Physicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics.

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by datadependent acquisition (DDA) experiments are required prior to DIA analysis, which is timeconsuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

An on-plate specific enrichment method is presented for the direct analysis of peptides phosphorylation. An array of sintered TiO 2 nanoparticle spots was prepared on a stainless steel plate to provide porous substrate with a very large specific surface and durable functions. These spots were used to selectively capture phosphorylated peptides from peptide mixtures, and the immobilized phosphopeptides could then be analyzed directly by MALDI MS after washing away the nonphosphorylated peptides. beta-Casein and protein mixtures were employed as model samples to investigate the selection efficiency. In this strategy, the steps of phosphopeptide capture, purification, and subsequent mass spectrometry analysis are all successfully accomplished on a single target plate, which greatly reduces sample loss and simplifies analytical procedures. The low detection limit, small sample size, and rapid selective entrapment show that this on-plate strategy is promising for online enrichment of phosphopeptides, which is essential for the analysis of minute amount of samples in high-throughput proteome research.

An electrostatic-spray ionization (ESTASI) method has been used for mass spectrometry (MS) analysis of samples deposited in or on an insulating substrate. The ionization is induced by a capacitive coupling between an electrode and the sample. In practice, a metallic electrode is placed close to but not in direct contact with the sample. Upon application of a high voltage pulse to the electrode, an electrostatic charging of the sample occurs leading to a bipolar spray pulse. When the voltage is positive, the bipolar spray pulse consists first of cations and then of anions. This method has been applied to a wide range of geometries to emit ions from samples in a silica capillary, in a disposable pipet tip, in a polymer microchannel, or from samples deposited as droplets on a polymer plate. Fractions from capillary electrophoresis were collected on a polymer plate for ESTASI MS analysis.

A new strategy using tandem (18)O stable isotope labeling (TOSIL) to quantify the N-glycosylation site occupancy is reported. Three heavy oxygen atoms are introduced into N-glycosylated peptides: two (18)O atoms are incorporated into the carboxyl terminal of all peptides during a tryptic digestion, and the third (18)O atom is incorporated into the N-glycosylation site of asparagines-linked sugar chains specifically via a N-glycosidase F (PNGase F)-mediated hydrolysis. Comparing samples treated in H(2)(18)O and samples treated in H(2)(16)O, a unique mass shift of 6 Da can be shown for N-glycosylated peptide with single glycosylation site, which could be easily distinguished from those nonglycosite peptide pairs with a mass difference of 4 Da only. The relative quantities of N-glycosylated and its parent protein-levels were obtained simultaneous by measuring the intensity ratios of (18)O/(16)O for glycosylated (6 Da) and for nonglycosylated (4 Da) peptides, respectively. Thus, a comparison of these two ratios can be utilized to evaluate the changes of occupancy of N-glycosylation at specific sites between healthy and diseased individuals. The TOSIL approach yielded good linearity in quantitative response within 10-fold dynamic range with the correlation coefficient r(2) > 0.99. The standard deviation (SD) ranged from 0.06 to 0.21, for four glycopeptides from two model glycoproteins. Furthermore, serums from a patient with ovarian cancer and healthy individual were used as test examples to validate the novel TOSIL method. A total of 86 N-glycosylation sites were quantified and N-glycosylation levels of 56 glycopeptides showed significant changes. Most changes in N-glycosylation at specific sites have the same trends as those of protein expression levels; however, the occupancies of three N-glycosylation sites were significantly changed with no change in proteins levels.

A nanoreactor based on mesoporous silicates is described for efficient tryptic digestion of proteins within the mesochannels. Cyano-functionalized mesoporous silicate (CNS), with an average pore diameter of 18 nm, is a good support for trypsin, with rapid in situ digestion of the model proteins, cytochrome c and myoglobin. The generated peptides were analyzed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Proteolysis by trypsin-CNS is much more efficient than in-solution digestion, which can be attributed to nanoscopic confinement and concentration enrichment of the substrate within the mesopores. Proteins at concentrations of 2 ng muL(-1) were successfully identified after digestion for 20 min. A biological complex sample extracted from the cytoplasm of human liver tissue was digested by using the CNS-based reactor. Coupled with reverse-phase HPLC and MALDI-TOF MS/MS, 165 proteins were identified after standard protein data searching. This nanoreactor combines the advantages of short digestion time with retention of enzymatic activity, providing a promising way to advance the development of proteomics.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.