Spatio-temporal, optogenetic control of gene expression in organoids

Legnini, Ivano; Emmenegger, Lisa; Wurmus, Ricardo; Zappulo, Alessandra; Oliveras, Anna; Jara, Cledi Cerda; Boltengagen, Anastasiya; Hessler, Talé; Mastrobuoni, Guido; Rybak-Wolf, Agnieszka; Kempa, Stefan; Zinzen, Robert P.; Woehler, Andrew; Rajewsky, Nikolaus

doi:10.1101/2021.09.26.461850

Cited by 10 publications

(5 citation statements)

References 65 publications

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“…Inferring the underlying mechanism for division of labor is a challenge since multiple potential mechanisms may result in the same phenotype. As comprehensive spatial transcriptomics data become more prevalent and extended to capture three-dimensional tissue structure (Wang et al 2018) and causation (Legnini et al 2022), our framework can be used to better infer or narrow down the potential underlying mechanisms of collective cellular division of labor.…”

Section: Discussionmentioning

confidence: 99%

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Emergence of division of labor in tissues through cell interactions and spatial cues

Adler

Moriel

Goeva

et al. 2022

Preprint

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Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions vs. instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division- of-labor mechanisms from single-cell RNA-seq and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

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Section: Discussionmentioning

confidence: 99%

“…) and causation (Legnini et al 2022), our framework can be used to better infer or narrow down the potential underlying mechanisms of collective cellular division of labor.…”

mentioning

confidence: 99%

Emergence of division of labor in tissues through cell interactions and spatial cues

Adler

Moriel

Goeva

et al. 2022

Preprint

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“…LOV domains make up the core of most non-ion-channel optogenetic systems [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] , presumably because they are small, reliable, and do not need exogenous chromophores. Interest in LOV domains is reflected in multiple diversification campaigns 34,[54][55][56] .…”

Section: )mentioning

confidence: 99%

Light-directed evolution of dynamic, multi-state, and computational protein functionalities

Gligorovski,

Labagnara,

Rahi

2024

Preprint

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Directed evolution is a powerful method in biological engineering. Current approaches were devised for evolving steady-state properties such as enzymatic activity or fluorescence intensity. A fundamental problem remains how to evolve dynamic, multi-state, or computational functionalities, e.g., folding times, on-off kinetics, state-specific activity, stimulus-responsiveness, or switching and logic capabilities. These require applying selection pressure on all of the states of a protein of interest (POI) and the transitions between them. We realized that optogenetics and cell cycle oscillations could be leveraged for a novel directed evolution paradigm (‘optovolution’) that is germane for this need: We designed a signaling cascade in budding yeast where optogenetic input switches the POI between off (0) and on (1) states. In turn, the POI controls a Cdk1 cyclin, which in the re-engineered cell cycle system is essential for one cell cycle stage but poisonous for another. Thus, the cyclin must oscillate (1-0-1-0…) for cell proliferation. In this system, evolution can act efficiently on the dynamics, transient states, and input-output relations of the POI in every cell cycle. Further, controlling the pacemaker, light, directs and tunes selection pressures. Optovolution isin vivo, continuous, self-selecting, and genetically robust. We first evolved two optogenetic systems, which relay 0/1 input to 0/1 output: We obtained 25 new variants of the widely used LOV transcription factor El222. These mutants were stronger, less leaky, or green- and red-responsive. The latter was conjectured to be impossible for LOV domains but is needed for multiplexing and lowering phototoxicity. Evolving the PhyB-Pif3 optogenetic system, we discovered that loss ofYOR1makes supplementing the expensive and unstable chromophore phycocyanobilin (PCB) unnecessary. Finally, we demonstrate the generality of the method by creating and evolving a destabilized rtTA transcription factor, which performs an AND operation between transcriptional and doxycycline input. Optovolution makes coveted, difficult-to-change protein functionalities evolvable.

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“…Similarly, a split CRISPR-Cas9-based photoactivatable transcription system, along with sgRNA for activating the SHH promoter, was used to locally induce and quantify increased SHH mRNA expression upon illumination in neuronal organoids to reveal its contribution to gene regulation in terms of the differentiation of neuronal progenitors, including the strong activation of IGF pathway modulators, the differentiation of cells expressing pericyte markers and the spatial modulation of axon guidance molecules. Programmable spatiotemporal patterns in a 3D neural organoid to study neurodevelopment were thus created using optogenetics [65].…”

Section: Measuring the Specific Dynamics Of Morphogen Activity With O...mentioning

confidence: 99%

Quantitative insights in tissue growth and morphogenesis with optogenetics

Mim,

Knight,

Zartman

2023

Phys. Biol.

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Cells communicate with each other to jointly regulate cellular processes during cellular differentiation and tissue morphogenesis. This multiscale coordination arises through spatiotemporal activity of morphogens to pattern cell signaling and transcriptional factor activity. This coded information controls cell mechanics, proliferation, and differentiation to shape the growth and morphogenesis of organs. While many of the molecular components and physical interactions have been identified in key model developmental systems, there are still many unresolved questions related to the dynamics involved due to challenges in precisely perturbing and quantitatively measuring signaling dynamics. Recently, a broad range of synthetic optogenetic tools have been developed and employed to quantitatively define relationships between signal transduction and downstream cellular responses. These optogenetic tools can control intracellular activities at the single cell or whole tissue scale to direct subsequent biological processes. In this brief review, we highlight a selected set of studies that develop and implement optogenetic tools to unravel quantitative biophysical mechanisms for tissue growth and morphogenesis across a broad range of biological systems through the manipulation of morphogens, signal transduction cascades, and cell mechanics. More generally, we discuss how optogenetic tools have emerged as a powerful platform for probing and controlling multicellular development.

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Spatio-temporal, optogenetic control of gene expression in organoids

Cited by 10 publications

References 65 publications

Emergence of division of labor in tissues through cell interactions and spatial cues

Emergence of division of labor in tissues through cell interactions and spatial cues

Light-directed evolution of dynamic, multi-state, and computational protein functionalities

Quantitative insights in tissue growth and morphogenesis with optogenetics

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