NATA: Advancing Spatial Biology Through Whole-Transcriptome in situ Profiling Across Complex Biological Systems
Introducing NATA Spatial Transcriptome Platform
Singapore, June 2026 – We are pleased to announce the launch of the NATA Spatial Transcriptome Platform (NATA), an integrated solution for whole-transcriptome spatial profiling. By combining broad RNA capture, high sensitivity, and spatially resolved analysis, NATA enables comprehensive characterization of tissue biology directly within intact tissue sections.
Built to address key limitations of existing spatial transcriptomics technologies, NATA supports unbiased detection of coding and non-coding RNAs, robust profiling of FFPE specimens, and simultaneous spatial analysis of host and pathogen transcripts.
To deliver this integrated workflow, the NATA Spatial Transcriptome Platform brings together dedicated instrumentation, chemistry, and bioinformatics into a single solution (Figure 1). The platform consists of:
- NATA Align Spatial Instrument
- NATA Thermal Control System
- NATA Omni Spatial Transcriptomics Kit for FFPE Samples
- NATA Insight Bioinformatics Software
Together, these components provide an end-to-end workflow for high-resolution spatial transcriptomic analysis across diverse biological systems.
Figure 1. NATA Spatial Transcriptome Platform
This integrated platform combines these components to deliver several key capabilities that distinguish NATA from conventional spatial transcriptomics approaches:
- High-Sensitivity FFPE Profiling - Enables robust spatial transcriptomic analysis from archived and challenging FFPE specimens.
- Whole-Transcriptome Spatial Profiling - Supports detection of coding and non-coding RNA species beyond targeted gene panels.
- Spatial Host-Pathogen Mapping - Simultaneously captures host and microbial transcripts to resolve infection-associated microenvironments.
Flexible and High Sensitivity Spatial Transcriptomics
The ability to analyze archived and clinically relevant specimens is essential for expanding the impact of spatial biology. NATA is designed to deliver high-sensitivity performance across a wide range of tissue types, including FFPE samples where RNA degradation, fragmentation, and crosslinking typically present significant challenges.
Using a random-primer-based capture strategy, NATA enables both tissue-scale mapping and high-resolution spatial interrogation. In a mouse brain FFPE section (50 µm spot size), NATA detected 1,973 spatial spots, with a median of 49,983 UMIs and 6,309 genes per spot (Figure 2)1. In a mouse olfactory bulb FFPE tissue (15 µm spot size), 4,471 spatial spots were detected, with a median of 3,700 UMIs and 941 genes per spot (Figure 3) 1, demonstrating high-resolution molecular characterization of complex tissue architecture.
Importantly, consistent transcript capture was observed across FFPE tissues from multiple species and organ systems (Table 1), highlighting its broad applicability for spatial transcriptomic analysis of archived tissues.
Figure 2. Spatial transcriptomic profiling of mouse brain FFPE. Left: Spatial UMI count map. Right: Spatial gene count map.
Figure 3. Spatial transcriptomic profiling of mouse olfactory bulb FFPE tissue. Left: Spatial UMI count map. Right: Spatial gene count map.
Table 1.NATA demonstrates robust spatial transcriptomic performance across FFPE tissues from diverse species and tissue types, consistently achieving robust transcript detection and high-quality spatial profiling in challenging archived samples.
Whole-Transcriptome Profiling and RNA Diversity
In addition to sensitive transcript detection, the breadth of captured RNA species is essential for fully resolving tissue complexity. Unlike poly(A)-dependent approaches, NATA uses a random-priming capture strategy that reduces transcript-end bias and enables more uniform gene-body coverage from 5′ to 3′ regions. This supports more complete transcript representation within intact tissue contexts (Figure 4) 1.
Figure 4. Random primer–based capture enables uniform gene-body coverage compared with oligo-dT priming.
Beyond mRNA detection, NATA captures a diverse spectrum of RNA species in situ, including long non-coding RNAs (lncRNAs), small nucleolar RNAs (snoRNAs), and other non-coding transcripts, expanding the scope of spatially resolved transcriptomic profiling (Figure 5).
Figure 5. NATA enables broad RNA class detection across coding and non-coding species.
Resolving Complex Microenvironments
Another important capability enabled by total RNA spatial profiling is the simultaneous characterization of host and pathogen transcriptomes within the same tissue section. Understanding host-pathogen interactions is critical for infectious disease research. However, resolving microbial signals alongside host responses within their native spatial context remains challenging.
NATA enables simultaneous spatial profiling of host and pathogen transcriptomes within the same tissue section, providing insights into infection dynamics, immune activation, and disease mechanisms. The following examples highlight how NATA can be applied to investigate host–pathogen interactions across distinct infectious disease models.
