Reconstructing complex lineage trees from scRNA-seq data using MERLoT

Parra, R. Gonzalo; Παπαδόπουλος, Νικόλαος; Ahumada-Arranz, Laura; Kholtei, Jakob El; Mottelson, Noah; Horokhovsky, Yehor; Treutlein, Barbara; Soeding, Johannes

doi:10.1093/nar/gkz706

Cited by 25 publications

(34 citation statements)

References 30 publications

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“…5A and Table S9). We inferred differentiation trajectories and pseudotemporal cell relationships (23) from a diffusion map embedding (16), and identified a trifurcated path, where multipotent blastema progenitors expressing embryonic limb and cell cycle markers (e.g. Prdx2, Nrep , Ccnb1 ) branch off to a non-skeletal lineage or a skeletal lineage that then bifurcates into either cartilage or bone (Fig.…”

Section: Connective Tissue Lineages Reemerge Through Multipotent Progmentioning

confidence: 99%

Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration

Gerber

Murawala

Knapp

et al. 2018

Science

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323

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Amputation of the axolotl forelimb results in the formation of a blastema, a transient tissue where progenitor cells accumulate prior to limb regeneration. Connective tissue (CT) – skeleton, periskeleton, tendon, dermis, interstitial fibroblasts – contributes the vast majority of cells that populate the blastema, however, it is unclear how individual CT cells may reprogram their fate in order to rebuild the tetrapod limb. Here we use a combination of Cre-loxP reporter lineage tracking and single-cell (sc) RNA-seq to molecularly track, for the first time, adult CT cell heterogeneity and its transition to a limb blastema state. We uncover a multi-phasic molecular program where CT cell types found in the uninjured adult limb revert to a relatively homogenous progenitor state that participates in inflammation and extracellular matrix disassembly prior to proliferation, establishment of positional information, and ultimately re-differentiation. While the early regeneration transcriptome states are unique to the blastema, the later stages recapitulate embryonic limb development. Notably, we do not find evidence of a pre-existing blastema-like precursor nor limb bud-like progenitors in the uninjured adult tissue. However, we find that distinct CT subpopulations in the adult limb differentially contribute to extending bone at the amputation plane versus regenerating new segments. Together, our data illuminates molecular and cellular reprogramming during complex organ regeneration in a vertebrate.

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Section: Connective Tissue Lineages Reemerge Through Multipotent Progmentioning

confidence: 99%

Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration

Gerber

Murawala

Knapp

et al. 2018

Science

Self Cite

323

435

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“…This stimulated the emergence of a number of tools in the field of bioinformatics for reconstructing so-called “branching cellular trajectories” and “pseudotime” (loosely defined as the length of the geodesic path between two data points along the graph approximating a datapoint cloud) [ 35 , 45 , 46 ]. Some of these tools exploit the notion of principal curves or graphs explicitly [ 32 , 47 ], while others sometimes use a different terminology closely related to principal graphs or principal trees in its purpose [ 48 , 49 ]. Biology is, however, only one of the possible domains for applicability of principal graphs.…”

Section: Introductionmentioning

confidence: 99%

Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph

Albergante

Mirkes

Bac

et al. 2020

Entropy

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179)Large datasets represented by multidimensional data point clouds often possess nontrivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples. Reducing the complexity and producing compact and interpretable representations of such data remains a challenging task. Most of the existing computational methods are based on exploring the local data point neighbourhood relations, a step that can perform poorly in the case of multidimensional and noisy data. Here we present ElPiGraph, a scalable and robust method for approximation of datasets with complex structures which does not require computing the complete data distance matrix or the data point neighbourhood graph. This method is able to withstand high levels of noise and is capable of approximating complex topologies via principal graph ensembles that can be combined into a consensus principal graph. ElPiGraph deals efficiently with large and complex datasets in various fields from biology, where it can be used to infer gene dynamics from single-cell RNA-Seq, to astronomy, where it can be used to explore complex structures in the distribution of galaxies.

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“…Droplet-based single cell RNA sequencing (scRNA-seq) [ 13 , 18 , 48 ] has provided many valuable insights into complex biological systems, such as rare cell-type identification [ 26 , 32 , 39 , 41 ], differential expression analysis at the single cell level [ 2 , 5 , 9 ], and cell lineage studies [ 9 , 15 , 24 , 30 ]. While the per-cell cost of library preparation has decreased over the years, the scalability of droplet-based scRNA-seq remains limited, mostly due to rapidly increasing, yet hard to anticipate, multiplet rates as more cells are loaded during single sequencing cell library preparation [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

et al. 2020

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Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

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Reconstructing complex lineage trees from scRNA-seq data using MERLoT

Cited by 25 publications

References 30 publications

Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration

Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration

Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

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