Spatial transcriptomics, or spatially resolved transcriptomics, is a method that captures positional context of transcriptional activity within intact tissue. The historical precursor to spatial transcriptomics is in situ hybridization, where the modernized omics terminology refers to the measurement of all the mRNA in a cell rather than select RNA targets. It comprises an important part of spatial biology.
Spatial transcriptomics includes methods that can be divided into two modalities, those based in next-generation sequencing for gene detection, and those based in imaging. Some common approaches to resolve spatial distribution of transcripts are microdissection techniques, fluorescent in situ hybridization methods, in situ sequencing, in situ capture protocols and in silico approaches.
History in situ hybridization was developed in the late 1960's by Joseph G. Gall and Mary-Lou Pardue and saw major developments in the 1980's with single molecule FISH (smFISH) and 2010's with RNAscope, seqFISH, MERFISH and osmFISH, seqFISH+, and DNA microscopy. Microdisecction techniques were first developed in the late 1990's (Laser Capture Microdissection) and combined with RNA-seq profiling in 2013 in Michael Eisen's lab using fruit fly embryos.
Spatial genomics as a technique, or now referred to as spatial transcriptomics, was initiated in 1990s by Michael Doyle (of Eolas), Maurice Pescitelli (of the University of Illinois at Chicago), Betsey Williams (of Harvard), and George Michaels (of George Mason University), as part of the Visible Embryo Project. Doyle and his co-investigators described a method called Spatial Analysis of Genomic Activity (SAGA).
This spatial indexing concept was expanded upon in 2016 by Jonas Frisén, Joakim Lundeberg, Patrik Ståhl and their colleagues in Stockholm, Sweden. In 2019, the first commercial platforms for spatial transcriptomics were launched with GeoMx Digital Spatial Profiler (DSP) by Nanostring Technologies and Visium by 10X Genomics. In 2019, at the Broad Institute, the labs of Fei Chen and Evan Macosko developed Slide-seq, which used barcoded oligos on beads.
Applications
Defining the spatial distribution of mRNA molecules allows for the detection of cellular heterogeneity in tissues, tumours, and immune cells as well as determining the subcellular distribution of transcripts in various conditions. This information provides a unique opportunity to decipher the cellular and subcellular architecture of tissues and individual cells, and to characterize how neighboring cells communicate through spatially organized molecular interactions. These methodologies provide crucial insights in the fields of embryology, oncology, immunology, neuroscience, pathology, and histology. The functioning of the individual cells in multicellular organisms can only be completely explained in the context of identifying their exact location in the body. Spatial transcriptomics techniques sought to elucidate cells' properties this way. Below, we look into the methods that connect gene expression to the spatial organization of cells.
Microdissection Laser capture microdissection Laser capture microdissection enables capturing single cells without causing morphologic alterations. It exploits transparent ethylene vinyl acetate film apposed to the histological section and a low-power infrared laser beam. Once such beam is directed at the cells of interest, film directly above the targeted area temporarily melts so that its long-chain polymers cover and tightly capture the cells. Then, the section is removed and cells of interest remain embedded in the film. This method allows further RNA transcript profiling and cDNA library generation of the retrieved cells.
RNA sequencing of individual cryosections RNA sequencing of the selected regions in individual cryosections is another method that can produce location-based genome-wide expression data. This method is carried out without laser capture microdissection. It was first used to determine genome-wide spatial patterns of gene expression in cryo-sliced Drosophila embryos. Essentially, it implies simple preparation of the library from the selected regions of the sample. This method had difficulties in obtaining high-quality RNA-seq libraries from every section due to the material loss as a result of the small amount of total RNA in each slice. This problem was resolved by adding RNA of a distantly related Drosophila species to each tube after initial RNA extraction.
GeoMx The GeoMx Digital Spatial Profiler (DSP) (NanoString Technologies) is the first automated commercial instrument developed for spatial profiling of RNAs and proteins in archival formalin-fixed, paraffin-embedded (FFPE) tissue sections. FFPE is a common sample type in the field of pathology and histology due to its long term preservation of tissue structure. The GeoMx DSP technology centers around a user's ability to perform "microdissection" based on histological structures, functional compartments, and cell types. However, unlike LCM, gene expression profiling is performed in a nondestructive manner through light, due to a UV-photocleavable barcode engineered into the in situ hybridization probe. To do this, tissue sections on microscope slides are stained with fluorescent antibodies and nuclear dye to visualize the whole tissue section at single cell resolution on the DSP instrument. Selection of region of interests occurs through automated segmentation on flurorescent signal intensities or drawing tools, including geometric or free hand shapes. Each region of interest is precisely exposed to UV light and the barcodes are cleaved, collected, and used to identify RNAs or proteins present in the tissue. The defined regions of interest can vary in size, between ten and six hundred micrometers, allowing a wide variety of structures and cells in the histological sample.Explore the Spatial Organ Atlas GeoMx DSP can spatially resolve and measure human or mouse whole transcriptomes, more than 1200 proteins, or both RNA and protein in multiomic same slide protocols.
