Shanghai Jiao Tong University Researchers Publish Breakthrough Study in Nature on Digital Colloid-Enhanced Raman Spectroscopy

Recently, the prestigious journal Nature published an article titled "Digital colloid-enhanced Raman spectroscopy by single-molecule counting." This study addresses the quantitative challenges within the field of surface-enhanced Raman spectroscopy (SERS) and systematically explains the quantitative technology based on digital colloid-enhanced Raman spectroscopy (dCERS). By leveraging single-molecule counting, dCERS successfully achieves reliable quantitative detection of target molecules at ultra-low concentrations, laying a significant foundation for the widespread application of SERS technology.

The first author of the paper is Xin Yuan Bi, a 2020 PhD student in the Zhiyuan Honors Program at the School of Biomedical Engineering, Shanghai Jiao Tong University. The corresponding author is Professor Jian Ye. As a senior author, Professor Zhifeng Shao made critical contributions to the basic concepts, data analysis, and the refinement and revision of the article. Professor Daniel M. Czajkowsky also significantly contributed to the understanding of the physical principles of the data and the revision of the manuscript. Shanghai Jiao Tong University is the sole completing and corresponding institution for this paper.

Research Background

Raman scattering, discovered by Chandrasekhara Venkata Raman in 1928, is a fingerprint-like, molecule-specific inelastic scattering spectrum. Raman was awarded the Nobel Prize in Physics in 1930 for this discovery. Raman spectra can directly determine the corresponding molecular structure, thus identifying specific types of molecules. This label-free advantage makes Raman spectroscopy valuable in various fields, including physics, chemistry, biology, geology, medicine, national defense, and public safety.

Raman signals are typically weak, making signal enhancement necessary. SERS originated from an important experiment by Martin Fleischmann and colleagues at the University of Southampton's Department of Chemistry in 1974. They discovered that the Raman scattering signals of pyridine molecules attached to a rough silver electrode surface were significantly enhanced. This phenomenon was later explained through electromagnetic field effects and charge transfer effects by David L. Jeanmaire and Richard P. Van Duyne at Northwestern University and M. Grant Albrecht and J. Alan Creighton at the University of Kent in 1977. In 1997, SERS achieved a milestone with the realization of single-molecule detection. Since then, SERS has been considered a potential candidate for a second Nobel Prize in Raman spectroscopy. Researchers have developed various nanostructured enhancement substrates, such as nanostars, nanourchins, nanoflowers, and nanoforests, using different wet chemical synthesis schemes and chip fabrication processes to create richer tip and gap structures on the substrate surface. These "hot spots" provide higher enhancement capabilities, enabling the detection of molecules at ultra-low concentrations.

However, with the continuous development of SERS research, it has been found that Raman signal intensity is highly inconsistent at low concentrations. Thus, achieving single-molecule detection sensitivity does not necessarily mean achieving ultra-sensitive quantitative detection. Higher enhancement factors are a necessary condition for high-sensitivity quantitative detection with SERS, but repeatable measurements are crucial for practical application and large-scale adoption. This problem, which has troubled the Raman field for decades, is difficult to solve within the existing technical framework.

Digital Colloid-Enhanced Raman Spectroscopy (dCERS)

Professors Jian Ye and Zhifeng Shao's team at the School of Biomedical Engineering, Shanghai Jiao Tong University, invented dCERS, which uses colloidal nanoparticles to achieve efficient single-molecule detection. While single-molecule signal fluctuations are significant, consistent with previous research findings, dCERS differs from conventional single-molecule SERS techniques by digitizing spectra based on the presence or absence of Raman characteristic peaks of target molecules. This results in negative and positive spectra (digital signals). By counting positive spectra in the solution, various molecules (e.g., dye molecules, small metabolites, nucleic acids, proteins) can be quantitatively detected, with detection limits reaching below 1 fM. The colloidal particles used in dCERS are synthesized through simple steps and are easy to scale up for production. In application, small amounts of particles from each batch can be used to establish standard curves for specific target molecules, enabling reliable quantitative detection of unknown concentration samples.

Quantitative Repeatability and Control

Experiments showed that the distribution of single-molecule events determined by threshold values followed Poisson statistics. Thus, the number of positive spectra directly determines quantitative sensitivity and accuracy, differing from traditional quantitative methods based on analog signals. As shown in Figure 3, by increasing the number of spectra detected, the number of positive spectra can be accumulated, effectively enhancing quantitative accuracy. The quantitative detection error conforms to Poisson noise, allowing control over quantitative detection accuracy by accumulating positive spectra based on the detection probability and requirements for accuracy and detection duration.

The findings also demonstrated that the dependency relationship between concentration and single-molecule counting for different target molecules, though requiring individual calibration, follows Gibbs thermodynamic theory. This is the first time the physical basis for single-molecule statistics has been clearly established, potentially applicable to other single-molecule counting techniques beyond Raman spectroscopy.

Practical Applications in Environmental Protection and Food Safety

To demonstrate dCERS's potential in practical measurements, the team chose paraquat and thiram as examples. Paraquat is a highly toxic herbicide that can induce Parkinson's disease, currently banned in 32 countries. Thiram is a highly toxic fungicide classified as a Category 2 carcinogen by the EU. Ultra-sensitive, accurate, and reliable quantitative detection techniques are crucial for detecting these molecules, especially carcinogens, as there is no safe dose.

Using ordinary lake water mixed with trace amounts of paraquat, the team achieved a detection sensitivity three orders of magnitude below the EU's maximum residue level. For thiram, using mung bean sprout extract as a background, the team achieved a detection sensitivity five orders of magnitude better than mass spectrometry. They demonstrated that through serial dilution, background interference could be perfectly suppressed, enabling accurate measurement of target molecule concentration. The ultra-high sensitivity and reliable statistical distribution of dCERS are key to achieving these quantitative measurements.

Conclusion and Future Prospects

This study demonstrates that dCERS, through single-molecule counting, achieves repeatable quantitative detection of ultra-low concentration target molecules in unknown complex backgrounds without requiring specific labeling of target molecules. Since different target molecules typically have unique SERS spectra, dCERS can simultaneously quantify various molecules, making it highly promising for diverse applications. Additionally, the colloidal nanoparticles used in this study can be easily mass-produced, and the detection method is relatively simple. Therefore, dCERS is expected to further drive advancements in high-sensitivity detection technology.

As this year marks the 50th anniversary of the discovery of SERS technology, it is anticipated that with the further maturation of dCERS, it will find widespread application in life sciences, clinical medicine, environmental protection, food safety, national defense, public safety, and basic research.

Team Members(from left to right): Zhifeng Shao, Jian Ye, Xin Yuan Bi, Daniel M. Czajkowsky

This work was supported by Professors Hongchen Gu, Hong Xu, and Feng Shen from Shanghai Jiao Tong University, and funded by the National Natural Science Foundation of China, National Key R&D Program, Shanghai Municipal Science and Technology Commission, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai Jiao Tong University, and Wang Kuancheng Education Foundation.