基于质谱的单细胞蛋白质组学技术的发展及应用

doi:10.13560/j.cnki.biotech.bull.1985.2024-0360

摘要/Abstract

摘要：

单细胞多组学技术为生命科学和医学注入新的活力，能够从更微观的细胞尺度、更高维的时空角度、更交叉的组学视野来探索生命的未知、拓展医学的边界。单细胞蛋白质组学是从时间和空间上研究单个或单一类型细胞的蛋白质表达异质性。尽管存在通量、灵敏度、分辨率等技术难点，基于质谱的细胞尺度的时空蛋白质组学技术凭借无偏检测、鉴定种类多、定量准确等特点而备受关注。本文综述了近年来单细胞蛋白质组学的发展，从单细胞分选、样品前处理、质谱检测和数据分析四个方面介绍相关技术进展，并探讨了其在生命科学与医学等研究中的应用，展望了未来面临的挑战和发展前景。在单细胞水平上进行时空蛋白质组学研究的挑战与机遇并存，而新兴的单细胞蛋白质组学技术无疑将为相关研究领域提供新的思路方法、认知线索和多模态数据等宝贵资源。

关键词: 单细胞蛋白质组学, 质谱, 细胞尺度的时空蛋白质组学技术

Abstract:

Single-cell multi-omics technologies have brought new vitality into life sciences and medicine and enable us to explore the unknowns of life and expand the boundaries of medicine from a more microscopic cellular scale, a higher-dimensional spatial-temporal perspective, and a more intersectional vision of genomics. Single-cell proteomics（SCP）is the study of the temporal and spatial heterogeneity of protein expression in a single or a single type of cell. Despite technical difficulties such as throughput, sensitivity, and resolution, mass spectrometry-based cell-scale spatiotemporal proteomics technologies have attracted much attention due to its characteristics of unbiased detection, multiple identification types and accurate quantification. This paper reviews the development of single-cell proteomics in recent years, introduces the related technological advances in four aspects, namely, single-cell sorting, sample pre-treatment, mass spectrometry detection, and data analysis, and discusses its applications and prospects for development in life science and medical research. Spatiotemporal proteomics research at the single-cell level presents both challenges and opportunities, and the emerging single-cell proteomics technologies will undoubtedly provide new ideas and methods, cognitive clues, and multimodal data, and other valuable resources for related research fields.

Key words: single-cell proteomics（SCP）, mass spectrometry（MS）, cell-scale spatiotemporal proteomics technology

顾蕾, 张誉露, 唐尚睿, 于浩月, 李辰. 基于质谱的单细胞蛋白质组学技术的发展及应用[J]. 生物技术通报, 2024, 40(11): 125-141.

GU Lei, ZHANG Yu-lu, TANG Shang-rui, YU Hao-yue, LI Chen. Development and Application of Mass Spectrometry-based Single-cell Proteomics Technologies[J]. Biotechnology Bulletin, 2024, 40(11): 125-141.

