CITE-seq

CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing)

一行要約

CITE-seq は oligonucleotide-tagged 抗体パネル (TotalSeq / AbSeq) で細胞表面タンパク質シグナルを cellular barcode と一緒に capture し、scRNA-seq の transcriptome と paired で読み出す multi-modal single-cell 手法。Flow cytometry の表面マーカー解像度と scRNA-seq の transcriptional state を 1 細胞単位で統合し、特に T cell exhaustion subset (Tex-prog / Tex-int / Tex-term) や myeloid polarization のように mRNA だけでは曖昧な系列・状態を highly resolved に annotate できる。

原理

(1) 細胞懸濁液を oligo-conjugated 抗体パネル (50-200 marker) と incubate → 表面タンパク発現に応じた antibody-derived tag (ADT) が細胞表面に結合。(2) 10x Chromium 等で droplet 化、cell barcode + UMI 付与。(3) cDNA library (transcriptome) と ADT library (protein quantification) を別々に prep → 同一 sequencing run で読む。(4) 解析パイプライン (Seurat / Scanpy + totalVI / CITE-seq-Count) で transcriptome × surface protein を joint embedding する。

派生として ECCITE-seq は CITE-seq + CRISPR perturbation + TCR/BCR の 4 modality 同時取得を実現、CAR-T や neoantigen-specific T cell の機能・系列・clonality を統合解析する基盤となる。

主要エビデンス / 適用領域

• TIL exhaustion taxonomy: PD-1 / TIM-3 / LAG-3 / TIGIT / CD39 / TCF1 / SLAMF6 等の表面マーカーで Tex-progenitor (TCF1+) と Tex-terminal (TIM-3+ TCF1−) を高解像度に分離。Tpex 識別は IO 応答性予測の中核
• CAR-T 製品 phenotyping: CD45RA / CD27 / CCR7 / CD62L で memory subset (Tscm / Tcm / Tem / Teff) を定量、infusion product の persistence・clinical response との相関解析
• Myeloid polarization: CD14 / CD16 / CD163 / CD206 / HLA-DR で TAM・MDSC subset を transcriptome と合わせて分類
• Multi-modal atlas: Immune Cell Atlas (Hao et al. Cell 2021) で PBMC reference を構築、weighted-nearest-neighbor (WNN) で multi-modal embedding 標準化

適用分野と限界

• 強み: Flow / CyTOF レベルの surface protein 解像度 + scRNA-seq の状態識別を 1 細胞で paired 取得、CD4/CD8 等 transcript レベルでは検出困難なマーカーを高感度補完
• 限界: 細胞内タンパク (TF / 細胞質シグナル) は不可 (細胞膜透過化が必要な intracellular CITE-seq は実験的)、抗体 panel の事前選定が必要 (unbiased でない)、ADT background noise (free antibody / non-specific binding) の補正が解析難点、cost が標準 scRNA-seq の 1.5-2 倍

Open Questions

• Multi-tissue / multi-modal reference atlas 整備 (lung TIL 専用 panel 標準化)
• Spatial CITE-seq / SM-Omics 等の空間統合
• Intracellular protein CITE-seq (TF, phospho-protein) の安定 protocol
• Clinical TAT / cost の壁を超える臨床応用 (IO 応答予測 product profiling)

関連エンティティ

• 上位手法: scRNA-seq / Multiome / Spatial-transcriptomics
• 補完手法: Flow-cytometry-CyTOF / TCR-seq / Multiplex-IF-imaging
• 関連細胞: Exhausted-CD8-T-cell / Tpex / CAR-T-cell / Macrophage-TAM / MDSC
• 関連概念: T-cell-exhaustion / Tumor-immune-microenvironment-classification

関連論文

直近 30 件のみ表示中 (関連 36 件)。 全件は MOC ページ または Obsidian (source Markdown) から辿れます。

Basic-Brain metastasis

• Guldner et al. Cell 2020 — CNS-native myeloid cells drive immune suppression in the brain metastatic niche through Cxcl10

Basic-Immunotherapy

• Koya et al. BloodCancerDiscov 2021 — Single-cell analysis of the multicellular ecosystem in viral carcinogenesis by HTLV-1
• George et al. CancerRes 2026 — Immune checkpoint blockade augments lymphodepleting chemotherapy-induced antitumor immunity by expanding effector CD8…
• Scharping et al. Cell 2026 — Proteostasis sustains t cell differentiation potential and tumor-infiltrating lymphocyte function
• Braun et al. Immunity 2020 — CD155 on tumor cells drives resistance to immunotherapy by inducing the degradation of the activating receptor CD226 in
• Ito et al. NatCommun 2026 — Phenotype of circulating tumor-reactive t cells predicts immune checkpoint inhibitor response in non-small cell lung
• Yofe et al. NatMed 2020 — Single-cell genomic approaches for developing the next generation of immunotherapies
• Legut et al. Nature 2022 — A genome-scale screen for synthetic drivers of T cell proliferation
• Kvedaraite et al. SciImmunol 2022 — Human dendritic cells in cancer

Basic-Neutrophil and myeloid cell

• Pillai et al. CancerDiscov 2026 — LIF-Induced Tumor Plasticity Establishes an Immunosuppressive Myeloid Niche in LKB1-Mutant Lung Cancer
• Cheng et al. Cell 2021 — A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells
• Gungabeesoon et al. Cell 2023 — A neutrophil response linked to tumor control in immunotherapy
• Hirschhorn et al. Cell 2023 — T cell immunotherapies engage neutrophils to eliminate tumor antigen escape variants
• Aegerter et al. Immunity 2022 — Biology of lung macrophages in health and disease
• Ng et al. Immunity 2025 — From complexity to consensus A roadmap for neutrophil classification
• Wigerblad et al. JImmunol 2022 — Single-cell analysis reveals the range of transcriptional states of circulating human neutrophils
• Grieshaber-Bouyer et al. NatCommun 2021 — The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments
• Adrover et al. Nature 2025 — Neutrophils drive vascular occlusion, tumour necrosis and metastasis
• Rys et al. TrendsCellBiol 2025 — Senescent neutrophils a hidden role in cancer progression

Basic-Oncogene and genetics

• Leader et al. CancerCell 2021 — Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification
• Argelaguet et al. NatBiotechnol 2021 — Computational principles and challenges in single-cell data integration
• AlKhafaji et al. NatBiotechnol 2024 — High-throughput RNA isoform sequencing using programmed cDNA concatenation
• Nam et al. NatRevGenet 2021 — Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics

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
• Frangieh et al. NatCancer 2026 — Single-cell and spatial profiling in cancer biology and clinical oncology
• Stuart et al. NatRevGenet 2019 — Integrative single-cell analysis

Clinical-Methodology

• Reitblat et al. UrolOncol 2021 — Radical prostatectomy versus external beam radiation therapy for high-grade, clinically localized prostate cancer

Clinical-Novel modalities

• Horowitz et al. SciTranslMed 2026 — Cd39+cd49a+cd103+ cytotoxic tissue-resident natural killer cells infiltrate and control solid epithelial tumor growth

Clinical-Others

• Medina et al. ClinCancerRes 2026 — AI-based pathology classifier predicts sensitivity to enzalutamide in metastatic hormone-sensitive prostate cancer A