Advances In Proteomics Research Articles

Unveiling Novel Insights in Helminth Proteomics: Advancements, Applications, and Implications for Parasitology and Beyond

Helminths have developed intricate mechanisms to survive and evade the host’s immune responses. Hence, understanding the excretory-secretory products (ESPs) by helminths is crucial for developing control tools, including drug targets, vaccines, and potential therapies for inflammatory and metabolic disorders caused by them. Proteomics, the large-scale analysis of proteins, offers a powerful approach to unravel the complex proteomes of helminths and gain insights into their biology. Proteomics, as a science that delves into the functions of proteins, has the potential to revolutionize clinical therapies against parasitic infections that have developed anthelminthic resistance. Proteomic technologies lay a framework for accompanying genomic, reverse genetics, and pharmacokinetic approaches to provide more profound or broader coverage of the cellular mechanisms that underlie the response to anthelmintics. With the development of vaccines against helminth infections, proteomics has brought a major change to parasitology. The proteome of helminths can be analyzed comprehensively, revealing the complex network of proteins that enable parasite survival and pathogenicity. Furthermore, it reveals how parasites interact with hosts’ immune systems. The current article reviews the latest advancements in helminth proteomics and highlights their valuable contributions to the search for anthelminthic vaccines.

Novel Biomarkers for Kidney Disease: New Frontiers in Early Diagnosis and Monitoring

Kidney diseases pose a significant threat to global public health, with early diagnosis and accurate monitoring being crucial for improving patient outcomes. However, traditional kidney disease markers such as serum creatinine and blood urea nitrogen have limitations in sensitivity and specificity. In recent years, rapid advancements in molecular biology and proteomics have led to the discovery of a series of novel biomarkers that are gradually being applied in clinical practice. This review examines novel kidney disease biomarkers, including cystatin C (Cys-C), neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule-1 (KIM-1). These markers not only reflect kidney injury earlier and more accurately but may also provide important bases for kidney disease classification, severity assessment, and prognosis prediction. Additionally, this paper explores the potential of multi-marker combined detection strategies in improving diagnostic accuracy. Although these novel markers show immense potential, they still face challenges such as standardization and cost-effectiveness before widespread clinical application. Future research should focus on optimizing detection methods, establishing clinical decision thresholds, and evaluating the value of these markers in personalized medicine, thereby paving new paths for precise diagnosis and treatment of kidney diseases.

Advances in understanding the roles of plant HAT and HDAC in non-histone protein acetylation and deacetylation.

This review focuses on HATs and HDACs that modify non-histone proteins, summarizes functional mechanisms of non-histone acetylation as well as the roles of HATs and HDACs in rice and Arabidopsis. The growth and development of plants, as well as their responses to biotic and abiotic stresses, are governed by intricate gene and protein regulatory networks, in which epigenetic modifying enzymes play a crucial role. Histone lysine acetylation levels, modulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), are well-studied in the realm of transcriptional regulation. However, the advent of advanced proteomics has unveiled that non-histone proteins also undergo acetylation, with its underlying mechanisms now being clarified. Indeed, non-histone acetylation influences protein functionality through diverse pathways, such as modulating protein stability, adjusting enzymatic activity, steering subcellular localization, influencing interactions with other post-translational modifications, and managing protein-protein and protein-DNA interactions. This review delves into the recent insights into the functional mechanisms of non-histone acetylation in plants. We also provide a summary of the roles of HATs and HDACs in rice and Arabidopsis, and explore their potential involvement in the regulation of non-histone proteins.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Global awareness of antigenic peptides - Unraveling the impact on sickle cell anemia patients in the presence of pathogens: A narrative review.

