Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona

Surface-engineered nanobeads for regioselective antibody binding: A robust immunoassay platform leveraging catalytic signal amplification

Regulating protein interactions and protein corona formation of nanomaterials is crucial for advancing nanomedicine, where surface engineering of nanomaterials plays a pivotal role in precise control over biological interactions. Here, we present a surface-engineered nanoparticle-based immunoassay platform using carboxyl-enriched polystyrene nanobeads (CEPS) with regioselectively controlled antibody-binding properties. Proteomic analysis and theoretical simulation revealed that CEPS has an enhanced Fc-specific binding affinity for immunoglobulins compared to conventional carboxylated polystyrene beads, with a higher surface carboxyl density critically mediating protein interactions. This regioselective antibody binding with unique Fc-specific affinity eliminates the need for complex surface modifications, streamlining the assay process and broadening the applicability across various immunoassay formats. Additionally, incorporating a palladium catalyst within CEPS enables solvent-triggered on-demand catalytic signal amplification using a leucodye substrate, providing a more stable alternative to enzyme-based methods while significantly enhancing detection sensitivity and stability. The platform demonstrated enhanced performance in detecting clinically relevant biomarkers, including C-reactive protein, interferon-gamma, and the receptor-binding domain of SARS-CoV2, achieving lower detection limits and faster response times compared to conventional enzyme-based ELISA systems. Notably, the CEPS-based assay retained catalytic activity for over 140 days at room temperature, underscoring its potential for reliable, long-term use in diverse diagnostic applications.

Ion exchange- and enrichment-based technology applied to large-scale plasma proteomic analysis of breast cancer neoadjuvant chemotherapy

Mass spectrometry (MS) based proteomics provides unbiased quantification of all proteins in plasma, which can dynamically reflect individual health states in real time. However, large-scale proteomics studies are constrained by the excessive dynamic range of plasma proteome and low throughput. Herein, two kinds of magnetic metal-organic frameworks (MOFs) modified with ion exchange functional groups (denoted as MHP-UiO-66-SAX and MHP-HKUST-1-SCX) were designed and fabricated to exhibit large protein adsorption capability, which were combined with an automated Liquid-handling System, thus realizing in-depth, high-throughput and automated proteomics studies. The constructed workflow could automatically complete the sample preparation before MS within only six hours and nearly a thousand protein groups per sample could be quantified. In the cohort study of nearly one hundred breast cancer neoadjuvant chemotherapy (NC) plasma samples, two differentially expressed proteins previously reported as biomarkers were related with the pathological complete response (PCR) of the breast cancer, demonstrating the feasibility of the developed technology for preparing large-scale clinical samples and exhibiting the potential application in monitoring the effect of chemotherapy.

Data-Independent Acquisition-Based Quantitative Proteomics for Pairwise Comparison of Serum and Plasma

Human blood contains proteins secreted by various organs, but there is no consensus on whether serum or plasma is preferable for proteome studies. Mass spectrometry employing data-independent acquisition has emerged as a transformative methodology in proteomics, enabling reproducible large-scale quantification of proteomes during one LC-MS/MS analytical run and facilitating identification of potential markers and elucidation of biological processes. Here, we profiled the proteome data of ten paired plasma and serum samples in the initial sample set. Functional analysis revealed similarities and differences in biological functions and the preference for different organs between serum and plasma. Furthermore, comparative proteomic analysis highlighted the different proteomic characteristics. Plasma-overrepresented pathways were related to the phagosome and immune, while serum-overrepresented pathways were associated with amino acid metabolism, which were further validated by the follow-up sample set composed of eight paired plasma and serum samples. We have detected potential markers in plasma and serum for various cancers and explored their association with prognosis using data from the TCGA pan-cancer cohort and HPA database. Further assessment is required to validate the reproducibility of the quantification for these markers. Overall, this study highlights the commonality and specificity of plasma and serum at the molecular level, underscoring their respective utility in biological exploration and clinical applications.

Bibliometric analysis of current trends and emerging patterns in the application of nanomaterials for non-small cell lung cancer

BACKGROUND

Lung cancer is one of the most prevalent tumor diagnoses and a leading cause of cancer-related mortality worldwide. Among its two primary subtypes, non-small cell lung cancer (NSCLC) accounts for approximately 80-85% of all lung cancer cases. Over the past decade, a significant number of publications have explored the application of nanomaterials in NSCLC. This study aimed to comprehensively evaluate the current state and evolving trends in research focused on utilizing nanomaterials as potent diagnostic and therapeutic tools for NSCLC.

METHODS

To identify all pertinent publications, we used the Web of Science Core Collection (WoSCC) database. Based on stringent inclusion and exclusion criteria, relevant publications were carefully selected. For the bibliometric and visual analyses, we employed VOSviewer (version 1.6.20), CiteSpace (version 6.1.6), and R-bibliometrix (version 4.3.2).

RESULTS

Our analysis encompassed 1880 studies that fulfilled the inclusion criteria. We observed a steady increase in annual publications from 2014 to June 22, 2024. China, the USA, and India have emerged as leading nations in this field. Notably, the Chinese Academy of Sciences and Wang J stood out as the most influential institutions and authors, respectively. Most publications are featured in The International Journal of Nanomedicine. The keywords used in these publications were closely tied to non-small cell lung cancer and nanomaterials. In the past three years, "green synthesis" exhibited the highest burst strength, while "immune response" and "nanocrystal" represented emerging areas of intense research interest.

CONCLUSION

Through our exhaustive analysis, we synthesized the current research trends and emerging landscapes of nanomaterials in NSCLC. We characterized the publication patterns, pinpointed the most influential nations, institutions, authors, journals, and hot topics related to nanomaterial applications in NSCLC, and proposed potential avenues for future development.

