Biology subjects

Bache, N.

Publications and source records attributed to Bache, N..

9 recordsLinked to original sources

bioRxiv2026

Pairing Data Independent Acquisition and High-Resolution Full Scan for Fast Urinary Tract Infection Diagnosis

BackgroundRapid and accurate identification of urinary tract infection (UTI) pathogens is critical for effective treatment and combating antimicrobial resistance. Conventional culture-based diagnostics are slow, and standard tandem mass spectrometry workflows are resource-intensive. MethodsWe present a proof-of-concept workflow that integrates high-resolution data-independent acquisition (DIA) MS/MS on the Thermo Scientific Orbitrap Astral with MS1-only spectra from the Orbitrap Exploris 480. DIA data establish a reference panel of pathogen-specific peptides, which are then identified in MS1 spectra from urine samples. Machine learning models trained on these matched MS1 features were used to classify eight common uropathogens and non-infected controls across synthetic inoculations, pure cultures, and clinical patient samples. Model development employed a one-vs-all Random Forest (Ranger) framework with nested cross-validation for feature selection and hyperparameter tuning, followed by evaluation on an independent held-out external patient cohort. ResultsThe approach accurately distinguished bacterial species in both controlled inoculated samples and clinical patient samples. Using repeated nested cross-validation, the model achieved a mean Matthews Correlation Coefficient (MCC) of 0.88, indicating robust classification performance across resampled training partitions. Performance generalized to an independent patient cohort, achieving an MCC of 0.822, confirming the models ability to maintain predictive accuracy under external validation. ConclusionsThis proof-of-concept demonstrates that pairing DIA-derived peptide panels with MS1-only data acquired on a cost-effective instrument suitable for routine analysis, enables rapid, culture-free identification of UTI pathogens. The method provides a scalable, high-throughput platform suitable for clinical applications and establishes a foundation for broader biomarker discovery and potential quantitative workflows. Key Points- DIA-derived peptide panels enable pathogen detection using MS1-only measurements. - Machine learning classifies eight common uropathogens covering 84% of infections. - MS1-only workflows enable culture-free pathogen identification of 300 samples/day.

bioinformatics↗

bioRxiv2025

A Solid-Phase Extraction Capture (SPEC) workflow in nanoliter volumes for fast, robust and ultrasensitive proteomics

Sample preparation remains a critical bottleneck in mass spectrometry (MS)-based proteomics, particularly for limited sample amounts where surface adsorption and dilution cause substantial losses. Here, we present Solid-Phase Extraction Capture (SPEC), a workflow that confines protein processing to nanoliter volumes within ion-exchange or C18 matrix inside a pipette tip. This achieves near-complete proteolysis within 5 minutes instead of hours and maintains full compatibility with strong detergents without cleanup steps, enabling effective lysis of challenging samples. From 200 ng FFPE tissue, SPEC achieves proteome depth and reproducibility exceeding conventional bulk protocols using 100 {micro}g, critical when sample is irreplaceable. The modular two-tip configuration enables on-tip chemical modifications for mTRAQ labeling and fractionation, while integration with enrichment workflows yields 2-fold improved glycopeptide identifications from plasma and 3-fold enhanced ubiquitin remnant identification at low amounts. SPEC enables nanoPhos for cell-type resolved tissue phosphoproteomics and provides a universal platform for proteomics sample preparation.

systems biology↗

bioRxiv2023

Fully automated workflow for integrated sample digestion and Evotip loading enabling high-throughput clinical proteomics

Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample-preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 (OT-2) robot that combines sample digestion, cleanup and Evotip loading in a fully automated manner, allowing the processing of up to 192 samples in 6 hours. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability even at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic Ti-IMAC beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.

molecular biology↗

bioRxiv2023

High-throughput and scalable single cell proteomics identifies over 5000 proteins per cell

