微生物学报

Core screening strategy	Representative technologies and methods	Technical advantages	References
Multi-omics driven targeted isolation	Metagenome-assembled genomes (MAGs) analysis, genome-targeted medium design	Obtain genomic blueprints directly from environmental samples, guide the rational design of culture media, and have strong targeting	[13-16]
Single-cell precise localization and sorting	Fluorescence in situ hybridization-fluorescence-activated cell sorting (FISH-FACS), microfluidic sorting	Realize accurate identification and high-throughput sorting of low-abundance cells in complex communities, and break away from the dependence on cultivation	[17-21]
Single-cell Raman tweezers sorting	Laser tweezers Raman spectroscopy system (LTRS)	Realize label-free, non-destructive, in-situ acquisition of single-cell chemical fingerprints, and achieve functional sorting and identification of viable cells	[22-28]
Function-oriented in-situ screening	Stable isotope probing (SIP), magnetic nanoparticle-based targeted capture, enrichment culture, high-throughput screening	Directly lock on viable cells with specific metabolic functions (e.g., degradation, pathogenicity) in the environment	[29-34]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Multi-omics driven targeted isolation	Metagenome-assembled genomes (MAGs) analysis, genome-targeted medium design	Obtain genomic blueprints directly from environmental samples, guide the rational design of culture media, and have strong targeting	[13-16]
Single-cell precise localization and sorting	Fluorescence in situ hybridization-fluorescence-activated cell sorting (FISH-FACS), microfluidic sorting	Realize accurate identification and high-throughput sorting of low-abundance cells in complex communities, and break away from the dependence on cultivation	[17-21]
Single-cell Raman tweezers sorting	Laser tweezers Raman spectroscopy system (LTRS)	Realize label-free, non-destructive, in-situ acquisition of single-cell chemical fingerprints, and achieve functional sorting and identification of viable cells	[22-28]
Function-oriented in-situ screening	Stable isotope probing (SIP), magnetic nanoparticle-based targeted capture, enrichment culture, high-throughput screening	Directly lock on viable cells with specific metabolic functions (e.g., degradation, pathogenicity) in the environment	[29-34]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Microenvironment simulation and high-throughput cultivation	Fungal isolation chips (FiChips), microfluidic spore detection, nutrient enrichment culture	Simulate natural microenvironments on chips, realize single-cell isolation culture, and significantly improve isolation efficiency and diversity	[35-37]
Artificial intelligence and automated platform	AI-driven colony imaging and identification, high-throughput colony picking robot, growth prediction model	High throughput and efficiency, reduce human error, identify weak growth and predict interspecific interaction relationships	[38-40]
Metabolomics-driven functional screening	High performance liquid chromatography-mass spectrometry (HPLC-MS/QTOF) metabolite analysis	Bypass time-consuming morphological identification, and rapidly screen and identify strains with application potential through chemical markers	[41-44]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Microenvironment simulation and high-throughput cultivation	Fungal isolation chips (FiChips), microfluidic spore detection, nutrient enrichment culture	Simulate natural microenvironments on chips, realize single-cell isolation culture, and significantly improve isolation efficiency and diversity	[35-37]
Artificial intelligence and automated platform	AI-driven colony imaging and identification, high-throughput colony picking robot, growth prediction model	High throughput and efficiency, reduce human error, identify weak growth and predict interspecific interaction relationships	[38-40]
Metabolomics-driven functional screening	High performance liquid chromatography-mass spectrometry (HPLC-MS/QTOF) metabolite analysis	Bypass time-consuming morphological identification, and rapidly screen and identify strains with application potential through chemical markers	[41-44]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Cultivation system precise optimization	Automated gas pressure control (GPC), high-pressure bioreactor, vacuum-vortex deoxygenation method	Accurately reproduce and dynamically regulate extreme environments (anaerobic, high temperature), and overcome bottlenecks such as oxygen sensitivity of core enzymes	[11,47-51]
Symbiotic interaction simulation and co-cultivation	Zoned co-cultivation on agar plates, co-cultivation with host bacteria, construction of electro-syntrophic systems	Solve the genomic nutrient dependence of archaea by providing essential symbiotic partners or metabolites	[46,52-56]
Extreme environment-adaptive medium design	Simulate the pH, salinity, temperature and trace element composition of natural habitats	Provide the basic growth foundation for special taxa by restoring key physicochemical factors of their native habitats	[6,57-59]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Cultivation system precise optimization	Automated gas pressure control (GPC), high-pressure bioreactor, vacuum-vortex deoxygenation method	Accurately reproduce and dynamically regulate extreme environments (anaerobic, high temperature), and overcome bottlenecks such as oxygen sensitivity of core enzymes	[11,47-51]
Symbiotic interaction simulation and co-cultivation	Zoned co-cultivation on agar plates, co-cultivation with host bacteria, construction of electro-syntrophic systems	Solve the genomic nutrient dependence of archaea by providing essential symbiotic partners or metabolites	[46,52-56]
Extreme environment-adaptive medium design	Simulate the pH, salinity, temperature and trace element composition of natural habitats	Provide the basic growth foundation for special taxa by restoring key physicochemical factors of their native habitats	[6,57-59]

微生物分离筛选技术的前沿进展

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马鸿钰 ¹ , 金烨薇 ¹ , 李淼 ¹ , 李雨娜 ¹ , 华威 ¹^,² , 武双 ¹^,² , 程艳玲 ¹^,² , 王晚晴 ¹^,² , 张娜 ¹^,² , 周成 ¹^,²

微生物学报 | 综述 2026,66(5): 2133-2147

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微生物学报 | 综述 2026, 66(5): 2133-2147

微生物分离筛选技术的前沿进展

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马鸿钰¹, 金烨薇¹, 李淼¹, 李雨娜¹, 华威¹^,², 武双¹^,², 程艳玲¹^,², 王晚晴¹^,², 张娜¹^,², 周成¹^,²

