Cell Optical Density and Molecular Composition Revealed by Simultaneous Multimodal Label-Free Imaging

We show how Raman imaging can be combined with independent but simultaneous phase measurements of unlabeled cells, with the resulting data providing information on how the light is retarded and/or scattered by molecules in the cell. We then show, for the first time to our knowledge, how the chemistry of the cell highlighted in the Raman information is related to the cell quantitative phase information revealed in digital holographic microscopy by quantifying how the two sets of spatial information are correlated. The results show that such a multimodal implementation is highly useful for the convenience of having video rate imaging of the cell during the entire Raman measurement time, allowing us to observe how the cell changes during Raman acquisition. More importantly, it also shows that the two sets of label-free data, which result from different scattering mechanisms, are complementary and can be used to interpret the composition and dynamics of the cell, where each mode supplies label-free information not available from the other mode.     

In situ label-free imaging of hemicellulose in plant cell walls using stimulated Raman scattering microscopy

Background

Plant hemicellulose (largely xylan) is an excellent feedstock for renewable energy production and second only to cellulose in abundance. Beyond a source of fermentable sugars, xylan constitutes a critical polymer in the plant cell wall, where its precise role in wall assembly, maturation, and deconstruction remains primarily hypothetical. Effective detection of xylan, particularly by in situ imaging of xylan in the presence of other biopolymers, would provide critical information for tackling the challenges of understanding the assembly and enhancing the liberation of xylan from plant materials.

Results

Raman-based imaging techniques, especially the highly sensitive stimulated Raman scattering (SRS) microscopy, have proven to be valuable tools for label-free imaging. However, due to the complex nature of plant materials, especially those same chemical groups shared between xylan and cellulose, the utility of specific Raman vibrational modes that are unique to xylan have been debated. Here, we report a novel approach based on combining spectroscopic analysis and chemical/enzymatic xylan removal from corn stover cell walls, to make progress in meeting this analytical challenge. We have identified several Raman peaks associated with xylan content in cell walls for label-free in situ imaging xylan in plant cell wall.

Conclusion

We demonstrated that xylan can be resolved from cellulose and lignin in situ using enzymatic digestion and label-free SRS microscopy in both 2D and 3D. We believe that this novel approach can be used to map xylan in plant cell walls and that this ability will enhance our understanding of the role played by xylan in cell wall biosynthesis and deconstruction.

     

Raman Spectroscopic Imaging of the Whole Ciona intestinalis Embryo during Development

Intracellular composition and the distribution of bio-molecules play central roles in the specification of cell fates and morphogenesis during embryogenesis. Consequently, investigation of changes in the expression and distribution of bio-molecules, especially mRNAs and proteins, is an important challenge in developmental biology. Raman spectroscopic imaging, a non-invasive and label-free technique, allows simultaneous imaging of the intracellular composition and distribution of multiple bio-molecules. In this study, we explored the application of Raman spectroscopic imaging in the whole Ciona intestinalis embryo during development. Analysis of Raman spectra scattered from C. intestinalis embryos revealed a number of localized patterns of high Raman intensity within the embryo. Based on the observed distribution of bio-molecules, we succeeded in identifying the location and structure of differentiated muscle and endoderm within the whole embryo, up to the tailbud stage, in a label-free manner. Furthermore, during cell differentiation, we detected significant differences in cell state between muscle/endoderm daughter cells and daughter cells with other fates that had divided from the same mother cells; this was achieved by focusing on the Raman intensity of single Raman bands at 1002 or 1526 cm⁻¹, respectively. This study reports the first application of Raman spectroscopic imaging to the study of identifying and characterizing differentiating tissues in a whole chordate embryo. Our results suggest that Raman spectroscopic imaging is a feasible label-free technique for investigating the developmental process of the whole embryo of C. intestinalis.     

