Biology subjects

Charkow, J.

Publications and source records attributed to Charkow, J..

4 recordsLinked to original sources

bioRxiv2026

Reference-Based Library Construction Improves Performance in low-input diaPASEF Workflows

In low input mass spectrometry-based proteomics, Data Independent Acquisition (DIA), is quickly becoming the method of choice for label free quantification. Whether using empirical or in silico spectral libraries, performance is dependent on the library; however, the optimal library construction strategy for low input proteomics remains an open question. To address this, we examine and develop library construction approaches that are compatible with both spectrum-centric and peptide-centric analysis workflows. These approaches leverage a closely related, high-quality sample to improve library quality. First, we validated our approach in bulk sample amounts where we observed that the effects of gas-phase fractionation based library construction is dependent on the software framework, with improvements more pronounced in OpenSWATH compared to DIA-NN. In OpenSWATH, our peptide-centric library reconstruction workflow consistently outperforms a transfer learning strategy, an emerging alternative approach. In DIA-NN, trends are dependent on library source highlighting OpenSWATHs stronger dependence on the search space. In low-input applications, such as single-cell-equivalent injection amounts (100 pg) of HeLa cell digest on a timsTOF SCP, our library construction approach provided more pronounced improvements across both software tools compared to bulk samples. Using a peptide-centric reconstruction approach with the OpenSWATH analysis framework, we detected over 15,000 peptide precursors (2480 protein groups), a 90% improvement over the original library. Furthermore, using a spectrum-centric construction approach, peptide precursor identification rates improved over 6-fold ( [~]1000 to [~]6000). Our strategy provides a practical solution for generating high-quality libraries in low-input applications.

bioinformatics↗

bioRxiv2026

ChatDIA: A zero-shot large language model workflow for targeted analysis of data-independent acquisition mass spectrometry data

Data-independent acquisition (DIA) proteomics enables reproducible, large-scale protein identification and quantification but remains challenging to analyze due to highly complex MS/MS spectra and chromatographic interference, particularly in low signal-to-noise single-cell proteomics. Here, we introduce ChatDIA, a zero-shot large language model (LLM)-based workflow for targeted DIA analysis that operates through an explicit reasoning-based decision framework. ChatDIA performs automated peptide identification and supports natural-language interaction with DIA data. Unlike purpose-built DIA software that relies on domain-specific models, ChatDIA employs general-purpose LLMs in a zero-shot setting to reason directly over extracted ion chromatograms and generate human-interpretable rationales for each decision. On an expert-annotated Streptococcus pyogenes DIA benchmark dataset, ChatDIA achieved 96.9% accuracy, matching the domain-specific state-of-the-art software DIA-NN (95.5%). In a challenging single-cell HEK-293T DIA proteomics dataset, ChatDIA further demonstrated excellent performance, achieving a lower risk-coverage area under the curve than DIA-NN (0.06 vs. 0.12) and identifying 38.75% and 46.25% of library peptides at 1% and 5% false discovery rate, respectively, compared with 16.25% and 48% for DIA-NN. Together, these results demonstrate that zero-shot LLM reasoning can competitively automate core targeted DIA decision-making while providing transparent, inspectable rationales that enable conversational, interactive validation and data exploration in noisy proteomics applications. More broadly, ChatDIA illustrates how reasoning-based AI systems can move beyond prediction to generate evidence-based decisions, offering a foundation for more general and interpretable computational analysis in proteomics.

bioinformatics↗

bioRxiv2024

MassDash: A Web-based Dashboard for Targeted Mass Spectrometry Visualization

With the increased usage, diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform, DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity providing additional insights into the data. The modular framework is easily extendable enabling rapid algorithm development of novel peak picker techniques, such as deep learning based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

bioinformatics↗

bioRxiv2021

Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow

In bottom-up mass spectrometry based proteomics, deep proteome coverage is limited by high cofragmentation rates. This occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry which separates peptides by their collisional cross section. Here we investigate the capability of the Trapped Ion Mobility Spectrometry (TIMS) device to effectively separate peptide ions and quantify the separation power of the TIMS device in the context of a Parallel Accumulation-Serial Fragmentation (PASEF) workflow. We found that TIMS IM separation increases the number of interference-free MS1 features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5 fold. In a Data Dependent Acquisition (DDA) PASEF workflow, IM separation increased the number of spectra without cofragmentation by a factor of 4.1 and the number of high quality spectra 17-fold. This observed decrease in spectral complexity results in a substantial increase in peptide identification rates when using our data-driven model. In the context of a Data Independent Acquisition (DIA), the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in isolation window width (from 25Da to 6.5Da). Our study shows that TIMS IM separation dramatically reduces cofragmentation rates leading to an increase in peptide identification rates.

bioinformatics↗