Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.
In Geographical Information Systems, spatial point pattern data are often analysed by dividing space into pixels, recording the presence or absence of points in each pixel, and fitting a logistic regression. We study weaknesses of this approach, propose improvements, and demonstrate an application to prospective geology in Western Australia. Models based on different pixel grids are incompatible (a 'change-of-support' problem) unless the pixels are very small. On a fine pixel grid, a spatial logistic 1151 A. Baddeley et al./Spatial logistic regression regression is approximately a Poisson point process with loglinear intensity; we give explicit distributional bounds. For a loglinear Poisson process, the optimal parameter estimator from pixel data is not spatial logistic regression, but complementary log-log regression with an offset depending on pixel area. If the pixel raster is randomly subsampled, logistic regression is conditionally optimal. Bias and efficiency depend strongly on the spatial regularity of the covariates. For discontinuous covariates, we propose a new algorithmic strategy in which pixels are subdivided, and demonstrate its efficiency.
The SSN package for R provides a set of functions for modeling stream network data. The package can import geographic information systems data or simulate new data as a 'SpatialStreamNetwork', a new object class that builds on the spatial sp classes. Functions are provided that fit spatial linear models (SLMs) for the 'SpatialStreamNetwork' object. The covariance matrix of the SLMs use distance metrics and geostatistical models that are unique to stream networks; these models account for the distances and topological configuration of stream networks, including the volume and direction of flowing water. In addition, traditional models that use Euclidean distance and simple random effects are included, along with Poisson and binomial families, for a generalized linear mixed model framework. Plotting and diagnostic functions are provided. Prediction (kriging) can be performed for missing data or for a separate set of unobserved locations, or block prediction (block kriging) can be used over sets of stream segments. This article summarizes the SSN package for importing, simulating, and modeling of stream network data, including diagnostics and prediction.
Recombinant protein production (RPP) in Escherichia coli (E. coli) often induces metabolic burden to the cells that compromise their overall growth and productivity. Amino acid starvation due to RPP is a major contributor of the metabolic burden on the cells and induces global stress response known as a stringent‐like response. In this study, the effect of amino acid supplementation in a chemically defined medium on cellular growth and recombinant pramlintide production was investigated. Based on the consumption profile, few amino acids were categorized as growth‐promoting (GP1) and protein production promoting (GP2). Feeding strategies of GP1 and GP2 were tested in shake flasks followed by scale up into the bioreactor. A 40% increase in the recombinant pramlintide (rPramlintide) production (protein concentration of 3.09 ± 0.12 g/L and yield of 227.69 ± 19.72 mg pramlintide per gram dry cell weight) was realized. Furthermore, transcriptomics data indicated the downregulation of several genes associated with global stress response and genes involved in amino acid biosynthesis in test culture, supported by proteomics analysis. These results signify that the external supply of critical amino acids decreases cellular stress during RPP and improves process productivity.
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