TIVA Transcriptome in vivo analysis (TIVA) is a technique that enables capturing mRNA in live single cells in intact live tissue sections. It uses a photoactivatable tag. The TIVA tag has several functional groups and a trapped poly(U) oligonucleotide coupled to biotin. A disulfide-linked peptide, which is adjacent to the tag, allows it to penetrate the cell membrane. Once inside, laser photoactivation is used to unblock poly(U) oligonucleotide in the cells of interest, so that TIVA tag hybridizes to mRNAs within the cell. Then, streptavidin capture of the biotin group is used to extract poly(A)-tailed mRNA molecules bound to unblocked tags, after which these mRNAs are analyzed by RNA sequencing. This method is limited by low throughput, as only a few single cells can be processed at a time.
tomo-seq An advanced alternative for RNA Sequencing of Individual Cryosections described above, RNA tomography (tomo-seq) features better RNA quantification and spatial resolution. It is also based on tissue cryosectioning with further RNA sequencing of individual sections, yielding genome-wide expression data and preserving spatial information. In this protocol, usage of carrier RNA is omitted due to linear amplification of cDNA in individual histological sections. The identical sample is sectioned in different directions followed by 3D transcriptional construction using overlapping data. Overall, this method implies using identical samples for each section and thus cannot be applied for processing clinical material.
LCM-seq LCM-seq utilizes laser capture microdissection (LCM) coupled with Smart-Seq2 RNA sequencing and is applicable down to the single cell level and can even be used on partially degraded tissues. The workflow includes cryosectioning of tissues followed by laser capture microdissection, where cells are collected directly into lysis buffer and cDNA is generated without the need for RNA isolation, which both simplifies the experimental procedures as well as lowers technical noise. As the positional identity of each cell is recorded during the LCM procedure, the transcriptome of each cell after RNA sequencing of the corresponding cDNA library can be inferred to the position where it was isolated from. LCM-seq has been applied to multiple cell types to understand their intrinsic properties, including oculomotor neurons, facial motor neurons, hypoglossal motor neurons, spinal motor neurons, red nucleus neurons, interneurons, dopamine neurons, and chondrocytes.
Geo-seq Geo-seq is a method that utilizes both laser capture microdissection and single-cell RNA sequencing procedures to determine the spatial distribution of the transcriptome in tissue areas approximately ten cells in size. The workflow involves removal and cryosectioning of tissue followed by laser capture microdissection. The extracted tissue is then lysed, and the RNA is purified and reverse transcribed into a cDNA library. Library is sequenced, and the transcriptomic profile can be mapped to the original location of the extracted tissue. This technique allows the user to define regions of interest in a tissue, extract said tissue and map the transcriptome in a targeted approach.
NICHE-seq The NICHE-seq method uses photoactivatable fluorescent markers and two-photon laser scanning microscopy to provide spatial data to the transcriptome generated. The cells bound by the fluorescent marker are photoactivated, dissociated and sorted via fluorescence-activated cell sorting. This provides sorting specificity to only labeled, photoactivated cells. Following sorting, single-cell RNA sequencing generates the transcriptome of the visualized cells. This method can process thousands of cells within a defined niche at the cost of losing spatial data between cells in the niche.
ProximID ProximID is a methodology based on iterative micro digestion of extracted tissue to single cells. Initial mild digestion steps preserve small interacting structures that are recorded prior to continued digestion. The single cells are then separated from each structure and undergo sc-RNAseq and clustered using t-distributed stochastic neighbour embedding. The clustered cells can be mapped to physical interactions based on the interacting structures prior to the micro digestions. While the throughput of this technique is relatively low it provides information on physical interaction between cells to the dataset.
Fluorescent in situ hybridization CosMx The CosMx Spatial Molecular Imager (NanoString Technologies) is the first high-plex in situ analysis platform to provide spatial multiomics with formalin-fixed paraffin-embedded (FFPE) and fresh frozen (FF) tissue samples at cellular and subcellular resolution. It enables rapid quantification and visualization of up to the whole transcriptome and 64 validated protein analytes and is the flexible, spatial single-cell imaging platform for cell atlasing, tissue phenotyping, cell-cell interactions, cellular processes, and biomarker discovery.
smFISH One of the first techniques able to achieve spatially resolved RNA profiling of individual cells was single-molecule fluorescent in situ hybridization (smFISH). It implemented short (50 base pairs) oligonucleotide probes conjugated with 5 fluorophores which could bind to a specific transcript yielding bright spots in the sample. Detection of these spots provides quantitative information about expression of certain genes in the cell. However, usage of probes labeled with multiple fluorophores was challenged by self-quenching, altered hybridization characteristics, their synthesis and purification.