A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post‐translational modifications. In top‐down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top‐down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

A new global post-translational modification (PTM) discovery strategy, G-PTM-D, is described. A proteomics database containing UniProt-curated PTM information is supplemented with potential new modification types and sites discovered from a first-round search of mass spectrometry data with ultrawide precursor mass tolerance. A second-round search employing the supplemented database conducted with standard narrow mass tolerances yields deep coverage and a rich variety of peptide modifications with high confidence in complex unenriched samples. The G-PTM-D strategy represents a major advance to the previously reported G-PTM strategy and provides a powerful new capability to the proteomics research community.

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We present an open-source, interactive program named Proteoform Suite that uses proteoform mass and intensity measurements from complex biological samples to identify and quantify proteoforms. It constructs families of proteoforms derived from the same gene, assesses proteoform function using gene ontology (GO) analysis, and enables visualization of quantified proteoform families and their changes. It is applied here to reveal systemic proteoform variations in the yeast response to salt stress.

A proteoform family is a group of related molecular forms of a protein (proteoforms) derived from the same gene. We have previously described a strategy to identify proteoforms and elucidate proteoform families in complex mixtures of intact proteins. The strategy is based upon measurements of two properties for each proteoform: (i) the accurate proteoform intact-mass, measured by liquid chromatography/mass spectrometry (LC–MS), and (ii) the number of lysine residues in each proteoform, determined using an isotopic labeling approach. These measured properties are then compared with those extracted from a catalog of theoretical proteoforms containing protein sequences and localized post-translational modifications (PTMs) for the organism under study. A match between the measured properties and those in the catalog constitutes an identification of the proteoform. In the present study, this strategy is extended by utilizing a global PTM discovery database and is applied to the widely studied model organism Escherichia coli, providing the most comprehensive elucidation of E. coli proteoforms and proteoform families to date.

In top-down proteomics, intact proteins are analyzed by tandem mass spectrometry and proteoforms, which are defined forms of a protein with specific sequences of amino acids and localized post-translational modifications, are identified using precursor mass and fragmentation data. Many proteoforms that are detected in the precursor scan (MS1) are not selected for fragmentation by the instrument and therefore remain unidentified in typical top-down proteomic workflows. Our laboratory has developed the open source software program Proteoform Suite to analyze MS1-only intact proteoform data. Here, we have adapted it to provide identifications of proteoform masses in precursor MS1 spectra of top-down data, supplementing the top-down identifications obtained using the MS2 fragmentation data. Proteoform Suite performs mass calibration using high-scoring top-down identifications and identifies additional proteoforms using calibrated, accurate intact masses. Proteoform families, the set of proteoforms from a given gene, are constructed and visualized from proteoforms identified by both top-down and intact-mass analyses. Using this strategy, we constructed proteoform families and identified 1861 proteoforms in yeast lysate, yielding an approximately 40% increase over the original 1291 proteoform identifications observed using traditional top-down analysis alone.

Aim To examine the risk of obstructive sleep apnea (OSA) in children with cerebral palsy (CP) and/or epilepsy. Method This cross‐sectional study employs the Pediatric Sleep Questionnaire (PSQ), the Gross Motor Function Classification System (GMFCS), and chart review to identify symptoms of OSA in children presenting to a multi‐specialty pediatric healthcare institution. Results Two‐hundred and fifteen patients were grouped into those with epilepsy (n=54), CP (n=18), both (n=55), and neither (comparison group, n=88). The comparison group comprised children with developmental disabilities but not children with typical development. Significantly increased PSQ scores (indicating increased risk of OSA) were found among children with CP (58%) and CP with epilepsy (67%) than among the comparison group (27%; p<0.001 and p<0.0001 respectively). Children with both CP and epilepsy had a greater number of increased PSQ scores compared with CP alone (p<0.05). Increased PSQ scores were observed with increasing CP severity as measured using the GMFCS. The PSQ identified more children at risk of OSA (46%) than did the medical record review for symptoms of OSA (8.2%, p<0.001). Interpretation Children with CP of greater severity or comorbid epilepsy are at increased risk of OSA. This study supports the routine questionnaire‐based assessment for OSA as a regular part of the care of all children with CP, especially in those with more severe CP and those with epilepsy.

The development of effective strategies for the comprehensive identification and quantification of proteoforms in complex systems is a critical challenge in proteomics. Proteoforms, the specific molecular forms in which proteins are present in biological systems, are the key effectors of biological function. Thus, knowledge of proteoform identities and abundances is essential to unraveling the mechanisms that underlie protein function. We recently reported a strategy that integrates conventional top-down mass spectrometry with intact-mass determinations for enhanced proteoform identifications and the elucidation of proteoform families and applied it to the analysis of yeast cell lysate. In the present work, we extend this strategy to enable quantification of proteoforms, and we examine changes in the abundance of murine mitochondrial proteoforms upon differentiation of mouse myoblasts to myotubes. The integrated top-down and intact-mass strategy provided an increase of ∼37% in the number of identified proteoforms compared to top-down alone, which is in agreement with our previous work in yeast; 1779 unique proteoforms were identified using the integrated strategy compared to 1301 using top-down analysis alone. Quantitative comparison of proteoform differences between the myoblast and myotube cell types showed 129 observed proteoforms exhibiting statistically significant abundance changes (fold change >2 and false discovery rate <5%).