Biology subjects

Egertson, J. D.

Publications and source records attributed to Egertson, J. D..

5 recordsLinked to original sources

bioRxiv2025

Development of a method for large-scale single-molecule analysis of tau proteoforms

Proteins exist as diverse proteoforms resulting from a combination of genetic variation, alternative splicing, and post-translational modifications. Current methods struggle to capture this complexity at the single-molecule level. Here we introduce Iterative Mapping of Proteoforms (IMaP), a method that enables the massively-parallel interrogation of millions to billions of single-protein molecules through iterative probing with fluorescently labeled antibodies. Using 12 site-specific antibodies, the method is capable of measuring 212 (4,096) potential proteoform groups. We used IMaP to measure proteoform group profiles of the tau protein, a key player in neurodegenerative diseases, using two pan anti-tau antibodies (Tau-13, Tau-216), three isoform-specific antibodies (Anti-0N, Anti-2N, Anti-4R), and seven phosphosite-specific antibodies (Anti-pT181, Anti-pS202+pT205, Anti-pT205, Anti-pS214, Anti-pT217, Anti-pT231, and Anti-pS396). The method demonstrates high sensitivity (detecting proteoforms at 0.1% abundance), high reproducibility (median CV <5.5%), and broad dynamic range (>3 orders of magnitude), outperforming conventional techniques in resolving closely related proteoform groups. We demonstrated that the method can be used on relevant biological samples by examining various neuronal models (iNeuron cells, organoids, MiBrains, and mouse brains) and human samples. This examination revealed 130 distinct tau proteoform groups with as many as six phosphorylation events. The non-random distribution of these phosphorylation events suggests ordered and site-specific modification processes rather than random, stochastic accumulation. Certain combinations of phosphorylation events were more abundant than others; for example, pT217 preferentially co-occurred with pT181. In validating the applicability of the assay to human disease samples, we noted a specific pattern of multiple phosphorylation events in an advanced Alzheimers disease patient that suggests a sequential pathway of pathological tau modification. Iterative Mapping of Proteoforms provides insights into proteoform complexity at the single-molecule level, with significant implications for understanding protein regulation in neurodegenerative diseases and beyond.

biochemistry↗

bioRxiv2021

A theoretical framework for proteome-scale single-molecule protein identification using multi-affinity protein binding reagents

The proteome is perhaps the most dynamic and valuable source of functional biological insight. Current proteomic techniques are limited in their sensitivity and throughput. A typical single experiment measures no more than 8% of the human proteome from blood or 35% from cells and tissues 1, 2. Here, we introduce a theoretical framework for a fundamentally different approach to proteomics that we call Protein Identification by Short-epitope Mapping (PrISM). PrISM utilizes multi-affinity reagents to target short linear epitopes with both a high affinity and low specificity. PrISM further employs a novel protein decoding algorithm that considers the stochasticity expected for single-molecule binding. In simulations, PrISM is able to identify more than 98% of proteins across the proteomes of a wide range of organisms. PrISM is robust to potential experimental confounders including false negative detection events and noise. Simulations of the approach with a chip containing 10 billion protein molecules show a dynamic range of 11.5 and 9.5 orders of magnitude for blood plasma and HeLa cells, respectively. If implemented experimentally, PrISM stands to rapidly quantify over 90% of the human proteome in a single experiment, potentially revolutionizing proteomics research.

systems biology↗

bioRxiv2018

Comprehensive peptide quantification for data independent acquisition mass spectrometry using chromatogram libraries

Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. We introduce a new experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in an experiment. These coordinates calibrate information in spectrum libraries or protein databases to a specific mass spectrometer and chromatography setup, and enable sensitive peptide detection in quantitative experiments. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect and quantify >50% more peptides from DIA experiments than with DDA-based spectrum libraries alone.

biochemistry↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗

bioRxiv2017

Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics

Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

bioinformatics↗