23rd Human Proteome Organization World Congress

Professor

PhD student

Development of Auto-2D: a fully automated two-dimensional electrophoresis system, and application for the top-down proteomics to identify proteoforms in tumor samples

New Technology: Non MS-based Proteomics

With the advancement of mass spectrometry, large-scale bottom-up proteomics has become common. However, it is still extremely difficult to elucidate in detail of proteoforms, i.e., the structural changes caused by the shift of various post-translational modification sites and splicing in biological proteins, using only bottom-up shotgun analysis at the peptide level. The two-dimensional electrophoresis method, which attracted much attention and was widely used in the early years of proteomics, is still the most useful method to easily and visually separate nearly 10,000 proteins from biological tissues, cells, body fluids, etc. However, it is difficult to perform because it requires techniques to separated proteins quantitatively with high reproducibility, and it is time-consuming, skill-consuming, and thus the use of 2D electrophoresis for proteomics has gradually declined. Against this background, we have previously succeeded in developing a fully automated 2D electrophoresis system (Auto2D), which is simple and easy to use with high resolution, sensitivity, and reproducibility in a short time, and proposed to apply it to proteoform analysis using top-down proteomics, which has recently attracted attention.In this presentation, we will show some successful experimental results obtained by using Auto-2D, with tumor samples including cranial nerve tumors such as neurofibroma, malignant peripheral schwannoma, drug-resistant malignant gliomas, glioma stem cells, and treatment-resistant prostate cancer, in order to identify their target proteins and molecules. In addition, the post translational modifications such as phosphorylation, glycosylation, proteolysis, alternative splicing, and other changes in the target molecule proteoforms involved in malignant transformation and drug resistance have been successfully identified. We present how the proteoforms identified with these strategies using Auto2D are useful for the development of drugs and clinical markers, and will discuss future perspectives of the top-down proteomics.

Poster presentation

Revolutionizing multiplexed protein profiling: dual-reporter capabilities of Luminex xMAP® technology

Luminex® xMAP® Technology is a flow cytometry-based multiplexing platform that utilizes colored magnetic microspheres (beads) coupled with target-specific molecules such as antigens, antibodies, receptors, enzyme substrates, as well as oligonucleotides. This technology facilitates the simultaneous detection of up to 500 analytes in a single reaction. The xMAP® Technology platform offers a wide range of preconfigured or custom applications, delivering results with sensitivity and reliability comparable to conventional singleplex assays like ELISA, western blot, and qPCR, but with greater efficiency and throughput. Consequently, it has become an invaluable tool in fields such as proteomics, genomics, clinical diagnostics, and drug discovery.Here we introduce the xMAP INTELLIFLEX® System, featuring innovative dual-reporter capabilities, as a novel tool for multiplexed protein profiling applications. This system enables the detection of two parameters per analyte on the same bead in a single well. The dual-reporter functionality has been successfully implemented in antibody isotyping assays to monitor immune responses to specific infections or vaccinations, facilitating the detection of two immunoglobulin (Ig) isotypes for multiple antigens simultaneously. Examples include the concurrent detection of IgG/IgM, IgA/IgM, and IgG/IgA responses to various pathogens, such as multiple Borrelia species (Häring et al. 2023), SARS-Cov-2 (Fhied et al 2022), Epstein-Barr Virus (Schrieber et al. 2023) and Neisseria gonorrhoeae (Stover et al. 2024). In addition to monitoring antibody titers, dual reporter assays can also determine antibody neutralization potential, as demonstrated by the ability of the ACE2 receptor to block antibody binding to S and RBD (Cameron et al. 2022).The dual-reporter capabilities extend to various assay formats where simultaneous measurements of analyte pairs are critical. Examples include analyzing post-translational modifications, such as phosphorylation in protein signaling pathways, and distinguishing between free and bound drug forms in pharmacokinetic/pharmacodynamic (PK/PD) studies. Additionally, dual-reporter assays can effectively characterize bi-specific/fusion proteins, used for profiling of extracellular vesicles, and bioparticles, like viral particles (Sahi et al. 2024), and detect mutant proteins in cell and tissue lysates. Moreover, xMAP® dual-reporter capabilities facilitate protein-protein interaction studies, allowing determination of interaction affinities or execution of competition experiments. Notably, this latter application has been proven sensitive in assessing vaccine-induced antibody blockade of PD-1/PD-L1 interactions (Overholster et al. 2023).In conclusion, Luminex xMAP® Technology, combined with intelligent workflow automation, revolutionizes protein identification and characterization. It elevates proteomics research in your laboratory by maximizing the number of results while minimizing sample input.

