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Real age prediction from the transcriptome with RAPToR

Nature Methods volume 19, pages 969–975 (2022)Cite this article

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Abstract

Transcriptomic data is often affected by uncontrolled variation among samples that can obscure and confound the effects of interest. This variation is frequently due to unintended differences in developmental stages between samples. The transcriptome itself can be used to estimate developmental progression, but existing methods require many samples and do not estimate a specimen’s real age. Here we present real-age prediction from transcriptome staging on reference (RAPToR), a computational method that precisely estimates the real age of a sample from its transcriptome, exploiting existing time-series data as reference. RAPToR works with whole animal, dissected tissue and single-cell data for the most common animal models, humans and even for non-model organisms lacking reference data. We show that RAPToR can be used to remove age as a confounding factor and allow recovery of a signal of interest in differential expression analysis. RAPToR will be especially useful in large-scale single-organism profiling because it eliminates the need for accurate staging or synchronisation before profiling.

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Fig. 1: Estimating age from the transcriptome using RAPToR.
Fig. 2: RAPToR precisely stages development and ageing, and works from whole-organism to single-cell data.
Fig. 3: Tissue-specific staging.
Fig. 4: Staging samples cross-species.
Fig. 5: Quantifying and correcting for developmental effects using RAPToR age estimates.

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Data availability

Source data for all figures is provided. Source data are provided with this paper.

Code availability

The code to download and (pre)process the data, perform the analyses and generate the figures of this paper can be found at https://gitbio.ens-lyon.fr/LBMC/qrg/raptor-analysis

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Acknowledgements

We are grateful to S. E. Hall, M. Sémon, and S. Pantalacci for providing data from their profiling experiments. We are also grateful to G. Yvert, D. Jost, M. Sémon, A. Piazza, S. Pantalacci, and B. Lehner for their critical reading of the manuscript. M.F. is supported by INSERM. Work in the laboratory of M.F. is supported by a grant from the Agence Nationale pour la Recherche (ANR-19-CE12-0009 ‘InterPhero’), Université de Lyon (IDEX IMPULSION G19002CC) and ENS-Lyon (Projet emergent 2019). R.B. PhD fellowship is funded by the French Ministry of Research.

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Authors and Affiliations

  1. Laboratoire de Biologie et Modelisation de la Cellule, Ecole normale superieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Universite Claude Bernard Lyon 1, Lyon, France

    Romain Bulteau & Mirko Francesconi

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  1. Romain Bulteau
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M.F. and R.B. conceived the method; R.B. developed the computational framework and performed the analyses; and M.F. and R.B. wrote the manuscript.

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Correspondence to Mirko Francesconi.

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Nature Methods thanks Helge Grosshans, Adam Alexander Thil Smith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 RAPToR estimates fit gene expression data better than chronological age.

a, RAPToR estimates of D. melanogaster single-embryo samples27 staged on a reference built from bulk data25 plotted against established BLIND ranks27. b, Percentage of genes better fitted by either RAPToR estimates or chronological age modeled with splines using 2-8 degrees of freedom in otherwise identical models. c, R² of models from (b) gene count in each half of the plot is indicated in the corners. d,e, Principal components plotted along chronological age (d), and RAPToR estimates (e) (as in Fig. 2d-f).

Source data

Extended Data Fig. 2 Reference interpolation allows RAPToR estimates at high resolution.

a, RAPToR estimates of a zebrafish embryonic time-series from 9 spawns28 staged on a reference built from Domazet et al. data23 plotted against original developmental ranks28. b, First 2 principal components of the zebrafish time-series plotted against RAPToR age estimates. Spawns are color-coded. c,d, RAPToR estimates of the zebrafish time-series on the non-interpolated reference (i.e the sampling time of the reference sample with the highest correlation) vs. original developmental ranks (c) and vs. standard RAPToR estimates (as in a) (d). In a,c,d, original reference time points within the plot area are shown on the right, in blue.

Source data

Extended Data Fig. 3 Tissue-specific staging yields soma and germline ages.

a, RAPToR estimates of C. elegans Recombinant Inbred Lines (RILs)11 staged on the larval to young-adult reference built from Meeuse et al.21 vs. Francesconi & Lehner12 estimates. b-d, Comparison of RAPToR estimates of global age vs. germline age (b), global age vs. soma age (c), and soma age vs. germline age (d). e, Distribution of soma–germline heterochrony.

Source data

Extended Data Fig. 4 A delayed germline and an advanced soma.

a, Independent Components from ICA on C. elegans Recombinant Inbred Lines (RILs)11 joined to the (non-interpolated) reference data21 plotted along chronological age and RAPToR global estimates for the reference (orange) and RILs (black) respectively. b, Gene loadings on ICA components for all genes (n = 14132), germline genes (oogen. n = 582, sperm. n = 596) and soma (n = 2005) categories. Each box within violins spans the interquartile range (IQR), the central white dot denotes the median, and whiskers extend to 1.5×IQR in either direction. Category enrichment p-values derive from a two-sided hypergeometric test on genes with absolute loadings above 1.96. From left to right, p-values are IC2: p > 0.99, p < 1e-10, and p > 0.99; IC3: p < 1e-10, p = 2.66e-06, and p = 0.022; IC4: p > 0.99, p > 0.99, and p < 1e-10; IC5: p > 0.99, p > 0.99, and p < 1e-10; IC6: p < 1e-10, p > 0.99, and p = 6.54e-04; IC7: p > 0.99, p > 0.99, and p < 1e-10; IC8: p > 0.99, p > 0.99, and p < 1e-10. c,d, Summed (c) and per-component (d) Root Mean Square Error (RMSE) between RILs and reference fit on IC2-IC8 when shifting RIL (global) age estimates. RMSE per-component shows heterochrony, with soma dynamics of RILs matching younger reference time and the reverse for germline dynamics. *: p < 0.05, **: p < 0.01, ***: p < 0.001.

