High-throughput fingerprinting of human pluripotent stem cell fate responses and lineage bias

Abstract

Populations of cells create local environments that lead to emergent heterogeneity. This is particularly evident with human pluripotent stem cells (hPSCs): microenvironmental heterogeneity limits hPSC cell fate control. We developed a high-throughput platform to screen hPSCs in configurable microenvironments in which we optimized colony size, cell density and other parameters to achieve rapid and robust cell fate responses to exogenous cues. We used this platform to perform single-cell protein expression profiling, revealing that Oct4 and Sox2 costaining discriminates pluripotent, neuroectoderm, primitive streak and extraembryonic cell fates. We applied this Oct4-Sox2 code to analyze dose responses of 27 developmental factors to obtain lineage-specific concentration optima and to quantify cell line-specific endogenous signaling pathway activation and differentiation bias. We demonstrated that short-term responses predict definitive endoderm induction efficiency and can be used to rescue differentiation of cell lines reticent to cardiac induction. This platform will facilitate high-throughput hPSC-based screening and quantification of lineage-induction bias.

Fig. 1. Optimization of the 96μCP platform

(a) Schematic of the assay design. Cell substrates are first patterned into standard 96-well plates using micro-contact printing. A single cell suspension of the population of interest is then generated and seeded onto the 96-well plates. The plates are then incubated for 6 h, allowing the cells to settle and adhere to the patterned substrates. Plates are then washed two times with PBS and test media (i.e. base media with factors) is added to each well. Plates are fixed 48 h after seeding and subsequently stained. (b) Sample image of a patterned hPSC colony viewed at 20x, and 25 stitched fields showing an array of colonies at the bottom of a well in a 96-well plate. DAPI staining is used to obtain nuclear masks which enable single cell analysis. The single cell features can be used to reconstruct vectorized figures of the wells. Using the x- and y-coordinates, cells can be clustered into colonies to enable colony-level analysis. (c) Colony size optimization. FnGel was patterned in different diameter islands as indicated. Cells were patterned and the response to murine embryonic fibroblast conditioned media (CM), base media alone (SF), SF with FGF2 and SB431542 (FTi), SF with BMP4 (B), or SF with BMP4 and activin A (BA) was tested. * ANOVA p < 0.01 and Tukey post-hoc p < 0.01. Error bars, s.d. (n = 3).

Fig. 2. Single cell protein profiling reveals Oct4 and Sox2 mark early cell fates

(a) Sample images of control conditions stained for Tra-1-60, Gata4, Nanog, Snail, and Brachyury (Bry). Scale bar 100 μm. (b) 2D hierarchical clustering of protein expression levels (%positive) of markers and sets of markers across control conditions. Left panel displays sample similarity tree, with samples clustered using a distance threshold of 0.4. Clusters are indicated in red numerals. (c) Sample plots of Oct4 and Sox2 intensity values from controls. Thresholds can be based on these controls to classify cells as positive or negative for each marker. (d) Quantification of Oct4 and Sox2 subpopulations in control conditions. Statistically compared to SF control value, * ANOVA p < 0.01 and Tukey post-hoc p < 0.01, # ANOVA p < 0.005 and Tukey post-hoc p < 0.05. Error bars, s.d. (n = 3). (e) Sample composite images showing DAPI (blue), Oct4 (green), and Sox2 (red) expression in controls. Scale bar 100 μm. (f) Quantification of Snail, Bry, Tra-1-60, and Gata4 expression in control conditions. * Statistically compared to SF control value, ANOVA p < 0.01 and Tukey post-hoc p < 0.01. Error bars, s.d. (n = 3). (g) Summary of Oct4 and Sox2 subpopulations.

Fig. 3. Characterization of factors modulating pluripotency, neuroectoderm, primitive streak, and extraembryonic cell fate choices

(a) Dose curves of development signaling factors. Title color indicates predominant effect of each factor: promoting pluripotency (blue), neuroectoderm (red), extraembryonic (green), or primitive streak (purple), or a mixed effect (orange). Ligand concentration is shown in ng/mL, small molecule concentration is shown in μM. Error bars, s.d. (n > 2). (b) Dose curve of Alk2/3/6 inhibitor LDN-193189 with and without 40ng/mL BMP4. Baseline pluripotency and neuroectoderm conditions in SF media alone or SF with BMP4 are shown with dotted lines. Statistically compared to “0” control values, * ANOVA p < 0.01 and Tukey post-hoc p < 0.01. Error bars, s.d. (n = 3). (c) LDN addition during stage two of endoderm differentiation enables generation of PDX1+ endoderm cells. Scale bar 100 μm. *ANOVA p < 0.0001. Error bars, s.d. (n = 3). (d) DKK1 inhibits pluripotency and promotes neuroectoderm, but this effect is masked by FGF2, activin A or BMP. * ANOVA p < 0.01 and Tukey post-hoc p < 0.01. Error bars, s.d. (n = 3).

Fig. 4. Quantitative assessment of cell line specific endogenous signaling and germ layer differentiation efficiency

(a) Hierarchical clustering of cell line responses to controls (Euclidean distance similarity metric, average linkage clustering). Left panel displays sample similarity tree, with samples clustered using a distance threshold of 1.4. Clusters are indicated in red numerals. (b) Germ layer differentiation of multiple cell lines can be compared on-chip in a rapid, controlled, and quantitative manner. Error bars, s.d. (n = 3). (c) i) Activin A has differential effects on day 2 primitive streak induction in ZAN3 and ZAN11 cell lines. * ANOVA, p < 0.005. Error bars, s.d. (n = 3) ii) At day 18 of cardiac induction, activin A has differential effects on cardiac Troponin T (TnT) expression in ZAN3 and ZAN11 cell lines (p < 0.005, Two-Factor ANOVA with n = 2, independent passages), with Activin A increasing primitive streak in ZAN11s and decreasing primitive streak in ZAN3s (both >95 percent confidence using linear regression). Error bars, s.d. (n = 2). (d) i) Definitive endoderm induction efficiencies of 12 cell lines. Error bars, s.d. (n = 2). ii) Correlation of cell line Mesendoderm prediction index values from day 2 to actual definitive endoderm induction efficiencies at day 5 for a panel of cell lines (r = 0.89, p < 0.0001). (e) i) The response of H9s to signaling pathway agonists and antagonists. Error bars, s.d. (n = 3). ii) Estimated endogenous signaling levels of specific pathways for H9 (Eqn. 1, Methods). iii) Estimated endogenous signaling levels for ZAN3 and ZAN11 cell lines. iv) Passage to passage correlation of endogenous signaling profiles. The 60 response outputs (4 readouts × 15 conditions) were obtained for multiple passages for ZAN11 and ZAN3 showing high passage-to-passage correlation (ZAN 11 r = 0.90, ZAN3 r = 0.88). Comparison of ZAN3 and ZAN11 shows low line-to-line correlation (r = 0.52).