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Single-cell transcriptomics enables the measurement of gene expression in complex biological systems at the resolution of individual cells. Multivariate analysis of single-cell data helps describe the variation in expression accompanying cellular processes during embryonic development, disease progression and in response to stimuli. Likewise, new methods have extended the possibilities of single-cell analysis by measuring the transcriptome while simultaneously capturing information on lineage or past molecular events. These emerging approaches have the common strategy of querying a static snapshot for information related to different temporal stages. Single-cell temporal-omics methods open new possibilities such as extrapolating the future or correlating past events to present gene expression. We highlight advancements in the single-cell field, describe novel toolkits for investigation, and consider the potential impact of temporal-omics approaches for the study of disease progression.
Didier Trono, Evaristo Jose Planet Letschert, Julien Léonard Duc, Alexandre Coudray, Julien Paul André Pontis, Delphine Yvette L Grun, Cyril David Son-Tuyên Pulver, Shaoline Sheppard
Vassily Hatzimanikatis, Georgios Fengos, Maria Masid Barcon, Daniel Robert Weilandt, Zhaleh Hosseini, Pierre Guy Rémy Salvy