Aptamer-Signatured Nanoparticle Protein Corona for Size-Dependent Fluorescent Barcoding Diagnosis

Cancer-specific nanoparticl protein corona (NPC) opens a new avenue for biomarker discovery and diagnosis. However, the simultaneous detection of multiplex protein biomarkers from NPC is very challenging owing to ultra-low abundance and limited detection probes. Here, an aptamer-signatured NPC (ASNC)-based size-dependent fluorescence barcode is developed for flow cytometry profiling (FBFC) sensing platform to diagnose hepatocellular carcinoma (HCC). First, HCC-specific NPC is obtained from incubating magnetic nanoparticles with clinical serum samples. A 12-aptamer panel is incubated with NPC for the assembly of ASNC. Six aptamers are selected from the preliminary profiling of 30 HCC and healthy controls, resulting in ASNC for subsequent multiplex detection. To achieve simultaneous and orthogonal detection in one pot, advantage of the size-dependent fluorescent microbeads are taken to profile the signature aptamers eluted from NPC via flow cytometry-distinguishable barcodes. With 84 clinical HCC and healthy serum samples, a diagnostic accuracy of 94.12%, have attained which is higher than other single biomarkers or any combinations. Overall, by transferring protein biomarkers to ASNC, a simultaneous and multiplex ASNC-based FBFC platform have developed which holds immense potential in facile and precise cancer diagnosis.

Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomics technologies are rapidly evolving and of critical importance to the field of biomedical research. Here, we report a technical evaluation of six notable plasma proteomics technologies─unenriched (Neat), acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, and Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total, we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4500 proteins detected), while Olink detected ∼2600 proteins. Other MS-based methods ranged from ∼500-2200 proteins detected. In our analysis, Neat, Mag-Net, Seer, and Olink had good reproducibility, while PreOmics and Acid had higher variability (>20% median coefficient of variation). All MS methods showed good linearity with spiked-in C-reactive protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer produced the highest number of quantifiable proteins with a measurable LOD (4407) and LOQ (2696). Olink had the next highest number of quantifiable proteins, with 2002 having an LOD and 1883 having an LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a nonsmall cell lung cancer (NSCLC) cohort. All six methods detected differentially abundant proteins between the cancer and healthy samples but disagreed on which proteins were significant, highlighting the contrast between each method.

Beyond Correlation: Establishing Causality in Protein Corona Formation for Nanomedicine

In contemporary studies on the role of the protein corona in specific biological applications, identifying correlation is widely used to draw conclusions from observations and statistical methods, yet it merely identifies associations without establishing a direct influence between variables. This over reliance on observation can lead to spurious connections where co-occurrence does not imply causation. In contrast, a causality-focused approach asserts the direct impact of one variable on another, offering a more robust framework for inference and the drawing of scientific conclusions. This approach allows researchers to better predict how changes in a nanoparticle's physicochemical properties or biological conditions will affect protein corona composition and decoration, in turn affecting their safety and therapeutic/diagnostic efficacies. As a proof of concept, we explore the concept of "actual causality" (introduced by Halpern and Pearl) to mathematically prove how spiking small molecules, including metabolites, lipids, vitamins, and nutrients, into plasma can induce diverse protein corona patterns on identical nanoparticles. This approach significantly enhances the depth of plasma proteome profiling. Our findings reveal that among the various spiked small molecules, phosphatidylcholine was the actual cause of the observed increase in the proteomic depth of the plasma sample. By considering the concept of causality in the field of protein coronas, the nanomedicine community can substantially improve the ability to design safer and more efficient nanoparticles for both diagnostic and therapeutic purposes.

Capillary Electrophoresis-Mass Spectrometry for Top-Down Proteomics

Mass spectrometry (MS)-based top-down proteomics (TDP) characterizes proteoforms in cells, tissues, and biological fluids (e.g., human plasma) to better our understanding of protein function and to discover new protein biomarkers for disease diagnosis and therapeutic development. Separations of proteoforms with high peak capacity are needed due to the high complexity of biological samples. Capillary electrophoresis (CE)-MS has been recognized as a powerful analytical tool for protein analysis since the 1980s owing to its high separation efficiency and sensitivity of CE-MS for proteoforms. Here, we review benefits of CE-MS for advancing TDP, challenges and solutions of the method, and the main research areas in which CE-MS-based TDP can make significant contributions. We provide a brief perspective of CE-MS-based TDP moving forward.

Debugging the dynamics of protein corona: Formation, composition, challenges, and applications at the nano-bio interface

The intricate interplay between nanomaterials and the biological molecules has garnered considerable interest in understanding the dynamics of protein corona formation at the nano-bio interface. This review provides an in-depth exploration of protein-nanoparticle interactions, elucidating their structural dynamics and thermodynamics at the nano-Bio interface and further on emphasizing its formation, composition, challenges, and applications in the biomedical and nanotechnological domains, such as drug delivery, theranostics, and the translational medicine. We delve the nuanced mechanisms governing protein corona formation on nanoparticle surfaces, highlighting the influence of nanoparticle and biological factors, and review the impact of corona formation on the biological identity and functionality of nanoparticles. Notably, emerging applications of artificial intelligence and machine learning have begun to revolutionize this field, enabling accurate prediction of corona composition and related biological outcomes. These tools not only enhance efficiency over traditional experimental methods but also help decode complex protein-nanoparticle interaction patterns, offering new insights into corona-driven cellular responses and disease diagnostics. Additionally, it discusses recent advancements in the field of protein corona, particularly in translational nanomedicine and associated applications entailing current and future strategies which are aimed at mitigating the adverse effects of protein-nanoparticle interactions at the biological interface, for tailoring the protein coronas by engineering of the nanomaterials. This comprehensive assessment from chemical, technological, and biological aspects serves as a guiding beacon for the development of future nanomedicine, enabling the more effective emulation of the biological milieu and the design of protein-NP systems for enhanced biomedical applications.

