Confocal fluorescence imaging of biological systems is an important method by which researchers can investigate
molecular processes occurring in live cells. We have developed a new 3D hyperspectral confocal fluorescence
microscope that can further enhance the usefulness of fluorescence microscopy in studying biological systems. The new
microscope can increase the information content obtained from the image since, at each voxel, the microscope records
512 wavelengths from the emission spectrum (490 to 800 nm) while providing optical sectioning of samples with
diffraction-limited spatial resolution. When coupled with multivariate curve resolution (MCR) analyses, the microscope
can resolve multiple spatially and spectrally overlapped emission components, thereby greatly increasing the number of
fluorescent labels, relative to most commercial microscopes, that can be monitored simultaneously. The MCR algorithm
allows the "discovery" of all emitting sources and estimation of their relative concentrations without cross talk, including
those emission sources that might not have been expected in the imaged cells. In this work, we have used the new
microscope to obtain time-resolved hyperspectral images of cellular processes. We have quantitatively monitored the
translocation of the GFP-labeled RelA protein (without interference from autofluorescence) into and out of the nucleus
of live HeLa cells in response to continuous stimulation by the cytokine, TNFα. These studies have been extended to
imaging live mouse macrophage cells with YFP-labeled RelA and GFP-labeled IRF3 protein. Hyperspectral imaging
coupled with MCR analysis makes possible, for the first time, quantitative analysis of GFP, YFP, and autofluorescence
without concern for cross-talk between emission sources. The significant power and quantitative capabilities of the new
hyperspectral imaging system are further demonstrated with the imaging of a simple fluorescence dye (SYTO 13)
traditionally used to stain the nucleus of live cells. We will demonstrate the microscope system's ability to actually
discover and quantify the presence of two separate SYTO 13 fluorescent species shifted in wavelength by only a few
nm. These two emission components exhibit very different spatial distributions in macrophage cells (i.e., nucleus vs.
cytoplasm + nucleus). Two highly overlapped autofluorescence components in addition to the two SYTO 13
components were also observed, and the spatial distributions of the two autofluorescence components were
quantitatively mapped throughout the cells in three dimensions.
The development of colloidal quantum dots (QDs) for biological imaging has brought a new level of sensitivity to live
cell imaging. Single particle tracking (SPT) techniques in particular benefit from the superior photostability, high
extinction coefficient and distinct emission spectra of QDs. Here we describe the use of QDs for SPT to study the
dynamics of membrane proteins in living cells. We work with the RBL-2H3 mast cell model that signals through the
high affinity IgE receptor, Fc&Vegr;RI. Using wide field or Total Internal Reflection Fluorescence (TIRF) microscopy we
have achieved simultaneous imaging of two spectrally distinct QDs with frame rates of up to 750 frames/s and
localization accuracy of ~10 nm. We also describe the imaging and analysis of QDs using a novel hyperspectral
microscope and multivariate curve resolution analysis for multi-color QD tracking. The same QD-tag used for SPT is
used to localize proteins at <10 nm resolution by electron microscopy (EM) on fixed membrane sheets.
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.
While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.
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.
The penetration depths possible with near-infrared spectroscopy make it well suited for reagentless monitoring of analytes in body fluids or noninvasive monitoring of human tissue. As an initial step in achieving these goals, we have conducted near-infrared in-vitro experiments of dilute aqueous solutions containing analytes of physiological importance. By combining partial least squares (PLS) multivariate calibration methods with Latin Hypercube statistical designs, we have obtained precise near-infrared spectral determinations of urea, creatinine, and NaCl in dilute aqueous solutions. Cross-validated PLS calibrations for the three analytes and temperature were very precise and resulted in R2 values greater than 0.997.
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in- situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mid- or near-infrared spectra of the blood. Progress toward the noninvasive determination of glucose levels in diabetics is an ultimate goal of this research.
Fourier transform infrared spectrophotometry (FTIR) is a valuable technique for monitoring thin films used in semiconductor device manufacture. Determinations of the constituent contents in borophosphosilicate (BPSG) phosphosilicate (PSG) silicon oxynitride (SiON:H and spin-on-glass (SOG) thin films are a few applications. Due to the nature of the technique FTIR instrumentation is one of the most extensively computer-dependent pieces of equipment that is likely to be found in a microelectronics plant. In the role of fab monitor or reactor characterization tool FTIR instruments can rapidly generate large amounts of data. Also the drive for greater accuracy and tighter precision is leading to the development of increasingly sophisticated data processing software that tax the computing abilities of most instrument local data stations. By linking a local FTIR data station to a remote minicomputer its capabilities are greatly improved. We discuss three classes of enhancement. First the FTIR in the fab area communicates and interacts in real time with the minicomputer: transferring data segments to it instructing it to perform sophisticated processing and returning the results to the operator in the fab. Characterizations of PSG thin films by this approach are discussed. Second the spectra of large numbers of samples are processed locally. The large database is then transmitted to the minicomputer for study by statistical/graphics software. Results of CVD-reactor spatial profiling experiments for plasma SiON are presented. Third processing of calibration spectra is performed
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.