A systems biology pipeline identifies regulatory networks for stem cell engineering

Abstract

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.

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Fig. 1: GRN dynamics capture cell fate specification.
Fig. 2: ErbB signaling is implicated in erythroid differentiation.
Fig. 3: ErbB4 is required for robust erythroid development.
Fig. 4: ErbB4 genetic deficiency leads to blood defects in the ErbB4−/−HER4heart mouse model.
Fig. 5: Modulation of pathways downstream of ErbB signaling augments iPSC-derived RBC generation.

Data availability

All RNA-seq data have been deposited to the GEO database under GSE108128.

Change history

  • 16 July 2019

    In the version of this article initially published, the second NIH grant “R24-DK49216” to author George Q. Daley contained an error. The grant number should have read U54DK110805. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

The authors thank G. Corfas (University of Michigan) for sharing the ErbB4−/− HER4heart mutant mice, which were generated in 2003 by M. Gassmann (University of Basel) and colleagues34 and L.I. Zon (Boston Children’s Hospital) for the globin:eGFP transgenic fish. The authors also thank P. Eser for the ErbB inhibitor library and T. Rosanwo for cells and reagents, as well as R. Mathieu and the BCH Flow Cytometry Core, J. Osborne, B. Joughin, J. Das and A. Zweemer for technical advice. This work is supported by grants to G.Q.D. from the NIH National Institute of Diabetes and Digestive and Kidney Diseases (nos. R24-DK092760, U54DK110805) and National Heart, Lung, and Blood Institute (Progenitor Cell Translation Consortium, no. U01HL134812; and nos. R01-HL04880 and NIH R24-OD017870-01). Additional support was given to D.A.L. from NIH National Institute of General Medical Sciences (no. R01-GM069668). M.A.K. is supported by a NIH T32 Training Grant from BWH Hematology. L.T.V. was supported by the NSF Graduate Research Fellowship. J.M.F. is supported by a NIH T32 Training Grant from the NHLBI. T.E.N. is a Leukemia and Lymphoma Society Scholar. G.Q.D. is an associate member of the Broad Institute and was supported by the Howard Hughes Medical Institute and the Manton Center for Orphan Disease Research.

Author information

M.A.K., D.A.L. and G.Q.D. conceived the project. M.A.K., L.T.V., J.M.F., J.B., A.J.C. and S.L. performed experimental work and data interpretation. K.-K.W., J.J.C., P.C., T.E.N., D.A.L. and G.Q.D. supervised research and participated in project planning. M.A.K., T.E.N., D.A.L. and G.Q.D. prepared the manuscript.

Correspondence to Douglas A. Lauffenburger or George Q. Daley.

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Integrated supplementary information

Supplementary Figure 1 CellNet classification of erythroid microarrays.

(a) Classification probability of hspc and erythroid microarrays using the original release of human CellNet. (b) CellNet classification of HSPC (n=127) and erythroid (n=164) microarrays by Random Forest (partitioned at 50% training/testing, 2-fold cross-validation) and classifier specificity (false positive rate of 5%) after training an erythroid GRN. (c) Status of the HSPC and erythroid GRNs across all cell types. GATA2 and GATA1 (d) expression and (e) first order networks associated with the HSPC (blue) and erythroid (red) GRNs, with overlapping genes annotated in green. (f) Enriched biological processes (gene ontology) within the entire erythroid GRN (235 genes) and the first order networks associated with each of the erythroid TFs. (g) overlap between cell and tissue specific GRNs (h) Classification probability (i) GRN establishment and (j) network influence score (NIS) for a subset of microarrays derived from sorted populations corresponding to temporal stages of differentiation (CFU-E, ProE, IntE, LateE). For this analysis, the classified arrays were excluded from the training data.

Supplementary Figure 2 Analysis of principal component characteristics and gene expression dynamics.

GSEA enrichment for Hallmark gene sets was calculated for each sample as a preranked list with respect to the average of the entire erythroid dataset. The correlation of the GSEA normalized enrichment scores (NES) with the principal component coordinates were ranked in order to determine the relative influence of gene sets to (a) PC1 and (b) PC2. (c) Distribution of regulators (i.e. TFs, DNA binding factors) within gene clusters in (c): G1 (green), G2 (blue) and G3 (red) and the distribution of target-regulator interactions across gene clusters (G1-G3). (d-g) The importance of genes within the GRN to each GMM cluster (C2, C4, C5 and C6 from Fig. 2a) were determined by calculating the dot product of the score cluster centroid with the coordinates of each gene on the PCA loadings plot (Fig. 2c). The top 20 genes corresponding to clusters C2 (d), C4 (e), C5 (f) and C6 (g) are plotted, with the full datasets available in Supplementary Table 6. (h) K-means clustering of significantly modulated genes across stages of differentiation (derived from clusters C2, C4, C5 and C6 from the PCA analysis in Fig. 2a). The number of genes in each cluster is denoted in the upper right corner and individual clusters are annotated with significantly enriched (Bonferroni corrected p < 0.05) gene ontology (GO) biological processes.

Supplementary Figure 3 Dissection of GRN loadings in RNA-seq datasets.