Lipid biosynthesis monitored at the single-cell level in Saccharomyces cerevisiae

There is increasing interest in bioengineering of lipids for use in functional foods, pharmaceuticals, and biofuels. Saccharomyces cerevisiae is a widely utilized cell factory for biotechnological production, thus a tempting alternative. Herein, we show how its neutral lipid accumulation varies throughout metabolic phases under nutritional conditions relevant for large-scale fermentation. Population-averaged metabolic data were correlated with lipid storage at the single-cell level monitored at submicron resolution by label-free coherent anti-Stokes Raman scattering (CARS) microscopy. While lipid droplet sizes are fairly constant, the number of droplets is a dynamic parameter determined by glucose and ethanol levels. The lowest number of lipid droplets is observed in the transition phase between glucose and ethanol fermentation. It is followed by a buildup during the ethanol phase. The surplus of accumulated lipids is then mobilized at concurrent glucose and ethanol starvation in the subsequent stationary phase. Thus, the highest amount of lipids is found in the ethanol phase, which is about 0.3 fL/cell. Our results indicate that the budding yeast, S. cerevisiae, can be used for the biosynthesis of lipids and demonstrate the strength of CARS microscopy for monitoring the dynamics of lipid metabolism at the single-cell level of importance for optimized lipid production.     

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering (CARS)

Coherent Raman imaging techniques have seen a dramatic increase in activity over the past decade due to their promise to enable label-free optical imaging with high molecular specificity ¹. The sensitivity of these techniques, however, is many orders of magnitude weaker than fluorescence, requiring milli-molar molecular concentrations ^1,2. Here, we describe a technique that can enable the detection of weak or low concentrations of Raman-active molecules by amplifying their signal with that obtained from strong or abundant Raman scatterers. The interaction of short pulsed lasers in a biological sample generates a variety of coherent Raman scattering signals, each of which carry unique chemical information about the sample. Typically, only one of these signals, e.g. Coherent Anti-stokes Raman scattering (CARS), is used to generate an image while the others are discarded. However, when these other signals, including 3-color CARS and four-wave mixing (FWM), are collected and compared to the CARS signal, otherwise difficult to detect information can be extracted ³. For example, doubly-resonant CARS (DR-CARS) is the result of the constructive interference between two resonant signals ⁴. We demonstrate how tuning of the three lasers required to produce DR-CARS signals to the 2845 cm^-1 CH stretch vibration in lipids and the 2120 cm^-1 CD stretching vibration of a deuterated molecule (e.g. deuterated sugars, fatty acids, etc.) can be utilized to probe both Raman resonances simultaneously. Under these conditions, in addition to CARS signals from each resonance, a combined DR-CARS signal probing both is also generated. We demonstrate how detecting the difference between the DR-CARS signal and the amplifying signal from an abundant molecule''s vibration can be used to enhance the sensitivity for the weaker signal. We further demonstrate that this approach even extends to applications where both signals are generated from different molecules, such that e.g. using the strong Raman signal of a solvent can enhance the weak Raman signal of a dilute solute.     

Hydrohalite spatial distribution in frozen cell cultures measured using confocal Raman microscopy

Hydrohalite, a crystalline rock salt hydrate, (NaCl·2H₂O), can form in cryopreservation samples under certain circumstances changing the local chemical environment of the preserved cells. Evidence of this crystalline phase was recently found by microspectroscopy measurements, and believed to form exclusively extracellular. We have studied the spatial distribution of hydrohalite in frozen mouse fibroblast cell samples by means of confocal Raman scanning microscopy (CRM). Hydrohalite has a unique Raman spectrum with several bands in the high frequency tail of the OH-stretching band which can be used for unambiguous identification. Hydrohalite can only form through eutectic crystallization in saline solutions without any cryoprotective agents and the spatial distribution thus gives a more detailed view on this crystallization process. This is important since eutectic crystallization has been empirically correlated to cell death, but the exact injury mechanism is unclear. By the means of colocalization of Raman bands we show that hydrohalite can indeed form intracellularly and is not a strictly extracellular phenomenon. We furthermore found that intracellular ice and intracellular hydrohalite very often coincide. Finally we show that the addition of 0.5 wt.% dimethyl sulfoxide (Me₂SO) inhibits formation of hydrohalite. This study shows how Raman microscopy and successive analysis can be employed non-invasively within cryobiology to give additional chemical and structural information compared to conventional imaging techniques.     