Massively parallel single-molecule protein quantitation using protein identification by short-epitope mapping (PrISM)

Introduction: Analyzing protein samples at single-molecule resolution holds the promise of achieving improved quantitative dynamic range, reproducibility, and sensitivity. By employing multi-affinity probes to interrogate immobilized, intact proteins, we aim to use Protein Identification by Short-epitope Mapping (PrISM) to analyze &gt;95% of the proteome with broad dynamic range. PrISM has the potential to revolutionize proteomics by delivering highly accurate, large-scale analyses of protein samples.Methods: Proteins were conjugated to DNA nanoparticles and deposited on a high-density patterned flow cell at optically resolvable locations. PrISM utilizes non-traditional affinity reagents called multi-affinity probes, designed with high affinity and low specificity in order to bind short epitopes present in multiple proteins across the proteome. Multi-affinity probes were applied to proteins to generate binding patterns for each single-molecule protein, which are translated to protein identifications and quantities using machine learning. We acquired PrISM data on a human osteosarcoma cell line (U2OS), protein mixtures, and control samples using hundreds of multi-affinity probes.Results: We demonstrate detection and quantification of proteins from the U2OS cell line using PrISM data comprising billions of single-molecule binding measurements per sample. The detected proteins span a wide dynamic range of abundance in the U2OS cell line sample. We compare PrISM protein identification to mass spectrometry analysis of the same cell line. Improvements in instrumentation, reagents, and data processing approaches yield improved quantitative performance defined by sensitivity, accuracy, reproducibility, and dynamic range. An in-depth analysis of binding data shows a high diversity among patterns generated and indicates an opportunity to identify more proteins by adding more multi-affinity probes.Conclusions: Steady improvement in the Nautilus platform driving the PrISM approach has resulted in detection of proteins from a human cell line sample at single-molecule resolution.

Single-molecule proteoform analysis of EGFR in cancer cells in response to therapeutic drug treatment

Alterations in both abundance and activation of the epidermal growth factor receptor (EGFR) have been correlatesd with poor prognosis in numerous cancers. Extensive studies of EGFR signaling have revealed dozens of phosphorylation sites that are believed to govern its role as a potent oncogene. Though numerous phosphorylation sites have been identified, the coordinated action of these sites within heterogeneous mixtures of proteoforms has yet to be elaborated. For example, it is not yet known how triple phosphorylation of EGFR at sites 1045,1086 and 1173 impacts cell behavior differently than triple phosphorylation of EGFR at sites 974, 992 and 1068. Here we describe the development and application of a high throughput single-molecule analysis platform to study EGFR proteoforms in cancer cells in response to ligand stimuli and drug treatments.The goal of the assay is to quantify the abundances, with single-molecule resolution, of a discrete set of proteoforms of EGFR, as defined by a pre-determined set of site-specific post-translational modifications. This measurement is achieved through first immobilizing billions of individual protein molecules onto a patterned flow cell. In parallel, each molecule is iteratively probed with fluorescently labeled affinity reagents that bind to site-specific post-translational modifications. A computational framework, then takes these measurements, accounts for cross-reactivity, false-positive and false-negative binding and infers the abundance of each proteoform.Titration studies have demonstrated that the assay is able to detect proteoforms that are present at less than 0.1% of the total population. To further enhance sensitivity, affinity purification strategies are used to enrich EGFR proteins from TCP 1016 lung cancer cells that have been treated with a monoclonal antibody therapeutic. The assay was first validated using in vitro phosphorylated recombinant EGFR (GST fusion of the intracellular domain, residuals 668-1210) as compared to un-phosphorylated control. Next, the assay was used to examine the impact of drug treatment on EGFR proteoform molecular heterogeneity.Ultimately, this novel assay may enable a deeper understanding of the regulation and dysregulation of EGFR during oncogenesis and in response to therapeutic intervention.

