Spatial multiomics aims to measure more than one molecular layer — typically protein and RNA — while keeping each measurement tied to its tissue position. Instead of running one experiment for transcriptomics and another for proteomics on adjacent sections, multiomic methods capture both from the same cells. Microscopy is central to this because most spatial read-outs are ultimately image-based: spots, bars, colours or fluorescent dots that an imager must resolve and a computer must decode.
1. What “spatial multiomics” actually means
A true multiomic experiment correlates two or more data types from the same spatial coordinates. For example:
- Protein + morphology: multiplex immunofluorescence that shows cell types, activation states and tissue architecture.
- RNA + morphology: spatial transcriptomics that maps transcript identity onto a tissue image.
- Protein + RNA: combined protein and RNA imaging in the same section, enabling direct comparison of transcript and translated product.
- Protein + RNA + morphology: the most complete view, linking gene expression, protein abundance and structural context.
2. Imaging-based vs sequencing-based spatial platforms
Spatial multiomics splits into two families. Sequencing-based methods such as 10x Genomics Visium place barcoded capture spots on a slide, sequence the captured RNA, and map the reads back to tissue positions. Imaging-based methods read transcripts or proteins directly with microscopy, using FISH probes, antibodies or aptamers. Imaging methods generally give higher spatial resolution — down to subcellular for MERFISH, seqFISH and CosMx — but require an instrument capable of resolving small signals across large areas.
EVOS S1000 for protein multiomics: While RNA-centric platforms such as Xenium and CosMx dominate spatial transcriptomics, the EVOS S1000 Spatial Imaging System offers a protein-only route: 9-plex multiplex immunofluorescence in a single round, compatible with both directly conjugated Alexa Fluor primaries and Aluora signal amplification dyes. It outputs standard OME-TIFF files that can be overlaid later with transcriptomic data from adjacent sections or registered spatial transcriptomics datasets.
3. Key imaging-based platforms in 2026
| Platform | Read-out | Resolution | Multiplex depth | Microscope needs |
|---|---|---|---|---|
| CODEX (Akoya) | Protein | ~1 µm (cellular) | 20–60+ proteins | Automated fluorescence microscope with filter wheel / cycling fluidics |
| MERFISH / seqFISH | RNA | Subcellular | 100s–1000s of genes | High-NA widefield or light-sheet with sensitive camera; multi-round imaging |
| Xenium (10x Genomics) | RNA | Subcellular | 100s–1000s of genes | Integrated benchtop imager; no separate microscope required |
| CosMx (Bruker/NanoString) | RNA + protein | Subcellular | 1000s RNA / 100s protein | Integrated benchtop imager; no separate microscope required |
| GeoMx DSP (NanoString/Bruker) | RNA + protein (region selection) | Region-of-interest (ROIs) | 100s–1000s targets | Fluorescence microscope for ROI visualisation; sequencer for read-out |
| Stereo-seq / seq-Scope | RNA | Subcellular / nanoball array | Whole transcriptome | Specialised high-resolution imager or sequencer-coupled system |
| EVOS S1000 (Thermo Fisher) | Protein (multiplex IF) | Subcellular to whole slide | Up to 8 + DAPI (9-plex) | Integrated LED/spectral unmixing slide imager; works with Aluora amplification and direct conjugates |
4. Where a benchtop fluorescence microscope fits
Integrated spatial transcriptomics systems such as Xenium and CosMx contain their own optics and cameras. But many multiomic workflows still need a separate fluorescence microscope for steps such as:
- Quality control of sections: checking tissue integrity, autofluorescence and marker penetration before running an expensive multiomic assay.
- H&E or IF overlay: capturing a morphology image to register against the spatial transcriptomics or proteomics output.
- CODEX cycling: reading one fluorophore per cycle on a conventional automated microscope, then reconstructing the multiplex stack computationally.
- GeoMx ROI selection: visualising tissue under fluorescence to pick regions of interest for collection and downstream profiling.
The EVOS S1000 Spatial Imaging System is positioned as an accessible benchtop option for the imaging side of spatial proteomics. Its LED illumination and automated stage can scan multiplex-stained sections, and its software handles tile scanning and spectral unmixing. It is not a sequencing-based spatial transcriptomics platform, but it can feed protein-level spatial data into a multiomic analysis when combined with RNA data from another method.
5. Data and sample considerations
Multiomic datasets are large. A single whole-slide spatial transcriptomics run can produce tens to hundreds of gigabytes of images and count matrices. Storage, backup and compute must be planned before the experiment. Equally important is sample handling: FFPE sections are compatible with most imaging-based RNA and protein methods, but RNA quality degrades with age and fixation conditions. Antibody and probe validation is also harder when protein and RNA assays are run on the same section because cross-reactions and quenching can occur.
6. Choosing a workflow for a UK lab
Labs starting out should ask three questions. First, do you need protein or RNA (or both)? Second, do you need single-cell or subcellular resolution, or are region-level measurements enough? Third, what throughput and budget are realistic? A simple answer might be multiplex IF on an EVOS or similar widefield system for protein neighbourhoods, plus GeoMx or an outsourced Xenium run for RNA. A more advanced setup combines both imaging modalities in the same section and merges them in spatial analysis software.
Video: Bruker / NanoString GeoMx DSP — region-of-interest spatial profiling.
Video: Overview of next-generation single-cell multiplexed spatial proteomics.