Modality

Spatial Omics

Multiplex tissue imaging and in-situ transcriptomics pipelines that combine cyclic-IF or RNA-imaging rounds, cross-cycle registration, and spatial neighborhood analysis to map cell types and tissue niches in situ.

The problem — Spatial omics experiments layer complexity on top of every challenge already present in standard multiplexed imaging: each additional acquisition round introduces registration drift, signal bleed-through, and photobleaching that compound silently. Skip the cross-cycle QC step and your cell-type map reflects slide mechanics, not biology.

What it is / how it works — A spatial omics pipeline begins at ingest: cyclic-IF or transcript images (MERFISH, seqFISH, Visium) are read alongside a codebook or antibody panel that maps channels to markers. The preprocessing stage is where the work is hardest — ASHLAR or SimpleITK performs cross-cycle registration to stitch each round into a single aligned mosaic, and illumination correction removes residual shading. Cell segmentation then runs on the registered stack (Cellpose or Mesmer for membrane-based approaches; Baysor for FISH-based transcript-to-cell assignment). Features are extracted per cell as marker intensities or expression counts using squidpy and scanpy, which also compute spatial neighborhood profiles — the key readout that pure scRNA-seq cannot provide. Batch and sample harmonization with Harmony or scVI follows the same logic as any multi-batch experiment: see Batch Correction Without Erasing Biology for the failure modes that apply equally here. The pipeline closes with cell-type clustering and spatial niche detection, exported to AnnData for downstream analysis or visualization.

Where it breaks — Registration is the single biggest failure point. Sub-pixel drift accumulates across cycles and is easy to miss if QC checks only the first and last round; intermediate rounds can be silently misaligned. Transcript assignment in FISH-based assays is sensitive to segmentation mask quality — an oversized nucleus pulls in transcripts from neighbouring cells, inflating co-expression. Panel normalization across samples follows the same provenance principles described in Metadata & Provenance: The Run Manifest: without a locked panel manifest, comparing cell-type abundances across batches is unreliable.

Cross-cycle registration drift is cumulative. Inspect per-round alignment metrics, not just the final composite — a 0.3-pixel shift per round is invisible at round 2 and catastrophic at round 10.

How we run it — the Spatial Omics pipeline, stage by stage — the nine-stage imaging backbone reshaped for this modality, with the tools, sub-steps and deliverable at each stage.

References

Appears in these notes

  • The QC-Aware ReportA QC-aware report doesn't just present a result — it surfaces the evidence that the result is trustworthy, structured by the four nested levels of quality and ending in an explicit decision. Its job is to make a number un-trustable on sight when it shouldn't be trusted.
  • Choosing a Bioimage Analysis Pipeline — Off-the-Shelf vs CustomMost microscopy questions are answered by composing existing, validated tools — and a custom pipeline is justified only when off-the-shelf options fail on a specific, identifiable axis. This note gives the decision criteria and the signals that you have genuinely crossed into custom territory.
  • Data Standards and Scalable StorageThe OME data model and OME-NGFF/OME-Zarr exist because a microscopy image without a shared data model and a chunked, cloud-native layout is neither interpretable nor analyzable at scale. Format is not plumbing — it decides whether a petabyte dataset can be opened at all.
  • Evaluating Segmentation Models in Real Microscopy WorkflowsA segmenter's benchmark Dice rarely predicts its behavior in a real workflow. Instance counting, merge/split errors, and cross-instrument generalization are the quantities that decide whether the downstream readout is correct — and standard semantic overlap scores are blind to all three.
  • Foundation Models Are Not PipelinesA foundation model is a powerful feature extractor, not a deployable measurement pipeline. It still needs ingestion, QC, preprocessing, confounder correction, validation, and provenance around it — and a frozen encoder makes the surrounding system more important, not less.
  • Image Quality Is Not One NumberFovea treats microscopy quality as four nested levels — image-space, run/sample, readout, and decision — because a metric at one level says nothing about the levels above it. Full-reference scalars like SSIM and PSNR measure only the first, yet teams report as if they measured the third.
  • Why Interoperability Matters in MicroscopyA microscopy pipeline is always several tools in sequence, so the seams between them — file formats, metadata, coordinate conventions — decide whether the pipeline composes or quietly corrupts. Open standards like OME-Zarr/NGFF turn hand-offs from lossy conversions into clean interfaces, and avoid lock-in to any one vendor or tool.
  • Why mAP, Dice, and SSIM Are Not EnoughDice, IoU, mAP, and SSIM measure overlap or similarity against a reference — not whether the result supports the decision the experiment exists to make. The discipline is problem-aware metric selection: derive the metric from the question, not from what is easy to compute.
  • Model Zoos for Bioimage Analysis — From a Zoo Model to a Validated WorkflowThe BioImage Model Zoo and community packaging tools make pre-trained models shareable, runnable, and reproducible across tools — solving distribution and provenance. They do not solve fitness: a downloaded model is a component, and the path from a zoo entry to a trustworthy readout still runs through validation.
  • Verification — A Pipeline You Can Re-RunVerification is the engineering discipline of making a microscopy pipeline deterministic, version-pinned, and re-executable from a provenance record. Without it, every result is a one-off, and "we changed nothing" is unprovable.
  • How to Prepare Microscopy Data for AIMost of the work of applying AI to microscopy is data preparation — formatting, normalization, tiling, and label hygiene — and the single most consequential decision is a split that respects the non-independence of microscopy data. Get the split wrong and every downstream metric is leaked, not earned.
  • Every Output Should Know Where It Came FromProvenance is a property of the artifact, not the pipeline — every mask, profile, and count matrix should carry the lineage that produced it. Where verification asks whether a run reproduces, provenance asks whether a single output, found alone, can still explain itself.
  • How to Make Imaging Data Ready for ReanalysisReanalysis-ready means a third party with no contact with the original lab can re-run, re-segment, or re-interpret the data correctly — which requires FAIR principles, complete metadata, and deposition in a public archive. The bar is reuse by a stranger, not retrieval by the author.
  • The Bioimage Analysis Ecosystem — Where Each Tool FitsBioimage analysis is not one tool but a landscape organized along two axes — interactive vs scriptable, and generalist vs specialist. Fiji, napari, CellProfiler, QuPath, ilastik, and the deep-learning segmenters each occupy a different cell of that grid, and choosing well means knowing which cell your problem lives in.
  • Uncertainty, Drift, and Failure Modes in Bioimage AnalysisModels fail silently when the data drifts away from what they were trained on — and an overconfident, miscalibrated model gives no warning. Trustworthy deployment needs calibrated uncertainty, explicit distribution-shift detection, and a way to flag failure when there is no ground truth to check against.
  • Validation Without a Ground TruthValidation asks whether a microscopy pipeline's outputs are biologically true and fit for purpose — but biology rarely supplies a clean answer key. The discipline is choosing metrics that reflect the question, manufacturing ground truth honestly, and conditioning performance on the experiment.
  • Verification and Validation Are Two Different QuestionsVerification asks whether the pipeline was built right — deterministic, reproducible, correct to spec. Validation asks whether it is the right pipeline — outputs that are biologically true and fit for purpose. Microscopy makes both hard, and conflating them is how silently wrong results get shipped.

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