SERS-Active Printable Hydrogel for 3D Cell Culture and Imaging

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Nanomaterials and clinical SERS technology: broad applications in disease diagnosis

The critical need for rapid cancer diagnosis and related illnesses is growing alongside the current healthcare challenges, unfavorable prognosis, and constraints in diagnostic timing. As a result, emphasis on surface-enhanced Raman spectroscopy (SERS) diagnostic methods, including both label-free and labelled approaches, holds significant promise in fields such as analytical chemistry, biomedical science, and physics, due to the user-friendly nature of SERS. Over time, the SERS detection sensitivity and specificity with nanostructured materials for SERS applications (NMs-SERS) in different media have been remarkable. An investigation into electronic dynamics and interactions has revealed a seemingly fair result regarding the complementary effects of electromagnetic (EM) and chemical enhancements (CM), underscoring the operational principles of SERS. Nevertheless, the focus on translational SERS applications, especially beyond preliminary proof-of-concept research, remains limited. This review focuses on the advancements made in clinical SERS diagnostics and the essential role of NMs-SERS, ranging from plasmonic to non-plasmonic materials and other related advancements. Furthermore, it outlines the significant achievements of biomedical SERS in tumor diagnosis, particularly in identifying circulating tumor cells (CTCs), alongside a clear focus on NMs-SERS characteristics such as surface charge, shape, size, detection sensitivity, specificity, signal reproducibility, and recyclability. Finally, it underscores the use of microfluidic chips within the labelled SERS strategy for isolating CTCs, the concept of Ramanomics, and the integration of artificial intelligence (AI) to strengthen SERS data analysis. We hope that this review will help guide and expedite the potential for precise SERS diagnosis of key chronic diseases.

Factors that Affect Quantification in Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) is an analytical technique capable of detecting trace amounts of specific species. The uniqueness of vibrational signatures is a major advantage of SERS. This combination of sensitivity and specificity has motivated researchers to develop diverse analytical methodologies leveraging SERS. However, even 50 years after its first observation, SERS is still perceived as an unreliable technique for quantification. This perception has precluded the application of SERS in laboratories that rely on consistent quantification (for regulatory purposes, for instance). In this review, we describe some of the aspects that lead to SERS intensity variations and how those challenges were addressed in the 50 years of the technique. The goal is to identify the sources of variations in SERS intensities and then demonstrate that, even with these pitfalls, the technique can be used for quantification when factors such as nature of the substrate, experimental conditions, sample preparation, surface chemistry, and data analysis are carefully considered and tailored for a particular application.

Monitoring kinetic processes of drugs and metabolites: Surface-enhanced Raman spectroscopy

Monitoring the kinetic changes of drugs and metabolites plays a crucial role in fundamental research, preclinical and clinical application. Raman spectroscopy (RS) is regarded as a fingerprinting technique that can reflect molecular structures but limited in applications due to poor sensitivity. Surface-enhanced Raman spectroscopy (SERS) significantly amplifies the detection sensitivity by plasmonic substrates, facilitating the identification and quantification of small molecules in biological samples, such as serum, urine, and living cells. This review will focus on advances in how SERS has been utilized to monitor the dynamic processes of small molecule drugs and metabolites in recent years. We first provide readers with a comprehensive overview of the mechanism and practical considerations of SERS, including enhancement theory, substrate design, sample pretreatment, molecule-substrate interactions and spectral analysis. Then we describe the latest advances in SERS for the detection and analysis of metabolites and drugs in cells, dynamic monitoring of drug in various biological matrices, and metabolic profiling for health assessment in biological fluids. We believe that high-performance SERS substrates, standardized technical regulations, and artificial intelligence spectral analysis will boost sensitive, accurate, reproducible, and universal molecular detection in the future. We hoped this review could inspire researchers working in related fields to better understand and utilize SERS for the analytical detection of drugs and metabolites.

Hydrogel Applications in the Diagnosis and Treatment of Glioblastoma

Glioblastoma multiforme (GBM), a common malignant neurological tumor, has boundaries indistinguishable from those of normal tissue, making complete surgical removal ineffective. The blood-brain barrier (BBB) further impedes the efficacy of radiotherapy and chemotherapy, leading to suboptimal treatment outcomes and a heightened probability of recurrence. Hydrogels offer multiple advantages for GBM diagnosis and treatment, including overcoming the BBB for improved drug delivery, controlled drug release for long-term efficacy, and enhanced relaxation properties of magnetic resonance imaging (MRI) contrast agents. Hydrogels, with their excellent biocompatibility and customizability, can mimic the in vivo microenvironment, support tumor cell culture, enable drug screening, and facilitate the study of tumor invasion and metastasis. This paper reviews the classification of hydrogels and recent research for the diagnosis and treatment of GBM, including their applications as cell culture platforms and drugs including imaging contrast agents carriers. The mechanisms of drug release from hydrogels and methods to monitor the activity of hydrogel-loaded drugs are also discussed. This review is intended to facilitate a more comprehensive understanding of the current state of GBM research. It offers insights into the design of integrated hydrogel-based GBM diagnosis and treatment with the objective of achieving the desired therapeutic effect and improving the prognosis of GBM.

