Remote assessment of physiological parameters has enabled patient diagnostics without the need for a medical professional to become exposed to potential communicable diseases. In particular, early detection of oxygen saturation, abnormal body temperature, heart rate, and/or blood pressure could affect treatment protocols. The modeling effort in this work uses an adding-doubling radiative transfer model of a seven-layer human skin structure to describe absorption and reflection of incident light within each layer. The model was validated using both abiotic and biotic systems to understand light interactions associated with surfaces consisting of complex topography as well as multiple illumination sources. Using literature-based property values for human skin thickness, absorption, and scattering, an average deviation of 7.7% between model prediction and experimental reflectivity was observed in the wavelength range of 500-1000 nm.
Fluorescence fluctuation analysis of dilute biomolecules can provide a powerful method for fast
and accurate determination of diffusion dynamics, local concentrations, and aggregation states in
complex environments. However, spectral overlap among multiple exogenous and endogenous
fluorescent species, photobleaching, and background inhomogeneities can compromise
quantitative accuracy and constrain useful biological implementation of this analytical strategy in
real systems. In order to better understand these limitations and expand the utility of fluctuation
correlation methods, spatiotemporal fluorescence correlation analysis was performed on
spectrally resolved line scanned images of modeled and real data from mixed fluorescent
nanospheres in a synthetic gel matrix. It was found that collecting images at a pixel sampling
regime optimal for spectral imaging provides a method for calibration and subsequent temporal
correlation analysis which is insensitive to spectral mixing, spatial inhomogeneity, and
photobleaching. In these analyses, preprocessing with multivariate curve resolution (MCR)
provided the local concentrations of each spectral component in the images, thus facilitating
correlation analysis of each component individually. This approach allowed quantitative
removal of background signals and showed dramatically improved quantitative results compared
to a hypothetical system employing idealized filters and multi-parameter fitting routines.
Cellular autofluorescence, though ubiquitous when imaging cells and tissues, is often assumed to be small in comparison
to the signal of interest. Uniform estimates of autofluorescence intensity obtained from separate control specimens are
commonly employed to correct for autofluorescence. While these may be sufficient for high signal-to-background
applications, improvements in detector and probe technologies and introduction of spectral imaging microscopes have
increased the sensitivity of fluorescence imaging methods, exposing the possibility of effectively probing the low signal-to-background regime. With spectral imaging, reliable monitoring of signals near or even below the noise levels of the
microscope is possible if autofluorescence and background signals can be accurately compensated for. We demonstrate
the importance of accurate autofluorescence determination and utility of spectral imaging and multivariate analysis
methods using a case study focusing on fluorescence confocal spectral imaging of host-pathogen interactions. In this
application fluorescent proteins are produced when bacteria invade host cells. Unfortunately the analyte signal is
spectrally overlapped and typically weaker than the cellular autofluorescence. In addition to discussing the advantages
of spectral imaging for following pathogen invasion, we present the spectral properties of mouse macrophage
autofluorescence. The imaging and analysis methods developed are widely applicable to cell and tissue imaging.
Hyperspectral imaging provides complex image data with spectral information from many fluorescent species contained within the sample such as the fluorescent labels and cellular or pigment autofluorescence. To maximize the utility of this spectral imaging technique it is necessary to couple hyperspectral imaging with sophisticated multivariate analysis methods to extract meaningful relationships from the overlapped spectra. Many commonly employed multivariate analysis techniques require the identity of the emission spectra of each component to be known or pure component pixels within the image, a condition rarely met in biological samples. Multivariate curve resolution (MCR) has proven extremely useful for analyzing hyperspectral and multispectral images of biological specimens because it can operate with little or no a priori information about the emitting species, making it appropriate for interrogating samples containing autofluorescence and unanticipated contaminating fluorescence. To demonstrate the unique ability of our hyperspectral imaging system coupled with MCR analysis techniques we will analyze hyperspectral images of four-color in-situ hybridized rat brain tissue containing 455 spectral pixels from 550 - 850 nm. Even though there were only four colors imparted onto the tissue in this case, analysis revealed seven fluorescent species, including contributions from cellular autofluorescence and the tissue mounting media. Spectral image analysis will be presented along with a detailed discussion of the origin of the fluorescence and specific illustrations of the adverse effects of ignoring these additional fluorescent species in a traditional microscopy experiment and a hyperspectral imaging system.
Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluroescent labels over the entire image. Since the concentration maps of the fluorescent labels are relativly uaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.
