Anuradha Ramoji

A comparative Raman and CARS imaging study of colon tissue

An experimental evaluation of the information content of two complimentary techniques, linear Raman and coherent anti-Stokes Raman scattering (CARS) microscopy, is presented. CARS is a nonlinear variant of Raman spectroscopy that enables rapid acquisition of images within seconds in combination with laser scanning microscopes. CARS images were recorded from thin colon tissue sections at 2850, 1660, 1450 and 1000 cm(-1) and compared with Raman images. Raman images were obtained from univariate and multivariate (k-means clustering) methods, whereas all CARS images represent univariate results. Variances within tissue sections could be visualized in chemical maps of CARS and Raman images. However, identification of tissue types and characterization of variances between different tissue sections were only possible by analysis of cluster mean spectra, obtained from k-means cluster analysis. This first comparison establishes the foundation for further development of the CARS technology to assess tissue.

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.

Raman spectroscopic differentiation of planktonic bacteria and biofilms

AbstractBoth biofilm formations as well as planktonic cells of water bacteria such as diverse species of the Legionella genus as well as Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli were examined in detail by Raman microspectroscopy. Production of various molecules involved in biofilm formation of tested species in nutrient-deficient media such as tap water was observed and was particularly evident in the biofilms formed by six Legionella species. Biofilms of selected species of the Legionella genus differ significantly from the planktonic cells of the same organisms in their lipid amount. Also, all Legionella species have formed biofilms that differ significantly from the biofilms of the other tested genera in the amount of lipids they produced. We believe that the significant increase in the synthesis of this molecular species may be associated with the ability of Legionella species to form biofilms. In addition, a combination of Raman microspectroscopy with chemometric approaches can distinguish between both planktonic form and biofilms of diverse bacteria and could be used to identify samples which were unknown to the identification model. Our results provide valuable data for the development of fast and reliable analytic methods based on Raman microspectroscopy, which can be applied to the analysis of tap water-adapted microorganisms without any cultivation step. Graphical abstractBiofilm and planktonic forms of L. pneumophila ssp. pneumophila exhibit different Raman spectra. L. pneumophila ssp. pneumophila in biofilms display a significant increase in the synthesis of lipids compared to the planktonic state

High-Throughput Screening Raman Spectroscopy Platform for Label-Free Cellomics

We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications. (1) HTS-RS-based differential white blood cell count. A classification model was trained using Raman spectra of 52 218 lymphocytes, 48 220 neutrophils, and 7 294 monocytes from four volunteers. The model was applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. (2) HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9, and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90, and 3:97, respectively. Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics.

Markierungsfreies Hochdurchsatzscreening mit Raman-Spektroskopie

Raman microspectroscopy displays a label-free and non-destructive modality providing highly specific information on single-cell level. Thus, it bears great potential as a standard tool in biomedical studies. We have developed a high-throughput screening Raman spectroscopy platform combining both automated imaging microscopy and spectra acquisition. The system allows for rapid screening of whole cell populations enabling differential white blood cell count and circulating tumor cell detection.

Photonic monitoring of treatment during infection and sepsis: development of new detection strategies and potential clinical applications

AbstractDespite the strong decline in the infection-associated mortality since the development of the first antibiotics, infectious diseases are still a major cause of death in the world. With the rising number of antibiotic-resistant pathogens, the incidence of deaths caused by infections may increase strongly in the future. Survival rates in sepsis, which occurs when body response to infections becomes uncontrolled, are still very poor if an adequate therapy is not initiated immediately. Therefore, approaches to monitor the treatment efficacy are crucially needed to adapt therapeutic strategies according to the patient’s response. An increasing number of photonic technologies are being considered for diagnostic purpose and monitoring of therapeutic response; however many of these strategies have not been introduced into clinical routine, yet. Here, we review photonic strategies to monitor response to treatment in patients with infectious disease, sepsis, and septic shock. We also include some selected approaches for the development of new drugs in animal models as well as new monitoring strategies which might be applicable to evaluate treatment response in humans in the future. FigureLabel-free probing of blood properties using photonics

Leukocyte subtypes classification by means of image processing

The classification of leukocyte subtypes is a routine method to diagnose many diseases, infections, and inflammations. By applying an automated cell counting procedure, it is possible to decrease analysis time and increase the number of analyzed cells per patient, thereby making the analysis more robust. Here we propose a method, which automatically differentiate between two white blood cell subtypes, which are present in blood in the highest fractions. We apply generalized pseudo-Zernike moments to transfer morphological information of the cells to features and subsequently to a classification model. The first results indicate that information from the morphology can be used to obtain efficient automatic classification, which was demonstrated for the leukocyte subtype classification of neutrophils and lymphocytes. The approach can be extended to other imaging modalities, like different types of staining, spectroscopic techniques, dark field or phase contrast microscopy.

Raman Spectroscopic Investigation of Dyes in Spices

In this study, a number of synthetic colorants for spices have been investigated by means of Raman spectroscopy, resonance Raman spectroscopy, and surface enhanced (resonance) Raman spectroscopy (SER(S)). The aim of the study was the determination of limits of detection for each dye separately and in binary mixtures of dyes in spiked samples of the spices. Most of the investigated dyes have been azo dyes, some being water‐soluble, the other being fat‐soluble.Investigating the composition of food preparations is an ongoing and important branch of analytical sciences. On one hand, new ingredients have to be analyzed with regard to their contents, on the other hand, raw materials that have been tampered have to be eliminated from food production processes.In the last decades, the various Raman spectroscopic methods have proven to be successful in many areas of life and materials sciences. The ability of Raman spectroscopy to distinguish even structural very similar analytes by means of their vibrational fing...