Molecular & Cellular Proteomics

Huntingtons disease (HD) is a life-altering genetic neurodegenerative disorder, with cognitive, motor, and psycho-social effects that have consequential impacts on the individuals and their families. While current treatments improve disease symptoms, there are no FDA-approved therapies that prevent disease progression. Converging lines of evidence from human GWAS and mouse models point to DNA repair and handling (R/H) proteins as promising therapeutic targets due to their ability to modulate somatic expansion of the CAG repeat of HTT. The roles of DNA R/H HD modulator proteins are incompletely understood, in part, due to their relatively low cellular abundance and technical challenges in quantification. Here, we developed and validated targeted mass spectrometry assays quantifying DNA R/H proteins, spanning functions in mismatch repair, Fanconi anemia, and transcriptional regulation, using complementary workflows for timsTOF and Orbitrap platforms. We built species-specific experiment spectral libraries that outperformed in silico libraries for target detection. Applying this pipeline to an HTT-Q140 knock-in mouse HD model, we observed that DNA R/H protein abundances were largely unchanged in HD mice, while HTT and HAP40 showed increased nuclear association with disease progression. To facilitate translational research applications, we further developed a stable isotope dilution assay for absolute quantification of 11 human mismatch repair-associated proteins and generated an HTT knock-out human neuroblastoma cell line. Additionally, we used thermal proximity coaggregation profiling to characterize the endogenous interactomes of MMR proteins. We observed that HTT KO caused proteome down-regulation in selected DNA R/H proteins and reshaped the MMR protein interactome, with the most pronounced changes observed for MLH1 and PMS1 interactions. Overall, we established a validated, transferable assay for quantifying DNA R/H proteins in perturbation studies using human and mouse HD model systems and provide evidence that HTT influences the abundance and interaction landscape of proteins central to CAG repeat instability.

Peptide spectrum annotation is critical for the assignment of peptides and the localisation of modifications. While many existing tools provide spectrum annotation capacities, they often lack the flexibility required to allow bespoke spectral annotation of peptides containing multiple labile modifications or the accurate assignment of peptides in which fragmentation deviates from canonical patterns. In these cases, user-guided annotation is widely used to improve assignment completeness, however it typically does not integrate peptide scoring, making it challenging to assess the empirical improvement of the associated annotation and its impact on downstream false-discovery rate estimations. Here, we introduce an interactive annotation environment, the 'MassSpectrum Analyzer', which aims to streamline the exploration and analysis of modified peptides by enabling user-defined customisation with peptide scoring. Using (2-Aminoethyl)trimethylammonium carboxyl-derivatised peptides and glycopeptides as case studies we demonstrate the capacity of the MassSpectrum Analyzer to rapidly explore and allow the assessment of modified peptide datasets. By enabling direct assessment of the impact of user-guided choices on peptide scoring, we show how the detection of highly modified peptides can be improved through post-search integration of modification fragmentation information in a statistically robust manner. Similarly, by permitting comparisons of peptide ion intensities across spectra, we show that global fragmentation patterns can be quantified allowing the interrogation of trends that only become clear when spectra are assessed en masse. Combined, the MassSpectrum Analyzer streamlines the generation of publication-ready spectra and provides a means to assess how the inclusion of annotated features influences assignment scores.

