The Past, Present and Future of Spatial Biology

Listicle 1 The Past, Present and Future of Spatial Biology Bree Foster, PhD The phrase "nothing exists in a vacuum" applies to almost every aspect of life, including the very cells that make up our bodies. Just as human behavior cannot be fully understood in isolation from external forces; cells, tissues and organs must be studied within their natural, dynamic surroundings to truly understand their functions. Cells are constantly interacting with their environment, responding to external stimuli, communicating with neighboring cells and reacting to distant signals within the body. This is precisely what spatial biology explores, adding a new dimension to the study of life by examining how molecules, cells and tissues are organized and interact within their natural environments. Spatial biology, encompassing fields like spatial transcriptomics and spatial proteomics, allows researchers to capture the complexity of living systems, leading to more accurate diagnostics and personalized treatments.1 Recognized by Nature Methods as the “method of the year” in 2021, spatial omics has the potential to transform disease research, clinical medicine and drug discovery.2 This cutting-edge approach enables the identification of disease biomarkers, drug resistance mechanisms and the optimization of treatment strategies.3,4 For example, researchers have discovered that certain cancer patients have unique spatial patterns of cell communication that make them resistant to conventional therapies.5,6 By blocking these communication pathways through immunotherapy, the effectiveness of standard treatments can be significantly enhanced.5 While spatial transcriptomics and proteomics methods are quickly becoming standard practice, researchers have been trying to understand cellular function in a true morphological context for a long time. In this listicle, we explore the key milestones of spatial profiling methods from their inception to advances in resolution and multiplex detection. Immunohistochemistry and in situ hybridization Conducting spatial molecular measurements on tissue sections has a long history. Techniques such as immunohistochemistry (IHC) and in situ hybridization (ISH) have been employed for over 50 years. These methods involve applying dyes or fluorescent probes to thin tissue sections mounted on microscope slides to detect specific proteins and nucleic acids. IHC uses antibodies to identify the location of proteins and other antigens within the tissue, while ISH targets specific DNA or RNA sequences. Fluorescence microscopy enables the spatial mapping of gene expression, protein localization and me- Credit: iStock THE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 2 Listicle tabolites using techniques like fluorescence ISH (FISH) and immunofluorescence (Figure 1).7 However, these approaches have several limitations, including the need for prior knowledge of target molecules, restricted multiplexing capabilities and challenges with spatial resolution. Fixation Hybridization Fluorescence microscopy presence of the target – triggers a signal that can be visualized. This method was first described in the 1960s and used radiolabeling. Since then, ISH has been redefined using different types of probes and methods to increase signal intensity. Arguably the most well-known method being fluorophore in situ hybridization (FISH). In situ sequencing (ISS) General concept: RNA is sequenced in a cell that remains in its original tissue sample, preserving its morphology. The first approach, published in 2013, utilized padlock probes that target known RNA sequences. FISH ISS WITH PADLOCK PROBES Reverse transcriptase creates cDNA of RNA target. Padlock probe is introduced that can hybridize two regions of the cDNA. Amplification of the target sequence is achieved via rolling-circle amplification (RCA). RCA products are sequenced by ligation, in situ. Derivatives of the FISH method (developed to overcome issues such as throughput, autofluorescence background noise and spectral overlaps) include but are not limited to: Single molecule FISH (smFISH) — 2008 Sequential hybridization FISH (seqFISH) — 2014 Reverse transcription Hybridization Amplification Sequencing In situ sequencing In situ sequencing (ISS) refers to the targeted sequencing of RNA fragments within morphologically preserved tissues or cells without RNA extraction (Figure 2). This method includes in situ cDNA synthesis by padlock probes or stably cross-linked cDNA amplicons in fluorescent in situ RNA sequencing (FISSEQ) in addition to in situ amplification by rolling-circle amplification (RCA).8,9 This technology allows for rapid gene expression analysis within intact tissue samples at single-cell resolution, offering invaluable insights into complex biological and pathological processes. For example, in a recent study, ISS was used to explore the role of amyloid-beta plaques in Alzheimer’s disease.10 The results showed that amyloid plaques trigger a strong, coordinated response among all cell types, contributing to the surrounding inflammatory environment. ISS revealed transcriptional changes at the single-cell level in both mouse and human brain sections, pinpointing early changes in oligodendrocyte-related gene networks and later phase networks involving inflammation and oxidative stress. However, this technique still requires prior knowledge of target