A good control group for a spatial molecular study is one that represents the right biological comparison for the research question and is replicated across enough independent donors, animals, or other experimental units to support the intended conclusion. Assay checks such as positive and negative probes serve a different purpose: they test whether the measurement works and do not replace biological controls.
Start with the comparison you want to make
Choose a control by defining the biological contrast first: what condition is being compared, and to which population should the conclusion apply? A generic “normal” sample is not automatically an adequate baseline. The comparator should match the causal question and relevant features of the study, such as treatment exposure, tissue type, or disease status.
Depending on the question, an appropriate comparator might be untreated, vehicle-treated, matched tissue, or a disease-comparison group. None is universally correct. State why the selected group answers the question and which matching criteria matter.
Identify the independent experimental unit
The number that supports generalization is the count of independent biological or experimental units—not the count of measurements taken from them. In many studies, donors or animals are the biological units. The experimental unit is the smallest unit independently assigned to a condition; depending on the design, it may be a tissue block. If treatment is assigned to an animal, multiple sections, cells, or spots from that animal do not create additional independent treatment replicates.
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Spots, bins, cells, fields of view, and repeated sections are observations or technical repeats. Treating them as independent biological replicates creates pseudoreplication and can overstate the evidence. The Bioconductor methods chapter Orchestrating Spatial Transcriptomics Analysis with Bioconductor explains the distinction between biological, experimental, and observational units.
Keep biological controls separate from assay controls
A biological comparator tests the study hypothesis. Assay controls check performance, expected signal, analyte integrity, or background. Both may be needed, but they answer different questions.
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| Control or design element | What it answers | What it cannot establish |
|---|---|---|
| Biological comparator group | Whether the outcome differs between conditions relevant to the hypothesis | It cannot support population-level inference without replication at the appropriate independent unit. |
| Positive assay control | Whether the assay detects expected target signal or the analyte is sufficiently intact | It does not show that the biological comparator is appropriate. |
| Negative assay control | How much signal may come from background, nonspecific binding, or staining | It does not estimate biological variability; a particular negative probe is not universal across platforms. |
| Reference tissue or cell-line pellet | Whether known material supports quality control, normalization, or orientation across slides or batches | It may not represent the biology or tissue context of study samples. |
| Technical replicate or adjacent section | How reproducible measurement is for a given biological unit | It does not increase the independent biological sample size. |
For RNA in situ hybridization (RNA-ISH), published examples use ActB as a positive control for RNA integrity and bacterial dapB as a negative control for background and nonspecific signal. These are assay-specific checks, not substitutes for biological replication. See the example in Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity and the RNAscope ISH Reference Guide.
Balance samples across slides and processing batches
Where feasible, distribute conditions across slides, batches, runs, and processing order rather than letting condition track a particular slide or batch. If all control samples are processed together and all experimental samples separately, technical differences can become difficult to distinguish from biological differences. Randomization and balanced processing help manage this risk; they do not guarantee that batch effects disappear.
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For plate-based assays, distribute controls across positions when practical to help identify or limit position and edge effects. The Advanced Assay Development Guidelines for Image-Based High Content Screening and Analysis discusses positive and negative controls and their placement in relation to plate bias.
Make tissue and region sampling representative
Choose regions of interest (ROIs) and fields of view that capture the tissue architecture and the feature under study. Use pathology or morphology to identify comparable regions, and consider whether the sampled area covers the expected scale and heterogeneity of the feature. A control that samples a different tissue compartment—or too little of the relevant area—may not represent the intended baseline.
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Tissue availability, quality, and a platform’s field-of-view constraints can limit what a sample represents. These constraints also affect comparisons between platforms, which can differ in spatial resolution, gene coverage, and sample compatibility. A practical guide to spatial transcriptomics: lessons from over 1000 samples discusses ROI selection, tissue quality, and design constraints.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan replication and power for the study
There is no universal sample-size number for spatial molecular studies. The number of independent units needed depends on the biological variation, tissue architecture, feature size, assay resolution, sampled area, and the intended inference. Use a study-specific power rationale where available rather than treating a large count of cells or spots as a substitute for independent replication.
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Technical repeats can improve measurement precision for a given biological unit, but they do not increase the independent sample size. The Bioconductor chapter on experimental design covers replication, power, randomization, and pseudoreplication in spatial transcriptomics.
A practical design checklist
- Write the estimand in plain language. For example: “difference in expression in a specified cell type or region between condition A and its matched comparator across independent donors.”
- Name both units. State the biological unit used to generalize and the experimental unit to which the condition is assigned.
- Justify the comparator. Specify why it fits the causal question and what features must be matched.
- Choose assay controls for likely failure modes. Select positive and negative probes or reference material appropriate to the platform and analyte.
- Block and randomize. Distribute conditions across slides, batches, runs, and processing order where feasible.
- Predefine tissue and ROI selection. Identify comparable regions and ensure sampling covers the relevant feature and tissue heterogeneity.
- Report each level of sampling. Give donor or animal counts, tissue blocks, sections, slides, ROIs, fields, spots or cells, exclusions, and the level used for statistical inference.
Reporting these levels makes clear which observations are independent replicates and which are nested measurements. It also lets readers judge whether the selected tissue areas and processing design support the comparison.
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