A good control group is the biological comparator that answers the study’s specific question, sampled from enough independent donors or animals to support the intended conclusion. It is not a collection of extra cells, spots, or tissue sections, and assay controls such as positive and negative probes cannot replace it. Sound design also balances conditions across slides and batches and samples regions that represent the tissue feature under study.
What makes a good control group for a spatial molecular study?
Start by defining the contrast: which condition is being compared with which comparator, and to what population should the result apply? A generic “normal” sample is not automatically suitable. Depending on the intervention and causal question, the relevant comparator might be untreated, vehicle-treated, matched tissue, or a disease comparator. State why it is appropriate and how it is matched; no one option is right for every study.
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For example, a study might ask whether expression in a specified cell type or region differs between a treated condition and its matched comparator across independent donors. That plain-language question helps determine what must be matched, what counts as an independent replicate, and which tissue regions need to be sampled.
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Distinguish the biological unit, the experimental unit, and the observations collected from them. The biological unit is often a donor or animal; the experimental unit is the smallest unit independently assigned to a condition, which may be a tissue block. Sections, fields of view, regions of interest (ROIs), spots, bins, and cells are usually observations or technical repeats within those units. Counting them as independent donors or treatment assignments creates pseudoreplication. The Bioconductor methods chapter “Experimental design – Orchestrating Spatial Transcriptomics Analysis with Bioconductor” explains these distinctions and cautions that technical replicates do not increase biological N.
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If treatment is assigned to an animal, several sections or thousands of cells from that animal do not create several independent treatment replicates. Technical repeats can improve measurement precision for that animal or donor, but the independent sample size remains tied to the unit assigned or sampled independently.
How should biological and assay controls differ?
A biological comparator addresses the hypothesis: does the outcome differ between the conditions of interest? Assay controls instead check whether the measurement process behaves as expected—for example, whether target signal can be detected or whether background is present. Both are useful, but they answer different questions.
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| Control or design element | What it helps assess | What it cannot establish |
|---|---|---|
| Biological comparator group | Whether the biological outcome differs between conditions relevant to the hypothesis. | It must still be replicated at the appropriate independent unit and matched to the causal question. |
| Positive assay control | Whether the assay detects expected signal or whether analyte integrity is adequate. | It does not show that the biological comparator is appropriate. |
| Negative assay control | How much signal may arise from background, nonspecific binding, or staining. | It does not estimate biological variability; the right negative control depends on the platform and analyte. |
| 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 the study samples. |
| Technical replicate or adjacent section | How reproducible measurement is for a given biological unit. | It does not add independent biological replicates. |
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 or nonspecific signal. These are assay-specific examples, not universal choices for every platform. See “Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity” and the RNAscope ISH Reference Guide. Neither probe supplies the biological replication needed to compare study conditions.
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Conditions should be distributed across slides, runs, batches, and processing order where feasible. If every control is on one slide and every treated sample is on another, a slide difference can become entangled with the biological contrast. Randomization and balanced processing help prevent technical variation from masquerading as biology; they do not guarantee that batch effects disappear. Spatial study design guidance recommends randomizing across slides and batches (Bioconductor). For plate-based assays, distributing controls where practical can also help reveal position and edge effects, as described in the NCBI Bookshelf guide to image-based high-content screening and analysis.
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Define tissue and ROI selection before comparing groups. Use pathology or morphology to identify comparable regions, and sample enough of the tissue architecture to capture the feature’s expected scale and relevant heterogeneity. Tissue quality, limited area, and a platform’s field-of-view constraints can affect whether the sampled region represents the question. A practical guide to spatial transcriptomics discusses ROI selection and these design constraints.
How many biological replicates are enough?
There is no universal minimum N established for spatial molecular studies. The appropriate number depends on biological variation, tissue architecture, feature size, assay resolution, and sampled area. Technical repeats may improve precision for each independent unit, but they do not compensate for too few independent donors or animals. Use a study-specific power rationale when available rather than treating the number of cells or spots as a substitute for biological replication.
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What should the study protocol and report specify?
- Write the contrast in plain language. Name the conditions, the population the conclusion is intended to address, and the tissue feature or region being compared.
- Name the biological and experimental units. State which units support generalization and which unit receives the condition assignment.
- Justify the comparator and matching. Explain why the selected control answers the causal question and what characteristics are matched.
- Choose assay controls for specific failure modes. Identify positive and negative checks appropriate to the platform and analyte; do not count them as biological replicates.
- Plan balanced processing and spatial sampling. Randomize conditions across slides and batches where feasible, and define ROI selection and tissue coverage.
- Report each sampling level. Give donor or animal counts, tissue blocks, sections, slides, ROIs, fields, spots or cells, exclusions, and the level used for statistical inference.
Reporting each level makes clear whether the inference rests on independent biological units or on repeated observations within them.
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