Sickle cell anemia (SCA) is a genetic blood disorder characterized by the production of abnormal hemoglobin S (HbS), leading to sickle-shaped red blood cells and various complications, including increased susceptibility to infections. The presence of antigenic peptides, short amino acid sequences derived from pathogens or altered self-proteins, plays a crucial role in immune responses. This review explores the global awareness of antigenic peptides, their role in immune responses in SCA patients, and the challenges and opportunities in managing infections within this vulnerable population. Antigenic peptides are central to the adaptive immune response, facilitating the recognition and elimination of pathogens by T-cells. In SCA, altered antigen presentation and impaired T-cell responses due to chronic inflammation, functional asplenia, and ongoing hemolysis contribute to increased susceptibility to infections. Pathogens such as Streptococcus pneumoniae and Haemophilus influenzae pose significant risks to SCA patients, highlighting the importance of robust immune responses mediated by antigenic peptides. Strategies such as vaccination and immunotherapy aim to enhance immune function by targeting specific antigenic peptides, thereby reducing infection rates and improving patient outcomes. Advances in genomics and proteomics offer insights into individual variations in antigen presentation and immune responses, guiding the development of tailored therapeutic interventions. Global collaborations are essential to address disparities in healthcare access and implement effective preventive measures, ensuring equitable outcomes for SCA patients worldwide.

Direct Cardiac Reprogramming in the Age of Computational Biology.

Heart disease continues to be one of the most fatal conditions worldwide. This is in part due to the maladaptive remodeling process by which ischemic cardiac tissue is replaced with a fibrotic scar. Direct cardiac reprogramming presents a unique solution for restoring injured cardiac tissue through the direct conversion of fibroblasts into induced cardiomyocytes, bypassing the transition through a pluripotent state. Since its inception in 2010, direct cardiac reprogramming using the transcription factors Gata4, Mef2c, and Tbx5 has revolutionized the field of cardiac regenerative medicine. Just over a decade later, the field has rapidly evolved through the expansion of identified molecular and genetic factors that can be used to optimize reprogramming efficiency. The integration of computational tools into the study of direct cardiac reprogramming has been critical to this progress. Advancements in transcriptomics, epigenetics, proteomics, genome editing, and machine learning have not only enhanced our understanding of the underlying mechanisms driving this cell fate transition, but have also driven innovations that push direct cardiac reprogramming closer to clinical application. This review article explores how these computational advancements have impacted and continue to shape the field of direct cardiac reprogramming.

Unveiling novel serum biomarkers in intrahepatic cholangiocarcinoma: a pilot proteomic exploration.

Recent advancements in proteomics have shown promise in identifying biomarkers for various cancers. Our study is the first to compare the serum proteomes of intrahepatic cholangiocarcinoma (iCCA) with cirrhosis (CIR), primary sclerosing cholangitis (PSC), and hepatocellular carcinoma (HCC), aiming to identify a proteomic signature that can effectively distinguish among these conditions. Utilizing high-throughput mass spectrometry on serum samples, we identified 845 proteins, of which 646 were suitable for further analysis. Unique clustering patterns were observed among the five groups, with significant proteomic differences. Our key findings include: S100 calcium-binding protein A9 (S100A9) and haptoglobin (HP) were more abundant in iCCA, while intercellular adhesion molecule 2 (ICAM2) was higher in HCC. Serum amyloid A1 (SAA1) and A4 (SAA4) emerged as potential biomarkers, with SAA1 significantly different in the iCCA vs healthy controls (HC) comparison, and SAA4 in the HCC vs HC comparison. Elevated levels of vascular cell adhesion molecule 1 (VCAM-1) in HCC suggested its potential as a differentiation and diagnostic marker. Angiopoietin-1 receptor (TEK) also showed discriminatory and diagnostic potential in HCC. ELISA validation corroborated mass spectrometry findings. Our study underscores the potential of proteomic profiling in distinguishing iCCA from other liver conditions and highlights the need for further validation to establish robust diagnostic biomarkers.

Research Progress on PGE and Retrograde Signaling Under Environmental Stress

Plastid gene expression (PGE) and retrograde signaling are pivotal processes in plant cellular function, particularly under environmental stress conditions. This literature review synthesizes current research on the role of PGE and retrograde signaling in plant stress responses. Environmental stresses such as drought, salinity, and extreme temperatures significantly alter PGE and trigger retrograde signaling pathways, which in turn modulate nuclear gene expression to maintain cellular homeostasis. Key insights include the identification of the GENOMES UNCOUPLED (GUN) proteins, especially GUN1, as crucial mediators that accumulate under stress and interact with plastid proteins to transmit signals to the nucleus. Technological advances in genomics, transcriptomics, and proteomics have provided comprehensive insights into these mechanisms, highlighting the dynamic nature of plant stress responses. Understanding these processes offers potential strategies for enhancing crop resilience through genetic engineering and other biotechnological interventions. This review underscores the importance of continued research in this field to improve crop productivity and sustainability under adverse environmental conditions.

