Diving into the World of Spatial Biology Techniques
Tracing the Spatial Transcriptomics Timeline
Born from whole tissue in situ hybridization methods and evolved through sequencing and imaging advances, spatial transcriptomics led the charge for positional molecular analysis.
The Beginning of Spatial In Situ Insights
Scientists first reported a method to locate RNA within tissue samples in the late 1960s, using in situ hybridization (ISH) to fix cells within their spatial context, label specific RNA molecules with complementary oligonucleotide probes, and detect targets via microscopy.1 Today, many herald ISH as the foundation for spatial biology, and modern iterations such as single molecule fluorescent in situ hybridization (smFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH) are among the most widely used imaging-based spatial transcriptomic methods.
Spatial Transcriptomic Sequencing Comes of Age
From ISH to next-generation sequencing (NGS), RNA detection methods have rapidly progressed from low-throughput, single-target assays to transcriptome-wide spatial analyses.1 Researchers generally categorize spatial transcriptomics approaches as imaging-based or sequencing-based.1 In contrast to ISH-related methods that rely on microscopy to see a target’s position in space, in situ barcoding array-based capture methods―introduced in 2016―use positionally-distinct probes to detect and convert mRNA into cDNA for sequencing.2 Array methods such as Slide-seq have various advantages and disadvantages; they often profile larger tissue sections than ISH and in situ sequencing (ISS), but typically provide lower spatial resolution and mRNA recovery rates.2
Make Way for More Spatial Methods
Although technologies for transcriptional profiling in tissues have existed for decades, sequencing-enabled profiling and other high-throughput techniques such as mass spectrometry imaging bolstered the spatial biology field as a whole. Spatial transcriptomics was named the 2020 Method of the Year by Nature Methods, followed by spatial proteomics not long after, in 2024.2,3 Spatial genomic and epigenomic measurements are also increasingly performed using techniques such as ISS, seqFISH+, MERFISH, microfluidic barcoding, Slide-seq, Cut&Tag, and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq).4
The Future Is Multiomic
Although current technologies mostly permit scientists to spatially profile only one or two molecular groups at a time, spatial multiomic technologies that combine several omic insights while preserving positional context are on the rise.4 For instance, researchers have recently begun integrating spatial barcoding with single-cell methods such as ATAC-seq and RNA-seq, capturing epigenetic, transcriptomic, and genomic data alongside spatial information from complex tissues such as healthy and wound-healing skin and melanoma samples.4,5
References
- Cheng M, et al. Spatially resolved transcriptomics: A comprehensive review of their technological advances, applications, and challenges. J Genet Genomics. 2023;50(9):625-640.
- Williams CG, et al. An introduction to spatial transcriptomics for biomedical research. Genome Med. 2022;14(1):68.
- Method of the year 2024: Spatial proteomics. Nat Methods. 2024;21(12):2195-2196.
- Bressan D, et al. The dawn of spatial omics. Science. 2023;381(6657):eabq4964.
- Foster DS, et al. Integrated spatial multiomics reveals fibroblast fate during tissue repair. Proc Natl Acad Sci U S A. 202;118(41):e2110025118.
Illuminating the RNA Landscape with Subcellular Profiling
A cutting-edge spatial transcriptomics approach enables researchers to map RNA distribution at subcellular resolution.
By Charlene Lancaster, PhD
RNA partitioning is critical for cell fate decisions and overall cellular function. Cells sort RNA molecules into specific organelles, such as mitochondria, and membraneless subcellular compartments, including stress granules (SGs).1 This spatial distribution affects the molecules’ processing, translation, degradation, and storage.2 For example, researchers have observed that cells under stress partition particular mRNA species into SGs to regulate their translation and stability.3
Drawbacks of Existing Subcellular RNA Profiling Techniques
Although numerous approaches exist for evaluating RNA localization, these methods face several limitations.
Cell fractionation techniques, such as density gradient centrifugation, separate subcellular compartments based on their different physiochemical properties, enabling subsequent RNA sequencing of the desired fractions.4 However, researchers cannot purify all compartments through fractionation, and subcellular compartments of similar density can co-fractionate. These methods also necessitate the use of millions of cells.5
High-throughput imaging techniques, including multiplexed error-robust fluorescence in situ hybridization, employ probes to examine the distribution of thousands of RNA molecules simultaneously.4 But optical constraints limit the total number of RNA species that scientists can visualize at one time. Moreover, these techniques often require highly trained personnel and specialized equipment.
Proximity labeling approaches, such as APEX sequencing, use enzymes fused to targeting peptides or proteins to label RNA molecules within particular subcellular compartments before enrichment and sequencing.2 However, these methods require that the cells express engineered fusion proteins.6
Advancing RNA Mapping with a Novel Approach
Haiqi Chen, a reproductive biologist and biotechnology researcher from the University of Texas Southwestern Medical Center, and his team developed a new subcellular spatial transcriptomics method called photoselection of transcriptome over nanoscale (PHOTON), which combines imaging and sequencing.5
PHOTON uses photocleavable primers to bind to RNA molecules within fixed specimens. Following in situ reverse transcription to build the photocaged cDNA library, the researchers labeled the selected subcellular compartments using a fluorescent dye. They then employed a high-resolution microscope and image segmentation pipeline to automatically identify the stained compartments and apply near-ultraviolet laser light to just those regions.5 This illumination breaks the photocleavable link attaching the fluorophores to the cDNA molecules, thereby exposing the molecules’ phosphate groups. Moreover, it restores the ability of modified nucleotides in the incorporated primers to form base pairs, known as uncaging. Chen and his group then extracted the nucleic acids and attached PCR handles to the uncaged cDNA molecules only. Following magnetic bead isolation and PCR amplification, they prepared the sequencing library and subjected it to next-generation sequencing.
Uncovering Compartment-Specific RNA Signatures with PHOTON
To identify which RNA species the nucleoli and mitochondria of cultured cells preferentially accumulate, Chen and his team used PHOTON to analyze the molecules within those compartments and in whole cells.5 They detected more small nucleolar RNA species in the nucleoli compared to the whole cell transcriptomic data, confirming previous predictions that these RNA molecules localize to the nucleolus. Additionally, the researchers observed the enrichment of several known mitochondrial transcripts within the mitochondrial network. Collectively, these results suggest that PHOTON can accurately assess the subcellular distribution of RNA.
This powerful method also offers scientists an opportunity to examine how subcellular compartments, such as SGs, recruit RNA.5 After treating the cells with sodium arsenite to induce SG formation, the team found that long mRNA molecules were more abundant in SGs compared to shorter transcripts, consistent with earlier findings.7 Researchers had previously hypothesized that N6-methyladenosine (m6A) modifications could be important for this length-dependent partitioning. By treating the cells with a drug to decrease the transcripts' m6A methylation, Chen and his group detected fewer long mRNA molecules within SGs. This suggested that the m6A marks affect the sorting of RNA into SGs, where this mechanism may play a critical role in how cells respond to stress under normal and disease-related conditions.
References
1. Buxbaum AR, et al. In the right place at the right time: Visualizing and understanding mRNA localization. Nat Rev Mol Cell Biol. 2015;16(2):95-109.
2. Liu J, et al. Mapping subcellular RNA localization with proximity labeling. Acta Biochim Biophys Sin. 2024;57(1):101-107.
3. Park C, et al. Stress granules contain Rbfox2 with cell cycle-related mRNAs. Sci Rep. 2017;7(1):11211.
4. Biayna J, Dumbović G. Decoding subcellular RNA localization one molecule at a time. Genome Biol. 2025;26:45.
5. Rajachandran S, et al. Subcellular level spatial transcriptomics with PHOTON. Nat Commun. 2025;16(1):4457.
6. Fazal FM, et al. Atlas of subcellular RNA localization revealed by APEX-seq. Cell. 2019;178(2):473-490.e26.
7. Khong A, et al. The stress granule transcriptome reveals principles of mRNA accumulation in stress granules. Mol Cell. 2017;68(4):808-820.e5.
Spatial Omics for the 21st Century
Jasmine Plummer aims to make spatial omics more powerful and accessible to the scientific community.
Interviewed by Nathan Ni, PhD
Image Caption
Jasmine Plummer, director of the Center for Spatial Omics at St. Jude Children’s Research Hospital, wants to make spatial omics more routine and accessible for scientists.
Scientists have used high-throughput genomic and proteomic technologies to greatly enhance understanding of human cell biology. However, these technologies often force researchers to remove cells from their local environments, and cellular behaviors are heavily affected by their surroundings. Jasmine Plummer, associate member and director of the Center for Spatial Omics at St. Jude Children’s Research Hospital, wants to help researchers put cells back into their appropriate contexts to better understand what drives their actions in health and disease.
