SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium

Skvortsova, A.; Trelin, Andrii; Sedlář, Antonín; Erzina, Mariia; Trávníčková, Martina; Svobodova, L.; Kolská, Zdeňka; Siegel, Jakub; Bačáková, Lucie; Švorčı́k, Václav; Lyutakov, Oleksiy

doi:10.1016/j.snb.2022.132812

Cited by 12 publications

(4 citation statements)

References 64 publications

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“…The SERS spectra exhibit complex patterns with overlapping peaks, making a direct, precise, and accurate determination of the flakes’ surface termination by manual analysis challenging. However, this challenge can be overcome by utilizing ANN, which have the capability to reveal and interpret hidden features within complex spectral patterns. − …”

Section: Resultsmentioning

confidence: 99%

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

Trelin,

Skvortsova,

Olshtrem

et al. 2024

J. Phys. Chem. C

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The properties of MXene flakes, a new class of twodimensional materials, are strictly determined by their surface termination. The most common termination groups are oxygencontaining (�O or −OH) and fluorine (−F), and their relative ratio is closely related to flake stability and catalytic activity. The surface termination can vary significantly among MXene flakes depending on the preparation route and is commonly determined after flake preparation by using X-ray photoelectron spectroscopy (XPS). In this paper, as an alternative approach, we propose the combination of surface-enhanced Raman spectroscopy (SERS) and artificial neural networks (ANN) for the precise and reliable determination of MXene flakes' (Ti 3 C 2 T x ) surface chemistry. Ti 3 C 2 T x flakes were independently prepared by three scientific groups and subsequently measured using three different Raman spectrometers, employing resonant excitation wavelengths. Manual analysis of the SERS spectra did not enable accurate determination of the flake surface termination. However, the combined SERS-ANN approach allowed us to determine the surface termination with a high accuracy. The reliability of the method was verified by using a series of independently prepared samples. We also paid special attention to how the results of the SERS-ANN method are affected by the flake stability and differences in the conditions of flake preparation and Raman measurements. This way, we have developed a universal technique that is independent of the above-mentioned parameters, providing the results with accuracy similar to XPS, but enhanced in terms of analysis time and simplicity.

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Section: Resultsmentioning

confidence: 99%

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

Trelin,

Skvortsova,

Olshtrem

et al. 2024

J. Phys. Chem. C

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“…SpecATNet is a combination of commonly used CNNs and Transformers. A DenseNet-like architecture was used as the CNN component due to its successful application in the recognition of Raman spectra 67 , 68 , providing good performance and fast convergence while being relatively simple. DenseNet is a type of deep-learning NN where sequences of input data are processed stepwise.…”

Section: Methodsmentioning

confidence: 99%

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Guselnikova,

Trelin,

Kang

et al. 2024

Nat Commun

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Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.

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“…Therefore, they are ideal for tasks such as feature extraction and spectral classification. For instance, the integration of SERS and CNN has demonstrated its ability to distinguish between normal and tumor cells [ 34 ], as well as its potential for identifying stem cell states [ 35 ]. However, the classification of cellular states in existing works is relatively coarse, focusing on the classification of different types of cells [ 32 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, to the best of our knowledge, no studies have addressed the identification of cellular states during the EB stage. Skvortsova et al achieved a 95.9% accuracy in identifying mesenchymal stem cell states at different time points using SERS and CNN, but the refinement of classification categories remains to be improved [ 35 ]. Germond et al attained an 88.6% accuracy in the reprogramming process of mouse stem cells using Raman spectroscopy and CNN, yet the classification categories and accuracy were inadequate [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate identification of stem cell differentiation stages via SERS and convolutional neural networks

Zhang,

Wan,

Huang

et al. 2024

Biomed. Opt. Express

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Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining surface-enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.

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SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium

Cited by 12 publications

References 64 publications

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams

Rapid and accurate identification of stem cell differentiation stages via SERS and convolutional neural networks

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