Applications and Prospects of Raman Spectroscopy-based “Screen-First， Culture-Second” Strategy in Functional Microbial Resource Exploration

doi:10.13560/j.cnki.biotech.bull.1985.2025-0480

Abstract

Abstract:

With the deepening of microbial research, the traditional "Culture-First, Screen-Second" strategy has shown pronounced limitations when confronted with complex environmental samples and in the quest for functionally significant microbes. Taking gut and natural-environment samples as examples, more than 99% of prokaryotes remain uncultivable under standard laboratory conditions, leaving myriad microorganisms with crucial functions undiscovered and uncharacterised. To address this challenge, a "Screen-First, Culture-Second" workflow based on single-cell Raman spectroscopy (SCRS) combined with heavy-water (D₂O) metabolic labelling has been proposed. This approach employs in situ, label-free SCRS to rapidly pinpoint individual cells exhibiting specific metabolic activities, followed by precise cultivation and taxonomic identification of those cells. By measuring the assimilation rate of D₂O in single cells, SCRS enables the efficient isolation of microbes endowed with targeted functions—for instance, phosphate solubilisation or pollutant degradation.This review summarises the core techniques underpinning this strategy and showcases representative applications. Then it analyses in detail its advantages for functional-microbe discovery, highlighting its considerable potential to enhance screening throughput, sensitivity and activity retention. Meanwhile, the review also discusses the trends, including automation, AI-assisted spectral recognition, multi-omics integration and genome-informed medium design. Finally, the review envisions broad prospects for this strategy in building microbial resource libraries, monitoring antimicrobial resistance and other emerging fields. Overall, the "Screen-First, Culture-Second" platform offers a systematic reference framework for high-throughput functional screening and sorting and is aimed to accelerate the widespread adoption of single-cell Raman technologies in microbiological research and application.

Key words: single-cell Raman spectroscopy, Raman-activated cell sorting, single-cell high-throughput sorting, single-cell cultivation, deuterium oxide metabolic labeling, "Screen-First, Culture-Second" strategy, functional screening, microbial resource

LIU Jia, REN Yi-shang, JING Xiao-yan, XU La. Applications and Prospects of Raman Spectroscopy-based “Screen-First， Culture-Second” Strategy in Functional Microbial Resource Exploration[J]. Biotechnology Bulletin, 2026, 42(5): 1-13.

