surface-enhanced+Raman+spectroscopy
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KAIST Develops CamBio - a New Biotemplating Method
- Professor Jae-Byum Chang and Professor Yeon Sik Jung’s joint research team of the Department of Materials Science and Engineering developed a highly tunable bio-templating method “CamBio” that makes use of intracellular protein structures
- Substrate performance improvement of up to 230% demonstrated via surface-enhanced Raman spectroscopy (SERS)
- Expected to have price competitiveness over bio-templating method as it expands the range of biological samples
- Expected to expand the range of application of nanostructure synthesis technology by utilizing various biological structures
< Photo 1. (From left) Professor Yeon Sik Jung, Ph.D. candidate Dae-Hyeon Song, Professor Jae-Byum Chang, and (from top right) Dr. Chang Woo Song and Dr. Seunghee H. Cho of the Department of Materials Science and Engineering >
Biological structures have complex characteristics that are difficult to replicate artificially, but biotemplating methods* that directly utilize these biological structures have been used in various fields of application. The KAIST research team succeeded in utilizing previously unusable biological structures and expanding the areas in which biotemplate methods can be applied.
*Biotemplating: A method of using biotemplates as a mold to create functional structural materials, utilizing the functions of these biological structures, from viruses to the tissues and organs that make up our bodies
KAIST (President Kwang Hyung Lee) announced on the 10th that a joint research team of Professors Jae-Byum Chang and Professor Yeon Sik Jung of the Department of Materials Science and Engineering developed a biotemplating method that utilizes specific intracellular proteins in biological samples and has high tunability.
Existing biotemplate methods mainly utilize only the external surface of biological samples or have limitations in utilizing the structure-function correlation of various biological structures due to limited dimensions and sample sizes, making it difficult to create functional nanostructures.
To solve this problem, the research team studied a way to utilize various biological structures within the cells while retaining high tunability.
< Figure 1. CamBio utilizing microtubules, a intracellular protein structure. The silver nanoparticle chains synthesized along the microtubules that span the entire cell interior can be observed through an electron microscope, and it is shown that this can be used as a successful SERS substrate. >
As a result of the research, the team developed the “Conversion to advanced materials via labeled Biostructure”, shortened as “CamBio”, which enables the selective synthesis of nanostructures with various characteristics and sizes from specific protein structures composed of diverse proteins within biological specimens.
The CamBio method secures high tunability of functional nanostructures that can be manufactured from biological samples by merging various manufacturing and biological technologies.
Through the technology of repeatedly attaching antibodies, arranging cells in a certain shape, and thinly slicing tissue, the functional nanostructures made with CamBio showed improved performance on the surface-enhanced Raman spectroscopy (SERS)* substrate used for material detection.
*Surface-enhanced Raman spectroscopy (SERS): A technology that can detect very small amounts of substances using light, based on the principle that specific substances react to light and amplifies signals on surfaces of metals such as gold or silver.
The research team found that the nanoparticle chains made using the intracellular protein structures through the process of repeated labeling with antibodies allowed easier control, and improved SERS performance by up to 230%.
In addition, the research team expanded from utilizing the structures inside cells to obtaining samples of muscle tissues inside meat using a cryostat and successfully producing a substrate with periodic bands made of metal particles by performing the CamBio process. This method of producing a substrate not only allows large-scale production using biological samples, but also shows that it is a cost-effective method.
< Figure 2. A method for securing tunability using CamBio at the cell level. Examples of controlling characteristics by integrating iterative labeling and cell pattering techniques with CamBio are shown. >
The CamBio developed by the research team is expected to be used as a way to solve problems faced by various research fields as it is to expand the range of bio-samples that can be produced for various usage.
The first author, Dae-Hyeon Song, a Ph.D. candidate of KAIST Department of Materials Science and Engineering said, “Through CamBio, we have comprehensively accumulated biotemplating methods that can utilize more diverse protein structures,” and “If combined with the state-of-the-art biological technologies such as gene editing and 3D bioprinting and new material synthesis technologies, biostructures can be utilized in various fields of application.”
