NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells

Abstract

Oligonucleotide-conjugated antibodies 1 have allowed for joint measurement of surface protein abundance and the transcriptome in single cells using high-throughput sequencing.Extending these measurements to gene regulatory proteins in the nucleus would provide a powerful means to link changes in abundance of trans-acting TFs to changes in activity of cisacting elements and expression of target genes. Here, we introduce Nuclear protein Epitope, chromatin Accessibility, and Transcriptome sequencing (NEAT-seq), a technique to simultaneously measure nuclear protein abundance, chromatin accessibility, and the transcriptome in single cells. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive distinct helper T cell subsets and regulatory T cells (Tregs) and identify examples of TFs with regulatory activity gated by three distinct mechanisms: transcription, translation, and regulation of chromatin binding. Furthermore, we identify regulatory elements and target genes associated with each TF, which we use to link a non-coding GWAS SNP within a GATA motif to both strong allele-specific chromatin accessibility in cells expressing high levels of GATA3 protein, and a putative target gene. MainRecent progress in diverse multimodal single cell technologies has revolutionized our ability to characterize cell states and identify gene regulatory programs across various cell types.For example, methods pairing ATAC-seq and RNA-seq in single cells have allowed association of epigenetic status with transcriptional output, enabling identification of putative target genes of regulatory elements and uncovering regulatory mechanisms controlling specific gene networks, such as epigenetic priming of genes during development 2 . Antibodies linked to barcoded oligonucleotides have enabled surface protein measurements using a sequencing read-out, which, when combined with RNA-or ATAC-seq in single cells, has been particularly informative for profiling immune populations traditionally isolated based on surface protein markers 1,3 .

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

“…These examples clearly show the potential of the technology and provide an outlook into where the field will be moving as more population-scale scRNA-seq datasets become available. We foresee that newly developed methodology, such as inCITE-seq 53 and NEAT-seq 77 , combining measurements of multiple omics layers from the same cell, including RNA and nuclear protein levels (which allows measuring active transcription factors levels), will further enhance the interpretability of the identified co-expression QTLs in the future.…”

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles

“…In addition, we obtained eight public data sets with 129,672 cells. In total we had data from 160,565 cells 46,[57][58][59][60][61] 1). After clustering cells and annotating them with cell labels, we applied SCENT individually to each of the cell types with n cells > 500 within each dataset to construct 23 enhancer-gene maps.…”

Recent advances in the field of single-cell proteomics

“…Cytometry by time of flight (CyTOF), further enhances the number of antibodies that can be quantified in parallel by combining the principles of flow-cytometry with MS [ 4 , 84 ]. Furthermore, sequencing-based techniques that utilize protein targeting antibodies fused to DNA oligonucleotides, allows the characterization of multiple modalities such from individual cells [ 14 , 57 , 77 ]. While these techniques allow the processing of thousands of cells, their ability to quantify only dozens to hundreds of proteins limits their utility in hypothesis-free, global data-driven biological interrogations.…”