Rohit Bhargava

Infrared spectroscopic imaging for histopathologic recognition

The process of histopathology, comprising tissue staining and morphological pattern recognition, has remained largely unchanged for over 140 years. Although it is integral to clinical and research activities, histopathologic recognition remains a time-consuming, subjective process to which only limited statistical confidence can be assigned because of inherent operator variability. Although immunohistochemical approaches allow limited molecular detection, significant challenges remain in using them for quantitative, automated pathology. Vibrational spectroscopic approaches, by contrast, directly provide nonperturbing molecular descriptors, but a practical spectroscopic protocol for histopathology is lacking. Here we couple high-throughput Fourier transform infrared (FTIR) spectroscopic imaging of tissue microarrays with statistical pattern recognition of spectra indicative of endogenous molecular composition and demonstrate histopathologic characterization of prostatic tissue. This automated histologic segmentation is applied to routine archival tissue samples, incorporates well-defined tests of statistical significance and eliminates any requirement for dyes or molecular probes. Finally, we differentiate benign from malignant prostatic epithelium by spectroscopic analyses.

Using Fourier transform IR spectroscopy to analyze biological materials

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams

Conventional Fourier-transform infrared (FTIR) microspectroscopic systems are limited by an inevitable trade-off between spatial resolution, acquisition time, signal-to-noise ratio (SNR) and sample coverage. We present an FTIR imaging approach that substantially extends current capabilities by combining multiple synchrotron beams with wide-field detection. This advance allows truly diffraction-limited high-resolution imaging over the entire mid-infrared spectrum with high chemical sensitivity and fast acquisition speed while maintaining high-quality SNR.

Infrared Spectroscopic Imaging: The Next Generation

Infrared (IR) spectroscopic imaging seemingly matured as a technology in the mid-2000s, with commercially successful instrumentation and reports in numerous applications. Recent developments, however, have transformed our understanding of the recorded data, provided capability for new instrumentation, and greatly enhanced the ability to extract more useful information in less time. These developments are summarized here in three broad areas—data recording, interpretation of recorded data, and information extraction—and their critical review is employed to project emerging trends. Overall, the convergence of selected components from hardware, theory, algorithms, and applications is one trend. Instead of similar, general-purpose instrumentation, another trend is likely to be diverse and application-targeted designs of instrumentation driven by emerging component technologies. The recent renaissance in both fundamental science and instrumentation will likely spur investigations at the confluence of conventional spectroscopic analyses and optical physics for improved data interpretation. While chemometrics has dominated data processing, a trend will likely lie in the development of signal processing algorithms to optimally extract spectral and spatial information prior to conventional chemometric analyses. Finally, the sum of these recent advances is likely to provide unprecedented capability in measurement and scientific in-sight, which will present new opportunities for the applied spectroscopist.

Coupling of plasmonic and optical cavity modes in quasi-three-dimensional plasmonic crystals.

The field of plasmonics has emerged as an interesting area for fundamental studies, with important application possibilities in miniaturized photonic components. Plasmonic crystals are of particular relevance because of large field enhancements and extraordinary transmission that arise from plasmonic interactions between periodic arrays of metallic elements. Here we report methods to enhance and modify the plasmonic resonances in such structures by strongly coupling them to optical modes of Fabry-Perot type cavities. First, we illustrate a type of plasmonic, narrow-band (~15 nm), high-contrast (>20 dB) absorber and an opto-fluidic modulator based on this component. Second, we use optimized samples as substrates to achieve strong amplification (>350%) and modulation (>4×) of surface-enhanced Raman scattering from surface-bound monolayers. Cavity-coupling strategies appear to be useful not only in these two examples, but also in applications of plasmonics for optoelectronics, photovoltaics and related technologies.

Off-resonance surface-enhanced Raman spectroscopy from gold nanorod suspensions as a function of aspect ratio: not what we thought.

Design of nanoparticles for surface-enhanced Raman scattering (SERS) within suspensions is more involved than simply maximizing the local field enhancement. The enhancement at the nanoparticle surface and the extinction of both the incident and scattered light during propagation act in concert to determine the observed signal intensity. Here we explore these critical aspects of signal generation and propagation through experiment and theory. We synthesized gold nanorods of six different aspect ratios in order to obtain longitudinal surface plasmon resonances that incrementally spanned 600-800 nm. The Raman reporter molecule methylene blue was trap-coated near the surface of each nanorod sample, generating SERS spectra, which were used to compare Raman signals. The average number of reporter molecules per nanorod was quantified against known standards using electrospray ionization liquid chromatography mass spectrometry. The magnitude of the observed Raman signal is reported for each aspect ratio along with the attenuation due to extinction in suspension. The highest Raman signal was obtained from the nanorod suspension with a plasmon resonance blue-shifted from the laser excitation wavelength. This finding is in contrast to SERS measurements obtained from molecules dried onto the surface of roughened or patterned metal substrates where the maximum observed signal is near or red-shifted from the laser excitation wavelength. We explain these results as a competition between SERS enhancement and extinction, at the excitation and scattered wavelengths, on propagation through the sample.

