Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and “digital drying”

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection‐Fourier transform infrared (ATR‐FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR‐FTIR on both liquid and air‐dried samples to investigate “digital drying” as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least‐squares method, have demonstrated a greater random forest (RF) classification performance than the air‐dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL‐IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep‐penetration light source on disease classification. The RF classification of QCL‐IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.     

Comparison between high definition FT‐IR,Raman and AFM‐IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones, especially in the case of demanding biological samples. Three vibrational spectroscopy techniques (high definition Fourier‐transform infrared [FT‐IR], Raman and atomic force microscopy infrared [AFM‐IR]) were applied for subcellular chemical imaging of cholesteryl esters in PC‐3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern and chemical information. All tested techniques were found to be useful in chemical imaging of cholesterol derivatives in cancer cells. The results obtained from FT‐IR and Raman imaging showed to be comparable, whereas those achieved from AFM‐IR study exhibited higher spectral heterogeneity. It confirms AFM‐IR method as a powerful tool in local chemical imaging of cells at the nanoscale level. Furthermore, due to polarization effect, p‐polarized AFM‐IR spectra showed strong enhancement of lipid bands when compared to FT‐IR.     

Hyperspectral characterization of re‐epithelialization in an in vitro wound model

In vitro wound models are useful for research on wound re‐epithelialization. Hyperspectral imaging represents a non‐destructive alternative to histology analysis for detection of re‐epithelialization. This study aims to characterize the main optical behavior of a wound model in order to enable development of detection algorithms. K‐Means clustering and agglomerative analysis were used to group spatial regions based on the spectral behavior, and an inverse photon transport model was used to explain differences in optical properties. Six samples of the wound model were prepared from human tissue and followed over 22 days. Re‐epithelialization occurred at a mean rate of 0.24 mm²/day after day 8 to 10. Suppression of wound spectral features was the main feature characterizing re‐epithelialized and intact tissue. Modeling the photon transport through a diffuse layer placed on top of wound tissue properties reproduced the spectral behavior. The missing top layer represented by wounds is thus optically detectable using hyperspectral imaging.     

Infrared attenuated total reflection spectroscopic surface analysis of bovine‐tail intervertebral discs after UV‐light‐activated riboflavin‐induced collagen cross‐linking

The tensile strength of the intervertebral disc (IVD) is mainly maintained by collagen cross‐links. Loss of collagen cross‐linking combined with other age‐related degenerative processes contributes to tissue weakening, biomechanical failure, disc herniation and pain. Exogenous collagen cross‐linking has been identified as an effective therapeutic approach for restoring IVD tensile strength. The current state‐of‐the‐art method to assess the extent of collagen cross‐linking in tissues requires destructive procedures and high‐performance liquid chromatography. In this study, we investigated the utility of infrared attenuated total reflection (IR‐ATR) spectroscopy as a nondestructive analytical strategy to rapidly evaluate the extent of UV‐light‐activated riboflavin (B2)‐induced collagen cross‐linking in bovine IVD samples. Thirty‐five fresh bovine‐tail IVD samples were equally divided into five treatment groups: (a) untreated, (b) cell culture medium Dulbecco's Modified Eagle's Medium only, (c) B2 only, (d) UV‐light only and (e) UV‐light‐B2. A total of 674 measurements have been acquired, and were analyzed via partial least squares discriminant analysis. This classification scheme unambiguously identified individual classes with a sensitivity >91% and specificity >92%. The obtained results demonstrate that IR‐ATR spectroscopy reliably differentiates between different treatment categories, and promises an excellent tool for potential in vivo, nondestructive and real‐time assessment of exogenous IVD cross‐linking.     

Discrimination of geographical origin and adulteration of radix astragali using fourier transform infrared spectroscopy and chemometric methods

Introduction – Radix Astragali, one of most widely used and important traditional Chinese medicines, is cultivated in different geographical regions. Because of varying growing conditions, the qualities of Radix Astragali vary, which can give rise to differences in clinical therapy. Detecting adulteration is a routine requirement in pharmaceutical practice. Objective – To develop a simple and accurate approach to discriminate the geographical origin and potential adulteration of Radix Astragali, derived from the root of Astragalus membranaceus (Fischer) Bunge var. mongholicus (Bunge) Hsiao, using Fourier transform infrared (FT‐IR) spectroscopy and chemometric methods. Methodology – To obtain characteristic IR spectra for accurate discrimination, a one‐solvent extraction method was utilised following a novel evaluation method for selecting appropriate solvents. Samples of Radix Astragali from different geographical origins were discriminated using FT‐IR spectroscopy and discriminant partial least squares (DPLS) methods. FT‐IR spectroscopy combined with Mahalanobis distance was employed to detect adulteration of Radix Astragali. Results – In comparison with other solvents, butanone was more effective at extracting samples. Radix Astragali samples were accurately assigned to their corresponding geographical origins by using FT‐IR spectroscopy and DPLS method. Most adulterated samples were detected accurately by application of FT‐IR spectroscopy combined with Mahalanobis distance. Conclusion – FT‐IR spectroscopy combined with chemometric method was developed and demonstrated to be a useful tool to discriminate geographical origin and adulteration of Radix Astragali. Copyright © 2010 John Wiley & Sons, Ltd.     

Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics

Fourier transform infrared (FT‐IR) spectroscopy combined with multivariate statistical analyses was investigated as a physicochemical tool for monitoring secreted recombinant antibody production in cultures of Chinese hamster ovary (CHO) and murine myeloma non‐secreting 0 (NS0) cell lines. Medium samples were taken during culture of CHO and NS0 cells lines, which included both antibody‐producing and non‐producing cell lines, and analyzed by FT‐IR spectroscopy. Principal components analysis (PCA) alone, and combined with discriminant function analysis (PC‐DFA), were applied to normalized FT‐IR spectroscopy datasets and showed a linear trend with respect to recombinant protein production. Loadings plots of the most significant spectral components showed a decrease in the C–O stretch from polysaccharides and an increase in the amide I band during culture, respectively, indicating a decrease in sugar concentration and an increase in protein concentration in the medium. Partial least squares regression (PLSR) analysis was used to predict antibody titers, and these regression models were able to predict antibody titers accurately with low error when compared to ELISA data. PLSR was also able to predict glucose and lactate amounts in the medium samples accurately. This work demonstrates that FT‐IR spectroscopy has great potential as a tool for monitoring cell cultures for recombinant protein production and offers a starting point for the application of spectroscopic techniques for the on‐line measurement of antibody production in industrial scale bioreactors. Biotechnol. Bioeng. 2010; 106: 432–442. © 2010 Wiley Periodicals, Inc.     

Inside Cover: Comparison between high definition FT‐IR,Raman and AFM‐IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells (J. Biophotonics 5/2020)

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones. Three vibrational spectroscopy techniques (High Definition FT‐IR, Raman and AFM‐IR) were applied for subcellular chemical imaging of cholesteryl esters in PC‐3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern, and chemical information. Further details can be found in the article by Maciej Roman, Tomasz P. Wrobel, Czeslawa Paluszkiewicz, and Wojciech M. Kwiatek ( e201960094 ).

     

Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins,cephalosporins, and fluoroquinolones using specific Raman marker bands

A Raman‐based, strain‐independent, semi‐automated method is presented that allows the rapid (<3 hours) determination of antibiotic susceptibility of bacterial pathogens isolated from clinical samples. Applying a priori knowledge about the mode of action of the respective antibiotic, we identified characteristic Raman marker bands in the spectrum and calculated batch‐wise weighted sum scores from standardized Raman intensity differences between spectra of antibiotic exposed and nonexposed samples of the same strains. The lead substances for three relevant antibiotic classes (fluoroquinolone ciprofloxacin, third‐generation cephalosporin cefotaxime, ureidopenicillin piperacillin) against multidrug‐resistant Gram‐negative bacteria (MRGN) revealed a high sensitivity and specificity for the susceptibility testing of two Escherichia coli laboratory strains and 12 clinical isolates. The method benefits from the parallel incubation of control and treated samples, which reduces the variance due to alterations in cultivation conditions and the standardization of differences between batches leading to long‐term comparability of Raman measurements.     

Using metabolic fingerprinting of plants for evaluating nitrogen deposition impacts on the landscape level

Nitrogen emissions and atmospheric deposition are globally significant with the potential to alter ecosystem nutrient balance, provoking changes in vegetation composition. Shifts in plant biochemistry are good indicators of nitrogen pollution and have been used to monitor vegetation health. Fourier transform‐infrared (FT‐IR) spectroscopy has previously been shown to be a rapid and relatively inexpensive method for evaluating leaf biochemistry. In the present study, FT‐IR spectra were collected from Galium saxatile samples taken from sites across the United Kingdom. Spectral changes in the tissue samples were correlated with a gradient of N deposition using partial least squares regression analysis. We show that FT‐IR analysis of G. saxatile leaf tissue is an effective way to evaluate nitrogen deposition across the entire UK landscape.     

