Mapping Tissues with Spatial Biology
Introduction
Spatial biology enables scientists to examine the distribution of molecules such as RNA and protein within tissues, revealing new insights into cellular organization, cell-cell interactions, and molecular patterns in health and disease.1 Numerous spatial transcriptomics and proteomics technologies are now available to researchers, providing tools to explore these molecular landscapes.
Spatial Transcriptomics Technologies
Imaging-Based Spatial Transcriptomics
These approaches use microscopy to map RNA molecules directly within tissue sections.2 Imaging-based spatial transcriptomics technologies include in situ hybridization (ISH)-based and in situ sequencing (ISS)-based methods.
ISH-Based Methods
Detects and localizes RNA molecules using fluorescently-labeled probes2
Methods
- Single-molecule fluorescence in situ hybridization (smFISH)
- Sequential fluorescence in situ hybridization (seqFISH)
- Multiplexed error-robust fluorescence in situ hybridization (MERFISH)
- Split-probe fluorescence in situ hybridization (Split-FISH)
Recent Applications
- Map receptor expression in the murine gut3
- Analyze age-related spatial changes in the cerebellum4
- Assess subnuclear RNA organization across cerebellar cell types5
ISS-Based Methods
Sequences RNA molecules within tissue sections using an imaging-based readout6
Methods
- Padlock probe-based ISS
- Fluorescent in situ sequencing (FISSEQ)
- Hybridization-based in situ sequencing (HybISS)
- Spatially-resolved transcript amplicon readout mapping (STARmap)
Recent Applications
- Map gene expression changes associated with kidney injury and repair7
- Examine spatial transcriptional states and histopathology in an Alzheimer’s disease model8
- Characterize brain‑wide spatial transcriptional patterns and cortical area identity9
Sequencing-Based Spatial Transcriptomics
These approaches capture or isolate RNA from defined regions of tissue sections for next-generation sequencing while preserving spatial context.2 Sequencing-based spatial transcriptomics technologies include array-based and region of interest (ROI)-based methods.
Array-Based Methods
Captures RNA on spatially barcoded surfaces, such as clustered arrays, beads, or microfluidic channels, enabling barcode-based spatial reconstruction of gene expression2,10
Methods
- Seq-Scope
- Slide-seq
- Deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq)
- Spatial reconstruction via oligonucleotide proximity encoding (SCOPE)
Recent Applications
- Characterize multicellular neighborhoods in colorectal cancer11
- Assess the spatial organization of various cell types in abdominal aortic aneurysm12
- Examine transcriptional changes across the prefrontal cortex’s layers in Alzheimer’s disease13
ROI-Based Methods
Isolates RNA from defined tissue regions via microdissection or tagged labeling approaches while preserving spatial context2
Methods
- Laser capture microdissection coupled with RNA sequencing (LCM-Seq)
- Transcriptome in vivo analysis (TIVA)
- RNA tomography (tomo‐seq)
- ZipSeq
Recent Applications
- Examine intratumor heterogeneity and immune cell states in extramedullary myeloma14
- Map gene expression patterns in tumors, lymph nodes, and wound healing models15
- Characterize the transcriptional changes in retinal ganglion cells during optic nerve regeneration16
Spatial Proteomics Technologies
Antibody-Based Spatial Proteomics
These approaches use fluorescently-labeled or metal-tagged antibodies to detect and spatially localize proteins in tissue sections.17 Antibody-based spatial proteomics technologies include multiplexed immunofluorescence and metal-tagged antibody-based imaging methods.
Multiplexed Immunofluorescence Methods
Uses fluorescently-labeled antibodies and iterative microscopy cycles to detect multiple proteins in tissues17
Methods
- Cyclic immunofluorescence (cycIF)
- Co-detection by indexing (CODEX)
- Iterative bleaching extends multiplexity (IBEX)
- Immunostaining with signal amplification by exchange reaction (Immuno-SABER)
Recent Applications
- Examine spatial immune cell patterns within dermatologic adverse events induced by immune checkpoint inhibitor therapy18
- Characterize immune niche organization associated with clinical outcomes in small cell lung cancer19
- Analyze inter- and intra-tumoral heterogeneity in primary cutaneous melanoma20
Metal-Tagged Antibody-Based Imaging Methods
Uses metal-tagged antibodies and mass spectrometry detection to spatially resolve multiple proteins in tissues17
Methods
- Imaging mass cytometry (IMC)
- Multiplexed ion beam imaging (MIBI)
Recent Applications
- Examine cell neighborhood architecture and vascular niche organization in pancreatic ductal adenocarcinoma21
- Characterize the spatial arrangement of fibroblast subpopulations in systemic sclerosis22
- Assess microglial proteomic and spatial alterations in Alzheimer’s disease23
Mass Spectrometry-Based Spatial Proteomics
These approaches map or quantify proteins from tissue sections using mass spectrometry without requiring antibodies.24 Mass spectrometry-based spatial proteomics technologies include matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and laser capture microdissection coupled mass spectrometry (LCM-MS).
MALDI-MSI
Uses a pulsed laser to ionize and detect peptides and other biomolecules directly from tissue sections24
Recent Applications
- Characterize the aberrant glycan and extracellular matrix protein patterns in prostate cancer25
- Predict chemoradiotherapy treatment outcomes from spatial proteomic signatures in head and neck cancer26
LCM-MS
Uses laser capture microdissection to isolate defined tissue regions, followed by mass spectrometry to quantify proteins24
Recent Applications
- Characterize mesangial IgA2 deposition and its role in IgA nephropathy pathogenesis27
- Analyze proteomic alterations in morphologically intact regions of fibrotic lungs28
Beyond Spatial Transcriptomics and Proteomics
Approaches such as spatial metabolomics and spatial epigenomics help reveal additional insights into tissue organization and disease mechanisms, complementing transcriptomic and proteomic analyses. Scientists are also combining these spatial techniques to generate integrated multiomic views, offering a more complete understanding of complex biological systems.29
References
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