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Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution

Nature Genetics volume 56, pages 2174–2184 (2024)Cite this article

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Abstract

Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution.

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Fig. 1: Phylogenetic correlations quantify the heritability versus plasticity of single-cell phenotypes.
Fig. 2: Measuring heritability, plasticity and cell state transition dynamics in somatic evolution.
Fig. 3: Quantifying EMT plasticity during metastasis in pancreatic cancer.
Fig. 4: Heritable transcriptional modules and cell state transition dynamics in human GBM.
Fig. 5: Quantifying differentiation state heritability in B-ALL using single-cell whole-genome sequencing.

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Data availability

The published pancreatic cancer mouse data were downloaded from the NCBI Gene Expression Omnibus (GEO) accession GSE173958. The published GBM data are available from GEO under accession number GSE151506. The gliomasphere scRNAseq data are available from GEO under accession number GSE273357. For the B-ALL data, all SNV and CNV variant calls, as well as all trees, can be found at https://doi.org/10.5281/zenodo.13143937. To protect pediatric patient privacy, unprocessed single-cell whole-genome sequences are available to qualified researchers upon request to D.A.L.

Code availability

The code used to measure phylogenetic correlations and to infer cell state transitions is available as part of our PATH R software package at https://github.com/landau-lab/PATH and archived at https://doi.org/10.5281/zenodo.13144052 (ref. 93). Scripts for simulations, benchmarking and data analysis are available at https://github.com/landau-lab/PATHpaper and are archived at https://doi.org/10.5281/zenodo.13143937 (ref. 94).

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Acknowledgements

We thank members of the Landau laboratory and Norbert Fehér for thoughtful discussions throughout the development of this work. We thank N. Yosef for critical comments on the manuscript. We thank A. McKenna (Dartmouth College) and B. Raj (University of Pennsylvania Perelman School of Medicine) for sharing data and code related to the scGESTALT phylogenies. We thank A. Meissner’s (Max Planck Institute for Molecular Genetics) group and the authors of Chan et al. 2019, including M. Chan (Princeton University), for sharing their cell-type assignment and code. A.R.D. is supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. C.G. is supported by a Burroughs Wellcome Fund Career Award for Medical Scientists, National Institutes of Health Director’s New Innovator Award (DP2-CA239145) and Chan Zuckerberg Investigator Award. D.A.L. is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists, the Valle Scholar Award, the William Rhodes and Louise Tilzer-Rhodes Center for Glioblastoma at New York-Presbyterian Hospital (NYPH 203205-01), the Sontag Foundation (Distinguished Scientist Award, SFI 203261-01), Leukemia Lymphoma Scholar Award and the Mark Foundation Emerging Leader Award. This work was supported by the National Heart Lung and Blood Institute (R01HL157387-01A1), National Cancer Institute (R33CA267219), a Tri-Institutional Stem Cell Initiative award, the National Institutes of Health Common Fund Somatic Mosaicism Across Human Tissues (UG3NS132139-01) and the National Human Genome Research Institute, Center of Excellence in Genomic Science (RM1HG011014). D.A.L. and M.L.S. are jointly supported by NCI R01CA258763 and a grant from the STARR Cancer Consortium. This work was made possible by the MacMillan Family Foundation and the MacMillan Center for the Study of the Non-Coding Cancer Genome at the New York Genome Center.

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Author notes

  1. These authors contributed equally: Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto.

Authors and Affiliations

  1. New York Genome Center, New York, NY, USA

    Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto, Srinivas Rajagopalan, Catherine Potenski & Dan A. Landau

  2. Weill Cornell Medicine, New York, NY, USA

    Joshua S. Schiffman, Andrew R. D’Avino, Tamara Prieto, Srinivas Rajagopalan, Catherine Potenski & Dan A. Landau

  3. Tri-Institutional MD-PhD Program, Weill Cornell Medicine, Rockefeller University, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Andrew R. D’Avino

  4. Stanford University, Stanford, CA, USA

    Yakun Pang & Charles Gawad

  5. Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Yilin Fan, Toshiro Hara & Mario L. Suvà

  6. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Yilin Fan, Toshiro Hara & Mario L. Suvà

  7. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Charles Gawad

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Contributions

J.S.S. and D.A.L. conceived the project and designed the study. J.S.S. developed PATH and PATHpro and performed simulations. J.S.S., A.R.D., T.P. and S.R. performed analyses. Y.F. and T.H. generated the gliomasphere data. Y.P. and C.G. generated the single-cell PTA data. J.S.S., A.R.D., T.P., C.P., M.L.S., C.G. and D.A.L. helped interpret the results. M.L.S., Y.F., C.G. and T.H. provided critical comments on the paper. J.S.S., A.R.D., T.P., C.P. and D.A.L. wrote the paper. All authors reviewed and approved the paper.

