Single‐cell genomics based on Raman sorting reveals novel carotenoid‐containing bacteria in the Red Sea

Cell sorting coupled with single‐cell genomics is a powerful tool to circumvent cultivation of microorganisms and reveal microbial ‘dark matter’. Single‐cell Raman spectra (SCRSs) are label‐free biochemical ‘fingerprints’ of individual cells, which can link the sorted cells to their phenotypic information and ecological functions. We employed a novel Raman‐activated cell ejection (RACE) approach to sort single bacterial cells from a water sample in the Red Sea based on SCRS. Carotenoids are highly diverse pigments and play an important role in phototrophic bacteria, giving strong and distinctive Raman spectra. Here, we showed that individual carotenoid‐containing cells from a Red Sea sample were isolated based on the characteristic SCRS. RACE‐based single‐cell genomics revealed putative novel functional genes related to carotenoid and isoprenoid biosynthesis, as well as previously unknown phototrophic microorganisms including an unculturable Cyanobacteria spp. The potential of Raman sorting coupled to single‐cell genomics has been demonstrated.     

Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution

Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.     

Using Raman spectroscopy and chemometrics to identify the growth phase of <Emphasis Type="Italic">Lactobacillus casei</Emphasis> Zhang during batch culture at the single-cell level

Background

As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment.

Methods

In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS.

Results

Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%.

Conclusions

To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

     

一种酵母细胞生长现象的实时单细胞拉曼光谱观察

用拉曼镊子观察单个即发活性干酵母(Saccharomy cescerevisiae)细胞在2.0%葡萄糖溶液中的活化过程,收集其拉曼光谱。结果发现,在某一批次的产品中,酵母细胞的1364cm-1峰强度随着细胞的活化而显著增加,531cm-1、652cm-1、1053cm-1等源自葡萄糖或葡萄糖基的信号峰也会随细胞的生长而增强,随后增强的还有1432cm-1、1448cm-1、1561cm-1等源自脂类物质的峰,而源自蛋白质及脂类的1000cm-1、1445cm-1、1655cm-1等峰的信号强度基本不变,酵母细胞代谢活跃的标志峰1603cm-1也基本不变。该批次产品中,10次实验有7次观察到上述现象,而在别的批次产品中并没有观察到该现象。用单细胞拉曼光谱实时记录了这一特殊的生长现象。     

Research Progress of Raman Spectroscopy in Drug Analysis

Raman spectroscopy is a spectroscopic analysis technique that enables rapid qualitative and quantitative detection based on inelastic collision and Raman scattering intensity. This review detailed the generation principle, instrument composition, influencing factors, and common classifications of Raman spectrum. Furthermore, it summarized and forecast the research progress of Raman spectroscopy in the field of drug analysis simultaneously over the past decade, including the identification of active pharmaceutical ingredients (APIs), qualitative and quantitative studies of pharmaceutical preparations, detection of illicit drugs, the identification of Chinese herbal medicines, and the combination with other technologies. The development of Raman spectroscopy in other fields is additionally summarized.     

Raman and Autofluorescence Spectrum Dynamics along the HRG-Induced Differentiation Pathway of MCF-7 Cells

Cellular differentiation proceeds along complicated pathways, even when it is induced by extracellular signaling molecules. One of the major reasons for this complexity is the highly multidimensional internal dynamics of cells, which sometimes causes apparently stochastic responses in individual cells to extracellular stimuli. Therefore, to understand cell differentiation, it is necessary to monitor the internal dynamics of cells at single-cell resolution. Here, we used a Raman and autofluorescence spectrum analysis of single cells to detect dynamic changes in intracellular molecular components. MCF-7 cells are a human cancer-derived cell line that can be induced to differentiate into mammary-gland-like cells with the addition of heregulin (HRG) to the culture medium. We measured the spectra in the cytoplasm of MCF-7 cells during 12 days of HRG stimulation. The Raman scattering spectrum, which was the major component of the signal, changed with time. A multicomponent analysis of the Raman spectrum revealed that the dynamics of the major components of the intracellular molecules, including proteins and lipids, changed cyclically along the differentiation pathway. The background autofluorescence signals of Raman scattering also provided information about the differentiation process. Using the total information from the Raman and autofluorescence spectra, we were able to visualize the pathway of cell differentiation in the multicomponent phase space.     

