Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts.
Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts.
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DOI：
10.1038/s41598-023-39282-4
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发表时间：
2023-07-26
影响因子：
4.6
通讯作者：
Lee, Ha Youn
中科院分区：
文献类型：
--
作者：
Park, Sung Yong;Ter-Saakyan, Sonia;Faraci, Gina;Lee, Ha Youn
单细胞RNA测序在免疫图谱中起着核心作用，将特定的免疫细胞识别为疾病标记物，并建议免疫细胞的治疗靶基因。为了从多细胞血液和器官样本中解剖复杂的免疫信号，从单细胞转录组中进行免疫细胞类型注释是非常必要的。然而，仅从单细胞RNA测序数据进行准确的细胞类型分配就会因为高度的基因表达异质性而变得复杂。为了应对这一挑战，已经开发了许多计算方法，但免疫细胞注释的准确性并不是很理想。通过将标记基因与机器学习方法相结合，我们提出了一种简单而健壮的免疫细胞识别和分类工具。在66个单细胞RNA测序研究中，免疫免疫芯片拥有200多万个免疫细胞和50万个非免疫细胞，在识别免疫细胞方面显示出98%的准确性。免疫细胞分类器优于现有的免疫细胞分类器，将免疫细胞分类为10种免疫细胞类型，准确率为92%。我们使用之前发表的单细胞转录数据确定了严重新冠肺炎患者和健康对照组的外周血单核细胞组成，从而能够识别免疫细胞类型的特定差异通路。我们公开可用的工具可以作为独立的生物信息细胞分类器，最大限度地发挥单细胞RNA图谱的效用，促进细胞类型特异性免疫图谱的发现，以发现疾病特异性免疫特征和治疗靶点。
Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.
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影响因子：
82.9
作者：
Fernandez, Dawn M.;Rahman, Adeeb H.;Giannarelli, Chiara
通讯作者：
Giannarelli, Chiara
人肝细胞来源的肝祖样细胞的扩增和分化及其在嗜肝病原体研究中的应用
影响因子：
44.1
作者：
Fu GB;Huang WJ;Zeng M;Zhou X;Wu HP;Liu CC;Wu H;Weng J;Zhang HD;Cai YC;Ashton C;Ding M;Tang D;Zhang BH;Gao Y;Yu WF;Zhai B;He ZY;Wang HY;Yan HX
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Yan HX
DOI：
10.1126/science.abl5197
发表时间：
2022-05-13
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
Domínguez Conde C;Xu C;Jarvis LB;Rainbow DB;Wells SB;Gomes T;Howlett SK;Suchanek O;Polanski K;King HW;Mamanova L;Huang N;Szabo PA;Richardson L;Bolt L;Fasouli ES;Mahbubani KT;Prete M;Tuck L;Richoz N;Tuong ZK;Campos L;Mousa HS;Needham EJ;Pritchard S;Li T;Elmentaite R;Park J;Rahmani E;Chen D;Menon DK;Bayraktar OA;James LK;Meyer KB;Yosef N;Clatworthy MR;Sims PA;Farber DL;Saeb-Parsy K;Jones JL;Teichmann SA
通讯作者：
Teichmann SA
影响因子：
64.8
作者：
通讯作者：
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DOI：
10.1016/j.omto.2020.12.003
发表时间：
2021-03-26
期刊：
Molecular therapy oncolytics
影响因子：
--
作者：
Deng W;Ma Y;Su Z;Liu Y;Liang P;Huang C;Liu X;Shao J;Zhang Y;Zhang K;Chen J;Li R
通讯作者：
Li R