Prof.

Characterization of exosome surface proteins related to colon cancer using proximity barcoding assay to profile single-exosomes

Colorectal cancer (CRC) is a common disease with high incidence around the world. A large body of evidence reveals exosomes play vital roles in regulating CRC proliferation, metastasis, and prognosis. It is generally acceptable that exosomes are heterogeneous in size, membrane composition, content, and function. Proximity barcoding assay (PBA) is a mass spectrometry-free approach that can resolve exosome surface proteomes at a single exosome level. For the first time, we have initiated a systematic investigation to profile and cluster the CRC-related exosomes in vitro using PBA. The study goals at classifying exosome subtypes secreted from CRC cell lines and clarifying differences in exosome subtypes among cancer cell lines.A total of 9 CRC and 2 gastric cancer (GC) cell lines were cultured and the corresponding exosomes were harvested through differential ultracentrifugation. After assessment with nanoparticle tracking analysis and transmission electron microscopy, the prepared exosomes were loaded onto microtiters followed by adding PBA probes with 262 antibodies. Then the mixture incubated with RCA products was put into a PCR machine to amplify the tag signals. The MGI T7 sequencer was employed for acquiring sequence and readout information. The sequencing data was treated by EVisualizer, and the treated data was further statistically evaluated by GraphPad Prism.The exosomes measured by PBA in individual cancer cell lines varied very much, from 0.2 to 5.2 million. Of 262 exosome biomarkers, only 27 proteins were identified in the exosomes of these cell lines if a cutoff set at S/N &gt;= 30. To inquire about whether a cell line secrets similar exosomes or not, the qualitative and quantitative surface proteins in the exosomes from individual cell lines were clustered under unsupervised mode using Partitioning Around Medoids. Of these CRC cell lines, 5 exhibited simply hierarchical subgroups of exosomes with 2-4 clusters, while the other cell lines had exosome clusters around 20. The next question was whether CRC cell lines shared similar exosome subgroups. There were 5 proteins generally detected in these exosomes, EPCAM (58.7%), CD151 (50.0%), ITGB1 (46.7%), ITGA6 (27.2%) and ITGB4 (23.9%). Thus, the patterns of their abundance distribution could decide the exosome subgroups. For instance, based on the expression level of CD151, the exosomes in CRC cell lines were broadly divided into two categories, while the other four exosome biomarkers were decisive factors to several exosome subgroups. With the same approach, the single exosomes from two GC cell lines were examined as well, indicating that CD151 was consistently identified in these exosomes, however, THBD and EGFR were the key proteins to define the exosome subgroups of GC cell lines. This study, therefore, offers the convincing evidence that the CRC cell lines indeed share some exosome subgroups, leading to a hypothesis of exosome subgroups involved in cancer metastasis and therapy.

Quantum-Si's next-generation protein sequencer™ enables protein detection and peptide characterization from biological samples