Nanophotonic sensing and label-free imaging of extracellular vesicles

This review examines imaging-based nanophotonic biosensing and interferometric label-free imaging, with a particular focus on vesicle detection. It specifically compares dielectric and plasmonic metasurfaces for label-free protein and extracellular vesicle detection, highlighting their respective advantages and limitations. Key topics include: (i) refractometric sensing principles using resonant dielectric and plasmonic surfaces; (ii) state-of-the-art developments in both plasmonic and dielectric nanostructured resonant surfaces; (iii) a detailed comparison of resonance characteristics, including amplitude, quality factor, and evanescent field enhancement; and (iv) the relationship between sensitivity, near-field enhancement, and analyte overlap in different sensing platforms. The review provides insights into the fundamental differences between plasmonic and dielectric platforms, discussing their fabrication, integration potential, and suitability for various analyte sizes. It aims to offer a unified, application-oriented perspective on the potential of these resonant surfaces for biosensing and imaging, aiming at addressing topics of interest for both photonics experts and potential users of these technologies.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

SysQuan: Repurposing SILAC Mice for the Cost-Effective Absolute Quantitation of the Human Proteome

Relative quantitation, used by most mass spectrometry-based proteomics laboratories to determine protein fold-changes, requires samples being processed and analyzed together for best comparability through minimizing batch differences. This limits the adoption of mass spectrometry-based proteomics in population-wide studies and the detection of subtle but relevant changes in heterogeneous samples. Absolute quantitation circumvents these limitations and enables comparison of results across laboratories, studies, and over time. However, high cost of the essential stable isotope labeled (SIL) standards prevents widespread access and limits the number of quantifiable proteins. Our new approach, called "SysQuan", repurposes SILAC mouse tissues/biofluids as system-wide internal standards for matched human samples to enable absolute quantitation of, theoretically, two-thirds of the human proteome using 157,086 shared tryptic peptides, of which 73,901 with lysine on the c terminus. We demonstrate that SysQuan enables quantification of 70% and 31% of the liver and plasma proteomes, respectively. We demonstrate for 14 metabolic proteins that abundant SIL mouse tissues enable cost-effective reverse absolute quantitation in, theoretically, 1000s of human samples. Moreover, 10,000s of light/heavy doublets in untargeted SysQuan datasets enable unique postacquisition absolute quantitation. SysQuan empowers researchers to replace relative quantitation with affordable absolute quantitation at scale, making data comparable across laboratories, diseases and tissues, enabling completely novel study designs and increasing reusability of data in repositories.

Serum proteomics reveals survival-associated biomarkers in pancreatic cancer patients treated with chemoimmunotherapy

Immunotherapy has transformed the landscape of cancer treatment but remains largely ineffective for patients with pancreatic ductal adenocarcinoma (PDAC). Some patients, however, show improved outcomes when treated with a combination of immunotherapy and chemotherapy. Here, we conducted deep serum proteome analysis to investigate the protein profiles of PDAC patients and changes during this combinatorial treatment. Utilizing an advanced serum workflow, we quantified 1,011 proteins across 211 samples from 62 patients. Glycolytic enzymes were associated with survival in anti-PD-1-treated patients, with their abundances significantly correlating with expression levels in tumor biopsies. Notably, a set of protein biomarkers was found to be highly predictive of survival in anti-PD-1-treated patients (area under the curve [AUC] = 0.91). Overall, our data demonstrate the potential of deep serum proteomics for precision medicine, offering a powerful tool to guide patient selection for treatment through minimally invasive serum protein biomarker measurements.

In Situ Metal-Organic Framework Growth in Serum Encapsulates and Depletes Abundant Proteins for Integrated Plasma Proteomics

Protein biomarkers in human serum provide critical insights into various physiological conditions and diseases, enabling early diagnosis, prognosis, and personalized treatment. However, detecting low-abundance protein biomarkers is challenging due to the presence of highly abundant proteins that make up ∼99% of the plasma proteome. Here, we report the use of in situ metal-organic framework (MOF) growth in serum to effectively deplete highly abundant serum proteins for integrated proteomic analysis. Through biomolecule-mediated nucleation of a zeolitic imidazolate framework (ZIF-8), abundant plasma proteins are selectively encapsulated within ZIF-8 and removed from serum via centrifugation, leaving a depleted protein fraction in the supernatant. Bottom-up proteomics analysis confirmed significant depletion of the topmost abundant proteins, many at depletion levels exceeding 95%. Such depletion enabled the identification of 277 total proteins in the supernatant (uncaptured) fraction in a single-shot analysis, including 54 proteins that were only identified after depletion, 12 drug targets, and many potential disease biomarkers. Top-down proteomics characterization of the captured and uncaptured protein fractions at the proteoform-level confirmed this method is not biased toward any specific proteoform of individual proteins. These results demonstrate that in situ MOF growth can selectively and effectively deplete high-abundance proteins from serum in a simple, low cost, one-pot synthesis to enable integrated top-down and bottom-up proteomic analysis of serum protein biomarkers.

Top-Down Proteomic Profiling of Protein Corona by High-Throughput Capillary Isoelectric Focusing-Mass Spectrometry

In the rapidly evolving field of nanomedicine, understanding the interactions between nanoparticles (NPs) and biological systems is crucial. A pivotal aspect of these interactions is the formation of a protein corona when NPs are exposed to biological fluids (e.g., human plasma), which significantly influences their behavior and functionality. This study introduces an advanced capillary isoelectric focusing tandem mass spectrometry (cIEF-MS/MS) platform designed to enable high-throughput and reproducible top-down proteomic analysis of protein corona. Our cIEF-MS/MS technique completed each analysis within 30 min. It produced reproducible proteoform measurements of protein corona for at least 50 runs regarding the proteoforms' migration time [relative standard deviations (RSDs) <4%], the proteoforms' intensity (Pearson's correlation coefficients between any two runs >0.90), the number of proteoform identifications (71 ± 10), and the number of proteoform-spectrum matches (PrSMs) (196 ± 30). Of the 53 identified genes, 33 are potential biomarkers of various diseases (e.g., cancer, cardiovascular disease, and Alzheimer's disease). We identified 1-102 proteoforms per potential protein biomarker, containing various sequence variations or post-translational modifications. Delineating proteoforms in protein corona by our cIEF-MS/MS in a reproducible and high-throughput fashion will benefit our understanding of nanobiointeractions and advance both diagnostic and therapeutic nanomedicine technologies.

