An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5′ single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and non-coding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type–specific, and disease heritability is strongly enriched in these enhancers. The resulting cell type–resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases., All experiments using human samples were approved by the ethical review committee of RIKEN [approval no. H30-9(13)]. Written informed consent was obtained from all donors. CD4+ T cells were isolated by the immunomagnetic negative selection method. Stained CD4+ T cells were sorted using a FACSAria IIu Cell Sorter (BD Biosciences). Human CD4+ T cells and FACS-sorted heterogenous populations were processed with a Chromium Next GEM Single Cell 5′ kit (10x Genomics). Libraries were sequenced on an Illumina NovaSeq 6000 sequencing platform using 2 × 150 bp paired-end sequencing. Multiome assay (10x Genomics) was performed according to the manufacturer’s instructions. Multiome libraries were pooled and sequenced as above with 10 cycles for i7 index and 24 cycles for i5 index. Micro-C libraries were generated using a Dovetail Micro-C Kit (Cantata Bio, Cat#21006) and were sequenced on an Illumina NovaSeq 6000 platform using 2 × 150 bp paired-end sequencing. Chromium scRNA-seq, snRNA-seq, and CIT..., , This repository contains Data S1 to S10. The detailed description is as follows.

**Data S1: Promoter-level gene expression (log2CPM) across 136 CD4+ T cell clusters.**
Promoters were defined as transcription start sites (TSSs) of protein-coding transcripts ±300 bp (hg38; GENCODE version 39 [‘primary assembly’]. scRNA-seq reads (TSS signals) mapped to the promoters were counted at TSS level in a strand-specific manner using the UCSC software bigWigAverageOverBed. Count normalization was based on counts per million (CPM). When count was converted to log2CPM, a prior count of 0.25 was added to the raw counts.

DataS1_log2cpm_MASK_136subsets_90565.xlsx: Expression data for promoter levels (listed in rows) for each of the 136 CD4+ T cell clusters (listed in columns). Count was converted to log2CPM.

**Data S2: RDS files of single-cell analyses.**

DataS2_Single_cell_rds_files.zip: There are 13 RDS files containing Seurat objects for single-cell data for human CD4+ T cell and subpopulations....