Label-free biochemical imaging of heart tissue with high-speed spontaneous Raman microscopy

Label-free imaging is desirable for elucidating morphological and biochemical changes of heart tissue in vivo. Spontaneous Raman microscopy (SRM) provides high chemical contrast without labeling, but presents disadvantage in acquiring images due to low sensitivity and consequent long imaging time. Here, we report a novel technique for label-free imaging of rat heart tissues with high-speed SRM combined with resonance Raman effect of heme proteins. We found that individual cardiomyocytes were identified with resonance Raman signal arising mainly from reduced b- and c-type cytochromes, and that cardiomyocytes and blood vessels were imaged by distinguishing cytochromes from oxy- and deoxy-hemoglobin in intact hearts, while cardiomyocytes and fibrotic tissue were imaged by distinguishing cytochromes from collagen type-I in infarct hearts with principal component analysis. These results suggest the potential of SRM as a label-free high-contrast imaging technique, providing a new approach for studying biochemical changes, based on the molecular composition, in the heart.     

Shedding new light on lipid biology with coherent anti-Stokes Raman scattering microscopy

Despite the ubiquitous roles of lipids in biology, the detection of lipids has relied on invasive techniques, population measurements, or nonspecific labeling. Such difficulties can be circumvented by a label-free imaging technique known as coherent anti-Stokes Raman (CARS) microscopy, which is capable of chemically selective, highly sensitive, and high-speed imaging of lipid-rich structures with submicron three-dimensional spatial resolution. We review the broad applications of CARS microscopy to studies of lipid biology in cell cultures, tissue biopsies, and model organisms. Recent technical advances, limitations of the technique, and perspectives are discussed.     

Noninvasive detection and imaging of molecular markers in live cardiomyocytes derived from human embryonic stem cells

Raman microspectroscopy (RMS) was used to detect and image molecular markers specific to cardiomyocytes (CMs) derived from human embryonic stem cells (hESCs). This technique is noninvasive and thus can be used to discriminate individual live CMs within highly heterogeneous cell populations. Principal component analysis (PCA) of the Raman spectra was used to build a classification model for identification of individual CMs. Retrospective immunostaining imaging was used as the gold standard for phenotypic identification of each cell. We were able to discriminate CMs from other phenotypes with >97% specificity and >96% sensitivity, as calculated with the use of cross-validation algorithms (target 100% specificity). A comparison between Raman spectral images corresponding to selected Raman bands identified by the PCA model and immunostaining of the same cells allowed assignment of the Raman spectral markers. We conclude that glycogen is responsible for the discrimination of CMs, whereas myofibril proteins have a lesser contribution. This study demonstrates the potential of RMS for allowing the noninvasive phenotypic identification of hESC progeny. With further development, such label-free optical techniques may enable the separation of high-purity cell populations with mature phenotypes, and provide repeated measurements to monitor time-dependent molecular changes in live hESCs during differentiation in vitro.     

Large Uptake of Titania and Iron Oxide Nanoparticles in the Nucleus of Lung Epithelial Cells as Measured by Raman Imaging and Multivariate Classification

It is a challenging task to characterize the biodistribution of nanoparticles in cells and tissue on a subcellular level. Conventional methods to study the interaction of nanoparticles with living cells rely on labeling techniques that either selectively stain the particles or selectively tag them with tracer molecules. In this work, Raman imaging, a label-free technique that requires no extensive sample preparation, was combined with multivariate classification to quantify the spatial distribution of oxide nanoparticles inside living lung epithelial cells (A549). Cells were exposed to TiO₂ (titania) and/or α-FeO(OH) (goethite) nanoparticles at various incubation times (4 or 48 h). Using multivariate classification of hyperspectral Raman data with partial least-squares discriminant analysis, we show that a surprisingly large fraction of spectra, classified as belonging to the cell nucleus, show Raman bands associated with nanoparticles. Up to 40% of spectra from the cell nucleus show Raman bands associated with nanoparticles. Complementary transmission electron microscopy data for thin cell sections qualitatively support the conclusions.     

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure

Background

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.

Results

We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.

Conclusions

The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.     

Label-free Evaluation of Hepatic Microvesicular Steatosis with Multimodal Coherent Anti-Stokes Raman Scattering Microscopy

