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:10.1126/science.abb3099

Cited by 102 publications

(79 citation statements)

References 63 publications

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“…Second, these landing pads could be useful in the development of engineered cells or cellular therapies, where custom combinations of genes can be introduced to induce cell type-specific differentiation or to control cell behavior via synthetic gene circuits. Third, both endogenous and synthetically inserted LSR attachment sites can be used as scratchpads to record and reconstruct cell lineages during cell fate specification in development or disease models (Chow et al 2021). Finally, these LSRs could also enable larger scale genome engineering, including controlled models of large structural rearrangements by installing the attachment sites at distal sites in a genome (Esvelt and Wang 2013).…”

Section: Discussionmentioning

confidence: 99%

Large-scale discovery of recombinases for integrating DNA into the human genome

Durrant

Fanton

Tycko

et al. 2021

Preprint

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SUMMARYRecent microbial genome sequencing efforts have revealed a vast reservoir of mobile genetic elements containing integrases that could be useful genome engineering tools. Large serine recombinases (LSRs), such as Bxb1 and PhiC31, are bacteriophage-encoded integrases that can facilitate the insertion of phage DNA into bacterial genomes. However, only a few LSRs have been previously characterized and they have limited efficiency in human cells. Here, we developed a systematic computational discovery workflow that identifies thousands of new LSRs and their cognate DNA attachment sites by. We validate this approach via experimental characterization of LSRs in human cells, leading to three classes of LSRs distinguished from one another by their efficiency and specificity. We identify landing pad LSRs that efficiently integrate into synthetically installed attachment sites orthogonal to the human genome, human genome-targeting LSRs with computationally predictable pseudosites, and multi-targeting LSRs that can unidirectionally integrate cargos at with similar efficiency and superior specificity to commonly used transposases. LSRs from each category were functionally characterized in human cells, overall achieving up to 7-fold higher plasmid recombination than Bxb1 and genome insertion efficiencies of 40-70% with cargo sizes over 7 kb. Overall, we establish a paradigm for large-scale discovery of microbial recombinases and reconstruction of their target sites directly from microbial sequencing data. This strategy provides a rich resource of over 60 experimentally characterized LSRs that can function in human cells and thousands of additional candidates for large-payload genome editing without exposed DNA double-stranded breaks.

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Section: Discussionmentioning

confidence: 99%

Large-scale discovery of recombinases for integrating DNA into the human genome

Durrant

Fanton

Tycko

et al. 2021

Preprint

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“…In the future, TEMPO could be combined with barcoding methods to drive recorder editing in a predefined order while extending recorder availability over longer periods of time (17). Further, combining TEMPO with high-density clonal labelling methods such as intMEMOIR (38), or Brainbow (39), to distinguish cell clones while maintaining both temporal and spatial resolution would be very powerful for multiplexed lineage tracing in complex organisms. In addition, TEMPO is compatible with spatial-transcriptomic methods (40) enabling combined studies of cell identity and temporal patterning.…”

Section: Discussionmentioning

confidence: 99%

TEMPO: A system to sequentially label and genetically manipulate vertebrate cell lineages

Espinosa-Medina

Feliciano

Belmonte-Mateos

et al. 2021

Preprint

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During development, regulatory factors appear in a precise order to determine cell fates over time. To investigate complex tissue development, one should not just label cell lineages but further visualize and manipulate cells with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labelling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation/inactivation of reporters/effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.One-Sentence SummaryGaining sequential genetic access to vertebrate cell lineages.

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“…We fix the sampling proportion at present to the ratio of cells in the alignment over the total number of cells in the colony (e.g. from [8], Supplementary Table 2. )…”

Section: Methodsmentioning

confidence: 99%

TiDeTree: A Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Seidel

Stadler

2022

Preprint

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The development of a multicellular organism is governed by an elaborate balance between cell division, death, and differentiation. These core developmental processes can be quantified from single-cell phylogenies. Here we present TiDeTree, a Bayesian phylogenetic framework for inference of time-scaled single-cell phylogenies and population dynamic parameters such as cell division, death, and differentiation rates from genetic lineage tracing data. We show that the performance of TiDeTree can be improved by incorporating multiple sources of additional independent information into the inference. Finally, we apply TiDeTree to a lineage tracing dataset to estimate time-scaled phylogenies, cell division, and apoptosis rates. We envision TiDeTree to find wide application in single-cell lineage tracing data analysis which will improve our understanding of cellular processes during development. The source code of TiDeTree is publicly available at https://github.com/seidels/tidetree.

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Imaging cell lineage with a synthetic digital recording system

Cited by 102 publications

References 63 publications

Large-scale discovery of recombinases for integrating DNA into the human genome

Large-scale discovery of recombinases for integrating DNA into the human genome

TEMPO: A system to sequentially label and genetically manipulate vertebrate cell lineages

TiDeTree: A Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

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