The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

This is a detailed and user-friendly protocol for the cultivation and successful crossing of Lotus japonicus (L. japonicus) e.g. for the generation of higher order mutants, based on methods previously reported (Grant et al., 1962; Handberg and Stougaards, 1992; Jiang and Gresshoff, 1997; Pajuelo and Stougaard, 2005).

The smut fungus Ustilago maydis is an established model organism for elucidating how biotrophic pathogens colonize plants and how gene families contribute to virulence. Here we describe a step by step protocol for the generation of CRISPR plasmids for single and multiplexed gene editing in U. maydis. Furthermore, we describe the necessary steps required for generating edited clonal populations, losing the Cas9 containing plasmid, and for selecting the desired clones.

Chronic exposure to ocean acidification and elevated sea-surface temperatures pose significant stress to marine ecosystems. This in turn necessitates costly acclimation responses in corals in both the symbiont and host, with a reorganization of cell metabolism and structure. A large-scale untargeted metabolomics approach comprising gas chromatography mass spectrometry (GC–MS) and ultraperformance liquid chromatography coupled to high resolution mass spectrometry (UPLC–MS) was applied to profile the metabolite composition of the soft coral Sarcophyton ehrenbergi and its dinoflagellate symbiont. Metabolite profiling compared ambient conditions with response to simulated climate change stressors and with the sister species, S. glaucum. Among ∼300 monitored metabolites, 13 metabolites were modulated. Incubation experiments providing four selected upregulated metabolites (alanine, GABA, nicotinic acid, and proline) in the culturing water failed to subside the bleaching response at temperature-induced stress, despite their known ability to mitigate heat stress in plants or animals. Thus, the results hint to metabolite accumulation (marker) during heat stress. This study provides the first detailed map of metabolic pathways transition in corals in response to different environmental stresses, accounting for the superior thermal tolerance of S. ehrenbergi versus S. glaucum, which can ultimately help maintain a viable symbiosis and mitigate against coral bleaching.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

In addition to synthesizing and secreting copious amounts of pectic polymers (Young et al., 2008), Arabidopsis thaliana seed coat epidermal cells produce small amounts of cellulose and hemicelluloses typical of secondary cell walls (Voiniciuc et al., 2015c). These components are intricately linked and are released as a large mucilage capsule upon hydration of mature seeds. Alterations in the structure of minor mucilage components can have dramatic effects on the architecture of this gelatinous cell wall. The immunolabeling protocol described here makes it possible to visualize the distribution of specific polysaccharides in the seed mucilage capsule.

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

The Arabidopsis thaliana seed coat epidermis produces copious amounts of mucilage polysaccharides (Haughn and Western, 2012). Characterization of mucilage mutants has identified novel genes required for cell wall biosynthesis and modification (North et al., 2014). The biochemical analysis of seed mucilage is essential to evaluate how different mutations affect cell wall structure (Voiniciuc et al., 2015c). Here we describe a robust method to screen the monosaccharide composition of Arabidopsis seed mucilage using ion chromatography (IC). Mucilage from up to 48 samples can be extracted and prepared for IC analysis within 24 h (only 4 h hands-on). Furthermore, this protocol enables fast separation (31 min per sample), automatic detection and quantification of both neutral and acidic sugars.

The Arabidopsis thaliana seed coat produces large amounts of cell wall polysaccharides, which swell out of the epidermal cells upon hydration of the mature dry seeds. While most mucilage polymers immediately diffuse in the surrounding solution, the remaining fraction tightly adheres to the seed, forming a dense gel-like capsule (Macquet et al., 2007). Recent evidence suggests that the adherence of mucilage is mediated by complex interactions between several cell wall components (Griffiths et al., 2014; Voiniciuc et al., 2015a). Therefore, it is important to evaluate how different cell wall mutants impact this mucilage property. This protocol facilitates the analysis of monosaccharides in sequentially extracted mucilage fractions, and quantifies the detachment of each component from seeds.

Damage to plant organs through both biotic and abiotic injury is very common in nature. Arabidopsis thaliana 5-day-old (5-do) seedlings represent an excellent system in which to study plant responses to mechanical wounding, both at the site of the damage and in distal unharmed tissues. Seedlings of wild type, transgenic or mutant lines subjected to single or repetitive cotyledon wounding can be used to quantify morphological alterations (e.g., root length, Gasperini et al., 2015), analyze the dynamics of reporter genes in vivo (Larrieu et al., 2015; Gasperini et al., 2015), follow transcriptional changes by quantitative RT-PCR (Acosta et al., 2013; Gasperini et al., 2015) or examine additional aspects of the wound response with a plethora of downstream procedures. Here we illustrate how to rapidly and reliably wound cotyledons of young seedlings, and show the behavior of two promoters driving the expression of β-glucuronidase (GUS) in entire seedlings and in the primary root meristem, following single or repetitive cotyledon wounding respectively. We describe two procedures that can be easily adapted to specific experimental needs.