Sonication is a fundamental technique in proteome sample preparation, primarily used for protein solubilization and shearing of genomic DNA. Although the mechanical shearing of DNA is well-characterized, its unintended impact on protein structural integrity remains a significant "blind spot" in high-throughput analytical workflows. In this study, we systematically investigated sonication-induced protein fragmentation by combining gel-based fractionation (PEPPI-MS) with sequence-level compositional analysis and bioinformatic mapping. Our results demonstrate that sonication does not significantly alter overall proteome identification or the recovery of membrane proteins; however, it induces extensive and non-random protein fragmentation. Sonication caused an approximately three-fold increase in the abundance of >45 kDa protein-derived fragments migrating into the <40 kDa fraction, and 1,620 high-molecular-weight (MW) proteins were uniquely detected in the lower-MW fraction upon sonication, an eight-fold increase over non-sonicated controls. Peptide-level amino acid composition analysis revealed subtle but directional shifts in the sonication-derived fragments. This residue-level signature is reinforced by two orthogonal structural analyses (MobiDB peptide-level mapping and protein-level profiling using metapredict V3 software), which show that sonication-susceptible proteins harbor more than twice the disordered content of length-matched controls (median 40% vs. 18%). This study identifies a previously unrecognized "structural bias" whereby intrinsically disordered region (IDR)-rich proteins are selectively compromised during sample preparation. Because these fragments are indistinguishable from enzymatic digestion products in conventional bottom-up proteomics, the underlying structural damage is effectively masked in global quantitative datasets, potentially distorting biological interpretations related to protein size, isoforms, and stability, particularly for IDR-rich classes, such as transcription factors and signaling molecules. We propose that optimizing and standardizing sonication parameters is essential for ensuring the accuracy and reproducibility of quantitative proteomic analyses.

Cancer cell lines are widely used in preclinical research, yet the clinical translation of findings from cell lines remains limited. Identifying cell lines that best resemble patient tumors requires integration of molecular profiles across biologically distinct sample types. Recent advances in transcriptomic integration have demonstrated the potential of deep learning for aligning data across different sample types. However, comparable approaches for proteomic data integration remain lacking, potentially because of the prevalence of missing values in proteomic datasets. Here, we introduce ProtInt, a deep learning-based framework that integrates proteomic data from cell lines and patient tumors by combining principles from proteomic imputation and transcriptomic integration methods. We applied ProtInt to integrate label-free proteomic profiles from 771 cancer cell lines and 550 treatment-naive tumors. ProtInt outperformed batch correction and transcriptomic integration methods in aligning cell line and tumor proteomes. Comparison of the cell line proteomes before and after integration revealed recurrent increase of proteins associated with immune reaction, cell-cell communication, and interaction with the extracellular matrix, and reduction of proteins involved in transcription, post-transcriptional processing, and mitochondrial gene expression as proteomes of cell lines were adapted to resemble tumors. These results establish ProtInt as a framework for joint analysis of proteomic datasets across distinct sample types and may facilitate the identification of cell lines best suited for clinically relevant studies.

Protein secretion represents a key functional output of cellular signalling, capturing dynamic responses to stimulation and pharmacological perturbation that shape immune behaviour. In macrophages, activation of Toll-like receptors (TLRs) drives tightly regulated secretion programmes that mediate inflammatory responses and provide a biologically meaningful readout of pathway activity. Whilst mass spectrometry (MS)-based secretomics enables unbiased profiling of these processes, broader application in drug discovery remains constrained by sample preparation workflows that limit scalability. Here, we describe a plate-based in-solution digestion and solid-phase extraction (ISD-SPE) workflow that enables 96-well processing of conditioned media for integrated proteome and secretome analysis from the same sample well. Benchmarking against a precipitation-based approach demonstrated comparable proteomic depth with improved quantitative reproducibility and robust performance across multiple plates. Coupled with dia-PASEF acquisition, this workflow enabled in-depth profiling of macrophage responses to TLR activation, resolving receptor-specific secretory programmes following TLR3, TLR4 and TLR7/8 activation. Extension of the approach to concentration-response studies enabled quantitative characterisation of pharmacological perturbation across intracellular and extracellular protein landscapes, revealing both shared and compartment-specific responses to TLR inhibition, as well as differences in apparent potency linked to secretion dynamics. Together, this workflow provides a scalable strategy for integrated analysis of intracellular signalling and downstream protein secretion, enabling systems-level characterisation of inflammatory responses and compound mechanisms of action.

