Jesse G. Meyer

The paper uses proteomic analysis of APP-London mutant neurons to show early cholesterol remodeling and altered A beta secretion without changes in total cholesterol.

The paper reanalyzes a proteomic atlas using audited vibe coding to assess if tumors show fetal-like proteomic convergence, finding partial alignment with reverse fetal-to-adult maturation.

This paper develops a platform combining robotic proteomics and AI to enable scalable drug mechanism discovery through high-throughput data analysis and hypothesis generation.

scTransient detects transient gene expression events in single-cell data by combining trajectory inference with wavelet analysis, revealing previously unreported biological spikes in three datasets.

SMAD-MS enables rapid single-injection multi-omics analysis of proteomics, metabolomics, and lipidomics without liquid chromatography, achieving high throughput and efficiency.

This paper uses single-cell proteomics to identify novel cell phenotypes in Marfan mouse aorta, revealing sex differences and angiotensin signaling roles in aneurysm formation.

xTracer identifies peptides in PAMAF data by integrating chromatogram and mobiligram correlations, improving identification accuracy over single-domain methods.

The paper identifies UCHL1 dysregulation and synaptic loss in the retina as markers of Alzheimer's disease severity using integrated multi-omics analysis.

The paper identifies Chlamydia pneumoniae infection and NLRP3 inflammasome activation in Alzheimer's disease retinas, linking them to disease progression and cognitive impairment.

The paper introduces a web platform for no-code single-cell omics analysis pipelines, enabling sharing of data, pipelines, and results to improve accessibility and reproducibility.

The paper shows that extended NGN2 expression in iPSCs improves neuronal culture purity by reducing heterogeneity caused by suboptimal differentiation.

This study identifies proteomic changes in HepG2 cells treated with antibiotics, showing off-target effects on ribosomal and mitochondrial proteins and highlighting the need for careful antibiotic use in cell culture.

This study examines how different cell storage conditions affect single-cell proteomic profiles using DIA-diaPASEF in 293T cells, finding significant changes in morphology and proteome composition.

The paper develops deep learning models to predict FAIMS ion mobility of peptides from sequence, achieving high F2 score on E. coli peptides.

This paper introduces a hybrid method combining direct infusion shotgun proteome analysis with nanoparticle protein corona enrichment to achieve rapid and efficient plasma proteomic profiling.

The paper improves proteome prediction from transcriptomes using deep learning with residual connections and shows functionally important transcripts.

This study identifies high stoichiometry lysine succinylation sites in mouse liver, showing their functional role in urea cycle regulation and ammonia metabolism through SIRT5.

This paper uses machine learning to differentiate between descending thoracic aortic diseases using plasma proteomic data, identifying key proteins and pathways associated with each condition.

This paper uses machine learning to integrate proteome and metabolome data, revealing new protein-metabolite connections and gene functions in yeast.

This paper introduces PoSHAP, a framework for interpreting machine learning models trained on biological sequences, demonstrating its ability to reproduce known peptide binding motifs and provide insights into amino acid interactions.

The paper introduces PeCorA, a method to detect discordant peptide quantities in shotgun proteomics data, identifying 15% of proteins with one discordant peptide in mouse microglia.

This study quantifies changes in the intracellular and secreted proteomes of stem cell-derived RPE under acute and chronic ROS stress, revealing key molecular responses to oxidative stress.

The paper predicts drug classification from chemical structure using CNN and random forests, achieving better accuracy than transcriptomic-based methods.

The paper investigates how excess dietary sugar and fat affect liver mitochondria protein acetylation and succinylation in mice through multi-omic profiling.

DIA-Pipe enables automated identification and quantification of post-translational modification peptides from DIA data without a pre-existing spectral library.