Identification of Key Signaling Pathways Orchestrating Substrate Topography Directed Osteogenic Differentiation Through High-Throughput siRNA Screening

Abstract

Fibrous scaffolds are used for bone tissue engineering purposes with great success across a variety of polymers with different physical and chemical properties. It is now evident that the correct degree of curvature promotes increased cytoskeletal tension on osteoprogenitors leading to osteogenic differentiation. However, the mechanotransductive pathways involved in this phenomenon are not fully understood. To achieve a reproducible and specific cellular response, an increased mechanistic understanding of the molecular mechanisms driving the fibrous scaffold mediated bone regeneration must be understood. High throughput siRNA mediated screening technology has been utilized for dissecting molecular targets that are important in certain cellular phenotypes. In this study, we used siRNA mediated gene silencing to understand the osteogenic differentiation observed on fibrous scaffolds. A high-throughput siRNA screen was conducted using a library collection of 863 genes including important human kinase and phosphatase targets on pre-osteoblast SaOS-2 cells. The cells were grown on electrospun poly(methyl methacrylate) (PMMA) scaffolds with a diameter of 0.938 ± 0.304 µm and a flat surface control. The osteogenic transcription factor RUNX2 was quantified with an in-cell western (ICW) assay for the primary screen and significant targets were selected via two sample t-test. After selecting the significant targets, a secondary screen was performed to identify osteoinductive markers that also effect cell shape on fibrous topography. Finally, we report the most physiologically relevant molecular signaling mechanisms that are involved in growth factor free, fibrous topography mediated osteoinduction. We identified GTPases, membrane channel proteins, and microtubule associated targets that promote an osteoinductive cell shape on fibrous scaffolds.

Figure 1

(A) Schematic workflow of the primary screen. (B) High-throughput siRNA library screen of 863 genes were distributed into designated wells in triplicates in 96 well plates. For each plate, SaOS2 cells grown in McKoy’s media (blue wells), SaOS2 cells grown in mineralization media (green wells), scrambled siRNA (orange wells), cell death siRNA (magenta wells), SaOS2 cells with only secondary Ab (yellow well) and only secondary Ab (red well) wells were used as internal controls. Three 96 well plate replicate was used. (C) Representative ICW images for Cell death, Scrambled, Mineralization Media Control, A test gene that upregulated osteogenesis, A test gene that downregulated osteogenesis, A test gene that has no effect osteogenesis compared to control. White circles represent the region of interest for quantification of RUNX2 ICW signal.

Figure 2

SEM images of smooth (A) and fibrous (B) topographies covered polystyrene sheet show fibers are sparsely distributed to allow cell spreading yet still create a fibrous topography over the flat underlying polystyrene layer. Cellular spreading and cell fiber interactions shown in C and D. Cells possess a spreading morphology (C) and present adhesive extensions that are wrapping around single fibers (D).

Figure 3

Summary of the filtered genes are shown in the diagram for each library (kinase and phosphatase). The unbiased selection criteria used in the statistical analysis step allowed distinguishing of upregulating, downregulating and no change targets for each topography. There were a significant number of intersecting genes in both smooth and fiber topography categories. A two sample t-test statistical analysis was performed to distinguish the different genes in this category.

Figure 4

Comparison Heatmap comprised of the total number of HTS hits for each topography within the specified signaling cascade using INGENUITY Comparison function. Green color indicates the number of upregulated signaling molecules were higher within the particular signaling cascade and red indicates number of downregulated signaling molecules were higher within the particular signaling cascade.

Figure 5

Morphological characteristics of cells treated with siRNA. (A) The shape of the actin cytoskeleton is represented by thresholded images after automated segmentation. Representative images shown for the indicated siRNA after thresholding. (B) The compactness, extent and form factor are derived from the primary parameters as indicated in the example cell diagram. (C) Morphological parameter classification based on the up or down regulation of the parameter only when the comparison was statistically significant. (p < 0.05 by ANOVA with Fisher’s test). Green and red indicate up or down significance respectively within an siRNA treatment (n = 5 to 26, mean n = 11 ± 4.3 cells per group).