Spatially Resolved Omics Techniques
Spatial omics technologies have the potential to advance our understanding of tumour ecosystems and improve clinical outcomes. The essence of spatial omics lies in its aptitude for the simultaneous detection of molecular constituents at exact spatial coordinates [1]. However different techniques vary greatly in resolution, scale and molecular complexity [2].
Spatial proteomics
Spatial proteomics encompasses technologies that enable the in-situ profiling of proteins within tissues, preserving their spatial localization. Most spatial proteomics techniques detect proteins using antibodies tagged with fluorophores, metals, or DNA barcodes. These tags are then read using technologies such as fluorescence microscopy, mass spectrometry, or DNA-based imaging to map the spatial distribution of proteins within tissues [1-5]. A summary of key features across these technologies is provided in Table1.
Immunohistochemistry (IHC) is one of the most established clinical tools for protein detection [6]. It uses enzyme-linked antibodies to generate a chromogenic signal visible under brightfield microscopy. While widely available and routinely used in diagnostics, traditional IHC is generally limited to detecting one or a few markers per tissue section, making it unsuitable for high-dimensional spatial profiling.
Among the fluorescence-based approaches, Immunofluorescence (IF) [7] is widely used but limited in multiplexing. More advanced cyclic immunofluorescence methods, such as tissue-based cyclic immunofluorescence (t-CyCIF) [8] and IBEX [9], use iterative staining and imaging cycles to overcome spectral limitations, enabling the detection of 40-60+ markers with spatial resolution down to 200-300 nm.
DNA-barcoded approaches such as CODEX [10] and Immuno-SABER [11] further enhance multiplexing. CODEX utilizes DNA-barcoded antibodies and sequential hybridization of fluorescent probes, achieving high multiplexing (up to 60 proteins) with single-cell resolution (∼500 nm) in a single imaging plane [12]. Immuno-SABER employs orthogonal DNA concatemers for signal amplification achieving 32-plex with same resolution as COSMX [11].
Mass spectrometry-based approaches, notably Imaging Mass Cytometry (IMC) [13] and multiplexed ion beam imaging by time of flight (MIBI-TOF) [14], use antibodies conjugated to isotopically pure lanthanide metals, which are detected using laser ablation (IMC) or ion beams (MIBI-TOF). These methods avoid fluorescence background and allow simultaneous quantification of 40-50 proteins per tissue section. IMC offers spatial resolution of approximately 1 μm, while MIBI achieves higher resolution (∼300 nm), albeit with more complex instrumentation.
Table1-Comparison table of key features of spatial proteomics profiling methods.
| Method | Tag Type |
Detection | Multiplexing Capacity |
Resolution | Resolution Scale |
|---|---|---|---|---|---|
| IHC | Enzyme (chromogen) |
Brightfield Microscopy |
1--2 | ~1--5 µm | Cellular |
| IF | Fluorophore | Fluorescence Microscopy |
4--7 | ~200--500 nm | Subcellular |
| cyCIF / 4i / IBEX | Fluorophore (iterative) |
Cyclic Fluorescence Imaging |
>60 | ~250 nm | Subcellular |
| CODEX | DNA Barcode | Fluorescence Microscopy |
>40--60+ | ~250 nm | Subcellular |
| Immuno-SABER | DNA Barcode (concatemers) |
Fluorescence Microscopy |
>50+ | ~200--300 nm | Subcellular |
| IMC | Metal Isotopes | Laser Ablation + MS |
~40 | ~1 µm | Cellular |
| MIBI | Metal Isotopes | Ion Beam + MS |
~40 | ~300 nm | Subcellular |
Together, these spatial proteomics platforms offer complementary advantages in terms of marker throughput, resolution, and sensitivity, enabling comprehensive characterization of the tumour microenvironment, immune cell infiltration, and cellular architecture in situ.
