Biology subjects

Spicer, V.

Publications and source records attributed to Spicer, V..

4 recordsLinked to original sources

bioRxiv2024

SysQuan: repurposing SILAC mice for the affordable absolute quantitation of the human proteome

Relative quantitation, used by most MS-based proteomics laboratories to determine protein fold-changes, requires samples being processed and analyzed together for best comparability through minimizing batch differences. This limits the adoption of MS-based proteomics in population-wide studies, and the detection of subtle but relevant changes in heterogeneous samples. Absolute quantitation circumvents these limitations and enables comparison of results across laboratories, studies, and longitudinally. However, high costs of the essential stable isotope labeled (SIL) standards prevents widespread access and limits the number of quantifiable proteins. Our new approach, called "SysQuan", repurposes SILAC mouse tissues/biofluids as system-wide internal standards for matched human samples to enable absolute quantitation of, theoretically, two-thirds of the human proteome using 157,086 shared tryptic peptides. We demonstrate that SysQuan enables quantification of 70% and 31% of the liver and plasma proteomes, respectively. We demonstrate for 14 metabolic proteins that abundant SIL mouse tissues enable cost-effective reverse absolute quantitation in, theoretically, 1000s of human samples. Moreover, 10,000s of light/heavy doublets in untargeted SysQuan datasets enable unique post-acquisition absolute quantitation. SysQuan empowers researchers to replace relative quantitation with affordable absolute quantitation at scale, making data comparable across laboratories, diseases and tissues, enabling completely novel study designs and increasing reusability of data in repositories. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/622109v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c7a58org.highwire.dtl.DTLVardef@1344320org.highwire.dtl.DTLVardef@2321e3org.highwire.dtl.DTLVardef@85bf2d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

bioinformatics↗

bioRxiv2020

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

The contribution of peptide amino-acid sequence to collision cross-section values (CCS) has been investigated using a dataset of [~]134,000 peptides of four different charge states (1+ to 4+). The migration data was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of [~]0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptides ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position dependent ISP behaviour consistent with electrostatic interaction with the peptide macro-dipole, which affects the peptide helicity.

bioinformatics↗

bioRxiv2020

Integrating Lung Tissue and Lavage Proteomes Reveals Unique Pathways in Allergen-Challenged Mice

To capture interplay between biological pathways we analyzed the proteome from matched lung tissue and bronchoalveolar lavage fluid (BALF) of individual allergen-naive and house dust mite (HDM)-challenged BALB/c mice, a model of allergic asthma. Unbiased label-free LC-MS/MS analysis quantified 2,675 proteins from tissue and BALF of allergen-naive and HDM-exposed mice. In comparing the four datasets we found significantly greater diversity in proteins between lung tissue and BALF than in the changes induced by HDM challenge. The biological pathways enriched after allergen exposure were compartment-dependent. Lung tissue featured innate immune responses and oxidative stress, while BALF most strongly revealed changes in metabolism. We combined lung tissue and BALF proteomes, which principally highlighted oxidation reduction (redox) pathways, a finding influenced chiefly by the lung tissue dataset. Integrating lung and BALF proteomes also uncovered new proteins and biological pathways that may mediate lung tissue and BALF interactions after allergen challenge, for example, B Cell Receptor signaling. We demonstrate that enhanced insight is fostered when different biological compartments from the lung are investigated in parallel. Integration of proteomes from lung tissue and BALF compartments reveals new information about protein networks in the response to environmental challenge and interaction between intracellular and extracellular process.

systems biology↗