{"id":55150,"date":"2024-12-05T09:43:28","date_gmt":"2024-12-05T10:43:28","guid":{"rendered":"https:\/\/peymantaeidi.net\/stem-cell\/?p=55150"},"modified":"2024-12-05T11:36:03","modified_gmt":"2024-12-05T11:36:03","slug":"the-past-present-and-future-of-spatial-biology","status":"publish","type":"post","link":"https:\/\/peymantaeidi.net\/stem-cell\/2024\/12\/05\/the-past-present-and-future-of-spatial-biology\/","title":{"rendered":"The Past, Present and Future of Spatial Biology"},"content":{"rendered":"<div><\/div>\n<div class=\"inner_content_body_text\" id=\"inner_content_body_text\" data-content-id=\"393370\">\n<p>Spatial biology is transforming biomedical research by revealing how cells, molecules and tissues interact in their native environments. This discipline opens a new frontier for understanding complex biological systems, leading to breakthroughs in disease diagnosis, drug development and personalized medicine. However, as the field grows, challenges in data integration and analysis are surfacing, demanding robust technological advancements.<\/p>\n<p>This listicle uncovers spatial biology&#8217;s journey, from foundational techniques to cutting-edge technologies that define the present.<\/p>\n<p><b>Download this listicle to explore:<\/b><\/p>\n<ul>\n<li>Key developments that have shaped spatial biology<\/li>\n<li>Applications of spatial biology in cancer research, neurodegenerative diseases and drug discovery<\/li>\n<li>The use of emerging technologies to overcome challenges in data complexity&nbsp;<\/li>\n<\/ul>\n<\/div>\n<p>\n    Listicle<br \/>\n1<br \/>\nThe Past, Present and Future of<br \/>\nSpatial Biology<br \/>\nBree Foster, PhD<br \/>\nThe phrase &#8220;nothing exists in a vacuum&#8221; applies to almost every aspect of life, including the very cells<br \/>\nthat make up our bodies. Just as human behavior cannot be fully understood in isolation from external<br \/>\nforces; cells, tissues and organs must be studied within their natural, dynamic surroundings to truly<br \/>\nunderstand their functions. Cells are constantly interacting with their environment, responding to external<br \/>\nstimuli, communicating with neighboring cells and reacting to distant signals within the body.<br \/>\nThis is precisely what spatial biology explores, adding a new dimension to the study of life by examining<br \/>\nhow molecules, cells and tissues are organized and interact within their natural environments.<br \/>\nSpatial biology, encompassing fields like spatial transcriptomics and spatial proteomics, allows<br \/>\nresearchers to capture the complexity of living systems, leading to more accurate diagnostics and<br \/>\npersonalized treatments.1<br \/>\nRecognized by Nature Methods as the \u201cmethod of the year\u201d in 2021, spatial omics has the potential to<br \/>\ntransform disease research, clinical medicine and drug discovery.2 This cutting-edge approach enables<br \/>\nthe identification of disease biomarkers, drug resistance mechanisms and the optimization of treatment<br \/>\nstrategies.3,4 For example, researchers have discovered that certain cancer patients have unique<br \/>\nspatial patterns of cell communication that make them resistant to conventional therapies.5,6 By blocking<br \/>\nthese communication pathways through immunotherapy, the effectiveness of standard treatments<br \/>\ncan be significantly enhanced.5<br \/>\nWhile spatial transcriptomics and proteomics methods are quickly becoming standard practice, researchers<br \/>\nhave been trying to understand cellular function in a true morphological context for a long<br \/>\ntime. In this listicle, we explore the key milestones of spatial profiling methods from their inception to<br \/>\nadvances in resolution and multiplex detection.<br \/>\nImmunohistochemistry and in situ hybridization<br \/>\nConducting spatial molecular measurements on tissue sections has a long history. Techniques such<br \/>\nas immunohistochemistry (IHC) and in situ hybridization (ISH) have been employed for over 50 years.