Quantitative serum proteomic analysis for biomarker discovery in post-COVID-19 postural orthostatic tachycardia syndrome (PC-POTS) patients

Postural orthostatic tachycardia syndrome (POTS) is a chronic, debilitating condition that is characterized by an excessive increase in heart rate upon orthostatic challenge. Before the COVID-19 pandemic, POTS affected 0.5 % to 1 % of the U.S. population. Since the pandemic, the incidence has risen sharply, adding an estimated 6-7 million new cases in the U.S. Despite its importance, there is currently no reliable biomarker for POTS, leading to significant diagnostic delays. A major hurdle in identifying biomarkers is the heterogeneous nature of the syndrome. To address this, we focused on a homogeneous subgroup of post-COVID-19 POTS (PC-POTS) patients. We conducted quantitative proteomics on sera from 9 PC-POTS patients and 9 healthy controls, identifying 31 proteins with significantly different abundances in PC-POTS patients. Most elevated proteins were linked to actin filaments or immune functions/inflammation. Weighted Gene Co-Expression Network Analysis revealed module 7 (M7) correlated strongly with PC-POTS diagnosis and related traits. The key proteins in M7 included MTPN, TAGLN2, ADP-ribosylation factor 1, PDLIM1, PPIA, CNN2, LGALSL, TXN, TLN1, TUBA4A, IL4, TREML1, GP1BA, and, all highly correlated with these traits. Cell-type enrichment analysis revealed that M7 was highly associated with immune and neuronal cells. The main pathways identified in M7 included the integrin signaling pathway, blood coagulation, and glycolysis. These findings suggest that the key proteins in M7 could serve as biomarkers for PC-POTS. This study uses quantitative proteomics to identify potential biomarkers that differentiate PC-POTS patients from healthy controls, establishing a foundation for further research and validation.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

AI-Driven Prediction of Cardio-Oncology Biomarkers Through Protein Corona Analysis

Protein corona, a layer predominantly composed of proteins and other biomolecules that forms on nanoparticle surfaces upon interaction with biological fluids, has recently been extensively utilized to enhance the depth of plasma proteomics and biomarker discovery. In this study, we integrate protein corona profiling with mass spectrometry (MS)-based bottom-up proteomics (BUP), machine learning, and causality analysis to identify potential biomarkers in the field of cardio-oncology. We selected prostate cancer (PC) and atherosclerosis as model cardio-oncology diseases, given that PC is the most prevalent cancer among men in the United States and frequently coexists with atherosclerotic cardiovascular disease (ASCVD), which contributes to the progression of metastatic PC (mPC). Protein corona profiles were generated from 35 plasma samples categorized into four groups: mPC with ASCVD, nonmetastatic PC (nmPC) with ASCVD, mPC without ASCVD, and nmPC without ASCVD. MS-based BUP analysis identified 887 unique proteins within the protein corona. Gene Ontology (GO) analysis of the 260 proteins common to all samples revealed key plasma proteomic pathways significantly associated with ASCVD and mPC. Using Least Absolute Shrinkage and Selection Operator (LASSO) regularization, we isolated 22 proteins strongly associated with ASCVD or mPC, including chaperonin containing TCP1 subunit 7 (CCT7), which was common to both conditions. Automated formal reasoning and causality analysis of these 22 proteins identified thromboxane-A synthase 1 (TBXAS1) as a primary causal factor linked to both ASCVD and mPC. TBXAS1 plays a critical role in promoting platelet aggregation, vascular smooth muscle cell proliferation, endothelial dysfunction, and thrombosis. In this proof-of-concept study, CCT7 and TBXAS1 emerged as potential biomarkers for both ASCVD and mPC, suggesting their utility as dual biomarkers for early detection and targeted therapeutic interventions. By combining nanomedicine with advanced analytical methods, our integrated approach provides a robust framework for uncovering causal relationships between biomarkers and disease states.

Integrating -Omic Technologies across Modality, Space, and Time to Decipher Remodeling in Cardiac Disease

PURPOSE OF REVIEW

Despite significant efforts to understand pathophysiological processes underlying cardiac diseases, the molecular causes for the most part remain unresolved. Rapid advancements in -omics technologies, and their application in cardiac research, offer new insight into cardiac remodeling in disease states. This review aims to provide an accessible overview of recent advances in omics approaches for studying cardiac remodeling, catering to readers without extensive prior expertise.

RECENT FINDINGS

We provide a methodologically focused overview of current methods for performing transcriptomics and proteomics, including their extensions for single-cell and spatial measurements. We discuss approaches to integrate data across modalities, resolutions and time. Key recent applications within the cardiac field are highlighted. Each -omics modality can provide insight, yet each existing experimental method has technical or conceptual limitations. Integrating data across multiple modalities can leverage strengths and mitigate weaknesses, ultimately enhancing our understanding of cardiac pathophysiology.

Deep, Unbiased, and Quantitative Mass Spectrometry-Based Plasma Proteome Analysis of Individual Responses to mRNA COVID-19 Vaccine

Global campaign against COVID-19 have vaccinated a significant portion of the world population in recent years. Combating the COVID-19 pandemic with mRNA vaccines played a pivotal role in the global immunization effort. However, individual responses to a vaccine are diverse and lead to varying vaccination efficacy. Despite significant progress, a complete understanding of the molecular mechanisms driving the individual immune response to the COVID-19 vaccine remains elusive. To address this gap, we combined a novel nanoparticle-based proteomic workflow with tandem mass tag (TMT) labeling, to quantitatively assess the proteomic changes in a cohort of 12 volunteers following two doses of the Pfizer-BioNTech mRNA COVID-19 vaccine. This optimized protocol seamlessly integrates comprehensive proteome analysis with enhanced throughput by leveraging the enrichment of low-abundant plasma proteins by engineered nanoparticles. Our data demonstrate the ability of this workflow to quantify over 3,000 proteins, providing the deepest view into COVID-19 vaccine-related plasma proteome study. We identified 69 proteins with boosted responses post-second dose and 74 proteins differentially regulated between individuals who contracted COVID-19 despite vaccination and those who did not. These findings offer valuable insights into individual variability in response to vaccination, demonstrating the potential of personalized medicine approaches in vaccine development.

Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts

Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies enable large-scale cohort proteomic and proteogenomic analyses. As such, the data infrastructure and search engines required to process data must also scale. This challenge is amplified in search engines that rely on library-free match between runs (MBR) search, which enable enhanced depth-per-sample and data completeness. However, to date, no MBR-based search could scale to process cohorts of thousands or more individuals. Here, we present a strategy to deploy search engines in a distributed cloud environment without source code modification, thereby enhancing resource scalability and throughput. Additionally, we present an algorithm, Scalable MBR, that replicates the MBR procedure of popular DIA-NN software for scalability to thousands of samples. We demonstrate that Scalable MBR can search thousands of MS raw files in a few hours compared to days required for the original DIA-NN MBR procedure and demonstrate that the results are almost indistinguishable to those of DIA-NN native MBR. We additionally show that empirical spectra generated by Scalable MBR better approximates DIA-NN native MBR compared to semiempirical alternatives such as ID-RT-IM MBR, preserving user choice to use empirical libraries in large cohort analysis. The method has been tested to scale to over 15,000 injections and is available for use in the Proteograph Analysis Suite.

In-Depth Proteomic Analysis of Tissue Interstitial Fluid Reveals Biomarker Candidates Related to Varying Differentiation Statuses in Gastric Adenocarcinoma

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

Extreme Tolerance of Nanoparticle-Protein Corona to Ultra-High Abundance Proteins Enhances the Depth of Serum Proteomics

The serum nanoparticle-protein corona (NPC) provides specific disease information, thus opening a new avenue for high-throughput in-depth proteomics to facilitate biomarker discovery. Yet, little is known about the interactions between NPs and proteins, and its role in enhanced depth of serum proteomics. Herein, a series of protein spike-in experiments are conducted to systematically investigate protein depletion and enrichment during NPC formation. Proteomic depth is serum concentration-dependent, and NPC exhibits powerful tolerance to ultra-high abundant proteins. In addition, protein-protein interactions (PPI), especially those involving albumin, play a pivotal role in promoting proteomic depth. Furthermore, a triple-protein assay is established to interrogate the relationship between protein binding affinity and concentration. NPC formation is a product of balancing binding affinity, concentration, and PPI. Overall, this study elucidates how NPs achieve protein depletion and enrichment for enhanced serum proteomic depth to gain a better understanding of NPC as an essential tool of proteome profiling.

Lanthanide Metal-Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples

Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a MOF-flower assisted simplified and single-pot Sample Preparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources.

Micro scale chromatography of human plasma proteins for nano LC-ESI-MS/MS

Organic precipitation of proteins with acetonitrile demonstrated complete protein recovery and improved chromatography of human plasma proteins. The separation of 25 μL of human plasma into 22 fractions on a QA SAX resin facilitated more effective protein discovery despite the limited sample size. Micro chromatography of plasma proteins over quaternary amine (QA) strong anion exchange (SAX) resins performed best, followed by diethylaminoethyl (DEAE), heparin (HEP), carboxymethyl cellulose (CMC), and propyl sulfate (PS) resins. Two independent statistical methods, Monte Carlo comparison with random MS/MS spectra and the rigorous X!TANDEM goodness of fit algorithm protein p-values corrected to false discovery rate q-values (q ≤ 0.01) agreed on at least 12,000 plasma proteins, each represented by at least three fully tryptic corrected peptide observations. There was qualitative agreement on 9393 protein/gene symbols between the linear quadrupole versus orbital ion trap but also quantitative agreement with a highly significant linear regression relationship between log observation frequency (F value 4,173, p-value 2.2e-16). The use of a QA resin showed nearly perfect replication of all the proteins that were also found using DEAE-, HEP-, CMC-, and PS-based chromatographic methods combined and together estimated the size of the size of the plasma proteome as ≥12,000 gene symbols.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Inflammatory disease progression shapes nanoparticle biomolecular corona-mediated immune activation profiles

Polymeric nanoparticles (NPs) are promising tools used for immunomodulation and drug delivery in various disease contexts. The interaction between NP surfaces and plasma-resident biomolecules results in the formation of a biomolecular corona, which varies patient-to-patient and as a function of disease state. This study investigates how the progression of acute systemic inflammatory disease influences NP corona compositions and the corresponding effects on innate immune cell interactions, phenotypes, and cytokine responses. NP coronas alter cell associations in a disease-dependent manner, induce differential co-stimulatory and co-inhibitory molecule expression, and influence cytokine release. Integrated multi-omics analysis of proteomics, lipidomics, metabolomics, and cytokine datasets highlight a set of differentially enriched TLR4 ligands that correlate with dynamic NP corona-mediated immune activation. Pharmacological inhibition and genetic knockout studies validate that NP coronas mediate this response through TLR4/MyD88/NF-κB signaling. Our findings illuminate the personalized nature of corona formation under a dynamic inflammatory condition and its impact on NP-mediated immune activation profiles and inflammation, suggesting that disease progression-related alterations in plasma composition can manifest in the corona to cause unintended toxicity and altered therapeutic efficacy.

