SERS-based approaches toward genetic profiling

Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

Present and Future of Surface-Enhanced Raman Scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Recent Advancement in the Surface-Enhanced Raman Spectroscopy-Based Biosensors for Infectious Disease Diagnosis

Diagnosis is the key component in disease elimination to improve global health. However, there is a tremendous need for diagnostic innovation for neglected tropical diseases that largely consist of mosquito-borne infections and bacterial infections. Early diagnosis of these infectious diseases is critical but challenging because the biomarkers are present at low concentrations, demanding bioanalytical techniques that can deliver high sensitivity with ensured specificity. Owing to the plasmonic nanomaterials-enabled high detection sensitivities, even up to single molecules, surface-enhanced Raman spectroscopy (SERS) has gained attention as an optical analytical tool for early disease biomarker detection. In this mini-review, we highlight the SERS-based assay development tailored to detect key types of biomarkers for mosquito-borne and bacterial infections. We discuss in detail the variations of SERS-based techniques that have developed to afford qualitative and quantitative disease biomarker detection in a more accurate, affordable, and field-transferable manner. Current and emerging challenges in the advancement of SERS-based technologies from the proof-of-concept phase to the point-of-care phase are also briefly discussed.

Prenatal detection of thalassemia by cell-free fetal DNA (cffDNA) in maternal plasma using surface enhanced Raman spectroscopy combined with PCR

Thalassemias are widely occurring genetic hemoglobin disorders; patients with severe thalassemia often require regular blood transfusions for survival. Prenatal detection of thalassemia is currently invasive and carries the risk of miscarriage and infection. A polymerase chain reaction (PCR)-based surface enhanced Raman spectroscopy (SERS) technique was investigated in this paper for the purpose of detecting prenatal α-thalassemia Southeast Asian (SEA) type deletion using maternal plasma. Couples with the same SEA thalassemia (-SEA/αα) were selected, and the quantification of SEA and wild type (WT) alleles in the maternal plasma sample predicted the fetal genotype. PCR was performed using two pairs of fluorescence tag-labeled primers to produce tag-labeled PCR products for both the SEA (labeled with R6G) and WT (labeled with Cy3) alleles. Then, the labeled PCR products containing the two fluorescence tags were measured by SERS. The ratios between the R6G and Cy3 tags were obtained using multiple linear regressions (MLR), and these ratios corresponded with the physical ratio of WT and SEA concentrations in maternal plasma. After verifying this technique on DNA mixtures with known SEA and WT ratios, the plasma from 24 pregnant women was screened. An accuracy of 91.7% was achieved for detecting the fetal genotypes of Hb Bart's, alpha-trait, and normal trait. The results indicated that the simple PCR-SERS method may be sensitive enough for use on cell free fetal DNA (cffDNA) in maternal plasma for non-invasive prenatal detection (NIPD).

Surface Enhanced Raman Spectroscopy (SERS) for the Multiplex Detection of Braf, Kras, and Pik3ca Mutations in Plasma of Colorectal Cancer Patients

In this paper, we discuss the use of a procedure based on polymerase chain reaction (PCR) and surface enhanced Raman spectroscopy (SERS) (PCR-SERS) to detect DNA mutations.

Methods: This method was implemented by first amplifying DNA-containing target mutations, then by annealing probes, and finally by applying SERS detection. The obtained SERS spectra were from a mixture of fluorescence tags labeled to complementary sequences on the mutant DNA. Then, the SERS spectra of multiple tags were decomposed to component tag spectra by multiple linear regression (MLR).

Results: The detection limit was 10^-11 M with a coefficient of determination (R²) of 0.88. To demonstrate the applicability of this process on real samples, the PCR-SERS method was applied on blood plasma taken from 49 colorectal cancer patients to detect six mutations located at the BRAF, KRAS, and PIK3CA genes. The mutation rates obtained by the PCR-SERS method were in concordance with previous research. Fisher's exact test showed that only two detected mutations at BRAF (V600E) and PIK3CA (E542K) were significantly positively correlated with right-sided colon cancer. No other clinical feature such as gender, age, cancer stage, or differentiation was correlated with mutation (V600E at BRAF, G12C, G12D, G12V, G13D at KRAS, and E542K at PIK3CA). Visually, a dendrogram drawn through hierarchical clustering analysis (HCA) supported the results of Fisher's exact test. The clusters drawn by all six mutations did not conform to the distributions of cancer stages, differentiation or cancer positions. However, the cluster drawn by the two mutations of V600E and E542K showed that all samples with those mutations belonged to the right-sided colon cancer group.

Conclusion: The suggested PCR-SERS method is multiplexed, flexible in probe design, easy to incorporate into existing PCR conditions, and was sensitive enough to detect mutations in blood plasma.