<br \/>\nThese methods involve applying dyes or fluorescent probes to thin tissue sections mounted on microscope<br \/>\nslides to detect specific proteins and nucleic acids. IHC uses antibodies to identify the location of<br \/>\nproteins and other antigens within the tissue, while ISH targets specific DNA or RNA sequences. Fluorescence<br \/>\nmicroscopy enables the spatial mapping of gene expression, protein localization and me-<br \/>\nCredit: iStock<br \/>\nTHE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 2<br \/>\nListicle<br \/>\ntabolites using techniques like fluorescence ISH (FISH) and immunofluorescence (Figure 1).7 However,<br \/>\nthese approaches have several limitations, including the need for prior knowledge of target molecules,<br \/>\nrestricted multiplexing capabilities and challenges with spatial resolution.<br \/>\nFixation Hybridization Fluorescence microscopy<br \/>\npresence of the target \u2013 triggers a signal that can be<br \/>\nvisualized.<br \/>\nThis method was first described in the 1960s and used<br \/>\nradiolabeling. Since then, ISH has been redefined using<br \/>\ndifferent types of probes and methods to increase signal<br \/>\nintensity. Arguably the most well-known method being<br \/>\nfluorophore in situ hybridization (FISH).<br \/>\nIn situ sequencing (ISS)<br \/>\nGeneral concept: RNA is sequenced in a cell that remains in<br \/>\nits original tissue sample, preserving its morphology. The<br \/>\nfirst approach, published in 2013, utilized padlock probes that<br \/>\ntarget known RNA sequences.<br \/>\nFISH<br \/>\nISS WITH PADLOCK PROBES<br \/>\nReverse transcriptase creates cDNA of RNA target. Padlock<br \/>\nprobe is introduced that can hybridize two regions of the<br \/>\ncDNA. Amplification of the target sequence is achieved via<br \/>\nrolling-circle amplification (RCA). RCA products are sequenced<br \/>\nby ligation, in situ.<br \/>\nDerivatives of the FISH method (developed<br \/>\nto overcome issues such as throughput,<br \/>\nautofluorescence background noise and<br \/>\nspectral overlaps) include but are not limited to:<br \/>\nSingle molecule FISH (smFISH) \u2014 2008<br \/>\nSequential hybridization FISH (seqFISH) \u2014 2014<br \/>\nReverse transcription Hybridization Amplification Sequencing<br \/>\nIn situ sequencing<br \/>\nIn situ sequencing (ISS) refers to the targeted sequencing of RNA fragments within morphologically preserved<br \/>\ntissues or cells without RNA extraction (Figure 2). This method includes in situ cDNA synthesis by padlock<br \/>\nprobes or stably cross-linked cDNA amplicons in fluorescent in situ RNA sequencing (FISSEQ) in addition to<br \/>\nin situ amplification by rolling-circle amplification (RCA).8,9 This technology allows for rapid gene expression<br \/>\nanalysis within intact tissue samples at single-cell resolution, offering invaluable insights into complex biological<br \/>\nand pathological processes.<br \/>\nFor example, in a recent study, ISS was used to explore the role of amyloid-beta plaques in Alzheimer\u2019s<br \/>\ndisease.10 The results showed that amyloid plaques trigger a strong, coordinated response among all cell<br \/>\ntypes, contributing to the surrounding inflammatory environment. ISS revealed transcriptional changes at the<br \/>\nsingle-cell level in both mouse and human brain sections, pinpointing early changes in oligodendrocyte-related<br \/>\ngene networks and later phase networks involving inflammation and oxidative stress. However, this<br \/>\ntechnique still requires prior knowledge of target genes and can struggle to accurately quantify gene expression<br \/>\nlevels.<br \/>\nFigure 1: Fluorescent in situ hybridization (FISH). FISH involves hybridizing a labeled complementary fluorescent<br \/>\nprobe to the target nucleic acid within the sample. After hybridization, any unbound probes are washed away and the<br \/>\nbound probes are visualized under a microscope. Credit: Technology Networks.<br \/>\nFigure 2: In situ sequencing (ISS) detects mRNA within tissues using padlock probes, which bind to target cDNA<br \/>\nsequences after mRNA is reverse transcribed and degraded. The probes are ligated to form circular DNA, which<br \/>\nis amplified by rolling circle amplification, producing localized rolling circle products (RCPs). These RCPs are then<br \/>\nsequenced using fluorescent probes to identify specific transcripts. Credit: Technology Networks.