Imaging vs Nonimaging Raman Spectroscopy for High-Throughput Single-Cell Phenotyping.

Raman spectroscopy can provide nonbiased single-cell analysis based on the endogenous ensemble of biomolecules, with alterations in cellular content indicative of cell state and disease. The measurements themselves can be performed in a variety of modes: generally, full imaging takes the most time but can provide the most information. By reducing the imaging resolution and generating the most characteristic single-cell Raman spectrum in the shortest time, we optimize the utility of the Raman measurement for cell phenotyping. Here, we establish methods to compare these different measurement approaches and assess what, if any, undesired effects occur in the cell. Assuming that laser-induced damage should be apparent as a change in molecular spectra across sequential measurements, and by defining the information content as the Raman-based separability of two cell lines, we thereby establish a parameter range for optimum measurement sensitivity and single-cell throughput in single-cell Raman spectroscopic analysis. While the work here uses 532 nm irradiation, the same approach can be generalized to Raman analysis at other wavelengths.

Single-cell Raman microscopy with machine learning highlights distinct biochemical features of neutrophil extracellular traps and necrosis.

The defining biology that distinguishes neutrophil extracellular traps (NETs) from other forms of cell death is unresolved, and techniques which unambiguously identify NETs remain elusive. Raman scattering measurement provides a holistic overview of cell molecular composition based on characteristic bond vibrations in components such as lipids and proteins. We collected Raman spectra from NETs and freeze/thaw necrotic cells using a custom built high-throughput platform which is able to rapidly measure spectra from single cells. Principal component analysis of Raman spectra from NETs clearly distinguished them from necrotic cells despite their similar morphology, demonstrating their fundamental molecular differences. In contrast, classical techniques used for NET analysis, immunofluorescence microscopy, extracellular DNA, and ELISA, could not differentiate these cells. Additionally, machine learning analysis of Raman spectra indicated subtle differences in lipopolysaccharide (LPS)-induced as opposed to phorbol myristate acetate (PMA)-induced NETs, demonstrating the molecular composition of NETs varies depending on the stimulant used. This study demonstrates the benefits of Raman microscopy in discriminating NETs from other types of cell death and by their pathway of induction.

Non-invasive monitoring of T cell differentiation through Raman spectroscopy.

The monitoring of dynamic cellular behaviors remains a technical challenge for most established techniques used nowadays for single-cell analysis, as most of them are either destructive, or rely on labels that can affect the long-term functions of cells. We employ here label-free optical techniques to non-invasively monitor the changes that occur in murine naive T cells upon activation and subsequent differentiation into effector cells. Based on spontaneous Raman single-cell spectra, we develop statistical models that allow the detection of activation, and employ non-linear projection methods to delineate the changes occurring over a several day period spanning early differentiation. We show that these label-free results have very high correlation with known surface markers of activation and differentiation, while also providing spectral models that allow the identification of the underlying molecular species that are representative of the biological process under study.

Cellular Adhesion Is a Controlling Factor in Neutrophil Extracellular Trap Formation Induced by Anti-Neutrophil Cytoplasmic Antibodies.

Anti-neutrophil cytoplasmic Ab (ANCA)-associated vasculitis (AAV) is a life-threatening condition characterized by improper activation of neutrophils and the release of neutrophil extracellular traps (NETs) in small vessels. This study aimed to explain the role of NETs in AAV pathogenesis by investigating a link between adhesion and NET release using human neutrophils. We leveraged an imaging flow cytometry-based assay and three-dimensional culture to demonstrate that neutrophil adhesion is essential for ANCA-induced NET formation. We confirmed this requirement for cell adhesion using standard microscopy on ultra-low attachment hydrogel surfaces and demonstrate that this depends on the focal adhesion kinase pathway as determined using inhibitors for multiple targets in this process. ANCA increased expression of ß2 integrins on neutrophils, and we confirmed that these integrins were required for NET formation using blocking Abs. Finally, inhibitors for oxidative burst prevented NET formation, and this oxidative burst was mediated by the focal adhesion pathway. Overall, our findings reveal a central role for neutrophil attachment in NET formation in response to ANCAs, helping to explain the restricted localization pattern of vessel damage, and suggesting that targeting neutrophil adhesion factors may be beneficial in preventing pathological damage from NETs during AAV.

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification.