genes and can struggle to accurately quantify gene expression levels. Figure 1: Fluorescent in situ hybridization (FISH). FISH involves hybridizing a labeled complementary fluorescent probe to the target nucleic acid within the sample. After hybridization, any unbound probes are washed away and the bound probes are visualized under a microscope. Credit: Technology Networks. Figure 2: In situ sequencing (ISS) detects mRNA within tissues using padlock probes, which bind to target cDNA sequences after mRNA is reverse transcribed and degraded. The probes are ligated to form circular DNA, which is amplified by rolling circle amplification, producing localized rolling circle products (RCPs). These RCPs are then sequenced using fluorescent probes to identify specific transcripts. Credit: Technology Networks. Fixation Hybridization Fluorescence microscopy In situ sequencing (ISS) General concept: RNA is sequenced in a cell that remains in its original tissue sample, preserving its morphology. The first approach, published in 2013, utilized padlock probes that target known RNA sequences. ISS WITH PADLOCK PROBES Reverse transcriptase creates cDNA of RNA target. Padlock probe is introduced that can hybridize two regions of the cDNA. Amplification of the target sequence is achieved via rolling-circle amplification (RCA). RCA products are sequenced by ligation, in situ. More recently a number of variations of ISS have been developed, using fluorescent probes and cross-linking of a cell to its environment, and barcode based-methods to improve amplification efficiency and to allow for un-targeted studies. Derivatives of the FISH method (developed to overcome issues such as throughput, autofluorescence background noise and spectral overlaps) include but are not limited to: Single molecule FISH (smFISH) — 2008 Sequential hybridization FISH (seqFISH) — 2014 Reverse transcription Hybridization Amplification Sequencing THE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 3 Listicle Imaging mass cytometry Flow cytometry and mass spectrometry are essential tools for "omics" level measurements, enabling comprehensive analysis of proteins on a large scale. First described in 2014, imaging mass cytometry (IMC) integrates the principles of both flow cytometry and mass spectrometry to generate high-parameter images of tissue sections.11 In IMC, metal-tagged antibodies bind to target proteins on the surface or within cells, and these proteins are subsequently detected through mass spectrometry (Figure 3). The use of metal isotopes in IMC significantly expands the number of proteins that can be targeted simultaneously compared to traditional flow cytometry. This technique allows the simultaneous analysis of up to 40 markers in a single tissue section and is compatible with both frozen and paraffin-embedded tissues. A recent study utilized IMC to investigate the composition and spatial organization of immune and stromal cells within the tumor microenvironments (TME) of advanced melanoma patients.12 By analyzing baseline tumor samples from 26 patients receiving anti-programmed cell death-1 (anti-PD-1) therapy, the researchers were able to conduct a detailed examination of both inter- and intra-tumor heterogeneity. This approach led to the identification of six distinct TME archetypes based on their multicellular compositions. Notably, the study revealed that patients with different TME archetypes exhibited varying responses to anti-PD-1 therapy, highlighting the potential of IMC to inform personalized treatment strategies. However, this approach still has disadvantages such as low resolution and time-consuming data acquisition.13 Spatial transcriptomics First published in 2016, spatial transcriptomics (ST) uses unique positional barcodes to enable the unbiased capture of RNA inside tissue sections.14 This technique combines traditional histological techniques with high-throughput RNA sequencing to visualize and quantitatively analyze the entire transcriptome with spatial distribution in tissue sections. Figure 3: Imaging Mass Cytometry uses metal-tagged antibodies to label target proteins in tissue sections. A laser ablates the tissue, releasing the metal tags, which are analyzed by mass spectrometry. The metal isotopes are measured and mapped back to the tissue, creating highly multiplexed images that simultaneously reveal the spatial distribution of multiple markers, enabling detailed analysis of tissue architecture and cell heterogeneity. Adapted from a figure created by Schlecht A et al. Credit: Technology Networks. UV-laser mass cytometer data analysis segmentation image processing image acqusition marker staining laser ablation and mass cytometer with metal-labeled antibodies THE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 4 Listicle ST has rapidly evolved since it was first published, integrating various technologies to deliver high-resolution gene expression mapping. ST technologies, including MERFISH and Slide-seq have transformed our ability to map the transcriptome, providing high-throughput analysis with a spatial resolution of sub-micrometer levels.15 These innovations led to ST being named "Method of the Year" by Nature Methods in 2021.16 Currently, ST has extensive applications in various fields, such as cancer research, developmental biology, pathology and organ chip technology.17,18,19,20 This field is continuing to expand and innovate, integrating