SRM/MRM assays for targeted proteomic analysis Selected/Multiple reaction monitoring assays, conducted on triple quadrupole instruments, can be coupled to liquid chromatography for the analysis of complex proteome digests. In SRM/MRM assays the first (Q1) and last (Q3) mass analyzers of a triple quadrupole mass spectrometer are used as mass filters to isolate a peptide ion and a corresponding fragment ion. The signal of the fragment ion is then monitored over the chromatographic elution time (Fig. 1). The selectivity resulting from the two filtering stages, combined with the high duty cycle, results in quantitative analyses with unmatched sensitivity and specificity. The specific pairs of m/z values associated to the precursor and fragment ions selected are referred to as "transitions" and effectively constitute mass spectrometric assays that allow to identify and quantify a specific peptide and, by inference, the corresponding protein in a complex protein digest. Figure 1. The process of establishing a SRM/MRM assay for a protein consists of a number of steps: 1. Selection of the appropriate peptide/s, unique to the protein of interest and showing high mass spectrometry signal response (proteotypic peptides, PTPs), to maximize the sensitivity of the assay 2. Selection of predominant peptide fragments specific to the peptide of interest to be used in the MRM transition 3. Eventually, for each peptide-fragment pair, optimization of specific MS parameters (e.g. the collision energy) to maximize the signal response/sensitivity 4. Validation of the MRM assay to confirm peptide identity, e.g. by acquiring a full MS2 spectrum of the peptide in the triple quadrupole instrument used for MRM 5. Extraction of the final “coordinates” of the MRM assay, including the selected peptide and peptide fragments, the corresponding mass-to-charge ratios, the fragment intensity ratios, the associated collision energy, and the chromatographic elution time to be optionally used in time-constrained MRM analyses Overall, this is a lengthy and iterative process, but, once an MRM assay for a protein is established, it becomes universally useful, i.e. the tedious assay development process needs to be performed only once, for a given type of mass spectrometer and fragmentation mechanism (e.g. collision induced-dissociation). In the MRMAtlas each protein assay is presented as a set of optimal MRM coordinates for the peptide(s) that represent a protein. Peptide identifications have been validated by acquiring the corresponding tandem mass spectra on the triple quadrupole mass spectrometer, which can be viewed as single or consensus spectra. The final assay coordinates can be directly downloaded in Excel table-format which can be directly pasted into a MRM/SRM method of a triple quadrupole instrument and used to specifically detect and quantify the protein of interest in a complex protein digest. (Adapted from: A. Schimdt, P. Picotti and R. Aebersold Proteomeanalyse und systembiologie BIOspektrum, 1/2008, S. 44)
Rauli Susmel Associate Professor of Finance Areas of Interest Econometrics, International Finance, Derivatives. Education B.A.: Economics - University of Buenos Aires (Argentina), 1983. M.A: Economics - Centro de Estudios Macroeconomicos de Argentina (Argentina), 1986. Ph.D: Economics - University of California, San Diego, 1991. UH Teaching Ph.D. Math Review Course Ph.D. Econometrics I Course Ph.D. Econometrics II Course Ph.D. Empirical Methods Course Ph.D. Financial Management Course MBA FINA 7360 Course Undergraduate FINA 4360 Course Instructions for Independent Studies/Special Projects Research Publications Working Papers, Work in Progress and Computer Programs Computer Programs Other Information CEMA Courses Department of Finance Brown Bag Seminar Series Submit a paper to the Journal of Applied Economics Family and Friends Lamar Soccer Personal Finance 101: Some Basic Concepts For Students Other Interests, Rock and Roll and Links Bauer College of Business Home Page Please send comments and suggestions to: email: rsusmel@uh.edu
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在本帖中,我们来讨论一下如何制作有吸引力的、使文章看起来专业的图片。形式规范的文章会更容易得到读者的重视,如果您制作图片时细心认真,读者会自然而然地认为您在做研究的时候也是严谨的。 制图时,请确保以下几点: 1. 刻度单位和标识清晰易读。 2. 避免不一致的字体以及生僻的刻度。 3. 尽量使用醒目、对比度强的颜色。 4. 如果图片有吸引力,读者就不会忽略图片所传达的信息。 在本帖中,我将给出图片示例。如果您想查看我如何用 Excel 修改图片,请登录我们的博客: 浏览演示稿,其中有截图和详细的解释。 In this post, I will discuss making figures worthy of your research. What do I mean by this? You have spent months building your experiment/designing your study/programming your simulation, collecting your data, and arguing with other researchers about what it means (This last item was a joke). Now you have some data you want to present to the broader community. What should you do? In this post, I will show example graphs. If you would like more details on how I made the changes to these plots using Microsoft Excel, please view the blog post titled: " Making Scienticic Plots: A Microsoft Excel Tutorial ," which has screenshots and detailed explanations. In addition, I would recommend using a full-featured plotting program such as Origin, Igor Pro, KalideGraph, or Matlab. As an author, I used Igor Pro followed by a second round of improvements in Adobe Illustrator. I often spend more time making figures than I do writing manuscript text, because they are more