Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

Gong, Wuming; Granados, Alejandro A; Hu, Jingyuan; Jones, Matthew G.; Raz, Ofir; Salvador-Martínez, Irepan; Zhang, Hanrui; Chow, Ke Huan; Kwak, Il-Youp; Retkute, Renata; Prusokas, Alidivinas; Prusokas, Augustinas; Khodaverdian, Alex; Zhang, Richard; Rao, Suhas; Wang, Robert; Rennert, Phil; Saipradeep, Vangala Govindakrishnan; Sivadasan, Naveen; Rao, Aditya; Joseph, Thomas; Srinivasan, R.; Peng, Jiajie; Han, Lu; Shang, Xuequn; Garry, Daniel J.; Yu, Thomas; Chung, Verena; Mason, Mike J.; Liu, Zhandong; Guan, Yuanfang; Yosef, Nir; Shendure, Jay; Telford, Maximilian J.; Shapiro, Ehud; Elowitz, Michael B.; Meyer, Pablo

doi:10.1016/j.cels.2021.05.008

Cited by 39 publications

(42 citation statements)

References 44 publications

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“…We benchmark TiDeTree on the intMEMOIR dataset (as available during the DREAM challenge) [14, 8] by comparing it against existing methods. The data is divided into a training and a test set.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…We apply TiDeTree to data used in the DREAM challenge [14], generated by the intMEMOIR system [8], where the ground truth cell phylogenies are known. In the analysis we quantify the ability of TiDeTree to recover the true tree topology, tree height, tree length and phylodynamic parameters.…”

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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“…Such stochastically evolving barcodes mark cells and enable inference of their developmental lineage relationships based on patterns of shared mutations. However, despite their promise, unordered lineage recorders remain sharply limited by several technical challenges, including: (1) a failure to explicitly record the order of editing events, which renders phylogenetic reconstruction of cell lineage highly challenging 24,25 ; (2) a reliance on doublestranded breaks (DSBs) and nonhomologous end-joining (NHEJ) to introduce edits; DSBs are cytotoxic and frequently delete consecutively located targets within a barcode; and (3) the number of target sites available to CRISPR-Cas9 decreases as sites are irreversibly edited, which makes it challenging to sustain a continuous rate of lineage recording throughout development.…”

Section: Ordered Recording and Decoding Of Cell Lineage Historiesmentioning

confidence: 99%

A temporally resolved, multiplex molecular recorder based on sequential genome editing

Choi

Chen

Minkina

et al. 2021

Preprint

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DNA is naturally well-suited to serve as a digital medium for in vivo molecular recording. However, DNA-based memory devices described to date are constrained in terms of the number of distinct signals that can be concurrently recorded and/or by a failure to capture the precise order of recorded events1. Here we describe DNA Ticker Tape, a general system for in vivo molecular recording that largely overcomes these limitations. Blank DNA Ticker Tape consists of a tandem array of partial CRISPR-Cas9 target sites, with all but the first site truncated at their 5’ ends, and therefore inactive. Signals of interest are coupled to the expression of specific prime editing guide RNAs2. Editing events are insertional, and record the identity of the guide RNA mediating the insertion while also shifting the position of the “write head” by one unit along the tandem array, i.e. sequential genome editing. In this proof-of-concept of DNA Ticker Tape, we demonstrate the recording and decoding of complex event histories or short text messages; evaluate the performance of dozens of orthogonal tapes; and construct “long tape” potentially capable of recording the order of as many as 20 serial events. Finally, we demonstrate how DNA Ticker Tape simplifies the decoding of cell lineage histories.

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“…In particular, we would like to find out whether the performances of lineage reconstruction are improved by integrating gene expressions with the lineage barcodes. We compare the performance of LinTIMaT [17] against DCLEAR [41] and Cassiopeia [16] , which are the best performing algorithms in the recent Allen Institute Cell Lineage Reconstruction DREAM Challenge [41] which use only the CRISPR/Cas9 induced lineage barcodes.…”

Section: Benchmarking Lineage Reconstruction Algorithmsmentioning

confidence: 99%

TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history

Pan

Zhang

2021

Preprint

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Recently, the combined scRNA-seq and CRISPR/Cas9 genome editing technologies have enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree, and makes it possible to trace the origin of each cell type. Computational methods are emerging to take advantage of the jointly profiled scRNA-seq and lineage barcode data to better reconstruct the cell division history or to infer the cell state trajectories. Here, we present TedSim (single cell Temporal dynamics Simulator), a simulator that simulates the cell division events from the root cell to present-day cells, simultaneously generating the CRISPR/Cas9 genome editing lineage barcodes and scRNA-seq data. In particular, TedSim generates cells from multiple cell types through cell division events. TedSim can be used to benchmark and investigate computational methods which use either or both of the two types of data, scRNA-seq and lineage barcodes, to study cell lineages or trajectories. TedSim is available at: https://github.com/Galaxeee/TedSim.

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Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

Cited by 39 publications

References 44 publications

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

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

A temporally resolved, multiplex molecular recorder based on sequential genome editing

TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history

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