Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets

Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides spatially resolved molecular absorption spectra, which can subsequently yield useful information by powerful artificial intelligence methods. A typical analysis pipeline in using IR imaging data for chemical pathology often involves iterative processes of segmentation, evaluation, and analysis that necessitate rapid data exploration. Here, we present a fast, reliable, and intuitive method based on a phasor representation of spectra and discuss its unique applicability for IR imaging data. We simulate different features extant in IR spectra and discuss their influence on the phasor waveforms; similarly, we undertake IR image analysis in the transform space to understand spectral similarity and variance. We demonstrate the potential of phasor analysis for biomedical tissue imaging using a variety of samples, using fresh frozen surgical prostate resections and formalin-fixed paraffin-embedded breast cancer tissue microarray samples as model systems that span common histopathology practice. To demonstrate further generalizability of this approach, we apply the method to data from different experimental conditionsincluding standard (5.5 μm × 5.5 μm pixel size) and high-definition (1.1 μm × 1.1 μm pixel size) Fourier transform IR (FTIR) spectroscopic imaging using transmission and transflection modes. Quantitative segmentation results from our approach are compared to previous studies, showing good agreement and quick visualization. The presented method is rapid, easy to use, and highly capable of deciphering compositional differences, presenting a convenient tool for exploratory analysis of IR imaging data

Probe–Sample Interaction-Independent Atomic Force Microscopy–Infrared Spectroscopy: Toward Robust Nanoscale Compositional Mapping

Nanoscale topological imaging using atomic force microscopy (AFM) combined with infrared (IR) spectroscopy (AFM-IR) is a rapidly emerging modality to record correlated structural and chemical images. Although the expectation is that the spectral data faithfully represents the underlying chemical composition, the sample mechanical properties affect the recorded data (known as the probe–sample-interaction effect). Although experts in the field are aware of this effect, the contribution is not fully understood. Further, when the sample properties are not well-known or when AFM-IR experiments are conducted by nonexperts, there is a chance that these nonmolecular properties may affect analytical measurements in an uncertain manner. Techniques such as resonance-enhanced imaging and normalization of the IR signal using ratios might improve fidelity of recorded data, but they are not universally effective. Here, we provide a fully analytical model that relates cantilever response to the local sample expansion which opens several avenues. We demonstrate a new method for removing probe–sample-interaction effects in AFM-IR images by measuring the cantilever responsivity using a mechanically induced, out-of-plane sample vibration. This method is then applied to model polymers and mammary epithelial cells to show improvements in sensitivity, accuracy, and repeatability for measuring soft matter when compared to the current state of the art (resonance-enhanced operation). Understanding of the sample-dependent cantilever responsivity is an essential addition to AFM-IR imaging if the identification of chemical features at nanoscale resolutions is to be realized for arbitrary samples

Analysis of Variance in Spectroscopic Imaging Data from Human Tissues

The analysis of cell types and disease using Fourier transform infrared (FT-IR) spectroscopic imaging is promising. The approach lacks an appreciation of the limits of performance for the technology, however, which limits both researcher efforts in improving the approach and acceptance by practitioners. One factor limiting performance is the variance in data arising from biological diversity, measurement noise or from other sources. Here we identify the sources of variation by first employing a high throughout sampling platform of tissue microarrays (TMAs) to record a sufficiently large and diverse set data. Next, a comprehensive set of analysis of variance (ANOVA) models is employed to analyze the data. Estimating the portions of explained variation, we quantify the primary sources of variation, find the most discriminating spectral metrics, and recognize the aspects of the technology to improve. The study provides a framework for the development of protocols for clinical translation and provides guidelines to design statistically valid studies in the spectroscopic analysis of tissue

Mapping Solvation Environments in Porous Metal–Organic Frameworks with Infrared Chemical Imaging

We report here the first mesoscale characterization of solvent environments in the metal–organic framework (MOF) Cu₃(BTC)₂ using infrared imaging. Two characteristic populations of the MOF structures corresponding to the carboxylate binding to the Cu­(II) (metal) ions were observed, which reflect a regular solvated MOF structure with axial solvents in the binuclear copper paddlewheel and an unsolvated defect mode that lacks axial solvent coordination. Infrared imaging also shows strong correlation between solvent localization and the spatial distribution of the solvated population within the MOF. This is a vital result as any remnant solvent molecules adsorbed inside of MOFs can render them less effective. We propose fast IR imaging as a potential characterization technique that can measure adsorbate and defect distributions in MOFs

List of metric definitions found useful to differentiate classes- peak height ratio; all values are in wavenumber (cm^-1).

<p>List of metric definitions found useful to differentiate classes- peak height ratio; all values are in wavenumber (cm^-1).</p

Baseline corrected absorption spectra, normalized using the Amide I peak, for all five classes of cells observed in the study.

<p>Important spectral differences observed over the fingerprint spectral region (1500–900 cm^-1) are highlighted in grey and zoomed in without offset.</p

Biopsy section array of 16 samples used for validation.

<p>Top panel: (i) H&E stained image of sections (scale bar represents 500μm); Asterisk marked samples showed no rejection in pathologist review. (ii) absorbance at 1236 cm^-1 demonstrating differences between samples and different cell types; (iii) Classified IR image showing color coded pixels indicating different pathological classes; Bottom panel: Magnified view of one sample from validation set with matched lower spatial resolution IR image. (iv) H&E stained image of section; (v) Classified 6.25 μm x 6.25 μm pixel size IR image; (vi) Classified 25 μm x 25 μm pixel image.</p

Receiver operating characteristic (ROC) curves demonstrating the accuracy of the classification algorithm (i) Training set at 6.25 μm x 6.25 μm pixel size; (ii) Validation set at 6.25 μm x 6.25 μm pixel size; (iii) Validation set at 25 μm x 25 μm pixel size.

<p>Receiver operating characteristic (ROC) curves demonstrating the accuracy of the classification algorithm (i) Training set at 6.25 μm x 6.25 μm pixel size; (ii) Validation set at 6.25 μm x 6.25 μm pixel size; (iii) Validation set at 25 μm x 25 μm pixel size.</p

Probability of detection at 10% probability of false alarm.

<p>Probability of detection at 10% probability of false alarm.</p

Spectroscopic signatures determined using 3D co-culture models can be translated to human breast tissue samples.

<p>(A) Tissue microarray (TMA) biopsy cores (1.5 mm core diameter) were IHC stained for ERα and also imaged using FT-IR imaging (N-H/O-H band at 3300 cm⁻¹ visualized here for clarity). Images classified using Bayesian classifier are displayed as well to highlight the ability of FT-IR to discriminate between cell types in complex samples. Scale bar represents 250 µm. (B) Differences between epithelial pixels in patient samples with high (>80%) and low (<20%) expression of ERα can be seen in peaks at 1080 cm⁻¹ (phosphate) and 1030 cm⁻¹ (glycosidic bonds). Interestingly, there are more apparent differences in these peaks when pixels from fibroblasts are analyzed. Full spectrum (3800 – 950 cm⁻¹), C-H stretching region (3000 – 2750 cm⁻¹), and biochemical fingerprint region (1750 – 950 cm⁻¹) are shown.</p