<br \/>\nFixation Hybridization Fluorescence microscopy<br \/>\nIn situ sequencing (ISS)<br \/>\nGeneral concept: RNA is sequenced in a cell that remains in<br \/>\nits original tissue sample, preserving its morphology. The<br \/>\nfirst approach, published in 2013, utilized padlock probes that<br \/>\ntarget known RNA sequences.<br \/>\nISS WITH PADLOCK PROBES<br \/>\nReverse transcriptase creates cDNA of RNA target. Padlock<br \/>\nprobe is introduced that can hybridize two regions of the<br \/>\ncDNA. Amplification of the target sequence is achieved via<br \/>\nrolling-circle amplification (RCA). RCA products are sequenced<br \/>\nby ligation, in situ.<br \/>\nMore recently a number of variations of ISS have<br \/>\nbeen developed, using fluorescent probes and<br \/>\ncross-linking of a cell to its environment, and<br \/>\nbarcode based-methods to improve amplification<br \/>\nefficiency and to allow for un-targeted studies.<br \/>\nDerivatives of the FISH method (developed<br \/>\nto overcome issues such as throughput,<br \/>\nautofluorescence background noise and<br \/>\nspectral overlaps) include but are not limited to:<br \/>\nSingle molecule FISH (smFISH) \u2014 2008<br \/>\nSequential hybridization FISH (seqFISH) \u2014 2014<br \/>\nReverse transcription Hybridization Amplification Sequencing<br \/>\nTHE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 3<br \/>\nListicle<br \/>\nImaging mass cytometry<br \/>\nFlow cytometry and mass spectrometry are essential tools for &#8220;omics&#8221; level measurements, enabling comprehensive<br \/>\nanalysis of proteins on a large scale. First described in 2014, imaging mass cytometry (IMC)<br \/>\nintegrates the principles of both flow cytometry and mass spectrometry to generate high-parameter images<br \/>\nof tissue sections.11<br \/>\nIn IMC, metal-tagged antibodies bind to target proteins on the surface or within cells, and these proteins are<br \/>\nsubsequently detected through mass spectrometry (Figure 3). The use of metal isotopes in IMC significantly<br \/>\nexpands the number of proteins that can be targeted simultaneously compared to traditional flow cytometry.<br \/>\nThis technique allows the simultaneous analysis of up to 40 markers in a single tissue section and is compatible<br \/>\nwith both frozen and paraffin-embedded tissues.<br \/>\nA recent study utilized IMC to investigate the composition and spatial organization of immune and stromal<br \/>\ncells within the tumor microenvironments (TME) of advanced melanoma patients.12 By analyzing baseline<br \/>\ntumor samples from 26 patients receiving anti-programmed cell death-1 (anti-PD-1) therapy, the researchers<br \/>\nwere able to conduct a detailed examination of both inter- and intra-tumor heterogeneity. This approach<br \/>\nled to the identification of six distinct TME archetypes based on their multicellular compositions. Notably, the<br \/>\nstudy revealed that patients with different TME archetypes exhibited varying responses to anti-PD-1 therapy,<br \/>\nhighlighting the potential of IMC to inform personalized treatment strategies. However, this approach still has<br \/>\ndisadvantages such as low resolution and time-consuming data acquisition.13<br \/>\nSpatial transcriptomics<br \/>\nFirst published in 2016, spatial transcriptomics (ST) uses unique positional barcodes to enable the unbiased<br \/>\ncapture of RNA inside tissue sections.14 This technique combines traditional histological techniques with<br \/>\nhigh-throughput RNA sequencing to visualize and quantitatively analyze the entire transcriptome with spatial<br \/>\ndistribution in tissue sections.<br \/>\nFigure 3: Imaging Mass Cytometry uses metal-tagged antibodies to label target proteins in tissue sections. A laser<br \/>\nablates the tissue, releasing the metal tags, which are analyzed by mass spectrometry. The metal isotopes are<br \/>\nmeasured and mapped back to the tissue, creating highly multiplexed images that simultaneously reveal the spatial<br \/>\ndistribution of multiple markers, enabling detailed analysis of tissue architecture and cell heterogeneity. Adapted<br \/>\nfrom a figure created by Schlecht A et al. Credit: Technology Networks.