Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

Controllable Construction of Aptamer-Modified Fe3O4@SiO2-Au Core-Shell-Satellite Nanocomposites with Surface-Enhanced Raman Scattering and Photothermal Properties and Their Effective Capture, Detection, and Elimination of Staphylococcus aureus

The early monitoring and inactivation of bacteria are of crucial importance in preventing the further spread of foodborne pathogens. Staphylococcus aureus (S. aureus), a prototypical foodborne pathogen, is widely present in the natural environment and has the capability to trigger a range of diseases at low concentrations. In this work, we designed Fe3O4@SiO2-Au core-shell-satellite nanocomposites (NCs) modified with aptamer for efficient capture, high-sensitivity surface-enhanced Raman scattering (SERS) detection, and photothermal therapy (PTT) against S. aureus. Fe3O4@SiO2-Au NCs with tunable Au nanocrystal nanogaps were prepared. By combining the finite-difference time-domain (FDTD) method and experimental results, we studied the electric field distribution of Fe3O4@SiO2-Au under different Au nanogaps and ultimately obtained the optimal SERS substrate FSA-60. The modification of aptamer on the surfaces of FSA-60 could be used for the specific capture and selective detection of S. aureus, achieving a detection limit of as low as 50 cfu/mL. Furthermore, Apt-FSA-60 possessed excellent photothermal properties, demonstrating the strong photothermal killing ability against S. aureus. Therefore, Apt-FSA-60 is a promising high-sensitivity SERS substrate and efficient photothermal agent and is expected to be widely applied and promoted in future disease prevention and treatment.

SERS microfluidic chip integrated with double amplified signal off-on strategy for detection of microRNA in NSCLC

In this work, based on Fe3O4@AuNPs and double amplified signal Off-On strategy, a simple and sensitive SERS microfluidic chip was constructed to detect microRNA associated with non-small cell lung cancer (NSCLC). Fe3O4@AuNPs have two advantages of SERS enhanced and magnetic adsorption, the introduction of microfluidic chip can realize double amplification of SERS signal. First, the binding of complementary ssDNA and hpDNA moved the Raman signaling molecule away from Fe3O4@AuNPs, at which point the signal was turned off. Second, in the presence of the target microRNA, they were captured by complementary ssDNA and bound to them. HpDNA restored the hairpin conformation, the Raman signaling molecule moved closer to Fe3O4@AuNPs. At this time, the signal was turned on and strong Raman signal was generated. And last, through the magnetic component of SERS microfluidic chip, Fe3O4@AuNPs could be enriched to realize the secondary enhancement of SERS signal. In this way, the proposed SERS microfluidic chip can detect microRNA with high sensitivity and specificity. The corresponding detection of limit (LOD) for miR-21 versus miR-125b was 6.38 aM and 7.94 aM, respectively. This SERS microfluidic chip was promising in the field of early detection of NSCLC.