Phylogenetic relationship of cells within tumors can help us to understand how cancer develops in space and time and identify driver mutations and other evolutionary events that enable cancer growth and spread. Numerous studies have reconstructed phylogenies from single-cell DNA-seq data. Here, we are looking into the problem of phylogenetic analysis of spatially resolved near single-cell RNA-seq data, which is a cost-efficient alternative (or complementary) data source that integrates multiple sources of evolutionary information, including point mutations, copy number changes, and epimutations. Recent attempts to use such data, although promising, raised many methodological challenges. Here, we explored data preprocessing and modeling approaches for evolutionary analyses of Visium spatial transcriptomics data. We conclude that using only highly variable genes and accounting for heterogeneous RNA capture across tissue-covered spots improves the reconstructed topological relationships and influences estimated branch lengths.
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References
1.
AlvesJM, TomasL, PosadaD. Unraveling the phylogenetic signal of gene expression from single-cell RNA-seq data. bioRxiv, 2024:2024–2004.
BaronCS, van OudenaardenA. Unravelling cellular relationships during development and regeneration using genetic lineage tracing. Nat Rev Mol Cell Biol, 2019; 20(12):753–765.
4.
BouckaertR. Phylogeography by diffusion on a sphere: Whole world phylogeography. PeerJ, 2016; 4:e2406.
5.
BouckaertR, VaughanTG, Barido-SottaniJ, et al.Beast 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol, 2019; 15(4):e1006650.
6.
ChenK, MoravecJC, GavryushkinA, et al.Accounting for errors in data improves divergence time estimates in single-cell cancer evolution. Mol Biol Evol, 2022b;39(8):msac143.
7.
ChenH-N, ShuY, LiaoF, et al.Genomic evolution and diverse models of systemic metastases in colorectal cancer. Gut, 2022a;71(2):322–332.
8.
ChurchSH, MahJL, WagnerG, et al.Normalizing need not be the norm: Count-based math for analyzing single-cell data. Theory Biosci, 2024; 143(1):45–62.
9.
DimayacyacJR, WuS, JiangD, et al.Evaluating the performance of widely used phylogenetic models for gene expression evolution. Genome Biol Evol, 2023; 15(12):evad211.
10.
DrummondAJ, HoSYW, PhillipsMJ, et al.Relaxed phylogenetics and dating with confidence. PLoS Biol, 2006; 4(5):e88.
11.
DuchêneDA, MatherN, Van Der WalC, et al.Excluding loci with substitution saturation improves inferences from phylogenomic data. Syst Biol, 2022; 71(3):676–689.
12.
FelsensteinJ. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am J Hum Genet, 1973; 25(5):471–492.
13.
HafemeisterC, SatijaR. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol, 2019; 20(1):296.
14.
HansenTF. Stabilizing selection and the comparative analysis of adaptation. Evolution, 1997; 51(5):1341–1351.
15.
HaoY, HaoS, Andersen-NissenE, et al.Integrated analysis of multimodal single-cell data. Cell, 2021; 184(13):3573–3587.e29.
16.
HicksSC, TownesFW, TengM, et al.Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics, 2018; 19(4):562–578.
17.
HuZ, DingJ, MaZ, et al.Quantitative evidence for early metastatic seeding in colorectal cancer. Nat Genet, 2019; 51(7):1113–1122.
18.
KochL. Using tumour phylogenies to identify drivers. Nat Rev Genet, 2022; 23(4):196–196.
19.
KozlovA, AlvesJM, StamatakisA, et al.Cellphy: Accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data. Genome Biol, 2022; 23(1):37.
20.
LemeyP, RambautA, DrummondAJ, et al.Bayesian phylogeography finds its roots. PLoS Comput Biol, 2009; 5(9):e1000520.
21.
LeungML, DavisA, GaoR, et al.Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res, 2017; 27(8):1287–1299.
22.
LewinsohnMA, BedfordT, MüllerNF, et al.State-dependent evolutionary models reveal modes of solid tumour growth. Nat Ecol Evol, 2023; 7(4):581–596.
23.
LewisPO. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst Biol, 2001; 50(6):913–925.
24.
LiangC, ConsortiumF, ForrestAR, et al.; FANTOM Consortium. The statistical geometry of transcriptome divergence in cell-type evolution and cancer. Nat Commun, 2015; 6(1):6066.
25.
LiuX, GriffithsJI, BisharaI, et al.Phylogenetic inference from single-cell RNA-seq data. Sci Rep, 2023; 13(1):12854.
26.
LiB, ZhangW, GuoC, et al.Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat Methods, 2022; 19(6):662–670.
27.
LiH, ZhouJ, LiZ, et al.A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics. Nat Commun, 2023; 14(1):1548.
28.
MahJL, DunnCW. Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data. Nat Ecol Evol, 2024:1–14.
29.
McKennaA, GagnonJA. Recording development with single cell dynamic lineage tracing. Development, 2019; 146(12):dev169730.
30.
MillerBF, HuangF, AttaL, et al.Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat Commun, 2022; 13(1):2339.
31.
MinhBQ, SchmidtHA, ChernomorO, et al.IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol, 2020; 37(5):1530–1534.
32.
MoravecJC, LanfearR, SpectorDL, et al.Testing for phylogenetic signal in single-cell RNA-seq data. J Comput Biol, 2023; 30(4):518–537.
33.
MosesL, PachterL. Museum of spatial transcriptomics. Nat Methods, 2022; 19(5):534–546.
34.
NavinN, KendallJ, TrogeJ, et al.Tumour evolution inferred by single-cell sequencing. Nature, 2011; 472(7341):90–94.
35.
NowellPC. The clonal evolution of tumor cell populations: Acquired genetic lability permits stepwise selection of variant sublines and underlies tumor progression. Science, 1976; 194(4260):23–28.
36.
PinkneyHR, RossCR, HodgsonTA, “Discovery of prognostic lncRNAs in colorectal cancer using spatial transcriptomics.” NPJ precision oncology 2024; 8.1: 230.
37.
RambautA, DrummondAJ, XieD, et al.Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst Biol, 2018; 67(5):901–904.
38.
SchlüterHM, UhlerC. Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns. Nat Commun, 2025; 16(1):1062.
39.
StrimmerK, Von HaeselerA. Likelihood-mapping: A simple method to visualize phylogenetic content of a sequence alignment. Proc Natl Acad Sci U S A, 1997; 94(13):6815–6819.
40.
TavaréS, MiuraRM. Lectures on mathematics in the life sciences. Am Math Soc, 1986; 17:57–86.
41.
YuG. Using ggtree to visualize data on tree-like structures. Curr Protoc Bioinformatics, 2020; 69(1):e96.
42.
YuG, LamTT-Y, ZhuH, et al.Two methods for mapping and visualizing associated data on phylogeny using ggtree. Mol Biol Evol, 2018; 35(12):3041–3043.
43.
ZhangR, DrummondAJ, MendesFK. Fast Bayesian Inference of Phylogenies from Multiple Continuous Characters. Syst Biol, 2024; 73(1):102–124; doi: 10.1093/sysbio/syad067
44.
ZuckerkandlE. Molecular disease, evolution, and genic heterogeneity. Horizons in Biochemistry, 1962:189–225.
45.
ZuckerkandlE, PaulingL. Evolutionary divergence and convergence in proteins. In: Evolving Genes and Proteins. Elsevier; 1965; pp. 97–166.
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