Theory of midinfrared absorption microspectroscopy: I. Homogeneous samples.

Midinfrared (IR) microspectroscopy is widely employed for spatially localized spectral analyses. A comprehensive theoretical model for the technique, however, has not been previously proposed. In this paper, rigorous theory is presented for IR absorption microspectroscopy by using Maxwells equations to model beam propagation. Focusing effects, material dispersion, and the geometry of the sample are accounted to predict spectral response for homogeneous samples. Predictions are validated experimentally using Fourier transform IR (FT-IR) microspectroscopic examination of a photoresist. The results emphasize that meaningful interpretation of IR microspectroscopic data must involve an understanding of the coupled optical effects associated with the sample, substrate properties, and microscopy configuration. Simulations provide guidance for developing experimental methods and future instrument design by quantifying distortions in the recorded data. Distortions are especially severe for transflection mode and for samples mounted on certain substrates. Last, the model generalizes to rigorously consider the effects of focusing. While spectral analyses range from examining gross spectral features to assessing subtle features using advanced chemometrics, the limitations imposed by these effects in the data acquisition on the information available are less clear. The distorting effects are shown to be larger than noise levels seen in modern spectrometers. Hence, the model provides a framework to quantify spectral distortions that may limit the accuracy of information or present confounding effects in microspectroscopy.

Theory of mid-infrared absorption microspectroscopy: II. Heterogeneous samples.

Fourier transform infrared (FT-IR) spectroscopic imaging combines the specificity of optical microscopy with the spectral selectivity of vibrational spectroscopy. There is increasing recognition that the recorded data may be dependent on the optical configuration and sample morphology in addition to its local material spectral response, but a quantitative framework for predicting such dependence is lacking. Here, a theory is developed to relate recorded data to the spectral and physical properties of heterogeneous samples. The modeling approach combines optical theory through rigorous coupled wave analysis with modeling of sampling geometry and sample structure. The interplay of morphology and dispersion are systematically explored using increasingly sophisticated samples to illustrate the dependence of the detected optical intensity on the spatial sample structure. Predictions of spectral distortions arising from the sample structure are quantified, and experimental validation of the developed theory is performed using a microfabricated standard from a commercial photoresist polymer. The developed framework forms a basis for understanding sample induced distortions in spectroscopic IR microscopy and imaging.

Multimodal microscopy for automated histologic analysis of prostate cancer

BackgroundProstate cancer is the single most prevalent cancer in US men whose gold standard of diagnosis is histologic assessment of biopsies. Manual assessment of stained tissue of all biopsies limits speed and accuracy in clinical practice and research of prostate cancer diagnosis. We sought to develop a fully-automated multimodal microscopy method to distinguish cancerous from non-cancerous tissue samples.MethodsWe recorded chemical data from an unstained tissue microarray (TMA) using Fourier transform infrared (FT-IR) spectroscopic imaging. Using pattern recognition, we identified epithelial cells without user input. We fused the cell type information with the corresponding stained images commonly used in clinical practice. Extracted morphological features, optimized by two-stage feature selection method using a minimum-redundancy-maximal-relevance (mRMR) criterion and sequential floating forward selection (SFFS), were applied to classify tissue samples as cancer or non-cancer.ResultsWe achieved high accuracy (area under ROC curve (AUC) >0.97) in cross-validations on each of two data sets that were stained under different conditions. When the classifier was trained on one data set and tested on the other data set, an AUC value of ~0.95 was observed. In the absence of IR data, the performance of the same classification system dropped for both data sets and between data sets.ConclusionsWe were able to achieve very effective fusion of the information from two different images that provide very different types of data with different characteristics. The method is entirely transparent to a user and does not involve any adjustment or decision-making based on spectral data. By combining the IR and optical data, we achieved high accurate classification.

Quantifying collagen structure in breast biopsies using second-harmonic generation imaging

Quantitative second-harmonic generation imaging is employed to assess stromal collagen in normal, hyperplastic, dysplastic, and malignant breast tissues. The cellular scale organization is quantified using Fourier transform-second harmonic generation imaging (FT-SHG), while the molecular scale organization is quantified using polarization-resolved second-harmonic generation measurements (P-SHG). In the case of FT-SHG, we apply a parameter that quantifies the regularity in collagen fiber orientation and find that malignant tissue contains locally aligned fibers compared to other tissue conditions. Alternatively, using P-SHG we calculate the ratio of tensor elements (d15/d31, d22/d31, and d33/d31) of the second-order susceptibility χ2 for collagen fibers in breast biopsies. In particular, d15/d31 shows potential differences across the tissue pathology. We also find that trigonal symmetry (3m) is a more appropriate model to describe collagen fibers in malignant tissues as opposed to the conventionally used hexagonal symmetry (C6). This novel method of targeting collagen fibers using a combination of two quantitative SHG techniques, FT-SHG and P-SHG, holds promise for breast tissue analysis and applications to characterizing cancer in a manner that is compatible with clinical practice.