<b>Super-Resolution Visual ProteomEx for Hard Tissues and Clinical Samples</b>

Raw sequencing reads were first assessed for quality using FastQC. Low-quality bases and adapter sequences were removed using standard trimming procedures by fastp. Cleaned reads were then aligned to the human reference genome (hg38) using Bowtie2 with default parameters. PCR duplicates were removed using Picard MarkDuplicates. Sorted and indexed BAM files were generated using SAMtools. To generate normalized genome-wide signal tracks, deepTools bamCoverage was used to convert BAM files into BigWig (bw) format. Global quality of the ChIP-seq data was processed using deepTools. Data quality and enrichment were first assessed using plotFingerprint to evaluate signal distribution across the genome. To examine sample reproducibility and overall similarity, read counts were summarized across the genome using multiBamSummary, followed by Pearson correlation analysis. For representative genes, ChIP-seq signal tracks were visualized using IGV. BigWig files were generated from aligned reads and displayed for GCMN and MM samples. Two downregulated proteins identified by proteomics, ELN and EHD2, were selected as examples to illustrate locus-specific H3K27me3 enrichment at transcription start sites. To assess H3K27me3 enrichment at transcription start sites of downregulated proteins identified in spatial proteomics data, log2(IP/Input) BigWig tracks were generated using deepTools bigwigCompare with identical bin sizes and normalization parameters. Transcription start site coordinates (±3 kb) of proteomics-downregulated genes were extracted and analyzed using computeMatrix in reference-point mode.