<br \/>\nUV-laser<br \/>\nmass cytometer<br \/>\ndata analysis segmentation image processing image acqusition<br \/>\nmarker staining laser ablation and mass cytometer<br \/>\nwith metal-labeled<br \/>\nantibodies<br \/>\nTHE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 4<br \/>\nListicle<br \/>\nST has rapidly evolved since it was first published, integrating various technologies to deliver high-resolution<br \/>\ngene expression mapping. ST technologies, including MERFISH and Slide-seq have transformed<br \/>\nour ability to map the transcriptome, providing high-throughput analysis with a spatial resolution of<br \/>\nsub-micrometer levels.15 These innovations led to ST being named &#8220;Method of the Year&#8221; by Nature<br \/>\nMethods in 2021.16 Currently, ST has extensive applications in various fields, such as cancer research,<br \/>\ndevelopmental biology, pathology and organ chip technology.17,18,19,20<br \/>\nThis field is continuing to expand and innovate, integrating cutting-edge technologies such as nanotechnology,<br \/>\nmicrofluidics and single-cell sequencing.<br \/>\nSpatial multi-omics technologies<br \/>\nThe future of spatial biology lies in integrating spatial transcriptomics and proteomics with other omics<br \/>\ndata to gain a deeper understanding of cellular functions within tissues. Spatial multi-omics has emerged<br \/>\nas a powerful method for comprehensive cell analysis in tissues, facilitating the parallel or even simultaneous<br \/>\nexamination of multiple data types such as transcriptome, epigenome and proteome.<br \/>\nAdvances in spatial multi-omics are rapidly evolving to allow the investigation of different molecular<br \/>\nanalytes at subcellular resolution while preserving their native tissue context. Recognized by Nature as<br \/>\none of the top technologies to watch in 2022, spatial multi-omics builds on established spatial techniques<br \/>\nsuch as IMC and ISS.21 These techniques can be applied on adjacent sections, serially on the<br \/>\nsame section \u2013 if analyte quality remains intact \u2013 or in parallel on the same section when joint targeting<br \/>\nand analysis of multiple analytes are feasible.<br \/>\nFor instance, techniques such as Spatial-CITE-seq (cellular indexing of transcriptomes and epitopes<br \/>\nby sequencing) and DBiT-seq (deterministic barcoding in tissue for spatial omics sequencing) combine<br \/>\ntranscriptomics with proteomics.22,23 Additionally, methods like Spatial Cut&amp;TAG and ATAC-RNAseq<br \/>\nhave been developed to simultaneously analyze both epigenomics and transcriptomics, providing a<br \/>\nmore comprehensive view of cellular processes.24 As this technology continues to develop, it\u2019s likely<br \/>\nthat even more processes will be combined for a comprehensive view of cellular function, allowing for<br \/>\ndeeper insights into the interplay between gene expression, protein activity and chromatin dynamics<br \/>\nwithin the spatial context of tissues<br \/>\nLimitations and challenges<br \/>\nSpatial omics technologies face significant challenges, such as data complexity, computational demands<br \/>\nand the need for standardized analysis methods.25 One of the biggest hurdles lies in the sheer<br \/>\ncomplexity and scale of the data generated. Spatial omics data are often noisy, heterogeneous and influenced<br \/>\nby multiple biological and technical factors, including tissue preservation methods, sequencing<br \/>\ndepth and sample preparation protocols. This makes it difficult to compare results across studies,<br \/>\nrequiring the development of robust quality control and normalization procedures. The absence of<br \/>\nstandardized pipelines for data analysis compounds these challenges, creating a barrier to the widespread<br \/>\nadoption of these techniques in clinical and research settings.26<br \/>\nWhile data acquisition is often seen as the most resource-intensive and technically demanding part of<br \/>\nspatial omics experiments, the downstream processes of data manipulation, analysis and visualization<br \/>\nare equally crucial. Advanced computational tools, including image analysis algorithms, dimensionality<br \/>\nreduction techniques and network-based approaches, are essential for processing and interpreting this<br \/>\ndata. Without the right tools, even well-designed and expensive experiments can result in misleading<br \/>\nor incomplete conclusions, wasting time and resources.