(a) Principal component scores plot and Gaussian Mixture Model (GMM) unsupervised clustering (S1-S4) of a RNA-seq dataset describing developmentally staged, purified populations of erythroblasts (GSE53983). The identity of samples in each cluster is represented in the graphical overlay. (b) Correlations between gene loadings from the erythroid GRN in microarray (Fig. 1d) and RNA-seq data * = p < 0.05. The loading distances were calculated as the Euclidean distance to each GMM cluster centroid. C2, C4, C5 and C6 represent the GMM clusters from microarray data and S1-S4 are the GMM clusters from RNA-seq data. (c) Representative loading distances comparing microarray (C2 and C5; x-axis) to RNA-seq (S1 and S3; y-axis) data. (d) The top 30 genes corresponding to each RNA-seq cluster, represented as the normalized Euclidean distance to the cluster centroid. The bar colors correspond to the gene cluster membership (G1-G3; Fig. 1d) from microarray classification.

Supplementary Figure 4 GRN dynamics of the T cell network and signaling during activation.

(a) Principal Component Analysis (PCA) scores of T cell microarrays, with Gaussian Mixture Model (GMM) derived clusters C1-C7. (b) Spatial organization of manual classifications and 95% confidence interval ellipses in PCA space, with PC1 related to treatments (HDAC inhibition, activation, resting, naïve) and PC2 related to cell type (CD4, CD8, NKT). Peripheral blood (PB) derived samples are shown with a solid line, and cord blood (CB) derived samples with a dashed line. (c) Distribution of manual classifications across unsupervised computational clusters C3 (primarily naïve PB CD4), C4 (PB CD8), C1 (primarily activated CB CD4) and C2 (activated PB CD4) from part (a). (d) Distance between loadings (genes) and cluster centroids, calculated as the dot product of their coordinates, with the top 5 genes shown for clusters C3, C4, C1 and C2. (e) Visualization of regulators from dynamic networks calculated using CLR inference across each of the clusters from C3, C4, C1 and C2. Node size correlates with the degree (number of targets) and line width corresponds to the CLR Z-score (confidence of interaction) between regulators. (f) Gene signature distinguishing clusters C3 (naïve PB CD4; green) and C2 (activated PB CD4; blue) from (a), as determined by Least Absolute Shrinkage and Selection Operator (LASSO). (g) Protein-protein interaction (PPI) network built from gene signature using the STRING database and the Prize Collecting Steiner Tree (PCSF) algorithm. PCSF parameters were ω=3, β=2, µ=8x10-5. Non-LASSO nodes (Steiner nodes) are depicted with the size proportional to the degree and LASSO nodes (terminals) are depicted in green and blue, corresponding to the representation in part (f). (h) P-values ranking node enrichment (Fisher’s test for connections in the LASSO network relative to the full STRING network) and corresponding Gene Ontology annotations. (i) Enriched signaling pathways from the Reactome database. (j) Coexpression network comprising genes highly correlated r > |0.86| with the LASSO signature. (k) Enrichment analyses for kinase perturbation and ligand regulation (both from LINCS L1000).

Supplementary Figure 5 Reticulocyte microarray characterization.

(a) Pearson’s correlation comparing all genes on microarrays from distinct stages of differentiation (corresponding to C2, C4, C5 and C6 from Fig. 2a). (b) Gene Ontology (GO) enrichment for biological processes that are differentially regulated between C5 and C6. (c) All genes within representative GO categories with coordinated decreased (blue) or increased (red) expression during erythroid maturation and reticulocytosis.

Supplementary Figure 6 PLSDA validation of LASSO genes.

(a) Lambda was chosen for LASSO regression based upon the minimum mean square error (MSE). The number of genes corresponding to each value of lambda are annotated above the graph. (b) PLSDA model built upon the 27 gene LASSO signature, with validation via comparison to PLSDA models with the (c) Y block (binary classification) randomly permuted and (d) based on random selections (1000 permutations) of 27 genes.

Supplementary Figure 7 Transcriptional regulatory network of hemoglobin genes.

(a) First order connection network derived from connecting genes corresponding to the hemoglobin metabolism biological process (GO: 0020027) within the CellNet global GRN. Significantly enriched regulators (p<0.05 by Fisher’s test for connections in the network compared to the global GRN) are shown in black, with community-derived modules depicted above in varying colors and the hemoglobin targets in green. (b) The degree of all regulators and hemoglobin target gene module membership. (e) Graphical representation of Module 1 with significantly enriched regulators (black) and a ranked list of the most enriched regulators by p-value (Fisher’s test).

Supplementary Figure 8 Signaling network parameter robustness analysis.