作者信息

^1.北京联合大学生物化学工程学院，北京

^2.北京联合大学，生物质废弃物资源化利用北京市重点实验室，北京

Advances in microbial isolation and screening technologies

Hongyu MA¹, Yewei JIN¹, Miao LI¹, Yuna LI¹, Wei HUA¹^,², Shuang WU¹^,², Yanling CHENG¹^,², Wanqing WANG¹^,², Na ZHANG¹^,², Cheng ZHOU¹^,²

Affiliations

^1.College of Biochemical Engineering, Beijing Union University, Beijing, China

^2.Beijing Key Laboratory of Biomass Waste Resource Utilization, Beijing Union University, Beijing, China

出版时间: 2026-05-04 doi: 10.13343/j.cnki.wsxb.20250930

文章导航

摘要

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微生物是地球上最大的未开发资源宝库，其分离培养技术的进步是生命科学领域取得关键突破的前提。本文聚焦细菌、真菌与古菌分离筛选技术的研究进展，系统阐释了前沿分离筛选技术的发展与应用，以及提高难培养及稀有微生物分离效率的方法。通过总结3类微生物的特异性分离筛选策略：细菌采用多组学靶向与单细胞精确定位技术、真菌运用代谢组学导向与微流控技术、古菌借助共培养与极端条件培养等技术，揭示了跨学科技术融合在连接基因组数据与原位功能验证中的核心价值。最后，本文前瞻性地提出了技术在数据整合与自动化闭环构建方面面临的挑战，为系统性发掘微生物资源指明了发展路径。

关键词

分离筛选 / 细菌 / 真菌 / 古菌

Abstract

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Microorganisms represent the largest untapped resource reservoir on the Earth, and breakthroughs in their isolation and cultivation are prerequisites for fundamental advances in the life sciences. This review focuses on recent progress in the isolation and screening technologies for bacteria, fungi, and archaea. It systematically elucidates how the development and application of cutting-edge isolation and screening technologies have enhanced the efficiency of isolating previously uncultivable and rare microbial taxa. By summarizing lineage-specific strategies—such as multi-omics targeting and single-cell precision localization for bacteria, metabolomics-guided screening and microfluidic technology for fungi, and co-culture systems coupled with extreme-condition cultivation for archaea—this review highlights the core value of interdisciplinary technology integration in bridging genomic data with in situ functional validation. Finally, the article prospectively addresses challenges in data integration and the construction of automated workflows, thereby outlining a strategic pathway for the systematic exploration of microbial resources.

Key words

isolation and screening / bacteria / fungi / archaea

引用本文

马鸿钰, 金烨薇, 李淼, 李雨娜, 华威, 武双, 程艳玲, 王晚晴, 张娜, 周成. 微生物分离筛选技术的前沿进展. 微生物学报, 2026 , 66 (5) : 2133 -2147 . DOI: 10.13343/j.cnki.wsxb.20250930

Hongyu MA, Yewei JIN, Miao LI, Yuna LI, Wei HUA, Shuang WU, Yanling CHENG, Wanqing WANG, Na ZHANG, Cheng ZHOU. Advances in microbial isolation and screening technologies[J]. Acta Microbiologica Sinica, 2026 , 66 (5) : 2133 -2147 . DOI: 10.13343/j.cnki.wsxb.20250930

正文

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微生物作为地球上最古老、多样性最丰富且分布最广泛的生物类群，是驱动地球生物化学循环、维持生态系统功能以及影响人类健康与产业发展的关键力量。获得纯培养或简化功能群落的微生物菌株，是深入解析其生物学特性、生态功能及开发其应用潜力的不可替代的基础^[1]。首先，作为基础认知的关键手段，它能够帮助科学家们直接解析常规微生物、极端环境古菌、活的但非可培养(viable but non-cultureable, VBNC)或未培养的细菌等的生理特性、遗传机制与进化关系^[2]，阐明各类微生物与其共生伙伴的互作网络^[3]；其次，微生物分离培养是应用开发的重要源头，可驱动活菌药物^[4]、天然药物、工业酶(嗜盐古菌/嗜热菌酶)^[5-6]、生物修复菌剂^[7]、微生物肥料(如氨氧化古菌)^[8]及生物防治剂^[9]的开发；最后，它更是连接宏基因组数据与功能验证的桥梁，唯有成功分离培养目标微生物，尤其是占比>99%的“不可培养”类群^[10]，才能实证基因功能，最终将海量遗传信息转化为有用的生物学认知与技术资源，从而进一步推动微生物资源的深度发掘与开发应用。

尽管微生物分离筛选意义重大，但其发展仍面临多维度瓶颈：绝大多数微生物(>99%)处于“难培养”状态，尤其低丰度物种易被优势菌掩盖^[10]；复杂的共生、竞争、拮抗作用难以在实验室条件下模拟，这导致依赖互作的严格厌氧古菌、细菌及真菌难以独立分离；并且不同微生物的生长特性差异显著，例如古菌与部分真菌增殖速度缓慢、生长条件需求苛刻^[11]，肠道菌群中的专性厌氧微生物对氧气极度敏感，需复杂无氧系统进行培养^[12]。传统上依据微生物形态特征与生化反应开展的鉴定工作，在面对未培养微生物类群时，难以充分挖掘其物种多样性，无法满足对这类微生物资源深入探索的需求^[2]。这些挑战共同构成了系统性解锁微生物“暗物质”的关键壁垒。

本文以细菌、真菌、古菌为核心分类展开论述，核心依据有三：(1) 三者为微生物域内演化独立、分类清晰的核心类群，覆盖已知绝大多数微生物；(2) 三者生态功能分工互补，分别主导物质循环、有机质降解与极端环境适应，可系统覆盖不同生态位资源挖掘；(3) 三者应用方向各有侧重，是工业酶、天然药物、极端工业功能菌株的主要来源，精准对接不同领域技术转化需求。本文旨在系统梳理细菌、真菌、古菌分离筛选技术的最新进展，通过整合多组学靶向设计、单细胞拉曼功能分选、人工智能驱动的高通量平台等跨学科创新，攻克难培养微生物、复杂互作依赖型微生物及严格厌氧微生物的核心瓶颈，并评述其对连接宏基因组数据与功能验证、加速资源发掘与应用转化的变革性价值，为系统性突破微生物“暗物质”壁垒提供前瞻性技术参考。