How did you personally become interested in spatial omics?
I was a molecular geneticist doing a lot of single-cell assays. Despite this, my main interests always pointed back to the tissue, because disease pathogenesis involves more than just individual cell genotypes. For example, if we look at COVID-19 responses, why did some people do better post-infection than others? Maybe they had structural impedances to the virus, or maybe the initial point of infection was at a physically different site. The same idea applies to the brain: we can look at the balance between excitatory and inhibitory neurons, but we also need to look at the location and connections that these neurons are making or are not making.
What does the Center for Spatial Omics do?
What spatial omics or spatial biology means, at its most fundamental, is looking at something visually—a stained tissue section on a slide, for example—that places the objects of interest within its environment. Where the “Center” part comes in is that the technology for spatial omics has caught up in terms of data volume and throughput compared to genomics. We want to be specialists at acquiring and looking at spatial data within its context, and St. Jude has invested a lot in this, not just in terms of money, but also intellectually, to recruit specialists in this field.
How does the Center promote spatial omics?
As a principal investigator (PI) myself, our starting place is always meeting with people and seeing if we could apply spatial omics to their research goals and hypotheses. That said, the broader goal is to improve accessibility to technology. What we do at the Center for Spatial Omics is expensive, and having a bigger budget makes us more responsive and flexible to changes in technology, strategic initiative, and scope.
How much does the Center collaborate with external researchers?
We are here to support St. Jude researchers and the St. Jude research community. However, St. Jude is a global place, and most of our PIs have global collaborators, so by virtue of this collaborative environment, we extend our technology to them through their link with St. Jude. Together with two others, I co-founded a community called the Global Alliance for Spatial Technologies (GESTALT), and it has reached 1,400 members in 43 countries within 18 months. We have been able to share some of the things that the Center has allowed us to do, and it has resulted in collaborations with other areas of the scientific community such as people who work on computational pipelines.
How do you determine the future direction of the Center?
I am grateful to have strong advisory committees at St. Jude so that I do not have to make unilateral decisions, and I am also lucky to work with other leaders in the spatial field. My long-term dream is to not become bottlenecked by instrument technology, but rather for the field to move to a place where the computational aspect—especially in terms of artificial intelligence and machine learning—has aligned with or perhaps surpassed it. I think having these computational advances means that we do not have to be married to a single technology, where inputs would be more universally compatible and outputs could be applied regardless of the instrument used to generate them.
What is your goal for spatial omics as both a PI and the director of the Center?
My immediate goal is to encourage people to think more about the context of their existing data and to not fear spatial omics technology. While it can look overwhelming, scientists do not have to start with the most complicated option. For the long term, and this might put me out of a job if it comes to fruition, I want to hopefully help lower the barriers to entry, whether that is cost or accessibility. I want spatial omics to be as routine, ubiquitous, and straightforward as a hematoxylin and eosin stain.
This interview has been condensed and edited for clarity.
Scaling Up Spatial Transcriptomics to Organs
A new method captures spatial RNA patterns without requiring expensive imaging.
Interviewed by Aparna Nathan, PhD
Image Caption
By leveraging barcode diffusion, Fei Chen and Chenlei Hu developed an imaging-free method to unlock large-scale spatial transcriptomics.
Organs have intricate spatial organization, whether it is the layers of the skin or the nephrons in the kidney. To study these spatial patterns, researchers use spatial transcriptomic technologies that measure RNA molecules from cells and map them back to their location in a sample. But these methods typically construct their spatial map by creating an image of the tissue, which can be expensive and time-consuming.
Fei Chen and Chenlei Hu at the Broad Institute of MIT and Harvard have developed a new imaging-free spatial transcriptomics method that tracks the diffusion of DNA barcodes between beads in an array to reconstruct the spatial organization of the tissue.1 In an interview with The Scientist, they described how they came up with this idea and their vision for a future with more scalable and accessible spatial transcriptomics.
What are the limitations of previous methods for spatial transcriptomics?
Chenlei Hu: One way that scientists perform spatial transcriptomics is by using sequencing to profile the RNA. Slide-seq, which we developed in our laboratory, is an example of such a method.2 It uses a spatial barcode conjugated to the RNA, enabling sequencing of all transcripts together. But we still need to use imaging to determine the position of those spatial barcodes, which requires advanced microscopes and takes time. Samples can only be up to three to five millimeters—even smaller than a mouse brain—so we often need to cut the tissue down to that size. Imaging limits the throughput of spatial transcriptomics, so we wanted to develop a system that no longer requires imaging.