Corresponding authors

Correspondence to Joshua S. Schiffman or Dan A. Landau.

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Competing interests

M.L.S. is equity holder, scientific co-founder and advisory board member of Immunitas Therapeutics. C.G. is a co-founder, equity holder and board member of BioSkryb Genomics. D.A.L. has served as a consultant for Abbvie, AstraZeneca and Illumina and is on the Scientific Advisory Board of Mission Bio, Pangea, Alethiomics, Montage Bio and Veracyte; D.A.L. has received prior research funding from BMS, 10x Genomics, Ultima Genomics and Illumina unrelated to the current manuscript. A.R.D. is a consultant for Montage Bio. The other authors declare no competing interests.

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Nature Genetics thanks Verena Körber, Arjun Raj and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Cell state transition dynamics and phylogenetic correlations.

Cell state transition dynamics can be linked with phylogenetic correlations using mathematical modeling (Methods).

Extended Data Fig. 2 Cell state transition dynamics predict phylogenetic correlations.

a, Simulated idealized phylogeny containing 26 = 64 cells (Supplementary Note) in which cells can transition between three possible cell states. Cell state transitions are represented as a discrete-time Markov chain (Supplementary Note). b, Simulated cell state transition dynamics and measured phylogenetic autocorrelations (Methods) for the first cell state for 1,000 independent simulations on idealized phylogenies, containing 210 cells, in which state transition probabilities were randomly generated for each trial. Phylogenetic correlations were computed using a weighting function that included only sibling cells (one-node only, as described in Methods). LOESS regression line (blue) with 95% confidence interval (light gray) is shown. c, (Left) Simulated versus PATH-inferred cell state self-transition (that is, stability) probabilities (Methods), by transforming the phylogenetic autocorrelations measured in b. (Right) Simulated versus PATH-inferred cell state transition probabilities from state 1 to 2, on idealized phylogenies (Supplementary Note). Dashed red lines both have slope 1 and pass through the origin. Linear regression lines (blue) with 95% confidence intervals (light gray) are shown for both plots. Spearman’s correlation coefficients shown in panels b and c had two-sided P values < 2.2e-16, the limit of precision in R.

Extended Data Fig. 3 PATH inferences and simulations of somatic evolution.

a, Same as Fig. 2b but for systems where heritability is not detectable for all cell states (at least one state phylogenetic autocorrelation z score ≤ 2) b, Comparing the state transition dynamic inference accuracy of PATH using phylogenetic correlations measured at different node depths. Transition inference error is measured as the Euclidean distance between simulated and inferred transition probabilities, and the number of possible states in a simulated system is shown on the x-axis. 1,000 phylogenies were simulated for each system and sampled at a rate of 10−2. c, Mean path length distance, phylogenetic correlation difference, and Robinson-Foulds distance between simulated true and reconstructed phylogenies (Supplementary Note). Phylogenies were simulated 1,000 times for each barcode length (x-axis). d, Expected pendant edge lengths for a sampled somatic evolutionary process, as a function of birth, death, and sampling rates (Supplementary Note). e, Transition inference error (Euclidean distance between inferred and true transition probabilities) using PATH or MLE for 3, 4, or 5 cell states in a phylogeny composed of either 100, 500, or 1,000 cells, representing a sample of 10−6 or 10−3 of the total population. Each parameter combination was simulated 1,000 times. f, Run times corresponding to simulations sampled at a rate of 10−6 depicted in e. Box plots represent median, bottom and upper quartiles; whiskers correspond to 1.5 times interquartile range.

Extended Data Fig. 4 PATHpro inferences from simulations of somatic evolution with cell state-specific proliferation rates.