High-throughput data-independent acquisition (DIA) workflows paired with short chromatographic separations are increasingly adopted for systems biology and clinical proteomics. However, narrower peak widths from rapid separations demand faster mass spectrometer cycle times to maintain quantitative depth and reproducibility. The synchro-PASEF acquisition mode on timsTOF mass spectrometers diagonally scans across ion mobility and m/z space, enabling efficient sampling of the precursor ion cloud with shortened cycle times. While synchro-PASEF has demonstrated competitive identification depth for global protein abundance samples compared to conventional dia-PASEF, its performance for phosphoproteomics - where the precursor ion cloud is characteristically broader and bimodally distributed - has not been evaluated. Here, we systematically optimized synchro-PASEF methods for phosphoproteomics and benchmarked performance against two dia-PASEF methods across three sub-hour separations. We found that synchro-PASEF performance depends critically on balancing diagonal window number, total isolation width, and gradient length, with longer gradients favoring more windows for selectivity and shorter gradients favoring fewer windows to preserve sampling frequency. An optimized configuration quantified over 19,000 localized phosphosites using a 23-minute separation. Retention time summation (RTsum) with a factor of 2 increased phosphopeptide identifications by 5-20% and reduced phosphosite-level coefficients of variation by up to 30% across all dia-PASEF and synchro-PASEF methods tested. Using {beta}2-adrenergic receptor (B2AR) activation as a signaling model, we demonstrate that label-free DIA phosphoproteomics can be used to model phosphoproteomics dose-response relationships, showing that synchro-PASEF and dia-PASEF produce highly concordant phosphoproteomic responses, with comparable numbers of responding phosphosites, similar effect sizes, and nearly identical predicted protein kinase A (PKA) substrates downstream of the activated B2AR. While synchro-PASEF did not surpass optimized dia-PASEF in identification depth, its comparable biological performance and amenability to post-acquisition optimization through RTsum support its utility for high-throughput phosphoproteomics. This work provides a transferable framework for synchro-PASEF method optimization and demonstrates the broad utility of retention time summation for PASEF-based phosphoproteomics workflows.

Inter-organellar communication is crucial for cellular function. Inside the cell, organelles interact with each other via membrane contact sites (MCSs). These structures mediate the close apposition of two organellar membranes to allow for the exchange of metabolites, ions and lipids. Most of what is known about MCSs comes only from a handful of well-studied metazoans, particularly yeast and mammals. Apicomplexans are parasites that drive human disease throughout the world. Yet, little is known about the makeup or function of their MCSs, leaving a gap in our understanding of how organelles communicate beyond conventional model eukaryotes. Here, we used a proximity biotinylation approach to map the surface proteome of three organelles in the model apicomplexan Toxoplasma gondii: the apicoplast--a non-photosynthetic plastid found only in apicomplexans--its single mitochondrion and the endoplasmic reticulum. By subtracting a cytosolic spatial reference, our high-stringency proteomic analysis uncovered candidate proteins localized simultaneously to multiple organellar surfaces suggesting their role as MCS components. We then validate our approach by characterizing a candidate involved in the association between the apicoplast and the mitochondrion. Overall, our findings provide a valuable approach to identify MCSs in apicomplexans and set the stage to apply our approach to other organelles in these pathogens. HighlightsO_LIGeneration of surface proteomes for the apicoplast, mitochondrion, and ER in Toxoplasma gondii C_LIO_LIMapping of the first endoplasmic reticulum and mitochondrial surface proteomes in T. gondii C_LIO_LIIdentified novel membrane contact site candidate proteins C_LIO_LIValidated a membrane contact site candidate mediating mitochondrion-apicoplast interactions C_LI