Prognostic Significance of Elevated UCHL1, SNRNP200, and PAK4 Expression in High-Grade Clear Cell Renal Cell Carcinoma: Insights from LC-MS/MS Analysis and Immunohistochemical Validation.

Recent advancements in proteomics have enhanced our understanding of clear cell renal cell carcinoma (CCRCC). Utilizing a combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by immunohistochemical validation, we investigated the expression levels of UCHL1, PAK4, and SNRNP200 in high-grade CCRCC samples. Our analysis also integrated Reactome pathway enrichment to elucidate the roles of these proteins in cancer-related pathways. Our results revealed significant upregulation of UCHL1 and SNRNP200 and downregulation of PAK4 in high-grade CCRCC tissues compared to non-cancerous tissues. UCHL1, a member of the ubiquitin carboxy-terminal hydrolase family, showed variable expression across different tissues and was notably involved in the Akt signaling pathway, which plays a critical role in cellular survival in various cancers. SNRNP200, a key component of the RNA splicing machinery, was found to be essential for proper cell cycle progression and possibly linked to autosomal dominant retinitis pigmentosa. PAK4's role was noted as critical in RCC cell proliferation and invasion and its expression correlated significantly with poor progression-free survival in CCRCC. Additionally, the expression patterns of these proteins suggested potential as prognostic markers for aggressive disease phenotypes. This study confirms the upregulation of UCHL1, SNRNP200, and PAK4 as significant factors in the progression of high-grade CCRCC, linking their enhanced expression to poor clinical outcomes. These findings propose these proteins as potential prognostic markers and therapeutic targets in CCRCC, offering novel insights into the molecular landscape of this malignancy and highlighting the importance of targeted therapeutic interventions.

Comparative Cross-Kingdom DDA- and DIA-PASEF Proteomic Profiling Reveals Novel Determinants of Fungal Virulence and a Putative Druggable Target.

Accurate and reliable detection of fungal pathogens presents an important hurdle to manage infections, especially considering that fungal pathogens, including the globally important human pathogen, Cryptococcus neoformans, have adapted diverse mechanisms to survive the hostile host environment and moderate virulence determinant production during coinfections. These pathogen adaptations present an opportunity for improvements (e.g., technological and computational) to better understand the interplay between a host and a pathogen during disease to uncover new strategies to overcome infection. In this study, we performed comparative proteomic profiling of an in vitro coinfection model across a range of fungal and bacterial burden loads in macrophages. Comparing data-dependent acquisition and data-independent acquisition enabled with parallel accumulation serial fragmentation technology, we quantified changes in dual-perspective proteome remodeling. We report enhanced and novel detection of pathogen proteins with data-independent acquisition-parallel accumulation serial fragmentation (DIA-PASEF), especially for fungal proteins during single and dual infection of macrophages. Further characterization of a fungal protein detected only with DIA-PASEF uncovered a novel determinant of fungal virulence, including altered capsule and melanin production, thermotolerance, and macrophage infectivity, supporting proteomics advances for the discovery of a novel putative druggable target to suppress C. neoformans pathogenicity.

