Phase-sensitive, angle-resolved light-scattering microscopy of single cells.

We report what is to our knowledge the first use of Fourier phase microscopy (FPM) to estimate diameters of individual single-micrometer beads and to classify cells based upon changes in scatterer size distribution. FPM, a quantitative phase imaging (QPI) method, combines the planar illumination typically used in off-axis QPI (ideal for Mie theory analysis) with the common-path geometry typically used in on-axis QPI (ideal for optimizing angular scattering range). Low-spatial-frequency imaging artifacts inherent to FPM have negligible impact upon these angular-domain applications. The system is simple to align and stable, and requires no external reference beam. Angular scattering patterns obtained from single 1 µm polystyrene beads in glycerol (Δn=0.11) display unprecedented fidelity to Mie theory, produce diameter estimates consistent with the manufacturer's specifications, and offer precision on the scale of tens of nanometers. Measurements of macrophages at different stages of antibody-dependent cellular phagocytosis demonstrate the ability to detect changes in a cell's scattering caused by the presence of phagocytosed material within.

Optical diffraction tomography for assessing single cell models in angular light scattering.

Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.

Angular-domain scattering interferometry.

We present an angular-scattering optical method that is capable of measuring the mean size of scatterers in static ensembles within a field of view less than 20 µm in diameter. Using interferometry, the method overcomes the inability of intensity-based models to tolerate the large speckle grains associated with such small illumination areas. By first estimating each scatterer's location, the method can model between-scatterer interference as well as traditional single-particle Mie scattering. Direct angular-domain measurements provide finer angular resolution than digitally transformed image-plane recordings. This increases sensitivity to size-dependent scattering features, enabling more robust size estimates. The sensitivity of these angular-scattering measurements to various sizes of polystyrene beads is demonstrated. Interferometry also allows recovery of the full complex scattered field, including a size-dependent phase profile in the angular-scattering pattern.

Mechanisms of bone fragility in a mouse model of glucocorticoid-treated rheumatoid arthritis: implications for insufficiency fracture risk.

OBJECTIVE: Glucocorticoid (GC) therapy is associated with increased risk of fracture in patients with rheumatoid arthritis (RA). To elucidate the cause of this increased risk, we examined the effects of chronic erosive inflammatory arthritis and GC treatment on bone quality, structure, and biomechanical properties in a murine model. METHODS: Mice with established arthritis and expressing human tumor necrosis factor α (TNFα) transgene (Tg) and their wild-type (WT) littermates were continually treated with GC (prednisolone 5 mg/kg/day via subcutaneous controlled-release pellet) or placebo for 14, 28, or 42 days. Microstructure, biomechanical properties, chemical composition, and morphology of the tibiae and lumbar vertebral bodies were assessed by micro-computed tomography, biomechanical testing, Raman spectroscopy, and histology, respectively. Serum markers of bone turnover were also determined. RESULTS: TNF-Tg and GC treatment additively decreased mechanical strength and stiffness in both the tibiae and the vertebral bodies. GC treatment in the TNF-Tg mice increased the ductility of tibiae under torsional loading. These changes were associated with significant alterations in the biochemical and structural composition of the mineral and organic components of the bone matrix, a decrease in osteoblast activity and bone formation, and an increase in osteoclast activity. CONCLUSION: Our findings indicate that the concomitant decrease in bone strength and increase in bone ductility associated with chronic inflammation and GC therapy, coupled with the significant changes in the bone quality and structure, may increase the susceptibility of the bone to failure under low-energy loading. This may explain the mechanism of symptomatic insufficiency fractures in patients with RA receiving GC therapy who do not have radiographic manifestations of fracture.

Three-dimensional angular scattering simulations inform analysis of scattering from single cells.

Significance: Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens. Aim: Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates. Approach: We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles' scattering, and detector noise. We quantified each noise source's effect upon the estimated mean and standard deviation of organelle size distributions. Results: The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%. Conclusions: The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.

Detection of osteoporotic-related bone changes and prediction of distal radius strength using Raman spectra from excised human cadaver finger bones.

While osteoporosis is reliably diagnosed using dual energy X-ray absorptiometry (DXA), screening rates are alarmingly low, contributing to preventable fractures. Raman spectroscopy (RS) can detect biochemical changes that occur in bones transcutaneously and can arguably be more accessible than DXA as a fracture risk assessment. A reasonable approach to translate RS is to interrogate phalangeal bones of human hands, where the soft tissues covering the bone are less likely to hamper transcutaneous measurements. To that end, we set out to first determine whether Raman spectra obtained from phalangeal bones correlate with distal radius fracture strength, which can predict subsequent osteoporotic fractures at the spine and hip. We performed RS upon diaphyseal and epiphyseal regions of exposed proximal phalanges from 12 cadaver forearms classified as healthy (n = 3), osteopenic (n = 4), or osteoporotic (n = 5) based on wrist T-scores measured by DXA. We observed a significant decrease in phosphate to matrix ratio and a significant increase in carbonate substitution in the osteoporotic phalanges relative to healthy and osteopenic phalanges. Multivariate regression models produced wrist T-score estimates with significant correlation to the DXA-measured values (r = 0.79). Furthermore, by accounting for phalangeal RS parameters, body mass index, and age, a multivariate regression significantly predicted distal radius strength measured in a simulated-fall biomechanical test (r = 0.81). These findings demonstrate the feasibility of interrogating the phalanges using RS for bone quality assessment of distant clinical sites of fragility fractures, such as the wrist. Future work will address transcutaneous measurement challenges as another requirement for scale-up and translation.

Calibration Technique for Suppressing Residual Etalon Artifacts in Slit-Averaged Raman Spectroscopy.

Back-illuminated charged-coupled device (BI-CCD) arrays increase quantum efficiency but also amplify etaloning, a multiplicative, wavelength-dependent fixed-pattern effect. When spectral data from hundreds of BI-CCD rows are combined, the averaged spectrum will generally appear etalon-free. This can mask substantial etaloning at the row level, even if the BI-CCD has been treated to suppress the effect. This paper compares two methods of etalon correction, one with simple averaging and one with row-by-row calibration using a fluorescence standard. Two BI-CCD arrays, both roughened by the supplier to reduce etaloning, were used to acquire Raman spectra of murine bone specimens. For one array, etaloning was the dominant source of noise under the exposure conditions chosen, even for the averaged spectrum across all rows; near-infrared-excited Raman peaks were noticeably affected. In this case, row-by-row calibration improved the spectral quality of the average spectrum. The other CCD's performance was shot-noise limited and therefore received no benefit from the extra calibration. The different results highlight the importance of checking for and correcting row-level fixed pattern when measuring weak Raman signals in the presence of a large fluorescence background.

Matching an immersion medium's refractive index to a cell's cytosol isolates organelle scattering.

Angularly-resolved light scattering has been proven to be an early detector of subtle changes in organelle size due to its sensitivity to scatterer size and refractive index contrast. However, for cells immersed in media with a refractive index close to 1.33, the cell itself acts as a larger scatterer and contributes its own angular signature. This whole-cell scattering, highly dependent on the cell's shape and size, is challenging to distinguish from the desired organelle scattering signal. This degrades the accuracy with which organelle size information can be extracted from the angular scattering. To mitigate this effect, we manipulate the refractive index of the immersion medium by mixing it with a water-soluble, biocompatible, high-refractive-index liquid. This approach physically reduces the amount of whole-cell scattering by minimizing the refractive index contrast between the cytosol and the modified medium. We demonstrate this technique on live cells adherent on a coverslip, using Fourier transform light scattering to compute the angular scattering from complex field images. We show that scattering from the cell: media refractive index contrast contributes significant scattering at angles up to twenty degrees and that refractive index-matching reduces such low-angle scatter by factors of up to 4.5. This result indicates the potential of refractive index-matching for improving the estimates of organelle size distributions in single cells.

Two-detector Corrected Near Infrared Spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS.

In near-infrared spectroscopy (NIRS) of human cerebral hemodynamics, detection of stimulus-related responses is confounded by the presence of unrelated trends in both the brain and the overlying scalp. A proposed strategy for reducing hemodynamic noise has been to record "scalp only" trends simultaneously via a second shorter-separation detector (~5 mm rather than ~30 mm) and perform a subtraction (C-NIRS, for "corrected near-infrared spectroscopy"). To compare the single- and dual-detector strategies, a 21-volunteer study of visual stimulation responses (6 stimulation blocks and 8 recording channels per measurement run) has been conducted. Activation-flagged channels were defined based upon (a) the significance (p-value) of the average rise in oxyhemoglobin concentration and (b) the average signal-to-noise over 6 stimulation epochs. At reasonable thresholds (p<0.025, SNR>1), the C-NIRS method increased the number of activation-flagged channels from 47 to 66, an increase of 40%, adding 24 channels and eliminating only 5. Of the 71 channels that were activation-flagged by at least one modality, the C-NIRS time series exhibited more significant oxyhemoglobin rise in 80% of such channels, and better signal-to-noise in 73%. In addition, single-subject C-NIRS stimulus responses were more consistent than NIRS over the six stimulation epochs, with significantly lower coefficients of variation in both amplitude and latency (i.e. time between stimulus onset and maximum hemoglobin rise). These results demonstrate that two-detector C-NIRS provides a straightforward way of (a) removing hemodynamic interference from NIRS data, (b) increasing the detection rate of cerebrally-unique responses, and (c) improving the quality of those recorded responses. Parallel insights regarding deoxyhemoglobin trends could not be drawn from this data set but should be attainable in future studies with higher signal to noise ratios.

Determination of best Raman spectroscopy spatial offsets for transcutaneous bone quality assessments in human hands.

Spatially offset Raman spectroscopy (SORS) is able to detect bone signal transcutaneously and could assist in predicting bone fracture risk. Criteria for optimal source-detector offsets for transcutaneous human measurements, however, are not well-established. Although larger offsets yield a higher percentage of bone signal, the absolute amount of bone signal decreases. Spectral unmixing into bone, adipose, and non-adipose components was employed to quantify changes in bone signal to noise ratio across a range of offsets, and optimal offsets for phalanx and metacarpal measurements were determined. The bone signal to noise ratio was maximized at offsets ranging from 4-6 mm.

Improved prediction of femoral fracture toughness in mice by combining standard medical imaging with Raman spectroscopy.

Bone fragility and fracture risk are assessed by measuring the areal bone mineral density (aBMD) using dual-energy X-ray absorptiometry (DXA). While aBMD correlates with bone strength, it is a poor predictor of fragility fracture risk. Alternatively, fracture toughness assesses the bone's resistance to crack propagation and fracture, making it a suitable bone quality metric. Here, we explored how femoral midshaft measurements from DXA, micro-computed tomography (µCT), and Raman spectroscopy could predict fracture toughness. We hypothesized that ovariectomy (OVX) decreases aBMD and fracture toughness compared to controls and we can optimize a multivariate assessment of bone quality by combining results from X-ray and Raman spectroscopy. Female mice underwent an OVX (n = 5) or sham (n = 5) surgery at 3 months of age. Femurs were excised 3 months after ovariectomy and assessed with Raman spectroscopy, µCT, and DXA. Subsequently, a notch was created on the anterior side of the mid-diaphysis of the femurs. Three-point bending induced a controlled fracture that initiated at the notch. The OVX mice had a significantly lower aBMD, cortical thickness, and fracture toughness when compared to controls (p < 0.05). A leave one out cross-validated (LOOCV) partial least squares regression (PLSR) model based only on the combination of aBMD and cortical thickness showed no significant predictive correlations with fracture toughness, whereas a PLSR model based on principal components derived from the full Raman spectra yielded significant prediction (r2 = 0.71, p < 0.05). Further, the PLSR model was improved by incorporating aBMD, cortical thickness, and principal components from Raman spectra (r2 = 0.92, p < 0.001). This exploratory study demonstrates combining X-ray with Raman spectroscopy leads to a more accurate assessment of bone fracture toughness and could be a useful diagnostic tool for the assessment of fragility fracture risk.

Soft-tissue spectral subtraction improves transcutaneous Raman estimates of murine bone strength in vivo.

Transcutaneous determination of a bone's Raman spectrum is challenging because the type I collagen in the overlying soft tissue is spectroscopically identical to that in bone. In a previous transcutaneous study of murine tibiae, we developed a library-based model called SOLD to unmix spatially offset Raman measurements into three spectra: a bone estimate, a soft tissue estimate, and a residual. Here, we demonstrate the value of combining the bone estimate and the residual to produce a "top layer subtracted" (tls) spectrum. We report superior prediction of two standard bone metrics (volumetric bone mineralization density and maximum torque) using partial least squares regression models based upon tls spectra rather than SOLD bone estimates, implying that the spectral residuals contain useful information. Simulations reinforce experimental in vivo findings. This chemometric approach, which we denote as SOLD/TLS, could have broad applicability in situations where comprehensive spectral libraries are difficult to acquire.

Silicon Nanomembrane Filtration and Imaging for the Evaluation of Microplastic Entrainment along a Municipal Water Delivery Route.

To better understand the origin of microplastics in municipal drinking water, we evaluated 50 mL water samples from different stages of the City of Rochester's drinking water production and transport route, from Hemlock Lake to the University of Rochester. We directly filtered samples using silicon nitride nanomembrane filters with precisely patterned slit-shaped pores, capturing many of the smallest particulates (<20 µm) that could be absorbed by the human body. We employed machine learning algorithms to quantify the shapes and quantity of debris at different stages of the water transport process, while automatically segregating out fibrous structures from particulate. Particulate concentrations ranged from 13 to 720 particles/mL at different stages of the water transport process and fibrous pollution ranged from 0.4 to 8.3 fibers/mL. A subset of the debris (0.2-8.6%) stained positively with Nile red dye which identifies them as hydrophobic polymers. Further spectroscopic analysis also indicated the presence of many non-plastic particulates, including rust, silicates, and calcium scale. While water leaving the Hemlock Lake facility is mostly devoid of debris, transport through many miles of piping results in the entrainment of a significant amount of debris, including plastics, although in-route reservoirs and end-stage filtration serve to reduce these concentrations.

Method for automated background subtraction from Raman spectra containing known contaminants.

The use of Raman spectroscopy for biomedical applications requires overcoming the obstacle of the broad background that is also generated by biological samples. This background, which is often largely attributed to fluorescence, is frequently orders of magnitude greater than the Raman signal and needs to be removed in order to use Raman spectra in sample analysis. Several methods have been proposed for removing fluorescent signal, both instrumental and computational. Of the computational methods, polynomial fitting has become increasingly popular. Typically, a polynomial of approximately fifth order is used in the fitting. This method alone is not always capable of fitting some more tightly featured spectra that may be present in data, potentially coming from a contaminant in the sample itself or from the experimental design. If this signal is present in varying amounts, the polynomial background removal method can leave the residual spectra with non-uniform artifacts that hinder classification results. If a reference spectrum can be obtained for this interfering signal, however, it can be incorporated into the polynomial fit and removed separately. An automated method for the removal of broad and/or moderately featured background signal is described. In addition to simulations, the method has been applied to spectra from biofilms of Streptococcus mutans.

Validation of an integrated Raman- and angular-scattering microscopy system on heterogeneous bead mixtures and single human immune cells.

A microscopy system has been constructed that is capable of simultaneously acquiring both Raman spectra and angle-resolved elastic-scattering patterns in either epi- or transillumination modes with a 7 mum spot size. The benefits and drawbacks of the epi- and transillumination modalities are discussed. Validation studies have been performed on single beads of a few micrometers in size, as well as on ensembles of submicrometer particles. In addition, transilluminated Raman and elastic-scattering spectra were obtained from single granulocytes and peripheral blood monocytes. Both the Raman- and the elastic-scattering channels show clear differences between the two types of immune cells.

Angularly resolved, finely sampled elastic scattering measurements of single cells: requirements for robust organelle size extractions.