What motivated you to design the new barcode diffusion-based approach?
Fei Chen: There is a theory that you can reconstruct spatial information from sequencing without ever having to image the sample. One advantage of this idea is that sequencing costs have been exponentially decreasing. Converting the spatial problem to a sequencing problem would make the method incredibly scalable and very easy for researchers to use.
How do you reconstruct the spatial map of beads and RNA profiles in the tissue?
CH: Although it seems intuitive that scientists can use diffusion to infer spatial information about the beads, it is hard to actually do so. We started with physical diffusion models, but this was difficult because of the large amount of data and its noisiness.
FC: We spent a long time trying to figure out how to do it, and we kind of gave up for a while.
CH: One day I realized the data were similar to single-cell data. In single-cell analysis, researchers use dimensionality reduction to visualize the similarity between cells based on their gene expression. This is analogous to what we want in the spatial reconstruction problem: if two beads are always interacting, they should also be close together. We realized that we could just borrow a common dimensionality reduction method from single-cell analysis called uniform manifold approximation and projection.
What do you hope researchers achieve with this new method?
FC: The cost of collecting spatial transcriptomic data is very high right now, and this method makes it at least an order of magnitude cheaper. There are experiments, for example, in cancer where scientists are currently collecting one representative section of each tumor. But they might gain more statistical power if they can sample the entire tumor.
We are collecting a lot of large human organs, mainly brains. We think we will be able to find cell type and gene expression patterns across really large length scales that we would never be able to observe otherwise—for example, across an entire human brain section.
Our technique is very scalable and easy for researchers to adopt. The cool thing is that we just send them beads, and they can follow the protocol. They do not need any special equipment. We want to lower the barrier to spatial transcriptomic analysis.
This interview has been condensed and edited for clarity.
References
1. Hu C, et al. Scalable spatial transcriptomics through computational array reconstruction. Nat Biotechnol. 2025;1–7.
2. Rodriques SG, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463-1467.
Beyond the Slide: Multidimensional and Multiomic Microscopy in Spatial Biology
By Andrea J. Radtke, PhD, Leica Microsystems, Inc.
Microscopy at the Core of Spatial Biology
Spatial biology is fundamental for major atlasing initiatives like the Human Cell Atlas (HCA) and Human BioMolecular Atlas Program (HuBMAP), as well as translational efforts to map tumor microenvironments of human cancers through the Human Tumor Atlas Network (HTAN).1-3 The two main branches of spatial biology, spatial transcriptomics and spatial proteomics, are deeply rooted in microscopy, the original single-cell technology offering spatial insights.4
The microscope’s invention centuries ago was foundational to the development of the spatial biology field for several reasons. Insights gathered from examining tissues prepared using histological stains under a light microscope provided a critical framework for understanding the cellular composition and tissue architecture of healthy and diseased tissues. Parallel advancements in fluorescence microscopy and immunology resulted in the development of immunofluorescence (IF) imaging in 1941, with the first description of a FITC-conjugated antibody being used to detect pneumococcal antigen in tissues.5 Today, advanced IF applications now allow scientists to visualize more than 60 targets in a single tissue section using iterative rounds of imaging and dye inactivation of fluorescently conjugated probes. This paved the way for pioneering techniques such as Cell DIVE.6 In situ hybridization (ISH), which researchers first described in 1961, is at the core of several spatial transcriptomic technologies.