Hepatic microvesicular steatosis is a hallmark of drug-induced hepatotoxicity and early-stage fatty liver disease. Current histopathology techniques are inadequate for the clinical evaluation of hepatic microvesicular steatosis. In this paper, we explore the use of multimodal coherent anti-Stokes Raman scattering (CARS) microscopy for the detection and characterization of hepatic microvesicular steatosis. We show that CARS microscopy is more sensitive than Oil Red O histology for the detection of microvesicular steatosis. Computer-assisted analysis of liver lipid level based on CARS signal intensity is consistent with triglyceride measurement using a standard biochemical assay. Most importantly, in a single measurement procedure on unprocessed and unstained liver tissues, multimodal CARS imaging provides a wealth of critical information including the detection of microvesicular steatosis and quantitation of liver lipid content, number and size of lipid droplets, and lipid unsaturation and packing order of lipid droplets. Such information can only be assessed by multiple different methods on processed and stained liver tissues or tissue extracts using current standard analytical techniques. Multimodal CARS microscopy also permits label-free identification of lipid-rich non-parenchymal cells. In addition, label-free and non-perturbative CARS imaging allow rapid screening of mitochondrial toxins-induced microvesicular steatosis in primary hepatocyte cultures. With its sensitivity and versatility, multimodal CARS microscopy should be a powerful tool for the clinical evaluation of hepatic microvesicular steatosis.     

Single cell Raman spectroscopy for cell sorting and imaging

Single cell Raman spectroscopy (SCRS) is a non-invasive and label-free technology, allowing in vivo and multiple parameter analysis of individual living cells. A single cell Raman spectrum usually contains more than 1000 Raman bands which provide rich and intrinsic information of the cell (e.g. nucleic acids, protein, carbohydrates and lipids), reflecting cellular genotypes, phenotypes and physiological states. A Raman spectrum serves as a molecular 'fingerprint' of a single cell, making it possible to differentiate various cells including bacterial, protistan and animal cells without prior knowledge of the cells. However, a key drawback of SCRS is the fact that spontaneous Raman signals are naturally weak; this review discusses recent research progress in significantly enhancing and improving the signal of spontaneous Raman spectroscopy, including resonance Raman spectroscopy (RRS), coherent anti-Stokes Raman spectroscopy (CARS), stimulated Raman spectroscopy (SRS) and surface enhanced Raman scattering (SERS). This review focuses on the biotechnological development and the associated applications of SCRS, including Raman activated cell sorting (RACS) and Raman imaging and mapping.     

Characterising live cell behaviour: Traditional label-free and quantitative phase imaging approaches

Label-free imaging uses inherent contrast mechanisms within cells to create image contrast without introducing dyes/labels, which may confound results. Quantitative phase imaging is label-free and offers higher content and contrast compared to traditional techniques. High-contrast images facilitate generation of individual cell metrics via more robust segmentation and tracking, enabling formation of a label-free dynamic phenotype describing cell-to-cell heterogeneity and temporal changes. Compared to population-level averages, individual cell-level dynamic phenotypes have greater power to differentiate between cellular responses to treatments, which has clinical relevance e.g. in the treatment of cancer. Furthermore, as the data is obtained label-free, the same cells can be used for further assays or expansion, of potential benefit for the fields of regenerative and personalised medicine.     

High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis.     

Visualizing the lipid dynamics role in infrared neural stimulation using stimulated Raman scattering

Infrared neural stimulation (INS) uses pulsed infrared light to yield label-free neural stimulation with broad experimental and translational utility. Despite its robust demonstration, INS’s mechanistic and biophysical underpinnings have been the subject of debate for more than a decade. The role of lipid membrane thermodynamics appears to play an important role in how fast IR-mediated heating nonspecifically drives action potential generation. Direct observation of lipid membrane dynamics during INS remains to be shown in a live neural model system. We used hyperspectral stimulated Raman scattering microscopy to study biochemical signatures of high-speed vibrational dynamics underlying INS in a live neural cell culture model. The findings suggest that lipid bilayer structural changes occur during INS in vitro in NG108-15 neuroglioma cells. Lipid-specific signatures of cell stimulated Raman scattering spectra varied with stimulation energy and radiation exposure. The spectroscopic observations agree with high-speed ratiometric fluorescence imaging of a conventional lipophilic membrane structure reporter, 4-(2-(6-(dibutylamino)-2-naphthalenyl)ethenyl)-1-(3-sulfopropyl)pyridinium hydroxide. The findings support the hypothesis that INS causes changes in the lipid membrane of neural cells by changing the lipid membrane packing order. This work highlights the potential of hyperspectral stimulated Raman scattering as a method to safely study biophysical and biochemical dynamics in live cells.     