Phenotypic evolution in stochastic environments: The contribution of frequency- and density-dependent selection

Understanding how environmental variation affects phenotypic evolution requires models based on ecologically realistic assumptions that include variation in population size and specific mechanisms by which environmental fluctuations affect selection. Here we generalize quantitative genetic theory for environmentally induced stochastic selection to include general forms of frequency- and density-dependent selection. We show how the relevant fitness measure under stochastic selection relates to Fisher's fundamental theorem of natural selection, and present a general class of models in which density regulation acts through total use of resources rather than just population size. In this model, there is a constant adaptive topography for expected evolution, and the function maximized in the long run is the expected factor restricting population growth. This allows us to generalize several previous results and to explain why apparently “-selected” species with slow life histories often have low carrying capacities. Our joint analysis of density- and frequency-dependent selection reveals more clearly the relationship between population dynamics and phenotypic evolution, enabling a broader range of eco-evolutionary analyses of some of the most interesting problems in evolution in the face of environmental variation.     

Fourier transform infrared spectroscopy as a metabolite fingerprinting tool for monitoring the phenotypic changes in complex bacterial communities capable of degrading phenol

The coking process produces great volumes of wastewater contaminated with pollutants such as cyanides, sulfides and phenolics. Chemical and physical remediation of this wastewater removes the majority of these pollutants; however, these processes do not remove phenol and thiocyanate. The removal of these compounds has been effected during bioremediation with activated sludge containing a complex microbial community. In this investigation we acquired activated sludge from an industrial bioreactor capable of degrading phenol. The sludge was incubated in our laboratory and monitored for its ability to degrade phenol over a 48 h period. Multiple samples were taken across the time‐course and analysed by Fourier transform infrared (FT‐IR) spectroscopy. FT‐IR was used as a whole‐organism fingerprinting approach to monitor biochemical changes in the bacterial cells during the degradation of phenol. We also investigated the ability of the activated sludge to degrade phenol following extended periods (2–131 days) of storage in the absence of phenol. A reduction was observed in the ability of the microbial community to degrade phenol and this was accompanied by a detectable biochemical change in the FT‐IR fingerprint related to cellular phenotype of the microbial community. In the absence of phenol a decrease in thiocyanate vibrations was observed, reflecting the ability of these communities to degrade this substrate. Actively degrading communities showed an additional new band in their FT‐IR spectra that could be attributed to phenol degradation products from the ortho‐ and meta‐cleavage of the aromatic ring. This study demonstrates that FT‐IR spectroscopy when combined with chemometric analysis is a very powerful high throughput screening approach for assessing the metabolic capability of complex microbial communities.     

A new mixed‐effects regression model for the analysis of zero‐modified hierarchical count data

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero‐modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)–(b) and (b)–(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed‐effects regression model based on the hurdle version of the Poisson–Lindley distribution. In this framework, the zero‐modification is incorporated by assuming that a binary probability model determines which outcomes are zero‐valued, and a zero‐truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well‐established mixed‐effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian ‐value and the randomized quantile residuals were considered for model diagnostics.     

Phytochemical Findings Evidencing Botanical Origin of New Propolis Type from North‐West Argentina

Propolis samples from north‐west Argentina (Amaicha del Valle, Tucumán) were evaluated by palynology, FT‐IR spectra, and RP‐HPTLC. In addition, the volatile fraction was studied by HS‐SPME‐GC/MS. The botanical species most visited by Apis mellifera L. near the apiaries were collected and their RP‐HPTLC extracts profiles were compared with propolis samples. In addition, GC/MS was performed for volatile compounds from Zuccagnia punctata Cav. (Fabaceae). FT‐IR spectra and RP‐HPTLC fingerprints of propolis samples showed similar profiles. In RP‐HPTLC analyses, only Z. punctata presented a similar fingerprint to Amaicha propolis. The major volatile compounds present in both were trans‐linalool oxide (furanoid), 6‐camphenone, linalool, trans‐pinocarveol, p‐cymen‐8‐ol, and 2,3,6‐trimethylbenzaldehyde. Potential variations for the Amaicha del Valle propolis volatile fraction as consequence of propolis sample preparation were demonstrated.     

