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Sci-fate characterizes the dynamics of gene expression in single cells

Nature Biotechnology volume 38, pages 980–988 (2020)Cite this article

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Abstract

Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, we present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell-state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.

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Fig. 1: Sci-fate enables joint profiling of whole and newly synthesized transcriptomes.
Fig. 2: Characterizing TF modules driving concurrent, dynamic gene, regulatory processes in populations of single cells.
Fig. 3: Inferring single-cell transcriptional dynamics with sci-fate.
Fig. 4: Constructing a state transition network for GR response and cell cycle.

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Data availability

The data generated by this study can be downloaded in raw and processed forms from the National Center for Biotechnology Information’s Gene Expression Omnibus (GSE131351).

Code availability

Scripts for processing sci-fate sequencing were written in Python and R with code available at https://github.com/JunyueC/sci-fate_analysis.

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Acknowledgements

We thank the members of the Shendure labs for helpful discussions, particularly X. Huang, R. Blecher, B. Martin, F. Chardon and R. Qiu; J. Rose, D. Maly, L. VandenBosch, and T. Reh’s lab for sharing the NIH/3T3 cell line; and J. McFaline-Figueroa for sharing the A549 cell line. This work was funded by the Paul G. Allen Frontiers Foundation (Allen Discovery Center grant to J.S. and C.T.), grants from the National Institutes of Health (grant nos. DP1HG007811 and R01HG006283 to J.S.; grant no. DP2 HD088158 to C.T.), the W. M. Keck Foundation (to C.T. and J.S.), the Dale. F. Frey Award for Breakthrough Scientists (to C.T.), the Alfred P. Sloan Foundation Research Fellowship (to C.T.) and the Brotman Baty Institute for Precision Medicine. J.S. is an investigator of the Howard Hughes Medical Institute.

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Junyue Cao, Wei Zhou, Cole Trapnell & Jay Shendure

  2. Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA

    Wei Zhou

  3. Illumina, San Diego, CA, USA

    Frank Steemers

  4. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Cole Trapnell & Jay Shendure

  5. Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA

    Jay Shendure

  6. Howard Hughes Medical Institute, Seattle, WA, USA

    Jay Shendure

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Contributions

J.S. and J.C. designed the research. J.C. developed the technique and performed the experiments with the assistance of F.S. J.C. performed the computation analysis with suggestions from W.Z. and C.T. J.S. and J.C. wrote the paper.

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Correspondence to Junyue Cao or Jay Shendure.

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F.J.S. declares competing financial interests in the form of stock ownership and paid employment by Illumina, Inc. One or more embodiments of one or more patents and patent applications filed by Illumina may encompass the methods, reagents and data disclosed in this article.

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Integrated supplementary information

Supplementary Figure 1 Performance and QC-related analyses for sci-fate.

(a) Scatter plot of mouse (NIH/3T3) vs. human (HEK293T) UMI counts per cell in sci-fate. (b-d) Boxplot showing the proportion of reads mapping to the expected species (b), number of UMIs (c) and ratio of 4sU labeled reads (d) per cell from HEK293T (n = 932) and NIH/3T3 (n = 438) cells. For all box plots: thick middle lines, medians; upper and lower box edges, first and third quartiles, respectively; whiskers, 1.5 times the interquartile range; circles, outliers. (e-f) Spearman’s correlation between gene expression measurements in aggregated profiles of HEK293T (Gene number n = 22,687) (e) and NIH/3T3 cells (Gene number n = 21,973) (f) from sci-fate (y-axis) vs. sci-RNA-seq (x-axis) cells.

Supplementary Figure 2 Performance of sci-fate on dexamethasone-treated A549 cells.