Imaging the invisible—Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super‐multiplexing capability can be seen in the recent development of Raman‐active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug‐cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence‐based imaging without the need of bulky fluorescent tags.     

Raman Tweezers Spectroscopy of Live,Single Red and White Blood Cells

An optical trap has been combined with a Raman spectrometer to make high-resolution measurements of Raman spectra of optically-immobilized, single, live red (RBC) and white blood cells (WBC) under physiological conditions. Tightly-focused, near infrared wavelength light (1064 nm) is utilized for trapping of single cells and 785 nm light is used for Raman excitation at low levels of incident power (few mW). Raman spectra of RBC recorded using this high-sensitivity, dual-wavelength apparatus has enabled identification of several additional lines; the hitherto-unreported lines originate purely from hemoglobin molecules. Raman spectra of single granulocytes and lymphocytes are interpreted on the basis of standard protein and nucleic acid vibrational spectroscopy data. The richness of the measured spectrum illustrates that Raman studies of live cells in suspension are more informative than conventional micro-Raman studies where the cells are chemically bound to a glass cover slip.     

Multiplex single‐cell quantification of rare RNA transcripts from protoplasts in a model plant system

Here we demonstrate multiplex and simultaneous detection of four different rare RNA species from plant, Arabidopsis thaliana, using surface‐enhanced Raman spectroscopy (SERS) and gold nanoprobes at single‐cell resolution. We show the applicability of nanoparticle‐based Raman spectroscopic sensor to study intracellular RNA copies. First, we demonstrate that gold‐nanoparticles decorated with Raman probes and carrying specific nucleic acid probe sequences can be uptaken by the protoplasts. We confirm the internalization of gold nanoprobes by transmission electron microscopy, inductively‐coupled plasma‐mass spectrometry and fluorescence imaging. Second, we show the utility of a SERS platform to monitor individual alternatively spliced (AS) variants and miRNA copies within single cells. Finally, the distinctive spectral features of Raman‐active dyes were exploited for multiplex analysis of AtPTB2, AtDCL2, miR156a and miR172a. Furthermore, single‐cell studies were validated by in vitro quantification and evaluation of nanotoxicity of gold probes. Raman tag functionalized gold nanosensors yielded an approach for the tracking of rare RNAs within the protoplasts. The SERS‐based approach for quantification of RNAs has the capability to be a highly sensitive, accurate and discerning method for single‐cell studies including AS variants quantification and rare miRNA detection in specific plant species.     

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.     

单细胞拉曼技术在病原微生物检测中的研究进展

单细胞拉曼技术是基于拉曼光谱分析原理实现非培养、无标签、快速、高效、低成本揭示物质本质的新方法。近年来,单细胞拉曼技术也开始在病原微生物检测领域崭露头角。本文结合单细胞拉曼技术基本原理这一理论基础,阐述该技术在病原微生物鉴定、药物敏感性检测中的研究进展及最新技术方向,并探讨其在临床实验室应用的可行性,为未来病原微生物检测技术提供新的方向。     

In situ non-invasive spectral discrimination between bone cell phenotypes used in tissue engineering

