Data analysis code for NexProQ, a targeted mass spectrometry workflow for the absolute quantitation of proteins from dried blood spots (DBS) without stable isotope-labelled internal standards.
NexProQ multiplexes a five-point internal calibration curve and the unknown samples into a single TMTpro-labelled acquisition, using non-adjacent reporter ion channels and PRM-PASEF on a timsTOF HT. Every plex therefore carries its own matrix-matched standard curve, which is what makes absolute quantitation possible without synthesising heavy peptide standards.
This repository accompanies:
Richard VR, Ebrahimi Ghahnavieh L, Kubiniok P, Geib T, Willetts M, Hornburg D, Kruppa G, Borchers CH. Multiplexed targeted protein quantitation using isobaric labelling-based internal calibration and PRM-PASEF for the quantification of proteins from dried blood spots.
| Path | Description |
|---|---|
app_v6.py |
Current Streamlit application: builds the acquisition template, fits the per-plex calibration curves and exports the quantitation tables |
app_v2.py, app_v3.py, app_v4.py |
Earlier releases of the application, retained for reproducibility of previously generated results |
R/ |
R scripts for assay characterisation and for generating the manuscript figures (see R/README.md) |
python/ |
Analysis scripts that re-implement the application's calibration fit, used for the calibration-curve figure |
requirements.txt |
Python dependencies |
The application is hosted and needs no local installation:
- Large-cohort version: https://gip-nexproq-largecohort.streamlit.app/
- Standard version: https://gip-nexproq.streamlit.app/
It takes the reporter ion intensities exported from the acquisition software together with a template describing which TMT channel holds which calibration level or sample, fits a weighted least-squares regression per peptide, and returns absolute concentrations with the below-limit-of-quantitation values flagged.
Requires Python 3.10 or newer.
pip install -r requirements.txt
streamlit run app_v6.py
Requires R 4.2 or newer with tidyverse, patchwork and scales.
The scripts read their inputs from data/ and write figures to plots/, both
relative to the working directory, so run them from the root of this repository
after placing the exported quantitation tables in data/. R/README.md lists
which script produces which figure and which input files it expects.
Rscript R/manuscript_figures.R
The mass spectrometry proteomics data are deposited in the ProteomeXchange Consortium via the PRIDE partner repository. Accession numbers are given in the Data Availability statement of the manuscript.
Distributed under the terms of the LICENSE file in this repository.