The number of spatial biology technologies using optical microscopes is vast and includes dozens of imaging-based spatial transcriptomics technologies (FISSEQ, ISS, MERFISH, seqFISH, and STARmap) and multiplexed antibody-based imaging methods (4i, Cell DIVE, CyCIF, IBEX, and Immuno-SABER).7,8 Furthermore, Deep Visual Proteomics (DVP), an advanced application of laser microdissection and mass spectrometry, relies on optical microscopy for visualizing regions of interest (ROIs), precise cutting of desired ROIs, and verification of dissected samples into specimen collectors.9 By combining mass spectrometry’s depth with spatial information, DVP empowers the quantification of up to 10,000 proteins.10 This capability represents a hundredfold increase over antibody-based methods, which are typically limited to far fewer protein targets and require extensive resources and expertise to construct an antibody panel.8,11
The Future of Spatial Biology: 3D, Subcellular, Multiomic, and Community-Driven
While techniques like ISH and IF have existed for over 60 years, the last decade has seen an explosion of commercial and open-source methods for comprehensive tissue analysis. Importantly, the majority of these methods are limited to the evaluation of thin tissue sections (<5-10 microns) and hindered by the microscope’s optical resolution or, in the case of sequencing-based spatial transcriptomics methods, the capture array design.12 As highlighted in the special 2024 Nature Methods issue on spatial proteomics, significant advances include capturing larger tissue volumes (>100 microns to several millimeters) and visualizing more targets at improved resolution (see subcellular spatial proteomics as a "Method to Watch").13,14 Large-scale scientific endeavors are driving the demand for more comprehensive multiomic analysis (RNA, protein, and metabolites) across more dimensions and with higher plex. To meet this challenge, SpectraPlex for STELLARIS, a recent recipient of a Microscopy Today Innovation Award by the Microscopy Society of America, allows researchers to image up to 15 targets at subcellular resolution in 3D tissue volumes. SpectraPlex is designed for high-plex, spatially resolved analysis of complex tissues, with key applications in cancer immunology, neuroscience, and developmental biology.
As microscopy remains foundational to spatial biology, continued advancements in multiplexing, sensitivity, and throughput are critical for the field's future. Several studies emphasize that no single method can provide a holistic view of complex tissues, making multiomic integration (spatial transcriptomics, spatial proteomics, scRNA-seq, etc.) the rule rather than the exception for discovery and translational research.15,16 Community-driven initiatives, such as the HuBMAP Affinity Reagents Working Group, IBEX Knowledge Base, European Society for Spatial Biology (ESSB), and the Global Alliance for Spatial Technologies (GESTALT), are essential to reduce entry barriers, disseminate knowledge, and establish best practices for spatial data acquisition, analysis, and interpretation while additionally fostering a collaborative spatial biology community.8,11,17, 18
References
1. Regev A, et al. The human cell atlas. eLife. 2017;6:e27041.
2. Snyder MP, et al. The human body at cellular resolution: The NIH Human Biomolecular Atlas Program. Nature. 2019;574(7777):187-192.
3. Rozenblatt-Rosen O, et al. The human tumor atlas network: Charting tumor transitions across space and time at single-cell resolution. Cell. 2020;181(2):236-249.
4. Chen X, et al. Optical and digital microscopic imaging techniques and applications in pathology. Anal Cell Pathol. 2011;34(1-2):150563.
5. Coons AH, et al. Immunological properties of an antibody containing a fluorescent group. Proc Soc Exp Biol Med. 1941;47(2):200-202.
6. Gerdes MJ, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci. 2013;110(29):11982-11987.
7. Moffitt JR, et al. The emerging landscape of spatial profiling technologies. Nat Rev Genet. 2022;23(12):741-759.
8. Hickey JW, et al. Spatial mapping of protein composition and tissue organization: A primer for multiplexed antibody-based imaging. Nat Methods. 2022;19(3):284-295.
9. Mund A, et al. Deep Visual Proteomics defines single-cell identity and heterogeneity. Nat Biotechnol. 2022;40(8):1231-1240.
10. Nordmann TM, et al. A new understanding of tissue biology from MS-based proteomics at single-cell resolution. Nat Methods. 2024;21(12):2220-2222.
11. Quardokus EM, et al. Organ Mapping Antibody Panels: A community resource for standardized multiplexed tissue imaging. Nat Methods. 2023;20(8):1174-1178.
12. Radtke AJ, Roschewski M. The follicular lymphoma tumor microenvironment at single-cell and spatial resolution. Blood. 2024;143(12):1069-1079.
13. Marx V. Atlases galore: Where to next? Nat Methods. 2024;21(12):2203-2208.
14. Strack R. Subcellular spatial proteomics. Nat Methods. 2024;21(12):2227-2227.
15. Yayon N, et al. A spatial human thymus cell atlas mapped to a continuous tissue axis. Nature. 2024;635(8039):708-718.
16. Radtke AJ, et al. Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients. Cancer Cell. 2024;42(3):444-463.e10.
17. Radtke AJ, et al. The IBEX Knowledge-Base: A central resource for multiplexed imaging techniques. PLOS Biol. 2025;23(3):e3003070.
18. Plummer JT, et al. Introducing the Global Alliance for Spatial Technologies (GESTALT). Nat Genet. 2025;57(2):275-279.