Angularly resolved elastic light scattering is an established technique for probing the average size of organelles in biological tissue and cellular ensembles. Focusing of the incident light to illuminate no more than one cell at a time restricts the minimum forward-scattering angle θmin that can be detected. Series of simulated single-cell angular-scattering patterns have been generated to explore how size estimates vary as a function of θmin. At a setting of θmin = 20 deg, the size estimates hop unstably between multiple minima in the solution space as simulated noise (mimicking experimentally observed levels) is varied. As θmin is reduced from 20 deg to 10 deg, the instability vanishes, and the variance of estimates near the correct answer also decreases. The simulations thus suggest that robust Mie theory fits to single-cell scattering at 785 nm excitation require measurements down to at least 15 deg. Notably, no such instability was observed at θmin = 20 deg for narrow bead distributions. Accurate sizing of traditional calibration beads is, therefore, insufficient proof that an angular-scattering system is capable of robust analysis of single cells. Experimental support for the simulation results is also presented using measurements on cells fixed with formaldehyde.

Spatially offset Raman spectroscopy for in vivo bone strength prediction.

Bone strength is a worldwide health concern. Although multiple techniques have been developed to evaluate bone quality, there are still gaps to be filled. Here we report a non-invasive approach for the prediction of bone strength in vivo using spatially offset Raman spectroscopy. Raman spectra were acquired transcutaneously from the tibiae of mice from 4 to 23 weeks old and subsequently on the exposed bones. Partial least squares regression was applied to generate predictions of the areal bone mineral density (aBMD), volumetric bone mineralization density (vBMD), and maximum torque (MT) of each tibia as quantified by dual-energy X-ray absorptiometry, microCT imaging, and biomechanical tests, respectively. Significant correlations were observed between Raman spectral predictions and the reference values in all three categories. To our knowledge, this is the first demonstration of Raman spectroscopy predicting a biomechanical bone parameter (MT) in vivo with an uncertainty much smaller than the spread in the reference values.

Determination of uncertainty in parameters extracted from single spectroscopic measurements.

The ability to quantify uncertainty in information extracted from spectroscopic measurements is important in numerous fields. The traditional approach of repetitive measurements may be impractical or impossible in some measurements scenarios, while chi-squared analysis does not provide insight into the sources of uncertainty. As such, a need exists for analytical expressions for estimating uncertainty and, by extension, minimum detectable concentrations or diagnostic parameters, that can be applied to a single noisy measurement. This work builds on established concepts from estimation theory, such as the Cramer-Rao lower bound on estimator covariance, to present an analytical formula for estimating uncertainty expressed as a simple function of measurement noise, signal strength, and spectral overlap. This formalism can be used to evaluate and improve instrument performance, particularly important for rapid-acquisition biomedical spectroscopy systems. We demonstrate the experimental utility of this expression in assessing concentration uncertainties from spectral measurements of aqueous solutions and diagnostic parameter uncertainties extracted from spectral measurements of human artery tissue. The measured uncertainty, calculated from many independent measurements, is found to be in good agreement with the analytical formula applied to a single spectrum. These results are intended to encourage the widespread use of uncertainty analysis in the biomedical optics community.

Raman spectroscopic measurement of relative concentrations in mixtures of oral bacteria.

Near-infrared Raman spectroscopy has been used for species identification of pure microbial specimens for more than a decade. More recently, this optical method has been extended to the analysis of specimens containing multiple species. In this report, we demonstrate rapid, reagent-free quantitative analysis of a simplified model of oral plaque containing three oral bacteria species, S. mutans, S. sanguis, and S. gordonii, using near-infrared Raman spectroscopy. Raman spectra were acquired from bacterial mixtures in 200 seconds. A prediction model was calibrated by the partial least squares method and validated by additional samples. On a scale from 0 to 1, relative fractions of each species could be predicted with a root mean square error of 0.07. These results suggest that near-infrared Raman spectroscopy is potentially useful in quantification of microbial mixtures in general and oral plaques in particular.

Sensitivity of spatially offset Raman spectroscopy (SORS) to subcortical bone tissue.

The development of spatially offset Raman spectroscopy (SORS) has enabled deep, non-invasive chemical characterization of turbid media. Here, we use SORS to measure subcortical bone tissue and depth-resolved biochemical variability in intact, exposed murine bones. We also apply the technique to study a mouse model of the genetic bone disorder osteogenesis imperfecta. The results suggest that SORS is more sensitive to disease-related biochemical differences in subcortical trabecular bone and marrow than conventional Raman measurements.