Iwan W. Schie

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.

Rapid acquisition of mean Raman spectra of eukaryotic cells for a robust single cell classification

Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization requires the acquisition of multiple Raman spectra to properly characterize them. A Raman spectrum from a diffraction-limited spot at an arbitrary location within a cell results in spectral variations that affect classification approaches. To probe whole cells with Raman imaging at high spatial resolution is time consuming, because a large number of Raman spectra need to be collected, resulting in low cell throughput and impairing statistical analysis due to low cell numbers. Here we propose a method to overcome the effects of cellular heterogeneity by acquiring integrated Raman spectra covering a large portion of a cell. The acquired spectrum represents the mean macromolecular composition of a cell with an exposure time that is comparable to acquisition of a single Raman spectrum. Data sets were collected from T lymphocyte Jurkat cells, and pancreatic cell lines Capan1 and MiaPaca2. Cell classification by support vector machines was compared for single spectra, spectra of images and integrated Raman spectra of cells. The integrated approach provides better and more stable prediction for individual cells, and in the current implementation, the mean macromolecular information of a cell can be acquired faster than with the acquisition of individual spectra from a comparable region. It is expected that this approach will have a major impact on the implementation of Raman based cell classification.

Design and first applications of a flexible Raman micro-spectroscopic system for biological imaging

Typical commercial Raman micro-spectroscopic systems do not offer much flexibility to the end user, thus limiting potential research applications. We present a design of a simple, highly flexible and portable confocal Raman microscope with a detailed list of parts. The system can perform spectral acquisition in different modes: single-point spectroscopy, hyperspectral point mapping or hyperspectral line mapping. Moreover, the microscope can be easily converted between inverted and upright configurations, which can be beneficial for specific situations. Fiber coupling enables to connect various lasers for excitation and spectrometer/CCD combinations for signal detection. The performance of the instrument is demonstrated via Raman spectroscopy at 785 nm excitation wavelength, single point mapping of pancreatic cancer cells placed onto a quartz substrate and line mapping of polystyrene beads.

Evaluation of Shifted Excitation Raman Difference Spectroscopy and Comparison to Computational Background Correction Methods Applied to Biochemical Raman Spectra

Raman spectroscopy provides label-free biochemical information from tissue samples without complicated sample preparation. The clinical capability of Raman spectroscopy has been demonstrated in a wide range of in vitro and in vivo applications. However, a challenge for in vivo applications is the simultaneous excitation of auto-fluorescence in the majority of tissues of interest, such as liver, bladder, brain, and others. Raman bands are then superimposed on a fluorescence background, which can be several orders of magnitude larger than the Raman signal. To eliminate the disturbing fluorescence background, several approaches are available. Among instrumentational methods shifted excitation Raman difference spectroscopy (SERDS) has been widely applied and studied. Similarly, computational techniques, for instance extended multiplicative scatter correction (EMSC), have also been employed to remove undesired background contributions. Here, we present a theoretical and experimental evaluation and comparison of fluorescence background removal approaches for Raman spectra based on SERDS and EMSC.

Surface-enhanced Raman spectroscopy of cell lysates mixed with silver nanoparticles for tumor classification

The throughput of spontaneous Raman spectroscopy for cell identification applications is limited to the range of one cell per second because of the relatively low sensitivity. Surface-enhanced Raman scattering (SERS) is a widespread way to amplify the intensity of Raman signals by several orders of magnitude and, consequently, to improve the sensitivity and throughput. SERS protocols using immuno-functionalized nanoparticles turned out to be challenging for cell identification because they require complex preparation procedures. Here, a new SERS strategy is presented for cell classification using non-functionalized silver nanoparticles and potassium chloride to induce aggregation. To demonstrate the principle, cell lysates were prepared by ultrasonication that disrupts the cell membrane and enables interaction of released cellular biomolecules to nanoparticles. This approach was applied to distinguish four cell lines – Capan-1, HepG2, Sk-Hep1 and MCF-7 – using SERS at 785 nm excitation. Six independent batches were prepared per cell line to check the reproducibility. Principal component analysis was applied for data reduction and assessment of spectral variations that were assigned to proteins, nucleotides and carbohydrates. Four principal components were selected as input for classification models based on support vector machines. Leave-three-batches-out cross validation recognized four cell lines with sensitivities, specificities and accuracies above 96%. We conclude that this reproducible and specific SERS approach offers prospects for cell identification using easily preparable silver nanoparticles.

