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PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Nature Biotechnology (2023)Cite this article

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Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR–Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

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Fig. 1: Schematic of the PhyloVelo framework.
Fig. 2: PhyloVelo recovers complex cell lineages in simulations.
Fig. 3: PhyloVelo reconstructs the embryonic differentiation trajectories of C. elegans.
Fig. 4: PhyloVelo reconstructs the cellular trajectory of mouse erythroid maturation.
Fig. 5: PhyloVelo identifies a dedifferentiation trajectory in lung tumor evolution.
Fig. 6: PhyloVelo inference with clonal lineage-tracing data and MEGs are enriched in ribosome-mediated processes.

Data availability

All data analyzed in this article are publicly available through online sources. The annotated data, lineage trees, results and Python implementation are available at https://phylovelo.readthedocs.io/. The raw data for the C. elegans dataset14 can be accessed with GSE126954, and the lineage tree can be accessed at http://dulab.genetics.ac.cn/TF-atlas/Cell.html. The CRISPR lineage-tracing datasets from the mouse embryos32 can be accessed with GSE117542. The scRNA-seq data of mouse brain development48 can be accessed with PRJNA637987. The time-course scRNA-seq data of whole mouse embryos (E6.5–E8.5)19 can be accessed with E-MTAB-6967. The dataset of mouse primary lung tumors51 can be accessed with PRJNA803321 and from Zenodo (https://zenodo.org/record/5847462#.Yt4-PewRXUI). The dataset of mouse pancreatic cancer cell line KPCY62 can be accessed with GSE173958 and from Mendeley (https://doi.org/10.17632/t98pjcd7t6.1). The dataset of human lung cancer cell line A549 (ref. 63) can be accessed with GSE161363. The dataset of human kidney cell line HEK293T64 can be accessed with PRJNA757179. The LARRY lentiviral barcoding dataset of hematopoiesis37 can be accessed with GSE140802. The single-cell TCR and RNA sequencing data of T cells in BCC57 can be accessed with GSE123813.

Code availability

PhyloVelo86 is freely available as a Python package at https://github.com/kunwang34/PhyloVelo. Detailed workflows to reproduce figures and results in this paper are written as Jupyter Notebook in the repository. The annotated data, lineage trees, results and Python implementation are available at https://phylovelo.readthedocs.io/.

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Acknowledgements

We thank Y. Huang, J. Wang, J. Xu, L. Ma, W. Chen and members of the Hu laboratory for constructive discussions. This work was supported by the National Key R&D Program of China (2021YFA1302500 to Z.H.), the National Natural Science Foundation of China (11971405 to D.Z. and 32270693 to Z.H.), the Guangdong Basic and Applied Basic Research Foundation (2021B1515020042 to Z.H.), Fundamental Research Funds for the Central Universities (20720230023 to D.Z.) and the China Postdoctoral Science Foundation (2021M693303 to Z.L and 2022M723301 to X.W.).

Author information

Authors and Affiliations

  1. CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Kun Wang, Liangzhen Hou, Xin Wang, Zhaolian Lu, Zhike Zi & Zheng Hu

  2. School of Mathematical Sciences, Xiamen University, Xiamen, China

    Kun Wang & Da Zhou

  3. Faculty of Health Sciences, University of Macau, Taipa, Macau, China

    Liangzhen Hou

  4. MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China

    Xiangwei Zhai & Xionglei He

  5. CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Weiwei Zhai

  6. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Weiwei Zhai

  7. Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA

    Christina Curtis

  8. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Christina Curtis

  9. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Christina Curtis

  10. National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China

    Da Zhou

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  1. Kun Wang
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Contributions

Z.H. and K.W. conceived the concept of phylogenetic velocity. Z.H., K.W. and D.Z. designed the study. K.W. developed the mathematical framework and implemented the software. K.W., Z.H., L.H., Z.L., X.W., X.Z. and Z.Z. analyzed the data. W.Z. and Z.Z. provided constructive suggestions on the model. K.W., Z.H., D.Z., C.C. and X.H. interpreted results. Z.H. and K.W. wrote the manuscript, with contributions from all co-authors. Z.H. and D.Z. supervised the study.

