arXiv subjects

Michael J. MacCoss

Publications and source records attributed to Michael J. MacCoss.

5 recordsLinked to original sources

arXiv2023

Sampling the proteome by emerging single-molecule and mass-spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules. This larger number of molecules and the associated larger dynamic range have major implications in the development of proteomics technologies. We examine these implications for both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on how many molecules are routinely measured in proteomics experiments by LC-MS/MS. We review strategies that have been helpful for counting billions of protein molecules by LC-MS/MS and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome. We also examine the theoretical possibilities for scaling up single-molecule and mass spectrometry proteomics approaches to quantifying the billions of protein molecules that make up the proteomes of our cells.

q-bio.QM↗

arXiv2022

Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.

q-bio.OT↗

arXiv2021

New views of old proteins: clarifying the enigmatic proteome

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide provocative projections for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we offer potential futures for a next generation of disruptive approaches to define, quantify and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

q-bio.BM↗

arXiv2012

On the feasibility and utility of exploiting real time database search to improve adaptive peak selection

Rationale: In a shotgun proteomics experiment with data-dependent acquisition, real-time analysis of a precursor scan results in selection of a handful of peaks for subsequent isolation, fragmentation and secondary scanning. This peak selection protocol typically focuses on the most abundant peaks in the precursor scan, while attempting to avoid re-sampling the same m/z values in rapid succession. The protocol does not, however, incorporate analysis of previous fragmentation scans into the peak selection procedure. Methods: In this work, we investigate the feasibility and utility of incorporating analysis of previous fragmentation scans into the peak selection protocol. We demonstrate that real-time identification of fragmentation spectra is feasible in principle, and we investigate, via simulations, several strategies to make use of the resulting peptide identifications during peak selection. Results: Our simulations fail to provide evidence that peptide identifications can provide a large improvement in the total number of peptides identified by a shotgun proteomics experiment. Conclusions: These results are significant because they point out the feasibility of using peptide identifications during peak selection, and because our experiments may provide a starting point for others working in this direction.

q-bio.QM↗

arXiv2010

A nested mixture model for protein identification using mass spectrometry

Mass spectrometry provides a high-throughput way to identify proteins in biological samples. In a typical experiment, proteins in a sample are first broken into their constituent peptides. The resulting mixture of peptides is then subjected to mass spectrometry, which generates thousands of spectra, each characteristic of its generating peptide. Here we consider the problem of inferring, from these spectra, which proteins and peptides are present in the sample. We develop a statistical approach to the problem, based on a nested mixture model. In contrast to commonly used two-stage approaches, this model provides a one-stage solution that simultaneously identifies which proteins are present, and which peptides are correctly identified. In this way our model incorporates the evidence feedback between proteins and their constituent peptides. Using simulated data and a yeast data set, we compare and contrast our method with existing widely used approaches (PeptideProphet/ProteinProphet) and with a recently published new approach, HSM. For peptide identification, our single-stage approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some scenarios in which the existing methods perform poorly.

stat.AP↗