Spatial transcriptomics
Spatial transcriptomics enables the study of gene expression within the tissue architecture, preserving spatial context at cellular or subcellular resolution. These platforms can be broadly divided into two main categories based on detection strategy: imaging-based methods, including n Situ Hybridization (ISH) and In Situ Sequencing (ISS), and spatial barcoding methods, which rely on capture-based approaches followed by sequencing. Each approach presents trade-offs in terms of resolution, transcriptome coverage, throughput, and tissue compatibility [1, 15-17]
In Situ Hybridization Imaging-Based Approaches
ISH-based techniques use fluorescently labelled probes that hybridize directly to target RNA molecules in fixed tissue sections. These methods are highly accurate and can offer single-molecule and single-cell resolution, but typically have limitations in multiplexing capacity unless cyclic imaging or barcode strategies are used.
Basic single molecule FISH (smFISH) can detect individual transcripts with high spatial precision but is limited in the number of genes detectable due to the finite number of distinguishable fluorophores.
Advanced multiplexed methods, such as Multiplexed error-robust FISH (MERFISH), and seqFISH+, overcome these limits by iterative cycles of hybridization and imaging, enabling detection of hundreds to thousands of genes. MERFISH [18] uses combinatorial labelling and error-correcting barcodes to detect thousands of RNA species in single cells. Sequential fluorescence in situ hybridization (seqFISH)+ [19] leverages sequential rounds of hybridization with an expanded pseudo-color palette, enabling detection of over 10,000 genes at subcellular resolution.
Commercial platforms such as CosMx (Nanostring/Bruker Spatial) [18], MERscope (Vizgen), and Xenium (10x Genomics) [20] implement these strategies to achieve high-resolution imaging of hundreds to thousands of RNA species, often with optional co-detection of proteins.
In situ sequencing Imaging-Based Approaches
ISS techniques sequence RNA molecules directly within tissues, preserving both spatial context and nucleotide identity. Unlike ISH, ISS provides sequence information, enabling mutation and splice isoform detection.
STARmap [21] improves detection efficiency by using DNA nanostructures and hydrogel-tissue chemistry, while FISSEQ [22], enables untargeted, whole-transcriptome analysis through in situ reverse transcription and random-hexamer priming. These methods retain spatial localization while offering a more detailed molecular readout than hybridization alone.
Both ISH and ISS require fluorescence microscopy for imaging readouts and are collectively referred to as imaging-based spatial transcriptomics.
Spatial Barcoding and Sequencing-Based Methods
Unlike imaging methods, spatial barcoding techniques rely on sequencing-based detection. They use spatially encoded oligonucleotides (barcodes) fixed to a surface (e.g., slide, bead, or grid) to capture RNA from overlying tissue. After RNA capture, reverse transcription and sequencing are performed, and spatial information is reconstructed based on barcode identity.
Prominent examples include Visium (10X Genomics) [23], which captures RNA on slide-mounted barcoded spots (∼55 µm resolution), and it's new version, Visium HD, that offers spatial resolution to ∼2-5 µm. Other examples include Slide-seq [24] and Slide-seqV2 [25] that utilise barcoded beads with known spatial locations (∼10 µm resolution).
These methods offer broader transcriptomic coverage-often approaching the whole transcriptome-and are scalable to larger tissue sections. However, they typically offer lower spatial resolution than imaging-based platforms and may not reveal single-cell or subcellular detail.
Comparison table of key features of spatial transcriptomics profiling methods.