(a-b) Network characteristics upon varying the Prize Collecting Steiner Forest (PCSF) parameters, including (a) the degree penalty, µ and (b) the number of trees, ω and node prize scaling, β. The degree centrality was used to assess the changes in µ, demonstrating the decreasing influence of highly connected regulators at increasing values of µ for 100 permutations of random networks, as well as the LASSO network (red). The fraction of LASSO nodes (top) and the total node size (bottom) as a function of β and ω demonstrate a global maximum after an empirically determined threshold. Accordingly, the network composition changes with increasing β and ω, with decreasing density and centralization, yet increasing significant Gene Ontology (GO) terms at p<0.05 in larger networks. (c) Example of a highly centralized network with µ = 0 and examples of small (d) and medium (e) sized networks. The Steiner node (non-LASSO terminal) size is represented in proportion to the connectivity degree and LASSO terminals are shown in red and blue, corresponding to the representation in Fig. 2a. (f) Comparison of the maximum network betweenness and the P53 node betweenness, when comparing random networks (100 permutations) to the LASSO network, demonstrating that P53 is has a significantly (p<0.001) more centralized role in the topology of the LASSO network than would be predicted randomly.

Supplementary Figure 9 Correlation network threshold robustness analysis.

(a) Network topology upon varying correlation thresholds (absolute value of Pearson’s r) for the co-expression network from Fig. 2i-j. Insets show the limited range from correlation cutoffs from r > |0.8| to r = |1|. (b) Representative networks across values from r > |0.92| to r > |0.8| represented as heatmaps of the Pearson’s r value. Predictions of enriched (c) transcription factors (ChEA/ENCODE), (d) kinase perturbations (downregulated genes upon kinase knockdown from LINCS L1000) and (e) ligand regulation (upregulated upon ligand stimulation from LINCS L1000) associated with each correlated gene set.

Supplementary Figure 10 ErbB4 expression in primary human bone marrow and during in vitro RBC differentiation.

(a) Temporal morphological analysis of erythroid cells differentiated from BM CD34+ cells and corresponding ErbB4 gene expression relative to MCF7 cells. n=4 independent samples from 2 experiments; p=0.03 by Kruskal-Wallis rank sum test (unequal variances assumed based upon Levene’s test p=0.005). (b) ERRB4 gene expression (relative to MCF7 cells) in sorted populations from primary human bone marrow corresponding to early (CD71+GlyA-), intermediate (CD71+GlyA+) and late (CD71-GlyA+) stages of erythropoiesis. (c) Raw microarray gene expression of the ErbB family of receptor tyrosine kinases across stages of differentiation (clusters C2, C4, C5 and C6 from Fig. 2a). * = p<0.05 compared to all other clusters via ANOVA and post hoc Tukey HSD tests.

Supplementary Figure 11 Peripheral blood composition in Neratinib treated mice.

Percentages of reticulocytes, neutrophils, lymphocytes and monocytes in the peripheral blood of mice treated with vehicle (hydroxypropyl methylcellulose; HPMC) or Neratinib (60 mg/kg) for 7 days. * = p<0.05 by unpaired, two-sided t-test.

Supplementary Figure 12 Zebrafish phenotypes with ErbB4 morpholinos.

(a) gata1:dsred and (b) mpo:gfp transgenic zebrafish lines, demonstrating the relatively proportion of erythroid and neutrophil phenotypes, respectively. ErbB4 morpholinos were injected at 0 hours post fertilization (hpf) and analysis was conducted at 48 hpf. n > 5 per condition, across at least 2 clutches. ** = p < 0.01 by two sided, unpaired t-test.

Supplementary Figure 13 Blood defects in ErbB4-/-HER4heart mice.

(a) Bone marrow profiles (CD71 vs Ter119) of wild type, heterozygous, and homozygous ErbB4 knockouts, with quantification of each gate I-IV in (b). (c) Peripheral blood complete blood count results for RBC parameters such as Cellular Hemoglobin Concentration Mean (CHCM) and hemoglobin distribution width (HDW). (d) Quantification and images of gross spleen anatomy and weight. (a), (b), and (d): ErbB4+/+ and ErbB4-/- n=3; ErbB4+/- n=4. (c): ErbB4+/+ and ErbB4-/- n=8; ErbB4+/- n=17. * p < 0.05, ** p < 0.01, *** p < 0.001 by one-way ANOVA with post hoc Tukey HSD.

Supplementary Figure 14 Expression of ErbB4 targets upon inhibitor treatment.

(a) GSEA analysis of targets of ErbB4 CYT-1, ErbB4 CYT-2, and common targets3,4,28 upon treatment with Lapatinib (increased affinities for EGFR and HER2), as well as Neratinib, Dacomitinib, and Afatinib (pan ErbB inhibitors) for 24 hours. (b) GSEA plots of ErbB4 CYT-2 targets. (c) Expression of genes within the erythroid GRN (GMM clusters G1, G2, G3 from Fig. 2c) compared to all genes. * = p<0.05, indicating differential probability distributions via Kolmogorov-Smirnov (KS) test.

Supplementary Figure 15 Flow cytometry gating schemes.

Gating schemes of representative flow cytometry plots (acquired on BD Fortessa cytometer and analyzed in FlowJo software) for (a) human bone marrow (BM) CD34+ differentiation (day 17) and (b) native mouse BM. Gating was performed by identifying cells on the FSC/SSC plots, excluding dead cells (DAPI+) and gating for GlyA/CD71 (human) and Ter119/CD71 (mouse). In all cases, gates were set based upon unstained controls and compensated with automated compensation with anti-mouse Igk and negative beads.

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