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2000
Volume 21, Issue 2
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Transcriptomics covers the in-depth analysis of RNA molecules in cells or tissues and plays an essential role in understanding cellular functions and disease mechanisms. Advances in spatial transcriptomics (ST) in recent times have revolutionized the field by combining gene expression data with spatial information, enabling the analysis of RNA molecules within their tissue context. The evolution of spatial transcriptomics, particularly the integration of artificial intelligence (AI) in data analysis, and its diverse applications have been found to be superior methods in developmental research. Spatial transcriptomics technologies, along with single-cell RNA sequencing (scRNA-seq), offer unprecedented possibilities to unravel intricate cellular interactions within tissues. It emphasizes the importance of accurate cell localization for in-depth discoveries and developments high-throughput spatial transcriptome profiling. The integration of artificial intelligence in spatial transcriptomics analysis is a key focus, showcasing its role in detecting spatially variable genes, clustering cell populations, communication analysis, and enhancing data interpretation. The evolution of AI methods tailored for spatial transcriptomics is highlighted, addressing the unique challenges posed by spatially resolved transcriptomic data. Applications of spatial transcriptomics integrated with other omics data, such as genomics, proteomics, and metabolomics, provide a detailed view of molecular processes within tissues and emerge in diverse applications. Integrating spatial transcriptomics with AI represents a transformative approach to understanding tissue architecture and cellular interactions. This innovative synergy not only enhances our understanding of gene expression patterns but also offers a holistic view of molecular processes within tissues, with profound implications for disease mechanisms and therapeutic development.

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References

  1. LoweR. ShirleyN. BleackleyM. DolanS. ShafeeT. Transcriptomics technologies.PLOS Comput. Biol.2017135e100545710.1371/journal.pcbi.1005457 28545146
     [Google Scholar]
  2. RahmanH. JagadeeshselvamN. ValarmathiR. Transcriptome analysis of salinity responsiveness in contrasting genotypes of finger millet (Eleusine coracana L.) through RNA-sequencing.Plant Mol. Biol.2014854-548550310.1007/s11103‑014‑0199‑4 24838653
     [Google Scholar]
  3. JayakodiM. LeeS.C. ParkH.S. Transcriptome profiling and comparative analysis of Panax ginseng adventitious roots.J. Ginseng Res.201438427828810.1016/j.jgr.2014.05.008 25379008
     [Google Scholar]
  4. DongZ. ChenY. Transcriptomics: Advances and approaches.Sci. China Life Sci.2013561096096710.1007/s11427‑013‑4557‑2 24091688
     [Google Scholar]
  5. KarthikeyanA. RenganathanV.G. KarthikeyanM. SenthilN. Pearl millet transcriptomics in response to abiotic stresses: Current status and future prospects.1st EdNova Science Publishers2023167182
     [Google Scholar]
  6. SelvapandianU. NallusamyS. SinghS.K. Transcriptome profiling and in silico docking analysis of phosphine resistance in rice weevil, Sitophilus oryzae (Coleoptera: Curculionidae).J. Insect Sci.20232362910.1093/jisesa/iead110 38159032
     [Google Scholar]
  7. KamalanathanV. SevugapperumalN. NallusamyS. AshrafS. KailasamK. AfzalM. Metagenomic approach deciphers the role of community composition of mycobiome structured by Bacillus velezensis VB7 and Trichoderma koningiopsis TK in tomato Rhizosphere to suppress root-knot nematode infecting tomato.Microorganisms20231110246710.3390/microorganisms11102467 37894125
     [Google Scholar]
  8. VinothiniK NakkeeranS SaranyaN Metagenomic profiling of tomato rhizosphere delineates the diverse nature of uncultured microbes as influenced by Bacillus velezensis VB7 and Trichoderma koningiopsis TK towards the suppression of root-knot nematode under field conditions.3 Biotech2024141210.1007/s13205‑023‑03851‑1
     [Google Scholar]
  9. SaranyaN. SrinivasanK. KumarP.S.K. FBCNN-TSA: An optimal deep learning model for banana ripening stages classification.J. Intell. Fuzzy Syst.20234435257527310.3233/JIFS‑221841
     [Google Scholar]
  10. GiacomelloS. SalménF. TerebieniecB.K. Spatially resolved transcriptome profiling in model plant species.Nat. Plants2017361706110.1038/nplants.2017.61 28481330
     [Google Scholar]
  11. SongQ. SuJ. DSTG: Deconvoluting spatial transcriptomics data through graph-based artificial intelligence.Brief. Bioinform.2021225bbaa41410.1093/bib/bbaa414 33480403
     [Google Scholar]
  12. YinR. XiaK. XuX. Spatial transcriptomics drives a new era in plant research.Plant J.202311661571158110.1111/tpj.16437 37651723
     [Google Scholar]
  13. CangZ. ZhaoY. AlmetA.A. Screening cell–cell communication in spatial transcriptomics via collective optimal transport.Nat. Methods202320221822810.1038/s41592‑022‑01728‑4 36690742
     [Google Scholar]
  14. GriffinR.C. StechemesserA. FinchJ. LucasE. OttS. SchäferP. Single-cell transcriptomics: A high-resolution avenue for plant functional genomics.Trends Plant Sci.202025218619710.1016/j.tplants.2019.10.008 31780334
     [Google Scholar]
  15. LiuT. FangZ-Y. ZhangZ. YuY. LiM. YinM-Z. A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics T.Comput. Struct. Biotechnol. J.20242310612810.1016/j.csbj.2023.11.055
     [Google Scholar]
  16. AndersonA.C. YanaiI. YatesL.R. Spatial transcriptomics.Cancer Cell202240989590010.1016/j.ccell.2022.08.021 36099884
     [Google Scholar]
  17. DriesR. ChenJ. del RossiN. KhanM.M. SistigA. YuanG.C. Advances in spatial transcriptomic data analysis.Genome Res.202131101706171810.1101/gr.275224.121 34599004
     [Google Scholar]
  18. WilliamsC.G. LeeH.J. AsatsumaT. TormoV.R. HaqueA. An introduction to spatial transcriptomics for biomedical research.Genome Med.20221416810.1186/s13073‑022‑01075‑1 35761361
     [Google Scholar]
  19. LongoS.K. GuoM.G. JiA.L. KhavariP.A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.Nat. Rev. Genet.2021221062764410.1038/s41576‑021‑00370‑8 34145435
     [Google Scholar]
  20. MosesL. PachterL. Museum of spatial transcriptomics.Nat. Methods202219553454610.1038/s41592‑022‑01409‑2 35273392
     [Google Scholar]
  21. LovattD. RubleB.K. LeeJ. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue.Nat. Methods201411219019610.1038/nmeth.2804 24412976
     [Google Scholar]
  22. PavithranS. MuruganM. MannuJ. Salivary gland transcriptomics of the cotton aphid Aphis gossypii and comparative analysis with other sap‐sucking insects.Arch. Insect Biochem. Physiol.20241162e2212310.1002/arch.22123 38860775
     [Google Scholar]
  23. VickovicS. EraslanG. SalménF. High-definition spatial transcriptomics for in situ tissue profiling.Nat. Methods2019161098799010.1038/s41592‑019‑0548‑y 31501547
     [Google Scholar]
  24. CanozoG.F.J. ZuoZ. MartinJ.F. SameeM.A.H. Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain.Cell Syst.20221315870.e510.1016/j.cels.2021.09.004 34626538
     [Google Scholar]
  25. StåhlP.L. SalménF. VickovicS. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.Science20163536294788210.1126/science.aaf2403 27365449
     [Google Scholar]
  26. GeissG.K. BumgarnerR.E. BirdittB. Direct multiplexed measurement of gene expression with color-coded probe pairs.Nat. Biotechnol.200826331732510.1038/nbt1385 18278033
     [Google Scholar]
  27. JinL. LloydR.V. In situ hybridization: Methods and applications.J. Clin. Lab. Anal.19971112910.1002/(SICI)1098‑2825(1997)11:1<2::AID‑JCLA2>3.0.CO;2‑F 9021518
     [Google Scholar]
  28. TangF. BarbacioruC. WangY. mRNA-Seq whole-transcriptome analysis of a single cell.Nat. Methods20096537738210.1038/nmeth.1315 19349980
     [Google Scholar]
  29. ChenT.Y. YouL. HardilloJ.A.U. ChienM.P. Spatial transcriptomic technologies.Cells20231216204210.3390/cells12162042 37626852
     [Google Scholar]
  30. SvenssonV. TormoV.R. TeichmannS.A. Exponential scaling of single-cell RNA-seq in the past decade.Nat. Protoc.201813459960410.1038/nprot.2017.149 29494575
     [Google Scholar]
  31. ChenW.T. LuA. CraessaertsK. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease.Cell20201824976991.e1910.1016/j.cell.2020.06.038 32702314
     [Google Scholar]
  32. RodriquesS.G. StickelsR.R. GoevaA. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.Science201936364341463146710.1126/science.aaw1219 30923225
     [Google Scholar]
  33. GiesenC. WangH.A.O. SchapiroD. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.Nat. Methods201411441742210.1038/nmeth.2869 24584193
     [Google Scholar]
  34. DanaherP. HasleN. NguyenE.D. Single cell spatial transcriptomic profiling of childhood-onset lupus nephritis reveals complex interactions between kidney stroma and infiltrating immune cells.bioRxiv202311.09-56650310.1101/2023.11.09.566503
     [Google Scholar]
  35. EdsgärdD. JohnssonP. SandbergR. Identification of spatial expression trends in single-cell gene expression data.Nat. Methods201815533934210.1038/nmeth.4634 29553578
     [Google Scholar]
  36. HouX. YangY. LiP. Integrating spatial transcriptomics and single-cell RNA-seq reveals the gene expression profling of the human embryonic liver.Front. Cell Dev. Biol.2021965240810.3389/fcell.2021.652408 34095116
     [Google Scholar]
  37. RaoA. BarkleyD. FrançaG.S. YanaiI. Exploring tissue architecture using spatial transcriptomics.Nature2021596787121122010.1038/s41586‑021‑03634‑9 34381231
     [Google Scholar]
  38. BaccinC. Al-SabahJ. VeltenL. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization.Nat. Cell Biol.2020221384810.1038/s41556‑019‑0439‑6 31871321
     [Google Scholar]
  39. ManiatisS. ÄijöT. VickovicS. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis.Science20193646435899310.1126/science.aav9776 30948552
     [Google Scholar]
  40. XuX. WenZ. ZhaoN. MicroRNA-1906, a novel regulator of toll-like receptor 4, ameliorates ischemic injury after experimental stroke in mice.J. Neurosci.20173743104981051510.1523/JNEUROSCI.1139‑17.2017 28924010
     [Google Scholar]
  41. EberwineJ. YehH. MiyashiroK. Analysis of gene expression in single live neurons.Proc. Natl. Acad. Sci.19928973010301410.1073/pnas.89.7.3010 1557406
     [Google Scholar]
  42. AhmedR. ZamanT. ChowdhuryF. Single-cell RNA sequencing with spatial transcriptomics of cancer tissues.Int. J. Mol. Sci.2022236304210.3390/ijms23063042 35328458
     [Google Scholar]
  43. FanZ. LuoY. LuH. SPASCER: Spatial transcriptomics annotation at single-cell resolution.Nucleic Acids Res.202351D1D1138D114910.1093/nar/gkac889 36243975
     [Google Scholar]
  44. HwangB. LeeJ.H. BangD. Single-cell RNA sequencing technologies and bioinformatics pipelines.Exp. Mol. Med.201850811410.1038/s12276‑018‑0071‑8 30089861
     [Google Scholar]
  45. DainiakM.B. KumarA. GalaevI.Y. MattiassonB. “Methods in cell separations,” cell separation: Fundamentals.Analytical and Preparative Methods2007118
     [Google Scholar]
  46. MiltenyiS. MüllerW. WeichelW. RadbruchA. High gradient magnetic cell separation with MACS.Cytometry199011223123810.1002/cyto.990110203 1690625
     [Google Scholar]
  47. BuckE.M.R. BonnerR.F. SmithP.D. Laser capture microdissection.Science19962745289998100110.1126/science.274.5289.998 8875945
     [Google Scholar]
  48. SchulzK.R. DannaE.A. KrutzikP.O. NolanG.P. Single-cell phospho-protein analysis by flow cytometry.Curr Protoc Immunol2012Chapter 8117.120 22314834
     [Google Scholar]
  49. BatleyJ. EdwardsD. SNP applications in plants. Association mapping in plants.Springer20079510210.1007/978‑0‑387‑36011‑9_6
     [Google Scholar]
  50. NijveenH. van KaauwenM. EsselinkD.G. HoegenB. VosmanB. QualitySNPng: A user-friendly SNP detection and visualization tool.Nucleic Acids Res.201341W1W587-9010.1093/nar/gkt333 23632165
     [Google Scholar]
  51. LiY. StanojevicS. GarmireL.X. Emerging artificial intelligence applications in spatial transcriptomics analysis.Comput. Struct. Biotechnol. J.2022202895290810.1016/j.csbj.2022.05.056 35765645
     [Google Scholar]
  52. ChelebianE. AvenelC. KartasaloK. Morphological features extracted by AI associated with spatial transcriptomics in prostate cancer.Cancers20211319483710.3390/cancers13194837 34638322
     [Google Scholar]
  53. KleinoI. FrolovaitėP. SuomiT. EloL.L. Computational solutions for spatial transcriptomics.Comput. Struct. Biotechnol. J.2022204870488410.1016/j.csbj.2022.08.043 36147664
     [Google Scholar]
  54. LeeK. LockhartJ.H. XieM. Deep learning of histopathology images at the single cell level.Front. Artif. Intell.2021475464110.3389/frai.2021.754641 34568816
     [Google Scholar]
  55. HuK.H. EichorstJ.P. McGinnisC.S. ZipSeq: Barcoding for real-time mapping of single cell transcriptomes.Nat. Methods202017883384310.1038/s41592‑020‑0880‑2 32632238
     [Google Scholar]
  56. MasedaF. CangZ. NieQ. DEEPsc: A deep learning-based map connecting single-cell transcriptomics and spatial imaging data.Front. Genet.20211263674310.3389/fgene.2021.636743 33833776
     [Google Scholar]
  57. FangS. ChenB. ZhangY. Computational approaches and challenges in spatial transcriptomics.Gen Prot Bioinform2023211244710.1016/j.gpb.2022.10.001 36252814
     [Google Scholar]
  58. LiuB. LiY. ZhangL. Analysis and visualization of spatial transcriptomic data.Front. Genet.20221278529010.3389/fgene.2021.785290 35154244
     [Google Scholar]
  59. LinM.K. UngC.Y. ZhangC. SPIN-AI: A deep learning model that identifies spatially predictive genes.Biomolecules202313689510.3390/biom13060895 37371475
     [Google Scholar]
  60. ZhangK. FengW. WangP. Identification of spatially variable genes with graph cuts.Nat. Commun.2022131548810.1038/s41467‑022‑33182‑3 36123336
     [Google Scholar]
  61. SvenssonV. TeichmannS.A. StegleO. SpatialDE: Identification of spatially variable genes.Nat. Methods201815534334610.1038/nmeth.4636 29553579
     [Google Scholar]
  62. WangT. Spatial transcriptome: Variable genes identification methods.Highlights Sci Eng Technol20237426026810.54097/kt4d4e47
     [Google Scholar]
  63. ZhuJ. SunS. ZhouX. SPARK-X: Non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies.Genome Biol.202122118410.1186/s13059‑021‑02404‑0 34154649
     [Google Scholar]
  64. LiB. ZhangW. GuoC. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.Nat. Methods202219666267010.1038/s41592‑022‑01480‑9 35577954
     [Google Scholar]
  65. WeberL.M. SahaA. DattaA. HansenK.D. HicksS.C. nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes.Nat. Commun.2023141405910.1038/s41467‑023‑39748‑z 37429865
     [Google Scholar]
  66. AdhikariD.S. YangJ. WangJ. CuiY. Recent advances in spatially variable gene detection in spatial transcriptomics.Comput. Struct. Biotechnol. J.20242388389110.1016/j.csbj.2024.01.016 38370977
     [Google Scholar]
  67. YuanX. MaY. GaoR. HEARTSVG: A fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.Nat. Commun.2024151570010.1038/s41467‑024‑49846‑1 38972896
     [Google Scholar]
  68. ChenC. KimH.J. YangP. Evaluating spatially variable gene detection methods for spatial transcriptomics data.Genome Biol.20242511810.1186/s13059‑023‑03145‑y 38225676
     [Google Scholar]
  69. HaoM. HuaK. ZhangX. SOMDE: A scalable method for identifying spatially variable genes with self-organizing map.Bioinformatics202137234392439810.1093/bioinformatics/btab471 34165490
     [Google Scholar]
  70. SunS. ZhuJ. ZhouX. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies.Nat. Methods202017219320010.1038/s41592‑019‑0701‑7 31988518
     [Google Scholar]
  71. WangL. XuY. LiJ. PowellR.A. XuZ. ChongK. Transgenic rice plants ectopically expressing AtBAK1 are semi-dwarfed and hypersensitive to 24-epibrassinolide.J. Plant Physiol.2007164565566410.1016/j.jplph.2006.08.006 17027118
     [Google Scholar]
  72. MoffittJ.R. HaoJ. WangG. ChenK.H. BabcockH.P. ZhuangX. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization.Proc. Natl. Acad. Sci.201611339110461105110.1073/pnas.1612826113 27625426
     [Google Scholar]
  73. AchimK. PettitJ.B. SaraivaL.R. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.Nat. Biotechnol.201533550350910.1038/nbt.3209 25867922
     [Google Scholar]
  74. VahidM.R. BrownE.L. SteenC.B. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE.Nat. Biotechnol.202341111543154810.1038/s41587‑023‑01697‑9 36879008
     [Google Scholar]
  75. WeiR. HeS. BaiS. Spatial charting of single-cell transcriptomes in tissues.Nat. Biotechnol.20224081190119910.1038/s41587‑022‑01233‑1 35314812
     [Google Scholar]
  76. YinW. WuX. ChenL. WanY. ZhouY. Accurate and flexible single cell to spatial transcriptome mapping with celloc.Small Sci.2024410240013910.1002/smsc.202400139
     [Google Scholar]
  77. TippaniM. DivechaH.R. CatalliniJ.L.II VistoSeg: Processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics databioRxiv20212021-0810.1101/2021.08.04.452489
     [Google Scholar]
  78. StringerC. WangT. MichaelosM. PachitariuM. Cellpose: A generalist algorithm for cellular segmentation.Nat. Methods202118110010610.1038/s41592‑020‑01018‑x 33318659
     [Google Scholar]
  79. GoldbergA.V. KennedyR. An efficient cost scaling algorithm for the assignment problem.Math. Program.199571215317710.1007/BF01585996
     [Google Scholar]
  80. MaA. WangC. ChangY. IRIS3: Integrated cell-type-specific regulon inference server from single-cell RNA-Seq.Nucleic Acids Res.202048W1W275-8610.1093/nar/gkaa394 32421805
     [Google Scholar]
  81. MaY. ZhouX. Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics.Nat. Methods20242171231124410.1038/s41592‑024‑02284‑9 38844627
     [Google Scholar]
  82. LongY. AngK.S. SethiR. Deciphering spatial domains from spatial multi-omics with spatialglue.Nat. Methods20242191658166710.1038/s41592‑024‑02316‑4 38907114
     [Google Scholar]
  83. LongY. AngK.S. LiM. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.Nat. Commun.2023141115510.1038/s41467‑023‑36796‑3 36859400
     [Google Scholar]
  84. XuH. FuH. LongY. Unsupervised spatially embedded deep representation of spatial transcriptomics.Genome Med.20241611210.1186/s13073‑024‑01283‑x 38217035
     [Google Scholar]
  85. RavichandranA. SaranyaN. MannuJ. Deciphering millet diversity: Proteomic clusters and phylogenetic insights.Int. J. Plant Soil Sci.2023352012513310.9734/ijpss/2023/v35i203792
     [Google Scholar]
  86. HaoY. HaoS. NissenA.E. Integrated analysis of multimodal single-cell data.Cell20211841335733587.e2910.1016/j.cell.2021.04.048 34062119
     [Google Scholar]
  87. XuY. McCordR.P. CoSTA: Unsupervised convolutional neural network learning for spatial transcriptomics analysis.BMC Bioinformatics202122139710.1186/s12859‑021‑04314‑1 34372758
     [Google Scholar]
  88. BurgessD.J. Spatial transcriptomics coming of age.Nat. Rev. Genet.2019206317710.1038/s41576‑019‑0129‑z 30980030
     [Google Scholar]
  89. LiZ. WangT. LiuP. HuangY. SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns.Nat. Commun.2023141399510.1038/s41467‑023‑39608‑w 37414760
     [Google Scholar]
  90. TangZ. ZhangT. YangB. SuJ. SongQ. spaCI: Deciphering spatial cellular communications through adaptive graph model.Brief. Bioinform.2023241bbac56310.1093/bib/bbac563 36545790
     [Google Scholar]
  91. TengH. YuanY. JosephB.Z. Clustering spatial transcriptomics data.Bioinformatics2022384997100410.1093/bioinformatics/btab704 34623423
     [Google Scholar]
  92. YuanY. JosephB.Z. GCNG: Graph convolutional networks for inferring gene interaction from spatial transcriptomics data.Genome Biol.202021130010.1186/s13059‑020‑02214‑w 33303016
     [Google Scholar]
  93. FischerD.S. SchaarA.C. TheisF.J. Learning cell communication from spatial graphs of cellsBioRxiv20212021-0710.1101/2021.07.11.451750
     [Google Scholar]
  94. ZhangL. ChenD. SongD. Clinical and translational values of spatial transcriptomics.Signal Transduct. Target. Ther.20227111110.1038/s41392‑022‑00960‑w 35365599
     [Google Scholar]
  95. ZhangM. EichhornS.W. ZinggB. Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomicsBioRxiv20202020-0610.1101/2020.06.04.105700
     [Google Scholar]
  96. KleshchevnikovV. ShmatkoA. DannE. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomicsBioRxiv20202020-1110.1101/2020.11.15.378125
     [Google Scholar]
  97. ZhouZ. ZhongY. ZhangZ. RenX. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.Nat. Commun.2023141793010.1038/s41467‑023‑43600‑9 38040768
     [Google Scholar]
  98. MaY. ZhouX. Spatially informed cell-type deconvolution for spatial transcriptomics.Nat. Biotechnol.20224091349135910.1038/s41587‑022‑01273‑7 35501392
     [Google Scholar]
  99. BiancalaniT. ScaliaG. BuffoniL. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.Nat. Methods202118111352136210.1038/s41592‑021‑01264‑7 34711971
     [Google Scholar]
  100. TothS.T.R. JensM. KaraiskosN. RajewskyN. Spacemake: Processing and analysis of large-scale spatial transcriptomics data.Gigascience202211giac06410.1093/gigascience/giac064 35852420
     [Google Scholar]
  101. LopezR. LiB. ShaulK.H. DestVI identifies continuums of cell types in spatial transcriptomics data.Nat. Biotechnol.20224091360136910.1038/s41587‑022‑01272‑8 35449415
     [Google Scholar]
  102. BergenstråhleL. HeB. BergenstråhleJ. Super-resolved spatial transcriptomics by deep data fusion.Nat. Biotechnol.202240447647910.1038/s41587‑021‑01075‑3 34845373
     [Google Scholar]
  103. RonnebergerO. FischerP. BroxT. U-net: Convolutional networks for biomedical image segmentation presented at the Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Proceedings, part III 18, Springer.Germany. 2015; pp. October 5-9; 234-41.10.1007/978‑3‑319‑24574‑4_28
     [Google Scholar]
  104. MonjoT. KoidoM. NagasawaS. SuzukiY. KamataniY. Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation.Sci. Rep.2022121413310.1038/s41598‑022‑07685‑4 35260632
     [Google Scholar]
  105. SimonyanK ZissermanA Very deep convolutional networks for large-scale image recognitionarXiv20141409-556
     [Google Scholar]
  106. BoopathiM.N. WilliamsM. RanjaniV.R. Development of novel SSR markers derived from genomic and transcriptomic data of Moringa oleifera L. var. PKM1 and their applicability.J. Hortic. Sci. Biotechnol.202297448749510.1080/14620316.2021.2014991
     [Google Scholar]
  107. PavithranS. MuruganM. JayakanthanM. BalasubramaniV. HarishS. SenthilN. Proteomic analysis of cowpea aphid iAphis craccivora/i Koch salivary gland using LC-MS/MS analysis.Indian J. Entomol.2024Feb1610.55446/IJE.2024.1897
     [Google Scholar]
  108. PavithranS. MuruganM. MannuJ. Identification of salivary proteins of the cowpea aphid Aphis craccivora by transcriptome and LC-MS/MS analyses.Insect Biochem. Mol. Biol.202416510406010.1016/j.ibmb.2023.104060 38123026
     [Google Scholar]
  109. KadiriM. SevugapperumalN. NallusamyS. Pan-genome analysis and molecular docking unveil the biocontrol potential of Bacillus velezensis VB7 against Phytophthora infestans.Microbiol. Res.202326812727710.1016/j.micres.2022.127277 36577205
     [Google Scholar]
  110. PalaniyappanS. GanesanK. ManivannanN. RavichandranV. SenthilN. Genetic distance as a predictor of heterosis in single cross hybrids of fodder maize (Zea mays L.).Electron. J. Plant Breed.2023142625632
     [Google Scholar]
  111. SathyamurthyD. MannuJ. NatesanS. Comparative chloroplast genome analysis of six millet species along with related Poaceae family members.Nucleus Nucleus2025681324
     [Google Scholar]
  112. AspM. BergenstråhleJ. LundebergJ. Spatially resolved transcriptomes—next generation tools for tissue exploration.BioEssays20204210190022110.1002/bies.201900221 32363691
     [Google Scholar]
  113. ChowdhuryR. NallusamyS. ShanmugamV. Genome-wide understanding of evolutionary and functional relationships of rice Yellow Stripe-Like (YSL) transporter family in comparison with other plant species.Biologia2022771395310.1007/s11756‑021‑00924‑5
     [Google Scholar]
  114. TüreiD. ValdeolivasA. GulL. Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis.Mol. Syst. Biol.2021173e992310.15252/msb.20209923 33749993
     [Google Scholar]
  115. FuY. XiaoW. TianL. Spatial transcriptomics uncover sucrose post-phloem transport during maize kernel development.Nat. Commun.2023141719110.1038/s41467‑023‑43006‑7 37938556
     [Google Scholar]
  116. JadhavK.P. KarthikeyanA. MohanapriyaB. Quantitative trait locus mapping reveals the genomic regions associated with yield-related traits in maize (Zea mays L.).Cereal Res. Commun.20245241337134810.1007/s42976‑024‑00510‑w
     [Google Scholar]
  117. DaiD. MaZ. SongR. Maize kernel development.Mol. Breed.2021411210.1007/s11032‑020‑01195‑9 37309525
     [Google Scholar]
  118. IndhuS. RavikesavanR. SenthilN. ChitdeshwariT. JoelA.J. Genetic diversity and decoding the genetics of phytic acid by investigating the inheritance of lpa 2 allele in maize (Zea mays L.).Electron. J. Plant Breed.2024151110119
     [Google Scholar]
  119. NivethithaT. RavikesavanR. VinodhanaN.K. SenthilN. Development and genetic evaluation of single cross super-sweet (shrunken 2) sweet corn hybrids (Zea mays var. saccharata L.): A novel choice for commercial market.Electron. J. Plant Breed.2023142429438
     [Google Scholar]
  120. SongX. GuoP. XiaK. Spatial transcriptomics reveals light-induced chlorenchyma cells involved in promoting shoot regeneration in tomato callus.Proc. Natl. Acad. Sci.202312038e231016312010.1073/pnas.2310163120 37703282
     [Google Scholar]
  121. DaiX. ZhuangZ. ZhaoP.X. psRNATarget: A plant small RNA target analysis server (2017 release).Nucleic Acids Res.201846W1W49-5410.1093/nar/gky316 29718424
     [Google Scholar]
  122. HeS. BhattR. BrownC. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.Nat. Biotechnol.202240121794180610.1038/s41587‑022‑01483‑z 36203011
     [Google Scholar]
  123. RosenbloomA. BonnettS. ConnerM. A complete pipeline for high‐plex spatial proteomic profiling and analysis of neural cell phenotypes on the CosMx™ Spatial Molecular Imager and AtoMx™ Spatial Informatics Platform.Alzheimers Dement.202319S13e07666510.1002/alz.076665
     [Google Scholar]
  124. EversonP.T. LewisZ. OngG. Abstract 4617: A complete pipeline for high-plex spatial proteomic profiling and analysis on the cosmxtm spatial molecular imager and atomtm spatial informatics platform.Cancer Res.202383S74617710.1158/1538‑7445.AM2023‑4617
     [Google Scholar]
  125. ZimmermanS.M. FropfR. KulasekaraB.R. Spatially resolved whole transcriptome profiling in human and mouse tissue using digital spatial profiling.bioRxiv202146244210.1101/2021.09.29.462442
     [Google Scholar]
  126. KishiJ.Y. LiuN. WestE.R. Light-Seq: Light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing.Nat. Methods202219111393140210.1038/s41592‑022‑01604‑1 36216958
     [Google Scholar]
  127. ZollingerD.R. LingleS.E. SorgK. BeechemJ.M. MerrittC.R. GeoMxTM RNA assay: High multiplex, digital, spatial analysis of RNA in FFPE tissue.Methods Mol. Biol.20202148331345
     [Google Scholar]
  128. EngC.H.L. LawsonM. ZhuQ. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+.Nature2019568775123523910.1038/s41586‑019‑1049‑y 30911168
     [Google Scholar]
  129. WilliamsC. ReevesJ. DanaherP. Spatial insights into tumor immune evasion illuminated with 1000-plex RNA profiling with CosMx spatial molecular imager.Cancer Res.2023836765101158
     [Google Scholar]
  130. ChenJ. SuoS. TamP.P.L. HanJ.D.J. PengG. JingN. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq.Nat. Protoc.201712356658010.1038/nprot.2017.003 28207000
     [Google Scholar]
  131. MedagliaC. GiladiA. BarakS.L. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq.Science201735863701622162610.1126/science.aao4277 29217582
     [Google Scholar]
  132. ChoeK. PakU. PangY. HaoW. YangX. Advances and challenges in spatial transcriptomics for developmental biology.Biomolecules202313115610.3390/biom13010156 36671541
     [Google Scholar]
/content/journals/cbio/10.2174/0115748936352261241224053340
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An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System
Curr Bioinform 21, 91 (2026); https://doi.org/10.2174/0115748936352261241224053340
/content/journals/cbio/10.2174/0115748936352261241224053340
/content/journals/cbio/10.2174/0115748936352261241224053340
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  • Article Type:     Review Article
Keyword(s): artificial intelligence; cellular interactions; scRNA sequencing; spatial omics data; Spatial transcriptomics; transcriptomics

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