A quartz crystal microbalance method to quantify the size of hyaluronan and other glycosaminoglycans on surfaces.

Hyaluronan (HA) is a major component of peri- and extra-cellular matrices and plays important roles in many biological processes such as cell adhesion, proliferation and migration. The abundance, size distribution and presentation of HA dictate its biological effects and are also useful indicators of pathologies and disease progression. Methods to assess the molecular mass of free-floating HA and other glycosaminoglycans (GAGs) are well established. In many biological and technological settings, however, GAGs are displayed on surfaces, and methods to obtain the size of surface-attached GAGs are lacking. Here, we present a method to size HA that is end-attached to surfaces. The method is based on the quartz crystal microbalance with dissipation monitoring (QCM-D) and exploits that the softness and thickness of films of grafted HA increase with HA size. These two quantities are sensitively reflected by the ratio of the dissipation shift (ΔD) and the negative frequency shift (- Δf) measured by QCM-D upon the formation of HA films. Using a series of size-defined HA preparations, ranging in size from ~ 2 kDa tetrasaccharides to ~ 1 MDa polysaccharides, we establish a monotonic yet non-linear standard curve of the ΔD/ - Δf ratio as a function of HA size, which reflects the distinct conformations adopted by grafted HA chains depending on their size and surface coverage. We demonstrate that the standard curve can be used to determine the mean size of HA, as well as other GAGs, such as chondroitin sulfate and heparan sulfate, of preparations of previously unknown size in the range from 1 to 500 kDa, with a resolution of better than 10%. For polydisperse samples, our analysis shows that the process of surface-grafting preferentially selects smaller GAG chains, and thus reduces the average size of GAGs that are immobilised on surfaces comparative to the original solution sample. Our results establish a quantitative method to size HA and other GAGs grafted on surfaces, and also highlight the importance of sizing GAGs directly on surfaces. The method should be useful for the development and quality control of GAG-based surface coatings in a wide range of research areas, from molecular interaction analysis to biomaterials coatings.

Visualization of Perineuronal Nets in Central Nervous System Tissue Sections.

The perineuronal net (PNN) is a specialized extracellular matrix structure that surrounds subpopulations of neurons in the central nervous system (CNS). The appearance of PNNs on the cell surface marks the closure of the critical period during development and has been observed to reduce synaptic plasticity. Perineuronal nets comprise hyaluronan, chondroitin sulfate proteoglycans (CSPGs), link proteins, tenascin-R, and other components, some of which are substrates for a disintegrin-like and metalloprotease domain with thrombospondin type 1 motifs (ADAMTS) proteases. There is a high heterogeneity of PNNs in the CNS. Depending on which part of the CNS is studied, the PNNs may be observed surrounding the soma, or both the soma and proximal dendrites. The most robust marker for PNN is a lectin called Wisteria floribunda agglutinin. Here, we describe a method for preparing tissue for visualization of PNNs in CNS.

A gene expression resource generated by genome-wide lacZ profiling in the mouse.

Knowledge of the expression profile of a gene is a critical piece of information required to build an understanding of the normal and essential functions of that gene and any role it may play in the development or progression of disease. High-throughput, large-scale efforts are on-going internationally to characterise reporter-tagged knockout mouse lines. As part of that effort, we report an open access adult mouse expression resource, in which the expression profile of 424 genes has been assessed in up to 47 different organs, tissues and sub-structures using a lacZ reporter gene. Many specific and informative expression patterns were noted. Expression was most commonly observed in the testis and brain and was most restricted in white adipose tissue and mammary gland. Over half of the assessed genes presented with an absent or localised expression pattern (categorised as 0-10 positive structures). A link between complexity of expression profile and viability of homozygous null animals was observed; inactivation of genes expressed in ≥ 21 structures was more likely to result in reduced viability by postnatal day 14 compared with more restricted expression profiles. For validation purposes, this mouse expression resource was compared with Bgee, a federated composite of RNA-based expression data sets. Strong agreement was observed, indicating a high degree of specificity in our data. Furthermore, there were 1207 observations of expression of a particular gene in an anatomical structure where Bgee had no data, indicating a large amount of novelty in our data set. Examples of expression data corroborating and extending genotype-phenotype associations and supporting disease gene candidacy are presented to demonstrate the potential of this powerful resource.