Studies of the proteome would greatly benefit from methods that enable direct protein detection and peptide characterization with exceptional sensitivity. In this context, we present the unique capabilities of Next-Generation Protein Sequencing™ on Quantum-Si&quot;s Platinum® instrument, which utilizes single-molecule protein sequencing to achieve detection of unknown proteins and protein variants. Individual peptides are digested from proteins, immobilized on a semiconductor chip, and probed by dye-labeled N-terminal amino acid (NAA) recognizers, followed by subsequent aminopeptidase cleavage to expose each NAA in the peptide for recognition. Through the recording and analysis of fluorescent intensity, lifetime, and binding kinetics of each NAA binding event, we can successfully identify proteins and detect PTMs.To demonstrate the power of protein sequencing, we identified proteins from serum without prior knowledge, identified pathological PTMs, and utilized protein barcodes to screen and characterize proteins. We isolated proteins from human serum via immunoprecipitation or SDS-PAGE and correctly identified them from the sequencing data with high confidence by mapping to an 8,000-protein reference panel. Additionally, we demonstrated the power of Platinum to detect PTMs based on kinetic changes by detecting citrullination and dimethylation of arginine—two PTMs that play key roles in disease states such as cardiovascular disease, autoimmune disease, and cancer. Furthermore, we identified both the wild-type and single-amino-acid variants of both hemoglobin alpha and beta from clinical samples of heterozygous patients. Finally, we showcased the detection of peptide biomarkers along with the utilization of barcoding techniques to streamline protein engineering, antibody engineering, and enzyme engineering.These results demonstrate the transformative potential of single-molecule protein sequencing using Platinum for comprehensive proteome analysis. Our technology enables the detection and characterization of proteins and peptides in complex mixtures and biofluids, as well as the detection of critical PTMs implicated in human pathophysiology. As proteomics continues to advance, Quantum-Si's platform offers a promising approach to drive new insights into protein research and disease mechanisms.

Analytical validation of NULISAseq inflammation panel 250 for multiplexed detection of inflammation-related proteins in autoimmune diseases

The plasma proteome holds great significance in tracking the development, progression and therapeutic response of autoimmune diseases. However, current methodologies lack the sensitivity and dynamic range to measure the diverse array and abundance of proteins in blood. NULISA™, Nucleic acid-linked immunosandwich assay, integrates proximity ligation-based specificity with background suppression and immunocomplex purification using a dual capture and release mechanism to achieve attomolar sensitivity and 12 log dynamic range of detection for multiplexed proteomic analysis. Here, we report the analytical validation of fully automated NULISA on ARGO HT™ with the NULISAseq Inflammation Panel 250.Limit of detection and dynamic range were determined for each assay from standard curves generated by serial dilution of pooled recombinant proteins. Intra- and inter-assay precision and reproducibility were evaluated across instruments and with different lots of conjugated antibodies for the inflammation panel. Dilutional linearity of each assay was evaluated using a pooled plasma sample. Assay specificity was evaluated by testing for cross reactivity both within the panel of targeted proteins and to untargeted proteins with high homology to targeted proteins. Assay sensitivity was further assessed for target detectability in plasma.The NULISA Inflammation Panel demonstrated a 12-log dynamic range spanning low abundance targets such as IFNL1 at attomolar concentrations and higher abundance targets like CRP at micromolar concentrations without sample dilution or enrichment. The median intra- and inter-plate CV across the panel were &lt;10%, and the median total CV including different reagent lots was &lt;15%. Dilutional linearity studies demonstrated excellent relative quantitation linearity for 97% of the targets. Cross reactivity studies demonstrated high specificity with &lt;1% cross reactivity for 95% of the targets in the panel. Overall detectability in plasma samples from healthy and diseased donors was over 95%.Overall, the fully automated NULISA on the ARGO HT platform demonstrated high analytical performance and enables both single-plex and multiplexed detection of a wide dynamic range of proteins in blood for discovery research and validation studies.