Nonresonant confocal Raman imaging of DNA and protein distribution in apoptotic cells

Nonresonant confocal Raman imaging has been used to map the DNA and the protein distributions in individual single human cells. The images are obtained on an improved homebuilt confocal Raman microscope. After statistical analysis, using singular value decomposition, the Raman images are reconstructed from the spectra covering the fingerprint region. The data are obtained at a step interval of approximately 250 nm and cover a field from 8- to 15- micro m square in size. Dwell times at each pixel are between 0.5 and 2 s, depending on the nature and the state of the cell under investigation. High quality nonresonant Raman images can only be obtained under these conditions using continuous wave high laser powers between 60 and 120 mW. We will present evidence that these laser powers can still safely be used to recover the chemical distributions in fixed cells. The developed Raman imaging method is used to image directly, i.e., without prior labeling, the nucleotide condensation and the protein distribution in the so-called nuclear fragments of apoptotic HeLa cells. In the control (nonapoptotic) HeLa cells, we show, for the first time by Raman microspectroscopy, the presence of the RNA in a cell nucleus.     

Elastic light scattering from single cells: orientational dynamics in optical trap

Light-scattering diagrams (phase functions) from single living cells and beads suspended in an optical trap were recorded with 30-ms time resolution. The intensity of the scattered light was recorded over an angular range of 0.5-179.5 degrees using an optical setup based on an elliptical mirror and rotating aperture. Experiments revealed that light-scattering diagrams from biological cells exhibit significant and complex time dependence. We have attributed this dependence to the cell's orientational dynamics within the trap. We have also used experimentally measured phase function information to calculate the time dependence of the optical radiation pressure force on the trapped particle and show how it changes depending on the orientation of the particle. Relevance of these experiments to potential improvement in the sensitivity of label-free flow cytometry is discussed.     

Study of cell-matrix adhesion dynamics using surface plasmon resonance imaging ellipsometry

The interaction of cells with extracellular matrix, termed cell-matrix adhesions, importantly governs multiple cellular phenomena. Knowledge of the functional dynamics of cell-matrix adhesion could provide critical clues for understanding biological phenomena. We developed surface plasmon resonance imaging ellipsometry (SPRIE) to provide high contrast images of the cell-matrix interface in unlabeled living cells. To improve the contrast and sensitivity, the null-type imaging ellipsometry technique was integrated with an attenuated total reflection coupler. We verified that the imaged area of SPRIE was indeed a cell-matrix adhesion area by confocal microscopy imaging. Using SPRIE, we demonstrated that three different cell types exhibit distinct features of adhesion. SPRIE was applied to diverse biological systems, including during cell division, cell migration, and cell-cell communication. We imaged the cell-matrix anchorage of mitotic cells, providing the first label-free imaging of this interaction to our knowledge. We found that cell-cell communication can alter cell-matrix adhesion, possibly providing direct experimental evidence for cell-cell communication-mediated changes in cell adhesion. We also investigated shear-stress-induced adhesion dynamics in real time. Based on these data, we expect that SPRIE will be a useful methodology for studying the role of cell-matrix adhesion in important biological phenomena.     

Using lod-score differences to determine mode of inheritance: a simple, robust method even in the presence of heterogeneity and reduced penetrance.

Determining the mode of inheritance is often difficult under the best of circumstances, but when segregation analysis is used, the problems of ambiguous ascertainment procedures, reduced penetrance, heterogeneity, and misdiagnosis make mode-of-inheritance determinations even more unreliable. The mode of inheritance can also be determined using a linkage-based method (maximized maximum lod score or mod score) and association-based methods, which can overcome many of these problems. In this work, we determined how much information is necessary to reliably determine the mode of inheritance from linkage data when heterogeneity and reduced penetrance are present in the data set. We generated data sets under both dominant and recessive inheritance with reduced penetrance and with varying fractions of linked and unlinked families. We then analyzed those data sets, assuming reduced penetrance, both dominant and recessive inheritance, and no heterogeneity. We investigated the reliability of two methods for determining the mode of inheritance from the linkage data. The first method examined the difference (delta) between the maximum lod scores calculated under the two mode-of-inheritance assumptions. We found that if delta was > 1.5, then the higher of the two maximum lod scores reflected the correct mode of inheritance with high reliability and that a delta of 2.5 appeared to practically guarantee a correct mode-of-inheritance inference. Furthermore, this reliability appeared to be virtually independent of alpha, the fraction of linked families in the data set, although the reliability decreased slightly as alpha fell below .50.(ABSTRACT TRUNCATED AT 250 WORDS)