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.

Raman-based identification of circulating tumor cells for cancer diagnosis

Circulating tumor cells (CTCs) that can be extracted from body fluids offer new prospects in cancer diagnostics. An overview about our recent achievements is presented to use Raman-based methodologies to distinguish cancer cells from normal blood cells. In a first approach, a microfluidic chip was developed to collect Raman spectra from optically trapped cells. Whereas sensitivities and specificities were promising, the throughput was not compatible with the expected low number of CTCs per million white blood cells. A second strategy immobilized up to 200,000 cells onto a microhole array made of silicon nitride. Rapid microscopic screening can be applied to pre-select a subset of cells from which Raman spectra are collected for specific CTC identification. As this approach is compatible with living cells and Raman spectroscopy with 785 nm excitation is non-destructive, a robotic arm can select positively identified CTCs for in-depth biochemical assessment. Finally, an in vivo approach directly collects CTCs from the blood stream. This way reduces the cell number to a manageable size that is subjected to Raman spectroscopy for cell typing and enumeration. An integrated acquisition mode was introduced to further increase the throughput and robustness of single cell classification.

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.

Quantitation of acute monocytic leukemia cells spiked in control monocytes using surface-enhanced Raman spectroscopy

Surface enhanced Raman spectroscopy (SERS) was used to quantify leukemia cells spiked in control cells. The novelty of the technique lies in preparing cell lysates by ultrasound sonication and mixing with silver nanoparticles which allow reproducible interaction of biomolecules and nanoparticles. The SERS spectra of these mixtures not only exhibit enhanced bands of intracellular proteins and nucleic acids, but also spectral variations for accurate cell identification. Here, samples from an acute monocytic leukemia cell line, THP-1, and control monocytes from three donors served as the in vitro model system for leukemia. For quantitative analysis, seven mixtures containing different percentile amounts of leukemia lysates and lysates from control monocytes were prepared ranging from 0% to 100% and SERS spectra were measured. The more intense spectral contributions of proteins relative to nucleic acids correlated with the larger cytoplasm to nucleus ratio of leukemia cells than control monocytes. The experimental SERS spectra were fitted by a non-negative least squares (NNLS) algorithm to calculate the percentile amounts of each of the cell types and to determine their contributions to the mixtures. Even in a mixture with control monocytes (360 μl), a small amount (5 μl) of leukemia cells was detected, which represents 10% of leukemia cells considering their twofold larger diameter and eightfold larger volume. As this value is well below the threshold of 20% blast cells for leukemia diagnosis, this approach is very promising for both qualitative and quantitative analysis of human cell mixtures. This study demonstrates the potential of SERS and NNLS fitting as a rapid method for diagnosis of acute monocytic leukemia in human blood or bone marrow samples because only a single spectrum is required.

A combination of low-resolution Raman spectroscopy (LRRS) and rapid acquisition of mean Raman spectra for the identification of cells (Conference Presentation)

It has been shown that Raman spectroscopy provides superb ability to differentiate individual cell types, and can also be used to detect circulating tumor cells (CTCs).1 CTCs have been recently identified as a main culprit for the development of cancer metastasis in cancer patients.2 It is also well known that the presence of CTCs is negatively associated with the development of metastasis and the progression of cancer. Hence, a reliable method for CTC identification will have a major impact on cancer diagnostic, monitoring of cancer progression, and cancer therapy. There are, however, two general problems of using Raman spectroscopy for the identification of cells. On the one hand, it is not clear from which cellular location a Raman spectrum that reliably represents the given cell should be acquired. On the other hand, the Raman signal intensity is weak, so that acquisition times of several seconds are required, prohibiting a high-throughput cell sampling. In this work we firstly show that by rapidly scanning a diffraction-limited spot over the cell and continuously acquiring a Raman spectrum it is possible to overcome the intracellular heterogeneity of a cell. And the resulting chemometric models provide a better and more robust cell classification. Secondly, we can show that the spectral resolution of a Raman spectrum is not as crucial to distinguish between different cell types. By reducing the spectral resolution 6-fold, we can achieve a signal gain 5-fold and still reliably identify single cells.