Reconstructing Complex Lineage Trees from ScRNA-seq Data Using MERLoT

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2019 Aug 21

PMID 31428793

Citations 10

Authors

R Gonzalo Parra

Nikolaos Papadopoulos

Laura Ahumada-Arranz

Jakob El Kholtei

Noah Mottelson

Yehor Horokhovsky

Barbara Treutlein

Johannes Soeding

Affiliations

Soon will be listed here.

Abstract

Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT's capabilities on various real cases and hundreds of simulated datasets.

Citing Articles

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome.

Iida K, Okada M Cancers (Basel). 2024; 16(10).

PMID: 38791962 PMC: 11119661. DOI: 10.3390/cancers16101884.

Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells.

Cannoodt R, Saelens W, Deconinck L, Saeys Y Nat Commun. 2021; 12(1):3942.

PMID: 34168133 PMC: 8225657. DOI: 10.1038/s41467-021-24152-2.

Minimum Spanning vs. Principal Trees for Structured Approximations of Multi-Dimensional Datasets.

Chervov A, Bac J, Zinovyev A Entropy (Basel). 2020; 22(11).

PMID: 33287042 PMC: 7711596. DOI: 10.3390/e22111274.

Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph.

Albergante L, Mirkes E, Bac J, Chen H, Martin A, Faure L Entropy (Basel). 2020; 22(3).

PMID: 33286070 PMC: 7516753. DOI: 10.3390/e22030296.

Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data.

Golovenkin S, Bac J, Chervov A, Mirkes E, Orlova Y, Barillot E Gigascience. 2020; 9(11).

PMID: 33241287 PMC: 7688475. DOI: 10.1093/gigascience/giaa128.

References

Regev A, Teichmann S, Lander E, Amit I, Benoist C, Birney E . The Human Cell Atlas. Elife. 2017; 6. PMC: 5762154. DOI: 10.7554/eLife.27041. View

Zappia L, Phipson B, Oshlack A . Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017; 18(1):174. PMC: 5596896. DOI: 10.1186/s13059-017-1305-0. View

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

Chen H, Albergante L, Hsu J, Lareau C, Lo Bosco G, Guan J . Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun. 2019; 10(1):1903. PMC: 6478907. DOI: 10.1038/s41467-019-09670-4. View

Welch J, Hartemink A, Prins J . SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 2016; 17(1):106. PMC: 4877799. DOI: 10.1186/s13059-016-0975-3. View

Saelens W, Cannoodt R, Todorov H, Saeys Y . A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019; 37(5):547-554. DOI: 10.1038/s41587-019-0071-9. View

Papadopoulos N, Gonzalo P, Soding J . PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes. Bioinformatics. 2019; 35(18):3517-3519. PMC: 6748774. DOI: 10.1093/bioinformatics/btz078. View

Treutlein B, Lee Q, Camp J, Mall M, Koh W, Shariati S . Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature. 2016; 534(7607):391-5. PMC: 4928860. DOI: 10.1038/nature18323. View

Cao J, Packer J, Ramani V, Cusanovich D, Huynh C, Daza R . Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017; 357(6352):661-667. PMC: 5894354. DOI: 10.1126/science.aam8940. View

10.

Ji Z, Ji H . TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016; 44(13):e117. PMC: 4994863. DOI: 10.1093/nar/gkw430. View

11.

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M . The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4):381-386. PMC: 4122333. DOI: 10.1038/nbt.2859. View

12.

Klein A, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V . Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015; 161(5):1187-1201. PMC: 4441768. DOI: 10.1016/j.cell.2015.04.044. View

13.

Rostom R, Svensson V, Teichmann S, Kar G . Computational approaches for interpreting scRNA-seq data. FEBS Lett. 2017; 591(15):2213-2225. PMC: 5575496. DOI: 10.1002/1873-3468.12684. View

14.

Street K, Risso D, Fletcher R, Das D, Ngai J, Yosef N . Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018; 19(1):477. PMC: 6007078. DOI: 10.1186/s12864-018-4772-0. View

15.

Angerer P, Haghverdi L, Buttner M, Theis F, Marr C, Buettner F . destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics. 2015; 32(8):1241-3. DOI: 10.1093/bioinformatics/btv715. View

16.

Wagner A, Regev A, Yosef N . Revealing the vectors of cellular identity with single-cell genomics. Nat Biotechnol. 2016; 34(11):1145-1160. PMC: 5465644. DOI: 10.1038/nbt.3711. View

17.

Trapnell C . Defining cell types and states with single-cell genomics. Genome Res. 2015; 25(10):1491-8. PMC: 4579334. DOI: 10.1101/gr.190595.115. View

18.

Gerber T, Murawala P, Knapp D, Masselink W, Schuez M, Hermann S . Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration. Science. 2018; 362(6413). PMC: 6669047. DOI: 10.1126/science.aaq0681. View

19.

Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner H . Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017; 14(10):979-982. PMC: 5764547. DOI: 10.1038/nmeth.4402. View

20.

Saitou N, Nei M . The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987; 4(4):406-25. DOI: 10.1093/oxfordjournals.molbev.a040454. View