RNA sequencing of sort-purified ILC3s from IBD patients
Group 3 innate lymphoid cells (ILC3s) are abundant in the developing or healthy intestine to critically support tissue homeostasis in response to microbial cues and other environmental signals. However, during gastrointestinal disease including infections, colorectal cancer, or inflammatory bowel disease (IBD), intestinal ILC3 numbers are dramatically reduced and the remaining ILC3s become dysfunctional which fuels disease and barrier breakdown. To define the underlying transcriptomic changes, we employed RNA sequencing of ILC3s from IBD patients. This may help to gain a deeper understanding of the mechanisms driving these alterations and ultimately lead to novel preventive, diagnostic, or therapeutic opportunities in IBD. Overall design: Human ILC3s (CD45+CD3-CD4-CD8-CD19-CD94-CD14-CD123-FceRIa-CD34-CD11c-CRTH2-CD127+CD117+) were sort-purified from surgical-resection samples from the colon of patients with Ulcerative Colitis or Crohn's Disease. Sorted cells were used to prepare RNA sequencing libraries by the Epigenomics Core at Weill Cornell Medicine, using the Clontech SMARTer Ultra Low Input RNA Kit V4 (Clontech Laboratories). Sequencing was performed on an Illumina HiSeq 4000, yielding 50-bp single-end reads. Raw sequencing reads were demultiplexed with Illumina CASAVA (v.1.8.2). Adapters were trimmed from reads using FLEXBAR (v.2.4) (Dodt et al., 2012) and reads were aligned to the NCBI GRCh37/hg19 human genome using the STAR aligner (v.2.3.0) (Dobin et al., 2013) with default settings. Reads per gene were counted using Rsubread (Liao et al., 2019). Prior to differential expression analysis, genes were prefiltered, keeping only those genes with 50 or more counts in at least two samples. Differential expression analysis was performed using DESeq2 version 1.20.0 (Love et al., 2014) using both site (inflamed/adjacent tissue) and patient ID as factors in the design. A false discovery rate of 0.1 was taken to indicate significance. Principal components analysis (PCA) was performed using the top 500 highest variance genes after applying DESeq2's variance stabilizing transformation. The degree to which samples clustered by site (inflamed versus adjacent tissue) in the PCA was assessed using PERMANOVA (Anderson, 2001) as implemented by the adonis function of the vegan R package (Oksanen et al., 2019) using the Euclidean metric and 20,000 permutations.