When a garden can be surveyed in its entirety, visitors were more likely to consume it from afar than to indulge in its experiential qualities.Also implicit in the collective negotiated design process and the dynamic edge between the centre and external periphery of the garden was that gardens operate somehow as test-beds for operations at the landscape scale—in the same way as the pavilion is typically revered within architecture as an incubator for more expansive architectural praxis. However, the relationship of the garden to the landscape is far more dialectical than its architectural equivalent, and what goes in the garden is not necessarily an experiment for subsequent deployment in the landscape. The garden is more of a counterbalance than a small fragment of landscape; the two interact of course, but from a garden, a landscape does not necessarily grow. There are certainly exceptions to this rule—such as ‘seed dispersal’ concepts that were popular in the 1980’s where the garden was engineered to disseminate its genetic produce on the wind—but the point is that in these examples the garden is sacrificed to its expansion or duplication into the landscape Nevertheless, while not prefiguring landscape-scale operations, gardens have a more encompassing role as potent cultural litmus papers; as Bernard Lassus notes, ‘gardens have almost always foretold in advance the relationships between … society and nature’.In this regard, gardens are more persuasive as reflectors— either of self or society—than empirical experiments that generate results applicable to the world at large. This efficacy of the garden differentiates it from the landscape on the whole, although when we start to consider the consciously designed landscape as opposed to the general cultural landscape,plastic pots for seedlings the issue becomes more obfuscated.
My interpretation of James Corner’s characterization of the real limits to landscape architectural practice in the world illuminates this convergence. Given that landscape architecture influences only a very small percentage of outdoor construction projects, with other aesthetically unconscious operations undertaking the lion’s share, Corner positions landscape design as a primarily ‘metaphorical and ideological’ rather than solely demonstrative or performative praxis; one that uses its cultural currency to edify and illuminate an ecological message—to provide a foundation on which to reflect, rather than attempt to physically cure the world within its own diminutive footprint.This is, I would argue, is also descriptive of the role of the garden. Therefore, while a garden doesn’t necessarily equate to the landscape, the two genres increasingly converge and overlap in contemporary theory and praxis. At a conceptual level, the university gardens pertinently navigated the convergent muddy territory between gardens as reflectors and gardens as demonstrative landscapes. The move to de-frame is the key mechanism in engaging this terrain, although the one threshold that the design teams had no control over restrains its effectiveness: the fence around the Expo site itself. In this regard, the perimeter boundary is physical but also social; while the frame may enable representation by physically separating nature from the continuum of the world, division is also imposed through less tangible but equally powerful social forces. Indeed, to conflate the picturesque as an example, the ultimate frame was formed less from ha-ha’s or the limits of representation, than along lines of society and class. Beyond entrance gates and perimeter fences, garden shows are historically typically also be framed within these societal terms.Whereas William Kent may have ‘leaped the fence, and saw that all nature was a garden’, 66 to jump or destroy the wall of a horticultural expo is to typically find the periphery of a city, complete with its own implied social delineations.
It is in this context that the dissolution of the physical and psychological frame of the institutionalized expo itself— rather than the frames of the individual gardens within—that is the more potent force in contemporary landscape and urbanism.To provide the highest quantification accuracy when comparing samples one needs to minimize differences introduced in the processing of samples and acquiring the data. This can be best achieved through the introduction of stable isotopes into samples that allow samples to be mixed and then analyzed in the mass spectrometer. The application of metabolic labeling, which uses stable-isotope labeled amino acids in cell culture or 15N nitrogen-containing salts into the whole cell or organism in vivo, enables relative quantifications of proteins on a global scale. In such a quantitative experiment, one sample is labeled with the natural abundance , and the other with a stable isotope of low natural abundance during growth. The samples are mixed, processed, and analyzed by the mass spectrometer. Chemically identical peptides from these light- and heavy-labeled mixed samples co-elute by chromatography into the mass spectrometer, which can distinguish between the light and heavy peptides based on their mass difference, and thereby quantify the difference in peptide, and hence protein abundance between the samples. An alternative stable isotope-based strategy is to chemically tag peptides after enzymatic digestion; the most popular reagents for this strategy are isobaric tagging reagents Tandem Mass Tags . The TMT isobaric tagging reagents allow comparison of a larger number of samples, but the labeling is done at the peptide level after sample digestion and then samples are mixed. In contrast, the metabolic labeling is introduced into the proteins during growth, thus samples can be combined at the beginning, minimizing variations introduced by sample processing that can compromise quantification accuracy .