Single-molecule library preparation of a complex cell lysate

Protein Identification by Short-epitope Mapping (PrISM) is a novel, single-molecule based proteome analysis approach that is designed to quantify nearly the entire human proteome in a single experiment. A key attribute of this approach includes the ability to provide comprehensive coverage of sample constituents and a wide dynamic range suitable to quantify high- and low-abundant proteins in the proteome. The first step in PrISM involves the generation of a single-molecule library from the sample, which must be simple-to-execute, efficient, reproducible and comprehensive to consistently provide a representative portion of the sample for subsequent deposition and detection on the billions of landing pads in the flowcell used for analysis.Using both a model-protein mixture and a U2-OS cell line, we prepared a library of each mixture by denaturing the sample using SDS and functionalizing the lysine residues and N-terminal primary amines using an STP methyltetrazine group (mTz), which puts a click reagent onto each of the proteins. To complete the single pot reaction, we reduced and alkylated the disulfide bonds. Cleanup steps removed the reactants, resulting in protein molecules labeled with the click group mTz. These labeled proteins were then conjugated to a proprietary nanoparticle scaffold for deposition onto our hyper dense flowcell.We first investigated the efficiency of labeling model-protein mixture and lysate samples with STP-mTz by applying bottom-up LCMS to the modified samples. As we expect many lysine residues to be modified, samples were digested with both trypsin and arginase-C to maximize sequence coverage. We obtained a broad coverage of the U2-OS proteome, providing a direct read out of the lysine residue and N-terminal primary amine modifications on the lysate. The results indicate that, on average, more than one lysine residue per protein is modified by mTz allowing for the efficient covalent reaction with the complimentary click group TCO on the nanoparticle scaffolds. We did not see any obvious classes of proteins that were unable to be modified. We next investigated the efficiency of conjugation between mTz modified proteins and nanoparticle scaffolds through LCMS analysis of the proteins that had been successfully conjugated to the nanoparticles. Comparing our U2-OS LCMS analysis with previously published U2-OS proteome analysis under similar LCMS conditions indicates that our simple, straightforward library preparation method enables the U2-OS proteome to be deposited as single proteins at high density on the flowcell for subsequent analysis by PrISM.

A deep-learning based approach for spot segmentation of indigenous protein microarray images

Senior Scientific Liaison

Integrated sequencing of single transcript and protein molecules discerns alternatively spliced tropomyosin proteoforms

Coupling detection of distinct RNA isoforms and alternatively spliced proteoforms is a key goal in proteogenomics. For example, actin-binding tropomyosin (TPM1-4) genes generate over 40 spliceoforms in mammals, several of which are functionally distinct. Notably, genetic and transcriptomic studies in human models have associated several TPM spliceoforms with complex diseases such as osteoporosis and coronary artery disease. However, subtle amino acid variations hamper unequivocal identification of TPM proteoforms. Here we integrate LR-RNA seq (PacBio) of transcript isoforms with Platinum (Quantum-Si), a benchtop platform that reveals the primary structure of single protein molecules, to distinguish TPM1/2 (87% amino acid identity).We created a database for long read-guided interpretation of protein isoforms and constructed a map of protein isoforms for splicing events associated with bone mineral density in the human fetal osteoblast (hFOB) cell line. We derived a list of TPM1/2-informative peptides and TPM2 isoform-informative peptides from our long-read transcript database, then utilized Platinum to differentiate synthetic peptides that correspond to TPM1/2 paralogs and spliceoforms.In the Platinum workflow, derivatized peptides are conjugated via C-termini and loaded into nanoscale reaction chambers of a semiconductor chip, with the goal of one peptide per aperture. During sequencing, dye-labeled N-terminal amino acid (NAA) recognizers reversibly bind to cognate NAAs, generating distinct binding and pulsing patterns. Aminopeptidases sequentially cleave NAAs to expose subsequent amino acids for recognition. Upon completion of a sequencing run, data is transmitted for cloud-based analysis. Traces are segmented into contiguous groups of pulses, or recognition segments (RSs), each representing interactions between recognizers and NAAs. The order of recognizer binding and kinetic properties of RSs are aligned to reference sequences to determine peptide sequence. Platinum sequencing discriminates TPM peptides with similar physicochemical properties.NAA recognizers that bind glutamate (E) distinguish SLEAQAEK (TPM1) and SLEAQADK (TPM2). Also, differential pulse durations (PDs) discern isobaric TPM1/2 peptides. At the exon level, NAA recognizers for aromatic residues reveal an isoform-informative peptide in TPM2. Overall, single molecule sequencing with orthogonal methods facilitates proteogenomics and the transition from the discovery to proteoform scoring phase.