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of information to create models that can predict the state of new samples. We study here linear models, whose separation coefficients can be used to interpret which bands are contributing to the discrimination, and compare the performance of principal component analysis coupled with linear discriminant analysis (PCA/LDA), with regularized logistic regression (Lasso). By applying these methods to single-cell measurements for the detection of macrophage activation, we found that PCA/LDA yields poorer performance in classification compared to Lasso, and underestimates the required sample size to reach stable models. Direct use of Lasso (without PCA) also yields more stable models, and provides sparse separation vectors that directly contain the Raman bands most relevant to classification. To further evaluate these sparse vectors, we apply Lasso to a well-defined case where protein synthesis is inhibited, and show that the separating features are consistent with RNA accumulation and protein levels depletion. Surprisingly, when features are selected purely in terms of their classification power (Lasso), they consist mostly of side bands, while typical strong Raman peaks are not present in the discrimination vector. We propose that this occurs because large Raman bands are representative of a wide variety of intracellular molecules and are therefore less suited for accurate classification.

Label-free Raman mapping of saturated and unsaturated fatty acid uptake, storage, and return toward baseline levels in macrophages.

Macrophage uptake and metabolism of fatty acids is involved in a large number of important biological pathways including immune activation and regulation of macrophages, as well as pathological conditions including obesity, atherosclerosis, and others lifestyle diseases. There are few methods available to directly probe both the uptake and later redistribution/metabolism of fatty acids within living cells as well as the potential changes induced within the cells themselves. We use Raman imaging and analysis to evaluate the effects of different fatty acids following their uptake in macrophages. The label-free nature of the methods means that we can evaluate the fatty acid dynamics without modifying endogenous cellular behavior and metabolism.

Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods.

Measurement techniques that allow the global analysis of cellular responses while retaining single-cell sensitivity are increasingly needed in order to understand complex and dynamic biological processes. In this context, compromises between sensitivity, degree of multiplexing, throughput, and invasiveness are often unavoidable. We present here a noninvasive optical approach that can retrieve quantitative biomarkers of both morphological and molecular phenotypes of individual cells, based on a combination of quantitative phase imaging and Raman spectroscopy measurements. We then develop generalized statistical tools to assess the influence of both controlled (cell sub-populations, immune stimulation) and uncontrolled (culturing conditions, animal variations, etc.) experimental parameters on the label-free biomarkers. These indicators can detect different macrophage cell sub-populations originating from different progenitors as well as their activation state, and how these changes are related to specific differences in morphology and molecular content. The molecular indicators also display further sensitivity that allow identification of other experimental conditions, such as differences between cells originating from different animals, allowing the detection of outlier behaviour from given cell sub-populations.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning.

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Label-free Raman imaging of the macrophage response to the malaria pigment hemozoin.

Hemozoin, the 'malaria pigment', is engulfed by phagocytic cells, such as macrophages, during malaria infection. This biocrystalline substance is difficult to degrade and often accumulates in phagocytes. The macrophage response to hemozoin relates to the severity of the disease and the potential for malaria-related disease complications. In this study we have used Raman spectroscopy as a label-free method to investigate the biochemical changes occurring in macrophages during the first few hours of hemozoin uptake. We found a number of distinct spectral groups, spectrally or spatially related to the presence of the hemozoin inside the cell. Intracellular hemozoin was spectrally identical to extracellular hemozoin, regardless of the location in the cell. A small proportion of hemozoin was found to be associated with lipid-based components, consistent with the uptake of hemozoin into vesicles such as phagosomes and lysosomes. The spatial distribution of the hemozoin was observed to be inhomogeneous, and its presence largely excluded that of proteins and lipids, demonstrating that cells were not able to break down the biocrystals on the time scales studied here. These results show that Raman imaging can be used to answer some of the open questions regarding the role of hemozoin in the immune response. How different combinations of hemozoin and other molecules are treated by macrophages, whether hemozoin can be broken down by the cell, and more importantly, which co-factors or products are involved in the subsequent cell reaction are the expected issues to be elucidated by this technique.

Maximizing throughput in label-free microspectroscopy with hybrid Raman imaging.