Figures/Tables 3

References 67

[1]	Olicón-Hernández DR, Guerra-Sánchez G, Porta CJ, et al. Fundaments and concepts on screening of microorganisms for biotechnological applications. mini review [J]. Curr Microbiol, 2022, 79(12): 373.
[2]	Lagier JC, Hugon P, Khelaifia S, et al. The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota [J]. Clin Microbiol Rev, 2015, 28(1): 237-264.
[3]	Thrash JC. Culturing the uncultured: risk versus reward [J]. mSystems, 2019, 4(3): e00130-19.
[4]	Li HZ, Bi QF, Yang K, et al. D₂O-isotope-labeling approach to probing phosphate-solubilizing bacteria in complex soil communities by single-cell Raman spectroscopy [J]. Anal Chem, 2019, 91(3): 2239-2246.
[5]	Zheng RK, Liu R, Shan YQ, et al. Characterization of the first cultured free-living representative of Candidatus Izemoplasma uncovers its unique biology [J]. ISME J, 2021, 15(9): 2676-2691.
[6]	Liu C, Du MX, Abuduaini R, et al. Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank [J]. Microbiome, 2021, 9(1): 119.
[7]	Schloss PD, Handelsman J. Metagenomics for studying unculturable microorganisms: cutting the gordian knot [J]. Genome Biol, 2005, 6(8): 229.
[8]	Wilson CG, Nowell RW, Barraclough TG. Cross-contamination explains "inter and intraspecific horizontal genetic transfers" between asexual bdelloid rotifers [J]. Curr Biol, 2018, 28(15): 2436-2444.e14.
[9]	Xu JB, Zhu D, Ibrahim AD, et al. Raman deuterium isotope probing reveals microbial metabolism at the single-cell level [J]. Anal Chem, 2017, 89(24): 13305-13312.
[10]	Wang X, Liu NM, Zhao YF, et al. Research progress in the medical application of heavy water, especially in the field of D₂O-Raman spectroscopy [J]. Int J Med Sci, 2022, 19(8): 1357-1363.
[11]	Song YZ, Cui L, López JÁS, et al. Raman-Deuterium Isotope Probing for in situ identification of antimicrobial resistant bacteria in Thames River [J]. Sci Rep, 2017, 7(1): 16648.
[12]	Watterson WJ, Tanyeri M, Watson AR, et al. Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes [J]. eLife, 2020, 9: e56998.
[13]	Jing XY, Gong YH, Pan HH, et al. Single-cell Raman-activated sorting and cultivation (scRACS-Culture) for assessing and mining in situ phosphate-solubilizing microbes from nature [J]. ISME Commun, 2022, 2(1): 106.
[14]	Jones BV, Sun F, Marchesi JR. Using skimmed milk agar to functionally screen a gut metagenomic library for proteases may lead to false positives [J]. Lett Appl Microbiol, 2007, 45(4): 418-420.
[15]	Wang L, Liu W, Tang JW, et al. Applications of Raman spectroscopy in bacterial infections: principles, advantages, and shortcomings [J]. Front Microbiol, 2021, 12: 683580.
[16]	Hekmatara M, Heidari Baladehi M, Ji YT, et al. D₂O-probed Raman microspectroscopy distinguishes the metabolic dynamics of macromolecules in organellar anticancer drug response [J]. Anal Chem, 2021, 93(4): 2125-2134.
[17]	Fakruddin M, Mannan KS, Andrews S. Viable but nonculturable bacteria: food safety and public health perspective [J]. ISRN Microbiol, 2013, 2013: 703813.
[18]	Bao QH, Bo XY, Chen L, et al. Comparative analysis using Raman spectroscopy of the cellular constituents of Lacticaseibacillus paracasei Zhang in a normal and viable but nonculturable state [J]. Microorganisms, 2023, 11(5): 1266.
[19]	Lee KS, Palatinszky M, Pereira FC, et al. An automated Raman-based platform for the sorting of live cells by functional properties [J]. Nat Microbiol, 2019, 4(6): 1035-1048.
[20]	Wang Y, Xu JB, Kong LC, et al. Raman-deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota [J]. Microb Biotechnol, 2020, 13(2): 572-583.
[21]	Zhang J, Lin HN, Xu JB, et al. High-throughput single-cell sorting by stimulated Raman-activated cell ejection [J]. Sci Adv, 2024, 10(50): eadn6373.
[22]	Wang XX, Ren LH, Diao ZD, et al. Robust spontaneous Raman flow cytometry for single-cell metabolic phenome profiling via pDEP-DLD-RFC [J]. Adv Sci, 2023, 10(16): e2207497.