Case 1: Host-Viral Interactions in FFPE Human HCC Tissue
Deciphering tumor heterogeneity and viral infection remains challenging, particularly in FFPE clinical specimens where RNA degradation and crosslinking can limit comprehensive transcriptomic profiling.
NATA was applied to FFPE human hepatocellular carcinoma (HCC) and adjacent non-tumor liver tissue at 15 μm spatial resolution (Figure 6) 1. Transcriptomic profiling captured rich molecular signals across both tumor and non-tumor regions. Hepatitis B virus (HBV) transcripts were strongly enriched in tumor regions whereas only minimal viral signal was detected in the adjacent non-tumor liver tissue, supporting the specificity of viral transcript detection rather than nonspecific background signal1.
Figure 6. Spatial mapping of detected gene numbers (left) and HBV RNA localization (right) in FFPE human HCC tissue. Tumor tissue is shown in the upper panels and adjacent non-tumor tissue in the lower panels.
Beyond viral localization, integrated spatial analysis resolved CNV-defined tumor subclones, pseudotime-inferred cellular trajectories, and CytoTRACE-based differentiation states (Figure 7A) 1. Differential HBV enrichment was observed across tumor subpopulations, linking viral localization with tumor architecture and cellular state heterogeneity (Figure 7B) 1. Regions with higher HBV transcript abundance showed preferential enrichment in specific tumor subclones and were associated with elevated CNV scores and less differentiated cellular states, highlighting spatial associations between viral localization, tumor heterogeneity, and cellular state dynamics1.
These findings demonstrate NATA’s ability to resolve viral localization, tumor heterogeneity, and cellular states within clinical FFPE specimens.
Figure 7. A. Spatial mapping of CNV-defined tumor subclones, pseudotime trajectories, and CytoTRACE-predicted differentiation states. B. Observed-to-expected HBV-positive spatial spots across tumor subclones in FFPE human HCC tissue.
Case 2: Host-Bacterial Interactions in Infectious Lung Tissue
While the HBV-HCC study highlights NATA’s ability to resolve viral localization and host-associated molecular heterogeneity in clinical FFPE specimens, the platform also extends to bacterial infection models, enabling investigation of spatial immune responses within infected tissues.
NATA was applied to Klebsiella pneumoniae-infected (KP-infected) mouse lung FFPE tissue to characterize infection-associated microenvironments and host-pathogen interactions. Spatial transcriptomic profiling revealed localized enrichment of bacterial transcripts in infected regions, alongside host gene expression patterns between normal and infected lung tissue (Figure 8) 1.
Figure 8. A. Spatial mapping of detected gene numbers in normal (top) and corresponding KP-infected lung tissue (bottom). B. Spatial mappings of the proportion of KP transcripts among total transcripts per spot in normal (top) and infected (bottom).
Co-localization analysis revealed overlap between neutrophil-enriched regions and bacterial transcript-enriched areas, consistent with targeted immune recruitment to infection sites (Figure 9) 1. This provides insight into the spatial organization of immune cells relative to pathogen burden during infection.
Figure 9. A. Spatial distributions of neutrophil-enriched and KP-enriched spots in normal lung tissue. B. Co-localization of neutrophil-enriched spots and KP-enriched spots in KP-infected lung tissue.
In addition, spatial mapping of alveolar epithelial cell populations showed reduced abundance of both alveolar type I and type II cells in infected regions, reflecting infection-associated disruption of lung architecture and local tissue remodeling (Figure 10) 1. Together, these results demonstrate NATA’s ability to simultaneously resolve pathogen localization, immune cell recruitment, and tissue remodeling, offering an integrated view of host-pathogen interactions in infected tissues.
Figure 10. Spatial distribution of alveolar type I (AT1) and alveolar type II (AT2) cells in normal (left) and KP-infected (right) mouse lung tissue.
Collectively, these applications highlight NATA’s ability to resolve complex tissue microenvironments across tumor and infection models, integrating tissue architecture, molecular programs, and spatially organized biological processes within a unified spatial framework.
Future Insights
By integrating random primer-based whole-transcriptome capture with spatial tissue analysis, NATA expands spatial biology beyond panel-based and species-restricted approaches. The platform enables simultaneous interrogation of tissue architecture, RNA diversity, cellular heterogeneity, and host-pathogen interactions within a single spatial framework.
As spatial biology continues to evolve, comprehensive molecular profiling while preserving spatial organization will be essential for uncovering new mechanisms underlying health and disease. NATA is designed to support this next generation of discovery, empowering researchers to uncover previously inaccessible molecular signals and generate deeper insights from both research and clinical specimens.
For ordering information, please contact info@m20genomics.sg
References:
- Liao, et al. Small,2026 (Accepted)