SERS Sensors with Bio-Derived Substrates Under the Way to Agricultural Monitoring of Pesticide Residues

Uncontrolled use of pesticides in agriculture leads to negative consequences for the environment, as well as for human and animal health. Therefore, timely detection of pesticides will allow application of measures to eliminate the excess of maximum residue limits and reduce possible negative consequences in advance. Common methods of pesticide analysis suffer from high costs, and are time consuming, and labor intensive. Currently, more attention is being paid to the development of surface-enhanced Raman scattering (SERS) sensors as a non-destructive and highly sensitive tool for detecting various chemicals in agricultural applications. This review focuses on the current developments of biocompatible SERS substrates based on natural materials with unique micro/nanostructures, flexible SERS substrates based on biopolymers, as well as functionalized SERS substrates, which are close to the current needs and requirements of agricultural product quality control and environmental safety assessment. The impact of herbicides on the process of photosynthesis is considered and the prospects for the application of Raman spectroscopy and SERS for the detection of herbicides are discussed.

Fast On-Site Speciation and High Spatial Resolution Imaging of Labile Arsenic in Freshwater and Sediment Using the DGT-SERS Sensor

Diffusive gradients in thin films (DGT) technique is renowned for in situ passive sampling but not for rapid on-site analysis, whereas surface-enhanced Raman spectroscopy (SERS) excels in ultrasensitive on-site detection but is limited by substrate contamination from complex matrices. Here, a hierarchical nanostructure of silver (Ag) mirror-supported large Ag nanoparticles (∼120 nm) was grown in situ in polyacrylamide hydrogel with a restricted pore size (PAM/Ag mirror/AgNPs) to serve as both the DGT binding phase and the SERS substrate. The substrate exhibited a maximum electric field enhancement factor of 9.9 × 108 and a signal relative standard error of 4.8%. Using the DGT-SERS sensor, As(III) and As(V) in freshwater were simultaneously detected at limits of 0.9 and 0.8 μg L-1, respectively, applicable across a wide range of environmental conditions. The DGT-SERS effectively mitigated the interfacial reduction of As(V) caused by humic acid by excluding it from plasmonic hotspots through size exclusion of the diffusive layer. The Raman analysis of a DGT sample in the field requires only 2 s using a portable spectrometer without DGT device disassembly. More importantly, the DGT-SERS captured the first two-dimensional image of As(III) and As(V) in one DGT at the micron scale resolution, revealing their spatially supplementary distribution patterns at the sediment-water interface. This study paves the way for next-generation speciation imaging DGT and the application of SERS in complex environments.

SERS microscopy as a tool for comprehensive biochemical characterization in complex samples

Surface enhanced Raman scattering (SERS) spectra of biomaterials such as cells or tissues can be used to obtain biochemical information from nanoscopic volumes in these heterogeneous samples. This tutorial review discusses the factors that determine the outcome of a SERS experiment in complex bioorganic samples. They are related to the SERS process itself, the possibility to selectively probe certain regions or constituents of a sample, and the retrieval of the vibrational information in order to identify molecules and their interaction. After introducing basic aspects of SERS experiments in the context of biocompatible environments, spectroscopy in typical microscopic settings is exemplified, including the possibilities to combine SERS with other linear and non-linear microscopic tools, and to exploit approaches that improve lateral and temporal resolution. In particular the great variation of data in a SERS experiment calls for robust data analysis tools. Approaches will be introduced that have been originally developed in the field of bioinformatics for the application to omics data and that show specific potential in the analysis of SERS data. They include the use of simulated data and machine learning tools that can yield chemical information beyond achieving spectral classification.

A Scaffold-Assisted 3D Cancer Cell Model for Surface-Enhanced Raman Scattering-Based Real-Time Sensing and Imaging

Despite recent advances in the development of scaffold-based three-dimensional (3D) cell models, challenges persist in imaging and monitoring cell behavior within these complex structures due to their heterogeneous cell distribution and geometries. Incorporating sensors into 3D scaffolds provides a potential solution for real-time, in situ sensing and imaging of biological processes such as cell growth and disease development. We introduce a 3D printed hydrogel-based scaffold capable of supporting both surface-enhanced Raman scattering (SERS) biosensing and imaging of 3D breast cancer cell models. The scaffold incorporates plasmonic nanoparticles and SERS tags, for sensing and imaging, respectively. We demonstrate the scaffold's adaptability and modularity in supporting breast cancer spheroids, thereby enabling spatial and temporal monitoring of tumor evolution.