a, Comparison of PATH versus PATHpro transition inference errors (Euclidean distance between inferred and simulated transition probabilities) on simulated data when proliferation rates depend on cell state (Methods). Phylogenies were simulated in forward time, starting with one cell and ending after reaching 104 cells, and then subsampled to 103 cells. The state of the first cell was randomly chosen, and \({\gamma }_{1}\), the proliferation rate of state 1, is shown by the x-axis, and \({\gamma }_{2}\), the proliferation rate of state 2, was fixed at 1. Cell state transitions between states were symmetric with \({P}_{12}={P}_{21}=0.1\). 100 phylogenies were simulated for each proliferation rate. b, Diagrams of cell state transition probabilities used for benchmarking PATHpro (Supplementary Note). c, Comparison of transition probability inferences between PATHpro and SSE-MLE for systems shown in b, when the true proliferation rates are known. Inference error is the Euclidean distance between inferred and true transition probabilities. d, Comparison of transition probability and proliferation rate inferences for networks shown in b when proliferation rates are not known. Inference error is measured as the Euclidean distance between true/simulated and inferred transition probabilities or proliferation rates and values reported are rounded to the nearest thousandth. e, Comparison of PATHpro and SSE-MLE run times for the inferences shown in d, with the y-axis is scaled by log10(seconds). For all box plots, boxes represent the interquartile range (IQR); the center line represents the median; minima and maxima shown represent 1.5⋅IQR. For violin plots, white lines correspond to the median, and dashed lines represent the mean. Box and violin plots represent 100 simulated phylogeny replicates each.

Extended Data Fig. 5 Quantifying the heritability versus plasticity of EMT transcriptional states.

a, Tumor cell harvest site phylogenetic correlations. b, EMT bin phylogenetic correlations (z scores). Colors represent putative states. Full table of EMT bin phylogenetic correlations can be found in Supplementary Table 1. c, Single-cell phylogeny from Mouse 1, Clone 1 from Simeonov et al.9, containing 700 of 7,968 randomly chosen cells for visualization. Each leaf represents a single cell. Cells are colored by PATH-defined states (T1, T2, T3, M). d, EMT bin phylogenetic correlation (z score) heat maps using different bin sizes (0.5, 2, 3). e, Box and whisker plot of EMT bin phylogenetic correlations (z scores) across phylogenies that contain cells from only one harvest site. Dots correspond to EMT bins. (T1, n = 7 bins; T2, n = 11 bins; T3, n = 6 bins; M, n = 1 bin). Bins are grouped and colored by transition state membership. f, PATHpro-inferred transition probabilities between states (T1, T2, T3, M) for branch lengths imputed using a death rate of 0, 0.01, or 0.02. Transition probabilities inferred for 100 iterations of subsampling 5,248 out of 10,495 cells for each imputation (Supplementary Note). For all box plots, boxes represent the interquartile range (IQR); the center line represents the median; minima and maxima shown represent 1.5⋅IQR.

Extended Data Fig. 6 PATH-inferred cell state transitions and gene set enrichment in human glioblastoma.

a, Heat map of the phylogeny-replicate (n = 9) mean phylogenetic correlations (Methods) for the top 100 most heritable genes (determined by phylogeny-replicate mean gene phylogenetic autocorrelation z scores) in MGH115. Over-representation analysis (ORA) performed on the genes in each of the two clusters, defined by hierarchical clustering using Ward’s method, separately. Phylogenetic correlations were computed using an inverse-node-distance weighting (Methods). Only select gene sets are depicted for Cluster 2; remaining significantly enriched gene sets are in Supplementary Table 4. b, PATHpro-inferred transition probabilities for MGH115. Transitions and proliferations, for \(t=\tau /2\), depicted correspond to the median ± MAD from ML phylogeny replicates, with low transition probabilities (Pij < 0.05) omitted. c, PATH-inferred transition probabilities \({\widehat{\bf{P}}\left(\tau \right)}\) (Methods) from neurodevelopmental-like (Stem-/AC-like) cell states to the MES-like cell state in human patient-derived GBM samples MGH115 and MGH122. Points correspond to PATH inferences for each phylogeny ML replicate (n = 9) per sample. Significance determined by two-sided t-test for Stem-like vs AC-like in MGH115 and MGH122, respectively. Colors correspond to cell state. Box plots represent median, bottom and upper quartiles; whiskers correspond to 1.5 times the interquartile range.

Extended Data Fig. 7 Quantifying cell state heterogeneity in B-ALL using single-cell whole-genome sequencing.

Genome-wide copy-number deletion annotations projected onto the B-ALL single-cell phylogeny from Fig. 5a. 25-kb copy-number events detected in at least 5 cells were concatenated and displayed next to the phylogeny. Single-cell CNV events were colored based on chromosomes. chr4.q31.21, chr6.q16.3-chr6.q22.33, chr11.p11.2 and chr16.p13.12 represent the largest deletions.

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Schiffman, J.S., D’Avino, A.R., Prieto, T. et al. Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution. Nat Genet 56, 2174–2184 (2024). https://doi.org/10.1038/s41588-024-01920-6

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