Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

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.

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.

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.

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.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

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.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

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

The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant 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 10 5 -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.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

A novel deep proteomic approach in human skeletal muscle unveils distinct molecular signatures affected by aging and resistance training

The skeletal muscle proteome alterations to aging and resistance training have been reported in prior studies. However, conventional proteomics in skeletal muscle typically yields wide protein abundance ranges that mask the detection of lowly expressed proteins. Thus, we adopted a novel deep proteomics approach whereby myofibril (MyoF) and non-MyoF fractions were separately subjected to protein corona nanoparticle complex formation prior to digestion and Liquid Chromatography Mass Spectrometry (LC-MS). Specifically, we investigated MyoF and non-MyoF proteomic profiles of the vastus lateralis muscle of younger (Y, 22±2 years old; n=5) and middle-aged participants (MA, 56±8 years old; n=6). Additionally, MA muscle was analyzed following eight weeks of resistance training (RT, 2d/week). Across all participants, the number of non-MyoF proteins detected averaged to be 5,645±266 (range: 4,888-5,987) and the number of MyoF proteins detected averaged to be 2,611±326 (range: 1,944-3,101). Differences in the non-MyoF (8.4%) and MyoF (2.5%) proteomes were evident between age cohorts, and most differentially expressed non-MyoF proteins (447/543) were more enriched in MA versus Y. Biological processes in the non-MyoF fraction were predicted to be operative in MA versus Y including increased cellular stress, mRNA splicing, translation elongation, and ubiquitin-mediated proteolysis. RT in MA participants only altered ~0.3% of MyoF and ~1.0% of non-MyoF proteomes. In summary, aging and RT predominantly affect non-contractile proteins in skeletal muscle. Additionally, marginal proteome adaptations with RT suggest more rigorous training may stimulate more robust effects or that RT, regardless of age, subtly alters basal state skeletal muscle protein abundances.

Personalized Cancer Nanomedicine: Overcoming Biological Barriers for Intracellular Delivery of Biopharmaceuticals

The success of personalized medicine in oncology relies on using highly effective and precise therapeutic modalities such as small interfering RNA (siRNA) and monoclonal antibodies (mAbs). Unfortunately, the clinical exploitation of these biological drugs has encountered obstacles in overcoming intricate biological barriers. Drug delivery technologies represent a plausible strategy to overcome such barriers, ultimately facilitating the access to intracellular domains. Here, an overview of the current landscape on how nanotechnology has dealt with protein corona phenomena as a first and determinant biological barrier is presented. This continues with the analysis of strategies facilitating access to the tumor, along with conceivable methods for enhanced tumor penetration. As a final step, the cellular barriers that nanocarriers must confront in order for their biological cargo to reach their target are deeply analyzed. This review concludes with a critical analysis and future perspectives of the translational advances in personalized oncological nanomedicine.

Size-exclusion chromatography combined with DIA-MS enables deep proteome profiling of extracellular vesicles from melanoma plasma and serum

Extracellular vesicles (EVs) are important players in melanoma progression, but their use as clinical biomarkers has been limited by the difficulty of profiling blood-derived EV proteins with high depth of coverage, the requirement for large input amounts, and complex protocols. Here, we provide a streamlined and reproducible experimental workflow to identify plasma- and serum- derived EV proteins of healthy donors and melanoma patients using minimal amounts of sample input. SEC-DIA-MS couples size-exclusion chromatography to EV concentration and deep-proteomic profiling using data-independent acquisition. From as little as 200 µL of plasma per patient in a cohort of three healthy donors and six melanoma patients, we identified and quantified 2896 EV-associated proteins, achieving a 3.5-fold increase in depth compared to previously published melanoma studies. To compare the EV-proteome to unenriched blood, we employed an automated workflow to deplete the 14 most abundant proteins from plasma and serum and thereby approximately doubled protein group identifications versus native blood. The EV proteome diverged from corresponding unenriched plasma and serum, and unlike the latter, separated healthy donor and melanoma patient samples. Furthermore, known melanoma markers, such as MCAM, TNC, and TGFBI, were upregulated in melanoma EVs but not in depleted melanoma plasma, highlighting the specific information contained in EVs. Overall, EVs were significantly enriched in intact membrane proteins and proteins related to SNARE protein interactions and T-cell biology. Taken together, we demonstrated the increased sensitivity of an EV-based proteomic workflow that can be easily applied to larger melanoma cohorts and other indications.

1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

Nano-bio interactions in mRNA nanomedicine: Challenges and opportunities for targeted mRNA delivery

Upon entering the biological milieu, nanomedicines swiftly interact with the surrounding tissue fluid, subsequently being enveloped by a dynamic interplay of biomacromolecules, such as carbohydrates, nucleic acids, and cellular metabolites, but with predominant serum proteins within the biological corona. A notable consequence of the protein corona phenomenon is the unintentional loss of targeting ligands initially designed to direct nanomedicines toward particular cells or organs within the in vivo environment. mRNA nanomedicine displays high demand for specific cell and tissue-targeted delivery to effectively transport mRNA molecules into target cells, where they can exert their therapeutic effects with utmost efficacy. In this review, focusing on the delivery systems and tissue-specific applications, we aim to update the nanomedicine population with the prevailing and still enigmatic paradigm of nano-bio interactions, a formidable hurdle in the pursuit of targeted mRNA delivery. We also elucidate the current impediments faced in mRNA therapeutics and, by contemplating prospective avenues-either to modulate the corona or to adopt an 'ally from adversary' approach-aim to chart a course for advancing mRNA nanomedicine.

Superparamagnetic Composite Nanobeads Anchored with Molecular Glues for Ultrasensitive Label-free Proteomics

Mass spectrometry has emerged as a mainstream technique for label-free proteomics. However, proteomic coverage for trace samples is constrained by adsorption loss during repeated elution at sample pretreatment. Here, we demonstrated superparamagnetic composite nanoparticles functionalized with molecular glues (MGs) to enrich proteins in trace human biofluid. We showed high protein binding (>95 %) and recovery (≈90 %) rates by anchor-nanoparticles. We further proposed a Streamlined Workflow based on Anchor-nanoparticles for Proteomics (SWAP) method that enabled unbiased protein capture, protein digestion and pure peptides elution in one single tube. We demonstrated SWAP to quantify over 2500 protein groups with 100 HEK 293T cells. We adopted SWAP to profile proteomics with trace aqueous humor samples from cataract (n=15) and wet age-related macular degeneration (n=8) patients, and quantified ≈1400 proteins from 5 μL aqueous humor. SWAP simplifies sample preparation steps, minimizes adsorption loss and improves protein coverage for label-free proteomics with previous trace samples.

'Passive' nanoparticles for organ-selective systemic delivery: design, mechanism and perspective

Nanotechnology has shown tremendous success in the drug delivery field for more effective and safer therapy, and has recently enabled the clinical approval of RNA medicine, a new class of therapeutics. Various nanoparticle strategies have been developed to improve the systemic delivery of therapeutics, among which surface modification of targeting ligands on nanoparticles has been widely explored for 'active' delivery to a specific organ or diseased tissue. Meanwhile, compelling evidence has recently been reported that organ-selective targeting may also be achievable by systemic administration of nanoparticles without surface ligand modification. In this Review, we highlight this unique set of 'passive' nanoparticles and their compositions and mechanisms for organ-selective delivery. In particular, the lipid-based, polymer-based, and biomimetic nanoparticles with tropism to different specific organs after intravenous administration are summarized. The underlying mechanisms (e.g., protein corona and size effect) of these nanosystems for organ selectivity are also extensively discussed. We further provide perspectives on the opportunities and challenges in this exciting area of organ-selective systemic nanoparticle delivery.

Local Environments Created by the Ligand Coating of Nanoparticles and Their Implications for Sensing and Surface Reactions

ConspectusThe ligand shells of colloidal nanoparticles (NPs) can serve different purposes. In general, they provide colloidal stability by introducing steric repulsion between NPs. In the context of biological applications, the ligand shell plays a critical role in targeting, enabling NPs to achieve specific biodistributions. However, there is also another important feature of the ligand shell of NPs, namely, the creation of a local environment differing from the bulk of the solvent in which the NPs are dispersed. It is known that charged ligand shells can attract or repel ions and change the effective charge of a NP through Debye-Hückel screening. Positively charged ions, such as H+ (or H3O+) are attracted to negatively charged surfaces, whereas negatively charged ions, such as Cl- are repelled. The distribution of the ions around charged NP surfaces is a radial function of distance from the center of the NP, which is governed by a balance of electrostatic forces and entropy of ions and ligands. As a result, the ion concentration at the NP surface is different from its bulk equilibrium concentration, i.e., the charged ligand shell around the NPs has formed a distinct local environment. This not only applies to charged ligand shells but also follows a more general principle of induced condensation and depletion. Polar/apolar ligand shells, for example, result in a locally increased concentration of polar/apolar molecules. Similar effects can be seen for biocatalysts like enzymes immobilized in nanoporous host structures, which provide a special environment due to their surface chemistry and geometrical nanoconfinement. The formation of a local environment close to the ligand shell of NPs has profound implications for NP sensing applications. As a result, analyte concentrations close to the ligand shell, which are the ones that are measured, may be very different from the analyte concentrations in bulk. Based on previous work describing this effect, it will be discussed herein how such local environments, created by the choice of used ligands, may allow for tailoring the NPs' sensing properties. In general, the ligand shell around NPs can be attractive/repulsive for molecules with distinct properties and thus forms an environment that can modulate the specific response. Such local environments can also be optimized to modulate chemical reactions close to the NP surface (for example, by size filtering within pores) or to attract specific low abundance proteins. The importance hereby is that this is based on interaction with low selectivity between the ligands and the target molecules.

