Imaging cell lineage with a synthetic digital recording system

Chow, Ke-Huan K.; Budde, Mark W.; Granados, Alejandro A; Cabrera, María Eugenia; Yoon, Shinae; Cho, Soomin; Huang, Tung Po; Koulena, Noushin; Frieda, Kirsten L.; Cai, Long; Lois, Carlos; Elowitz, Michael B.

doi:10.1101/2020.02.21.958678

Cited by 21 publications

(27 citation statements)

References 63 publications

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“…3D wholemount maps would further resolve the processes associated with embryo patterning, in particular processes that are associated with the left-right axis. Moreover, the recent development of novel image-based cell lineage tracing methods, such as Zombie 106 or intMEMOIR 107 , allow a cell’s lineage to be recorded while preserving spatial information. These methods are compatible with seqFISH and therefore afford the possibility to record spatial gene expression profiles and cell history from the same cell in intact tissue.…”

Section: Discussionmentioning

confidence: 99%

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Lohoff

Ghazanfar

Missarova

et al. 2020

Preprint

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Transcriptional and epigenetic profiling of single-cells has advanced our knowledge of the molecular bases of gastrulation and early organogenesis. However, current approaches rely on dissociating cells from tissues, thereby losing the crucial spatial context that is necessary for understanding cell and tissue interactions during development. Here, we apply an image-based single-cell transcriptomics method, seqFISH, to simultaneously and precisely detect mRNA molecules for 387 selected target genes in 8-12 somite stage mouse embryo tissue sections. By integrating spatial context and highly multiplexed transcriptional measurements with two single-cell transcriptome atlases we accurately characterize cell types across the embryo and demonstrate how spatially-resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary and the developing gut tube. Our spatial atlas uncovers axes of resolution that are not apparent from single-cell RNA sequencing data – for example, in the gut tube we observe early dorsal-ventral separation of esophageal and tracheal progenitor populations. In sum, by computationally integrating high-resolution spatially-resolved gene expression maps with single-cell genomics data, we provide a powerful new approach for studying how and when cell fate decisions are made during early mammalian development.

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Section: Discussionmentioning

confidence: 99%

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Lohoff

Ghazanfar

Missarova

et al. 2020

Preprint

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“…More states could be distinguished by fluorescence microscopy by fusing fluorescent proteins to subcellular localization tags 49 , expressing various combinations of fluorescent proteins and performing spectral deconvolution 50 , or expressing barcoded mRNA and detect them through smFISH. 31 Because intermediate recombination states are encoded within DNA, we developed a rapid prototyping workflow (OSIRiS) based on total synthesis and characterization of all recombination intermediates. We found that the results from scaffold characterization using OSIRiS closely matched device behavior when operating in real conditions, i.e., responding to recombinase mediated inversions or excisions.…”

Section: Discussionmentioning

confidence: 99%

“…Recombinase switches are analog-to-digital converters and have been engineered to encode complex Boolean logic using reduced, single-layer architectures 28,29 . Stochastic, mutually exclusive recombination reactions have been used to randomly generate mosaics of reporter gene expression and enable cell lineage tracking in various organisms 30,31 . Researchers also built recombinase devices to control gene expression according to the number of inputs received by the cells and the order in which they appear.…”

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confidence: 99%

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Rational programming of history-dependent logic in cellular populations

et al. 2020

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Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

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“…In parallel has been the development of genetic lineage recorders, which register the genealogical history of cells based on somatic mutations, opening up the possibility of reconstructing the entire lineage history of complex organisms (e.g. [4][5][6][7][8]. The visualisation and integration of these complex data requires new specialised tools.…”

Section: Introductionmentioning

confidence: 99%

CeLaVi: An Interactive Cell Lineage Visualisation Tool

Salvador-Martínez

Grillo

Averof

et al. 2020

Preprint

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Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualising, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualisation tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualising the spatial distribution, identities and properties of cells. CeLaVi's principal functions include the ability to explore and manipulate the cell lineage tree; to visualise the spatial distribution of cell clones at different depths of the tree; to colour cells in the 3D viewer based on lineage relationships; to visualise various cell qualities on the 3D viewer (e.g. gene expression, cell type, tissue layer) and to annotate selected cells/clones. All these capabilities are demonstrated with four different example data sets. CeLaVi is available at http://www.celavi.pro.

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Imaging cell lineage with a synthetic digital recording system

Cited by 21 publications

References 63 publications

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

Rational programming of history-dependent logic in cellular populations

CeLaVi: An Interactive Cell Lineage Visualisation Tool

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