(a, b) Violin plot showing the number of UMIs (a) and genes (b) per cell in each of six treatment conditions. Cell number n = 1,054 (0h), 1,049 (2h), 949 (4h), 1,262 (6h), 1,041 (8h), and 1,325 (10h). For all violin plots: thick middle lines, medians; upper and lower box edges, first and third quartiles, respectively; whiskers, 1.5 times the interquartile range; circles, outliers. (c) Barplot showing the distribution of T > C mutation counts across labeled reads, estimated using reads from 100 randomly sampled cells and normalized by the total read number. (d) Violin plot showing the fraction of 4sU unlabeled reads per cell, split out by the subsets that map to exons vs. introns (Cell number n = 6,680). (e) Violin plot showing the fraction of exonic reads per cell, split out by the subsets that are labeled or unlabeled by 4sU (Cell number n = 6,680). (f) The x and y coordinates correspond to the joint information UMAP space (Cell number n = 6,680) shown in the rightmost panel of Fig. 1e, colored by DEX treatment time. Grey lines represent inferred cell state transition directions by RNA velocity with intronic reads (left) or newly synthesised reads (right). (g) Correlation plot showing the Pearson’s correlation between different treatment conditions, using either whole transcriptomes (upper right; circles, gene number n = 43,167) or newly synthesized transcriptomes (bottom left; squares, gene number n = 43,167). For each condition, we generated pseudobulk transcriptomes for either the newly synthesized or whole transcriptomes (i.e. aggregating across cells), and compared these in a pairwise fashion between conditions (e.g. whole transcriptome at 0 hrs vs. 4 hrs; newly synthesized transcriptome at 2 hrs vs. 6 hrs, etc.). The lowest correlations corresponded to the newly synthesized transcriptome with no DEX treatment (0 hrs) vs. the newly synthesized transcriptomes of any DEX treated condition. (h) Bar plot showing the expression fold-change before and after 2 hour DEX treatment for FGD4 and FKBP5, calculated using either whole RNA and newly synthesised RNA. Error bars represent standard deviation estimated by sampling (without replacement) 200 cells five times from each time point (n = 5, centre: mean). (i) Scatter plot showing the expression fold-change before and after 2 hour DEX treatment for differentially expressed (DE) genes identified using either whole transcriptomes or newly synthesized transcriptomes. The blue line represents y = x. (j-k) UMAP visualization of A549 cells (n = 6,680) via joint information of the whole and newly synthesized transcriptomes, colored by normalized expression of S phase marker genes in the whole (j) and newly synthesized (k) transcriptomes. UMI counts for these genes are scaled for library size, log-transformed, aggregated and then mapped to Z-scores.

Supplementary Figure 3 TF modules driving cell state transitions in DEX-treated A549 cells.

(a) Scatter plot showing the Spearman’s correlations between either the overall expression (x-axis) or newly synthesized RNA expression (y-axis) for each TF-gene gene pair identified by correlation analysis using newly synthesized mRNA (blue, n = 448), full mRNA expression (red, n = 1,606) or found in both approaches (green, n = 538) (Methods). (b) Line plot showing the number of identified TF-gene links with varied numbers of sampled UMIs per cell, by the same TF-gene linkage analysis using newly synthesized mRNA or full mRNA expression. (c) Identified gene targets (grey, n = 5) of CEBPB (orange). Only links with regularized regression coefficients from LASSO > 0.06 are shown. (d) UMAP visualization of A549 cells (n = 6,680) via joint information of the whole and newly synthesized transcriptomes, colored by CEBPB expression (left) and activity (right). (e) similar to panel d, colored by the YOD1 expression (left) or activity (right). (f) similar to panel d, colored by the GTF2IRD1 expression (left) or activity (right). (g) similar to panel d, colored by the E2F1 expression (left), activity (middle) or aggregated whole transcriptome expression of E2F1 linked genes (right). (h) similar to panel d, colored by the expression and activity of KLF6 (left two panels), GATA3 (middle two panels) or NRF1 (right two panels).

Supplementary Figure 4 Twenty-seven cell states defined by combinations of TF module-defined states.

(a) Schematic of strategy for characterizing cell states as the combination of TF modules. (b) Supplementary Table showing the observed proportion of cells (black numbers) falling into each of 27 cell states that each correspond to one of 3 states defined by GR response (rows) and one of 9 states defined by the cell cycle module (columns). The red numbers in parentheses correspond to the expected proportions, assuming that the distributions are independent.

Supplementary Figure 5 Estimating rates of new RNA detection and of RNA degradation.