We performed a comprehensive quantitative proteomic analysis of mouse oocytes using as few as 40 oocytes per condition, comparing wild-type and separase knockout oocytes at metaphase I and metaphase II. To this end, we generated a deep proteomic library spanning oocyte cell cycle stages, enabling the identification of numerous phosphosites without phosphopeptide enrichment. We further combined data-dependent (DDA) and data-independent (DIA) acquisition strategies, analyzed through multiple software pipelines in both library-based and library-free modes. Our results reveal extensive proteome remodeling during the metaphase I to metaphase II transition in wild-type oocytes, consistent with dynamic regulation of meiotic processes. As a proof of concept for our workflow, we asked whether separase knockout oocytes--unable to separate chromosomes in meiosis I--progress into meiosis II. Direct comparison of wild-type and separase knockout oocytes at the metaphase II stage revealed minimal global differences, supporting the idea that both conditions converge toward a comparable metaphase II-like cellular state despite distinct chromosomal configurations. However, at a finer scale, specific alterations were detected among chromosome-associated proteins. Notably, Meikin was enriched in separase-deficient metaphase II oocytes, consistent with defective separase-dependent cleavage and subsequent turnover. More broadly, several proteins involved in chromosome organization displayed behavior similar to Meikin, suggesting that separase activity regulates multiple substrates to orchestrate chromosome segregation during female meiosis.

Equitable access to large sample cohorts for robust, high-throughput proteomics for biomarker discovery is a major barrier to widescale clinical implementation. Dried blood spots (DBS) offer a minimally invasive alternative to venous blood draws, enabling at-home microsampling (<50 {micro}L) for centralised analysis, thus enhancing research participation. This approach is particularly relevant for under-represented groups, including children, the elderly, minority backgrounds and those with long-term health conditions such as chronic kidney disease (CKD), where disease fluctuations may occur outside the clinic, and vein preservation is critical. Proteomic analysis has demonstrated great utility in monitoring disease progression, and for biomarker/therapeutic target discovery. However, liquid chromatography-tandem mass spectrometry (LC-MS/MS) of whole blood is hindered by the wide dynamic range and the relatively high abundance of proteins such as haemoglobin, compromising biomarker discovery. Here, we establish an optimised workflow for protein extraction and haemoglobin depletion from microsamples obtained using DBS, enabling sensitive and high-throughput proteomic analysis. We demonstrate that haemoglobin depletion increases protein identifications by [~]50%, mitigating ion suppression and dynamic range effects, enabling the identification of putative biomarkers from patients with stage 5 CKD on dialysis. We also evaluated a commercial cell-free DBS device which yielded a sample more representative of plasma compared to traditional DBS and enabled greater depletion of haemoglobin compared to traditional DBS with haemoglobin depletion methods. Our findings offer a scalable approach for biomarker discovery, facilitating remote, longitudinal clinical studies.

Dried blood spots (DBS) are well-established microsamples used in clinical testing and newborn screening. However, their use in deep proteomics is hindered by highly abundant blood proteins and inefficient protein recovery from filter paper matrices. The non-targeted analysis of non-specifically DBS-absorbed proteins (NANDA) workflow partially overcomes the impact of abundant blood proteins and has enabled the identification of over 5,000 proteins from DBS samples. Nonetheless, residual abundant proteins, including hemoglobin and fibrinogen, constrain deep proteomic analysis. Therefore, this study aimed to evaluate the effects of the metal chelator ethylenediaminetetraacetic acid (EDTA) on the depth of DBS proteomic analysis. An optimized EDTA-enhanced NANDA protocol that incorporated a 100 mM EDTA wash step was compatible with standard DBS collection procedures and required no modification of current clinical workflows, markedly enhancing the depletion of abundant proteins and facilitating its potential use in clinical and translational settings. When combined with Orbitrap Astral data-independent acquisition mass spectrometry, this approach enabled the single-shot identification of more than 7,000 proteins from DBS samples; to the best of our knowledge, this represents the deepest proteome coverage reported to date, and the workflow further supported high-throughput and highly reproducible analyses. Additionally, its application to mouse disease models revealed disease-specific systemic immune signatures from minimal blood volumes. Collectively, these results establish EDTA-enhanced NANDA as a practical and scalable workflow that overcomes longstanding limitations of DBS proteomics, thereby enabling deep, high-throughput, minimally invasive proteomic profiling across diverse biological and experimental contexts.

Sample preparation increasingly sets the throughput and reproducibility of mass spectrometry (MS)-based proteomics. StageTips (stop-and-go extraction tips) and variants thereof have long been common implements to purify samples, and we recently extended the concept to solid-phase extraction capture (SPEC) tips, in which the entire digestion takes place in sub-microliter volumes. Here we replace the hand-packed bed with a strong anion-exchange (SAX) monolith photopolymerized directly inside the pipette tip from a defined recipe (pSPEC). A liquid-handling robot casts 384 tunable tips in minutes, at low cost and in any format, adding negligibly to the workflows variance. Across biofluids, pSPEC added [~]20% more identifications than in-solution plasma and reached 3,500 protein groups from a single injection of healthy urine and 4,800 from saliva at 100 samples per day, depths usually requiring depletion or fractionation. The same light-cast chemistry should extend to single cells, affinity capture, and population-scale studies.

Subcellular omics technologies now allow us to obtain insights into steady-state localisation and re-localisation of biomolecules in high-throughput. However, robust analysis of these experiments can be slow and challenging. Here, we show that existing approaches to differential localisation fall into two classes, marker-dependent and marker-free, that fail for fundamentally different statistical reasons, with failure modes that are not completely overlapping. We exploit this observation in SANDLE (statistical analysis of differential localisation experiments), which combines a marker-dependent generative model of subcellular niches with a marker-free regression test for changes in fractionation profiles. This dual strategy provides better control of false positives, up to 100-fold reduction in analysis time, and accommodates more complex experimental designs than existing methods. We demonstrate SANDLE's versatility across a wide range of subcellular omics experiments, including drug-treatment responses, cross-species and life-cycle comparisons, post-translationally modified proteoforms, and RNA re-localisation. Co-analysing transcriptome and proteome dynamics during the unfolded protein response, we further reveal lncRNAs whose steady-state localisation is condition-dependent. By addressing key methodological limitations, SANDLE enables broad applications of subcellular omics from fundamental biology to clinical research.

The discovery of alternative proteins (AltProts), translated from non-canonical ORFs, has expanded the human proteome and revealed a hidden layer known as the "ghost proteome". Despite increasing evidence, AltProts detection remains challenging due to their small size, physicochemical heterogeneity, and lack of annotation. Here, we developed an integrated bioinformatic and proteomic workflow to benchmark the detection of reference proteins (RefProts), isoforms, and alternative microproteins (MicroAltProts) in colorectal cancer cells using four extraction protocols--HCl, RIPA buffer, RIPA with chloroform, and RIPA followed by 30 kDa filtration--combined with high-resolution data-independent acquisition mass spectrometry. We identified and quantified using the Orbitrap Astral mass spectrometer a total of 66,438 peptides corresponding to 12,584 different protein groups across methods, with RIPA-based extraction approaches providing the most comprehensive coverage. To reduce redundancy in the OpenProt database and focus on MicroAltProts, we curated the dataset by removing known isoforms and long proteins, yielding a non-redundant set of 183,937 MicroAltProts. K-means clustering based on eight ProtParam-derived features grouped MicroAltProts into four physicochemical clusters. Among them, 43 MicroAltProts (<200 amino acids) were experimentally validated by mass spectrometry and classified into tiers following recent recommended international guidelines. Cluster assignment of detected MicroAltProts revealed that HCl extraction favored disordered, alkaline proteins, while RIPA-based protocols enabled the identification of membrane-associated and amphipathic -helical MicroAltProts. Structural prediction indicated the presence of diverse folding determinants, including transmembrane helices, disordered regions, and nucleic acid-binding-like motifs. Altogether, this study provides a roadmap framework for the unbiased simultaneous detection of RefProts, isoforms, and AltProts, and supports a broader functional role for MicroAltProts.