<br \/>\nTHE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 5<br \/>\nListicle<br \/>\nTo address these challenges and push the field forward, several key developments are needed:<br \/>\n\u2022 Enhancing spatial resolution to achieve subcellular detail<br \/>\n\u2022 Expanding molecular coverage, throughput and multiplexing capabilities<br \/>\n\u2022 Developing standardized, integrated experimental and computational methods for multi-modal spatial<br \/>\ndata analysis<br \/>\n\u2022 Creating comprehensive atlases that span entire organisms, mapping cellular and molecular features<br \/>\nacross tissues and systems<br \/>\nThe future of spatial biology<br \/>\nSpatial biology represents a groundbreaking fusion of molecular biology and advanced imaging tools, transforming<br \/>\nour understanding of cellular organization and function in tissues. It provides unparalleled insights into<br \/>\ngenes, proteins and RNA within a tissue\u2019s context, reshaping research, diagnostics and therapeutic strategies.<br \/>\nWhile challenges remain in data complexity, computational tools and standardization, advancements in spatial<br \/>\nomics technologies are rapidly overcoming these barriers. As the field continues to evolve, it holds the promise<br \/>\nof more precise and personalized medicine, driving progress in areas like cancer research, neurodegenerative<br \/>\ndiseases and drug discovery.<br \/>\nSpatial biology is not just the future of biomedical research \u2013 it is the key to unlocking the full complexity of life.<br \/>\nReferences:<br \/>\n1. Locke D, Hoyt CC. Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral<br \/>\nimaging. Front Mol Biosci. 2023;10. doi: 10.3389\/fmolb.2023.1051491<br \/>\n2. Cheng M, Jiang Y, Xu J, et al. Spatially resolved transcriptomics: a comprehensive review of their technological advances,<br \/>\napplications, and challenges. J Genet Genom. 2023;50(9):625-640. doi: 10.1016\/j.jgg.2023.03.011<br \/>\n3. Zhang X, Wang X, Shivashankar GV, Uhler C. Graph-based autoencoder integrates spatial transcriptomics with chromatin<br \/>\nimages and identifies joint biomarkers for Alzheimer\u2019s disease. Nat Commun. 2022;13(1):7480. doi: 10.1038\/s41467-022-<br \/>\n35233-1<br \/>\n4. Fu F, Nowak MA, Bonhoeffer S. Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to<br \/>\ncancer therapy. PLoS Comput Biol. 2015;11(3):e1004142. doi: 10.1371\/journal.pcbi.1004142<br \/>\n5. Dominiak A, Che\u0142stowska B, Olejarz W, Nowicka G. Communication in the cancer microenvironment as a target for therapeutic<br \/>\ninterventions. Cancers. 2020;12(5):1232. doi: 10.3390\/cancers12051232<br \/>\n6. Sharma P, Aaroe A, Liang J, Puduvalli VK. Tumor microenvironment in glioblastoma: Current and emerging concepts.<br \/>\nNeuro-oncol adv. 2023;5(1):vdad009. doi: 10.1093\/noajnl\/vdad009<br \/>\n7. Alexandrov T, Saez\u2010Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol<br \/>\nSyst Biol. 2023;19(11):e10571. doi: 10.15252\/msb.202110571<br \/>\n8. Ke R, Mignardi M, Pacureanu A, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods.<br \/>\n2013;10(9):857-860. doi: 10.1038\/nmeth.2563<br \/>\n9. Lee JH, Daugharthy ER, Scheiman J, et al. Highly multiplexed subcellular RNA sequencing in situ. Science.<br \/>\n2014;343(6177):1360-1363. doi: 10.1126\/science.1250212<br \/>\n10. Chen WT, Lu A, Craessaerts K, et al. Spatial transcriptomics and in situ sequencing to study Alzheimer\u2019s disease. Cell.