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Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications. Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.

Raman microspectroscopy is useful for the analysis of biological samples, because chemical and structural information can be obtained without using labels. This protocol brings together practical guidelines from expert research groups. Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting their components’ unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modeling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% are achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high-dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices. Label-free surface-enhanced Raman spectroscopy is an emergent method for the detection and discrimination of biological analytes. Here, the authors describe SERS sensors with arrayed mildly-selective surface chemistries to give a fingerprint based on different interactions for analysing biological samples.

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies. Most current cell sorting methods are based on fluorescence detection with no imaging capability. Here the authors generate and use Raman image-activated cell sorting with a throughput of around 100 events per second, providing molecular images with no need for labeling.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Nanometric probes based on surface-enhanced Raman scattering (SERS) are promising candidates for all-optical environmental, biological and technological sensing applications with intrinsic quantitative molecular specificity. However, the effectiveness of SERS probes depends on a delicate trade-off between particle size, stability and brightness that has so far hindered their wide application in SERS imaging methodologies. In this Article, we introduce holographic Raman microscopy, which allows single-shot three-dimensional single-particle localization. We validate our approach by simultaneously performing Fourier transform Raman spectroscopy of individual SERS nanoparticles and Raman holography, using shearing interferometry to extract both the phase and the amplitude of wide-field Raman images and ultimately localize and track single SERS nanoparticles inside living cells in three dimensions. Our results represent a step towards multiplexed single-shot three-dimensional concentration mapping in many different scenarios, including live cell and tissue interrogation and complex anti-counterfeiting applications.Holography of incoherent emission from SERS probes allows multiplexed single-particle localization in three dimensions in one shot using a wide-field microscope.

Live-cell Raman imaging based on bioorthogonal Raman probes with distinct signals in the cellular Raman-silent region (1800–2800 cm −1 ) has attracted great interest in recent years. We report here a class of water-soluble and biocompatible polydiacetylenes with intrinsic ultrastrong alkyne Raman signals that locate in this region for organelle-targeting live-cell Raman imaging. Using a host-guest topochemical polymerization strategy, we have synthesized a water-soluble and functionalizable master polydiacetylene, namely poly(deca-4,6-diynedioic acid) (PDDA), which possesses significantly enhanced (up to ~10 4 fold) alkyne vibration compared to conventional alkyne Raman probes. In addition, PDDA can be used as a general platform for multi-functional ultrastrong Raman probes. We achieve high quality live-cell stimulated Raman scattering imaging on the basis of modified PDDA. The polydiacetylene-based Raman probes represent ultrastrong intrinsic Raman imaging agents in the Raman-silent region (without any Raman enhancer), and the flexible functionalization of this material holds great promise for its potential diverse applications. Raman probes which operate in the cellular silent region are of interest for live cell imaging. Here, the authors report on the development of a water soluble polydiacetylene Raman probe with enhanced Raman signal in the silent region which can be functionalised for organelle targeting and demonstrate application.

One of the biggest challenges in microbiome research in environmental and medical samples is to better understand functional properties of microbial community members at a single-cell level. Single-cell isotope probing has become a key tool for this purpose, but the current detection methods for determination of isotope incorporation into single cells do not allow high-throughput analyses. Here, we report on the development of an imaging-based approach termed stimulated Raman scattering–two-photon fluorescence in situ hybridization (SRS-FISH) for high-throughputmetabolism and identity analyses of microbial communities with single-cell resolution. SRS-FISH offers an imaging speed of 10 to 100 ms per cell, which is two to three orders ofmagnitude faster than achievable by state-of-the-art methods. Using this technique, we delineated metabolic responses of 30,000 individual cells to various mucosal sugars in the human gut microbiome via incorporation of deuterium from heavy water as an activity marker. Application of SRS-FISH to investigate the utilization of host-derived nutrients by two major human gut microbiome taxa revealed that response to mucosal sugars tends to be dominated by Bacteroidales, with an unexpected finding that Clostridia can outperform Bacteroidales at foraging fucose.With high sensitivity and speed, SRS-FISH will enable researchers to probe the fine-scale temporal, spatial, and individual activity patterns of microbial cells in complex communities with unprecedented detail.

From the famous 1918 H1N1 influenza to the present COVID-19 pandemic, the need for improved viral detection techniques is all too apparent. The aim of the present paper is to show that identification of individual virus particles in clinical sample materials quickly and reliably is near at hand. First of all, our team has developed techniques for identification of virions based on a modular atomic force microscopy (AFM). Furthermore, femtosecond adaptive spectroscopic techniques with enhanced resolution via coherent anti-Stokes Raman scattering (FASTER CARS) using tip-enhanced techniques markedly improves the sensitivity [M. O. Scully, et al., Proc. Natl. Acad. Sci. U.S.A. 99, 10994–11001 (2002)].

Surface-Enhanced Raman Scattering has been studied for decades as a recognition technique due to its high sensitivity and discriminative power, particularly in biological applications. Inspired by nanopore sequencing technology, we developed a plasmonic device capable of operating in a flow-through configuration to detect individual molecules passing through plasmonic hotspots. This device is a permeable plasmonic film, through which single molecules are sequentially delivered via electrophoresis, while Raman spectra are recorded in real-time. A very effective light-matter coupling, combined with the ultrasmall size of plasmonic spots, enabled access to angstrom spatial and microsecond temporal scales compatible with polymer sequencing. We successfully slowed the translocation process and captured Raman spectra of the four nucleotides at a time resolution down to 20 μs under our experimental conditions. We achieved a discrimination accuracy of 89% at the single-molecule level. Also, we demonstrated a spatial resolution on the order of a few nucleotides, suggesting the potential for sequencing applications. Ultrafast SERS combined with nanopore sequencing allows distinguishing similar nucleotides at the single-molecule level, offering a step towards molecular sequencing with the aid of machine learning.