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Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis. Biopsy is recommended for definitive diagnosis of liver carcinoma, however, this process requires staining and expert pathologists to confirm diagnosis. Here, the authors employ a portable Raman spectroscopy system combined with deep learning to detect carcinoma from normal tissue in real time.

All molecules consist of chemical bonds, and much can be learned from mapping the spatiotemporal dynamics of these bonds. Since its invention a decade ago, stimulated Raman scattering (SRS) microscopy has become a powerful modality for imaging chemical bonds with high sensitivity, resolution, speed and specificity. We introduce the fundamentals of SRS microscopy and review innovations in SRS microscopes and imaging probes. We highlight examples of exciting biological applications, and share our vision for potential future breakthroughs for this technology.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1 . The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive 2 , 3 . Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce 4 . In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH) 5 – 7 , a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20–30 min) 2 . In a multicenter, prospective clinical trial ( n  = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory. A prospective, multicenter, case–control clinical trial evaluates the potential of artificial intelligence for providing accurate bedside diagnosis of patients with brain tumors.

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.

Coherent Raman imaging techniques have evolved to become powerful tools for biomedical imaging without the need for labelling. Raman scattering provides an intrinsic fingerprint of chemical composition. Spontaneous Raman spectroscopy has been used for many decades to interrogate biological materials and systems. In spite of its valuable information content, Raman imaging is rarely used compared with modalities such as fluorescence microscopy because of its relatively slow signal acquisition. Coherent Raman imaging technologies have evolved over the past fifteen years to now capture rich chemical information with improved acquisition speeds. As a result, coherent Raman imaging methods are now poised to begin emerging as widely used tools for obtaining functional information in a label-free manner from biological systems. We briefly review the development and application of these methods.

An imaging platform based on broadband coherent anti-Stokes Raman scattering has been developed that provides an advantageous combination of speed, sensitivity and spectral breadth. The system utilizes a configuration of laser sources that probes the entire biologically relevant Raman window (500–3,500 cm –1 ) with high resolution (<10 cm –1 ). It strongly and efficiently stimulates Raman transitions within the typically weak ‘fingerprint’ region using intrapulse three-colour excitation, and utilizes the non-resonant background to heterodyne-amplify weak Raman signals. We demonstrate high-speed chemical imaging in two- and three-dimensional views of healthy murine liver and pancreas tissues as well as interfaces between xenograft brain tumours and the surrounding healthy brain matter. A high-resolution, broadband imaging system based on coherent anti-Stokes Raman spectroscopy performs rapid, chemically specific imaging of biological tissue. It employs three-colour excitation and operates across the entire biological window.

Optical multiplexing has a large impact in photonics, the life sciences and biomedicine. However, current technology is limited by a 'multiplexing ceiling' from existing optical materials. Here we engineered a class of polyyne-based materials for optical supermultiplexing. We achieved 20 distinct Raman frequencies, as 'Carbon rainbow', through rational engineering of conjugation length, bond-selective isotope doping and end-capping substitution of polyynes. With further probe functionalization, we demonstrated ten-color organelle imaging in individual living cells with high specificity, sensitivity and photostability. Moreover, we realized optical data storage and identification by combinatorial barcoding, yielding to our knowledge the largest number of distinct spectral barcodes to date. Therefore, these polyynes hold great promise in live-cell imaging and sorting as well as in high-throughput diagnostics and screening.

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies. Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool to detect biomolecules in aqueous environments. However, it is challenging to identify protein structures at low concentrations, especially for the proteins existing in an equilibrium mixture of various conformations. Here, we develop an in situ optical tweezers-coupled Raman spectroscopy to visualize and control the hotspot between two Ag nanoparticle-coated silica beads, generating tunable and reproducible SERS enhancements with single-molecule level sensitivity. This dynamic SERS detection window is placed in a microfluidic flow chamber to detect the passing-by proteins, which precisely characterizes the structures of three globular proteins without perturbation to their native states. Moreover, it directly identifies the structural features of the transient species of alpha-synuclein among its predominant monomers at physiological concentration of 1 μM by reducing the ensemble averaging. Hence, this SERS platform holds the promise to resolve the structural details of dynamic, heterogeneous, and complex biological systems. It is currently challenging to identify protein structures at low concentrations. Here the authors report optical tweezers-coupled Raman spectroscopy to generate tunable and reproducible SERS enhancements with single-molecule level sensitivity and use the method to detect protein structural features.

Surface-enhanced Raman spectroscopy (SERS) sensing of DNA bases by plasmonic nanopores could pave a way to novel methods for DNA analyses and new generation single-molecule sequencing platforms. The SERS discrimination of single DNA bases depends critically on the time that a DNA strand resides within the plasmonic hot spot. In fact, DNA molecules flow through the nanopores so rapidly that the SERS signals collected are not sufficient for single-molecule analysis. Here, we report an approach to control the residence time of molecules in the hot spot by an electro-plasmonic trapping effect. By directly adsorbing molecules onto a gold nanoparticle and then trapping the single nanoparticle in a plasmonic nanohole up to several minutes, we demonstrate single-molecule SERS detection of all four DNA bases as well as discrimination of single nucleobases in a single oligonucleotide. Our method can be extended easily to label-free sensing of single-molecule amino acids and proteins. Sensing DNA bases by surface-enhanced Raman spectroscopy (SERS) in plasmonic nanopores has suffered from rapid flow through of molecules. Here, the authors attach DNA molecules to gold nanoparticles which, due to electro-plasmonic trapping, allow for controlled residence times and discrimination of single nucleotides.