Supplementary Data Figures S1-S14 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

Rockne, Russell C.; Branciamore, Sergio; Qi, Jing; Frankhouser, David E.; O'Meally, Denis; Hua, Wei-Kai; Cook, Guerry; Carnahan, Emily; Zhang, Lianjun; Marom, Ayelet; Wu, Herman; Maestrini, Davide; Wu, Xiwei; Yuan, Yate-Ching; Liu, Zheng; Wang, Leo D.; Forman, Stephen; Carlesso, Nadia; Kuo, Ya-Huei; Marcucci, Guido

doi:10.1158/0008-5472.22426288.v1

- No file added yet -

Supplementary Data Figures S1-S14 from State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

journal contribution

posted on 2023-03-31, 03:46 authored by Russell C. Rockne, Sergio Branciamore, Jing Qi, David E. Frankhouser, Denis O'Meally, Wei-Kai Hua, Guerry Cook, Emily Carnahan, Lianjun Zhang, Ayelet Marom, Herman Wu, Davide Maestrini, Xiwei Wu, Yate-Ching Yuan, Zheng Liu, Leo D. Wang, Stephen Forman, Nadia Carlesso, Ya-Huei Kuo, Guido Marcucci

Figure S1. Details of animal model and fusion gene CM, Figure S2. Ex vivo flow cytometry analysis of bone marrow, Figure S3. Details of principal component analysis, Figure S4. Comparison of state-space construction with different dimension reduction methods, Figure S5. Hierarchical clustering of time-series RNA-seq data and relation to state-space, Figure S6. Details of critical point estimation, construction of quasi-potential, and state-space dynamics, Figure S7. Correlation of state-space geometry with Kit and CM gene expression. Sensitivity of state-space geometry to inclusion of Kit and CM, Figure S8. Volcano plots of critical-point based differential gene expression analysis, Figure S9. Heatmaps of selected GO pathways in early, transition, persistent, and leukemic events, Figure S10. Computation of eigengene angle in state-space, Figure S11. Details of principal component analysis of validation cohorts and state-space dynamics, Figure S12. Mean-squared displacement analysis of particle trajectories in state-space and calibration of Fokker-Planck diffusion coefficient with training cohort, Figure S13. Sensitivity analysis of state-space construction to sample and normalization thresholds, Figure S14. Bootstrap cross validation of state-space construction.

Funding

NIH

History

ARTICLE ABSTRACT

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072