Alahmari Saeed, Schultz Andrew, Albrecht Jordan, Tagal Vural, Siddiqui Zaid, Prabhakaran Sandhya, El Naqa Issam, Anderson Alexander, Heiser Laura, Andor Noemi
Department of Computer Science, Najran University, Najran 66462, Saudi Arabia.
Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
bioRxiv. 2024 Sep 8:2024.09.04.610488. doi: 10.1101/2024.09.04.610488.
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.
目前存在多种从测序数据推断细胞周期的方法，且被广泛采用。相比之下，从成像数据分类细胞周期状态的方法却很稀少。我们首次整合了测序和成像得出的细胞周期伪时间，以分别将449个成像细胞与693个测序细胞进行匹配，测序数据和成像数据的平均分辨率分别为3.4个细胞和2.4个细胞。数据整合揭示了数千种相互关联的通路和细胞器特征，包括一些先前已知的相互作用和新的关联。将已分析细胞的转录组状态与其仍在活跃生长和扩展的最接近的活细胞亲属进行匹配的能力，为及时以单细胞分辨率进行基因型-表型映射打开了大门。
