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SPDB:一个用于单细胞分辨率蛋白质组学数据的综合资源和知识库。

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution.

机构信息

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.

AI Lab, Tencent, Shenzhen 518000, China.

出版信息

Nucleic Acids Res. 2024 Jan 5;52(D1):D562-D571. doi: 10.1093/nar/gkad1018.


DOI:10.1093/nar/gkad1018
PMID:37953313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10767837/
Abstract

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

摘要

单细胞蛋白质组学能够直接定量单个细胞分辨率下的蛋白质丰度,提供了超越转录组分析所能推断的细胞表型的有价值的见解。然而,缺乏大规模集成数据库阻碍了研究人员访问和探索单细胞蛋白质组学,阻碍了该领域的发展。为了弥补这一不足,我们提出了一个全面的数据库,即单细胞蛋白质组学数据库(SPDB,https://scproteomicsdb.com/),用于一般的单细胞蛋白质组学数据,包括基于抗体或质谱的单细胞蛋白质组学。SPDB 配备了标准化的数据处理和用户友好的网络界面,为与下游分析的方便交互提供了统一的数据格式,并提供了数据集级和蛋白质级别的数据搜索和探索功能。为了能够详细展示单细胞蛋白质组学数据,SPDB 还提供了一个从细胞元数据或蛋白质特征角度可视化数据的模块。目前的 SPDB 版本包含 133 个基于抗体的单细胞蛋白质组学数据集,涉及超过 3 亿个细胞和超过 800 个标记/表面蛋白,以及 10 个基于质谱的单细胞蛋白质组学数据集,涉及超过 4000 个细胞和超过 7000 个蛋白质。总的来说,SPDB 有望被探索为一个有用的资源,通过提供单细胞蛋白质组学的详细见解,为更广泛的研究社区提供便利。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/62979f5e278f/gkad1018fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/cc4a34b4a45c/gkad1018figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/8e9890ad9bd9/gkad1018fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/f8480331514b/gkad1018fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/62979f5e278f/gkad1018fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/cc4a34b4a45c/gkad1018figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/8e9890ad9bd9/gkad1018fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/f8480331514b/gkad1018fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9309/10767837/62979f5e278f/gkad1018fig3.jpg

相似文献

[1]
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[2]
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[5]
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Biomark Res. 2024-9-18

[6]
ULV: A robust statistical method for clustered data, with applications to multi-subject, single-cell omics data.

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[7]
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本文引用的文献

[1]
Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics.

J Proteome Res. 2023-9-1

[2]
The technological landscape and applications of single-cell multi-omics.

Nat Rev Mol Cell Biol. 2023-10

[3]
Single-cell analysis targeting the proteome.

Nat Rev Chem. 2020-3

[4]
Targeting CXCL16 and STAT1 augments immune checkpoint blockade therapy in triple-negative breast cancer.

Nat Commun. 2023-4-13

[5]
Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics.

Nat Methods. 2023-5

[6]
Single-cell proteomics enabled by next-generation sequencing or mass spectrometry.

Nat Methods. 2023-3

[7]
The Current State of Single-Cell Proteomics Data Analysis.

Curr Protoc. 2023-1

[8]
Exploring functional protein covariation across single cells using nPOP.

Genome Biol. 2022-12-16

[9]
UniProt: the Universal Protein Knowledgebase in 2023.

Nucleic Acids Res. 2023-1-6

[10]
The ProteomeXchange consortium at 10 years: 2023 update.

Nucleic Acids Res. 2023-1-6

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