[23]	Wang C, Chen RZ, Xu J, et al. Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities [J]. Front Microbiol, 2022, 13: 936726.
[24]	Liu L, Feng B, Song Y, et al. Detecting and classifying metabolic activity of Staphylococcus aureus by D2O-probed single-cell Raman spectroscopy and machine learning [J]. Biosaf Health, 2025, 7(2): 94-102.
[25]	Schaible GA, Cliff JB, Crandall JA, et al. Comparing Raman and NanoSIMS for heavy water labeling of single cells [J]. bioRxiv, 2025: 2024.07.05.602271.
[26]	Li HZ, Yang K, Liao H, et al. Active antibiotic resistome in soils unraveled by single-cell isotope probing and targeted metagenomics [J]. Proc Natl Acad Sci USA, 2022, 119(40): e2201473119.
[27]	Wang JW, Luo YJ, Wu Y, et al. Single-cell Raman spectroscopy as a novel platform for unveiling the heterogeneity of mesenchymal stem cells [J]. Talanta, 2025, 292: 127933.
[28]	Zhu LJ, Yang YN, Xu F, et al. Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments [J]. Sci Adv, 2025, 11(2): eadp7991.
[29]	Xu T, Gong YH, Su XL, et al. Phenome-genome profiling of single bacterial cell by Raman-activated gravity-driven encapsulation and sequencing [J]. Small, 2020, 16(30): e2001172.
[30]	Wang JK, Kong K, Guo C, et al. Cultureless enumeration of live bacteria in urinary tract infection by single-cell Raman spectroscopy [J]. Front Microbiol, 2023, 14: 1144607.
[31]	Li JB, Zhang DY, Luo CL, et al. In situ discrimination and cultivation of active degraders in soils by genome-directed cultivation assisted by SIP-Raman-activated cell sorting [J]. Environ Sci Technol, 2023, 57(44): 17087-17098.
[32]	Mahdizade Ari M, Scholz KJ, Cieplik F, et al. Viable but non-cultivable state in oral microbiota: a critical review of an underexplored microbial survival strategy [J]. Front Cell Infect Microbiol, 2025, 15: 1533768.
[33]	Kage D, Heinrich K, Volkmann KV, et al. Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification [J]. Commun Biol, 2021, 4(1): 1144.
[34]	Telford WG. Flow cytometry and cell sorting [J]. Front Med: Lausanne, 2023, 10: 1287884.
[35]	Bellais S, Nehlich M, Ania M, et al. Species-targeted sorting and cultivation of commensal bacteria from the gut microbiome using flow cytometry under anaerobic conditions [J]. Microbiome, 2022, 10(1): 24.
[36]	Plouffe BD, Murthy SK, Lewis LH. Fundamentals and application of magnetic particles in cell isolation and enrichment: a review [J]. Rep Prog Phys, 2015, 78(1): 016601.
[37]	van Gogh M, Louwers JM, Celli A, et al. Next-generation IgA-SEQ allows for high-throughput, anaerobic, and metagenomic assessment of IgA-coated bacteria [J]. Microbiome, 2024, 12(1): 211.
[38]	Kim JA, Choi SS, Lim JK, et al. Unlocking marine treasures: isolation and mining strategies of natural products from sponge-associated bacteria [J]. Nat Prod Rep, 2025. doi:10.1039/D5NP00013K .
[39]	Neun S, Kaminski TS, Hollfelder F. Single-cell activity screening in microfluidic droplets [J]. Methods Enzymol, 2019, 628: 95-112.
[40]	Zhang Q, Wang TT, Zhou Q, et al. Development of a facile droplet-based single-cell isolation platform for cultivation and genomic analysis in microorganisms [J]. Sci Rep, 2017, 7: 41192.
[41]	Beneyton T, Thomas S, Griffiths AD, et al. Droplet-based microfluidic high-throughput screening of heterologous enzymes secreted by the yeast Yarrowia lipolytica [J]. Microb Cell Fact, 2017, 16(1): 18.
[42]	Staskiewicz K, Dabrowska-Zawada M, Kozon L, et al. Droplet microfluidic system for high throughput and passive selection of bacteria producing biosurfactants [J]. Lab Chip, 2024, 24(7): 1947-1956.
[43]	Saito K, Ota Y, Tourlousse DM, et al. Microdroplet-based system for culturing of environmental microorganisms using FNAP-sort [J]. Sci Rep, 2021, 11(1): 9506.
