Hepes-glutamic acid buffer-mediated organic solvent protection effect (HOPE)-fixation has been introduced as an alternative to formalin fixation of clinical samples. Beyond preservation of morphological structures for histology, HOPE-fixation was demonstrated to be compatible with recent methods for RNA and DNA sequencing. However, the suitability of HOPE-fixed materials for the inspection of proteomes by mass spectrometry so far remained undefined. This is of particular interest, since proteins constitute a prime resource for drug research and can give valuable insights into the activity status of signaling pathways. In this study, we extracted proteins from human lung tissue and tested HOPE-treated and snap-frozen tissues comparatively by proteome and phosphoproteome analyses. High confident data from accurate mass spectrometry allowed the identification of 2603 proteins and 3036 phosphorylation sites. HOPE-fixation did not hinder the representative extraction of proteins, and investigating their biochemical properties, covered subcellular localizations, and cellular processes revealed no bias caused by the type of fixation. In conclusion, proteome as well as phosphoproteome data of HOPE lung samples were qualitatively equivalent to results obtained from snap-frozen tissues. Thus, HOPE-treated tissues match clinical demands in both histology and retrospective proteome analyses of patient samples by proteomics.
To better understand the dynamics of the underlying processes in cells, it is necessary to take measurements over a time course. Modern high-throughput technologies are often used for this purpose to measure the behavior of cell products like metabolites, peptides, proteins, [Formula: see text]RNA or mRNA at different points in time. Compared to classical time series, the number of time points is usually very limited and the measurements are taken at irregular time intervals. The main reasons for this are the costs of the experiments and the fact that the dynamic behavior usually shows a strong reaction and fast changes shortly after a stimulus and then slowly converges to a certain stable state. Another reason might simply be missing values. It is common to repeat the experiments and to have replicates in order to carry out a more reliable analysis. The ideal assumptions that the initial stimulus really started exactly at the same time for all replicates and that the replicates are perfectly synchronized are seldom satisfied. Therefore, there is a need to first adjust or align the time-resolved data before further analysis is carried out. Dynamic time warping (DTW) is considered as one of the common alignment techniques for time series data with equidistant time points. In this paper, we modified the DTW algorithm so that it can align sequences with measurements at different, non-equidistant time points with large gaps in between. This type of data is usually known as time-resolved data characterized by irregular time intervals between measurements as well as non-identical time points for different replicates. This new algorithm can be easily used to align time-resolved data from high-throughput experiments and to come across existing problems such as time scarcity and existing noise in the measurements. We propose a modified method of DTW to adapt requirements imposed by time-resolved data by use of monotone cubic interpolation splines. Our presented approach provides a nonlinear alignment of two sequences that neither need to have equi-distant time points nor measurements at identical time points. The proposed method is evaluated with artificial as well as real data. The software is available as an R package tra (Time-Resolved data Alignment) which is freely available at: http://public.ostfalia.de/klawonn/tra.zip .
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