Erwin M. Schoof

This study identifies CD59 as a critical regulator of Ras-MAPK signaling in AML, showing its depletion impairs leukemic growth and identifies rILYd4 as a potential therapeutic target.

The paper introduces spatialproteomics, a Python toolbox for analyzing highly multiplexed fluorescence images, demonstrating its use on lymph node and lymphoma data.

This study establishes quality control standards for single-cell proteomics through inter-laboratory benchmarking, identifying software and technical factors affecting data reliability.

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

The paper introduces a capillary-based subcellular sampling method combined with LC-MS to analyze stress granule proteomes in single cells, identifying 405 high-confidence proteins with minimal cytosolic contamination.

This paper introduces a workflow for direct protein sequencing using AI-driven methods, validated across diverse proteins to improve accuracy and reduce ambiguity in therapeutic and scientific applications.

This study identifies cholesterol biosynthesis as a therapeutic target in CEBPA-mutant AML, showing that statins enhance chemosensitivity in cells with co-mutations in TET2 or WT1.

This paper introduces AbTags, a method to enhance detection of low-abundant proteins in single-cell proteomics by mass spectrometry through antibody-based signal amplification.

The paper identifies that tryptophan-binding pockets in Arabidopsis AGO1 are essential for RDR6-dependent siRNA amplification through interactions with SGS3.

The paper integrates single-cell proteomics and transcriptomics to study human hematopoietic stem and progenitor cells, identifying protein markers and dynamics not captured by mRNA alone.

The paper identifies molecular determinants for phosphotyrosine recognition in tyrosine kinases and shows their roles in tumor promotion and suppression.

The paper introduces iDIA, a method that enhances sensitivity for quantifying regulatory proteins in single cells, revealing lineage priming and cell-cycle coordination in hematopoietic stem and progenitor cells.

This study introduces MAP-MS, a method to improve proteomic dynamic range using Orbitrap mass spectrometers without hardware or software changes, achieving nearly 2x dynamic range with minimal impact.

This study evaluates the Orbitrap Astral MS's sensitivity for single-cell proteomics, showing improved proteome coverage and quantitative accuracy in low-input samples.

InstaNovo is a transformer model for de novo peptide sequencing that outperforms existing methods, enabling accurate identification of peptides and discovery of novel sequences and organisms.

This study identifies distinct gene regulatory networks in hematopoietic stem cells and leukemia stem cells using integrated gene expression and chromatin accessibility data from AML patients and human umbilical cord blood.

This study develops a low-input proteomics workflow to analyze rare hematopoietic stem and progenitor cells, identifying 7,000 proteins and a novel marker for long-term hematopoietic stem cells.

CLIPPER 2.0 is a computational tool for peptide-level annotation and analysis of positional proteomics data, demonstrating its use on GluC and MMP9 cleavage in HeLa lysates.

The paper evaluates the impact of different trypsin variants on proteomics experiments across multiple sample types and methods, identifying variations in performance and providing handling recommendations.

This study evaluates the Astral mass analyzer's performance in single-cell proteomics using DIA, showing improved proteome depth and quantitative precision for ultra-low input samples.

The paper develops a tailored DIA method for single-cell proteomics to improve quantification from low-input samples and establishes a complete scp-MS workflow.

The paper develops a method combining linear ion traps and FAIMS for low-input proteomics, improving sensitivity and throughput for limited material samples.

The paper presents a workflow for single-cell proteomics to study cellular heterogeneity in leukemia, demonstrating the ability to quantify thousands of proteins per cell.