Investigations on chemometric approaches for diagnostic applications utilizing various combinations of spectral and image data types

In the presented work, several data fusion and machine learning approaches were explored within the frame of the data combination for various measurement techniques in biomedical applications. For each of the measurement techniques used in this work, the data was ana-lyzed by means of machine learning. Prior to applying these machine learning algorithms, a specific preprocessing pipeline for each type of data had to be established. These pipelines made it possible to standardize the data and to decrease sample-to-sample variations which originate from the instability of devices or small deviations in the sample preparation or measurement routine. The preprocessed data sets were used for various analyses of biological samples. Separate data analyses were performed for microscopic images, Raman spectra, and SERS data. However, this work mainly focused on the application of data fusion methods for the analy-sis of biological tissues and cells. To do so, different data fusion pipelines were constructed for each task, depending on the data structure. Both low-level (centralized) and high-level (distributed) data fusion approaches were tested and investigated within in this work. To demonstrate centralized and distributed data fusion, two examples were implemented for tissue investigation. In both examples, a combination of Raman spectroscopic and MALDI spectrometric data were analyzed. One example demonstrated centralized data fusion for the analysis of the chemical composition of a mouse brain section, and the other example employed distributed data fusion for liver cancer detection. Other data fusion examples were demonstrated for cell-based analysis. It was demonstrated that leukocyte cell subtype identification can be improved by a centralized data fusion of Raman spectroscopic data and morphological features obtained from microscopic images of stained cells. The last example presented in this work demonstrated a sepsis diagnostic pipeline based on the combination of Raman spectroscopic data and biomarkers. Besides the measured values, the demographic information of the patient was included in the analysis process for considering non-disease-related variations. During the construction of data fusion pipelines, such issues as unbalanced data contribu-tion, missing values, and variations that are not related to the investigated responses were faced. To resolve these issues, data weighting, missing data imputation, and the introduc-tion of additional responses were employed. For further improvement of analysis reliability, the data fusion pipelines and data processing routine were adjusted for each study in this work. As a result, the most suitable data fusion approach was found for every example, and a combination of the machine learning methods with data fusion approaches was demon-strated as a powerful tool for data analysis in biomedical applications

Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples

Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis

Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples

Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods

Use of polymers as wavenumber calibration standards in deep-UVRR

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400–1900 cm−1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique

Label‐free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli , Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV‐Resonance Raman spectroscopy (UVRR) and single‐cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine‐learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications

Deep learning a boon for biophotonics

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Toward food analytics: fast estimation of lycopene and β-carotene content in tomatoes based on surface enhanced Raman spectroscopy (SERS)

Carotenoids are molecules that play important roles in both plant development and in the well-being of mammalian organisms. Therefore, various studies have been performed to characterize carotenoids’ properties, distribution in nature and their health benefits upon ingestion. Nevertheless, there is a gap regarding a fast detection of them at the plant phase. Within this contribution we report the results obtained regarding the application of surface enhanced Raman spectroscopy (SERS) toward the differentiation of two carotenoid molecules (namely, lycopene and β-carotene) in tomato samples. To this end, an e-beam lithography (EBL) SERS-active substrate and a 488 nm excitation source were employed, and a relevant simulated matrix was prepared (by mixing the two carotenoids in defined percentages) and measured. Next, carotenoids were extracted from tomato plants and measured as well. Finally, a combination of principal component analysis and partial least squares regression (PCA-PLSR) was applied to process the data, and the obtained results were compared with HPLC measurements of the same extracts. A good agreement was obtained between the HPLC and the SERS results for most of the tomato samples

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination

Raman spectroscopy follows time-dependent changes in T lymphocytes isolated from spleen of endotoxemic mice

T lymphocytes (T cells) are highly specialized members of the adaptive immune system and hold the key to the understanding the hosts’ response toward invading pathogen or pathogen-associated molecular patterns such as LPS. In this study, noninvasive Raman spectroscopy is presented as a label-free method to follow LPS-induced changes in splenic T cells during acute and postacute inflammatory phases (1, 4, 10, and 30 d) with a special focus on CD4+ and CD8+ T cells of endotoxemic C57BL/6 mice. Raman spectral analysis reveals highest chemical differences between CD4+ and CD8+ T cells originating from the control and LPS-treated mice during acute inflammation, and the differences are visible up to 10 d after the LPS insult. In the postacute phase, CD4+ and CD8+ T cells from treated and untreated mice could not be differentiated anymore, suggesting that T cells largely regained their original status. In sum, the biological information obtained from Raman spectra agrees with immunological readouts demonstrating that Raman spectroscopy is a well-suited, label-free method for following splenic T cell activation in systemic inflammation from acute to postacute phases. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.T lymphocytes (T cells) are highly specialized members of the adaptive immune system and hold the key to the understanding the hosts’ response toward invading pathogen or pathogen-associated molecular patterns such as LPS. In this study, noninvasive Raman spectroscopy is presented as a label-free method to follow LPS-induced changes in splenic T cells during acute and postacute inflammatory phases (1, 4, 10, and 30 d) with a special focus on CD4+ and CD8+ T cells of endotoxemic C57BL/6 mice. Raman spectral analysis reveals highest chemical differences between CD4+ and CD8+ T cells originating from the control and LPS-treated mice during acute inflammation, and the differences are visible up to 10 d after the LPS insult. In the postacute phase, CD4+ and CD8+ T cells from treated and untreated mice could not be differentiated anymore, suggesting that T cells largely regained their original status. In sum, the biological information obtained from Raman spectra agrees with immunological readouts demonstrating that Raman spectroscopy is a well-suited, label-free method for following splenic T cell activation in systemic inflammation from acute to postacute phases. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult