REFERENCES
1. Rossant J, Tam PP. Early human embryonic development: blastocyst formation to gastrulation. Dev Cell. 2022;57:152-65.
2. Xiao Z, Cui L, Yuan Y, et al. 3D reconstruction of a gastrulating human embryo. Cell. 2024;187:2855-74.e19.
3. Yuan Y, Wang X, Yan X, et al. 3D reconstruction of a human Carnegie stage 9 embryo provides a snapshot of early body plan formation. Cell Stem Cell. 2025;32:1006-24.e5.
4. Rivron N, Pera M, Rossant J, et al. Debate ethics of embryo models from stem cells. Nature. 2018;564:183-5.
5. Lovell-badge R, Anthony E, Barker RA, et al. ISSCR guidelines for stem cell research and clinical translation: the 2021 update. Stem Cell Rep. 2021;16:1398-408.
6. Dries R, Chen J, Del Rossi N, Khan MM, Sistig A, Yuan G. Advances in spatial transcriptomic data analysis. Genome Res. 2021;31:1706-18.
7. Cheng M, Jiang Y, Xu J, et al. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genom. 2023;50:625-40.
8. Liu Y, Dai Y, Wang L. Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications. Cancer Cell. 2026;44:24-49.
9. Jain S, Eadon MT. Spatial transcriptomics in health and disease. Nat Rev Nephrol. 2024;20:659-71.
10. Geng A, Cui C, Luo Z, et al. Computational methods for spatial multi-omics integration. Biotechnol Adv. 2026;87:108807.
11. Liang G, Yin H, Ding F. Technical advances and applications of spatial transcriptomics. GEN Biotechnol. 2023;2:384-98.
12. Clark AT, Cook-Andersen H, Franklin S, et al. Stem cell-based embryo models: the 2021 ISSCR stem cell guidelines revisited. Stem Cell Rep. 2025;20:102514.
13. Hyun I, Wilkerson A, Johnston J. Embryology policy: revisit the 14-day rule. Nature. 2016;533:169-71.
14. Xiang L, Yin Y, Zheng Y, et al. A developmental landscape of 3D-cultured human pre-gastrulation embryos. Nature. 2019;577:537-42.
15. Ma H, Zhai J, Wan H, et al. In vitro culture of cynomolgus monkey embryos beyond early gastrulation. Science. 2019;366:eaax7890.
16. Deglincerti A, Croft GF, Pietila LN, Zernicka-Goetz M, Siggia ED, Brivanlou AH. Self-organization of the in vitro attached human embryo. Nature. 2016;533:251-4.
17. Human Fertilisation and Embryology Act 1990. United Kingdom. Available from: https://www.legislation.gov.uk/ukpga/1990/37/contents [Last accessed on 1 Oct 2026].
18. Code of practice for the generation and use of human stem cell-based embryo models. 2024. Available from: https://www.repro.cam.ac.uk/sites/default/files/240704_scbem_code_of_practice.pdf [Last accessed on 1 Oct 2026].
19. Dickey-Wicker Amendment, 1996. Available from: https://embryo.asu.edu/pages/dickey-wicker-amendment-1996 [Last accessed on 1 Oct 2026].
20. Ethical guidelines for human embryonic stem cell research. 2003. Available from: https://www.cncbd.org.cn/TColumn/Detail/3376 [Last accessed on 1 Oct 2026].
21. Regulation on the Administration of Human Genetic Resources. 2019. Available from: https://www.nhc.gov.cn/bgt/gwywj2/201906/7f057bf005b44d87894e6764e73d557a.shtml [Last accessed on 1 Oct 2026].
22. Assisted Human Reproduction Act: S.C. 2004, c. 2. Available from: http://laws-lois.justice.gc.ca/eng/acts/A-13.4/FullText.html [Last accessed on 1 Oct 2026].
23. Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans - TCPS 2. 2022. Available from: https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_2022.html [Last accessed on 1 Oct 2026].
24. Embryo Protection Act. 1990. Available from: http://www.gesetze-im-internet.de/eschg/ [Last accessed on 1 Oct 2026].
25. Stem Cell Act. 2002. Available from: http://www.gesetze-im-internet.de/stzg/index.html [Last accessed on 1 Oct 2026].
26. Embryowet. 2002. Available from: https://wetten.overheid.nl/jci1.3:c:BWBR0013797&z=2019-04-02&g=2019-04-02 [Last accessed on 1 Oct 2026].
27. M’hamdi HI, De Wert G. Reconsidering the 14-day rule in human embryo research: advice from the Dutch Health Council. Cell Stem Cell. 2024;31:1560-2.
28. Biosafety Law. Brazil; 2005. Available from: https://www.wipo.int/wipolex/zh/legislation/details/8300 [Last accessed on 1 Oct 2026].
29. Shahbazi MN, Pasque V. Early human development and stem cell-based human embryo models. Cell Stem Cell. 2024;31:1398-418.
30. Amadei G, Handford CE, Qiu C, et al. Embryo model completes gastrulation to neurulation and organogenesis. Nature. 2022;610:143-53.
31. Itskovitz-Eldor J, Schuldiner M, Karsenti D, et al. Differentiation of human embryonic stem cells into embryoid bodies compromising the three embryonic germ layers. Mol Med. 2000;6:88-95.
32. Warmflash A, Sorre B, Etoc F, Siggia ED, Brivanlou AH. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat Methods. 2014;11:847-54.
33. Zheng Y, Xue X, Resto-Irizarry AM, et al. Dorsal-ventral patterned neural cyst from human pluripotent stem cells in a neurogenic niche. Sci Adv. 2019;5:eaax5933.
34. Haremaki T, Metzger JJ, Rito T, Ozair MZ, Etoc F, Brivanlou AH. Self-organizing neuruloids model developmental aspects of Huntington’s disease in the ectodermal compartment. Nat Biotechnol. 2019;37:1198-208.
35. Zheng Y, Xue X, Shao Y, et al. Controlled modelling of human epiblast and amnion development using stem cells. Nature. 2019;573:421-5.
36. Martyn I, Kanno TY, Ruzo A, Siggia ED, Brivanlou AH. Self-organization of a human organizer by combined Wnt and Nodal signalling. Nature. 2018;558:132-5.
37. Shao Y, Taniguchi K, Townshend RF, Miki T, Gumucio DL, Fu J. A pluripotent stem cell-based model for post-implantation human amniotic sac development. Nat Commun. 2017;8:208.
38. Shao Y, Taniguchi K, Gurdziel K, et al. Self-organized amniogenesis by human pluripotent stem cells in a biomimetic implantation-like niche. Nat Mater. 2016;16:419-25.
39. Moris N, Anlas K, Van Den Brink SC, et al. An in vitro model of early anteroposterior organization during human development. Nature. 2020;582:410-5.
40. Yamanaka Y, Hamidi S, Yoshioka-Kobayashi K, et al. Reconstituting human somitogenesis in vitro. Nature. 2022;614:509-20.
41. Liu X, Tan JP, Schröder J, et al. Modelling human blastocysts by reprogramming fibroblasts into iBlastoids. Nature. 2021;591:627-32.
42. Yu L, Wei Y, Duan J, et al. Blastocyst-like structures generated from human pluripotent stem cells. Nature. 2021;591:620-6.
43. Weatherbee BAT, Gantner CW, Iwamoto-Stohl LK, et al. Pluripotent stem cell-derived model of the post-implantation human embryo. Nature. 2023;622:584-93.
44. Oldak B, Wildschutz E, Bondarenko V, et al. Complete human day 14 post-implantation embryo models from naive ES cells. Nature. 2023;622:562-73.
45. Neupane J, Jowett GM, Wu B, et al. A post-implantation model of human embryo development includes a definitive hematopoietic niche. Cell Rep. 2025;44:116373.
46. Hamazaki N, Yang W, Kubo CA, et al. Retinoic acid induces human gastruloids with posterior embryo-like structures. Nat Cell Biol. 2024;26:1790-803.
47. Guo M, Wu J, Chen C, et al. Self-renewing human naïve pluripotent stem cells dedifferentiate in 3D culture and form blastoids spontaneously. Nat Commun. 2024;15:668.
48. Molè MA, Elderkin S, Zorzan I, et al. Modeling human embryo implantation in vitro. Cell. 2026;189:87-105.e28.
49. Li Q, Yuan Y, Zhao W, et al. A 3D in vitro model for studying human implantation and implantation failure. Cell. 2026;189:70-86.e20.
50. Clark AT, Brivanlou A, Fu J, et al. Human embryo research, stem cell-derived embryo models and in vitro gametogenesis: considerations leading to the revised ISSCR guidelines. Stem Cell Rep. 2021;16:1416-24.
51. Xue Z, Huang K, Cai C, et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature. 2013;500:593-7.
52. Meistermann D, Bruneau A, Loubersac S, et al. Integrated pseudotime analysis of human pre-implantation embryo single-cell transcriptomes reveals the dynamics of lineage specification. Cell Stem Cell. 2021;28:1625-1640.e6.
53. Tyser RCV, Mahammadov E, Nakanoh S, Vallier L, Scialdone A, Srinivas S. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature. 2021;600:285-9.
54. Xu Y, Zhang T, Zhou Q, et al. A single-cell transcriptome atlas profiles early organogenesis in human embryos. Nat Cell Biol. 2023;25:604-15.
55. Zhao C, Plaza Reyes A, Schell JP, et al. A comprehensive human embryo reference tool using single-cell RNA-sequencing data. Nat Methods. 2024;22:193-206.
56. Cui L, Lin S, Yang X, et al. Spatial transcriptomic characterization of a Carnegie stage 7 human embryo. Nat Cell Biol. 2025;27:360-9.
57. Zeng B, Liu Z, Lu Y, et al. The single-cell and spatial transcriptional landscape of human gastrulation and early brain development. Cell Stem Cell. 2023;30:851-66.e7.
58. Zhang B, He P, Lawrence JEG, et al. A human embryonic limb cell atlas resolved in space and time. Nature. 2023;635:668-78.
59. Hara Y, Kumamoto T, Yoshizawa-Sugata N, et al. The spatial transcriptome of the late-stage embryonic and postnatal mouse brain reveals spatiotemporal molecular markers. Sci Rep. 2025;15:12299.
60. Pan J, Li Y, Lin Z, et al. Spatiotemporal transcriptome atlas of human embryos after gastrulation. Nature. 2026;654:751-61.
61. Van Den Brink SC, Alemany A, Van Batenburg V, et al. Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature. 2020;582:405-9.
62. Farah EN, Hu RK, Kern C, et al. Spatially organized cellular communities form the developing human heart. Nature. 2024;627:854-64.
63. Qiu X, Zhu DY, Lu Y, et al. Spatiotemporal modeling of molecular holograms. Cell. 2024;187:7351-73.e61.
64. Chen J, Zhang X, Delaughter DM, et al. Molecular and spatial signatures of mouse embryonic endothelial cells at single-cell resolution. Circ Res. 2024;134:529-46.
65. Qian X, Coleman K, Jiang S, et al. Spatial transcriptomics reveals human cortical layer and area specification. Nature. 2025;644:153-63.
66. Jiang F, Zhou X, Qian Y, et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat Methods. 2023;20:1048-57.
67. Zhu J, Pang K, Hu B, et al. Custom microfluidic chip design enables cost-effective three-dimensional spatiotemporal transcriptomics with a wide field of view. Nat Genet. 2024;56:2259-70.
68. Wang H, Huang R, Nelson J, et al. Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues. Nat Commun. 2025;16:10215.
69. Hui T, Zhou J, Yao M, Xie Y, Zeng H. Advances in spatial omics technologies. Small Methods. 2025;9:2401171.
70. Maynard KR, Collado-Torres L, Weber LM, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci. 2021;24:425-36.
71. Zenobi R. Single-cell metabolomics: analytical and biological perspectives. Science. 2013;342:1243259.
72. Polański K, Bartolomé-Casado R, Sarropoulos I, et al. Bin2cell reconstructs cells from high resolution Visium HD data. Bioinformatics. 2024;40:btae546.
73. Chen A, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185:1777-92.e21.
74. Chen H, Li D, Bar-Joseph Z. SCS: cell segmentation for high-resolution spatial transcriptomics. Nat Methods. 2023;20:1237-43.
75. Stringer C, Pachitariu M. Cellpose3: one-click image restoration for improved cellular segmentation. Nat Methods. 2025;22:592-9.
76. Pachitariu M, Stringer C. Cellpose 2.0: how to train your own model. Nat Methods. 2022;19:1634-41.
77. Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2020;18:100-6.
78. Petukhov V, Xu RJ, Soldatov RA, et al. Cell segmentation in imaging-based spatial transcriptomics. Nat Biotechnol. 2021;40:345-54.
79. Srivatsan SR, Regier MC, Barkan E, et al. Embryo-scale, single-cell spatial transcriptomics. Science. 2021;373:111-7.
80. Tang XT, Chen LV, Zhou BO. Resolving the spatial organization of fetal liver hematopoiesis by SeekSpace. Cell Regen. 2025;14:15.
81. Wang X, Allen WE, Wright MA, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361:eaat5691.
82. Marco Salas S, Kuemmerle LB, Mattsson-Langseth C, et al. Optimizing Xenium in situ data utility by quality assessment and best-practice analysis workflows. Nat Methods. 2025;22:813-23.
83. Nichterwitz S, Chen G, Aguila Benitez J, et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat Commun. 2016;7:12139.
84. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096-8.
85. Chen J, Suo S, Tam PP, Han JJ, Peng G, Jing N. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat Protoc. 2017;12:566-80.
86. Kruse F, Junker JP, van Oudenaarden A, et al. Tomo-seq: a method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307.
87. Fang R, Halpern A, Rahman MM, et al. Three-dimensional single-cell transcriptome imaging of thick tissues. eLife. 2024;12:RP90029.
88. Schott M, León-Periñán D, Splendiani E, et al. Open-ST: high-resolution spatial transcriptomics in 3D. Cell. 2024;187:3953-72.e26.
89. Sui X, Lo JA, Luo S, et al. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks. Nat Methods. 2025;22:2574-84.
90. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363:1463-7.
91. Moffitt JR, Hao J, Bambah-Mukku D, Lu T, Dulac C, Zhuang X. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc Natl Acad Sci USA. 2016;113:14456-61.
92. Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019;568:235-9.
93. Janesick A, Shelansky R, Gottscho AD, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023;14:8353.
94. Cohen L, Halpern AR, Blosser TR, Che ZP, Pan X, Zhuang X. Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues. Cell. 2026;189:6189-207.e10.
95. Bergmann S, Penfold CA, Slatery E, et al. Spatial profiling of early primate gastrulation in utero. Nature. 2022;609:136-43.
96. Zeira R, Land M, Strzalkowski A, Raphael BJ. Alignment and integration of spatial transcriptomics data. Nat Methods. 2022;19:567-75.
97. Fang S, Xu M, Cao L, et al. Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics. Nat Commun. 2025;16:3741.
98. Wang M, Hu Q, Lv T, et al. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev Cell. 2022;57:1271-1283.e4.
99. Liu C, Li R, Li Y, et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell. 2022;57:1284-98.e5.
101. Qu F, Li W, Xu J, et al. Three-dimensional molecular architecture of mouse organogenesis. Nat Commun. 2023;14:4599.
102. Sampath Kumar A, Tian L, Bolondi A, et al. Spatiotemporal transcriptomic maps of whole mouse embryos at the onset of organogenesis. Nat Genet. 2023;55:1176-85.
103. To K, Fei L, Pett JP, et al. A multi-omic atlas of human embryonic skeletal development. Nature. 2024;635:657-67.
104. Lu T, Wang M, Zhou W, et al. Decoding transcriptional identity in developing human sensory neurons and organoid modeling. Cell. 2024;187:7374-93.e28.
105. Feng J, Janečková E, Guo T, et al. High-resolution spatial transcriptomics and cell lineage analysis reveal spatiotemporal cell fate determination during craniofacial development. Nat Commun. 2025;16:4396.
106. Li M, Liu H, Kang Q, et al. CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics. arXiv. 2024.
107. Carpenter AE, Jones TR, Lamprecht MR, et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;7:R100.
108. Gamarra M, Zurek E, Escalante HJ, Hurtado L, San-Juan-Vergara H. Split and merge watershed: a two-step method for cell segmentation in fluorescence microscopy images. Biomed Signal Process Control. 2019;53:101575.
109. Schmidt U, Weigert M, Broaddus C, Myers G. Cell detection with star-convex polygons. Medical image computing and computer assisted intervention - MICCAI 2018. Cham: Springer, 2018.
110. Si Y, Lee C, Hwang Y, et al. FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics. Nat Methods. 2024;21:1843-54.
111. Xie P, Shen J, Yang Y, et al. Digital reconstruction of full embryos during early mouse organogenesis. Cell. 2025;188:4754-72.e18.
112. Cheng M, Zheng H, Fang Q, et al. Three-dimension transcriptomics maps of whole mouse embryo during organogenesis. Dev Biol. 2024.
113. Wei X, Fu S, Li H, et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science. 2022;377:eabp9444.
114. Wan Y, El Kholtei J, Jenie I, et al. Whole-embryo spatial transcriptomics at subcellular resolution from gastrulation to organogenesis. Science. 2026;391:eadt3439.
115. Li H, Zhou J, Li Z, et al. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat Commun. 2023;14:1548.
116. Cable DM, Murray E, Zou LS, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2021;40:517-26.
117. Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022;40:661-71.
118. Tao Q, Xu Y, He Y, Luo T, Li X, Han L. Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data. Brief Bioinform. 2024;25:bbae250.
119. Sang-Aram C, Browaeys R, Seurinck R, et al. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics. eLife. 2024;12:RP88431.
120. Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods. 2021;18:1352-62.
121. Yang X, Xiang J, Feng Y, Zhao Y, Li H, Chen X. Spatialsmooth: a spatially-aware convolutional autoencoder framework for enhanced deconvolution of spatial transcriptomics data. BMC Genomics. 2025;26:791.
122. Zha Y, Feng S, Gao P, Zou Q, Ma X. Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model. Bioinformatics. 2025;41:btaf419.
123. Chen Y, Ruan F, Wang J. NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data. Bioinformatics. 2025;41:btae747.
124. Coleman K, Hu J, Schroeder A, Lee EB, Li M. SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning. Commun Biol. 2023;6:378.
125. Long Y, Ang KS, Li M, et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun. 2023;14:1155.
126. Zhang M, Parker J, An L, Liu Y, Sun X. Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach. BMC Bioinf. 2025;26:35.
127. Miller BF, Huang F, Atta L, Sahoo A, Fan J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat Commun. 2022;13:2339.
128. Singh R, He X, Wang X, et al. RETROFIT: reference-free deconvolution of cell-type mixtures in spatial transcriptomics. Nat Commun. 2026;17:9010.
129. Xie A, Steele NG, Cui Y. gwSPADE: gene frequency-weighted reference-free deconvolution in spatial transcriptomics. Nucleic Acids Res. 2025;53:gkaf966.
130. Liu X, Tang G, Chen Y, Li Y, Li H, Wang X. SpatialDeX is a reference-free method for cell-type deconvolution of spatial transcriptomics data in solid tumors. Cancer Res. 2025;85:171-82.
131. Liu Y, Wang T, Duggan B, et al. SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression. Brief Bioinform. 2022;23:bbac116.
132. Zhu Q, Shah S, Dries R, Cai L, Yuan G. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat Biotechnol. 2018;36:1183-90.
133. Dries R, Zhu Q, Dong R, et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 2021;22:78.
134. Zhao E, Stone MR, Ren X, et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat Biotechnol. 2021;39:1375-84.
135. Pham D, Tan X, Balderson B, et al. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat Commun. 2023;14:7739.
136. Hu J, Li X, Coleman K, et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods. 2021;18:1342-51.
137. Dong K, Zhang S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat Commun. 2022;13:1739.
138. Xu C, Jin X, Wei S, et al. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Res. 2022;50:e131.
139. Ren H, Walker BL, Cang Z, Nie Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat Commun. 2022;13:4076.
140. Bai Y, Guo X, Liu K, et al. SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics. Genome Biol. 2025;26:230.
141. Zhao C, Xu Z, Wang X, et al. Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression. Brief Bioinform. 2024;25:bbae052.