图/表 5

参考文献 72

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单细胞蛋白质组学工具 Single-cell proteomics tools	专用设备 Customized equipment	标记 Label	细胞类型 Cell types	细胞分选分离方法 Cell isolation methods	质谱仪 Mass spectrometers	样品前处理通量 Pretreatment throughput（Cells per run）	质谱检测通量 MS throughput（Cells per day）	单个细胞中平均鉴定的蛋白质数目 Average identified protein number per cell	单细胞蛋白质组覆盖总深度Depth of proteome coverage（n = number of cells）	参考文献 References	技术应用 Technical applications	应用相关文献Related references
iPAD-1	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Tribrid MS	1	24	271	406（n=10）	[10]	图谱研究	[10]
OAD	需要	非标记	HeLa	液滴微流控技术	Orbitrap Elite MS	1	4	51	/	[11]	发育研究	[36]
nanoPOTS	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Lumos Tribrid MS	27	/	669	/	[12]	疾病精准分析	[12]
CE-ESI-MS	需要	非标记	Xenopus laevis（D11）, zebrafish embryos	毛细管电泳	Orbitrap Q-Exactive Plus	/	/	450-800	/	[13]	发育研究	[13]
ULF LC-FAIMS-MS	需要	非标记	HeLa, U-937	纳升液相色谱毛细管	Orbitrap Fusion Lumos Tribrid MS	5	/	2 348	/	[14]	癌症研究	[14]
nested nanoPOTS	需要	TMT16	C10, RAW, SVEC	全自动单细胞分选技术	Orbitrap Eclipse Tribrid MS	243	108	1 716	2 457（n=108）	[4]	免疫研究	[4]
iProChip	需要	非标记	MEC-1	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	9	9	455	/	[16]	癌症研究	[16]
SciProChip	需要	非标记	PC-9	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	20	16	1 500	1 995（n=10）	[16]	癌症研究	[16]
PiSPA	需要	非标记	A549	液滴微流控技术	timsTOF Pro	1	/	3 008	5 093（n=37）	[17]	癌症研究	[17]
nPOP	需要	TMT18	U-937, WM989	全自动单细胞分选技术	Orbitrap Q-Exactive	2 016	212	997	2 844（n=1543）	[24]	癌症研究	[24]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 940（20× carrier）	3 674（n=276）	[28]	癌症研究	[28]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 598（no carrier）	3 674（n=276）	[28]	癌症研究	[28]
scSTAP	需要	非标记	小鼠oocytes	序控液滴微流控技术	timsTOF Pro	/	15-20	2 663	3 363（n=36）	[6]	发育研究	[6]
OCAM	需要	非标记	HeLa, 小鼠oocytes	毛细管烷基化微反应	Orbitrap Q-Exactive, Orbitrap Exploris 480 MS	100	/	3 457 (single mouse oocyte) 1 509 (single HeLa cell)	/	[37]	发育研究	[37]
SCoPE	不需要	TMT10	Jurkat, U-937	人工挑选	LTQ Orbitrap Elite	8	48	/	767（n=24）	[19]	图谱研究	[19]
SCoPE2	不需要	TMT16	Monocyte, macrophage	流式细胞荧光分选术	Orbitrap Q-Exactive	/	200	1 000	3 042（n=1490）	[20]	免疫研究	[20]
SOP-MS	不需要	非标记	MCF10A, MCF7	流式细胞荧光分选术，激光捕获显微切割	Orbitrap Q-Exactive Plus	/	/	146	/	[15]	癌症研究	[15]
SOP/C18-IMAC-C18/iBASIL	不需要	TMT标记	AML	流式细胞荧光分选术	Orbitrap Q-Exactive Plus	/	/	1 926	2 622（n=104）	[33]	免疫研究	[18]
autoPOTS	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Exploris 480 MS	/	10	301	/	[21]	免疫研究	[21]
T-SCP	不需要	非标记	HeLa	流式细胞荧光分选术	timsTOF Pro	308	41	2 083	2 501（n=231）	[5]	图谱研究	[5]
Mad-CASP	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Eclipse Tribrid MS	/	16	1 240	/	[22]	疾病精准分析	[22]
WinO	不需要	TMT10	RPMI8226	细胞分选仪	Orbitrap Fusion Tribrid MS	/	144	845	/	[23]	免疫研究	[23]
UE-SCP	不需要	TMT6	HeLa, HEK-293T	全自动单细胞分选技术	timsTOF Pro	308	96	2 249	4 230（n=128）	[25]	癌症研究	[25]
CS-UPT	不需要	非标记, TMT6, TMT8	小鼠MII oocytes, zygotes	人工挑选	timsTOF Pro	/	18 非标记,（70, 50×car rier,100×carrier）,（140, no carrier)	2 665非标记,（2 182, 50× carrier）,（2 371, 100× carrier）,（1 568, no carrier）	/	[26]	发育研究	[26,38]
REO-SCP	不需要	非标记	HeLa	全自动单细胞分选技术	Orbitrap Exploris 480 MS	384	/	1 208	/	[27]	图谱研究	[27]
DVP	不需要	非标记	U2OS	激光显微切割技术	timsTOF Pro	/	/	4 500-4 800（每个样品平均267个细胞核）	5 085（8个重复）	[30]	癌症研究	[30]
scDVP	不需要	非标记	小鼠肝细胞	激光显微切割技术	timsTOF SCP	/	80	1 700-2 700（1/3-1/2个细胞的体积）	/	[31]	图谱研究	[31]
LCM-MTA	不需要	非标记	人结直肠癌癌组织与癌旁组织	激光显微切割技术	timsTOF Pro	/	/	536（15个细胞的体积）	/	[29]	癌症研究	[29]
ProteomEx	不需要	非标记	小鼠脑组织	激光显微切割技术	timsTOF Pro	/	/	928（160 μm横向分辨率）	/	[32]	图谱研究	[32]
DISCO-MS	不需要	非标记	小鼠脑、心脏和肺组织	激光显微切割技术	timsTOF Pro	/	/	1 400（每个ROI）	/	[34]	癌症研究	[34]
SCPro	不需要	非标记	HEK 293T	激光显微切割技术	timsTOF Pro	/	/	800（2.4个细胞的体积）	/	[35]	疾病精准分析	[35]

