Characterization and sequence mapping of large RNA and mRNA therapeutics using mass spectrometry

Large RNA including mRNA (mRNA) has emerged as an important new class of therapeutics. Recently, this has been demonstrated by two highly efficacious vaccines based on mRNA sequences encoding for a modified version of the SARS-CoV-2 spike protein. There is currently significant demand for the development of new and improved analytical methods for the characterization of large RNA including mRNA therapeutics. In this study, we have developed an automated, high-throughput workflow for the rapid characterization and direct sequence mapping of large RNA and mRNA therapeutics. Partial RNase digestions using RNase T1 immobilized on magnetic particles were performed in conjunction with high-resolution liquid chromatography–mass spectrometry analysis. Sequence mapping was performed using automated oligoribonucleotide annotation and identifications based on MS/MS spectra. Using this approach, a >80% sequence of coverage of a range of large RNAs and mRNA therapeutics including the SARS-CoV-2 spike protein was obtained in a single analysis. The analytical workflow, including automated sample preparation, can be completed within 90 min. The ability to rapidly identify, characterize, and sequence map large mRNA therapeutics with high sequence coverage provides important information for identity testing, sequence validation, and impurity analysis

Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: Environmental and spatial effects

Aim: To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location: The study was carried out in southern Belgium using grid cells of 1 x 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods: Species found in 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) 'pure' environmental variation, (b) spatially-structured environmental variation, (c) 'pure' spatial variation and (d) unexplained, non-spatial variation. Results: The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst 'pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions: The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and 'noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of 'false correlation') and to bring out the 'pure' environmental variation for ecological interpretation