142. Cui H, Wang C, Maan H, et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods. 2024;21:1470-80.
143. Li L, Dong L, Zhang H, Xu D, Li Y. spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration. Brief Bioinform. 2025;26:bbaf304.
144. Blampey Q, Benkirane H, Bercovici N, et al. Novae: a graph-based foundation model for spatial transcriptomics data. Nat Methods. 2025;22:2539-50.
145. Singhal V, Chou N, Lee J, et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet. 2024;56:431-41.
146. Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol. 2024;25:73.
147. Qiu X, Hill A, Packer J, Lin D, Ma Y, Trapnell C. Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017;14:309-15.
148. Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566:496-502.
149. Street K, Risso D, Fletcher RB, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018;19:477.
151. Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020;38:1408-14.
152. Li S, Zhang P, Chen W, et al. A relay velocity model infers cell-dependent RNA velocity. Nat Biotechnol. 2023;42:99-108.
153. Cui H, Maan H, Vladoiu MC, Zhang J, Taylor MD, Wang B. DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics. Genome Biol. 2024;25:27.
154. Shang L, Zhou X. Spatially aware dimension reduction for spatial transcriptomics. Nat Commun. 2022;13:7203.
155. Qiu X, Zhang Y, Martin-Rufino JD, et al. Mapping transcriptomic vector fields of single cells. Cell. 2022;185:690-711.e45.
156. Gu Y, Liu J, Lee KH, et al. Topological velocity inference from spatial transcriptomic data. Nat Biotechnol. 2025;44:771-82.
157. Faure L, Soldatov R, Kharchenko PV, Adameyko I. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data. Bioinformatics. 2023;39:btac746.
158. Long W, Liu T, Xue L, Zhao H. spVelo: RNA velocity inference for multi-batch spatial transcriptomics data. Genome Biol. 2025;26:239.
159. Aivazidis A, Memi F, Kleshchevnikov V, et al. Cell2fate infers RNA velocity modules to improve cell fate prediction. Nat Methods. 2025;22:698-707.
160. Abdelaal T, Grossouw LM, Pasterkamp RJ, Lelieveldt BPF, Reinders MJT, Mahfouz A. SIRV: spatial inference of RNA velocity at the single-cell resolution. NAR Genom Bioinform. 2024;6:lqae100.
161. Gulati GS, Sikandar SS, Wesche DJ, et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science. 2020;367:405-11.
162. Kang M, Gulati GS, Brown EL, et al. Improved reconstruction of single-cell developmental potential with CytoTRACE 2. Nat Methods. 2025;22:2258-63.
163. Wolf FA, Hamey FK, Plass M, et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019;20:59.
164. Angerer P, Haghverdi L, Büttner M, Theis FJ, Marr C, Buettner F. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics. 2016;32:1241-3.
165. Jacomy M, Venturini T, Heymann S, Bastian M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE. 2014;9:e98679.
166. Vinyard ME, Rasmussen AW, Li R, Klein AM, Getz G, Pinello L. Learning cell dynamics with neural differential equations. Nat Mach Intell. 2025;7:1969-84.
167. Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc. 2024;20:180-219.
168. Troulé K, Petryszak R, Cakir B, et al. CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data. Nat Protoc. 2025;20:3412-40.
169. Shao X, Li C, Yang H, et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun. 2022;13:4429.
170. Cang Z, Zhao Y, Almet AA, et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods. 2023;20:218-28.
171. Almet AA, Tsai Y, Watanabe M, Nie Q. Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics. Nat Methods. 2024;21:1806-17.
172. Feng X, Zhang S, Li L. OrgaCCC: orthogonal graph autoencoders for constructing cell-cell communication networks on spatial transcriptomics data. PLoS Comput Biol. 2025;21:e1013212.