单细胞蛋白质组学工具 Single-cell proteomics tools	专用设备 Customized equipment	标记 Label	细胞类型 Cell types	细胞分选分离方法 Cell isolation methods	质谱仪 Mass spectrometers	样品前处理通量 Pretreatment throughput（Cells per run）	质谱检测通量 MS throughput（Cells per day）	单个细胞中平均鉴定的蛋白质数目 Average identified protein number per cell	单细胞蛋白质组覆盖总深度Depth of proteome coverage（n = number of cells）	参考文献 References	技术应用 Technical applications	应用相关文献Related references
iPAD-1	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Tribrid MS	1	24	271	406（n=10）	[10]	图谱研究	[10]
OAD	需要	非标记	HeLa	液滴微流控技术	Orbitrap Elite MS	1	4	51	/	[11]	发育研究	[36]
nanoPOTS	需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Fusion Lumos Tribrid MS	27	/	669	/	[12]	疾病精准分析	[12]
CE-ESI-MS	需要	非标记	Xenopus laevis（D11）, zebrafish embryos	毛细管电泳	Orbitrap Q-Exactive Plus	/	/	450-800	/	[13]	发育研究	[13]
ULF LC-FAIMS-MS	需要	非标记	HeLa, U-937	纳升液相色谱毛细管	Orbitrap Fusion Lumos Tribrid MS	5	/	2 348	/	[14]	癌症研究	[14]
nested nanoPOTS	需要	TMT16	C10, RAW, SVEC	全自动单细胞分选技术	Orbitrap Eclipse Tribrid MS	243	108	1 716	2 457（n=108）	[4]	免疫研究	[4]
iProChip	需要	非标记	MEC-1	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	9	9	455	/	[16]	癌症研究	[16]
SciProChip	需要	非标记	PC-9	双层聚二甲基硅氧烷（PDMS）装置	Orbitrap Eclipse Tribrid MS	20	16	1 500	1 995（n=10）	[16]	癌症研究	[16]
PiSPA	需要	非标记	A549	液滴微流控技术	timsTOF Pro	1	/	3 008	5 093（n=37）	[17]	癌症研究	[17]
nPOP	需要	TMT18	U-937, WM989	全自动单细胞分选技术	Orbitrap Q-Exactive	2 016	212	997	2 844（n=1543）	[24]	癌症研究	[24]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 940（20× carrier）	3 674（n=276）	[28]	癌症研究	[28]
proteoCHIP	需要	TMT16	HeLa, HEK-293	全自动单细胞分选技术	Orbitrap Exploris 480 MS	592	384	1 598（no carrier）	3 674（n=276）	[28]	癌症研究	[28]
scSTAP	需要	非标记	小鼠oocytes	序控液滴微流控技术	timsTOF Pro	/	15-20	2 663	3 363（n=36）	[6]	发育研究	[6]
OCAM	需要	非标记	HeLa, 小鼠oocytes	毛细管烷基化微反应	Orbitrap Q-Exactive, Orbitrap Exploris 480 MS	100	/	3 457 (single mouse oocyte) 1 509 (single HeLa cell)	/	[37]	发育研究	[37]
SCoPE	不需要	TMT10	Jurkat, U-937	人工挑选	LTQ Orbitrap Elite	8	48	/	767（n=24）	[19]	图谱研究	[19]
SCoPE2	不需要	TMT16	Monocyte, macrophage	流式细胞荧光分选术	Orbitrap Q-Exactive	/	200	1 000	3 042（n=1490）	[20]	免疫研究	[20]
SOP-MS	不需要	非标记	MCF10A, MCF7	流式细胞荧光分选术，激光捕获显微切割	Orbitrap Q-Exactive Plus	/	/	146	/	[15]	癌症研究	[15]
SOP/C18-IMAC-C18/iBASIL	不需要	TMT标记	AML	流式细胞荧光分选术	Orbitrap Q-Exactive Plus	/	/	1 926	2 622（n=104）	[33]	免疫研究	[18]
autoPOTS	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Exploris 480 MS	/	10	301	/	[21]	免疫研究	[21]
T-SCP	不需要	非标记	HeLa	流式细胞荧光分选术	timsTOF Pro	308	41	2 083	2 501（n=231）	[5]	图谱研究	[5]
Mad-CASP	不需要	非标记	HeLa	流式细胞荧光分选术	Orbitrap Eclipse Tribrid MS	/	16	1 240	/	[22]	疾病精准分析	[22]
WinO	不需要	TMT10	RPMI8226	细胞分选仪	Orbitrap Fusion Tribrid MS	/	144	845	/	[23]	免疫研究	[23]
UE-SCP	不需要	TMT6	HeLa, HEK-293T	全自动单细胞分选技术	timsTOF Pro	308	96	2 249	4 230（n=128）	[25]	癌症研究	[25]
CS-UPT	不需要	非标记, TMT6, TMT8	小鼠MII oocytes, zygotes	人工挑选	timsTOF Pro	/	18 非标记,（70, 50×car rier,100×carrier）,（140, no carrier)	2 665非标记,（2 182, 50× carrier）,（2 371, 100× carrier）,（1 568, no carrier）	/	[26]	发育研究	[26,38]
REO-SCP	不需要	非标记	HeLa	全自动单细胞分选技术	Orbitrap Exploris 480 MS	384	/	1 208	/	[27]	图谱研究	[27]
DVP	不需要	非标记	U2OS	激光显微切割技术	timsTOF Pro	/	/	4 500-4 800（每个样品平均267个细胞核）	5 085（8个重复）	[30]	癌症研究	[30]
scDVP	不需要	非标记	小鼠肝细胞	激光显微切割技术	timsTOF SCP	/	80	1 700-2 700（1/3-1/2个细胞的体积）	/	[31]	图谱研究	[31]
LCM-MTA	不需要	非标记	人结直肠癌癌组织与癌旁组织	激光显微切割技术	timsTOF Pro	/	/	536（15个细胞的体积）	/	[29]	癌症研究	[29]
ProteomEx	不需要	非标记	小鼠脑组织	激光显微切割技术	timsTOF Pro	/	/	928（160 μm横向分辨率）	/	[32]	图谱研究	[32]
DISCO-MS	不需要	非标记	小鼠脑、心脏和肺组织	激光显微切割技术	timsTOF Pro	/	/	1 400（每个ROI）	/	[34]	癌症研究	[34]
SCPro	不需要	非标记	HEK 293T	激光显微切割技术	timsTOF Pro	/	/	800（2.4个细胞的体积）	/	[35]	疾病精准分析	[35]