1 细菌分离筛选

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细菌分离筛选是微生物资源开发的核心环节，其策略已从依赖传统平板涂布/选择性培养基的经验模式转向多学科技术融合的“理性设计-原位验证”的确定模式。其更加侧重选择性富集，结合多组学靶向设计与单细胞分选，高效捕获低丰度、难培养的细菌类群。本节系统梳理了细菌分选的前沿创新技术。

1.1 多组学驱动的靶向分离策略

宏基因组组装基因组(metagenome-assembled genomes, MAGs)能够直接从环境样本中对未培养的微生物进行基因组分辨研究，彻底改变了微生物生态学。Mirete等^[13]指出，MAGs通过整合高通量测序与先进组装算法，使研究者无需培养即可获取微生物基因组信息，彻底改变了微生物生态学的研究模式。在海洋微生物研究中，Liu等^[14]通过对全球海洋样本的宏基因组分析，重建了82个隶属于玫瑰杆菌属(Roseobacter) RCA集群的MAGs，并结合5个分离株基因组，首次将该集群划分为海洋浮游菌属(Planktomarina)、假海洋浮游菌属(Pseudoplanktomarina)及候选副海洋浮游菌属(Candidatus Paraplanktomarina) 3个属。研究还通过MAGs的生物地理分布分析，发现不同物种对温带至极地海洋环境的适应性差异，证实了MAGs在解析未培养微生物分类地位与生态位分化中的不可替代性^[14]。在肠道微生物组研究中，Chen等^[15]对猪肠道样本进行深度宏基因组测序，构建了6 339个MAGs，聚类为2 673个物种水平基因组箱(species-level genome bins, SGBs)，其中2古菌 309个为未知物种；这些MAGs不仅拓展了猪肠道微生物的参考基因组库，还在菌株水平揭示了颤螺旋菌目(Oscillospirales)及嗜甲基甲烷科(Methanomethylophilaceae)等类群的富集模式，表明MAGs可有效捕捉紧密相关菌株的功能分化。

在细菌、古菌分离领域，MAGs的价值不仅在于揭示未培养微生物的“身份”与生态功能，更在于其能通过Cross等^[16]建立的“反向基因组学”逻辑，将细菌、古菌基因组信息转化为可操作的培养条件设计。在培养条件设计上，可依据MAGs注释的代谢特征，定制含特定碳源、调控盐度的培养基；在靶向筛选时，可以基于MAGs的特异性核心基因设计引物，通过菌落PCR快速排除非目标菌；在后续实验中，结合MAGs的物种共现网络，添加互作微生物培养上清液构建共培养体系，有效解决难培养菌株的生长需求，有效推动“组学驱动靶向分离”。

1.2 单细胞精确定位与分选技术

荧光原位杂交(fluorescence in situ hybridization, FISH)与荧光激活细胞分选(fluorescence-activated cell sorting, FACS)的耦合技术(FISH-FACS)，是靶向捕获特定微生物类群的创新方法，尤其为低丰度、难培养物种的分离提供了精准解决方案。其核心原理是通过荧光标记的寡核苷酸探针特异性识别目标细菌的rRNA或功能基因，再利用FACS依据荧光信号与细胞理化特性(大小、复杂度)实现单细胞水平的高效分选^[17]。Pereira等^[17]系统阐述了该技术的创新：针对环境样本中丰度<0.1%的稀有类群，可通过信号放大策略提升检测灵敏度，结合多标记探针，实现复杂群落中多个目标菌的同时分离；例如，对土壤中细菌的分离，通过16S rRNA靶向探针标记后，FISH-FACS可将其从海量背景菌群中特异性捕获，解决了传统平板法难以富集的难题。在应用层面，Liu等^[18]通过FACS实现单细胞精准加载，结合原位培养体系，成功分离出土壤中40%的未培养微生物，证明FISH-FACS在低丰度菌分选中的高效性。Rinke等^[19]进一步将FISH-FACS与单细胞基因组扩增结合，对分选的目标菌进行全基因组测序，验证了MAG的功能预测，实现了从“序列信息”到“功能验证”的闭环。此外，Oberpaul等^[20]结合微流体芯片与FISH-FACS，通过微滴包裹单细胞并进行荧光标记分选，显著提高了肠道互养厌氧菌的分离效率，为复杂共生菌群的靶向挖掘提供了新思路。该技术通过“标记-分选-鉴定”的闭环流程，有效连接了宏基因组数据与可培养资源，成为解析细菌系统发育与功能关联的关键工具^[17,19]。

单细胞精确定位与分选技术在细菌低丰度、难培养类群分离中的核心价值同样可通过类群特异性优化延伸至真菌与古菌。真菌和古菌共同面临形态复杂、共生依赖，以及丰度较低的分离瓶颈。然而，FISH-FACS耦合技术结合Gate ID算法，为真菌、古菌低丰度难培养类群的精准分离提供了高效方案。针对真菌多形态分化、共生依赖的特性，以及古菌极端生境、低丰度的分离瓶颈，Gate ID算法通过整合单细胞转录组与FACS index数据，仅利用细胞大小、线粒体含量等固有理化特性，结合FISH特异性探针及信号放大技术，大幅提升分选纯度与效率^[21]。然而该技术仍面临探针设计难、样本预处理易损伤细胞、实验与计算成本偏高的局限，需进一步改进优化以推动微生物的功能解析。