Raman spectroscopy is an optical method providing sample molecular composition, which can be analyzed (by point measurements) or spatially mapped by Raman imaging. These provide different information, signal-to-noise ratios, and require different acquisition times. Here, we quantitatively assess Raman spectral features and compare the two measurement methods by multivariate analysis. We also propose a hybrid method: scanning the beam through the sample but optically binning the signal at one location on the detector. This approach generates significantly more useful spectral signals in terms of peak visibility and statistical information. Additionally, by combination with a complementary imaging mode such as quantitative phase microscopy, hybrid imaging allows high throughput and robust spectral analysis while retaining sample spatial information. We demonstrate the improved ability to discriminate between cell lines when using hybrid scanning compared to typical point mode measurements, by quantitatively evaluating spectra taken from two macrophage-like cell lines. Hybrid scanning also provides better classification capability than the full Raman imaging mode, while providing higher signal-to-noise signals with shorter acquisition times. This hybrid imaging approach is suited for various applications including cytometry, cancer versus noncancer detection, and label-free discrimination of cell types or tissues.

Cell volume regulation monitored with combined epifluorescence and digital holographic microscopy.

Quantitative phase imaging emerged recently as a valuable tool for cell observation, by enabling label-free imaging through the intrinsic phase-contrast provided by transparent living cells, thus greatly simplifying observation protocols. The quantitative phase signal, unlike the one provided by the widely used phase-contrast microscope, can be related to relevant biological indicators including dry mass, cell volume regulation or transmembrane water movements. Here, we present quantitative phase imaging coupled with live fluorescence, making it possible to follow the phase signal in time to monitor the cell volume regulation, an early indicator of cell viability, along with specific information such as intracellular Ca2+ imaging with Fura-2 ratiometric fluorescence.

Laser-targeted photofabrication of gold nanoparticles inside cells.

Nanoparticle manipulation is of increasing interest, since they can report single molecule-level measurements of the cellular environment. Until now, however, intracellular nanoparticle locations have been essentially uncontrollable. Here we show that by infusing a gold ion solution, focused laser light-induced photoreduction allows in situ fabrication of gold nanoparticles at precise locations. The resulting particles are pure gold nanocrystals, distributed throughout the laser focus at sizes ranging from 2 to 20 nm, and remain in place even after removing the gold solution. We demonstrate the spatial control by scanning a laser beam to write characters in gold inside a cell. Plasmonically enhanced molecular signals could be detected from nanoparticles, allowing their use as nano-chemical probes at targeted locations inside the cell, with intracellular molecular feedback. Such light-based control of the intracellular particle generation reaction also offers avenues for in situ plasmonic device creation in organic targets, and may eventually link optical and electron microscopy.

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.

A practical inverse-problem approach to digital holographic reconstruction.

In this paper, we propose a new technique for high-quality reconstruction from single digital holographic acquisitions. The unknown complex object field is found as the solution of a nonlinear inverse problem that consists in the minimization of an energy functional. The latter includes total-variation (TV) regularization terms that constrain the spatial amplitude and phase distributions of the reconstructed data. The algorithm that we derive tolerates downsampling, which allows to acquire substantially fewer measurements for reconstruction compared to the state of the art. We demonstrate the effectiveness of our method through several experiments on simulated and real off-axis holograms.

Feature-based recognition of surface-enhanced Raman spectra for biological targets.

We propose and compare multiple approaches to automatically process data measured through surface-enhanced Raman scattering (SERS), in the context of intracellular molecule probing. It relies on locally detecting the most relevant spectra to retrieve all data independently through indexing, thus avoiding any pre-filtering which occurs with standard processing methods. We first assess our approach on simulated data of the spectrum of Rhodamine 6G, and then validate high-performing methods on experimental measurements of this compound. The optimized method is then utilized to extract and classify the complex SERS response behavior of gold nanoparticles taken in live cells.

Local demodulation of holograms using the Riesz transform with application to microscopy.

We propose a Riesz transform approach to the demodulation of digital holograms. The Riesz transform is a higher-dimensional extension of the Hilbert transform and is steerable to a desired orientation. Accurate demodulation of the hologram requires a reliable methodology by which quadrature-phase functions (or simply, quadratures) can be constructed. The Riesz transform, by itself, does not yield quadratures. However, one can start with the Riesz transform and construct the so-called vortex operator by employing the notion of quasi-eigenfunctions, and this approach results in accurate quadratures. The key advantage of using the vortex operator is that it effectively handles nonplanar fringes (interference patterns) and has the ability to compensate for the local orientation. Therefore, this method results in aberration-free holographic imaging even in the case when the wavefronts are not planar. We calibrate the method by estimating the orientation from a reference hologram, measured with an empty field of view. Demodulation results on synthesized planar as well as nonplanar fringe patterns show that the accuracy of demodulation is high. We also perform validation on real experimental measurements of Caenorhabditis elegans acquired with a digital holographic microscope.