173. Xia C, Cao Z, Tu X, Gao G. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nat Commun. 2023;14:7236.
174. Wang G, Zhao J, Yan Y, Wang Y, Wu AR, Yang C. Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks. Nat Mach Intell. 2023;5:1200-13.
175. Liu C, Yuen J, Torralba A. SIFT flow: dense correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell. 2011;33:978-94.
176. Preibisch S, Innerberger M, León-Periñán D, Karaiskos N, Rajewsky N. Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Syst. 2025;16:101264.
177. Xu H, Wang S, Fang M, et al. SPACEL: deep learning-based characterization of spatial transcriptome architectures. Nat Commun. 2023;14:7603.
178. Lin S, Wang Z, Cui Y, et al. Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases. Nat Methods. 2025;23:360-72.
179. Detomaso D, Yosef N. Hotspot identifies informative gene modules across modalities of single-cell genomics. Cell Syst. 2021;12:446-56.e9.
180. Cao Z, Keloth VK, Xie Q, et al. The development landscape of large language models for biomedical applications. Annu Rev Biomed Data Sci. 2025;8:251-74.
183. Wang L, Hu Y, Gao L. Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics. Brief Bioinform. 2024;25:bbae063.
184. Hao M, Gong J, Zeng X, et al. Large-scale foundation model on single-cell transcriptomics. Nat Methods. 2024;21:1481-91.
185. Theodoris CV, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature. 2023;618:616-24.
186. Tejada-Lapuerta A, Schaar AC, Gutgesell R, et al. Nicheformer: a foundation model for single-cell and spatial omics. Nat Methods. 2025;22:2525-38.
187. Sadia RT, Ahamed MA, Cheng Q. CausalGeD: blending causality and diffusion for spatial gene expression generation. arXiv. 2025.
188. Li X, Zhu F, Min W. SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq. Brief Bioinform. 2024;25:bbae571.
189. Abdelaal T, Michielsen L, Cats D, et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 2019;20:194.
190. Xia Y, Liu Y, Li T, et al. Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data. Methods. 2024;228:12-21.
191. Fu Q, Dong C, Liu Y, et al. A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset. Brief Bioinform. 2024;25:bbae392.
192. Heimberg G, Kuo T, Depianto DJ, et al. A cell atlas foundation model for scalable search of similar human cells. Nature. 2024;638:1085-94.
193. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417-25.
194. Roohani Y, Huang K, Leskovec J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol. 2023;42:927-35.
195. Norman TM, Horlbeck MA, Replogle JM, et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science. 2019;365:786-93.
196. Adamson B, Norman TM, Jost M, et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell. 2016;167:1867-82.e21.
197. Replogle JM, Norman TM, Xu A, et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat Biotechnol. 2020;38:954-61.
198. He S, Zhu Y, Tavakol DN, et al. Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nat Methods. 2025;23:65-77.
199. Bolton E, Venigalla A, Yasunaga M, et al. BioMedLM: a 2.7B parameter language model trained on biomedical text. arXiv. 2024.
201. Luo R, Sun L, Xia Y, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 2022;23:bbac409.
202. Jin Q, Yang Y, Chen Q, Lu Z. GeneGPT: augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics. 2024;40:btae075.
203. Jin Q, Wang Z, Yang Y, et al. AgentMD: empowering language agents for risk prediction with large-scale clinical tool learning. Nat Commun. 2025;16:9377.
204. Xiao Y, Liu J, Zheng Y, et al. CellAgent: LLM-driven multi-agent framework for natural language-based single-cell analysis. Bioinformatics. 2025:2024.05.13.593861.
205. Wang H, He Y, Coelho PP, et al. SpatialAgent: an autonomous AI agent for spatial biology. Bioinformatics. 2025:2025.04.03.646459.
206. Zhou J, Zhang B, Li G, et al. An AI agent for fully automated multi-omic analyses. Adv Sci. 2024;11:2407094.
207. Schaefer M, Peneder P, Malzl D, et al. Multimodal learning enables chat-based exploration of single-cell data. Nat Biotechnol. 2025;44:1722-32.