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

Background

The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.

Methods

Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.

Results

We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.

Conclusions

The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

A novel deep proteomic approach in human skeletal muscle unveils distinct molecular signatures affected by aging and resistance training

We examined the myofibrillar (MyoF) and non-myofibrillar (non-MyoF) proteomic profiles of the vastus lateralis (VL) muscle of younger (Y, 22±2 years old; n=5) and middle-aged participants (MA, 56±8 years old; n=6), and MA following eight weeks of knee extensor resistance training (RT, 2d/week). Shotgun/bottom-up proteomics in skeletal muscle typically yields wide protein abundance ranges that mask lowly expressed proteins. Thus, we adopted a novel approach whereby the MyoF and non-MyoF fractions were separately subjected to protein corona nanoparticle complex formation prior to digestion and Liquid Chromatography Mass Spectrometry (LC-MS) analysis. A total of 10,866 proteins (4,421 MyoF and 6,445 non-MyoF) were identified. Across all participants, the number of non-MyoF proteins detected averaged to be 5,645±266 (range: 4,888-5,987) and the number of MyoF proteins detected averaged to be 2,611±326 (range: 1,944-3,101). Differences in the non-MyoF (8.4%) and MyoF (2.5%) proteome were evident between age cohorts. Further, most of these age-related non-MyoF proteins (447/543) were more enriched in MA versus Y. Several biological processes in the non-MyoF fraction were predicted to be operative in MA versus Y including (but not limited to) increased cellular stress, mRNA splicing, translation elongation, and ubiquitin-mediated proteolysis. Non-MyoF proteins associated with splicing and proteostasis were further interrogated, and in agreement with bioinformatics, alternative protein variants, spliceosome-associated proteins (snRNPs), and proteolysis-related targets were more abundant in MA versus Y. RT in MA non-significantly increased VL muscle cross-sectional area (+6.5%, p=0.066) and significantly increased knee extensor strength (+8.7%, p=0.048). However, RT modestly altered the MyoF (~0.3%, 11 upregulated and two downregulated proteins) and non-MyoF proteomes (~1.0%, 56 upregulated and eight downregulated proteins, p<0.01). Further, RT did not affect predicted biological processes in either fraction. Although participant numbers were limited, these preliminary results using a novel deep proteomic approach in skeletal muscle suggest that aging and RT predominantly affects protein abundances in the non-contractile protein pool. However, the marginal proteome adaptations occurring with RT suggest either: a) this may be an aging-associated phenomenon, b) more rigorous RT may stimulate more robust effects, or c) RT, regardless of age, subtly affects skeletal muscle protein abundances in the basal state.

Fast and Dynamic Mapping of the Protein Corona on Nanoparticle Surfaces by Photocatalytic Proximity Labeling

Protein corona broadly affects the delivery of nanomedicines in vivo. Although it has been widely studied by multiple strategies like centrifugal sedimentation, the rapidly forming mechanism and the dynamic structure of the protein corona at the seconds level remains challenging. Here, a photocatalytic proximity labeling technology in nanoparticles (nano-PPL) is developed. By fabricating a "core-shell" nanoparticle co-loaded with chlorin e6 catalyst and biotin-phenol probe, nano-PPL technology is validated for the rapid and precise labeling of corona proteins in situ. Nano-PPL significantly improves the temporal resolution of nano-protein interactions to 5 s duration compared with the classical centrifugation method (>30 s duration). Furthermore, nano-PPL achieves the fast and dynamic mapping of the protein corona on anionic and cationic nanoparticles, respectively. Finally, nano-PPL is deployed to verify the effect of the rapidly formed protein corona on the initial interaction of nanoparticles with cells. These findings highlight a significant methodological advance toward nano-protein interactions in the delivery of nanomedicines in vivo.

An Isobaric Labeling Approach to Enhance Detection and Quantification of Tissue-Derived Plasma Proteins as Potential Early Disease Biomarkers

The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 μg kidney protein to 10 μg plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

Proteomics to study cancer immunity and improve treatment

Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.