<br \/>\n2020;182(4):976-991.e19. doi: 10.1016\/j.cell.2020.06.038<br \/>\n11. Giesen C, Wang HAO, Schapiro D, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass<br \/>\ncytometry. Nat Methods. 2014;11(4):417-422. doi: 10.1038\/nmeth.2869<br \/>\n12. Xiao X, Guo Q, Cui C, et al. Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments<br \/>\nlinked to immunotherapy responses in melanoma. Commun Med. 2022;2(1):1-14. doi: 10.1038\/s43856-022-00197-2<br \/>\n13. Milosevic V. Different approaches to Imaging Mass Cytometry data analysis. Bioinformatics Advances. 2023;3(1):vbad046.<br \/>\ndoi: 10.1093\/bioadv\/vbad046<br \/>\nTHE PAST, PRESENT AND FUTURE OF SPATIAL BIOLOGY 6<br \/>\nListicle<br \/>\n14. St\u00e5hl PL, Salm\u00e9n F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.<br \/>\nScience. 2016;353(6294):78-82. doi: 10.1126\/science.aaf2403<br \/>\n15. Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods. 2022;19(5):534-546. doi: 10.1038\/s41592-022-01409-2<br \/>\n16. Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9-14. doi: 10.1038\/s41592-020-<br \/>\n01033-y<br \/>\n17. Arora R, Cao C, Kumar M, et al. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures<br \/>\nthat predict survival and targeted therapy response. Nat Commun. 2023;14(1):5029. doi: 10.1038\/s41467-023-40271-4<br \/>\n18. Choe K, Pak U, Pang Y, Hao W, Yang X. Advances and challenges in spatial transcriptomics for developmental biology.<br \/>\nBiomolecules. 2023;13(1):156. doi: 10.3390\/biom13010156<br \/>\n19. Pang JMB, Byrne DJ, Bergin ART, et al. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology.<br \/>\nHistopathology. 2024;84(4):577-586. doi: 10.1111\/his.15093<br \/>\n20. Li D, Fang Z, Shi Q, et al. Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation<br \/>\nof liver microphysiological systems for regulatory application. J Pharm Anal. 2023;13(7):691-693. doi: 10.1016\/j.<br \/>\njpha.2023.06.013<br \/>\n21. Eisenstein M. Seven technologies to watch in 2022. Nature. 2022;601(7894):658-661. doi: 10.1038\/d41586-022-00163-x<br \/>\n22. Liu Y, DiStasio M, Su G, et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial<br \/>\nCITE-seq. Nat Biotechnol. 2023;41(10):1405-1409. doi: 10.1038\/s41587-023-01676-0<br \/>\n23. Liu Y, Yang M, Deng Y, et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell.<br \/>\n2020;183(6):1665-1681.e18. doi: 10.1016\/j.cell.2020.10.026<br \/>\n24. Li X. Harnessing the potential of spatial multiomics: a timely opportunity. Sig Transduct Target Ther. 2023;8(1):1-3. doi:<br \/>\n10.1038\/s41392-023-01507-3<br \/>\n25. Mulholland E, Leedham S. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. annals.<br \/>\n2024;106(4):305-312. doi: 10.1308\/rcsann.2023.0091<br \/>\n26. Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical<br \/>\nresearch. Genome Med. 2022;14(1):68. doi: 10.1186\/s13073-022-01075-1<br \/>\nAbout the author:<br \/>\nBree Foster, PhD, is a Science Writer for Technology Networks<br \/>\nSponsored by:\n<\/p>\n<div class=\"focusButton show-on-mobile hide-on-desktop\">\n<p>        <button type=\"\" class=\"gtm-event-click\" data-eventcategory=\"none\"><br \/>\n            View Listicle<br \/>\n        <\/button>\n<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Spatial biology is transforming biomedical research by revealing how cells, molecules and tissues interact in<\/p>\n","protected":false},"author":1,"featured_media":55152,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/posts\/55150"}],"collection":[{"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/comments?post=55150"}],"version-history":[{"count":2,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/posts\/55150\/revisions"}],"predecessor-version":[{"id":55153,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/posts\/55150\/revisions\/55153"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/media\/55152"}],"wp:attachment":[{"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/media?parent=55150"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/categories?post=55150"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/peymantaeidi.net\/stem-cell\/wp-json\/wp\/v2\/tags?post=55150"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}