cutting-edge technologies such as nanotechnology, microfluidics and single-cell sequencing. Spatial multi-omics technologies The future of spatial biology lies in integrating spatial transcriptomics and proteomics with other omics data to gain a deeper understanding of cellular functions within tissues. Spatial multi-omics has emerged as a powerful method for comprehensive cell analysis in tissues, facilitating the parallel or even simultaneous examination of multiple data types such as transcriptome, epigenome and proteome. Advances in spatial multi-omics are rapidly evolving to allow the investigation of different molecular analytes at subcellular resolution while preserving their native tissue context. Recognized by Nature as one of the top technologies to watch in 2022, spatial multi-omics builds on established spatial techniques such as IMC and ISS.21 These techniques can be applied on adjacent sections, serially on the same section – if analyte quality remains intact – or in parallel on the same section when joint targeting and analysis of multiple analytes are feasible. For instance, techniques such as Spatial-CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and DBiT-seq (deterministic barcoding in tissue for spatial omics sequencing) combine transcriptomics with proteomics.22,23 Additionally, methods like Spatial Cut&TAG and ATAC-RNAseq have been developed to simultaneously analyze both epigenomics and transcriptomics, providing a more comprehensive view of cellular processes.24 As this technology continues to develop, it’s likely that even more processes will be combined for a comprehensive view of cellular function, allowing for deeper insights into the interplay between gene expression, protein activity and chromatin dynamics within the spatial context of tissues Limitations and challenges Spatial omics technologies face significant challenges, such as data complexity, computational demands and the need for standardized analysis methods.25 One of the biggest hurdles lies in the sheer complexity and scale of the data generated. Spatial omics data are often noisy, heterogeneous and influenced by multiple biological and technical factors, including tissue preservation methods, sequencing depth and sample preparation protocols. This makes it difficult to compare results across studies, requiring the development of robust quality control and normalization procedures. The absence of standardized pipelines for data analysis compounds these challenges, creating a barrier to the widespread adoption of these techniques in clinical and research settings.26 While data acquisition is often seen as the most resource-intensive and technically demanding part of spatial omics experiments, the downstream processes of data manipulation, analysis and visualization are equally crucial. Advanced computational tools, including image analysis algorithms, dimensionality reduction techniques and network-based approaches, are essential for processing and interpreting this data. Without the right tools, even well-designed and expensive experiments can result in misleading or incomplete conclusions, wasting time and resources. THE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 5 Listicle To address these challenges and push the field forward, several key developments are needed: • Enhancing spatial resolution to achieve subcellular detail • Expanding molecular coverage, throughput and multiplexing capabilities • Developing standardized, integrated experimental and computational methods for multi-modal spatial data analysis • Creating comprehensive atlases that span entire organisms, mapping cellular and molecular features across tissues and systems The future of spatial biology Spatial biology represents a groundbreaking fusion of molecular biology and advanced imaging tools, transforming our understanding of cellular organization and function in tissues. It provides unparalleled insights into genes, proteins and RNA within a tissue’s context, reshaping research, diagnostics and therapeutic strategies. While challenges remain in data complexity, computational tools and standardization, advancements in spatial omics technologies are rapidly overcoming these barriers. As the field continues to evolve, it holds the promise of more precise and personalized medicine, driving progress in areas like cancer research, neurodegenerative diseases and drug discovery. Spatial biology is not just the future of biomedical research – it is the key to unlocking the full complexity of life. References: 1. Locke D, Hoyt CC. Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging. Front Mol Biosci. 2023;10. doi: 10.3389/fmolb.2023.1051491 2. Cheng M, Jiang Y, Xu J, et al. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. 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High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell. 2020;183(6):1665-1681.e18. doi: 10.1016/j.cell.2020.10.026 24. Li X. Harnessing the potential of spatial multiomics: a timely opportunity. Sig Transduct Target Ther. 2023;8(1):1-3. doi: 10.1038/s41392-023-01507-3 25. Mulholland E, Leedham S. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. annals. 2024;106(4):305-312. doi: 10.1308/rcsann.2023.0091 26. Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 2022;14(1):68. doi: 10.1186/s13073-022-01075-1 About the author: Bree Foster, PhD, is a Science Writer for Technology Networks Sponsored by: