NEAT-seq

NEAT-seq profiles nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in single cells to jointly characterize transcription factor activity and regulatory interactions between chromatin and gene expression.

Key Features:

• Multi-modal single-cell sequencing: Simultaneously sequences nuclear protein epitope abundance, chromatin accessibility, and the transcriptome in the same single cells.
• Intra-nuclear protein epitope profiling: Profiles intra-nuclear proteins including transcription factors (TFs) via nuclear protein epitope abundance measurements.
• Chromatin accessibility measurement: Measures chromatin accessibility to assess DNA accessibility states.
• Transcriptome profiling: Performs single-cell transcriptome sequencing to quantify gene expression and RNA synthesis.
• Integrated TF–chromatin–transcriptome analysis: Integrates measurements to explore interplay between transcription factors (TFs), chromatin dynamics, and transcriptomic outputs.
• Stage-resolved regulation analysis: Enables analysis across stages from DNA accessibility to RNA synthesis, linking chromatin state to transcriptomic output.
• Application to CD4 memory T cells: Has been applied to CD4 memory T cells using a panel of master transcription factors to interrogate T cell differentiation and function.
• Detection of TF regulatory modulation: Identifies TFs whose regulatory activities are modulated by transcription, translation, and chromatin binding dynamics.
• Variant-to-function mapping: Links noncoding genome-wide association study single-nucleotide polymorphisms (SNPs) within regulatory motifs, such as a GATA motif, to effects on target gene regulation including GATA3.

Scientific Applications:

• Regulatory mechanism dissection: Dissects regulatory networks coupling TF activity, chromatin dynamics, and gene expression at single-cell resolution.
• Immunology and T cell differentiation: Profiles CD4 memory T cell subsets and master transcription factors governing differentiation and function.
• Genetic variant functionalization: Maps noncoding GWAS SNPs in motifs (e.g., a GATA motif) to functional impacts on target genes such as GATA3.

Methodology:

The provided description does not specify computational methods, algorithms, or file formats.