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Cell identity revealed by precise cell cycle state mapping links data modalities.
Alahmari, Saeed; Schultz, Andrew; Albrecht, Jordan; Tagal, Vural; Siddiqui, Zaid; Prabhakaran, Sandhya; El Naqa, Issam; Anderson, Alexander; Heiser, Laura; Andor, Noemi.
Afiliación
  • Alahmari S; Department of Computer Science, Najran University, Najran 66462, Saudi Arabia.
  • Schultz A; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Albrecht J; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Tagal V; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Siddiqui Z; Department of Radiation Oncology, Baylor College of Medicine, Houston, TX, USA.
  • Prabhakaran S; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • El Naqa I; Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Anderson A; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
  • Heiser L; Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
  • Andor N; Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
bioRxiv ; 2024 Sep 08.
Article en En | MEDLINE | ID: mdl-39282313
ABSTRACT
Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita