Thomas Bocklitz

How to pre-process Raman spectra for reliable and stable models?

Raman spectroscopy in combination with chemometrics is gaining more and more importance for answering biological questions. This results from the fact that Raman spectroscopy is non-invasive, marker-free and water is not corrupting Raman spectra significantly. However, Raman spectra contain despite Raman fingerprint information other contributions like fluorescence background, Gaussian noise, cosmic spikes and other effects dependent on experimental parameters, which have to be removed prior to the analysis, in order to ensure that the analysis is based on the Raman measurements and not on other effects. Here we present a comprehensive study of the influence of pre-processing procedures on statistical models. We will show that a large amount of possible and physically meaningful pre-processing procedures leads to bad results. Furthermore a method based on genetic algorithms (GAs) is introduced, which chooses the spectral pre-processing according to the carried out analysis task without trying all possible pre-processing approaches (grid-search). This was demonstrated for the two most common tasks, namely for a multivariate calibration model and for two classification models. However, the presented approach can be applied in general, if there is a computational measure, which can be optimized. The suggested GA procedure results in models, which have a higher precision and are more stable against corrupting effects.

Sample size planning for classification models

In biospectroscopy, suitably annotated and statistically independent samples (e.g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classifiers. However, building a good model is actually not enough: the performance must also be proven. We discuss learning curves for typical small sample size situations with 5-25 independent samples per class. Although the classification models achieve acceptable performance, the learning curve can be completely masked by the random testing uncertainty due to the equally limited test sample size. In consequence, we determine test sample sizes necessary to achieve reasonable precision in the validation and find that 75-100 samples will usually be needed to test a good but not perfect classifier. Such a data set will then allow refined sample size planning on the basis of the achieved performance. We also demonstrate how to calculate necessary sample sizes in order to show the superiority of one classifier over another: this often requires hundreds of statistically independent test samples or is even theoretically impossible. We demonstrate our findings with a data set of ca. 2550 Raman spectra of single cells (five classes: erythrocytes, leukocytes and three tumour cell lines BT-20, MCF-7 and OCI-AML3) as well as by an extensive simulation that allows precise determination of the actual performance of the models in question.

Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells

Significance Measuring activity patterns of microbes in their natural environment is essential for understanding ecosystems and the multifaceted interactions of microorganisms with eukaryotes. In this study, we developed a technique that allows fast and nondestructive activity measurements of microbial communities on a single-cell level. Microbial communities were amended with heavy water (D2O), a treatment that does not change the available substrate pool. After incubation, physiologically active cells are rapidly identified with Raman microspectroscopy by measuring cellular D incorporation. Using this approach, we characterized the activity patterns of two dominant microbes in mouse cecum samples amended with different carbohydrates and discovered previously unidentified bacteria stimulated by mucin and/or glucosamine by combining Raman microspectroscopy and optical tweezer-based sorting. Microbial communities are essential to the function of virtually all ecosystems and eukaryotes, including humans. However, it is still a major challenge to identify microbial cells active under natural conditions in complex systems. In this study, we developed a new method to identify and sort active microbes on the single-cell level in complex samples using stable isotope probing with heavy water (D2O) combined with Raman microspectroscopy. Incorporation of D2O-derived D into the biomass of autotrophic and heterotrophic bacteria and archaea could be unambiguously detected via C-D signature peaks in single-cell Raman spectra, and the obtained labeling pattern was confirmed by nanoscale-resolution secondary ion MS. In fast-growing Escherichia coli cells, label detection was already possible after 20 min. For functional analyses of microbial communities, the detection of D incorporation from D2O in individual microbial cells via Raman microspectroscopy can be directly combined with FISH for the identification of active microbes. Applying this approach to mouse cecal microbiota revealed that the host-compound foragers Akkermansia muciniphila and Bacteroides acidifaciens exhibited distinctive response patterns to amendments of mucin and sugars. By Raman-based cell sorting of active (deuterated) cells with optical tweezers and subsequent multiple displacement amplification and DNA sequencing, novel cecal microbes stimulated by mucin and/or glucosamine were identified, demonstrating the potential of the nondestructive D2O-Raman approach for targeted sorting of microbial cells with defined functional properties for single-cell genomics.

Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging

Medical diagnosis can be improved significantly by fast, highly sensitive and quantitative cell identification from easily accessible body fluids. Prominent examples are disseminated tumor cells circulating in the peripheral blood of cancer patients. These cells are extremely rare and therefore difficult to detect. In this contribution we present the Raman spectroscopic characterization of different cells that can be found in peripheral blood such as leukocytes, leukemic cells and solid tumor cells. Leukocytes were isolated from the peripheral blood from healthy donors. Breast carcinoma derived tumor cells (MCF-7, BT-20) and myeloid leukaemia cells (OCI-AML3) were prepared from cell cultures. Raman images were collected from dried cells on calcium fluoride slides using 785 nm laser excitation. Unsupervised statistical methods (hierarchical cluster analysis and principal component analysis) were used to visualize spectral differences and cluster formation according to the cell type. With the help of supervised statistical methods (support vector machines) a classification model with 99.7% accuracy rates for the differentiation of the cells was built. The model was successfully applied to identify single cells from an independent mixture of cells based on their vibrational spectra. The classification was confirmed by fluorescence staining of the cells after the Raman measurement.