[44]	Batani G, Bayer K, Böge J, et al. Fluorescence in situ hybridization (FISH) and cell sorting of living bacteria [J]. Sci Rep, 2019, 9(1): 18618.
[45]	Silverman AP, Kool ET. Quenched autoligation probes allow discrimination of live bacterial species by single nucleotide differences in rRNA [J]. Nucleic Acids Res, 2005, 33(15): 4978-4986.
[46]	Sutermaster BA, Darling EM. Considerations for high-yield, high-throughput cell enrichment: fluorescence versus magnetic sorting [J]. Sci Rep, 2019, 9(1): 227.
[47]	Adekanmbi EO, Srivastava SK. Dielectrophoretic applications for disease diagnostics using lab-on-a-chip platforms [J]. Lab Chip, 2016, 16(12): 2148-2167.
[48]	Kaminski TS, Scheler O, Garstecki P. Droplet microfluidics for microbiology: techniques, applications and challenges [J]. Lab Chip, 2016, 16(12): 2168-2187.
[49]	Wang Y, Huang WE, Cui L, et al. Single cell stable isotope probing in microbiology using Raman microspectroscopy [J]. Curr Opin Biotechnol, 2016, 41: 34-42.
[50]	Zhang J, Ren LH, Zhang L, et al. Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products [J]. Imeta, 2023, 2(3): e117.
[51]	Berry D, Mader E, Lee TK, et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells [J]. Proc Natl Acad Sci USA, 2015, 112(2): E194-E203.
[52]	Lyu YK, Yuan XF, Glidle A, et al. Automated Raman based cell sorting with 3D microfluidics [J]. Lab Chip, 2020, 20(22): 4235-4245.
[53]	Tang XS, Wu QY, Shang LD, et al. Raman cell sorting for single-cell research [J]. Front Bioeng Biotechnol, 2024, 12: 1389143.
[54]	Diao ZD, Jing XY, Hou XB, et al. Artificial intelligence-assisted automatic Raman-activated cell sorting (AI-RACS) system for mining specific functional microorganisms in the microbiome [J]. Anal Chem, 2024, 96(46): 18416-18426.
[55]	Huang WE, Ward AD, Whiteley AS. Raman tweezers sorting of single microbial cells [J]. Environ Microbiol Rep, 2009, 1(1): 44-49.
[56]	Wang XX, Xin Y, Ren LH, et al. Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo [J]. Sci Adv, 2020, 6(32): eabb3521.
[57]	Brouzes E, Medkova M, Savenelli N, et al. Droplet microfluidic technology for single-cell high-throughput screening [J]. Proc Natl Acad Sci USA, 2009, 106(34): 14195-14200.
[58]	Hu BY, Xu P, Ma L, et al. One cell at a time: droplet-based microbial cultivation, screening and sequencing [J]. Mar Life Sci Technol, 2021, 3(2): 169-188.
[59]	Lindström S, Andersson-Svahn H. Single-cell culture in microwells [J]. Methods Mol Biol, 2012, 853: 41-52.
[60]	Hu JQ, Chen GJ, Xue CL, et al. RSPSSL: a novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization [J]. Light Sci Appl, 2024, 13(1): 52.
[61]	Li JB, Zhang DY, Li B, et al. Identifying the active phenanthrene degraders and characterizing their metabolic activities at the single-cell level by the combination of magnetic-nanoparticle-mediated isolation, stable-isotope probing, and Raman-activated cell sorting (MMI-SIP-RACS) [J]. Environ Sci Technol, 2022, 56(4): 2289-2299.
[62]	Ouyang Y, Chen DM, Fu Y, et al. Direct cell extraction from fresh and stored soil samples: Impact on microbial viability and community compositions [J]. Soil Biol Biochem, 2021, 155: 108178.
[63]	Jing XY, Gong YH, Xu T, et al. One-cell metabolic phenotyping and sequencing of soil microbiome by Raman-activated gravity-driven encapsulation (RAGE) [J]. mSystems, 2021, 6(3): e0018121.
[64]	Xu JB, Luo YJ, Wang JK, et al. Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy [J]. Front Microbiol, 2023, 14: 1125676.
[65]	Du JJ, Su YP, Qian CX, et al. Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells [J]. Nat Commun, 2020, 11(1): 4830.
[66]	Renesto P, Crapoulet N, Ogata H, et al. Genome-based design of a cell-free culture medium for Tropheryma whipplei [J]. Lancet, 2003, 362(9382): 447-449.
[67]	Armetta J, Li SS, Vaaben TH, et al. Metagenome-guided culturomics for the targeted enrichment of gut microbes [J]. Nat Commun, 2025, 16(1): 663.

