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.

Despite numerous advances in "omics" research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P < 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of blood plasma, a diagnostic accuracy of 74% was achieved, while the same classifier showed 93.3% accuracy for IR spectra of blood plasma. These observations suggest that a biospectroscopy approach could be applied to identify spectral alterations associated with the presence of insidious ovarian cancer.

We applied surface-enhanced Raman spectroscopy (SERS) to immunolabeled endothelial cells to derive enhanced spectra of the biomolecular makeup of the cellular surface. A two-step immunolabeling protocol with gold-conjugated antibodies coupled with silver enhancement to attach silver nanoparticles to the cell surface was employed. This approach generated ∼50-fold SERS enhancement of spectral signals. The SERS spectra exhibited several SERS-enhanced peaks associated with cell membrane components. The SERS detection of silver nanoparticles proved more far more sensitive than conventional light microscopy techniques. The SERS enhancement allowed us to carry out spectral mapping using wavenumbers associated with membrane components that correlated directly with the distribution of silver nanoparticles. SERS has the potential to detect immunolabeling at lower levels than is possible using conventional immunolabeling methods while simultaneously providing unique, spatially defined, biochemical information.

Nanotechnologies generate a wide range of engineered nanomaterials that enter into our ecosystem, especially carbon-based nanoparticles (CNPs). As these novel materials acquire ever increasing numbers of applications, they may pose a risk to organisms, including humans. However, our knowledge of nanoparticle-induced effects remains limited. We are yet to understand the interaction between nanoparticles and organisms, and classical toxicology fails to provide models for risk assessment. Biospectroscopy techniques were employed to identify the effects induced by real-world levels of a panel of CNPs. MCF-7 cells concentrated in S-phase or G0/G1-phase were treated for 24 h with short or long multiwalled carbon nanotubes (MWCNTs) or Fullerene (C60) at the following concentrations: 0.0025 mg/L, 0.005 mg/L, 0.01 mg/L, 0.025 mg/L, 0.05 mg/L, and 0.1 mg/L. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with computational analysis was then applied to interrogate the cells and significant dose-related effects were detected. From derived infrared spectra, distinct spectral biomarkers of cell alteration induced by each CNP type were identified. Additionally, Raman spectroscopy was applied and allowed us to determine that reactive oxygen species (ROS) were generated by CNPs. These observations highlight the potential of biospectroscopy techniques to determine CNP-induced alterations in target mammalian cells at ppb levels.

“Large” nanoparticles potentially are a good starting point in order to derive informative NIR/IR SERS analysis of biological samples.