Selected Ion Extraction of Peptides with Heavy Isotopes and Hydrogen Loss Reduces the Type II Error in Plasma Proteomics

Naturally occurring peptides display a wide mass distribution after ionization due to the presence of heavy isotopes of C, H, N, O, and S and hydrogen loss. There is a crucial need for sensitive methods that collect as much information as possible about all plasma peptide forms. Statistical analysis of the delta mass distribution of peptide precursors from MS/MS spectra that were matched to 63,077 peptide sequences by X!TANDEM revealed Gaussian peaks representing heavy isotopes and hydrogen loss at integer delta mass values of -3, -2, -1, 0, +1, +2, +3, +4, and +5 Da. Human plasma samples were precipitated in acetonitrile, and the resulting proteins were collected over a quaternary amine resin, eluted with NaCl, digested with trypsin, and analyzed by nano liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with an orbital ion trap (OIT). Fragment spectra (MS/MS) generated from the OIT data were fit to human fully tryptic peptides by X!TANDEM, which led to the identification of 3,888 protein gene symbols represented by three or more peptides (n ≥ 3). The peptide counts to plasma proteins from experimental MS/MS spectra were corrected against 29 blank LC-ESI-MS/MS spectra and 30 million random MS/MS control spectra to yield 2,784 true positive proteins (n ≥ 3; q ≤ 0.01). Peptides identified by fragmenting ions with Gaussian heavy isotopes and hydrogen loss that were matched to known plasma proteins, such as albumin (ALB), were shown to be true positives and agreed with the peptide sequences identified in the monoisotopic peak. Accepting the ions from the monoisotopic peak alone (±0.1 Da) yielded only 382 plasma proteins (n ≥ 3; type I error q ≤ 0.01; type II error ∼86%). In contrast, accepting all ions within ±0.1 Da around the hydrogen loss, monoisotopic, and heavy isotopic peaks led to the identification of 963 proteins (n ≥ 3; q ≤ 0.01; type II error ∼60%). Using the power of the OIT to resolve the Gaussian peaks from heavy isotopes and hydrogen loss resulted in the identification of three times more proteins with high confidence and a much lower type II error than analyzing peptides from the monoisotopic peak alone. The resolving power of the OIT may be exploited to increase observation frequencies and provide greater proteomic coverage and statistical power in comparative proteomics studies.

A Technical Evaluation of Plasma Proteomics Technologies

Plasma proteomic technologies are rapidly evolving and of critical importance to the field of biomedical research. Here we report a technical evaluation of six notable plasma proteomic technologies - unenriched (Neat), Acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, Olink Explore HT. The methods were compared on proteomic depth, reproducibility, linearity, tolerance to lipid interference, and limit of detection/quantification. In total we performed 618 LC-MS/MS experiments and 93 Olink Explore HT assays. The Seer method achieved the greatest proteomic depth (∼4,500), while Olink detected ∼2,600 proteins. Other MS-based methods ranged from ∼500-2,200. Neat, Mag-Net, Seer, and Olink had strong reproducibility, while PreOmics and Acid showed higher variability. All MS methods showed good linearity with spiked-in C-Reactive Protein (CRP); CRP was surprisingly not in the Olink assay. None of the methods were affected by lipid interference. Seer had more than double the number of quantifiable proteins (4,800) for both LOD and LOQ than the next best method. Olink was comparable to Neat and Mag-Net for LOD, but worse for LOQ. Finally, we tested the applicability of these methods for detecting differences between healthy and cancer groups in a non-small cell lung cancer (NSCLC) cohort.

Reproducible protein quantitation of 270 human proteins at increased depth using nanoparticle-based fractionation and multiple reaction monitoring mass spectrometry with stable isotope-labelled internal standards

Here we show that when using a mix of 274 light synthetic peptide standards (NAT) as surrogates for 270 human plasma proteins, as well as stable isotope-labelled standards (SIS) as normalizers (both from MRM Proteomics Inc.) for targeted quantitative analysis by liquid chromatography multiple reaction monitoring mass spectrometry (LC/MRM-MS), the Seer Proteograph™ platform allowed for the enrichment and absolute quantitation of up to an additional 62 targets (median) compared to two standard proteomic workflows without enrichment, representing an increase of 44%. The nanoparticle-based fractionation workflow resulted in improved reproducibility compared to a traditional proteomic workflow with no fractionation (median 8.3% vs. 13.1% CV). As expected, the protein concentrations in nanoparticle coronas were higher and had more compressed dynamic range in comparison to the concentrations determined either by a 3-hour Trypsin/LysC or overnight tryptic digestion methods. As the nanoparticle-based fractionation technology gains popularity, additional steps such as establishing technique-specific protein reference ranges across plasma samples and comparisons to well-established protein quantitation methods like enzyme-linked immunosorbent assay (ELISA) and LC/MRM-MS may be explored to enable absolute quantification of plasma proteins based on nanoparticle-based fractionation data.

Proteomic patterns associated with ketamine response in major depressive disorders

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD.

METHODS

Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered.

RESULTS

Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment.

CONCLUSIONS

These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.

Quantum dot-protein interface: Interaction of the CdS quantum dot with human hemoglobin for the study of the energy transfer process and binding mechanism along with detection of the unfolding of hemoglobin

In this study, the interaction of the human hemoglobin with cost effective and chemically fabricated CdS quantum dots (QDs) (average sizes ≈3nm) has been investigated. The semiconductor QDs showed maximum visible absorption at 445 nm with excitonic formation and band gap of ≈ 2.88 eV along with hexagonal crystalline phase. The binding of QDs-Hb occurs through corona formation to the ground sate complex formation. The life time of the heme pocket binding and reorganization were found to be t1 = 43 min and t2 = 642 min, respectively. The emission quenching of the Hb has been indicated large energy transfer between CdS QDs and Hb with tertiary deformation of Hb. The binding thermodynamics showed highly exothermic nature. The ultrafast decay during corona formation was studied from TCSPC. The results showed that the energy transfer efficiency increases with the increase of the QDs concentration and maximum ≈71.5 % energy transfer occurs and average ultrafast lifetime varies from 5.45 ns to1.51 ns. The deformation and unfolding of the secondary structure of Hb with changes of the α-helix (≈74 % to ≈51.07 %) and β-sheets (≈8.63 % to ≈10.25 %) have been observed from circular dichroism spectrum. The SAXS spectrum showed that the radius of gyration of CdS QDs-Hb bioconjugate increased (up to 23 ± 0.45 nm) with the increase of the concentration of QDs compare with pure Hb (11 ± 0.23 nm) and Hb becoming more unfolded.