< Figure 3. A method for securing tunability using CamBio at the tissue level. In order to utilize proteins inside muscle tissue, the frozen tissue sectioning technology is combined, and through this, a substrate with a periodic nanoparticle band pattern is successfully produced, and it is shown that large-area acquisition of samples and price competitiveness can be achieved. >
This study, in which the Ph.D. candidate Dae-Hyeon Song along with Dr. Chang Woo Song, and Dr. Seunghee H. Cho of the same department participated as the first authors, was published online in the international academic journal, Advanced Science, on November 13th, 2024.
(Paper title: Highly Tunable, Nanomaterial-Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species) https://doi.org/10.1002/advs.202406492
This study was conducted with a combination of support from various programs including the National Convergence Research of Scientific Challenges (National Research Foundation of Korea (NRF) 2024), Engineering Reseach Center (ERC) (Wearable Platform Materials Technology Center, NRF 2023), ERC (Global Bio-integrated Materials Center, NRF 2024), and the National Advanced Program for Biological Research Resources (Bioimaging Data Curation Center, NRF 2024) funded by Ministry of Science and ICT.
2025.01.10 View 2243 -
'Fingerprint' Machine Learning Technique Identifies Different Bacteria in Seconds
A synergistic combination of surface-enhanced Raman spectroscopy and deep learning serves as an effective platform for separation-free detection of bacteria in arbitrary media
Bacterial identification can take hours and often longer, precious time when diagnosing infections and selecting appropriate treatments. There may be a quicker, more accurate process according to researchers at KAIST. By teaching a deep learning algorithm to identify the “fingerprint” spectra of the molecular components of various bacteria, the researchers could classify various bacteria in different media with accuracies of up to 98%.
Their results were made available online on Jan. 18 in Biosensors and Bioelectronics, ahead of publication in the journal’s April issue.
Bacteria-induced illnesses, those caused by direct bacterial infection or by exposure to bacterial toxins, can induce painful symptoms and even lead to death, so the rapid detection of bacteria is crucial to prevent the intake of contaminated foods and to diagnose infections from clinical samples, such as urine. “By using surface-enhanced Raman spectroscopy (SERS) analysis boosted with a newly proposed deep learning model, we demonstrated a markedly simple, fast, and effective route to classify the signals of two common bacteria and their resident media without any separation procedures,” said Professor Sungho Jo from the School of Computing.
Raman spectroscopy sends light through a sample to see how it scatters. The results reveal structural information about the sample — the spectral fingerprint — allowing researchers to identify its molecules. The surface-enhanced version places sample cells on noble metal nanostructures that help amplify the sample’s signals.
However, it is challenging to obtain consistent and clear spectra of bacteria due to numerous overlapping peak sources, such as proteins in cell walls. “Moreover, strong signals of surrounding media are also enhanced to overwhelm target signals, requiring time-consuming and tedious bacterial separation steps,” said Professor Yeon Sik Jung from the Department of Materials Science and Engineering.
To parse through the noisy signals, the researchers implemented an artificial intelligence method called deep learning that can hierarchically extract certain features of the spectral information to classify data. They specifically designed their model, named the dual-branch wide-kernel network (DualWKNet), to efficiently learn the correlation between spectral features. Such an ability is critical for analyzing one-dimensional spectral data, according to Professor Jo.
“Despite having interfering signals or noise from the media, which make the general shapes of different bacterial spectra and their residing media signals look similar, high classification accuracies of bacterial types and their media were achieved,” Professor Jo said, explaining that DualWKNet allowed the team to identify key peaks in each class that were almost indiscernible in individual spectra, enhancing the classification accuracies. “Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time.”
The researchers plan to use their platform to study more bacteria and media types, using the information to build a training data library of various bacterial types in additional media to reduce the collection and detection times for new samples.
“We developed a meaningful universal platform for rapid bacterial detection with the collaboration between SERS and deep learning,” Professor Jo said. “We hope to extend the use of our deep learning-based SERS analysis platform to detect numerous types of bacteria in additional media that are important for food or clinical analysis, such as blood.”
The National R&D Program, through a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, supported this research.
-PublicationEojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon Park, Hanhwi Jang, Yeon Sik Jung, Sungho Jo, “Separation-free bacterial identification in arbitrary media via deepneural network-based SERS analysis,” Biosensors and Bioelectronics online January 18, 2022 (doi.org/10.1016/j.bios.2022.113991)
-ProfileProfessor Yeon Sik JungDepartment of Materials Science and EngineeringKAIST
Professor Sungho JoSchool of ComputingKAIST
2022.03.04 View 21113