ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

Emerging multianalyte biosensors for the simultaneous detection of protein and nucleic acid biomarkers

Traditionally, biosensors are designed to detect one specific analyte. Nevertheless, disease progression is regulated in a highly interactive way by different classes of biomolecules like proteins and nucleic acids. Therefore, a more comprehensive analysis of biomarkers from a single sample is of utmost importance to further improve both, the accuracy of diagnosis as well as the therapeutic success. This review summarizes fundamentals like biorecognition and sensing strategies for the simultaneous detection of proteins and nucleic acids and discusses challenges related to multianalyte biosensor development. We present an overview of the current state of biosensors for the combined detection of protein and nucleic acid biomarkers associated with widespread diseases, among them cancer and infectious diseases. Furthermore, we outline the multianalyte analysis in the rapidly evolving field of single-cell multiomics, to stress its significance for the future discovery and validation of biomarkers. Finally, we provide a critical perspective on the performance and translation potential of multianalyte biosensors for medical diagnostics.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Biotechnology applications of proteins functionalized with DNA oligonucleotides

The functionalization of proteins with DNA through the formation of covalent bonds enables a wide range of biotechnology advancements. For example, single-molecule analytical methods rely on bioconjugated DNA as elastic biolinkers for protein immobilization. Labeling proteins with DNA enables facile protein identification, as well as spatial and temporal organization and control of protein within DNA-protein networks. Bioconjugation reactions can target native, engineered, and non-canonical amino acids (NCAAs) within proteins. In addition, further protein engineering via the incorporation of peptide tags and self-labeling proteins can also be used for conjugation reactions. The selection of techniques will depend on application requirements such as yield, selectivity, conjugation position, potential for steric hindrance, cost, commercial availability, and potential impact on protein function.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Single-Molecule Peptide Identification Using Fluorescence Blinking Fingerprints

The ability to identify peptides with single-molecule sensitivity would lead to next-generation proteomics methods for basic research and clinical applications. Existing single-molecule peptide sequencing methods can read some amino acid sequences, but they are limited in their ability to distinguish between similar amino acids or post-translational modifications. Here, we demonstrate that the fluorescence intermittency of a peptide labeled with a spontaneously blinking fluorophore contains information about the structure of the peptide. Using a deep learning algorithm, this single-molecule blinking pattern can be used to identify the peptide. This method can distinguish between peptides with different sequences, peptides with the same sequence but different phosphorylation patterns, and even peptides that differ only by the presence of epimerized residues. This study builds the foundation for a targeted proteomics method with single-molecule sensitivity.

Target-switch SELEX: Screening with alternating targets to generate aptamers to conserved terminal dipeptides

Systematic evolution of ligands by exponential enrichment (SELEX) encompasses a wide variety of high-throughput screening techniques for producing nucleic acid binders to molecular targets through directed evolution. We describe here the design and selection steps for discovery of DNA aptamers with specificity for the two consecutive N-terminal amino acids (AAs) of a small peptide (8-10 amino acids). This bead-based method may be adapted for applications requiring binders which recognize a specific portion of the desired target. For complete details on the use and execution of this protocol, please refer to Hong et al. (2022).

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.