An Immunosignature system for diagnosis of cancer [Cancer immunosignaturing - test 1]
Ontology highlight
ABSTRACT: This dataset contains peptide array information from 120 patients from 5 different cancer types using classic blinded test/train method. This array is library 1 (GPL17600). A 1:500 dilution of human serum is added to a peptide array (GPL17600). This array is a two-up design, with 10420 peptides printed on the top and bottom of a standard glass microscope slide. Samples were run in duplicate. The average of the duplicates are listed here. 20 train and 20 blinded test samples were run.
Project description:This dataset contains peptide array information from 1516 patients from 12 different cancer types, 2 infectious diseases, and healthy controls using leave one out cross validation. This array is library 2 (GPL14921). A 1:500 dilution of human serum is added to a peptide array (GPL14921). This array is a one-up design, with 10286 peptides printed in duplicate on a standard glass microscope slide. 1516 patients samples from 14 different diseases and 1 control cohort were analyzed
Project description:Mice were immunized with either formalin fixed Influenza A/PR/8/34 (Killed PR8), the 2006-2007 seasonal influenza vaccine, the 2007-2008 seasonal influenza vaccine, a sublethal infection (live PR8) or mock immunized (PBS). Array data was used to distinguish the immunogens from each other and predict which of the three inactivated vaccines would be protective against A/PR/8/34 challenge. two replicates of each peptide was printed on 1 CIM_10kv3 peptide microarray. One microarray were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:An experiment was designed to use a computer program to create lithography masks using a pseudo-random pattern generator. The data in this file are results from immunosignaturing 8 different monoclonals using a 10,000 peptide random-sequence microarray. Peptides were synthesized by Sigma Aldrich, and printed onto glass slides and used to test several different parameters. One replicate of each peptide was printed on 1 CIM_10K_v2 peptide microarray. One microarray were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:In this paper several computer programs were used to simulate in situ synthesis of peptides using shadow masks and BOC synthesis. The peptides were designed to be random, or pseudo-random, but fulfill requirements of immunosignaturing. This file contains data from actual 330,000 peptide arrays that used the first iteration of the peptide generation algorithm. Monoclonal antibodies were bound to the microarrays and the total number of peptides that distinguished each monoclonal was measured. This provides a baseline against which to compare purely random sequences. One replicate of each peptide was printed on 1 330k peptide microarray. One microarray were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:A computer program was used to create random amino acid sequences based on and restricted by physical shadow masks which will be used for lithography-based synthesis of peptides. The output from this algorithm was used to create peptides that were synthesized by Sigma Aldrich, and printed onto glass slides. The arrays contained 384 peptides printed in duplicate for each of 4 different mask designs. 52 different monoclonal antibodies were incubated on these microarrays and analyzed for their propensity to bind the peptides created from each mask set. The diversity of binding served as a proxy for the 'randomness' of these peptides, and provided information about how many masks are needed to truly generate random sequence peptides. two replicates of each peptide was printed on 1 Mask peptide microarray. A minimum of Two microarrays were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:This dataset contains peptide array information from 120 patients from 5 different cancer types using classic blinded test/train method. This array is library 1 (GPL17600).
Project description:SARS-CoV-2 infection poses a worldwide public health problem affecting millions of people worldwide. There is a critical need for improvements in the noninvasive prognosis of COVID-19. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF MS) analysis combined with molecular-weight directed bottom-up proteomic analysis of plasma proteins may predict high and low risk cases of COVID-19. Patients and Methods: We used MALDI MS to analyze plasma small proteins and peptides isolated using C18 micro-columns from a cohort containing a total of 117 cases of high and low risk cases split into training (n = 88) and validation sets (n= 29). The plasma protein/peptide fingerprint obtained was used to train the algorithm before validation using a blinded test cohort. Several sample preparation, MS and data analysis parameters were optimized to achieve an overall accuracy of 85%, a sensitivity of 90%, and a specificity of 81% in the training set. In the blinded test set, this signature reached an overall accuracy of 93.1%, a sensitivity of 87.5%, and a specificity of 100%. From this signature, we identified two distinct regions corresponding to the single and doubly protonated proteins in the MALDI-TOF profile belonging to the same proteoforms. A combination of 1D SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins. We found a plasma proteomic profile that discriminates against patients with high and low risk COVID-19. Proteomic analysis of C18-fractionated plasma may have a role in the noninvasive prognosis of COVID-19. Further validation will be important to consolidate its clinical utility.
Project description:Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis translates TCR signal strength into proportional activation of enhancers and genes essential for T cell function. CD4+ T cells from transgenic AND mice were sequenced under the conditions indicated. Replicates are included for each type of data (RNA-Seq, ChIP-Seq), and are numbered accordingly. The No Peptide condition serves as the untreated control for the peptide-treated samples, and inputs are provided for ChIP-Sequencing samples.