Cell Fate Clusters in ICM Organoids Arise from Cell Fate Heredity & Division – a Modelling Approach

Abstract

During the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first cell fate decision, cells become either part of an outer trophectoderm or part of the inner cell mass. Subsequently, the inner cell mass segregates into an embryonic or an extraembryonic lineage giving rise to the epiblast and the primitive endoderm, respectively. Inner cell mass organoids represent an experimental model system for preimplantation development, mimicking the second cell fate decision taking place in in vivo mouse embryos. In a previous study, the spatial pattern of the di erent cell lineage types was investigated. The study revealed that cells of the same fate tended to cluster stronger than expected for purely random cell fate decisions. In order to investigate the emergence of the cell lineage type clustering behaviour, we developed an agent-based model. Hereby, cells are mechanically interacting with direct neighbours, and exert adhesion and repulsion forces. The model was applied to compare two current assumptions of how inner cell mass neighbourhood structures are generated. We tested how di erent assumptions regarding cell fate switches a ect the observed neighbourhood relationships. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. The model further shows that the observed neighbourhood structures can emerge due to cell fate heredity during cell division and allows the inference for a time point for the cell fate decision. STATEMENT OF SIGNIFICANCECell fate decisions in early embryogenesis have been considered random events, which also cause a random distribution of cells of di erent cell fates. Using an agent-based mathematical model, fitted to data derived from ICM organoids, we show that a random distribution occurs only for a short time interval, as cell fate heredity and cell division quickly lead to spatial cell fate clustering. Our results speak against neighbourhood interactions determining an individual cell fate. Instead our approach indicates four consecutive phases of early development: 1) co-expression of cell fate markers, 2) cell fate decision, 3) cell division and local cell fate clustering, and 4) phase separation, whereby only the phases 1-3 occur in ICM organoids during the first 24h of growth. NANOG and GATA6 are described as the first markers for Epi and PrE segregation, respectively. Expression levels of NANOG and GATA6 undergo progressive changes during the morula stage and the early blastocyst (9, 10). In early blastocysts (E3.0), all ICM cells seem to co-express NANOG and GATA6 (7,11,12). Subsequently, NANOG and GATA6 are gradually up-or down-regulated during the 32-cell stage. Thereby, both transcription factors repress each other locally (10, 13-17), leading to a mutually exclusive transcription factor expression in late blastocysts (64 cells) (18). Once a cell-fate is determined it is only possible to switch the fate by an external m...

The transition from local to global patterns governs the differentiation of mouse blastocysts

“…The functional relevance of the clustering effect might be to ensure an early coordinated PrE cell behaviour during their migration in later stages to occupy their final position at the blastocoele. Recent modelling results for cell population development in ICM organoids show that clonal expansion can play a role in clustering [58]. In addition, the substantial cellular rearrangements taking place during preimplantation development [28,59] might have a positive effect on cell fate clustering.…”

Computational insights in cell physiology

“…Indeed, the two populations of cells are generated in precise proportions and with a reproducible timing, but their spatial patterning is random as it exhibits a salt-andpepper pattern (Chazaud et al, 2006). Models of the associated GRN revealed that ICM differentiation corresponds to a self-organized system relying on bi-or tri-stability (Bessonnard et al, 2014;Liebisch et al, 2020;Saiz et al, 2020;Raina et al, 2021;. These models are all based on the cross-inhibition between the NANOG and GATA6 transcription factors that characterize the Epi and PrE states, respectively.…”