Retinoic acid signaling is critical for cardiac development and homeostasis. Dysregulation of all-trans retinoic acid metabolism contributes to vascular atherogenesis, restenosis, calcification, and heart failure. Therefore, assessment of proteins involved in retinoid metabolism and signaling has gained interest for identifying potential biomarkers and therapeutic targets in cardiovascular disease. However, quantifying these proteins remains challenging due to limitations of antibody-based methods. We developed a targeted proteomics approach using SureQuant internal standard-triggered parallel reaction monitoring mass spectrometry to profile these proteins. We designed a panel of 80 stable isotope-labeled (heavy) peptides representing proteins involved in retinoid signaling and metabolism, with sequences applicable to human samples and conserved across multiple species. Survey experiments using directed data-dependent acquisition on the Orbitrap Fusion Lumos mass spectrometer determined precursor and product ion masses for each heavy peptide, which were programmed into the SureQuant method for continuous monitoring. Upon detection of these heavy internal standards, the instrument transitions to a targeted PRM acquisition mode in which repeated high-resolution MS/MS spectra of both endogenous (light) and heavy peptides are acquired. Using this method, retinoid pathway-associated proteins were quantified to as low as 10 attomoles for selected targets across multiple tissues and developmental stages. Distinct tissue-specific retinoid metabolic networks were identified across lung, liver, retinal cell lines and cardiac tissues. Developmental profiling of mouse and rat hearts revealed remodeling of retinoid pathway proteins from embryonic to postnatal and adult stages, suggesting a functional transition from retinoid-driven cardiac development toward maintenance of retinoid homeostasis in the mature heart.

Despite its importance in cellular signaling and protein fate, the detection of protein ubiquitination in proteomics experiments presents many challenges for researchers. Importantly, current techniques that often rely on antibodies specific for lysine sidechain modifications may miss non-canonical ubiquitination sites in experiments. We envisioned a strategy that uses sortase, a bacterial transpeptidase enzyme, to selectively modify ubiquitination sites with a Biotin tag for enrichment and downstream proteomics experiments. In this work, we demonstrate our ability to selectively modify N-terminal diglycine remnants in digested proteins with a Biotin-modified peptide, enabling downstream enrichment of previously ubiquitinated proteins. We show this proof of concept on several recombinant proteins, revealing a site of autoubiquitination in the E2 conjugating enzyme Ubc13. We show that elution of the enriched peptides can be achieved by using common guanidinium elutions or by leveraging the reversibility of sortase. Finally, we include a bifunctional peptide that is labile to trypsinization to better streamline this strategy for downstream proteomics approaches. We envision that this approach will provide an accessible strategy for the detection of ubiquitinated proteins in proteomics experiments, with the goal of enabling researchers to better detect noncanonical protein ubiquitination.

Dual proteome-metabolome measurements from limited samples typically require sample splitting or sequential analyses using electrospray ionization mass spectrometry (ESI-MS). Here we show that capillary electrophoresis (CE) can avoid that tradeoff by organizing predominantly singly charged small molecules and multiply charged peptides into partially resolved, analyte-class-dependent regions of migration time-m/z space. Leveraging this intrinsic electrophoretic organization together with charge- and m/z-resolved precursor selection, we developed a single-run CE-ESI-MS workflow that combines single-vial sample processing with class-resolved tandem MS acquisition. In a HeLa digest spiked with 17 amino acids, the integrated analysis detected all amino acids while preserving proteomic depth relative to a dedicated proteomics run, yielding 1,221 versus 1,227 cumulative protein groups. Applied to identified single Xenopus laevis blastomeres, the method provided matched readouts of 86 metabolite features together with 1,097 and 1,083 protein groups from D1.1 and V1.1 cells, respectively. The paired measurements resolved cell-type-dependent molecular differences and mapped protein and metabolite changes into shared pathway context. These results establish analyte-class-dependent electrophoretic organization coupled to class-resolved MS acquisition as an analytical basis for single-run proteome-metabolome analysis by CE-ESI-MS in material-limited samples.

