ known CHIP/TET2-associated inflammatory genes compiled from literature search (PubMed, GeneCards) including CXCL2, CXCL3, IL8, IL1B, IL6, TNF, and all genes from Fuster 2017 and Jaiswal 2017 studies
• Detailed BMDM differentiation and stimulation protocol with timing, cell density (10^6 cells/well), M-CSF concentration (50 ng/ml for 7 days), and quality control criteria (>95% CD11b+F4/80+ by flow cytometry)
• Bioinformatics analysis pipeline specification: alignment tools (STAR v2.7+), DE analysis parameters (DESeq2, padj<0.05, log2FC>1.5), scRNA-seq clustering strategy (Seurat, resolution 0.5-1.0), and prioritization criteria (secreted proteins, surface markers, druggability scores from DGIdb and ChEMBL)
• qPCR primer design parameters for top candidates: amplicon 80-150bp, Tm 58-60°C, spanning exon junctions where possible, validated in silico with Primer-BLAST for specificity
• Data deposition plan for GEO submission with metadata templates and FASTQ file organization structure