Time-Series Spatial Transcriptomics Service
Standard spatial transcriptomics gives you a map. Time-series spatial transcriptomics gives you a movie.
Biological processes—development, disease progression, drug response—are not static. They are dynamic events that unfold over time and space. Our Time-Series Spatial service allows you to integrate the fourth dimension, tracking how molecular neighborhoods shift, expand, and reorganize throughout your study.
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- Advantages
- Our service
- Workflow
- Applications
- FAQs
- Sample requirements
- Case study
Why Add Time? The 4D Advantage
Most services offer a single snapshot. We offer the ability to connect the dots between those snapshots to reveal causality and progression.
- From "Where" to "When & Where": Don't just identify a tumor boundary; watch it advance. Don't just see a healed wound; quantify the phases of repair from inflammation to fibrosis.
- Catch Transient States: Many critical biological events (like immune cell infiltration or developmental signaling waves) are fleeting. Time-series analysis captures these transient "middle" states that endpoint analysis misses.
- Dynamic Niche Evolution: Observe how the cellular neighborhood changes. Does the arrival of Cell Type A precede the upregulation of Gene B in Cell Type C?
Service Features
1. Spatio-Temporal Differential Expression (stDE)
We go beyond simple group comparisons. We identify genes that show significant changes over time specifically within distinct regions.
The Insight: "Gene X is upregulated in the tissue core at Day 3, but shifts to the periphery by Day 7."
2. Spatial Trajectory & Pseudotime Analysis
Using advanced computational modeling, we reconstruct the developmental or disease trajectory of cells within the tissue context.
The Insight: Map the "flow" of differentiation or disease spread across the physical tissue coordinate system.
3. Pattern Propagation Modeling
We identify genes that behave like waves—starting in one region and propagating outward over time.
The Insight: Visualize how an inflammatory signal spreads from a vessel into the parenchyma over 24 hours.
Workflow
Applications
| Area of Study | Application Example |
|---|---|
| Developmental Biology | Organogenesis: Watch how signaling centers (morphogens) establish tissue gradients and induce differentiation layer-by-layer over embryonic days. |
| Oncology | Tumor Evolution & Resistance: Track the spatial response to therapy. Does the resistant clone emerge from the hypoxic core or the vascular edge after treatment starts? |
| Neuroscience | Degeneration Kinetics: Map the spatiotemporal spread of neurotoxicity or protein aggregation (e.g., Alzheimer's plaques) across brain regions over months. |
| Regenerative Medicine | Wound Healing: Quantify the precise timing of immune influx, re-epithelialization, and scarring in a distinct spatial sequence. |
FAQs
How many timepoints do I really need?
While more is always better for resolution, we generally recommend a minimum of 3 timepoints (e.g., Baseline, Peak Disease/Treatment, and Resolution/Endpoint) to mathematically establish a trend (linear or non-linear).
Can I compare tissues that change size drastically (e.g., a growing tumor)?
Yes. This is where our "Niche-Based Alignment" comes in. Since we cannot overlay a small tumor directly onto a large one, we align based on biological structure rather than physical coordinates. We compare "Core vs. Core" and "Margin vs. Margin" across time, regardless of how much the tissue has expanded.
How do you handle batch effects if I collect samples months apart?
Time-series datasets are highly susceptible to batch effects. We use advanced computational integration tools (such as Harmony, scVI, or specialized spatial integration algorithms) to "anchor" your datasets. This ensures that the differences we see are due to biology (Time), not technical variation (Batch).
What is "Trajectory Analysis" in a spatial context?
In single-cell data, trajectory analysis infers the "age" of a cell. In spatial data, we add physical direction. We can calculate vectors to show not just how a cell is changing, but where that change is moving (e.g., "Differentiation is flowing from the cortex into the medulla").
Learn about other Q&A.
Sample Requirements
- Tissue Samples: Fresh frozen or FFPE (Formalin-Fixed, Paraffin-Embedded) tissue sections.
- Tissue Blocks: Provide us with your tissue blocks (either frozen or fixed), and we will handle the sectioning and other necessary preparations.
- Time Points: Specify the time intervals you wish to analyze, and we will coordinate the experimental setup accordingly.
Time-Series Spatial Transcriptomics Case Study
Title: Spatially resolved transcriptomic analysis of the germinating barley grain
Journal: Nucleic Acids Research
Published: 2023
- Background
- Methods
- Results
- Conclusion
- Reference
Seed germination is a biologically and agriculturally pivotal process, underpinning global food security as seeds contribute approximately 70% of human dietary calories. Conventional transcriptomic approaches, particularly single-cell RNA sequencing, face significant limitations in plant systems due to irreversible loss of spatial context during tissue dissociation, technical hurdles in protoplast isolation caused by rigid and heterogeneous cell walls, and potential omission of rare cell types. Although spatial transcriptomics has emerged as a transformative technology for preserving tissue architecture while profiling gene expression, its adoption in cereal crop research remains limited. This study addresses this methodological gap by establishing an optimized spatial transcriptomics workflow tailored for barley seeds to unravel the spatiotemporal dynamics of gene regulation during early germination.
The researchers developed a plant-optimized spatial transcriptomics workflow to profile gene expression dynamics during barley seed germination. Samples were collected across key germination stages and analyzed using the 10x Genomics Visium platform with protocols adapted for plant tissues. Spatially variable genes were identified bioinformatically and validated via in situ hybridization, with cross-species comparisons supporting functional conservation.
Four major functional groups were identified: DNA/RNA metabolism, seed storage and metabolism, transport, and miscellaneous functions. Genes related to transcription, translation, and ribosomal proteins were highly expressed in the embryo, while genes for seed metabolism were more prominent in the endosperm and aleurone. A wordcloud analysis revealed functional categories associated with the processes of germination (Figure 3).
Figure3. Spatio-temporal analysis of key biological processes during barley germination.
Figure 4 examines the spatial and temporal expression of aquaporin genes, essential for water and nutrient transport. At 24 HAI, aquaporins like HORVU4Hr1G079230 (TIP1;1) and HORVU1Hr1G043890 (TIP3;1) exhibit distinct expression patterns across different tissues. TIP1;1 is predominantly expressed in the coleorhiza and later in the mesocotyl and radicle, while TIP3;1 is expressed in the embryo and aleurone at early time points, transitioning to the scutellum and aleurone at later stages. These results highlight the role of aquaporins in facilitating water movement during seed germination.
Figure 4. Spatio-temporal expression of aquaporin genes during barley germination.
Figure 5 explores the expression of genes involved in starch degradation, particularly alpha-amylases, during barley germination. These genes are highly expressed in the aleurone and endosperm, with peak expression occurring at 24 HAI. This temporal pattern reflects the critical role of starch breakdown in providing energy for the growing seedling during germination.
Figure 5. Spatio-temporal expression of carbohydrate-related genes during barley germination
Figure 6 examines the expression patterns of transcription factor genes during barley germination. Transcription factors like bZIP, bHLH, and AP2/EREB show tissue-specific expression, with bZIP factors primarily expressed in the embryo and AP2/EREB factors varying across tissues over time. These patterns suggest that these transcription factors play key roles in regulating gene expression during the early stages of seedling development.
Figure 6. Spatio-temporal expression of transcription factors during barley germination.
Figure 7 presents an analysis of spatially variable genes (SVGs), which show significant spatial autocorrelation, meaning their expression is correlated with specific tissue regions. Figure 7A shows the number of SVGs with significant spatial coherence across different time points, illustrating how certain genes exhibit distinct expression patterns over time. Figure 7B presents Moran's I statistics, which measure the spatial autocorrelation of gene expression. Higher Moran's I values indicate stronger spatial clustering of gene expression. Figure 7C highlights specific SVGs that were identified, such as the HD2B histone deacetylase gene, which showed high spatial correlation and was enriched in specific growth centers, such as the radicle and shoot meristem, indicating its role in seed dormancy and germination.
Figure 7. Spatial autocorrelation defines domainsin the embryo of the germinating barley grain.
Co-expression modules of SVGs, identified using weighted gene co-expression network analysis (WGCNA). These modules represent groups of genes that show similar expression patterns across the germination process.
Gene ontology (GO) enrichment analysis for co-expressed modules, highlighting the biological processes associated with each module. For instance, modules related to metabolic processes like sulfur metabolism and glutathione synthesis were enriched, suggesting their key roles in seedling development (Figure 8).
Figure 8. Co-expression of spatially variable genes in the embryo of 24 HAI germinating barley grain.
By integrating spatial transcriptomics, this study delineates germination-associated gene expression dynamics in barley seeds with unprecedented spatial resolution. It not only deepens fundamental understanding of seed biology but also furnishes precise molecular tools and targets to accelerate cereal breeding and genetic enhancement efforts.
Reference
- Peirats-Llobet, Marta et al. "Spatially resolved transcriptomic analysis of the germinating barley grain." Nucleic acids research vol. 51,15 (2023): 7798-7819. doi:10.1093/nar/gkad521
