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Abstract
Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
Author summary
Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell’s fate to those of the local neighborhood.
To unravel the role of single genes underlying certain biological processes, scientists often use amorphic or hypomorphic alleles. In the past, such mutants were often created by chance. Enormous approaches with many animals and massive screening effort for striking phenotypes were necessary to find a needle in the haystack. Therefore at the beginning chemical mutagens or radiation were used to induce mutations in the genome. Later P-element insertions and inaccurate jump-outs enabled the advantage of potential larger deletions or inversions. The mutations were characterized and subsequently kept in smaller populations in the laboratories. Thus additional mutations with unknown background effects could accumulate.
The precision of the knockout through homologous recombination and the additional advantage of being able to generate many useful rescue constructs that can be easily reintegrated into the target locus made us trying an ends-out targeting procedure of the two core clock genes period and timeless in Drosophila melanogaster. Instead of the endogenous region, a small fragment of approximately 100 base pairs remains including an attP-site that can be used as integration site for in vitro created rescue constructs. After a successful ends-out targeting procedure, the locus will be restored with e.g. flies expressing the endogenous gene under the native promoter at the original locus coupled to a fluorescence tag or expressing luciferase.
We also linked this project to other research interests of our work group, like the epigenetic related ADAR-editing project of the Timeless protein, a promising newly discovered feature of time point specific timeless mRNA modification after transcription with yet unexplored consequences. The editing position within the Timeless protein is likewise interesting and not only noticed for the first time. This will render new insights into the otherwise not-satisfying investigation and quest for functional important sequences of the Timeless protein, which anyway shows less homology to other yet characterized proteins.
Last but not least, we bothered with the question of the role of Shaggy on the circadian clock. The impact of an overexpression or downregulation of Shaggy on the pace of the clock is obvious and often described. The influence of Shaggy on Period and Timeless was also shown, but for the latter it is still controversially discussed. Some are talking of a Cryptochrome stabilization effect and rhythmic animals in constant light due to Shaggy overexpression, others show a decrease of Cryptochrome levels under these conditions. Also the constant light rhythmicity of the flies, as it was published, could not be repeated so far. We were able to expose the conditions behind the Cryptochrome stabilization and discuss possibilities for the phenomenon of rhythmicity under constant light due to Shaggy overexpression.
G-protein-coupled receptors (GPCRs) are typically regarded as chemosensors that control cellular states in response to soluble extracellular cues. However, the modality of stimuli recognized through adhesion GPCR (aGPCR), the second largest class of the GPCR superfamily, is unresolved. Our study characterizes the Drosophila aGPCR Latrophilin/dCirl, a prototype member of this enigmatic receptor class. We show that dCirl shapes the perception of tactile, proprioceptive, and auditory stimuli through chordotonal neurons, the principal mechanosensors of Drosophila. dCirl sensitizes these neurons for the detection of mechanical stimulation by amplifying their input-output function. Our results indicate that aGPCR may generally process and modulate the perception of mechanical signals, linking these important stimuli to the sensory canon of the GPCR superfamily.
Sleep is a highly conserved and essential behaviour in many species, including the fruit fly Drosophila melanogaster. In the wild, sensory signalling encoding environmental information must be integrated with sleep drive to ensure that sleep is not initiated during detrimental conditions. However, the molecular and circuit mechanisms by which sleep timing is modulated by the environment are unclear. Here we introduce a novel behavioural paradigm to study this issue. We show that in male fruit flies, onset of the daytime siesta is delayed by ambient temperatures above 29°C. We term this effect Prolonged Morning Wakefulness (PMW). We show that signalling through the TrpA1 thermo-sensor is required for PMW, and that TrpA1 specifically impacts siesta onset, but not night sleep onset, in response to elevated temperatures. We identify two critical TrpA1-expressing circuits and show that both contact DN1p clock neurons, the output of which is also required for PMW. Finally, we identify the circadian blue-light photoreceptor CRYPTOCHROME as a molecular regulator of PMW, and propose a model in which the Drosophila nervous system integrates information encoding temperature, light, and time to dynamically control when sleep is initiated. Our results provide a platform to investigate how environmental inputs co-ordinately regulate sleep plasticity.
Cryptochrome (CRY) is the primary photoreceptor of Drosophila’s circadian clock. It resets the circadian clock by promoting light-induced degradation of the clock protein Timeless (TIM) in the proteasome. Under constant light, the clock stops because TIM is absent, and the flies become arrhythmic. In addition to TIM degradation, light also induces CRY degradation. This depends on the interaction of CRY with several proteins such as the E3 ubiquitin ligases Jetlag (JET) and Ramshackle (BRWD3). However, CRY can seemingly also be stabilized by interaction with the kinase Shaggy (SGG), the GSK-3 beta fly orthologue. Consequently, flies with SGG overexpression in certain dorsal clock neurons are reported to remain rhythmic under constant light. We were interested in the interaction between CRY, Ramshackle and SGG and started to perform protein interaction studies in S2 cells. To our surprise, we were not able to replicate the results, that SGG overexpression does stabilize CRY, neither in S2 cells nor in the relevant clock neurons. SGG rather does the contrary. Furthermore, flies with SGG overexpression in the dorsal clock neurons became arrhythmic as did wild-type flies. Nevertheless, we could reproduce the published interaction of SGG with TIM, since flies with SGG overexpression in the lateral clock neurons shortened their free-running period. We conclude that SGG does not directly interact with CRY but rather with TIM. Furthermore we could demonstrate, that an unspecific antibody explains the observed stabilization effects on CRY.