Spatiotemporal Omics-Refining the landscape of precision medicine
Open accessAbstract
Current streamline of precision medicine uses histomorphological and molecular information to indicate individual phenotypes and genotypes to achieve optimal outcome of treatment. The knowledge of detected mutations and alteration can hardly describe molecular interaction and biological process which can finally be manifested as a disease. With molecular diagnosis revising the modalities of disease, there is a trend in precision medicine to apply multiomic and multidimensional information to decode tumors, regarding heterogeneity, pathogenesis, prognosis, etc. Emerging state-of-art spatiotemporal omics provides a novel vision for in discovering clinicopathogenesis associated findings, some of which show a promising potential to be translated to facilitate clinical practice. Here, we summarize the available spatiotemporal omic technologies and algorithms, highlight the novel scientific findings and explore potential applications in the clinical scenario. Spatiotemporal omics present the ability to provide impetus to rewrite clinical pathology and to answer outstanding clinical questions. This review emphasizes the novel vision of spatiotemporal omics to refine the landscape of precision medicine in the clinic.
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Spatial transcriptomics methods enable creation of detailed molecular maps with location information that is lost in scRNAseq.
“Spatial transcriptomics methods enable the creation of detailed molecular maps with location information that is lost in scRNAseq.19”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [58]
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Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162 · see citing works
Self-citation
- Authors
- Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X.
- Affiliations
- BGI Group (China) (CN)
- Weizmann Institute of Science (IL)
- University of Science and Technology of China (CN)
- Corresponding author
- Amit, Ido
- Bian, Xiu-wu
- Xu, Xun
- Venue
- Life Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-01
- Volume
- 1
- Issue
- 2
- Pages
- 84-102
- ISSN
- 2755-1733 (ISSN-L)
- PMC
- PMC11749813
- OpenAlex
- W4309006001
- Cited by
- 27
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- Funders
- Genome Institute of Singapore
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- precision medicine, multi-omics, tumor heterogeneity, carcinogenesis, prognosis, molecular diagnosis, clinical pathology, spatial transcriptomics, spatial proteomics, spatial genomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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