1.3 单细胞拉曼光镊分选技术

拉曼光谱属于散射光谱的一种，这一现象由印度物理学家Raman等^[22]于1928年首次发现。其形成源于化合物分子键被激发至虚能态后，在未完全恢复至原始状态时，引发入射光散射并导致频率改变。这种光谱信号与化学键的振动状态直接对应，借助激发光的非弹性散射作用以及分子共振效应，能够捕捉到细胞在特定生理状态下丰富的生化信息，包括核酸、蛋白质、脂质等物质的特征信号^[23]，进而为单个细胞生成独一无二的“化学指纹”。

单细胞拉曼光谱(single-cell Raman spectrum, SCRS)技术具备显著优势：它能基于这种“化学指纹”提供丰富的代谢表型相关信息，实现单细胞层面的精准测量；同时，该技术可进行非标记的原位检测，这一特性使其拥有广泛的普适性应用场景^[23-24]。正因如此，单细胞拉曼光谱技术成为实现高精度单细胞代谢功能探测的关键手段，能通过分子组成特征进一步揭示细胞的生理状态和生态功能。光学镊子结合拉曼光谱(laser tweezers and Raman spectroscopy, LTRS)是一项新兴的细胞筛分技术，光学镊子结合拉曼光谱技术流程见图1。这一技术中，光学镊子的工作原理基于光辐射压，通过高度聚焦的激光束产生引力或斥力，以此对微观中性物体实现物理层面的固定与移动操作^[25]。目前，该技术与拉曼光谱相结合已广泛应用于细菌分离筛选、细胞生理研究以及临床诊断等场景，成为连接微观操控与化学分析的关键纽带。

Liu等^[26]利用该技术针对深海微生物样本量少、信噪比低的挑战，创新性地将渐进生成对抗网络(progressive growing of generative adversarial n, PGGAN)与残差网络(residual network, ResNet)结合；他们从南海塑料垃圾中分离出5株海洋细菌，仅用每株300条原始光谱训练PGGAN生成1 000条高保真增强数据，使ResNet模型在测试集上达到(99.8±0.2)%的分类准确率；该方法将光谱采集时间缩短至传统需求的1/3，解决了深海中样本稀缺的难题。Du等^[27]进一步将LTRS与卷积神经网络(convolutional neural network, CNN)结合，实现了对5种芽孢杆菌孢子的100%准确鉴定；通过解析关键拉曼特征峰，发现660、826、1 017 cm^-1处的吡啶二羧酸(dipicolinic acid, DPA)振动模式是区分芽孢物种的核心依据。

LTRS在细菌、真菌、古菌的鉴定方面具有更广泛的应用，Lu等^[28]利用LTRS捕获14种微生物单细胞，结合卷积神经网络分析拉曼光谱，实现平均95.64%的分类准确率。该方法克服了传统方法需培养细胞的局限，且对细胞存活率影响极小(>95%)，为环境样本中不可培养微生物的鉴定提供了新方案。LTRS凭借单细胞微损捕获、原位分子检测及动态过程解析三重优势，在生理研究、环境监测及微生物鉴定领域中展现出不可替代的价值。

1.4 功能导向的其他创新分选鉴定方法

细菌分选鉴定领域存在多项前沿创新技术。Levy-Booth等^[29]利用稳定同位素探针(stable isotope probing, SIP)在热环境中研究木质素降解菌时，采用¹³C标记的合成木质素脱氢聚合物作为底物，通过监测¹³CO₂释放来验证微生物对木质素的矿化作用，并结合DNA密度梯度离心分离¹³C富集的微生物基因组，成功鉴定出热嗜油菌科(Thermoleophilaceae)、土壤红色杆形菌科(Solirubrobacteraceae)、科鲁比蒂氏菌属(Kyrpidia)、脂环酸芽孢杆菌属(Alicyclobacillus)等具有木质素降解功能的菌株。Li等^[30]利用纳米磁珠靶向捕获技术，通过修饰特异性抗体或寡核苷酸探针的磁珠，使其特异性结合目标细菌表面抗原或核酸序列，在外加磁场作用下实现快速分离，该技术已成功应用于耐甲氧西林金黄色葡萄球菌的高效富集。You等^[31]针对油藏环境功能微生物筛选，采用富集培养基梯度稀释涂布结合多相分类鉴定技术，以宝里格油田油井生产液为样品，经45 ℃选择性培养与反复纯化，通过形态观察、生理生化特征分析、化学分类特征检测及比较基因组分析，成功分离鉴定出栖珊瑚黄杆菌属(Coralloluteibacterium)、嗜热栖珊瑚黄杆菌(Coralloluteibacterium thermophilus)。同样地，Wang等^[32]采用富集培养与多相分类学方法，从中国青海盐土中分离得到一株中度嗜盐球菌W24^T，通过表型、化学分类及16S rRNA基因序列分析，确定其为盐水球菌属(Salinicoccus)的新种耐盐盐水球菌(Salinicoccus halodurans)。Xue等^[33]从中国腾冲碱性热泉中分离到一株嗜热嗜碱杆菌HA6^T，经多相分类鉴定确立其为芽孢杆菌科(Bacillaceae)的新属新种温泉热碱芽孢杆菌(Caldalkalibacillus thermarum)。Zhang等^[34]通过“两步法条形码PCR结合Illumina测序”技术，实现了籼稻和粳稻根系细菌的高通量分离与鉴定；该研究从田间根系样品中大规模分离纯培养细菌，成功获得水稻根系中70%的细菌种类，构建了首个系统性的水稻根系好氧细菌资源库。

细菌分离筛选借助多组学靶向明确培养方向，单细胞分选技术实现精准捕获，功能导向分选鉴定功能菌株，三者按流程阶段适配，突破了难培养、低丰度细菌分离的瓶颈，显著提升了分离效率。各项细菌分离筛选技术方法总结见表1。

2 真菌分离筛选

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真菌分离筛选技术已从传统上依赖孢子结构与菌落特征的形态学鉴定，逐步演进为采用自然微环境复刻、代谢物导向的筛选策略。这类新型筛选策略能有效改善真菌生长缓慢、依赖共生的培养难题，进而推动真菌资源发掘从“表型观察”向“功能驱动”的模式转变。本节将系统梳理真菌分离筛选的前沿创新技术。

