Polly’s High-Throughput Spatial Metabolomics Pipeline Accelerated Data-to-Insight by 3X
Key Highlights
Spatial metabolomics was too complex to scale at the core facility; Polly delivered a reusable, automated pipeline that cut data-to-insight time by 3× while standardizing outputs for consistency.
Diverse, proprietary instrument formats (.imzML/.ibd/.mat; labeled and unlabeled) slowed prep and analysis; the pipeline ingested and standardized all inputs and supported both targeted and untargeted workflows across studies.
Poorly annotated references and fragmented metadata led to inconsistent findings; LLM-based Harmonize plus a QA layer standardized sample/ROI metadata and benchmarked reproducibility across datasets.
Operational bottlenecks and high manual effort limited throughput; with Polly, the core processed 100+ samples/week, delivered actionable outputs in <5 minutes per sample, and cut QC-related reruns by >90% with ~6× less manual prep.
Teams needed tissue-aware insights, not just spectra; advanced spatial analytics (Scanpy/STAGATE clustering, H&E co-registration, isotopic flux quantification) resolved intra-tissue niches for precise, biologically meaningful interpretations.
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