Towards a quantitative SERS approach--online monitoring of analytes in a microfluidic system with isotope-edited internal standards.

In this contribution a new approach for quantitative measurements using surface-enhanced Raman spectroscopy (SERS) is presented. Combining the application of isotope-edited internal standard with the advantages of the liquid-liquid segmented-flow-based approach for flow-through SERS detection seems to be a promising means for quantitative SERS analysis. For the investigations discussed here a newly designed flow cell, tested for ideal mixing efficiency on the basis of grayscale-value measurements, is implemented. Measurements with the heteroaromatics nicotine and pyridine using their respective deuterated isotopomers as internal standards show that the integration of an isotopically labeled internal standard in the used liquid-liquid two-phase segmented flow leads to reproducible and comparable SERS spectra independent from the used colloid. With the implementation of an internal standard into the microfluidic device the influence of the properties of the colloid on the SERS activity can be compensated. Thus, the problem of a poor batch-to-batch reproducibility of the needed nanoparticle solutions is solved. To the best of our knowledge these are the first measurements combining the above mentioned concepts in order to correct for differences in the enhancement behaviour of the respective colloid.

Toward a Spectroscopic Hemogram: Raman Spectroscopic Differentiation of the Two Most Abundant Leukocytes from Peripheral Blood

The first response to infection in the blood is mediated by leukocytes. As a result crucial information can be gained from a hemogram. Conventional methods such as blood smears and automated sorting procedures are not capable of recording detailed biochemical information of the different leukocytes. In this study, Raman spectroscopy has been applied to investigate the differences between the leukocyte subtypes which have been obtained from healthy donors. Raman imaging was able to visualize the same morphological features as standard staining methods without the need of any label. Unsupervised statistical methods such as principal component analysis and hierarchical cluster analysis were able to separate Raman spectra of the two most abundant leukocytes, the neutrophils and lymphocytes (with a special focus on CD4(+) T-lymphocytes). For the same cells a classification model was built to allow an automated Raman-based differentiation of the cell type in the future. The classification model could achieve an accuracy of 94% in the validation step and could predict the identity of unknown cells from a completely different donor with an accuracy of 81% when using single spectra and with an accuracy of 97% when using the majority vote from all individual spectra of the cell. This marks a promising step toward automated Raman spectroscopic blood analysis which holds the potential not only to assign the numbers of the cells but also to yield important biochemical information.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells.

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohns disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Deeper Understanding of Biological Tissue: Quantitative Correlation of MALDI-TOF and Raman Imaging

In order to achieve a comprehensive description of biological tissue, spectral information about proteins, lipids, nucleic acids, and other biochemical components need to be obtained concurrently. Different analytical techniques may be combined to record complementary information of the same sample. Established techniques, which can be utilized to elucidate the biochemistry of tissue samples are, for instance, MALDI-TOF-MS and Raman microscopic imaging. With this contribution, we combine these two techniques for the first time. The combination of both techniques allows the utilization and interpretation of complementary information (i.e., the information about the protein composition derived from the Raman spectra with data of the lipids analyzed by the MALDI-TOF measurements). Furthermore, we demonstrate how spectral information from MALDI-TOF experiments can be utilized to interpret Raman spectra.

A study of Docetaxel-induced effects in MCF-7 cells by means of Raman microspectroscopy

AbstractChemotherapies feature a low success rate of about 25%, and therefore, the choice of the most effective cytostatic drug for the individual patient and monitoring the efficiency of an ongoing chemotherapy are important steps towards personalized therapy. Thereby, an objective method able to differentiate between treated and untreated cancer cells would be essential. In this study, we provide molecular insights into Docetaxel-induced effects in MCF-7 cells, as a model system for adenocarcinoma, by means of Raman microspectroscopy combined with powerful chemometric methods. The analysis of the Raman data is divided into two steps. In the first part, the morphology of cell organelles, e.g. the cell nucleus has been visualized by analysing the Raman spectra with k-means cluster analysis and artificial neural networks and compared to the histopathologic gold standard method hematoxylin and eosin staining. This comparison showed that Raman microscopy is capable of displaying the cell morphology; however, this is in contrast to hematoxylin and eosin staining label free and can therefore be applied potentially in vivo. Because Docetaxel is a drug acting within the cell nucleus, Raman spectra originating from the cell nucleus region were further investigated in a next step. Thereby we were able to differentiate treated from untreated MCF-7 cells and to quantify the cell–drug response by utilizing linear discriminant analysis models. FigureRaman microspectroscopy in combination with powerful chemometric methods (e.g. artificial neural networks) indicates morphological (nucleus fragmentation) and spectral changes in Docetaxel treated breast cancer cells (MCF-7) in comparison to untreated cell samples

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.