Early cell death detection with digital holographic microscopy.

BACKGROUND: Digital holography provides a non-invasive measurement of the quantitative phase shifts induced by cells in culture, which can be related to cell volume changes. It has been shown previously that regulation of cell volume, in particular as it relates to ionic homeostasis, is crucially involved in the activation/inactivation of the cell death processes. We thus present here an application of digital holographic microscopy (DHM) dedicated to early and label-free detection of cell death. METHODS AND FINDINGS: We provide quantitative measurements of phase signal obtained on mouse cortical neurons, and caused by early neuronal cell volume regulation triggered by excitotoxic concentrations of L-glutamate. We show that the efficiency of this early regulation of cell volume detected by DHM, is correlated with the occurrence of subsequent neuronal death assessed with the widely accepted trypan blue method for detection of cell viability. CONCLUSIONS: The determination of the phase signal by DHM provides a simple and rapid optical method for the early detection of cell death.

Determination of transmembrane water fluxes in neurons elicited by glutamate ionotropic receptors and by the cotransporters KCC2 and NKCC1: a digital holographic microscopy study.

Digital holographic microscopy (DHM) is a noninvasive optical imaging technique that provides quantitative phase images of living cells. In a recent study, we showed that the quantitative monitoring of the phase signal by DHM was a simple label-free method to study the effects of glutamate on neuronal optical responses (Pavillon et al., 2010). Here, we refine these observations and show that glutamate produces the following three distinct optical responses in mouse primary cortical neurons in culture, predominantly mediated by NMDA receptors: biphasic, reversible decrease (RD) and irreversible decrease (ID) responses. The shape and amplitude of the optical signal were not associated with a particular cellular phenotype but reflected the physiopathological status of neurons linked to the degree of NMDA activity. Thus, the biphasic, RD, and ID responses indicated, respectively, a low-level, a high-level, and an "excitotoxic" level of NMDA activation. Moreover, furosemide and bumetanide, two inhibitors of sodium-coupled and/or potassium-coupled chloride movement strongly modified the phase shift, suggesting an involvement of two neuronal cotransporters, NKCC1 (Na-K-Cl) and KCC2 (K-Cl) in the genesis of the optical signal. This observation is of particular interest since it shows that DHM is the first imaging technique able to monitor dynamically and in situ the activity of these cotransporters during physiological and/or pathological neuronal conditions.

Exact complex-wave reconstruction in digital holography.

We address the problem of exact complex-wave reconstruction in digital holography. We show that, by confining the object-wave modulation to one quadrant of the frequency domain, and by maintaining a reference-wave intensity higher than that of the object, one can achieve exact complex-wave reconstruction in the absence of noise. A feature of the proposed technique is that the zero-order artifact, which is commonly encountered in hologram reconstruction, can be completely suppressed in the absence of noise. The technique is noniterative and nonlinear. We also establish a connection between the reconstruction technique and homomorphic signal processing, which enables an interpretation of the technique from the perspective of deconvolution. Another key contribution of this paper is a direct link between the reconstruction technique and the two-dimensional Hilbert transform formalism proposed by Hahn. We show that this connection leads to explicit Hilbert transform relations between the magnitude and phase of the complex wave encoded in the hologram. We also provide results on simulated as well as experimental data to validate the accuracy of the reconstruction technique.

Microscopy image resolution improvement by deconvolution of complex fields.

Based on truncated inverse filtering, a theory for deconvolution of complex fields is studied. The validity of the theory is verified by comparing with experimental data from digital holographic microscopy (DHM) using a high-NA system (NA=0.95). Comparison with standard intensity deconvolution reveals that only complex deconvolution deals correctly with coherent cross-talk. With improved image resolution, complex deconvolution is demonstrated to exceed the Rayleigh limit. Gain in resolution arises by accessing the objects complex field - containing the information encoded in the phase - and deconvolving it with the reconstructed complex transfer function (CTF). Synthetic (based on Debye theory modeled with experimental parameters of MO) and experimental amplitude point spread functions (APSF) are used for the CTF reconstruction and compared. Thus, the optical system used for microscopy is characterized quantitatively by its APSF. The role of noise is discussed in the context of complex field deconvolution. As further results, we demonstrate that complex deconvolution does not require any additional optics in the DHM setup while extending the limit of resolution with coherent illumination by a factor of at least 1.64.