In this paper, we describe the use of linear unmixing algorithms to spatially and spectrally separate fluorescence emission signals from fluorophores having highly overlapping emission spectra. Hyperspectral image data for mixtures of Nile Blue and HIDC Iodide in a methanol/polymer matrix were obtained using the Information-efficient Spectral Imaging sensor (ISIS) operated in its Hadamard Transform mode. The data were analyzed with a combination of Principal Components Analysis (PCA), orthogonal rotation, and equality and non-negativity constrained least squares methods. The analysis provided estimates of the pure-component fluorescence emission spectra and the spatial distributions of the fluorophores. In addition, spatially varying interferences from the background and laser excitation were identified and separated. A major finding resulting from this work is that the pure-component spectral estimates are very insensitive to the initial estimates supplied to the alternating least squares procedures. In fact, random number starting points reliably gave solutions that were effectively equivalent to those obtained when measured pure-component spectra were used as the initial estimates. While our proximate application is evaluating the possibility of multivariate quantitation of DNA microarrays, the results of this study should be generally applicable to hyperspectral imagery typical of remote sensing spectrometers.
We employ infrared spectroscopy (IR) with attenuated total reflectance (ATR) as a sampling technique to monitor live and dried RAW cells (a murine macrophage cell line) during activation with g-interferon and lipopolysaccharide. By comparing the spectra of activated cells at various time points to the spectra of healthy control cells, we identify spectral bands associated with nucleic acids that are markers for the cell activation process. These spectral changes are slight and can be complicated with the normal metabolic changes that occur within cells. We will discuss the use of data pretreatment strategies to accurately correct for the contribution of the buffer to the live cell spectra. We find the standard background correction method inadequate for concentrated solutions of cells. Data presented shows the severe effect incorrect background subtraction has on the cell spectra. We report a more accurate correction for phosphate buffer spectral contribution using an interactive subtraction of the buffer spectrum. We will show classification of dried control and activated macrophage cell spectra using partial-least squares analysis with multiplicative scatter correction.
Raman microspectroscopy and imaging can be used to probe the chemical properties of newly mineralized bone tissue. In this study, our early mineralization models are neonatal murine cranial suture tissue and prostate cancer cell cultures. The murine cranial tissue was harvested from animals three weeks postnatal. On this time scale, remodeling does not corrupt the temporal record inherent in the spatial distribution of mineral species. When analyzing transects, line images, of the cranial tissue, multivariate data processing is required to generate chemical state plots from the hundreds of Raman spectra acquired during a single transect experiment. In most cranial tissue specimens more than one phosphate mineral environment is observed, allowing inferences on the relation between chemical structure and physiologically important properties. The prostate cancer cell cultures were cultured for up to nine days. Point microspectroscopy reveals the ratios of mineral species present and the amount of protein species in the cell cultures changes dramatically over the course of 9 days. Very low carbonation, typical of early-mineralized tissue, is observed in both of these models.
Underlying the contrast in a hyperspectral Raman image are complete Raman spectra at each of tens or hundreds of thousands of pixels. Multivariate statistics allows reduction of these large data sets to manageable numbers of chemically significant descriptors that become the image contrast. In most cases an object can be viewed as containing a small number (usually fewer than ten) chemically discrete components, each with its own vibrational spectrum. Principal component analysis (PCA) and exploratory factor analysis (FA) can be used to generate descriptors from the experimentally observed Raman spectra in image data sets. Additionally, PCA and FA can be viewed as optimized weighted signal averaging techniques. FA contrast is generated from all regions of a spectrum that are attributable to one component. The result is better signal/noise ratio than is obtained using the height or area of a single band as image contrast. We will discuss a variety of preprocessing steps such as removing outliers and selecting spectral subregions for data analysis optimization. We will illustrate these concepts using an image of bone tissue.
We discuss the use of Raman microprobe spectroscopy and Raman imaging to study the chemical composition of fresh, unmounted bone at a microscopic level. A specimen of human cortical bone was analyzed and evidence for the presence of amorphous-type calcium phosphate, a theoretical precursor in the bone formation process, was found. In general the amorphous4ype calcium phosphate appears away from osteons, in the interstitial tissue. This finding calls into question the role of amorphous-type calcium phosphate as a precursor to apatitic phosphate, since it was not found in the recently remodeled bone near the osteon center, but rather in older bone tissue. Some reasons for the presence of amorphous calcium phosphate are proposed. Possible relations ofthe amorphous mineral to bone damage and bone remodeling are discussed.
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