Plant Circadian Clocks: Unravelling the Molecular Rhythms of Nature

The circadian clock is a fundamental biological mechanism that allows organisms to synchronize their internal processes with the external environment, thereby optimizing growth, development, and physiology. In plants, circadian rhythms govern various aspects of their life cycle, including germination, leaf movement, flowering, and responses to environmental cues such as light and temperature. Understanding the molecular mechanisms underlying plant circadian clocks is essential not only for elucidating fundamental principles of plant biology but also for applications in agriculture and biotechnology. This review delves into the intricate molecular networks that comprise plant circadian clocks, focusing on key components and their interactions. At the core of these clocks are transcription-translation feedback loops (TTFLs) involving a set of clock genes. The central oscillator, composed of genes such as CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), LATE ELONGATED HYPOCOTYL (LHY), TIMING OF CAB EXPRESSION 1 (TOC1), and PSEUDO-RESPONSE REGULATOR (PRR) genes, drives rhythmic expression of downstream clock-controlled genes (CCGs). The interplay between positive and negative regulators within the TTFLs generates robust oscillations with a period of approximately 24 hours. Additionally, post-translational modifications, protein-protein interactions, and chromatin remodeling contribute to fine-tuning the circadian system, allowing plants to adapt to changing environmental conditions. Light perception through photoreceptors such as phytochromes and cryptochromes plays a crucial role in entraining the circadian clock to external cues, ensuring synchronization with the day-night cycle. Furthermore, recent advancements in omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have provided unprecedented insights into the complexity and plasticity of plant circadian clocks. Integration of multi-omics data has facilitated the construction of comprehensive regulatory networks and computational models, enabling predictive understanding of clock function and behavior under diverse conditions. Overall, unraveling the molecular rhythms of plant circadian clocks not only enhances our knowledge of fundamental biological processes but also holds promise for improving crop productivity, stress resilience, and sustainability in agriculture through targeted manipulation of clock components and their associated pathways. Future research endeavors will undoubtedly continue to unveil the intricacies of these fascinating timekeeping mechanisms, further enriching our understanding of the molecular rhythms of nature.

Dietary modulation of microRNAs in insulin resistance and type 2 diabetes

The prevalence of type 2 diabetes is increasing worldwide. Various molecular mechanisms have been proposed to interfere with the insulin signaling pathway. Recent advances in proteomics and genomics indicate that one such mechanism involves the post-transcriptional regulation of insulin signaling by microRNA (miRNA). These noncoding RNAs typically induce messenger RNA (mRNA) degradation or translational repression by interacting with the 3′ untranslated region (3′UTR) of target mRNA. Dietary components and patterns, which can either enhance or impair the insulin signaling pathway, have been found to regulate miRNA expression in both in vitro and in vivo studies. This review provides an overview of the current knowledge of how dietary components influence the expression of miRNAs related to the control of the insulin signaling pathway and discusses the potential application of these findings in precision nutrition.

Evolving concepts in meningioma management in the era of genomics.

Meningioma is the most common type of primary brain tumor. Surgical resection followed by surveillance is the first-line treatment for the majority of symptomatic meningiomas; however, recent advances in molecular sequencing, DNA methylation, proteomics, and single-cell sequencing provide insights into further characterizing this heterogeneous group of tumors with a wide range of prognoses. A subset of these tumors are highly aggressive and cause severe morbidity and mortality. Therefore, identifying those individuals with a poor prognosis and intervening are critical. This review aims to help readers interpret the molecular profiling of meningiomas to identify patients with worse prognoses and guide the management and strategy for surveillance.

A New Era of Discovery: How Artificial Intelligence has Revolutionized the Biotechnology

In the growing field of biotechnology, artificial intelligence (AI) has emerged as a pivotal force of innovation, unveiling a new era of discovery and advancement. The convergence of AI with biotechnology has revolutionized the landscape of scientific research and development. The dynamic interplay between AI and biotechnology highlights the transformative power of AI techniques in accelerating advancements in drug discovery, development, personalized medicine, biomolecular engineering, bioprocessing, CRISPR technology, genome editing, genomics, proteomics, metabolomics, transcriptomic, and AI-enabled robotics in biotechnology. This integration of AI with biotechnology helps us combat global challenges and offers environmentally friendly and sustainable solutions like bioremediation, bioplastics, biodiesel, and biofiltration. Current examples of these problems include waste management, air pollution, healthcare, clean water, energy access, sustainable practices, conservation of biodiversity, and ecosystems. This review article provides a comprehensive analysis, drawing on current literature, case studies, and emerging trends, to highlight the transformative potential of AI in reshaping the biotechnological landscape. It also addresses the challenges and opportunities associated with this AI-powered transformation, discussing future directions, ethical considerations, and the need for human-AI collaboration to ensure responsible and sustainable progress for a brighter future.