Corresponding authors

Correspondence to Da Zhou or Zheng Hu.

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Competing interests

C.C. is an advisor to and stockholder in Grail, Ravel and DeepCell and an advisor to Genentech, Bristol Myers Squibb, 3T Biosciences and NanoString. All other authors declare no competing interests.

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Peer review information

Nature Biotechnology thanks Junyue Cao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quantitative metrics for evaluating the performance of PhyloVelo using simulation data.

Two quantitative metrics with varied cell numbers (a), non-linear MEGs (b), different dimensionality reduction methods (c), varied data sparsity (d) and varied numbers of MEGs (e). All benchmarks are simulated 50 times independently. Bar, median; box, 25th to 75th interquartile range (IQR); vertical line, data within 1.5 times the IQR.

Extended Data Fig. 2 PhyloVelo velocity fields in three additional lineages of C. elegans.

Hypodermis, body wall muscle (BWM) and pharynx lineage cells, respectively. Colors are labeled by the estimated embryo time (minutes). (d-f) PhyloVelo velocity fields of the three lineages respectively each consisting of 2,000 randomly sampled cells from multiple embryos, which were reconstructed using the MEGs identified from 298 AB lineage cells. Colors are labeled by the PhyloVelo pseudotime. (g-i) The correlation between PhyloVelo pseudotime and embryo time for the cells in the three lineages. The Spearman correlation coefficients and P values are shown here.

Extended Data Fig. 3 High concordance of MEGs identified from 4 mouse embryos (E8.0/8.5) in Chan et al.

(a) Venn diagram showing the overlap of MEGs identified from four mouse embryos in the dataset of Chan et al. P-value is by one-sided SuperExactTest multi-set intersection test. (b-g) The correlation of phylogenetic velocities \({\boldsymbol{v}}\) for the overlapped MEGs between any two embryos. The Pearson correlation coefficients and P values are shown here.

Extended Data Fig. 4 The global differentiation trajectories of whole mouse embryos and brain tissues predicted by LT-MEGs.

(a) PhyloVelo velocity fields of mouse embryos (E6.5-8.5) mapped by 104 LT-MEGs with the temporal scRNA-seq dataset from Pijuan-Sala et al. (b-c) UMAP plot colored by PhyloVelo pseudotime (b) or sample capture time (c). (d) PhyloVelo velocity fields of mouse brain (E7-18) mapped by LT-MEGs with the temporal scRNA-seq dataset from La Manno et al. (e-f) tSNE plot colored by PhyloVelo pseudotime (e) or sample capture time (f). UMAP or tSNE coordinates were as the original studies.

Extended Data Fig. 5 PhyloVelo velocity fields and quantitative state transitions of mouse erythroid development for four embryos from Chan et al.

(a-d) PhyloVelo velocity fields. (e-h) The transition rate (backward) between any two cell types. (i-l) Cell-type transition graph (backward) visualized based on the cell-type transition rates. PhyloVelo velocity fields were used as the input of Dynamo.

Extended Data Fig. 6 PhyloVelo reconstructs the cellular trajectory of lung cancer evolution in 3435_NT_T1.

(a) Single-cell phylogenetic tree of primary lung tumor 3435_NT_T1 (n = 1,109 cells) from KP (KrasLSL-G12D/+;Trp53fl/fl) mouse model. The single-cell RNA data, cell type annotations and lineage tree were obtained from Yang et al. (b) RNA velocity fields (scVelo - dynamical mode). (c) PhyloVelo velocity fields. (d) The fitness signatures of single cells as defined by Yang et al. (e) The correlation between scVelo latent time and fitness signatures. The Spearman correlation coefficients and P values are shown here. (f) The correlation between PhyloVelo pseudotime and fitness signatures. The Spearman correlation coefficients and P values are shown here. (g) CytoTRACE score of individual cells. (h) The correlation between scVelo latent time and CytoTRACE scores. The Spearman correlation coefficients and P values are shown here. (i) The correlation between PhyloVelo pseudotime and CytoTRACE scores. (j) The correlation of phylogenetic velocities for the overlapped MEGs between KP primary tumor 3435_NT_T1 and 3726_NT_T1. The Pearson correlation coefficient and P value are shown here.