| Method | Category | Transcript Coverage |
Resolution | Resolution Scale |
|---|---|---|---|---|
| smFISH | ISH | 1–10 genes | ~200–300 nm | Subcellular |
| MERFISH | ISH | 1,000+ genes | ~100–300 nm | Subcellular |
| seqFISH seqFISH+ |
ISH-based | >10,000 genes | ~200 nm | Subcellular |
| CosMx | ISH / smFISH | ~1,000 genes | ~250 nm | Subcellular |
| MERscope | MERFISH | ~500–1,000 genes |
~300 nm | Subcellular |
| Xenium | smFISH | ~300–400 genes |
~280 nm | Subcellular |
| STARmap | ISS | ~1,000 genes |
~2–3 µm | Single-cell |
| FISSEQ | ISS | Whole transcriptome |
~300 nm–1 µm | Single-cell / Subcellular |
| Visium | Spatial Barcoding (Slide) | Whole transcriptome |
~55 µm | Multicellular |
| Visium HD | Spatial Barcoding (Slide) | Whole transcriptome |
~2–5 µm | Close to single-cell |
| Slide-seq | Spatial Barcoding (Beads) | Whole transcriptome |
~10 µm | Cellular |
| Slide-seqV2 | Spatial Barcoding (Beads) | Whole transcriptome |
~10 µm | Cellular |
Spatial metabolomics
Spatial metabolomics explores the spatial distribution of metabolites directly in tissue sections, providing insight into biochemical activity within the anatomical context [26]. The field is largely driven by Matrix-Assisted Laser Desorption-Ionization Mass Spectrometry Imaging (MALDI-MSI) [27], a label-free, untargeted technique capable of detecting a wide range of small molecules including lipids, neurotransmitters, and drugs.
In MALDI-MSI, tissue sections are coated with a matrix that facilitates ionization when hit by a laser. The resulting ions are analysed by mass spectrometry to reconstruct spatial metabolite maps. MALDI-MSI offers spatial resolution in the range of 10--50 um, with coverage of hundreds to thousands of metabolites, depending on the tissue and matrix.
Spatial multi-omics
Spatial multi-omics technologies (Table 3) enable the simultaneous or integrative profiling of multiple molecular layers-such as RNA, proteins, and metabolites- within their spatial tissue context, offering a more comprehensive understanding of cellular states and interactions.
One of the most established platforms, GeoMx Digital Spatial Profiler (DSP)(NanoString)[28], allows for high-plex profiling of both RNA and proteins within defined regions of interest using oligonucleotide-tagged probes and UV-directed barcode collection, though at limited spatial resolution (∼10–100um). Other advanced methods, such as deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) [29], achieve co-detection of RNA and proteins through microfluidic-based spatial barcoding on the same section, offering high spatial resolution (∼10um) and true multimodal readouts. Similarly, Spatial-CITE-seq [30] adapts co-indexing of transcriptomes and epitopes (CITE) to the spatial dimension, enabling the capture of transcriptomes alongside surface protein markers.
Furthermore, MALDI-MSI have been successfully combined on the same slide with both IMC [31] and spatial transcriptomics platforms like 10x Visium [32].
Comparison table of key features of spatial multi-omics methods.
| Method | RNA Targets |
Protein Targets |
Metabolite Coverage |
Spatial Resolution |
|---|---|---|---|---|
| GeoMx DSP | ~18,000 | 100+ | -- | ~10--100µm (ROI-based) |
| DBiT-seq | ~6,000-10,000 | ~30-100 | -- | ~10µm |
| Spatial-CITE-seq | ~5,000-10,000 | ~100-200 | -- | ~20--50µm |
| MALDI-MSI + IMC | -- | ~30-50 | 100s to 1,000s |
~1--10µm |
| MALDI-MSI + Visium | 18000 | -- | 100-1000 | ~10--50µm (MALDI); 55µm (ST) |
Altogether, spatial omics are accelerating insights into tissue biology and transforming multiple areas of medicine. In particular, they have been extensively applied to study complex microenvironments, such as tumours, where spatial context and cellular heterogeneity are critical and often obscured in bulk or single-modality data [17, 33-35]. They have also been used to study clonality differences in space [13,36]. As the applications are vast and rapidly evolving, they are reviewed in detail elsewhere [17,34,35].
With this foundation in the underlying technologies and their biological potential, the next section shifts focus to the analytical challenges and computational frameworks required to interpret and integrate spatial omics data.
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