important. However, it is possible to make high quality plots using only Excel. First and foremost, DO NOT under any circumstances use default plot settings, this is particularly true for Excel. Take extra time and care to make your plots readable and attractive. This means you will need to adjust the settings for every graph. Figure 1 . Typical default settings plot. It can be seen from the time that the author (me) took to make this plot that the data included in it is unimportant and should be ignored by the readers. In Fig. 1, I have made only minor changes to the default settings. I changed the spacing of the tics such that the numbers on the axes don’t overlap, and I moved the vertical axis to the left side of the plot such that its associated numbers are not on top of the data. This plot has some clear deficiencies. 1. The axes have no labels. Don’t rely on the caption (or the main text) convey all the details of a plot to the reader. Try to make your plots easy to interpret. 2. The plot does not use space well. The data is concentrated in the center of the plot, and surrounded by areas that are unused. Space is limited in journal submissions (if it is not limited than it is at least expensive). Therefore, you should not waste space. 3. The used in the scatter plot are too large. This makes it difficult to see trends in the data. 4. The figure key, which simply says “Series1”, does not provide useful information to the reader. In this case there is only one data set included, which means that the key is unnecessary. Figure 2 . Minimum effort plot. The author took the time to make a plot that others could read, but this plot still looks unprofessional. The author used scientific notation for small numbers which are all on the same order. Why does the time axis go from -700 ns to -580 ns? Should the intensity floor go to zero? In Fig. 2, I have made these changes. Now the graph can be interpreted by another scientist. This graph might meet the absolute minimum standards expected by journal editors, reviewers, and the broader scientific community. However, it still contains some poor choices, which make it look unprofessional and would reflect poorly on the author. 1. The axes have very strange ranges. These will distract readers that will think you are trying to convey something you are not. For this data, it would be better to normalize the data and set the intensity floor at zero. Be careful when making decisions like this. Setting the intensity floor to zero makes a significant quantitative change to the data. DO NOT alter data for the purposes of improving results! This is unethical and reflects poorly on the entire scientific community. This is far more important than having a clear plot. Be honest first, and then clear second. For the other axis, use a time interval that will not distract the reader. For example or would seem more natural. AVOID just using the ranges given to you by your instrument, because they often will not convey the intended meaning of the data. 2. The bottom axis uses scientific notation for numbers less than 1,000, which are all within one order of magnitude. This make the scale hard to read, and consequently hard to interpret. 3. The text is too small. In a print publication, figures are often compressed. If your text is too small in comparison to the plot it will become illegible when the figure is shrunk for publication. 4. The font choice is inconsistent. Notice that axis labels use two different fonts. This looks careless. In addition, the font choice is not the best. For most of text of the figure, the author used the font “Times New Roman,” which is considered a readable font. These fonts are best used on long sections of printed text. For figures and presentations, it is best to use a legible font such as “Arial.” Legible fonts are easy to read even when the text is small. 5. The figure is unattractive. This figure will not attract readers. Making plots using vibrant high-contrast (easy to see) colors will improve readership (citations!) Figure 3 . Professional looking plot. The author took time planning and making this figure. Readers will interpret the data the way the author intends. In addition, the use of bright red will attract readers to the figure, which may increase citations. This figure could be published in a top-tier international scientific journal. Figure 3 is an example of a plot that I as an author or reviewer would be satisfied with. 1. The scales and labels are easy to read. 2. Distractions like inconsistent fonts and strange scale ranges are eliminated. 3. Vibrant high-contrast (easy to see) colors are used. 4. The figure is attractive. Therefore readers will be interested in the meaning of the figure rather than ignoring it. In addition, I made some personal choices in for this figure. I like to close my plots in a box to separate them from the text of the paper. I also like to have my axis tics on the inside of the axis. I chose red rather than blue or green. Experiment with options like these. Ask yourself, “What looks attractive?” Taking time to think about these details will improve your plots even more. Your plots are more important than the main text of the paper! Give them the time they deserve. All the best, Daniel Broaddus, PhD Physical Sciences Editor, Edanz Group China www.liwenbianji.cn