Although SILAC has been widely used in animal cell lines and has been the gold standard for MS-based proteomics quantification , 15N-labeling based quantitative applications are still quite limited in plants despite it being cheaper . This could be due to the complexity of the data analysis. SILAC pairs are easily identifiable because they have well-defined mass differences as typically only lysine and arginine are labeled. In contrast, in 15N labeling, each amino acid in the expressed proteins is labeled, and therefore, the mass difference in 15N pairs varies depending on the number of nitrogen atoms in their composition. Also, as more amino acids are being labeled, the effect of incomplete incorporation of the heavy isotope can be more pronounced under some conditions, such that isotope clusters of heavy labeled peptides in the survey scan MS1 spectra are generally broader, making it harder to identify the monoisotopic peak.There are very few freely available software tools with work flows that can analyze large-scale 15N labeled samples. Such tools include MSQuant , pFIND , and Protein Prospector . The workflow using MSQuant normally requires manual inspection of the pairs of the light and heavy forms that both fit with expected isotope envelope distribution; those that don’t fit the criteria will be omitted from further analysis . This makes it very time-consuming for a large dataset because of the manual inspection prerequisite. In addition,dutch buckets if both forms need to be present for quantification, then there will be a high false-negative rate for some of those highly biologically interesting proteins which only express in one of the two conditions, or from immuno precipitated samples where those proteins will be only in the bait-IP but may be completely absent in the control IP. Here, we present the 15N quantification workflow based on the free web-based software Protein Prospector . After data search with respective 14N and 15N search parameters, quantification between the light and heavy peptide pairs is done based on the identification of either the light or heavy peptide, or both.Additional features in Protein Prospector include a Cosine Similarity score which can be utilized to reduce manual checking of spectra and a cache function that enables efficient result retrieval through cached result storage. This workflow allows us to report quantifications of thousands of proteins and is applicable to the quantification of the total proteomes, sub-proteomes, and immunoprecipitated samples.This workflow can quantify thousands of proteins simultaneously. We demonstrate its performance using three in different proteins is relatively constant . With less complete labeling, the identification rate of heavy labeled peptides is significantly lower than light due to errors in monoisotopic peak assignment . If the labeling efficiency is achieved 98.5% or above, the identification rate between 14N and 15N search is similar in our experience. High-level labeling depends on three factors: 15N containing salt needs to be over 99% purity; we find 15N chemicals from Cambridge Isotope Laboratories are generally high-purity. The labeling time. We recommend growing Arabidopsis for 14 days to achieve high labeling efficiency. If plants can only be labeled for a shorter time before harvesting, then it is recommended to label the plants for one generation using a hydroponic system and start the experiment using the labeled seeds.
If the Arabidopsis plants are small after 14 days of growth, then the labeling efficiency will be lower, for instance, our acinus-2 pinin mutants are smaller than wild-type plants, therefore the labeling efficiency is lower than wild-type with the same duration of labeling. The availability of the 15N salt. Seeds should not be sown too many on solid-medium plates or in the liquid medium. We recommend the Arabidopsis plants are labeled 14 days or more to achieve high labeling efficiency and high identification rate, but users should be cautious not to stress plants by leaving them on medium for too long. Almost all proteins except seed storage proteins are labeled. These are not synthesized during the seedling stage and therefore they don’t incorporate the 15N labeling during growth and will remain unlabeled.Co-eluting peptides are common problems, especially in highly complex samples, and interfere with quantification. High resolution scans in MS1 reduce peak overlap, improving the accuracy of quantification , so we typically acquire our data at 120K resolution. High mass accuracy in MS2 helps to reduce the false discovery rate . Higher FDR was reported in the 15N sequence assignments due to more isobaric amino acid forms present in 15N labeling when the MS2 fragmentation was done using a low-resolution and mass accuracy QTOF2. To check this possibility in MS2 data acquired at high resolution,we compared the FDR in our seven labeled experiments listed in Table 2. After we combined peptides for 14N and 15N searches together with 1% FDR, we parsed the target and decoy 14N and 15N matches and calculated the FDR separately. We found the 15N data results had a lower FDR than that of 14N data search when MS2 scans were done in the high resolution and high-mass accuracy Orbitrap mass spectrometer . This trend is more pronounced in the higher labeling efficiency datasets. If FDR was calculated based only on first three datasets , there was no significant difference between 14N and 15N FDR, despite the average of FDR is slightly lower in 15N search. When four more Col/sec-5 datasets were included for comparison, the 15N FDR is significantly lower than 14N one, indicating using high-resolution and high-accuracy measurement, the unique mass of 15N modification to the amino acid may empower less random matches in data searches.After each peptide is quantified, they are compiled into protein groups in “Search Compare”. The spread of ratios for peptides from the same proteins are measured using the interquartile range, and Q1 , median, and Q3 are reported, as illustrated in Figures 5A,B and quantification of the pairs can be visualized as Figures 5C,D). Here we include two biological experiments as a demonstration. To calculate the median and Q1, Q3, the log base 10 of all the ratios of the peptides from the same protein are first calculated in Protein Prospector to generate log base 10 of median and Q1, Q3, followed by converting these log values to normal values by raising 10 to the power. To plot these quantification results, an R script is written as in Supplementary Data 1. In the R script, the log base 2 of all the ratios of the peptides are calculated and converted back to normal values by ratio 2 to the power. The plot is the same as displayed in Protein Prospector, no matter base 10 or 2 is used.A median value is preferentially reported instead of a mean value, as outliers, which are not unusual, can significantly skew the mean ratio, whereas median values are more tolerant. In general, the more peptides quantified from a single protein, the more accurate the median number is to the actual ratio.