Development of a software platform for studying the protein expression of microarrays

IntroductionMicroarrays can house thousands of assays. The multiplexed output from each ligand is used to study the protein expression for a particular disease. The intensity extraction exercise, however, has to be performed manually for each spot. This not only delays the experiment, but is also prone to human errors. This work presents an automated technique to segregate the spot intensities from the background that has been embedded in an application software.MethodologyThe user interface also enables the operator to attach a file containing ligand position details. This generates an output file containing the protein names along with their respective intensities and other statistical parameters. The &quot;Project&quot; mode allows the user to load multiple images pertaining to the same assay and label them in subgroups as per the cohort or severity. In this mode, significant proteins or peptides are found and required visualizations are generated. User is given an option to set various parameters in the tasks of intensity extraction, statistical analysis and visualization.The imaging process involves the denoising of the image to remove the features containing abrupt intensity changes. Then the image is segmented into object and background using the local threshold instead of global. Out of the extracted objects, microarray spots are selected based on the criteria of convolution. The row and column details of the spots are identified to match them with the provided file.Statistical process begins with the preprocessing steps like background correction followed by the normalization or standardization techniques. The processed data is subjected to parametric or non-parametric according to the user preference to identify the novel biomarkers.ConclusionThe said technique has not only been tried on protein and peptide microarrays scanned under high-end instrument but it can also extract data from microarray chips imaged under indigenous instrument. Unlike the contemporary methods, the feature pixels are not enclosed inside regular grid cells like circle or rectangle but their boundary contours are extracted ignoring the background area.References1. Ray, Arka, Sanjeeva Srivastava, and Siddhartha P. Duttagupta. "MicroNET: Microarray Image Orientation Predictor and Automated Data Extraction." In 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy &amp; Communication (CIEC), pp. 118-123. IEEE, 2024.2. Acharjee, Arup, Arka Ray, Akanksha Salkar, Surbhi Bihani, Chaitanya Tuckley, Jayanthi Shastri, Sachee Agrawal, Siddhartha Duttagupta, and Sanjeeva Srivastava. "Humoral immune response profile of COVID-19 reveals severity and variant-specific epitopes: Lessons from SARS-CoV-2 peptide microarray." Viruses 15, no. 1 (2023): 248.

Autoantibody reactome profiling of health and disease using AAgAtlas 1.0 microarray platform

Each individual possesses a unique repertoire of autoantibodies, termed the &quot;autoantibody reactome,&quot; which exerts a range of biological effects, leading to both pathogenic and protective clinical outcomes. This diversity may serve as a valuable source for monitoring disease-specific pathology, facilitating earlier diagnosis and risk stratification [1-2]. However, the physiological significance of autoantibodies may have been underappreciated due to the absence of unbiased, high-throughput, and quantitative detection methods that could be leveraged among surveys at a proteome-wide scale. Here, we introduce an AAgAtlas 1.0 microarray that enables the profiling of autoantibody reactome in healthy individuals and patients with different diseases [3-6]. First, we developed a method to quantify autoantibody binding on microarray surfaces, enhancing sensitivity and accuracy over conventional methods. It boasted a linear dynamic range spanning three to four orders of magnitude, with the lowest limit of detection (LOD) of 68 pg/mL and an average coefficient of variation (CV) for quantification results of less than 5% intra-assay and 10% inter-assay. Second, we compared our platform with Chemiluminescence Immunoassay (CLIA) in 267 plasma samples. The classification accuracy achieved an average of over 95%, with a correlation coefficient of 0.94 with clinical quantitative data. Afterward, we designed and prepared an AAgAtlas 1.0 microarray according to the AAgAtlas 1.0 database [6], which was employed to screen ~1,000 serums of healthy controls and patients with Hepatitis B virus-infected hepatitis (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC). Machine learning algorithms were used to generate classification models to identify the signatures that could distinguish well between healthy and patient groups. This study underscores the potential of humoral immunity in detecting health and disease.Reference:Science. 2024 Feb 16;383(6684):705-707.J Proteome Res. 2023 Jun 2;22(6):1800-1815.Proteomics Clin Appl. 2022 Nov;16(6):e2100132.Cancer Immunol Immunother. 2022 Jul;71(7):1681-1691.Theranostics. 2020 May 16;10(14):6399-6410.Nucleic Acids Res. 2017 Jan 4;45(D1):D769-D776.

New Technology: Non MS-based Proteomics

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