Katherine Lau

Exploring the role of stem cells in cutaneous wound healing

Abstract:  The skin offers a perfect model system for studying the wound healing cascade, which involves a finely tuned interplay between several cell types, pathways and processes. The dysregulation of these factors may lead to wound healing disorders resulting in chronic wounds, as well as abnormal scars such as hypertrophic and keloid scars. As the contribution of stem cells towards tissue regeneration and wound healing is increasingly appreciated, a rising number of stem cell therapies for cutaneous wounds are currently under development, encouraged by emerging preliminary findings in both animal models and human studies. However, we still lack an in‐depth understanding of the underlying mechanisms through which stem cells contribute to cutaneous wound healing. The aim of this review is, therefore, to present a critical synthesis of our current understanding of the role of stem cells in normal cutaneous wound healing. In addition to summarizing wound healing principles and related key molecular and cellular players, we discuss the potential participation of different cutaneous stem cell populations in wound healing, and list corresponding stem cells markers. In summary, this review delineates current strategies, future applications, and limitations of stem cell‐based or stem cell‐targeted therapy in the management of acute and chronic skin wounds.

Advances in TERS (tip-enhanced Raman scattering) for biochemical applications

TERS (tip-enhanced Raman scattering) provides exceptional spatial resolution without any need for labelling and has become a versatile tool for biochemical analysis. Two examples will be highlighted here. On the one hand, TERS measurements on a single mitochondrion are discussed, monitoring the oxidation state of the central iron ion of cytochrome c, leading towards a single protein characterization scheme in a natural environment. On the other hand, a novel approach of single molecule analysis is discussed, again based on TERS experiments on DNA and RNA, further highlighting the resolution capabilities of this method.

Visualization and characterisation of defined hair follicle compartments by Fourier transform infrared (FTIR) imaging without labelling.

BACKGROUND To visualise and characterise skin architecture, the tissue usually has to be destroyed and labelled. OBJECTIVES The use of Fourier transform infrared (FTIR) spectroscopy as a label-free, minimally sample destructive method to define hair follicular structure has been explored and demonstrated in this paper. METHODS Human scalp skin cryosections were imaged using FTIR microscopy and the data was subsequently analysed with N-FINDR spectral unmixing algorithm. RESULTS This resulted in an excellent distinction of known hair follicle tissue layers, which could be discerned based on their molecular structure. CONCLUSION The development of a minimally sample-destructive, label-free spectroscopy based technique that can differentiate layers of cells in the dermal papilla and connective tissue sheath in the mesenchyme of the hair follicle paves the way forward to identifying spectral markers important in wound healing and stem cell therapies.

Multi-centre Raman spectral mapping of oesophageal cancer tissues: a study to assess system transferability.

The potential for Raman spectroscopy to provide early and improved diagnosis on a wide range of tissue and biopsy samples in situ is well documented. The standard histopathology diagnostic methods of reviewing H&E and/or immunohistochemical (IHC) stained tissue sections provides valuable clinical information, but requires both logistics (review, analysis and interpretation by an expert) and costly processing and reagents. Vibrational spectroscopy offers a complimentary diagnostic tool providing specific and multiplexed information relating to molecular structure and composition, but is not yet used to a significant extent in a clinical setting. One of the challenges for clinical implementation is that each Raman spectrometer system will have different characteristics and therefore spectra are not readily compatible between systems. This is essential for clinical implementation where classification models are used to compare measured biochemical or tissue spectra against a library training dataset. In this study, we demonstrate the development and validation of a classification model to discriminate between adenocarcinoma (AC) and non-cancerous intraepithelial metaplasia (IM) oesophageal tissue samples, measured on three different Raman instruments across three different locations. Spectra were corrected using system transfer spectral correction algorithms including wavenumber shift (offset) correction, instrument response correction and baseline removal. The results from this study indicate that the combined correction methods do minimize the instrument and sample quality variations within and between the instrument sites. However, more tissue samples of varying pathology states and greater tissue area coverage (per sample) are needed to properly assess the ability of Raman spectroscopy and system transferability algorithms over multiple instrument sites.