Comparison of normal distribution–based and nonparametric decision limits on the GH‐2000 score for detecting growth hormone misuse (doping) in sport

This paper is motivated by the GH‐2000 biomarker test, though the discussion is applicable to other diagnostic tests. The GH‐2000 biomarker test has been developed as a powerful technique to detect growth hormone misuse by athletes, based on the GH‐2000 score. Decision limits on the GH‐2000 score have been developed and incorporated into the guidelines of the World Anti‐Doping Agency (WADA). These decision limits are constructed, however, under the assumption that the GH‐2000 score follows a normal distribution. As it is difficult to affirm the normality of a distribution based on a finite sample, nonparametric decision limits, readily available in the statistical literature, are viable alternatives. In this paper, we compare the normal distribution–based and nonparametric decision limits. We show that the decision limit based on the normal distribution may deviate significantly from the nominal confidence level or nominal FPR when the distribution of the GH‐2000 score departs only slightly from the normal distribution. While a nonparametric decision limit does not assume any specific distribution of the GH‐2000 score and always guarantees the nominal confidence level and FPR, it requires a much larger sample size than the normal distribution–based decision limit. Due to the stringent FPR of the GH‐2000 biomarker test used by WADA, the sample sizes currently available are much too small, and it will take many years of testing to have the minimum sample size required, in order to use the nonparametric decision limits. Large sample theory about the normal distribution–based and nonparametric decision limits is also developed in this paper to help understanding their behaviours when the sample size is large.     

Implantable self‐aligning fiber‐optic optomechanical devices for in vivo intraocular pressure‐sensing in artificial cornea

Artificial cornea is an effective treatment of corneal blindness. Yet, intraocular pressure (IOP) measurements for glaucoma monitoring remain an urgent unmet need. Here, we present the integration of a fiber‐optic Fabry‐Perot pressure sensor with an FDA‐approved keratoprosthesis for real‐time IOP measurements using a novel strategy based on optical‐path self‐alignment with micromagnets. Additionally, an alternative noncontact sensor‐interrogation approach is demonstrated using a bench‐top optical coherence tomography system. We show stable pressure readings with low baseline drift (<2.8 mm Hg) for >4.5 years in vitro and efficacy in IOP interrogation in vivo using fiber‐optic self‐alignment, with good initial agreement with the actual IOP. Subsequently, IOP drift in vivo was due to retroprosthetic membrane (RPM) formation on the sensor secondary to surgical inflammation (more severe in the current pro‐fibrotic rabbit model). This study paves the way for clinical adaptation of optical pressure sensors with ocular implants, highlighting the importance of controlling RPM in clinical adaptation.     

Distinguishing metastatic triple‐negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Triple‐negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African‐American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time‐consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label‐free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole‐tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.     

Multispectral near infrared absorption imaging for histology of skin cancer

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800‐1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900‐1500 nm) was used for homogenous wide‐field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal‐cell carcinoma, squamous‐cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.      

Optical investigation of three‐dimensional human skin equivalents: A pilot study

Human skin equivalents (HSEs) are three‐dimensional living models of human skin that are prepared in vitro by seeding cells onto an appropriate scaffold. They recreate the structure and biological behaviour of real skin, allowing the investigation of processes such as keratinocyte differentiation and interactions between the dermal and epidermal layers. However, for wider applications, their optical and mechanical properties should also replicate those of real skin. We therefore conducted a pilot study to investigate the optical properties of HSEs. We compared Monte Carlo simulations of (a) real human skin and (b) two‐layer optical models of HSEs with (c) experimental measurements of transmittance through HSE samples. The skin layers were described using a hybrid collection of optical attenuation coefficients. A linear relationship was observed between the simulations and experiments. For samples thinner than 0.5 mm, an exponential increase in detected power was observed due to fewer instances of absorption and scattering.      

Fiber attenuated total reflection infrared spectroscopy of kidney tissue during live surgery

More than 90% of solid kidney tumors are cancerous and have to be treated by surgical resection where surgical outcomes and patient prognosis are dependent on the tumor discrimination. The development of alternative approaches based on a new generation of fiber attenuated total reflection (ATR) probes could aid tumor identification even under intrasurgical conditions. Herein, fiber ATR IR spectroscopy is employed to distinguish normal and cancerous kidney tissues. Freshly resected tissue samples from 34 patients are investigated under nearly native conditions. Spectral marker bands that allow a reliable discrimination between tumor and normal tissue are identified by a supervised classification algorithm. The absorbance values of the bands at 1025, 1155 and 1240 cm^?1 assigned to glycogen and fructose 1,6‐bisphosphatase are used as the clearest markers for the tissue discrimination. Absorbance threshold values for tumor and normal tissue are determined by discriminant analysis. This new approach allows the surgeon to make a clinical diagnosis.     

Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana

Availability of plant‐specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measured in vitro, often under non‐physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximal in vivo catalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome‐b6f complex, ATP‐citrate synthase, sucrose‐phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition‐specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements from Arabidopsis thaliana rosette with and fluxes through canonical pathways in a constraint‐based model of leaf metabolism. In comparison to findings in Escherichia coli, we demonstrate weaker concordance between the plant‐specific in vitro and in vivo enzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximal in vivo catalytic rates, and available quantitative metabolomics data are below reported values and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome‐wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.