2.1 微流控与芯片技术创新

真菌分选领域同样涌现出多项突破性创新技术，例如微流控与芯片技术(图2)。Li等^[35]新开发了一种真菌富集培养法，通过低营养培养基的连续富集，从红树林沉积物中分离的物种数量较直接培养增加近一倍，其中50个物种仅在富集后被发现；利用真菌分离芯片(fungal isolation chips, FiChips)作为原位培养装置，模拟自然环境营养条件，实现真菌单细胞的隔离培养。与传统稀释平板法相比，FiChips从红树林沉积物中分离出94个物种，其中44个为潜在新物种，且30个物种无法通过高通量测序检测，凸显了其在挖掘“暗物质”真菌方面的优势。与此同时，微流控系统为真菌孢子的快速检测与分选提供了新方案。Li等^[36]利用该系统集成富集芯片与检测芯片，通过免疫荧光分析实现空气中黑曲霉(Aspergillus niger)孢子的半定量检测，检测限低至20个孢子(相当于300个孢子/m³)，总分析时间仅2-3 h，为空气传播致病性真菌的即时监测奠定了基础。此外，Inman等^[37]通过优化的真菌培养基(fungal roswell park memorial institute, fRPMI)整合多种生长促进成分，显著改善12种真菌的生长效率。例如，白念珠菌(Candida albicans)、热带念珠菌(Candida tropicalis)在fRPMI中24 h的生长量显著高于传统RPMI，而慢生长的近平滑念珠菌(Candida parapsilosis)仅在fRPMI中达到可检测水平(OD₅₃₀≥0.2)，结合高通量筛选平台，成功从500个化学片段中识别出23个抗真菌活性化合物，为真菌功能研究与药物开发提供了高效工具。

2.2 人工智能与自动化高通量平台

人工智能与自动化技术的深度融合，正推动微生物分离筛选进入智能化、高通量的新阶段。相较于传统微生物培养方法高度依赖操作者经验且通量有限的局限，人工智能通过整合多维度数据，实现了对微生物生长模式与形态分析的精准预测，显著提升了难培养真菌的分离效率。这一变革的主要研究方向是自动化微生物分离培养与成像系统。例如，Huang等^[38]开发的CAMII系统，主要针对细菌和部分真菌，将自动化成像、机器人挑菌与机器学习分析无缝整合；该系统在厌氧环境下，通过双光源成像自动采集菌落的大小、形状、颜色、纹理等数十个形态学特征，并利用主成分分析等算法将其量化为可解析的表型数据；随后，系统采用“智能挑取”策略，在形态特征构建的多维欧氏空间中，优先选择距离最远的点(即形态差异最大的菌落)进行分离；实践证明，相较于随机挑取法，该策略分离出相同数量物种水平操作分类单元所需的菌落挑取量减少了约80%，显著提升了对稀有和难培养类群的挖掘效率。更重要的是，AI在此过程中实现了从“描述关联”到“主动预测”的功能跃迁。通过对海量菌落形态与基因组数据进行关联学习，AI模型能够建立从表型到基因型的预测桥梁。在CAMII构建的包含近2.7万个分离株的生物库基础上，研究者训练了随机森林分类器，例如通过辨别细菌、真菌菌落的形态特征即可对其所属的属级分类单元进行预测，对部分形态特征鲜明的类群预测精度超过70%^[38]。这一能力使得靶向分离成为可能：当研究者需要从复杂样本中特异性富集某一目标类群时，AI模型可对培养板上所有菌落进行实时扫描与鉴定，指导挑菌机器人精准抓取预测为目标属的菌落，将特定菌属的分离效率提升高达8倍，彻底改变了以往依赖“盲目大量挑取”的被动筛选模式。CAMII系统对真菌的分离筛选具备良好的适配性，其智能化的分析与筛选逻辑，为解决真菌培养的各类难题提供了全新技术思路，对真菌分离筛选的技术发展形成了强有力的实践与理论指导。

此外，AI算法还能从微生物组大数据中挖掘更深层的生物学规律，为真菌分离培养提供生态学视角。通过分析物种共现、互斥网络以及环境参数，AI可以预测微生物间的协同互作或竞争关系，从而指导设计更合理的共培养体系，以促进那些依赖其他微生物提供生长因子的难培养菌的生长^[39]。同时，结合宏基因组数据，AI还能预测微生物在特定生态位中的功能角色，提前锁定具有污染物降解、药物开发等应用潜力的目标菌株，实现从“盲目筛选”到“功能驱动筛选”的战略转变^[40]。

2.3 代谢组学驱动的功能筛选

真菌在生长过程中会产生种类丰富的次生代谢产物，这些代谢物具有物种特异性，可作为化学标志物用于功能菌株筛选^[41]。高效液相色谱-质谱联用技术(high-performance liquid chromatography, HPLC-MS)通过靶向检测特征代谢物，能够快速从复杂样本中识别目标真菌，克服了传统形态学鉴定耗时、依赖经验的局限性。2018年，Saldan等^[42]针对黄曲霉(Aspergillus flavus)的鉴定需求，建立了基于HPLC-MS的化学标志物筛选方法；该研究通过甲醇提取黄曲霉在马铃薯葡萄糖琼脂(potato dextrose agar, PDA)和察氏琼脂培养基(czapek dox agar, CDA)培养基中的次生代谢物，结合QTOF-MS的高分辨率检测，共鉴定出14种特异性代谢物，包括黄曲霉毒素B1、黄曲霉毒素G1、曲霉酸等；这一研究证实，HPLC-MS可通过化学标志物的特异性识别，实现对产毒真菌的快速筛查。2021年，Mohammed等^[43]对沙特阿拉伯沙漠土壤中分离的毛壳菌(Chaetomium sp.)、平脐蠕孢菌(Bipolaris sp.)、有毒镰刀菌(Fusarium venenatum)进行了液相色谱-四极杆飞行时间质谱(high performance liquid chromatography-mass spectrometry, LC-MS-QTOF)代谢组学分析；从Chaetomium sp.中鉴定出9种主要代谢物(如cochliodone A、细胞松弛素B)；从Bipolaris sp.中鉴定出3种特征化合物，并通过关联这些代谢物与菌株的抗菌、抗癌活性，发现F. venenatum的提取物对结直肠癌细胞HCT8的抑制活性与特定未鉴定代谢物的积累相关；该研究表明，LC-MS可通过代谢组学数据与生物活性的关联分析，定向筛选具有药用潜力的真菌菌株，为天然产物开发提供候选资源。李淼等^[44]在白地霉(Geotrichum candidum)应用研究的综述中，整合了不同研究团队的实践数据，明确HPLC-MS靶向检测脂肪酶、蛋白酶、抗菌肽等特征代谢产物，是白地霉功能菌株筛选的核心技术手段；不同来源的白地霉展现出差异化功能特性，而这些功能差异均可通过代谢组学技术精准捕获。