Source data

Extended Data Fig. 5 Soma–germline heterochrony among C. elegans recombinant lines.

Recombinant Inbred Lines (RILs)11 are staged on the larval to young-adult reference built from Meeuse et al. samples21. a, Percentage of genes better fitted by either RAPToR global, soma, or germline age estimates, modeled with splines with 4, 6, or 8 degrees of freedom in otherwise identical models. Genes are classified into spermatogenesis, oogenesis, somatic, or other (see methods). b, R² per gene of models with global, soma, or germline age estimates as predictors for 4, 6, and 8 spline degrees of freedom.

Source data

Extended Data Fig. 6 RAPToR age estimates synchronize expression dynamics across species.

a-c, Principal components of Drosophila embryogenesis in 6 species41 plotted along chronological age (a), linearly scaled chronological age41 (b), and RAPToR age estimates on a D. melanogaster reference25 (c).

Source data

Extended Data Fig. 7 Staging M. musculus single cells on H. sapiens reference.

Single cells from M. musculus embryos42 were staged on a H. sapiens single-cell embryogenesis reference39 using orthologs. a, First 2 principal components of a PCA done on the 1000 most variable genes. A principal curve is fit on the first 3 components. Cells are colored by RAPToR age estimate on the H. sapiens reference. b, RAPToR age estimates of M. musculus single cells on H. sapiens reference vs. cell ranks along principal curve (a). c, Chronological age of M. musculus single cells vs. RAPToR age estimates on H. sapiens reference using top 10% most correlated genes between mouse and human for staging (see methods). d, H. sapiens (red) and M. musculus (black) clustered gene expression profiles (aggregated per time point) of highest-correlated genes between both species (see methods).

Source data

Extended Data Fig. 8 Staging C. elegans embryogenesis with D. melanogaster.

a, C. elegans embryo samples from Levin et al.27 staged on the D. melanogaster reference built from Graveley et al.25 samples. Gaps appear in the estimates, likely at points where fly expression dynamics are incompatible with those of worms. b, As in (a), staging on the adjusted fly reference and using top 10% most correlated genes between fly and worm embryogenesis (see methods). c, D. melanogaster (red) and C. elegans (black) clustered gene expression profiles of highest-correlated genes between both species (see methods). d, ICA components of the C. elegans embryo time course plotted along sampling time. Both the red highlighted outlier and 4 samples with erroneous chronological age (circled in IC1) are omitted from analysis (see methods).

Source data

Extended Data Fig. 9 Estimating the impact of development by integrating reference data.

a-c, Cartoon detailing how the log-fold-changes (logFCs) of a differential expression analysis between two sample groups (a) and the logFCs of their matching time points in the RAPToR interpolated reference (b) can be compared to quantify the impact of development (c).

Extended Data Fig. 10 Correcting the effect of development by integrating reference data.

Samples from C. elegans time-course experiments of wildt-type (WT) and xrn-2 mutants, profiled by Miki et al.49, and staged on the larval to young-adult reference built from Meeuse et al. samples21, are used to validate developmental correction approach (see also Fig. 5f-i). a, Cartoon of a model integrating a window of reference data, with Strain and Batch coefficients shown in blue. b, Number of DE genes found by a standard differential expression model (FDR < 0.05) increases with the age gaps between compared groups, with a quasi-constant fraction of truly DE genes. c, Area under PR curves (AUPRC) in detecting gold-standard DE genes for standard differential expression model p-value, age-corrected logFCs, or the age-corrected classifier for each shifted WT subset. d, w parameter optimization for shifted WT sets, by maximizing area under the PR curves. e, PR curves of gold-standard gene detection by the age-corrected classifier for each shifted WT subset. f, Correlation of expected development logFCs and observed logFCs between the xrn-2 subset and combinations of 3-sample WT sets (note these are not the “WT -n” subsets, see Supplementary Table 13). g, Relationship between optimal w and sample-reference logFC correlation, as in (f). h, Optimal spline degree-of-freedom (df) selection for the different WT shifted sets by reaching a residual Sum of Square (SSQ) plateau. The selected df increases with the shift, which is expected since the reference window to include gets larger and may thus contain more complex dynamics. DE, Differentially Expressed. logFC, log2 fold-change. FDR, false discovery rate, PR: Precision-Recall.

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Bulteau, R., Francesconi, M. Real age prediction from the transcriptome with RAPToR. Nat Methods 19, 969–975 (2022). https://doi.org/10.1038/s41592-022-01540-0

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