Multiomics Approach Identifies Key Proteins and Regulatory Pathways in Colorectal Cancer

The prevalence rate of colorectal cancer (CRC) has dramatically increased in recent decades. However, robust CRC biomarkers with therapeutic value for early diagnosis are still lacking. To comprehensively reveal the molecular characteristics of CRC development, we employed a multiomics strategy to investigate eight different types of CRC samples. Proteomic analysis revealed 2022 and 599 differentially expressed tissue proteins between CRC and control groups in CRC patients and CRC mice, respectively. In patients with colorectal precancerous lesions, 25 and 34 significantly changed proteins were found between patients and healthy controls in plasma and white blood cells, respectively. Notably, vesicle-associated membrane protein-associated protein A (VAPA) was found to be consistently and significantly decreased in most types of CRC samples, and its level was also significantly correlated with increased overall survival of CRC patients. Furthermore, 37 significantly enriched pathways in CRC were further validated via metabolomics analysis. Ten VAPA-related pathways were found to be significantly enriched in CRC samples, among which PI3K-Akt signaling, central carbon metabolism in cancer, cholesterol metabolism, and ABC transporter pathways were also enriched in the premalignant stage. Our study identified VAPA and its associated pathways as key regulators, suggesting their potential applications in the early diagnosis and prognosis of CRC.

Comparative Proteomics and N-Glycoproteomics Reveal the Effects of Different Plasma Protein Enrichment Technologies

Human plasma proteomic and glycoproteomic analyses have emerged as an alternate avenue to identify disease biomarkers and therapeutic approaches. However, the vast number of high-abundance proteins in plasma can cause mass spectrometry (MS) suppression, which makes it challenging to detect low-abundance proteins (LAP). Currently, immunoaffinity-based depletion methods and strategies involving nanomaterial protein coronas have been developed to remove high-abundance proteins (HAP) and enhance the depth of plasma protein identification. Despite these advancements, there is a lack of systematic comparison and evaluation of the qualitative and quantitative effects of different strategies on the human plasma proteome and glycoproteome. In this study, we evaluated the performance of four depletion methods including combinatorial peptide ligand libraries (CPLL), Top 2, Top 14, and the nanomaterial protein corona formed by magnetic nanoparticles (MN) in both plasma proteomics and N-glycoproteomics. Compared to the CPLL, Top 2, and Top 14 strategies, the MN approach significantly increased the number of identified peptides and proteins. However, it demonstrated a relatively lower efficacy in identifying intact N-glycopeptides and N-glycoproteins. In contrast, the immunoaffinity-based depletion methods are better suited to glycoproteomics due to higher identification numbers. We believe that this work provides valuable insights and options for various research objectives, as well as clinical applications.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Personalized biocorona as disease biomarker: The challenges and opportunities

It is well known that when nanoparticles interact with biological fluids, a layer of proteins and biological components forms on them. This layer may alter the biological fate and efficiency of the nanomaterial. Recent studies have shown that illness states have a major impact on the structure of the biocorona, sometimes referred to as the "personalized protein corona." Physiological factors like illness, which impact the proteome and metabolome pattern and result in conformational changes in proteins, give rise to this structure of discrimination in biocorona decoration. Improving the efficiency of precise platforms for developing new molecular biomarkers for accurate illness diagnosis is vitally necessary. The biocorona pattern's discrimination may be a diagnostic tool for designing biosensors. As a result, in this review, we summarize the most current studies on the relationship between physiological conditions and the variety of biocorona patterns that influence the biological responses of nanosystems. The biocorona pattern's flexibility may provide new research directions and be utilized to create nanoparticle-based therapeutic and diagnostic products suited to certain physiological situations.

Omics-Enhanced Nanomedicine for Cancer Therapy

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

The role of patient-specific variables in protein corona formation and therapeutic efficacy in nanomedicine

Despite their potential, the adoption of nanotechnology in therapeutics remains limited, with only around eighty nanomedicines approved in the past 30 years. This disparity is partly due to the "one-size-fits-all" approach in medical design, which often overlooks patient-specific variables such as biological sex, genetic ancestry, disease state, environment, and age that influence nanoparticle behavior. Nanoparticles (NPs) must be transported through systemic, microenvironmental, and cellular barriers that vary across heterogeneous patient populations. Key patient-dependent properties impacting NP delivery include blood flow rates, body fat distribution, reproductive organ vascularization, hormone and protein levels, immune responses, and chromosomal differences. Understanding these variables is crucial for developing effective, patient-specific nanotechnologies. The formation of a protein corona around NPs upon exposure to biological fluids significantly alters NP properties, affecting biodistribution, pharmacokinetics, cytotoxicity, and organ targeting. The dynamics of the protein corona, such as time-dependent composition and formation of soft and hard coronas, depend on NP characteristics and patient-specific serum components. This review highlights the importance of understanding protein corona formation across different patient backgrounds and its implications for NP design, including sex, ancestry, age, environment, and disease state. By exploring these variables, we aim to advance the development of personalized nanomedicine, improving therapeutic efficacy and patient outcomes.

In situ characterization techniques of protein corona around nanomaterials

Nanoparticles (NPs) inevitably interact with proteins upon exposure to biological fluids, leading to the formation of an adsorption layer known as the "protein corona". This corona imparts NPs with a new biological identity, directly influencing their interactions with living systems and dictating their fates in vivo. Thus, gaining a comprehensive understanding of the dynamic interplay between NPs and proteins in biological fluids is crucial for predicting therapeutic effects and advancing the clinical translation of nanomedicines. Numerous methods have been established to decode the protein corona fingerprints. However, these methods primarily rely on prior isolation of NP-protein complex from the surrounding medium by centrifugation, resulting in the loss of outer-layer proteins that directly interact with the biological system and determine the in vivo fate of NPs. We discuss here separation techniques as well as in situ characterization methods tailored for comprehensively unraveling the inherent complexities of NP-protein interactions, highlighting the challenges of in situ protein corona characterization and its significance for nanomedicine development and clinical translation.