综上所述，微流控/芯片技术通过模拟自然微环境优化富集培养与单细胞隔离，AI则赋能菌落精准筛选与高通量分析，代谢组学依托特征代谢物检测实现靶标识别与功能验证。三者按分离流程阶段化适配，联合应用可突破传统技术局限，显著提升真菌分离效率与成功率，为挖掘“暗物质”真菌提供关键支撑。各项真菌分离筛选技术方法总结见表2。

3 古菌分离筛选

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古菌作为地球生命演化中最古老的分支之一，涵盖产甲烷古菌、嗜盐古菌、嗜热古菌及氨氧化古菌等类群，其分离筛选是解析极端环境适应机制、碳氮循环功能及生命进化起源的核心前提。与细菌和真菌相比，古菌因生存环境特殊、代谢依赖共生互作、生长缓慢，在传统平板培养中难以形成可见菌落，导致其多样性被严重低估，分离培养过程十分艰难，绝大多数类群仍处于未培养或难培养状态^[10,45-46]。近年来，通过古菌培养系统优化、共生互作模拟、设计极端环境适应性培养基及跨学科工程技术，古菌分离筛选已从“经验试错”转向“理性靶向”模式，逐步突破传统技术瓶颈。

3.1 古菌培养系统的精准优化

严格厌氧是产甲烷古菌、厌氧氨氧化古菌等类群分离的核心要求，这类微生物的核心代谢酶(如产甲烷古菌的甲基辅酶M还原酶、厌氧氨氧化古菌的肼氧化酶)对氧气极其敏感，即使纳摩尔级的氧气也会导致酶活性不可逆失活^[11,47]，因此整合气体调控对于这类古菌极为关键。Hofmann等^[48]研发了气体压力控制器(gas and pressure controller, GPC)装置，该装置专为封闭培养瓶的顶空气压自动化控制设计，能够实时完成通气、鼓泡、监测及顶空体积调节等操作，从根本上解决了传统厌氧培养中气体限制的核心问题，实现封闭式批次培养自动化，精准供给H₂/CO₂等气体底物；在产甲烷菌研究中，使火甲烷火温菌(Methanotorris igneus)的产甲烷效率再创新高，实现了封闭式批次培养的自动化与精准化革新。传统亨盖特(Hungate)厌氧操作技术^[49] (如厌氧手套箱、滚管法)虽能提供基础厌氧环境，但难以应对极端需求或长期培养中的微氧污染。Wolfe等^[50]提出了一种制备小体积缺氧溶液和培养基的方法，使用真空气体循环与对整个系统进行涡旋以增加液体表面积的组合，能有效地将氧气从培养容器中排出；该方法的主要优点是能快速制备出缺氧溶液，相较于使用传统的气体吹扫法更加节省时间。对于一些耐高温高压的古菌，Pappenreiter等^[51]开发的同步生物反应器系统可模拟5 MPa高压、145 ℃高温等极端生境，通过独立温控和压力传感，精准测定动力学参数，为耐高压古菌研究提供高效初筛平台。近年来，通过整合气体调控、培养装置自动化的改进，厌氧培养系统已实现从“静态维持”到“动态精准调控”的升级。

3.2 共生互作模拟与共培养策略

与纯培养相比，共培养能让古菌之间更有效地相互交流。许多古菌的基因组较小，缺乏负责关键代谢途径的重要基因^[46]。这些古菌只有在营养合适且丰富的培养基中生长，或者在另一种能够产生其生长所需代谢物的古菌周围生长。这种现象可能是单向的，也可能是双向的。例如在甲烷氧化菌和产甲烷菌的生长过程中，它们为彼此的生长提供了环境。甲烷氧化古菌利用氧气，为产甲烷菌提供厌氧条件，而产甲烷菌产生的甲烷被甲烷氧化菌用作碳源^[52]。常规古菌共培养策略见图3。

通过共培养方法成功分离出古菌有许多例子，Traore等^[53]开创性地提出“琼脂平板分区共培养法”，彻底摆脱了复杂的气体供给装置；该方法将厌氧琼脂平板划分为独立区域，分别接种目标产甲烷古菌与产氢细菌，通过气相扩散实现代谢互作。超门古菌(Diapherotrites、Parvarchaeota、Aenigmarchaeota、Nanoarchaeota、Nanohaloarchaeota superphylum, DPANN)普遍依赖宿主获取营养，其培养需严格模拟与宿主的共生关系。Johnson等^[54]发现多变共生微热菌(Microcaldus variisymbioticus) ARM-1与宿主形成稳定共培养体系，通过宿主产生的蛋白质纳米管实现细胞质间物质交换，宿主还会上调碳固定和氨基酸代谢相关基因以支持ARM-1生长；这表明识别并引入天然宿主是DPANN古菌培养的核心。此外，巴氏甲烷八叠球菌(Methanosarcina barkeri)与光合细菌沼泽红假单胞菌(Rhodopseudomonas palustris)的共培养体系中，R. palustris通过光合作用将硫代硫酸盐氧化为电子，直接或通过电子穿梭体传递给M. barkeri，驱动其将CO₂还原为甲烷，甲烷生成速率达(4.73±0.23) μmol/(L·h)，且依赖光照^[55]。古菌的共培养还使用了诸如荧光原位杂交、低倍二维冷冻电子显微镜以及冷冻断层扫描等其他技术，来鉴定和确认通过富集共培养分离出的古菌^[56]。

