bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.05.22.655475

Are Solid Particles Ready for Prime-Time Proteomics?

Kitano, E.·Demyanenko, Y.·Mohammed, S.
View original source ↗Export metadata ↓

Abstract

We evaluate the performance of nonporous C-18 stationary phases in high-speed proteomics workflows. We employed two commercially available sub-2 {micro}m nonporous particle (NPP) materials, ODS-IIIE (1.5 {micro}m) and SOLAD (1.0 {micro}m), to fabricate analytical columns using 150 {micro}m internal diameter (i.d.) fused silica capillaries to ensure compatibility with nano-UHPLC and nano-HPLC pressure regimes. Using long NPP columns (15 - 25 cm) connected to a conventional nano-UHPLC, we found that both materials supported efficient peptide separations within the flow rate and pressure ranges typical of nano-UHPLC systems. Shorter columns, used with the 10- and 16-minute Whisper Zoom 120 and Zoom 80 methods on the Evosep One HPLC, demonstrated competitive separation performance compared to columns packed with fully porous (FPP) and superficially porous particles (SPP), achieving full-width at half maximum (FWHM) values below 2 seconds (Zoom 120) and 3 seconds (Zoom 80). DIA LC-MS/MS analysis of the same digest demonstrated that NPP columns provided comparable or superior performance relative to FPP and SPP materials. These findings establish NPP-based columns as a viable and competitive alternative to FPP and SPP materials, particularly suited for high-throughput and high-sensitivity proteomics applications.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kitano, E., Demyanenko, Y., Mohammed, S.. 2025-05-27. Are Solid Particles Ready for Prime-Time Proteomics?. https://doi.org/10.1101/2025.05.22.655475

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues

H2O2 released to the extracellular space (ECS) regulates diverse physiological processes, yet its concentrations and spatial distribution in tissues remain poorly defined. This uncertainty hampers mechanistic understanding of redox signaling. Here, we used reaction-diffusion modeling to estimate extracellular H2O2 concentrations and transport ranges in various scenarios. Idealized analytical models were combined with numerical models incorporating localized NADPH oxidase (NOX) clusters, ECS microstructure, membrane permeability, and the thioredoxin- and GSH-dependent clearance systems. Using maximal neutrophil and NOX superoxide/H2O2 release rates, we obtained upper bounds for extracellular H2O2. Adjacent to isolated average-sized, fully active NOX2 clusters H2O2 peaked at ~540 nM at adhesion cell-cell separations, and decreased radially over ~50-100 nm. At the receptor cell surface, peak concentration decreased inversely with intercellular separation, to <5 nM at 1 m separation. Radial decrease here, for this wide separation, was over ~2.5 m. Even the former maximal extracellular concentrations induce just a minimal, highly localized oxidation of the intracellular Prdx, Trx and GSH pools. In turn, maximally activated neutrophils carry ~2000 such NOX2 clusters, inducing 10s of M peak ECS H2O2 concentrations. These cause extensive Prdx and Trx oxidation near the exposed membranes. However, the GSH-dependent system still sustains a strong transmembrane gradient if the permeation barrier remains intact, and ECS H2O2 concentrations decay to sub-M within a few m of the source cell. Extracellular H2O2 concentrations scaled linearly with source flux in all the examined conditions. These results establish stringent constraints on autocrine, juxtacrine and next-cell paracrine H2O2 signaling.

systems biology↗
bioRxiv2026

LSD-pipeline: Causal Inference of miRNA Network Effects in Alzheimer's Disease

MicroRNAs (miRNAs) are implicated in Alzheimer's disease (AD), but research has focused on individual miRNAs and direct targets. Existing approaches to miRNA regulation in AD identify associations rather than causal effects, and few methods estimate multi-stage chains from miRNAs through target genes to target transcription factor (TF) cascades. We developed the LSD pipeline (LASSO-SEM-DoWhy), integrating LASSO feature selection, multi-stage structural equation modeling, and DoWhy causal inference to identify and validate miRNA causal pathways in AD. Applying LSD to six blood miRNA and brain mRNA datasets, we identified four LSD-validated miRNAs (miR-30d-5p, miR-92a-3p, miR-296-5p, miR-193a-5p) as AD biomarkers, achieving >86% ROC accuracy in an independent validation cohort. Several miRNAs with no significant direct association with AD showed significant effects when estimated through their target networks, while others significant in direct analysis were not supported at the network level, underscoring the value of network-level analysis. Extending to the TF layer revealed complete miRNA [->] targets [->] TF cascades [->] AD causal chains, with HMGA1, NKX2-3, and PRRX2 as key intermediaries. Confirmed classic pathways converge primarily on tau pathology and synaptic dysfunction. miRNA effects were largely age-independent, suggesting miRNAs act as early initiators of AD pathogenesis. Beyond AD, the LSD pipeline provides a generalizable framework for uncovering causal regulatory mechanisms in other diseases.

systems biology↗
bioRxiv2026

PyKappa: Rule-based modeling in Python

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

systems biology↗