Small molecule modulation of protein corona for deep plasma proteome profiling

The protein corona formed on nanoparticles (NPs) has potential as a valuable diagnostic tool for improving plasma proteome coverage. Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules allows for the detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depletes the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. Employing an optimized data-independent acquisition approach, the inclusion of phosphatidylcholine leads to the detection of 1436 proteins in a single plasma sample. Our molecular dynamics results reveal that phosphatidylcholine interacts with albumin via hydrophobic interactions, H-bonds, and water bridges. The addition of phosphatidylcholine also enables the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate the widespread adoption of this methodology for the identification and clinical translation of biomarkers.

Protein Corona of Nanoparticles: Isolation and Analysis

Nanoparticles entering biological systems or fluids inevitably adsorb biomolecules, such as protein, on their surfaces, forming a protein corona. Ensuing, the protein corona endows nanoparticles with a new biological identity and impacts the interaction between the nanoparticles and biological systems. Hence, the development of reliable techniques for protein corona isolation and analysis is key for understanding the biological behaviors of nanoparticles. First, this review systematically outlines the approach for isolating the protein corona, including centrifugation, magnetic separation, size exclusion chromatography, flow-field-flow fractionation, and other emerging methods. Next, we review the qualitative and quantitative characterization methods of the protein corona. Finally, we underscore the necessary steps to advance the efficiency and fidelity of protein corona isolation and characterization on nanoparticle surfaces. We anticipate that these insights into protein corona isolation and characterization methodologies will profoundly influence the development of technologies aimed at elucidating bionano interactions and the role of protein corona in various biomedical applications.

Use of a Novel Whole Blood Separation and Transport Device for Targeted and Untargeted Proteomics

BACKGROUND

There is significant interest in developing alternatives to traditional blood transportation and separation methods, which often require centrifugation and cold storage to preserve specimen integrity. Here we provide new performance findings that characterize a novel device that separates whole blood via lateral flow then dries the isolated components for room temperature storage and transport.

METHODS

Untargeted proteomics was performed on non-small cell lung cancer (NSCLC) and normal healthy plasma applied to the device or prepared neat.

RESULTS

Significantly, proteomic profiles from the storage device were more reproducible than from neat plasma. Proteins depleted or absent in the device preparation were shown to be absorbed onto the device membrane through largely hydrophilic interactions. Use of the device did not impact proteins relevant to an NSCLC clinical immune classifier. The device was also evaluated for use in targeted proteomics experiments using multiple-reaction monitoring (MRM) mass spectrometry. Intra-specimen detection intensity for protein targets between neat and device preparations showed a strong correlation, and device variation was comparable to the neat after normalization. Inter-specimen measurements between the device and neat preparations were also highly concordant.

CONCLUSIONS

These studies demonstrate that the lateral flow device is a viable blood separation and transportation tool for untargeted and targeted proteomics applications.

Deciphering nanoparticle protein coronas by capillary isoelectric focusing-mass spectrometry-based top-down proteomics

The nanoparticle (NP) protein corona significantly influences the outcome of nanomedicine. We present the first example of top-down proteomics (TDP) measurement of the protein corona using capillary isoelectric focusing-mass spectrometry, identifying seventy proteoforms of 16 cancer-related genes. This technique has the potential to revolutionize our understanding of the protein corona and advance nanomedicine.

Dual Fractions Proteomic Analysis of Silica Nanoparticle Interactions with Protein Extracts

Dual-fraction proteomics reveals a novel class of proteins impacted by nanoparticle exposure.

BACKGROUND

Nanoparticles (NPs) interact with cellular proteomes, altering biological processes. Understanding these interactions requires comprehensive analyses beyond solely characterizing the NP corona.

METHODS

We utilized a dual-fraction mass spectrometry (MS) approach to analyze both NP-bound and unbound proteins in Saccharomyces cerevisiae sp. protein extracts exposed to silica nanoparticles (SiNPs). We identified unique protein signatures for each fraction and quantified protein abundance changes using spectral counts.

RESULTS

Strong correlations were observed between protein profiles in each fraction and non-exposed controls, while minimal correlation existed between the fractions themselves. Linear models demonstrated equal contributions from both fractions in predicting control sample abundance. Combining both fractions revealed a larger proteomic response to SiNP exposure compared to single-fraction analysis. We identified 302/56 proteins bound/unbound to SiNPs and an additional 196 "impacted" proteins demonstrably affected by SiNPs.

CONCLUSION

This dual-fraction MS approach provides a more comprehensive understanding of nanoparticle interactions with cellular proteomes. It reveals a novel class of "impacted" proteins, potentially undergoing conformational changes or aggregation due to NP exposure. Further research is needed to elucidate their biological functions and the mechanisms underlying their impact.

Biofunctionalized dissolvable hydrogel microbeads enable efficient characterization of native protein complexes

The characterization of protein complex is vital for unraveling biological mechanisms in various life processes. Despite advancements in biophysical tools, the capture of non-covalent complexes and deciphering of their biochemical composition continue to present challenges for low-input samples. Here we introduce SNAP-MS, a Stationary-phase-dissolvable Native Affinity Purification and Mass Spectrometric characterization strategy. It allows for highly efficient purification and characterization from inputs at the pico-mole level. SNAP-MS replaces traditional elution with matrix dissolving during the recovery of captured targets, enabling the use of high-affinity bait-target pairs and eliminates interstitial voids. The purified intact protein complexes are compatible with native MS, which provides structural information including stoichiometry, topology, and distribution of proteoforms, size variants and interaction states. An algorithm utilizes the bait as a charge remover and mass corrector significantly enhances the accuracy of analyzing heterogeneously glycosylated complexes. With a sample-to-data time as brief as 2 hours, SNAP-MS demonstrates considerable versatility in characterizing native complexes from biological samples, including blood samples.

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-Down Proteomics

The dynamic range challenge for the detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundance proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 μg/mL and ∼10 pg/mL in absolute abundance, providing up to a 105-fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 μg/mL) were detected. The ability to monitor medium and some low-abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins, and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Multiplex plasma protein assays as a diagnostic tool for lung cancer

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.