Single-cell Raman sorting

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level

Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Single-Cell Genomics and the Oral Microbiome

The human oral cavity is one of the first environments where microbes have been discovered and studied since the dawn of microbiology. Nevertheless, approximately 200 types of bacteria from the oral microbiota have remained uncultured in the laboratory. Some are associated with a healthy oral microbial community, while others are linked to oral diseases, from dental caries to gum disease. Single-cell genomics has enabled inferences on the physiology, virulence, and evolution of such uncultured microorganisms and has further enabled isolation and cultivation of several novel oral bacteria, including the discovery of novel interspecies interactions. This review summarizes some of the more recent advances in this field, which is rapidly moving toward physiologic characterization of single cells and ultimately cultivation of the yet uncultured. A combination of traditional microbiological approaches with genomic-based physiologic predictions and isolation strategies may lead to the oral microbiome being the first complex microbial community to have all its members cultivable in the laboratory. Studying the biology of the individual microbes when in association with other members of the community, in controlled laboratory conditions and in vivo, should lead to a better understanding of oral dysbiosis and its prevention and reversion.

Gold Nanoparticles in Single-Cell Analysis for Surface Enhanced Raman Scattering

The need for new therapeutic approaches in the treatment of challenging diseases such as cancer, which often consists of a highly heterogeneous and complex population of cells, brought up the idea of analyzing single cells. The development of novel techniques to analyze single cells has been intensively studied to fully understand specific alternations inducing abnormalities in cellular function. One of the techniques used for single cell analysis is surface-enhanced Raman spectroscopy (SERS) in which a noble metal nanoparticle is used to enhance Raman scattering. Due to its low toxicity and biocompatibility, gold nanoparticles (AuNPs) are commonly preferred as SERS substrates in single cell analysis. The intracellular uptake, localization and toxicity issues of AuNPs are the critical points for interpretation of data since the obtained SERS signals originate from molecules in close vicinity to AuNPs that are taken up by the cells. In this review, the AuNP-living cell interactions, cellular uptake and toxicity of AuNPs in relation to their physicochemical properties, and surface-enhanced Raman scattering from single cells are discussed.

Exploring microbial dark matter to resolve the deep archaeal ancestry of eukaryotes

The origin of eukaryotes represents an enigmatic puzzle, which is still lacking a number of essential pieces. Whereas it is currently accepted that the process of eukaryogenesis involved an interplay between a host cell and an alphaproteobacterial endosymbiont, we currently lack detailed information regarding the identity and nature of these players. A number of studies have provided increasing support for the emergence of the eukaryotic host cell from within the archaeal domain of life, displaying a specific affiliation with the archaeal TACK superphylum. Recent studies have shown that genomic exploration of yet-uncultivated archaea, the so-called archaeal ‘dark matter’, is able to provide unprecedented insights into the process of eukaryogenesis. Here, we provide an overview of state-of-the-art cultivation-independent approaches, and demonstrate how these methods were used to obtain draft genome sequences of several novel members of the TACK superphylum, including Lokiarchaeum, two representatives of the Miscellaneous Crenarchaeotal Group (Bathyarchaeota), and a Korarchaeum-related lineage. The maturation of cultivation-independent genomics approaches, as well as future developments in next-generation sequencing technologies, will revolutionize our current view of microbial evolution and diversity, and provide profound new insights into the early evolution of life, including the enigmatic origin of the eukaryotic cell.