Raman micro-spectroscopy was used to discriminate between different types of bone cells commonly used in tissue engineering of bone, with the aim of developing a method of phenotypic identification and classification. Three types of bone cells were analysed: human primary osteoblasts (HOB), retroviral transfected human alveolar bone cells with SV40 large T antigen (SV40 AB), and osteoblast-like human osteosarcoma derived MG63 cell line. Unsupervised principal component analysis (PCA) and linear discriminant analysis (LDA) of the Raman spectra succeeded in discriminating the osteosarcoma derived MG63 cells from the non-tumour cells (HOB and SV40 AB). No significant differences were observed between the Raman spectra of the HOB and SV40 AB cells, confirming the biochemical similarities between the two cell types. Difference spectra between tumour and non-tumour cells suggested that the spectral discrimination is based on the fact that MG63 osteosarcoma derived cells are characterised by lower concentrations of nucleic acids and higher relative concentrations of proteins compared to the non-tumour bone cells. A supervised classification model (LDA) was built and showed high cross-validation sensitivity (100%) and specificity (95%) for discriminating the MG63 cells and the non-tumour cells, with 96% of the cells being correctly classified either as tumour or non-tumour derived cells. This study proves the feasibility of using Raman spectroscopy to identify in situ phenotypic differences in living cells.     

Molecular-level investigation of the structure, transformation, and bioactivity of single living fission yeast cells by time- and space-resolved Raman spectroscopy

The structure, transformation, and bioactivity of single living Schizosaccharomyces pombe cells at the molecular level have been studied in vivo by time- and space-resolved Raman spectroscopy. A time resolution of 100 s and a space resolution of 250 nm have been achieved with the use of a confocal Raman microspectrometer. The space-resolved Raman spectra of living S. pombe cells at different cell cycle stages were recorded in an effort to elucidate the molecular compositions of organelles, including nuclei, cytoplasm, mitochondria, and septa. The time- and space-resolved measurement of the central part of a dividing yeast cell showed continuous spectral evolution from that of the nucleus to those of the cytoplasm and mitochondria and finally to that of the septum, in accordance with the transformation during the cell cycle. A strong Raman band was observed at 1602 cm(-)(1) only when cells were under good nutrient conditions. The effect of a respiration inhibitor, KCN, on a living yeast cell was studied by measuring the Raman spectra of its mitochondria. A sudden disappearance of the 1602 cm(-)(1) band followed by the change in the shape and intensity of the phospholipid bands was observed, indicating a strong relationship between the cell activity and the intensity of this band. We therefore call this band "the Raman spectroscopic signature of life". The Raman mapping of a living yeast cell was also carried out. Not only the distributions of molecular species but also those of active mitochondria in the cell were successfully visualized in vivo.     

基于拉曼技术的单细胞生长检测方法

目的 单细胞生长检测可以更加科学地揭示微生物代谢变化的规律,为后期微生物工程应用提供指导。针对微生物生长应用于食品安全期和最佳食用期的精准检测问题,本文提出一种基于拉曼技术的单细胞生长检测方法。方法 首先,通过同步培养实验采集了枯草芽孢杆菌两个批次共900个单细胞拉曼光谱（SCRS）数据,其中600个用于训练和测试,另一批次300个用于模型验证。其次,基于主成分分析的特征关系矩阵,提出CP-SP特征评估方法以筛选SCRS特征用于模型检测。再基于XGBoost构建检测模型,并应用网格搜索和交叉验证对检测模型进行调优。最后,应用混淆矩阵、ROC曲线评估模型对细胞滞后期、对数期和稳定期的检测准确率、敏感性和特异性。结果 选用CP-SP筛选的第一、第二和第四主成分较特征贡献率前3个主成分的分类性能提高了3.1%,调优后的细胞生长检测模型测试准确率为96.0%,验证准确率为92.3%。结论 基于拉曼技术的单细胞生长检测方法能准确识别单细胞生长状态且具有较高的泛化能力,可为食品安全和保鲜制定精准调控机制提供科学指导。     

Polarization sensitive coherent anti-Stokes Raman scattering spectroscopy of the amide I band of proteins in solutions.

