In situ identification of environmental microorganisms with Raman spectroscopy

被引：0

作者：

Dongyu Cui ^{[1
,2
]}

Lingchao Kong ^{[3
]}

Yi Wang ^{[1
,2
]}

Yuanqing Zhu ^{[2
,4
]}

Chuanlun Zhang ^{[1
,2
,5
,4
]}

机构：

[1] Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)

[2] Department of Ocean Science and Engineering,Southern University of Science and Technology

[3] State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science & Engineering,Southern University of Science and Technology

[4] Shanghai Sheshan National Geophysical Observatory,Shanghai Earthquake Agency

[5] Shenzhen Key Laboratory of Marine Archaea Geo-Omics,University of Southern University of Science and

来源：

Environmental Science & Ecotechnology | 2022年 / 03期

关键词：

D O I：

暂无

中图分类号：

O657.37 [拉曼光谱分析法]; Q93-331 [微生物鉴定];

学科分类号：

摘要：

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment.However,it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated.Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells.It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ.The first part of this review introduces the principles,advantages,progress,and analytical methods of Raman spectroscopy applied in environmental microbiology.The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing(SIP),fluorescence in situ hybridization(FISH),Raman-activated cell sorting and genomic sequencing,and machine learning in microbiological studies.Finally,this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms,especially for uncultured microorganisms.

引用

页码：86 / 97

页数：12

共 7 条

[1] Next-Generation Machine Learning for Biological Networks
Camacho, Diogo M.
Collins, Katherine M.
Powers, Rani K.
Costello, James C.
Collins, James J.
[J]. CELL, 2018, 173 (07) : 1581 - 1592
[2] Single cell stable isotope probing in microbiology using Raman microspectroscopy.[J].Yun Wang;Wei E Huang;Li Cui;Michael Wagner.Current Opinion in Biotechnology.2016,
[3] Carotenes and carotenoids in natural biological samples: a Raman spectroscopic analysis
de Oliveira, Vanessa E.
Castro, Harlem V.
Edwards, Howell G. M.
de Oliveira, Luiz Fernando C.
[J]. JOURNAL OF RAMAN SPECTROSCOPY, 2010, 41 (06) : 642 - 650
[4] Near-Infrared Micro-Raman Spectroscopy for in Vitro Detection of Cervical Cancer
Kamemoto, Lori E.
Misra, Anupam K.
Sharma, Shiv K.
Goodman, Marc T.
Luk, Hugh
Dykes, Ava C.
Acosta, Tayro
[J]. APPLIED SPECTROSCOPY, 2010, 64 (03) : 255 - 261
[5] Comparison of psychro-active arctic marine bacteria and common mesophillic bacteria using surface-enhanced Raman spectroscopy
Laucks, ML
Sengupta, A
Junge, K
Davis, EJ
Swanson, BD
[J]. APPLIED SPECTROSCOPY, 2005, 59 (10) : 1222 - 1228
[6] Diagnosis of Basal Cell Carcinoma by Raman Spectroscopy.[J].M.Gniadecka;H. C.Wulf;N.Nymark Mortensen;O.Faurskov Nielsen;D. H.Christensen.J. Raman Spectrosc..1998, 2‐3
[7] High-speed diagnosis of bacterial pathogens at the single cell level by Raman microspectroscopy with machine learning filters and denoising autoencoders..J.B.Xu;X.F.Yi;G.L.Jin;D.Peng;G.Y.Fan;X.G.Xu;X.Chen;H.B.Yin;J.M.Cooper;W.E.Huang;.ACS Chem.Biol.2022, 02

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