Label‐free Raman spectroscopic imaging to extract morphological and chemical information from a formalin‐fixed, paraffin‐embedded rat colon tissue section

Animal models and archived human biobank tissues are useful resources for research in disease development, diagnostics and therapeutics. For the preservation of microscopic anatomical features and to facilitate long‐term storage, a majority of tissue samples are denatured by the chemical treatments required for fixation, paraffin embedding and subsequent deparaffinization. These aggressive chemical processes are thought to modify the biochemical composition of the sample and potentially compromise reliable spectroscopic examination useful for the diagnosis or biomarking. As a result, spectroscopy is often conducted on fresh/frozen samples. In this study, we provide an extensive characterization of the biochemical signals remaining in processed samples (formalin fixation and paraffin embedding, FFPE) and especially those originating from the anatomical layers of a healthy rat colon. The application of chemometric analytical methods (unsupervised and supervised) was shown to eliminate the need for tissue staining and easily revealed microscopic features consistent with goblet cells and the dense populations of cells within the mucosa, principally via strong nucleic acid signals. We were also able to identify the collagenous submucosa‐ and serosa‐ as well as the muscle‐associated signals from the muscular regions and blood vessels. Applying linear regression analysis to the data, we were able to corroborate this initial assignment of cell and tissue types by confirming the biological origin of each layer by reference to a subset of authentic biomolecular standards. Our results demonstrate the potential of using label‐free Raman microspectroscopy to obtain superior imaging contrast in FFPE sections when compared directly to conventional haematoxylin and eosin (H&E) staining.

Mirrored stainless steel substrate provides improved signal for Raman spectroscopy of tissue and cells

Raman spectroscopy (RS) is a powerful technique that permits the non‐destructive chemical analysis of cells and tissues without the need for expensive and complex sample preparation. To date, samples have been routinely mounted onto calcium fluoride (CaF2) as this material possesses the desired mechanical and optical properties for analysis, but CaF2 is both expensive and brittle and this prevents the technique from being routinely adopted. Furthermore, Raman scattering is a weak phenomenon and CaF2 provides no means of increasing signal. For RS to be widely adopted, particularly in the clinical field, it is crucial that spectroscopists identify an alternative, low‐cost substrate capable of providing high spectral signal to noise ratios with good spatial resolution. Results show that these desired properties are attainable when using mirrored stainless steel as a Raman substrate. When compared with CaF2, data show that stainless steel has a low background signal and provides an average signal increase of 1.43 times during tissue analysis and 1.64 times when analyzing cells. This result is attributed to a double‐pass of the laser beam through the sample where the photons from the source laser and the forward scattered Raman signal are backreflected and retroreflected from the mirrored steel surface and focused towards collection optics. The spatial resolution on stainless steel is at least comparable to that on CaF2 and it is not compromised by the reflection of the laser. Steel is a fraction of the cost of CaF2 and the reflection and focusing of photons improve signal to noise ratios permitting more rapid mapping. The low cost of steel coupled with its Raman signal increasing properties and robust durability indicates that steel is an ideal substrate for biological and clinical RS as it possesses key advantages over routinely used CaF2.

The road map towards providing a robust Raman spectroscopy-based cancer diagnostic platform and integration into clinic

Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles in its adoption for cancer diagnosis. These hurdles include awareness and acceptance of the technology, practicality of integration into the histopathology workflow, data reproducibility and availability of transferrable models. We have formed a consortium, in joint efforts, to develop optimised protocols for tissue sample preparation, data collection and analysis. These protocols will be supported by provision of suitable hardware and software tools to allow statistically sound classification models to be built and transferred for use on different systems. In addition, we are building a validated gastrointestinal (GI) cancers model, which can be trialled as part of the histopathology workflow at hospitals, and a classification tool. At the end of the project, we aim to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define tumour margin, build cancer diagnostic models and discover novel disease bio markers.

Label‐Free Non‐Destructive Identification of Stem Cells in the Hair Follicle with Confocal Raman Spectrocopy

Institute of Photonic Technology, Albert-Einstein-Str. 9, Jena 07745, Germany. Institute for Physical Chemistry, F riedrich-Schiller-University, Lessing Str. 10, Jena 07743, Germany. Centre for Biospectroscopy School of Chemistry Monash University Wel lington Rd. Clayton, 3800 Victoria, Australia Department of Dermatology, University of Lubeck, Ratzeburger Allee 160, 23562 Lubeck, Germany School of Translational Medicine, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PL, UK