Rapid assay development for low input targeted proteomics using a versatile linear ion trap.
Brian C SearleAriana E ShannonRachael TeodorescuNo-Joon SongLilian HeilCristina JacobPhilip RemesZihai LiMark RubinsteinPublished in: Research square (2024)
Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4 + and CD8 + T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.
Keyphrases
- mass spectrometry
- liquid chromatography
- gas chromatography
- high performance liquid chromatography
- high resolution
- capillary electrophoresis
- cancer therapy
- flow cytometry
- high throughput
- patient reported outcomes
- label free
- tandem mass spectrometry
- cell therapy
- stem cells
- computed tomography
- simultaneous determination
- drug delivery
- single cell
- big data