Polarization sensitive coherent anti-Stokes Raman scattering (PCARS) spectroscopy is a fruitful technique to study Raman vibrations of diluted molecules under off-electron resonant conditions. We apply PCARS as a direct spectroscopic method to investigate the broad amide I band of proteins in heavy water. In spontaneous Raman spectroscopy, this band is not well resolved. We fit a number of spectra taken of each protein under different polarization conditions, with a single set of parameters. It then appears that some substructure is observed in the amide I band. From this substructure, we determine the percentage of alpha-helix, beta-sheet, and random coil for the proteins lysozyme, albumin, ribonuclease A, and alpha-chymotrypsin.     

Non-destructive analysis of the nuclei of transgenic living cells using laser tweezers and near-infrared raman spectroscopic technique

Transgenic cell lines of loblolly pine （Pinus taeda L.） were analyzed by a compact laser-tweezers-Raman-spectroscopy （LTRS） system in this investigation. A low power diode laser at 785 nm was used for both laser optical trapping of single transgenic cells and excitation for near-infrared Raman spectroscopy of the nuclei of synchronized cells, which were treated as single organic particles, at the S-phase of the cell cycle. Transgenic living cells with gfp and uidA genes were used as biological samples to test this LTRS technique. As expected, different Raman spectra were observed from the tested biological samples. This technique provides a high sensitivity and enables real-time spectroscopic measurements of transgenic cell lines. It could be a valuable tool for the study of the fundamental cell and molecular biological process by trapping single nucleus and by providing a wealth of molecular information about the nuclei of cells.     

Non-invasive analysis of cell cycle dynamics in single living cells with Raman micro-spectroscopy

Raman micro-spectroscopy is a laser-based technique which enables rapid and non-invasive biochemical analysis of cells and tissues without the need for labels, markers or stains. Previous characterization of the mammalian cell cycle using Raman micro-spectroscopy involved the analysis of suspensions of viable cells and individual fixed and/or dried cells. Cell suspensions do not provide cell-specific information, and fixing/drying can introduce artefacts which distort Raman spectra, potentially obscuring both qualitative and quantitative analytical results. In this article, we present Raman spectral characterization of biochemical changes related to cell cycle dynamics within single living cells in vitro. Raman spectra of human osteosarcoma cells synchronized in G(0)/G(1), S, and G(2)/M phases of the cell cycle were obtained and multivariate statistics applied to analyze the changes in cell spectra as a function of cell cycle phase. Principal components analysis identified spectral differences between cells in different phases, indicating a decrease in relative cellular lipid contribution to Raman spectral signatures from G(0)/G(1) to G(2)/M, with a concurrent relative increase in signal from nucleic acids and proteins. Supervised linear discriminant analysis of spectra was used to classify cells according to cell cycle phase, and exhibited 97% discrimination between G(0)/G(1)-phase cells and G(2)/M-phase cells. The non-invasive analysis of live cell cycle dynamics with Raman micro-spectroscopy demonstrates the potential of this approach to monitoring biochemical cellular reactions and processes in live cells in the absence of fixatives or labels.     

The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy

Laser tweezers Raman spectroscopy (LTRS) was used to characterize the effect of different chemical fixation procedures on the Raman spectra of normal and leukemia cells. Individual unfixed, paraformaldehyde-fixed, and methanol-fixed normal and transformed lymphocytes from three different cell lines were analyzed with LTRS. When compared to the spectra of unfixed cells, the fixed cell spectra show clear, reproducible changes in the intensity of specific Raman markers commonly assigned to DNA, RNA, protein, and lipid vibrations (e.g. 785, 1230, 1305, 1660 cm(-1)) in mammalian cells, many of which are important markers that have been used to discriminate between normal and cancer lymphocytes. Statistical analyses of the Raman data and classification using principal component analysis and linear discriminant analysis indicate that methanol fixation induces a greater change in the Raman spectra than paraformaldehyde. In addition, we demonstrate that the spectral changes as a result of the fixation process have an adverse effect on the accurate Raman discrimination of the normal and cancer cells. The spectral artifacts created by the use of fixatives indicate that the method of cell preparation is an important parameter to consider when applying Raman spectroscopy to characterize, image, or differentiate between different fixed cell samples to avoid potential misinterpretation of the data.