Cryoprotectant-specific alterations in the proteome of Siberian sturgeon spermatozoa induced by cryopreservation

Cryopreservation is crucial for conserving genetic diversity in endangered species including the critically endangered group of sturgeons (Acipenseridae), but it can compromise sperm quality and protein profiles. Although cryopreservation with dimethyl sulfoxide (DMSO) and methanol (MeOH) results in the recovery of good post-thaw motility, DMSO-preserved sperm show reduced fertilization ability. This study was conducted in Siberian sturgeon as a model for Acipenserid fishes to explore the effects of DMSO and MeOH on the proteome of semen using advanced proteomics methods—liquid chromatography‒mass spectrometry and two-dimensional difference gel electrophoresis. We analyzed the proteomic profiles of fresh and cryopreserved spermatozoa and their extracellular medium and showed that cryopreservation decreases motility and viability and increases reactive oxygen species levels, membrane fluidity, and acrosome damage. Despite having similar post-thaw semen motility, sperm treated with DMSO had significantly lower fertilization success (6.2%) than those treated with MeOH (51.2%). A total of 224 and 118 differentially abundant proteins were identified in spermatozoa preserved with MeOH and DMSO, respectively. MeOH-related proteins were linked to chromosomal structure and mitochondrial functionality, while DMSO-related proteins impacted fertilization by altering the acrosome reaction and binding of sperm to the zona pellucida and nuclear organization. Additionally, cryopreservation led to alterations in the proacrosin/acrosin system in both cryoprotectants. This study provides the first comprehensive proteomic characterization of Siberian sturgeon sperm after cryopreservation, offering insights into how cryoprotectants impact fertilization ability.

D-sORF: Accurate Ab Initio Classification of Experimentally Detected Small Open Reading Frames (sORFs) Associated with Translational Machinery.

Small open reading frames (sORFs; <300 nucleotides or <100 amino acids) are widespread across all genomes, and an increasing variety of them appear to be translating from non-genic regions. Over the past few decades, peptides produced from sORFs have been identified as functional in various organisms, from bacteria to humans. Despite recent advances in next-generation sequencing and proteomics, accurate annotation and classification of sORFs remain a rate-limiting step toward reliable and high-throughput detection of small proteins from non-genic regions. Additionally, the cost of computational methods utilizing machine learning is lower than that of biological experiments, and they can be employed to detect sORFs, laying the groundwork for biological experiments. We present D-sORF, a machine-learning framework that integrates the statistical nucleotide context and motif information around the start codon to predict coding sORFs. D-sORF scores directly for coding identity and requires only the underlying genomic sequence, without incorporating parameters such as the conservation, which, in the case of sORFs, may increase the dispersion of scores within the significantly less conserved non-genic regions. D-sORF achieves 94.74% precision and 92.37% accuracy for small ORFs (using the 99 nt medium length window). When D-sORF is applied to sORFs associated with ribosomes, the identification of transcripts producing peptides (annotated by the Ensembl IDs) is similar to or superior to experimental methodologies based on ribosome-sequencing (Ribo-Seq) profiling. In parallel, the recognition of putative negative data, such as the intron-containing transcripts that associate with ribosomes, remains remarkably low, indicating that D-sORF could be efficiently applied to filter out false-positive sORFs from Ribo-Seq data because of the non-productive ribosomal binding or noise inherent in these protocols.

A Zwitterionic Detergent and Catalyst-Based Single-Cell Proteomics Using a Loss-Free Microhole-Collection Disc.

Recent advances in single-cell proteomics have solved many bottlenecks, such as throughput, sample recovery, and scalability via nanoscale sample handling. In this study, we aimed for a sensitive mass spectrometry (MS) analysis capable of handling single cells with a conventional mass spectrometry workflow without additional equipment. We achieved seamless cell lysis and TMT labeling in a micro-HOLe Disc (microHOLD) by developing a mass-compatible single solution based on a zwitterionic detergent and a catalyst for single-cell lysis and tandem mass tag labeling without a heat incubation step. This method was developed to avoid peptide loss by surface adsorption and buffer or tube changes by collecting tandem mass tag-labeled peptide through microholes placed in the liquid chromatography injection vials in a single solution. We successfully applied the microHOLD single-cell proteomics method for the analysis of proteome reprogramming in hormone-sensitive prostate cells to develop castration-resistant prostate cancer cells. This novel single-cell proteomics method is not limited by cutting-edge nanovolume handling equipment and achieves high throughput and ultrasensitive proteomics analysis of limited samples, such as single cells.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics.

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

Rapid assay development for low input targeted proteomics using a versatile linear ion trap.

Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4 + and CD8 + T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.