![]() ![]() They had four investors including Y-Combinator where they started plus Will Smith (not the one you're thinking of) from Octave which also invested in another big note-taking app Notion and 27 other companies. Why? If that's where you're keeping your personal notes, it's good to know who is protecting them and keeping them safe from bad actors/actresses! Now that we know who started Workflowy, let's see who else owns it. What's interesting is Mike's LinkedIn profile shows that he studies focused in graphics design and then he built a completely text-based editor. and Mike in 2005 with a Masters in Comp Sci. Both graduated from Stanford, Jesse in 2003 with a BS in Product Design and Mechanical Eng. Workflowy was started in 2010 by Jesse Patel who seems to be there still 13 years later and Michael (Mike) Turitzin who sold most of his part and is now a Software Engineer at Figma. I highly recommend watching that video, not now, after you read this! :) Back then the app was called Work Flowy and somehow was renamed to Work flowy, so less emphasis on flow? I kid! Looking back at my old emails, the first time I heard of it was in Scott Hansleman's 2012 talk " It's not what you read, it's what you ignore". This week's app is Worflowy which my sister Janet started using back in 2016 mostly for work but then also with her family and our families. , Springer Nature.Welcome Ricardo from no-fixed address, Esther from New York, Reginald from Florida and Captain Nemo (!) from Italy. BCA, bicinchoninic acid HCD, higher-energy collision dissociation. PCC1–3 indicate Protein Characterization Centers 1 (Broad Institute), 2 (Johns Hopkins University), and 3 (Pacific Northwest National Laboratory), respectively. Intra-plex, intra-lab, and inter-lab comparisons were conducted to test depth of coverage and reproducibility. Each laboratory analyzed 2× TMT-10 plexes. Tumors of each subtype from multiple mice were cryofractured and aliquots of the homogenized powders were distributed to the three different laboratories for global proteome and phosphoproteome analysis. b, Multiple mice of basal (WHIM 2) and luminal (WHIM16) subtypes were grown, and the tumors of each subtype were pooled together. The relevant steps of the Procedure are indicated in red. ![]() Some of the conditions tested relative to the preexisting workflow were (i) digestion at higher protein concentrations, which effectively increases the enzyme concentration during digestion, resulting in lower missed cleavage rates (ii) reconstitution of lysyl endopeptidase in water, instead of 50 mM acetic acid, which better maintains the activity of the enzyme (iii) quantification of peptides by BCA before isobaric labeling, which yields more accurate input amounts than BCA at the protein level (iv) offline basic RP fractionation using either Agilent or Waters columns, which yield equivalent results and (v) optimization of HCD energy for each individual instrument, rather than the use of a common collision energy, which improved spectral quality. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.Ī, Multiple aspects of sample handling were optimized based on a preexisting workflow for global proteome and phosphoproteome analysis (Mertins et al.). The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in ~4 months using a single LC-MS/MS instrument. The maximum deviation for the phosphoproteome coverage was 37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. Each plex consisted of ten samples, each being 300 μg of peptide derived from 0.88. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. ![]()
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