单细胞蛋白质组学数据分析软件Software for single-cell proteomics data analysis	构建方式Construction method	输出方式 Output method	适配的数据采集模式 Compatible data acquisition modes	测试数据集 Test datasets	特点 Features	参考文献 References
DO-MS	R	HTML报告	DDA、DIA	SCoPE、SCoPE2	交互式可视化分析	[19-20]
SCPCompanion	C#	XML文件	DDA	SCoPE、N2	推荐仪器和数据分析参数	[4,9,19]
SCeptre	Python	YAML文件	DDA	AML细胞群	数据归一化处理	[67]
DART-ID	Python和R	HTML报告	DDA	SCoPE、SCoPE2	对齐肽段保留时间	[19-20]
IceR	R	TSV文件	DDA（PIP）、DIA	DeMix-Q、IonStar、HRM-DIA等	使用离子电流信息进行混合肽段鉴定	[68]
DeepSCP	Python（深度学习）	CSV文件	DDA（TDC）	SCoPE2、N2	首次应用深度学习技术进行DDA分析	[4,20,69]

单细胞蛋白质组学数据分析软件Software for single-cell proteomics data analysis	构建方式Construction method	输出方式 Output method	适配的数据采集模式 Compatible data acquisition modes	测试数据集 Test datasets	特点 Features	参考文献 References
DO-MS	R	HTML报告	DDA、DIA	SCoPE、SCoPE2	交互式可视化分析	[19-20]
SCPCompanion	C#	XML文件	DDA	SCoPE、N2	推荐仪器和数据分析参数	[4,9,19]
SCeptre	Python	YAML文件	DDA	AML细胞群	数据归一化处理	[67]
DART-ID	Python和R	HTML报告	DDA	SCoPE、SCoPE2	对齐肽段保留时间	[19-20]
IceR	R	TSV文件	DDA（PIP）、DIA	DeMix-Q、IonStar、HRM-DIA等	使用离子电流信息进行混合肽段鉴定	[68]
DeepSCP	Python（深度学习）	CSV文件	DDA（TDC）	SCoPE2、N2	首次应用深度学习技术进行DDA分析	[4,20,69]