2 Spatial omics
2.1 Introduction
Spatial omics (or spatially-resolved omics) refers to a set of recently-developed technologies that enable molecular measurements (e.g. gene expression) with spatial resolution. Spatially-resolved transcriptomics and spatial proteomics were named the Method of the Year 2020 (Nature Methods 2021) and Method of the Year 2024 (Nature Methods 2024) by the journal Nature Methods, and have each become widely applied in a range of biological contexts.
2.1.1 Data modalities and technological platforms
In general, there are now a wide variety of data modalities that can be measured (e.g. gene expression, chromatin accessibility, histone modifications, and antibody-based protein abundance), with diverse measurement streams (e.g. high-throughput sequencing, imaging, and mass spectrometry), all of which give molecular measurements in a spatial context for a given tissue area.
Technological platforms differ drastically in terms of the experimental procedures used (sequence or ion counts versus fluorescence intensities), the feature space (dozens of proteins in imaging mass cytometry, to full transcriptome in Visium or Visium HD, to genome-wide assessments of chromatin accessibility), and spatial resolution (e.g. single-cell resolution, or multiple cells per measurement location). In general, this also means there are tradeoffs between the spatial resolution, number of features, and sensitivity of the assays.
Platforms may be broadly grouped into “sequencing-based” and “imaging-based” technologies; some of the latter can be further classified into “molecule-based” or not. The main platforms are described in more detail below. Sequencing-based platforms tend to provide higher gene coverage (e.g. full transcriptome), while imaging-based platforms tend to provide higher spatial resolution (e.g. single-cell or subcellular resolution).
2.1.2 Commercially available platforms
In this book, we focus on commercially available platforms, since these are the most widely used and accessible. However, the data representations are often similar for other related platforms. The initial sections of the book are split into separate parts for sequencing-based and imaging-based platforms, since several analysis techniques are specific to each of these, followed by chapters for platform-independent and other analyses.
In the sections below, we give a brief overview of several commercially available platforms. For more in-depth background, several recent reviews are available, covering available platforms, analysis methods, outstanding challenges, and additional topics (Bressan, Battistoni, and Hannon 2023; Moses and Pachter 2022; Tian, Chen, and Macosko 2023; Lundberg and Borner 2019; Gulati et al. 2025; Paul et al. 2021; Mund, Brunner, and Mann 2022; Palla et al. 2022; Moffitt, Lundberg, and Heyn 2022; Rao et al. 2021; Cheng et al. 2023).
2.2 Sequencing-based spatial transcriptomics
Sequencing-based platforms capture molecular information (which could represent gene expression, DNA binding, antibody-conjugated tags, etc.) at a set of spatial measurement locations for a tissue section placed on a slide. The spatial location is tagged via a unique barcode for each measurement location, and reads are summarized (e.g. as counts) according to features such as genes or bins.
The advantage of sequencing is that typically the features represent an untargeted set of molecular entities, thus not requiring panel selection and optimization. In practice, some spatial assays still require panels (e.g. spatial variants of CITE-seq (Liu et al. 2023)), and assays such as Visium and Visium HD use genome-wide gene capture panels, and thus cannot always be applied to non-model organisms.
Spatial resolution varies between platforms, and depends on the size and spacing between the spatial measurement locations. Depending on the spatial resolution and tissue cell density in a given biological sample, each spatial measurement location may contain zero, one, or multiple cells. For these platforms, the spatial measurement locations are often referred to as “spots”, “beads”, or “bins”.
2.3 Imaging-based spatial transcriptomics
Imaging-based platforms (or molecule-based platforms) identify the spatial locations of individual RNA molecules by sequential in situ hybridization (ISH) or in situ sequencing (ISS), for targeted panels of up to hundreds or thousands of genes. Since transcripts are individually identified, the raw data is collected at subcellular spatial resolution.
Image segmentation is used to identify the boundaries of individual cells or nuclei, and assign RNA molecules to cells or nuclei during preprocessing. Segmentation into cells is challenging, especially due to overlapping cells (i.e. cells have 3-dimensional organization and the plane that a tissue section represents may have material from multiple cells at a given x/y location). After segmentation, gene counts may be aggregated to the cell level, or analyses may be performed directly at the molecule level. Cell-level analyses may re-use methods developed for spot-level spatial transcriptomics data or single-cell data.
The selection of targeted sets of biologically informative genes for an experiment, referred to as panel design, is a key consideration during experimental design (Baran and Doğan 2023; Kuemmerle et al. 2024; Y. Zhang et al. 2024). Several commercially available options for targeted gene sets suitable for certain biological contexts are available.
2.4 Other types of spatial omics data
2.4.1 Spatial proteomics
Imaging-based proteomics, also called multiplexed imaging, represents a broad array of spatial detection technologies, the vast majority of which are antibody-based. Semba and Ishimoto (2024) categorize these antibody-based technologies into either “single-shot” (e.g. imaging mass cytometry; IMC) or “multicycle” (e.g. Lunaphore) imaging approaches. Single-shot refers to the set of, for example, heavy metal ions (representing protein presence), resulting from a laser ablation of a pre-stained sample. Multicycle approaches refer to sets of antibodies that are sequentially stained and stripped, with an imaging step at each cycle. The two most common single-shot spatial proteomics platforms are IMC and MIBIscope, with maximum pixel resolution of 0.4 µm and 1 µm, respectively, and each platform measuring upwards of 40 channels (proteins) (Semba and Ishimoto 2024).
2.4.2 Other modalities
While the focus of the data analyses in this book is primarily on spatially-resolved gene and protein expression, here we also mention other modalities or data structures that are adjacent or emerging. These will not be directly covered in the data examples in the book, but some of the analysis steps discussed in the chapters may have applications to these other contexts.
Tissues are three-dimensional (3D) entities that are represented as 2D slices for many of the analyses discussed here. However, there will be various emerging datasets that have measurements made along a third dimension (Schott et al. 2024; Vickovic et al. 2022), whether this is directly measured (e.g. 3D imaging) or indirectly reconstructed from multiple per-slice measurements (Schott et al. 2025). In some cases, the analyses discussed in this book with either still apply, or can be applied successively on multiple slices.
Multi-omics datasets (e.g. RNA expression and protein abundance, or RNA expression and chomatin accessibility) that are collected in a spatial context are now emerging (e.g. (Liu et al. 2023; D. Zhang et al. 2023)). Epigenomic modalities, in particular, may require alternative preprocessing steps, but some of the analyses mentioned in the chapters here could be re-used (e.g. clustering given a low-dimensional embedding) or adapted.
Another emerging modality within a spatial context is the measurement of metabolites, lipids, or proteins via mass spectrometry imaging (MSI), such as MALDI-MSI (H. Zhang et al. 2024); these assays are sometimes known as imaging mass spectrometry. MSI typically involves coating tissues with a matrix layer that promotes the ionization of analytes of interest (e.g. glycans; (Palomino and Muddiman 2025)). Integration of MSI with other spatial modalities (e.g. to reveal cell types) may also be promising.