Biology subjects

Fellers, R. T.

Publications and source records attributed to Fellers, R. T..

7 recordsLinked to original sources

bioRxiv2026

Deep Proteoform Sequencing with Top-Down Direct Mass Technology

Individual Ion Mass Spectrometry (I2MS) using Direct Mass Technology mode on an Orbitrap mass spectrometer (DMTm) increases sensitivity, resolution, and mass range for protein analysis. Here, we present an end-to-end workflow for deep proteoform sequencing using top-down mass spectrometry with DMTm. By assigning the charge of individual fragment ions and converting spectra from the m/z to the mass domain, DMTm resolves overlapping isotopic distributions that have limited conventional top-down mass spectrometry. Across different fragmentation modes on Orbitrap mass spectrometers, top-down DMTm significantly outperformed conventional top-down mass spectrometry methods. For a glycosylated 50.8 kDa antibody heavy chain, sequence coverage was greatly increased, from 27.5% to 83.3%, in 10 minutes of acquisition using a single fragmentation mode. Coverage of the middle 350 residues improved from 0% to >95%, demonstrating near-complete coverage of the difficult-to-characterize internal region of a large protein. The fragmentation patterns of DMTm were found to be complementary to conventional top-down, with higher internal coverage for DMTm and higher terminal coverage for conventional. Accordingly, aggregation of the data from the two modes further increased heavy chain sequence coverage to 90.2%. A new software platform, Proteoform Studio, provided optimized ion processing for improved sequence coverage and enabled real-time experimental monitoring as individual ions were accumulated. The platform automatically integrates conventional and DMTm data to provide the most comprehensive sequence coverage possible. Together, these advances enable substantially deeper proteoform sequencing and establish a straightforward, complete top-down DMTm workflow to confidently define proteoforms in biological systems and biotherapeutic development.

bioinformatics↗

bioRxiv2023

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

MotivationPost-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. ResultsWe developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a proteins PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. AvailabilityThe source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

bioinformatics↗

bioRxiv2023

Top-down Proteomics of 10,000 Single Brain Cells

We introduce single-cell Proteoform imaging Mass Spectrometry (scPiMS), which realizes the benefit of direct analysis of intact proteins to process 10,836 single cells from the rat hippocampus. This new platform addresses the throughput bottleneck for single cell proteomics using mass spectrometry, boosting cell processing rates by >20-fold in the field. We identified 169 proteoforms <70 kDa from single brain cells and classified 2758 of them as neurons, astrocytes or microglia cell types.

biochemistry↗

bioRxiv2021

Direct Imaging and Identification of Proteoforms up to 70 kDa from Human Tissue

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by 4-fold compared to reported methods, and reveals tissue localization of proteoforms at <80 m spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization (nano-DESI) with ion detection using individual ion mass spectrometry (I2MS). We demonstrate the first proteoform imaging of human kidney, identifying 169 of 400 proteoforms <70 kDa using top-down mass spectrometry and database lookup from the human proteoform atlas, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues. TeaserNano-DESI combined with individual ion mass spectrometry generates images of proteoforms up to 70 kDa.

biochemistry↗

bioRxiv2019

Multiplexed Single Ion Mass Spectrometry Improves Measurement of Proteoforms and Their Complexes

A new Orbitrap-based single ion analysis procedure is shown to be possible by determining the direct charge on numerous measurements of individual protein ions to generate true mass spectra. The deployment of an Orbitrap system for charge detection enables the characterization of highly complicated mixtures of proteoforms and their complexes in both denatured and native modes of operation, revealing information not obtainable by traditional measurement of an ensemble of ions.

biochemistry↗

bioRxiv2018

Analysing protein post-translational modform regions by linear programming

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such \"modforms\". Estimating these \"modform distributions\" is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral \"modform region\". Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

systems biology↗

bioRxiv2018

A multi-modal proteomics strategy for characterizing posttranslational modifications of tumor suppressor p53 reveals many sites but few modified forms

Post-translational modifications (PTMs) are found on most proteins, particularly on \"hub\" proteins like the tumor suppressor p53, which has over 100 possible PTM sites. Substantial crosstalk between PTM sites underlies the ability of such proteins to integrate diverse signals and coordinate downstream responses. However, disentangling the combinatorial explosion in global PTM patterns across an entire protein (\"modforms\") has been challenging, as conventional peptide-based mass spectrometry strategies (so-called \"bottom-up\" MS) destroy such global correlations. Alternatively, direct analysis of intact and modified proteins using \"top-down\" MS retains global information. Here, we applied both strategies to recombinant p53 phosphorylated in vitro with Chk1 kinase, which exhibited 41 modified sites by bottom-up MS, but no more than 8 modified sites per molecule detected by top-down MS. This observation that many low-abundance modifications comprise relatively few modforms above a 1% threshold indicates that endogenous p53 PTM complexity may be more definable than previously thought.

systems biology↗