The emergence of mass spectrometry (MS)-based single-cell proteomics (SCP) promise to revolutionize the study of cellular biology and biomedicine by providing an unparalleled view of the proteome in individual cells. Despite its groundbreaking potential, SCP is nascent and faces challenges including limited sequence depth, throughput, and reproducibility, which have constrained its broader utility. This study introduces key methodological advances, which considerably improve the sensitivity, coverage and dependability of protein identification from single cells. We developed an almost lossless SCP workflow encompassing sample preparation to MS analysis, doubling the number of identified proteins from roughly 2000 to over 5000 in individual HeLa cells. A comprehensive evaluation of analytical software tools, alongside strict false discovery rate (FDR) controls solidified the reliability of our results. These enhancements also facilitated the direct detection of post-translational modifications (PTMs) in single cells, negating the need for enrichment and thereby simplifying the analytical process. Although throughput in MS remains a challenge, our study demonstrates the feasibility of processing up to 80 label-free SCP samples per day. Moreover, an optimized tissue dissociation buffer enabled effective single cell disaggregation of drug-treated cancer cell spheroids, refining the overall proteomic analysis. Our workflow sets a new benchmark in SCP for sensitivity and throughput, with broad applications ranging from the study of cellular development to disease progression and the identification of cell type-specific markers and therapeutic targets.

systems biology↗

bioRxiv2023

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes

We present One-Tip, a lossless proteomics methodology that seamlessly combines swift, one-pot sample preparation with narrow-window data-independent acquisition mass spectrometric analysis. With simplest sample processing, One-Tip reproducibly identifies > 9,000 proteins from [~]1000 cells and [~] 6,000 proteins in a single mouse zygote with a throughput of 40 samples-per-day. This easy-to-use method expands capabilities of proteomics research by enabling greater depth, scalability and throughput covering low to high input samples.

biochemistry↗

bioRxiv2020

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing1-3. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary4,5. Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions6,7. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material8, posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease.

systems biology↗

bioRxiv2019

Evosep One Enables Robust Quantitative Deep Proteome Coverage using Tandem Mass Tags while Significantly Reducing Instrument Time

The balance between comprehensively analyzing the proteome and using valuable mass spectrometry time is a genuine challenge in the field of proteomics. Multidimensional fractionation strategies have significantly increased proteome coverage, but often at the cost of increased mass analysis time, despite advances in mass spectrometer acquisition rates. Recently, the Evosep One liquid chromatography system was shown to analyze peptide samples in a high throughput manner without sacrificing in depth proteomics coverage. We demonstrate incorporation of Evosep One technology into our multiplexing workflow for quantitative analysis of tandem mass tag (TMT)-labeled non-small cell lung carcinoma (NSCLC) patient-derived xenografts (PDXs). Using a 30 samples per day Evosep workflow, >12,000 proteins were identified in 48 hours of mass spectrometry time, which is comparable to the number of proteins identified by our conventional concatenated EASY-nLC workflow in 67.5 hours. Shorter Evosep gradient lengths reduced the number of protein identifications by 10%, while decreasing mass analysis time by 50%. Thus, our Evosep workflow enables quantitative analysis of multiplexed samples in less time without conceding depth of proteome coverage.

systems biology↗

bioRxiv2018

Online parallel accumulation - serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, while mass spectra are acquired in about 100 microseconds with time-of-fight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive due to its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 800,000 fragmentation spectra in standard 120 min LC runs are easily achievable, which can be used for near exhaustive precursor selection in complex mixtures or re-sequencing weak precursors. MaxQuant identified more than 6,400 proteins in single run HeLa analyses without matching to a library, and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,900 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

systems biology↗

bioRxiv2018

A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics

To further integrate mass spectrometry (MS)-based proteomics into biomedical research and especially into clinical settings, high throughput and robustness are essential requirements. They are largely met in high-flow rate chromatographic systems for small molecules but these are not sufficiently sensitive for proteomics applications. Here we describe a new concept that delivers on these requirements while maintaining the sensitivity of current nano-flow LC systems. Low-pressure pumps elute the sample from a disposable trap column, simultaneously forming a chromatographic gradient that is stored in a long storage loop. An auxiliary gradient creates an offset, ensuring the re-focusing of the peptides before the separation on the analytical column by a single high-pressure pump. This simplified design enables robust operation over thousands of sample injections. Furthermore, the steps between injections are performed in parallel, reducing overhead time to a few minutes and allowing analysis of more than 200 samples per day. From fractionated HeLa cell lysates, deep proteomes covering more than 130,000 sequence unique peptides and close to 10,000 proteins were rapidly acquired. Using this data as a library, we demonstrate quantitation of 5200 proteins in only 21 min. Thus, the new system-termed Evosep One-analyzes samples in an extremely robust and high throughput manner, without sacrificing in depth proteomics coverage.

biochemistry↗