3.3 极端环境适应性培养基设计

针对不同生态类型的古菌设计特定培养基，需以其天然生境的理化特性和代谢需求为核心依据。例如嗜酸古菌硫化叶菌属(Sulfolobus)常栖息于pH 1.0-4.0的热泉环境，Brock等^[57]通过将培养基pH值调节至2.0-3.0模拟酸性条件，同时添加酵母提取物提供有机营养，硫作为能量代谢底物，这种培养基成功从美国黄石国家公园酸性热泉中分离出该类群；其关键在于精准匹配酸性环境下的营养利用偏好，因为硫的存在既满足能量需求，又维持环境平衡。嗜盐古菌依赖高盐环境维持细胞结构^[6]，Brown等^[58]将改良的产甲烷菌生长培养基(methanogenium growth medium, MGM)通过高浓度NaCl维持渗透压，同时添加硫酸镁、氯化钙等矿物质模拟天然盐湖成分，并以酵母提取物和酪蛋白水解物提供碳氮源；这种设计从伊朗沙漠高盐河流中分离出59株耐盐古菌，部分菌株可耐受30%盐浓度，这印证了盐浓度与营养供给的协同作用。氢营养型产甲烷菌的培养基需严格厌氧，除添加H₂和CO₂作为碳源和能源外，还需硫化物消除氧气干扰，并补充镍、钴等微量元素，这些是产甲烷阶段关键酶(如甲基辅酶M还原酶)的必需辅因子。此外，古菌自身合成维生素、氨基酸、核苷等必需生长因子的能力较弱，配制培养基时需针对性添加有机营养物质(如酵母提取物、维生素等)^[47]。然而，古菌在培养过程中易被杂菌污染，需同步做好杂菌防控的操作衔接，例如添加氨苄西林、青霉素等抗生素抑制细菌生长^[47]。针对其他超嗜热特性古菌的培养，培养基需在高温高压反应器中使用，确保其在122 ℃下仍能生长，确保对极端环境参数的精准复刻^[59]。这些培养基设计均遵循天然生境的理化特性和代谢需求，通过还原关键环境因子(如盐度、pH、气体组分)和靶向供给营养，为不同类群古菌提供了生长基础。

古菌的培养系统优化能够复刻极端微环境，共生共培养策略解决营养依赖，极端培养基设计匹配生境需求，三者相辅相成，显著提升古菌分离筛选效率，为解锁极端环境古菌资源奠定了坚实基础。各项古菌分离筛选技术方法总结见表3。

4 总结与展望

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尽管微生物分离筛选技术在多组学、人工智能、单细胞技术与微工程平台的推动下取得了突破性进展，显著提升了对细菌、真菌和古菌中“难培养”类群的捕获能力，但仍面临一系列关键挑战。从技术整合层面看，当前多数方法仍处于“技术孤岛”状态，多组学、人工智能与实验验证之间的数据流尚未实现高效闭环。例如，基于MAGs的靶向分离虽能锁定潜在新物种，但在缺乏原位功能验证的情况下，其代谢潜力与生态功能难以实证；单细胞拉曼与光镊分选技术虽具备对细胞损伤较小、精准原位探测等优势，却受限于通量低、成本高、光谱解析的标准化不足与数据库覆盖不全等问题。尤其在面对环境样本中高度多样性的微生物以及针对丝状或形态复杂的微生物，分类准确率与通量仍有提升空间。人工智能模型尽管在菌落识别与互作预测中展现出潜力，但其性能高度依赖高质量、多来源的训练数据，当前微生物表型-基因型关联数据的匮乏限制了模型的泛化能力与可解释性。在方法适用性方面，现有技术对极端环境微生物和严格互作依赖型古菌的培养仍存在明显短板。例如，尽管共生共培养策略成功分离了部分DPANN古菌与产甲烷菌，但其宿主范围狭窄、互作机制复杂，难以实现高通量推广；微流控与芯片技术虽能模拟微环境，但在长时间维持极端理化条件(如高温、高压、极酸)方面仍面临材料与稳定性瓶颈。此外，多数自动化平台尚未实现对厌氧过程的全程无缝集成，氧气敏感型微生物在分选、转移与培养过程中易因微氧暴露而失活。

未来发展方向应聚焦于构建数据和实验双向驱动的智能分离体系，推动技术从碎片化应用走向体系化。需强化多组学数据与培养实验的实时联动，开发基于宏基因组、代谢组动态反馈的培养基自适应优化系统，针对细菌、真菌、古菌的分离特性定制专属调控模块，让培养条件随微生物生长状态动态调整，打破传统培养基固定化的局限。此外，应推动跨平台技术融合，如将单细胞拉曼与纳米孔测序联用，实现“光谱识别-基因组验证”的一体化流程，同步完善微生物光谱解析数据库，提升环境样本中高多样性类群的分选准确率与通量；依托人工智能模型将机器人挑菌、厌氧培养微环境调控进行深度整合，实现菌落识别、种间互作因子预测，构建具备学习、决策、执行能力的全自动化培养平台。最后，还需要重点拓展极端环境、宿主相关微生物的标准化培养方案与表型数据库，为AI模型训练提供高质量多源数据，提升模型的泛化能力与可解释性。通过以上针对性的技术攻关，微生物分离筛选将真正实现从“经验驱动”向“功能与数据驱动”的跨越，系统性地解锁微生物“暗物质”，为生命科学基础研究与生物技术创新提供永不枯竭的资源基石。

基金

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国家重点研发计划(2022YFC3902403)

