Development and Validation of a SERS-Based Serological Test Combined with PLS-DA Method for Leishmaniasis Detection

Abstract

The fast and reliable anti-leishmaniasis antibody detection method was developed using citrate-capped gold nanoparticles (AuNPs) conjugated with an immunogenic peptide from promastigote surface antigens. These conjugates recognize specific antibodies that promote the aggregation of AuNPs in the solution, enabling a remarkable surface-enhanced Raman scattering (SERS) activity which allowed the development of an indirect detection test for human visceral leishmaniasis (VL). Dynamic and static light scattering methods were performed to investigate the aggregation process of the peptide-capped AuNPs induced by the addition of the specific polyclonal antibody anti-PSAdP23–47 in the diagnostic test. UV–vis spectroscopy and scanning electron microscopy demonstrated that AuNPs of different sizes are formed after interacting with antibodies against promastigote surface antigen (PSA)-derived peptides from the amino acid residues 23–47 (PSAdP23–47), confirmed by the plasmon resonance band position from 536 to 639 nm and light-scattering techniques. Experimental optimization of the SERS-based serological test showed that increasing the peptide and the citrate buffer concentration induces stronger Raman intensities. In addition, the multivariate curve resolution with alternating least squares allowed the identification of a fingerprint window within the range of 1043–1498 cm–1. Finally, a refined data analysis approach was successfully employed, enabling a robust differentiation between human sera from VL and non-VL patients. Prototype validation and proof of concept to demonstrate the feasibility and functionality of the sensor were carried out through calibration and optimization using a partial least squares discriminant analysis model with six latent variables, which validated the test reaching 100% sensitivity and 88.2% specificity. These results show the potential of the SERS platform for constructing a point-of-need serological test for diagnosing VL, a neglected tropical disease with a strong humoral immune response.

Recent Research Progress of Surface‐Enhanced Raman Scattering Dominated Analysis Strategies in Early Diagnosis of Diseases

“…Supervised learning algorithm is to obtain an optimal model which can realize prediction and classification of unknown data according to training samples (training datasets) with known categories. Common supervised learning algorithms include support vector method (SVM), [188] logistic regression, [189] classical least squares (CLS), [190] partial least square discriminate analysis (PLS-DA), [43,[191][192][193] neural network [23,47,194] and so on. [195] The combination of SERS sensor and one or more supervised learning algorithms can avoid "human error" and simultaneously achieve high throughput analysis during disease diagnosis.…”

Capillary Tube Surface-Enhanced Raman Scattering Substrate and High-Sensitivity Molecule Detection

“…In particular, such a highly sensitive Raman spectroscopy has potential in the field of security technology. Food safety check, − explosive detection, , toxic gas detection, detection of environmental water pollution, , cancer and disease detection, and screening in living cell and tissues, − as well as drag and medicine analysis − are the typical applications of SERS spectroscopy.…”