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Primary Msi2+ and Msi2- KPf/fC ChIP-seq for histone H3K27ac
Stem and non-stem tumor cell isolation followed by H3K27ac ChIP-sequencing
70,000 Msi2+/EpCAM+ (stem) and Msi2-/EpCAM+ (non-stem) MF498 were freshly isolated from a single mouse as described above. ChIP was performed as described previously (Deshpande et al., 2014); cells were pelleted by centrifugation and crosslinked with 1% formalin in culture medium using the protocol described previously (Deshpande et al., 2014). Fixed cells were then lysed in SDS buffer and sonicated on a Covaris S2 ultrasonicator. The following settings were used: Duty factor: 20%, Intensity: 4 and 200 Cycles/burst, Duration: 60 s for a total of 10 cycles to shear chromatin with an average fragment size of 200-400 bp. ChIP for H3K27Acetyl was performed using the antibody ab4729 (Abcam, Cambridge, UK) speciÞc to the H3K27Ac modiÞcation. Library preparation of eluted chromatin immunoprecipitated DNA fragments was performed using the NEBNext Ultra II DNA library prep kit (E7645S and E7600S-NEB) for Illumina as per the manufacturerÕs protocol. Library prepped DNA was then subjected to single-end, 75-nucleotide reads sequencing on the Illumina NexSeq500 sequencer at a sequencing depth of 20 million reads per sample.
H3K27ac signal quantification from ChIP-seq data
Pre-processed H3K27ac ChIP sequencing data was aligned to the UCSC mm10 mouse genome using the Bowtie2 aligner (version 2.1.0 (Langmead and Salzberg, 2012), removing reads with quality scores of < 15. Non-unique and duplicate reads were removed using samtools (version 0.1.16, Li et al., 2009) and Picard tools (version 1.98), respectively. Replicates were then combined using BEDTools (version 2.17.0). Absolute H3K27ac occupancy in stem cells and non-stem cells was determined using the SICER-df al-gorithm without an input control (version 1.1; (Zang et al., 2009), using a redundancy threshold of 1, a window size of 200bp, a frag-ment size of 150, an effective genome fraction of 0.75, a gap size of 200bp and an E-value of 1000. Relative H3K27ac occupancy in stem cells versus non-stem cells was determined as above, with the exception that the SICER-df-rb algorithm was used.
Determining the overlap between peaks and genomic features
Genomic coordinates for features such as coding genes in the mouse mm10 build were obtained from the Ensembl 84 build (Ensembl BioMart). The observed versus expected number of overlapping features and bases between the experimental peaks and these genomic features (datasets A and B) was then determined computationally using a custom python script, as described in (Cole et al., 2017). Brießy, the number of base pairs within each region of A that overlapped with each region of B was computed. An ex-pected background level of expected overlap was determined using permutation tests to randomly generate > 1000 sets of regions with equivalent lengths and chromosomal distributions to dataset B, ensuring that only sequenced genomic regions were consid-ered. The overlaps between the random datasets and experimental datasets were then determined, and p values and fold changes were estimated by comparing the overlap occurring by chance (expected) with that observed empirically (observed). This same pro-cess was used to determine the observed versus expected overlap of different experimental datasets.
Overlap between gene expression and H3K27ac modification
Genes that were up- or downregulated in stem cells were determined using the Cuffdiff algorithm, and H3K27ac peaks that were enriched or disfavored in stem cells were determined using the SICER-df-rb algorithm. The H3K27ac peaks were then annotated at the gene level using the ÔChippeakAnnoÕ (Zhu et al., 2010) and Ôorg.Mm.eg.dbÕ packages in R, and genes with peaks that were either exclusively upregulated or exclusively downregulated (termed Ôunique upÕ or Ôunique downÕ) were isolated. The correlation be-tween upregulated gene expression and upregulated H3K27ac occupancy, or downregulated gene expression and downregulated H3K27ac occupancy, was then determined using the Spearman method in R.