指标 Parameter	“先养后筛”传统培养法 "Culture-First， Screen-Second" traditional culture method	“先筛后养”单细胞培养策略 "Screen-First， Culture-Second" single-cell culture strategy
通量	低（实验繁琐，效率低下）	高（可达数百个至数千单细胞/h）
培养周期	长（需数周至数月）	短（约3-14 d）
培养偏倚	严重（细胞群体成分失真）	低（直接靶向原位分选）
假阳性率	较高（易引发非特异反应）	低（拉曼光谱可直接表征功能）
活性保持率	高（部分细胞在纯化时可能进入VBNC状态）	高（拉曼分选对活性几乎不造成影响）
假阴性率	高（细胞生长时漏检）	低（通过采取措施减少假阴性）

指标 Parameter	“先养后筛”传统培养法 "Culture-First， Screen-Second" traditional culture method	“先筛后养”单细胞培养策略 "Screen-First， Culture-Second" single-cell culture strategy
通量	低（实验繁琐，效率低下）	高（可达数百个至数千单细胞/h）
培养周期	长（需数周至数月）	短（约3-14 d）
培养偏倚	严重（细胞群体成分失真）	低（直接靶向原位分选）
假阳性率	较高（易引发非特异反应）	低（拉曼光谱可直接表征功能）
活性保持率	高（部分细胞在纯化时可能进入VBNC状态）	高（拉曼分选对活性几乎不造成影响）
假阴性率	高（细胞生长时漏检）	低（通过采取措施减少假阴性）

技术指标 Performance metrics	单细胞拉曼技术 Single-cell Raman technologya	荧光激活细胞分选 Fluorescence-activated cell sorting	磁性激活细胞分选 Magnetic-activated cell sorting	微流控液滴技术 Droplet-based microfluidics	活细胞荧光原位杂交 Live cell fluorescence in situ hybridization
可培养率	~17%	~7.3%	—	~3.3%	~2.5%
筛选准确率	100%	—	~79%	~32.2%	100%
培养周期	~14 d	数天至数周	数天至数周	~8-10 d	数天至数周
标记需求	无须荧光/磁性探针	需荧光标记（抗体或探针）	需抗体+磁珠标记	通常需荧光或化学底物作为功能报告	需荧光寡核苷酸探针标记
功能识别机制	代谢功能（D₂O吸收速率）	荧光标记抗原/报告基因	细胞表面抗原	代谢产物或酶活性（荧光/化学底物）	核酸序列（16S rRNA序列）
功能活性定量	可定量（C-D峰强度）	可半定量荧光强度	不可定量	可半定量荧光或底物产物	可半定量荧光强度
功能识别	可识别	可识别	可识别	可识别	可识别
分选通量	高（10²-10³细胞/分钟）	极高（10⁴-10⁶细胞/秒）	高（批量并行处理）	极高（10⁴-10⁶细胞/时）	高（结合FACS，受限于低活性）
活性保持率	无损	较高（>83%）	高（>90%）	高	低（＞1%）

技术指标 Performance metrics	单细胞拉曼技术 Single-cell Raman technologya	荧光激活细胞分选 Fluorescence-activated cell sorting	磁性激活细胞分选 Magnetic-activated cell sorting	微流控液滴技术 Droplet-based microfluidics	活细胞荧光原位杂交 Live cell fluorescence in situ hybridization
可培养率	~17%	~7.3%	—	~3.3%	~2.5%
筛选准确率	100%	—	~79%	~32.2%	100%
培养周期	~14 d	数天至数周	数天至数周	~8-10 d	数天至数周
标记需求	无须荧光/磁性探针	需荧光标记（抗体或探针）	需抗体+磁珠标记	通常需荧光或化学底物作为功能报告	需荧光寡核苷酸探针标记
功能识别机制	代谢功能（D₂O吸收速率）	荧光标记抗原/报告基因	细胞表面抗原	代谢产物或酶活性（荧光/化学底物）	核酸序列（16S rRNA序列）
功能活性定量	可定量（C-D峰强度）	可半定量荧光强度	不可定量	可半定量荧光或底物产物	可半定量荧光强度
功能识别	可识别	可识别	可识别	可识别	可识别
分选通量	高（10²-10³细胞/分钟）	极高（10⁴-10⁶细胞/秒）	高（批量并行处理）	极高（10⁴-10⁶细胞/时）	高（结合FACS，受限于低活性）
活性保持率	无损	较高（>83%）	高（>90%）	高	低（＞1%）