AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Abstract

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Figure 1

AutonoMS offers walkaway automation of ion mobility mass spectrometry data collection and analysis. (A) AutonoMS integrates software control layers with the Agilent RapidFire–6560 ion mobility mass spectrometry system to provide fully automated data acquisition, raw data handling, data processing, and metabolomic end-to-end analysis, resulting in tabular metabolite reports and interactive Skyline documents. (B) The AutonoMS software stack is hosted on a shared drive between the 6560 and RapidFire control computers. Human laboratory users or an upstream software agent may trigger AutonoMS runs using a tabular experiment definition file. The AutonoMS workflow control is written using Prefect which coordinates the event-triggered actions of modules responsible for instrument file compilation, instrument control (pywinauto), postacquisition raw data handling including ion mobility demultiplexing and CCS calibration (PNNL PreProcessor and DEIMoS), and metabolomic data analysis (Skyline^,). Additional modules may be written and incorporated into the workflow to accommodate different instruments or analysis workflows.

Figure 2

Automated targeted data analysis of the standards with AutonoMS. (A) Detected glutathione [M + H]⁺ ion intensity from an automated AutonoMS analysis of glutathione in 50/50 methanol/water at 0.1 mg/mL injected from separate wells in a robotically dispensed 384 well microplate. (B) Detected peak areas from AutonoMS analysis of robotically prepared triplicate serial dilutions of 5 chemical standards in positive and negative ionization modes robotically dispensed into a 384 well microplate.

Figure 3

Automated analysis of the extracted intracellular yeast samples with AutonoMS. (A) Detected peak areas in extracted yeast samples across plate injections (368 over 38 min per mode) of the 5 ions used in the chemical standards analysis. Ions correspond to the [M + H]⁺ and [M – H]⁻ adducts in positive and negative modes, respectively. Peak areas shown as the 6-injection moving average (solid lines) together with 6-injection standard deviation (shaded areas). (B) Untargeted metabolite features found across all extracted yeast samples across positive (blue) and negative (orange) ionization modes. Displayed features were present in at least 2/3 of samples in a given mode and had Agilent quality scores greater than 70. A total of 812 features were found, of which 404 involved multiple ions in various ionization states. Single ionization state (z = 1) ion features are shown in gray, and marker size is scaled according to log₁₀(abundance).

Figure 4

Use of AutonoMS for automated data collection and integration with background knowledge. Panel of 35 metabolites from the Yeast Metabolome Database (YMDB^,) with publicly available collision cross section (CCS) values from the Baker Lab Cross Collision Section Database (CCSDB). Chosen metabolites were detected by the acquisition method described in the Experimental Section and exhibited mean peak areas across extracted yeast injections greater than 10⁴. Metabolites are grouped by their ClassyFire/ChemOnt superclass labels and are plotted against their mean peak areas across extracted yeast injections per ionization mode. Error bars display the intensity of the standard deviations.