参考文献

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文献

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2026年第66卷第5期

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doi: 10.13343/j.cnki.wsxb.20250930

接收时间：2025-12-15
首发时间：2026-05-09
出版时间：2026-05-04

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收稿日期：2025-12-15
录用日期：2026-03-03

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The National Key Research and Development Program of China(2022YFC3902403)

国家重点研发计划(2022YFC3902403)

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^1.北京联合大学生物化学工程学院，北京

^2.北京联合大学，生物质废弃物资源化利用北京市重点实验室，北京

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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TxT

Core screening strategy	Representative technologies and methods	Technical advantages	References
Multi-omics driven targeted isolation	Metagenome-assembled genomes (MAGs) analysis, genome-targeted medium design	Obtain genomic blueprints directly from environmental samples, guide the rational design of culture media, and have strong targeting	[13-16]
Single-cell precise localization and sorting	Fluorescence in situ hybridization-fluorescence-activated cell sorting (FISH-FACS), microfluidic sorting	Realize accurate identification and high-throughput sorting of low-abundance cells in complex communities, and break away from the dependence on cultivation	[17-21]
Single-cell Raman tweezers sorting	Laser tweezers Raman spectroscopy system (LTRS)	Realize label-free, non-destructive, in-situ acquisition of single-cell chemical fingerprints, and achieve functional sorting and identification of viable cells	[22-28]
Function-oriented in-situ screening	Stable isotope probing (SIP), magnetic nanoparticle-based targeted capture, enrichment culture, high-throughput screening	Directly lock on viable cells with specific metabolic functions (e.g., degradation, pathogenicity) in the environment	[29-34]

Core screening strategy

Representative technologies and methods

Technical advantages

References

Multi-omics driven targeted isolation

Metagenome-assembled genomes (MAGs) analysis, genome-targeted medium design

Obtain genomic blueprints directly from environmental samples, guide the rational design of culture media, and have strong targeting

[13-16]

Single-cell precise localization and sorting

Fluorescence in situ hybridization-fluorescence-activated cell sorting (FISH-FACS), microfluidic sorting

Realize accurate identification and high-throughput sorting of low-abundance cells in complex communities, and break away from the dependence on cultivation

[17-21]

Single-cell Raman tweezers sorting

Laser tweezers Raman spectroscopy system (LTRS)

Realize label-free, non-destructive, in-situ acquisition of single-cell chemical fingerprints, and achieve functional sorting and identification of viable cells

[22-28]

Function-oriented in-situ screening

Stable isotope probing (SIP), magnetic nanoparticle-based targeted capture, enrichment culture, high-throughput screening

Directly lock on viable cells with specific metabolic functions (e.g., degradation, pathogenicity) in the environment

[29-34]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Microenvironment simulation and high-throughput cultivation	Fungal isolation chips (FiChips), microfluidic spore detection, nutrient enrichment culture	Simulate natural microenvironments on chips, realize single-cell isolation culture, and significantly improve isolation efficiency and diversity	[35-37]
Artificial intelligence and automated platform	AI-driven colony imaging and identification, high-throughput colony picking robot, growth prediction model	High throughput and efficiency, reduce human error, identify weak growth and predict interspecific interaction relationships	[38-40]
Metabolomics-driven functional screening	High performance liquid chromatography-mass spectrometry (HPLC-MS/QTOF) metabolite analysis	Bypass time-consuming morphological identification, and rapidly screen and identify strains with application potential through chemical markers	[41-44]

Core screening strategy

Representative technologies and methods

Technical advantages

References

Microenvironment simulation and high-throughput cultivation

Fungal isolation chips (FiChips), microfluidic spore detection, nutrient enrichment culture

Simulate natural microenvironments on chips, realize single-cell isolation culture, and significantly improve isolation efficiency and diversity

[35-37]

Artificial intelligence and automated platform

AI-driven colony imaging and identification, high-throughput colony picking robot, growth prediction model

High throughput and efficiency, reduce human error, identify weak growth and predict interspecific interaction relationships

[38-40]

Metabolomics-driven functional screening

High performance liquid chromatography-mass spectrometry (HPLC-MS/QTOF) metabolite analysis

Bypass time-consuming morphological identification, and rapidly screen and identify strains with application potential through chemical markers

[41-44]

Core screening strategy	Representative technologies and methods	Technical advantages	References
Cultivation system precise optimization	Automated gas pressure control (GPC), high-pressure bioreactor, vacuum-vortex deoxygenation method	Accurately reproduce and dynamically regulate extreme environments (anaerobic, high temperature), and overcome bottlenecks such as oxygen sensitivity of core enzymes	[11,47-51]
Symbiotic interaction simulation and co-cultivation	Zoned co-cultivation on agar plates, co-cultivation with host bacteria, construction of electro-syntrophic systems	Solve the genomic nutrient dependence of archaea by providing essential symbiotic partners or metabolites	[46,52-56]
Extreme environment-adaptive medium design	Simulate the pH, salinity, temperature and trace element composition of natural habitats	Provide the basic growth foundation for special taxa by restoring key physicochemical factors of their native habitats	[6,57-59]

Core screening strategy

Representative technologies and methods

Technical advantages

References

Cultivation system precise optimization

Automated gas pressure control (GPC), high-pressure bioreactor, vacuum-vortex deoxygenation method

Accurately reproduce and dynamically regulate extreme environments (anaerobic, high temperature), and overcome bottlenecks such as oxygen sensitivity of core enzymes

[11,47-51]

Symbiotic interaction simulation and co-cultivation

Zoned co-cultivation on agar plates, co-cultivation with host bacteria, construction of electro-syntrophic systems

Solve the genomic nutrient dependence of archaea by providing essential symbiotic partners or metabolites

[46,52-56]

Extreme environment-adaptive medium design

Simulate the pH, salinity, temperature and trace element composition of natural habitats

Provide the basic growth foundation for special taxa by restoring key physicochemical factors of their native habitats

[6,57-59]