Proteins undergo dynamic conformational rearrangements and interactions that are central to their biological functions. Quantitative crosslinking mass spectrometry enables the analysis of those dynamics and molecular interactions, but rigorous confidence assessment and empirical validation strategies for quantitative measurements remain underdeveloped, and integrated analysis of complementary structural features, including monolinks and protein-RNA adducts, remains limited. Here we present a data-independent acquisition (DIA)-based framework for quantitative crosslinking mass spectrometry (DIA-QCLMS) that combines optimized acquisition strategies, crosslink-aware spectral libraries and empirical false-discovery-rate (FDR) validation. The workflow supports crosslinks, monolinks and protein-RNA adducts and integrates spectral-library generation from two crosslinking search engines (xiSEARCH and MSAnnika). To enable robust confidence assessment in DIA data, we developed a four-state target-decoy spectral library strategy that explicitly models target-target, target-decoy, decoy-target and decoy-decoy crosslink spectra. Experimental entrapment datasets enabled empirical validation of confidence estimation, whereas benchmarking with PhoX-crosslinked Cas9 demonstrated improved quantitative completeness and reproducibility compared with data-dependent acquisition. Application of the workflow to the ATP-dependent RNA helicase UAP56 (DDX39B) resolved ligand-dependent changes in intramolecular restraints, residue accessibility and candidate RNA-contact sites associated with the transition from an open to a clamped conformation. These results establish DIA-QCLMS as a scalable framework for quantitative structural proteomics and provide practical strategies for confidence-controlled analysis of dynamic protein interactions and conformational states.

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

BackgroundThe developmental pattern of the crystalline lens provides a unique model to study biological aging and its effects on the posttranslational modification of long-lived proteins. The orderly differentiation of lens fiber cells leads to a spatiotemporal gradient where mature, organelle-free fiber cells are packed in the lens nucleus surrounded by con-centric rings of successively younger fiber cells in the cortex. MethodsPig lenses were separated into six layers by dissolution in a hypotonic buffer. The changes in protein abundance, oxidation, and phosphorylation that occur across the spatiotemporal gradient of the lens were assessed quantitatively by using data independent acquisition label-free proteomic analysis of these six fractions. ResultsExpected changes in protein abundance of major lens protein which reflect the maturation process of lens fiber cells across the spatiotemporal gradient were found. Significant differences were noted in phosphorylation sites on crystallins, phakinin, and actin. Significant changes in oxidation of residues on essential lens proteins, as well as several glycolytic enzymes, were found across the spatiotemporal gradient. ConclusionDissolution of the lens followed by high resolution data-independent acquisition proteomics is a powerful technique for spatial mapping of protein abundance and posttranslational modification changes in the lens. The oxidation and phosphorylation sites noted in this study may play important roles in both lens development and cataractogenesis.

Top-down proteomics (TDP) advances biomedical research by providing a birds-eye view of proteoforms in cells, tissues, and biofluids. Thousands of proteoforms can be characterized using well-established TDP technologies, and potential proteoform biomarkers of diseases have been discovered. However, there is a lack of an easy and biologically informative approach to present the quantitative global TDP data. Here, we present proteoform barcode as a straightforward visualization approach that simultaneously displays proteoform abundance and their associated Gene Ontology (GO) biological processes, converting a list of proteoforms to a biologically informative image. The proteoform barcode allows 1) a global view of proteoforms (i.e., relative abundance and functional information) in complex biological systems (i.e., bacteria, yeast, human cells, and human plasma) and 2) the accurate distinction of samples in diverse biological conditions (i.e., control and disease) assisted by machine learning approaches. The proteoform barcode, assisted by the random forest model, accurately separated the human plasma samples of healthy controls and early-stage breast cancer. The data demonstrates the high potential of the proteoform barcode-based approach for early diagnosis of diseases in an easy and biologically informative manner.