scATAC-seq

scATAC-seq (Single-cell Assay for Transposase-Accessible Chromatin)

一行要約

scATAC-seq は Tn5 transposase で open chromatin 領域を tagmentation し、単一細胞解像度で accessibility を測定する手法。scRNA-seq では捉えられない TF activity / regulatory element 動態 / epigenetic memory を解像し、特に T 細胞 exhaustion のように transcriptional state とは独立に固定される epigenetic landscape の研究で不可欠。10x Multiome では同一細胞で RNA+ATAC を paired 取得でき、cis-regulatory inference の解像度が劇的に向上。

原理

(1) 細胞 / 核を分離、Tn5 が DNA 切断時に sequencing adapter を挿入 (open chromatin に集中アクセス)。(2) droplet/well で cell barcode 付与。(3) Illumina sequencing → fragment per cell、peak calling (MACS2 / cellranger-atac) で accessibility matrix 化。(4) chromVAR / Signac / ArchR / SnapATAC で TF motif activity, gene activity (proximity-based), peak-to-gene linkage 推論。

10x Multiome (RNA+ATAC) では同一核から RNA と ATAC を paired 取得し、cis-regulatory element と発現変動を直接 link できる。snATAC は frozen / FFPE 組織で適用可能、tumor biopsy retrospective 解析に有用。

主要エビデンス / 適用領域

• T 細胞 exhaustion epigenetic fixation: Pace 2018 / Philip 2017 が exhaustion の epigenetic landscape は PD-1 blockade 後も維持されることを ChIP-seq で示し、scATAC で per-cell trajectory に解像。Tex-prog → Tex-int → Tex-term の階層が accessibility prof で固定化されている
• SCLC subtype regulome: ASCL1 / NEUROD1 / POU2F3 の TF motif activity と subtype-specific enhancer landscape を scATAC で解像
• NSCLC TKI 耐性: drug-tolerant persister の epigenetic state (BRD4 / chromatin remodeler) を scATAC で同定
• Hematopoietic lineage: HSPC → progenitor → mature の lineage-priming chromatin を高解像度 mapping、emergency granulopoiesis の epigenetic basis 解析にも応用

適用分野と限界

• 強み: TF activity の per-cell 推定、regulatory element の細胞種特異性、epigenetic memory (transcription 後でも残る accessibility) の検出
• 限界: data sparsity (peak per cell 約10,000-40,000)、peak calling の noise、gene activity score は proxy であり真の発現と乖離可能、computational pipeline の選択依存性 (ArchR vs Signac vs SnapATAC で結果差)

Open Questions

• TF binding 直接検出: scCUT&Tag / scNTT-seq / Multi-CUT&Tag による per-cell TF binding map
• Single-cell histone modification (H3K27ac / H3K4me3) の routine 化
• 3D chromatin (scHi-C) との統合
• Causal regulatory inference: peak-to-gene link の機能的検証 (CRISPRi screen との pairing)

関連エンティティ

• 上位 / paired 手法: scRNA-seq / Multiome / ATAC-seq / ChIP-seq / Cut-and-Tag
• 関連細胞: Exhausted-CD8-T-cell / Tpex / Drug-tolerant-persister
• 関連概念: T-cell-exhaustion / Lineage-plasticity / Epigenetic-therapy-resistance / NE-non-NE-transition

関連論文

Basic-Immunotherapy

• Foidart et al. CancerCell 2026 — Whole-genome doubling drives immune evasion by silencing antigen presentation
• Liu et al. CancerCell 2026 — Clonal lineage tracing of innate immune cells in human cancer
• Sharma et al. Cell 2023 — Immune checkpoint therapy-current perspectives and future directions
• Yofe et al. NatMed 2020 — Single-cell genomic approaches for developing the next generation of immunotherapies
• Gong et al. Nature 2025 — Multi-omic profiling reveals age-related immune dynamics in healthy adults

Basic-Neutrophil and myeloid cell

• Qian et al. Cell 2026 — Respiratory viral infections prime accelerated lung cancer growth
• Ng et al. NatRevImmunol 2019 — Heterogeneity of neutrophils

Basic-Oncogene and genetics

• LaFave et al. CancerCell 2020 — Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma
• Lim et al. CancerCell 2020 — Advancing cancer research and medicine with single-cell genomics
• Marjanovic et al. CancerCell 2020 — Emergence of a high-plasticity cell state during lung cancer evolution
• Concepcion et al. CancerDiscov 2022 — Smarca4 inactivation promotes lineage-specific transformation and early metastatic features in the lung
• he et al. Cell 2022 — A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial
• Argelaguet et al. NatBiotechnol 2021 — Computational principles and challenges in single-cell data integration
• Bhang et al. NatMed 2015 — Studying clonal dynamics in response to cancer therapy using high-complexity barcoding
• Nam et al. NatRevGenet 2021 — Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics
• Tanay et al. Nature 2017 — Scaling single-cell genomics from phenomenology to mechanism
• Fennell et al. Nature 2022 — Non-genetic determinants of malignant clonal fitness at single-cell resolution

Basic-Others

• Stuart et al. Cell 2019 — Comprehensive integration of single-cell data
• Hao et al. Cell 2021 — Integrated analysis of multimodal single-cell data
• Tzouanas et al. Cell 2026 — Hepatic adaptation to chronic metabolic stress primes tumorigenesis
• Butler et al. NatBiotechnol 2018 — Integrating single-cell transcriptomic data across different conditions, technologies, and species
• Laisne et al. NatRevCancer 2025 — Epigenomic heterogeneity as a source of tumour evolution
• Goodwin et al. NatRevGenet 2016 — Coming of age ten years of next-generation sequencing technologies
• Stuart et al. NatRevGenet 2019 — Integrative single-cell analysis
• Davalos et al. TrendsCancer 2026 — How DNA methylation predicts response to cancer therapies

Clinical-NSCLC-EGFR

• Kashima et al. CancerRes 2021 — Single-cell analyses reveal diverse mechanisms of resistance to EGFR tyrosine kinase inhibitors in lung cancer