Spatial Omics: Enabling New Insights into Cell Architecture and Function
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| Spatial Omics |
Spatial
omics techniques allow researchers to map biomolecules within intact tissue
samples at high resolution, providing information about the spatial organization
and localization of biomolecules. Various techniques have been developed that
enable spatially resolving the presence and quantities of different types of
biomolecules like RNA, proteins or metabolites within intact tissues. These
techniques overcome limitations of traditional bulk analysis methods that
average signals from whole tissues, cells or regions and lose spatial
information.
Spatial Transcriptomics
One of the earliest and widely used spatial omics techniques is spatial
transcriptomics. This technique enables researchers to map the distribution and
localization of mRNA transcripts within intact tissue samples. In spatial
transcriptomics, tissue sections are placed on slides coated with an array of
oligonucleotide probes with spatial barcodes. Cellular RNAs from the tissue
sample are then hybridized to the spatial barcodes. The cellular location of
each RNA molecule can be determined based on the spatial barcode it hybridizes
to. After sequencing, the Spatial
Omics expression profiles of thousands of genes can be reconstructed.
This provides insights into regions with distinct gene expression patterns
within tissues and cell type-specific gene expression signatures.
Multiple companies now offer commercial solutions for spatial transcriptomics.
The technique has been applied to profile a wide variety of human and animal
tissues like brain, lungs, kidney, liver and more. It has enhanced
understanding of cell type heterogeneity, disease pathology and altered gene
expression patterns associated with various diseases. The technique also
enables discovery of novel cell types or subpopulations based on distinct gene
expression profiles. Researchers are now coupling spatial transcriptomics with
other omic layers like proteomics or epigenomics to generate multi-omics
spatial maps of tissues.
Spatial Proteomics
Spatial proteomics enables mapping of protein expression and localization
within tissues. Major techniques in this category include mass spectrometry
imaging (MSI) and multiplexed ion beam imaging (MIBI). In MSI, tissue sections
are analyzed using mass spectrometers to detect protein and peptide signatures
directly from the tissue based on their mass-to-charge ratio without requiring
antibodies or probes. The detection signals are localized to generate spatially
resolved protein maps. MIBI utilizes secondary ion mass spectrometry to detect
protein-tagged antibodies within intact tissue sections with subcellular
resolution. Both MSI and MIBI enable mapping up to hundreds of proteins
simultaneously from intact tissues at subcellular resolution.
Spatial proteomics has provided novel insights into protein localization
patterns in the brain, implications of altered proteome organization in cancer
and more. However, the techniques still have limited throughput and resolution
compared to transcriptomics. Efforts are ongoing to develop high-throughput
imaging mass cytometry approaches amenable to studying clinical samples and
animal models at higher resolution and proteomic depth. Coupling spatial
proteomics data with transcriptomics or other modalities also holds promise for
generating multi-omic spatial maps of proteins, RNA and other biomolecules
within tissues.
Spatial Metabolomics
Spatial metabolomics enables mapping small molecule metabolites within intact
tissues. Major techniques in this category include metabolite imaging mass
spectrometry (MIMS), fluorescence imaging and Raman imaging. In MIMS, mass
spectrometry is used to detect spatial distribution of metabolites directly
from tissue sections. While initial applications focused on localization of
lipids, ongoing developments aim to increase detection of polar metabolites.
Fluorescence imaging utilizes metabolite-specific fluorescent tags and probes
for mapping distribution of targeted classes of metabolites like antioxidants
and reactive species within tissues. Raman imaging detects intrinsic
vibrational fingerprints of metabolites to build spatially resolved chemical
maps of tissues.
Early applications of spatial metabolomics included profiling altered
metabolite signatures in various cancers and localized changes associated with
ischemia injury. Current efforts aim to characterize spatial metabolome
organization within normal and diseased brains, analyze metabolic
microenvironments in tumors and understand implications of subcellular
metabolite compartmentalization. Integration of spatial metabolomics with other
omics modalities may help establish relationships between metabolite gradients,
proteomes and transcriptomes to generate multi-layer spatial maps elucidating
functional organization within healthy and diseased tissues.
Spatial omics techniques have transformed our understanding of complex
biomolecular architectures within tissues by enabling high-resolution spatial
mapping of biomolecules. Integration of multiple spatial omics modalities holds
promise for generating multi-dimensional spatial maps elucidating complex
interplay between genomes, transcriptomes, proteomes and metabolomes
orchestrating tissue architecture and function. Ongoing advancement of
techniques, analytical approaches and integration with other data types is
likely to uncover novel biology and insights into disease mechanisms with
spatial context in the future. Spatial omics also represents a powerful
approach towards developing spatially resolved maps of human tissues as part of
large-scale reference atlases characterizing normal human anatomy.
About Author:
Money Singh is a seasoned content writer with over four years of
experience in the market research sector. Her expertise spans various
industries, including food and beverages, biotechnology, chemical and
materials, defense and aerospace, consumer goods, etc. (https://www.linkedin.com/in/money-singh-590844163)
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