Extended Data Fig. 7 Comparison of PhyloVelo with scVelo, VeloVAE, DeepVelo, CellDancer and UniTVelo respectively on mouse erythroid data.

scVelo - RNA velocity fields (a), latent time (b) and the fractions of different cell types along latent time (c). VeloVAE - RNA velocity fields (d), latent time (e) and the fractions of different cell types along latent time (f). DeepVelo - RNA velocity fields (g), latent time (h) and the fractions of different cell types along latent time (i). cellDancer - RNA velocity fields (j), pseudotime (k) and the fractions of different cell types along pseudotime (l). UniTVelo - RNA velocity fields (m), latent time (n) and the fractions of different cell types along latent time (o). PhyloVelo - velocity fields (p), pseudotime (q) and the fractions of different cell types along pseudotime (r). PhyloVelo velocity fields are in backward directions.

Extended Data Fig. 8 The dynamic EMT trajectory in metastatic progression of pancreatic cancer KPCY cells.

(a) Phylogenetic tree of 601 non-repetitive terminal cells in tumor subclone M1.1 from Simeonov et al. Cell colors are labeled by EMT pseudotime as defined in the original study. (b) The total UMI count (normalized) of MEGs changing with the phylogenetic distance from the root. (c) Heatmap of MEG expressions (z-score normalized) with EMT pseudotime. (d) RNA velocity fields (scVelo - dynamical mode). Cell colors are labeled by EMT pseudotime. (e) scVelo latent time. (f) The correlation between scVelo latent time and EMT pseudotime. (g) PhyloVelo velocity fields. Cell colors are labeled by EMT pseudotime. (h) PhyloVelo pseudotime. (i) The correlation between PhyloVelo pseudotime and EMT pseudotime. The Spearman correlation coefficients and P values are shown here.

Extended Data Fig. 9 Continuous state transitions inferred by PhyloVelo after regressing out cell-cycle effect.

(a–d) PhyloVelo velocity fields after regressing out cell-cycle dynamics in KPCY, A549 lg1, A549 lg2 and HEK293T, respectively. (e–h) The correlation of PhyloVelo pseudotime between original analysis and post regressing out of cell-cycle effect in KPCY, A549 lg1, A549 lg2 and HEK293T, respectively. The Pearson correlation coefficients and P values are shown here.

Extended Data Fig. 10 Overlap of MEGs across organisms and tissue/cell types and the permutation analysis of MEG identification.

(a) The overlap of MEGs identified in different datasets as stratified by mouse vs human. (b) The overlap of MEGs identified in different datasets as stratified by normal vs tumor cells. P values are by one-sided hypergeometric test. (c) The q values of MEGs in standard and permutation analysis. Permutation analysis was performed by randomly shuffling the phylogenetic distances of the cells, followed by the PhyloVelo inference procedure. The number of detected MEGs using standard and permutation analysis: n = 1,724 and n = 941 genes in Embryo E8/E8.5; n = 681 and n = 445 genes in KP lung tumor; n = 424 and n = 141 genes in KPCY; n = 629 and n = 50 genes in A549; n = 243 and n = 90 genes in HEK293T; n = 419 and n = 112 genes in in vitro hematopoiesis; n = 368 and n = 270 genes in CD8 + T cells. Bar, median; box, 25th to 75th interquartile range (IQR); vertical line, data within 1.5 times the IQR. (d) The GO enrichment of pseudo-MEGs across the seven lineage tracing datasets.

Supplementary information

Supplementary Information

Supplementary Figs. 1–24 and Supplementary Note.

Reporting Summary

Supplementary Table 1

The MEGs and their phylogenetic velocity estimates in C. elegans, five CRISPR lineage-tracing datasets and two clonal lineage-tracing datasets.

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Wang, K., Hou, L., Wang, X. et al. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01887-5

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