Spiral packing and chiral selectivity in model membranes probed by phase-resolved sum-frequency generation microscopy.

Since the lipid raft model was developed at the end of the last century, it became clear that the specific molecular arrangements of phospholipid assemblies within a membrane have profound implications in a vast range of physiological functions. Studies of such condensed lipid islands in model systems using fluorescence and Brewster angle microscopies have shown a wide range of sizes and morphologies, with suggestions of substantial in-plane molecular anisotropy and mesoscopic structural chirality. Whilst these variations can significantly alter many membrane properties including its fluidity, permeability and molecular recognition, the details of the in-plane molecular orientations underlying these traits remain largely unknown. Here, we use phase-resolved sum-frequency generation microscopy on model membranes of mixed chirality phospholipid monolayers to fully determine the three-dimensional molecular structure of the constituent micron-scale condensed domains. We find that the domains possess curved molecular directionality with spiralling mesoscopic packing, where both the molecular and spiral turning directions depend on the lipid chirality, but form structures clearly deviating from mirror symmetry for different enantiomeric mixtures. This demonstrates strong enantioselectivity in the domain growth process and indicates fundamental thermodynamic differences between homo- and heterochiral membranes, which may be relevant in the evolution of homochirality in all living organisms.

Obtaining extended insight into molecular systems by probing multiple pathways in second-order nonlinear spectroscopy.

Second-order nonlinear spectroscopy is becoming an increasingly important technique in the study of interfacial systems owing to its marked ability to study molecular structures and interactions. The properties of such a system under investigation are contained within their intrinsic second-order susceptibilities which are mapped onto the measured nonlinear signals (e.g. sum-frequency generation) through the applied experimental settings. Despite this yielding a plethora of information, many crucial aspects of molecular systems typically remain elusive, for example the depth distributions, molecular orientation and local dielectric properties of its constituent chromophores. Here, it is shown that this information is contained within the phase of the measured signal and, critically, can be extracted through measurement of multiple nonlinear pathways (both the sum-frequency and difference-frequency output signals). Furthermore, it is shown that this novel information can directly be correlated to the characteristic vibrational spectra, enabling a new type of advanced sample characterization and a profound analysis of interfacial molecular structures. The theory underlying the different contributions to the measured phase of distinct nonlinear pathways is derived, after which the presented phase disentanglement methodology is experimentally demonstrated for model systems of self-assembled monolayers on several metallic substrates. The obtained phases of the local fields are compared to the corresponding phases of the nonlinear Fresnel factors calculated through the commonly used theoretical model, the three-layer model. It is found that, despite its rather crude assumptions, the model yields remarkable similarity to the experimentally obtained values, thus providing validation of the model for many sample classes.

Using hybrid atomic force microscopy and infrared spectroscopy (AFM-IR) to identify chemical components of the hair medulla on the nanoscale.

Atomic force microscopy integrated with infrared spectroscopy (AFM-IR) has been used to topographically and chemically examine the medulla of human hair fibres with nanometre scale lateral resolution. The mapping of cross-sections of the medulla showed two distinct structural components which were subsequently characterised spectroscopically. One of these components was shown to be closely similar to cortical cell species, consistent with the fibrillar structures found in previous electron microscope (EM) investigations. The other component showed large chemical differences from cortical cells and was assigned to globular vacuole species, also confirming EM observations. Further characterisation of the two components was achieved through spectral deconvolution of the protein Amide-I and -II bands. This showed that the vacuoles have a greater proportion of the most thermodynamically stable conformation, namely the antiparallel ß-sheet structures. This chimes with the observed lower cysteine concentration, indicating a lower proportion of restrictive disulphide cross-link bonding. Furthermore, the large α-helix presence within the vacuoles points to a loss of matrix-like material as well as significant intermolecular stabilisation of the protein structures. By analysing the carbonyl stretching region, it was established that the fibrillar, cortical cell-like components showed considerable stabilisation from H-bonding interactions, similar to the cortex, involving amino acid side chains whereas, in contrast, the vacuoles were found to only be stabilised significantly by structural lipids.

Infrared Nanospectroscopy of Air-Sensitive Biological Substrates Protected by Thin Hydrogel Films.

The air sensitivity of many substrates, and specifically biosurfaces, presents an experimental challenge for their analysis by vibrational spectroscopy and, in particular, infrared microscopy on a nanometer scale. The recent development of atomic-force-microscopy-based infrared spectroscopy (AFM-IR), which circumvents the Abbe diffraction limit, allows nanoscale chemical characterization of surfaces. Additionally, this technique has been shown to work for thin films under aqueous environments but is limited to substrates up to 10 nm thick, thus ruling out application to many biological surfaces. To circumvent this restriction, we have utilized hydrogels to cover such surfaces and maintain a more physiologically representative environment for biological substrates. We show that it is feasible to use AFM-IR to chemically characterize this type of substrate buried under a thin hydrogel film. Specifically, this work describes the AFM-IR spectra of red blood cells under polyvinyl alcohol hydrogels.

Nanoscale Molecular Characterization of Hair Cuticle Cells Using Integrated Atomic Force Microscopy-Infrared Laser Spectroscopy.

The hair cuticle provides significant protection from external sources, as well as giving rise to many of its bulk properties, e.g., friction, shine, etc. that are important in many industries. In this work, atomic force microscopy-infrared spectroscopy (AFM-IR) has been used to investigate the nanometer-scale topography and chemical structure of human hair cuticles in two spectral regions. AFM-IR combines atomic force microscopy with a tunable infrared laser and circumvents the diffraction limit that has impaired traditional infrared spectroscopy, facilitating surface-selective spectroscopy at ultra-spatial resolution. This high resolution was exploited to probe the protein secondary structures and lipid content, as well as specific amino acid residues, e.g., cystine, within individual cuticle cells. Characterization across the top of individual cells showed large inhomogeneity in protein and lipid contributions that suggested significant changes to physical properties on approaching the hair edge. Additionally, the exposed layered sub-structure of individual cuticle cells allowed their chemical compositions to be assessed. The variation of protein, lipid, and cystine composition in the observed layers, as well as the measured dimensions of each, correspond closely to that of the epicuticle, A-layer, exocuticle, and endocuticle layers of the cuticle cell sub-structure, confirming previous findings, and demonstrate the potential of AFM-IR for nanoscale chemical characterization within biological substrates.

Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts.

The challenge of deriving quantitative information from the infrared spectra of proteins arises from the large number of secondary structures and amino acid side-chain functional groups that all contribute to the spectral intensity, such as within the amide I band (1600-1700 cm-1). The band is invariably heavily convoluted from overlapping spectral features, thereby making interpretation difficult such that deconvolution is usually required. This work critically examines the methods available to deconvolute the spectra and assesses the commonly used methods and algorithms applied to vibrational spectra for smoothing and peak identification. We show that unless their spectra have very high signal-to-noise ratios, quantitative analysis to decipher protein constituents is not feasible. The advantages and disadvantages of spectral smoothing using adjacent averaging, the Savitzky-Golay filter and the fast Fourier transform filter are examined in detail. The use of derivative spectra to identify peaks is described with particular reference to the influence and reduction of interfering water bands in the amide I region. The reliability of band narrowing techniques such as second-derivative analysis or Fourier deconvolution that lead to the identification of the contributing protein peaks is investigated. Both methods are shown to be limited in their capacity to resolve features with very similar frequencies. Additionally, the presence of narrow bands arising from high-frequency noise whether from atmospheric water vapor, acoustic vibrations, or electrical interference results in both methods becoming increasingly unusable as narrow bands are preferentially enhanced at the expense of broad ones such as the amide I bands. An optimal strategy is critically developed to allow accurate determination and quantification of protein constituents and their conformations. Additionally, quantitative methods are proposed to account for baseline shifts, which would otherwise introduce significant errors in similarity indices.