Data quality control and mass recalibration standardize MALDI Imaging and enhance interpretability across large datasets

Significance of the topic

Mass accuracy and precision are foundational for reliable interpretation of MALDI imaging mass spectrometry (IMS) data. Small mass shifts across pixels or between experiments cause peak misalignment, erroneous molecular annotations, and reduced comparability across large spatial datasets. Systematic quality-control (QC) and robust mass recalibration or alignment are therefore essential to support reproducibility, cross-laboratory standardization, and confident biological conclusions from spatial proteomics and metabolomics studies.

Aims and study overview

This work introduces three complementary software capabilities implemented in SCiLS Lab 2027a: QC View, Mass Variation Statistics, and a Mass Recalibration/Alignment workflow. The goals are to (1) provide visual and quantitative metrics for mass precision and ion-intensity variation across images, (2) enable automated or guided correction of m/z drift using internal reference features, and (3) standardize quality assessment across large MALDI imaging datasets to improve interpretability and reproducibility.

Methodology

Human tissue samples (including hepatocellular carcinoma and prostate cancer sections) were prepared for on-tissue bottom-up proteomics and analyzed by MALDI imaging. Data acquisition used a timsTOF fleX instrument in oTOF mode over m/z 500–2,500 with pixel sizes of 50×50 µm2 and 60×60 µm2. Acquired datasets were imported into a preview release of SCiLS Lab 2027a. The new QC View provided per-ion and total-ion diagnostics; Mass Variation Statistics quantified mass precision metrics across pixels; and the Mass Recalibration/Alignment workflow performed spectral recalibration or alignment based on internal reference peaks or user-selected high-quality features.

Used instrumentation

• timsTOF fleX mass spectrometer (oTOF mode), m/z 500–2,500 acquisition range
• Pixel sizes: 50×50 µm2 and 60×60 µm2
• On-tissue bottom-up proteomics sample preparation workflows
• SCiLS Lab 2027a (preview) software: QC View, Mass Variation Statistics, Mass Recalibration/Alignment
• Internal reference peptides (multi-peptide calibrant and [Glu]-Fibrinopeptide B used as internal standards)
• Instrument hardware detail: water-cooled timsTOF flight tube (temperature stability important to maintain constant drift path length)

Main results and discussion

Key outcomes demonstrate the usefulness of integrated QC and recalibration tools for detecting and correcting mass drift artifacts:

• QC View panels visualize mean spectra, ion intensity/TIC, mass-drift maps, and mass precision statistics to quickly identify problematic regions or whole datasets.
• Mass Variation Statistics quantify per-m/z mass precision (e.g., m/z standard deviation and isolated peak rate across pixels), enabling objective selection of calibration targets.
• Mass Recalibration/Alignment can operate in two modes: a recalibration that requires internal reference standards to yield a physically correct m/z axis, and an alignment driven by SCiLS Lab feature lists that improves spectral consistency but may not fully restore absolute m/z physical accuracy.
• Practical example: a water-cooler malfunction in the timsTOF caused significant intra- and inter-dataset mass drift in prostate cancer samples. The nominal internal calibrant ([Glu]-Fibrinopeptide B) had a low isolated peak rate (1.4%) and could not be used for recalibration. An alternative endogenous feature at m/z 1302.6545, with a high isolated peak rate (92.7%) across a 165 ppm interval, was used for spectral alignment and fully corrected the mass-precision artifact. After alignment, the [Glu]-Fibrinopeptide B marker achieved 1.8 ppm mass accuracy.
• Application of an externally added multi-peptide reference standard in a hepatocellular carcinoma tissue section produced measurable improvement in the overall calibration error after recalibration in SCiLS Lab.

Benefits and practical applications

The introduced QC and recalibration tools deliver several practical advantages:

• Improved molecular annotation confidence by reducing peak misalignment and mass inaccuracy.
• Standardized, reproducible QC metrics that support inter-experiment and inter-laboratory comparability when internal calibrants are used.
• Rapid identification and correction of localized or instrument-wide mass drift, enabling rescue of valuable datasets that otherwise could be unusable.
• Objective selection of calibration/alignment targets through metrics such as isolated peak rate and per-m/z mass variation.

Future trends and potential applications

Expected directions and opportunities for further development include:

• Automation and integration of drift-detection and recalibration routines into acquisition pipelines for real-time QC feedback.
• Enhanced algorithms combining internal calibrants with robust endogenous features and machine-learning methods to distinguish true biological shifts from instrumental artifacts.
• Standardized multi-site QC schemes and reference materials to improve cross-laboratory comparability in clinical and translational IMS studies.
• Closer integration with spatial-omics data layers (e.g., single-cell transcriptomics, imaging immunohistochemistry) where reliable mass accuracy is critical for multimodal annotation.
• Development of metrics and visualizations tailored for large cohort studies to streamline batch correction and quality-trending analyses.

Conclusion

Systematic QC, quantitative mass-variation metrics, and flexible recalibration/alignment workflows materially improve the interpretability and reproducibility of MALDI imaging datasets. When internal reference standards are available, recalibration can restore a physically correct m/z axis; when not, alignment to robust endogenous features offers an effective remedial path. Together, these tools enable rescue of drift-afflicted datasets, foster standardization across experiments, and increase confidence in spatial molecular annotations.

References

Note: The original report is a Bruker Life Sciences application note describing SCiLS Lab 2027a features and example datasets acquired on a timsTOF fleX. Specific figures and numerical examples (e.g., isolated peak rates, ppm values, and m/z markers) are derived from that report.