(a) We selected genes showing higher differences in normalized newly synthesis rate between 0 hrs and 2 hrs time points. For this, we first tested a series of thresholds for gene filtering and calculated the detection rate (α) for all genes. We then plotted the relationship between threshold and the ratio of genes with out-range α values (< 0 or > 1). Blue line represents loess smooth line for the data. We selected the threshold that was at the knee point of the plot, resulting in 186 genes selected. (b) Scatter plot (Gene number n = 186) showing differences between the normalized transcriptomes with no DEX treatment vs. 2 hours DEX treatment for each of 186 genes exhibiting the largest differences in new transcription between the two conditions. X-axis shows absolute differences between the conditions in the whole transcriptome. Y-axis shows absolute differences between the conditions in the newly synthesized transcriptome. Blue line is the linear regression line. Both whole transcriptome and newly synthesized transcriptome of each time point are normalized by the library size of whole transcriptome. (c) Histogram showing the distribution of the estimated detection rate of newly synthesized mRNA for each of 186 genes. (d) Scatter plot showing the median detection rate computed with different number of genes ordered by absolute differences in normalized newly synthesis rate between 0 hrs and 2 hrs time points. (e-f) Line plot showing the estimated new RNA detection rate (e) or Pearson’s correlation of estimated gene degradation rate with the full data (f, gene number n = 13,343) using varied numbers of sampled UMIs per cell. (g) Below-left of diagonal: correlation plots showing the Pearson’s correlations (r) of gene degradation rates between treatment conditions (Gene number n = 14,587). Diagonal: plots showing the distribution of gene degradation rates at each time point. Top-right of diagonal: correlation plots showing the Pearson’s correlations (r) values and mean squared errors (MSE) of gene degradation rates between treatment conditions. (h) Scatter plot between published per-gene mRNA half-lives (log transformed) in K562 cells (Nat. Methods, 221–225, 2018) (x-axis) vs. as estimated by sci-fate in A549 cells (y-axis) (Shared gene number n = 4,963).

Supplementary Figure 6 Reconstructed cellular past states overlapped with real cell states in the corresponding past time point even without Seurat integration.

(a) UMAP visualization of cells of 0 hrs treatment (Cell number n = 1,054) and original states (left) or reconstructed past states (right) from 2 hrs treatment (Cell number n = 1,049). (b) UMAP visualization of cells for reconstructed cellular past states of 4 hrs cells (Cell number n = 949) and profiled cells from 0 hrs (left, cell number n = 1,054) or 2 hrs (right, cell number n = 1,049). (c) Similar to Fig. 3f, barplots showing the contributions of the 9 different cell cycle states to each of three cell trajectory clusters by simply aligning neighboring time points, but without knowledge of newly synthesized mRNA.

Supplementary Figure 7 Constructing a state transition network for GR response and cell cycle.

(a-b) Scatter plots of cell state transition probabilities (a) or cell state proportions (Cell state number n = 27) (b), comparing inferences made at different timepoints against one another. More specifically, we computed the cell state transition probability from each detected cell state (with at least 50 cells profiled) to all 27 cell states (shown in Fig. 4a) for the last three time intervals (4-6h, 6-8h, 8-10h), and then compared these against one another (Point number n = 91, 77 and 90 for plots on the left, middle and right in a). The first two time points (0h and 2h) were excluded as they had few cell states (with at least 50 cells profiled) shared with other time points. (c) Cell state transition network (Cell state number = 27) similar to Fig. 4a (left), but after permuting the parent and child cell links (middle), or by similar approaches but based on mature mRNA only to link cells from different time points (right). For the permutation-based control (middle), each child cell of each time point was linked to a randomly selected parent cell from the immediately preceding time point. (d) The x and y coordinates correspond to UMAP visualization of cells from 0h and 2h treatment groups (Cell number n = 1,054 for 0h and 1,049 for 2h) colored by DEX treatment time (top) or inferred cell cycle stage (bottom). For both panels, black lines and arrows represent inferred cells state transitions by either RNA velocity (left), treatment time-informed RNA velocity (middle) or sci-fate analysis (right). Reference: Schofield, J. A., Duffy, E. E., Kiefer, L., Sullivan, M. C. & Simon, M. D. TimeLapse-seq: adding a temporal dimension to RNA sequencing through nucleoside recoding. Nat. Methods 15, 221–225 (2018).

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Cao, J., Zhou, W., Steemers, F. et al. Sci-fate characterizes the dynamics of gene expression in single cells. Nat Biotechnol 38, 980–988 (2020). https://doi.org/10.1038/s41587-020-0480-9

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