Data for Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip
Publication Date
September 18, 2020
Creators
Description
Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the “hit” materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers.
Collection dates:
2017-2019
Subjects
Subjects (JACS)
Subjects (LC)
Divisions
University of Nottingham, UK Campus
Data type
Microscope images, processed image data, topographical descriptors, image processing pipelines
Grant Number
EP/N006615/1
Data collection method
Optical microscopy, fluorescence microscopy, CellProfiler image analysis software processing
Resource languages
en_US
Publisher
The University of Nottingham
Date Issued
September 18, 2020
Except where otherwise noted, this item's license is described as
File(s)![Thumbnail Image]()
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Name
CTC_Image_Metadata.xlsx
Description
Metadata file containing chemistry and topography information for the raw .czi image files. Please contact authors for .czi files.
Size
430.01 KB
Format
Microsoft Excel XML
Checksum (MD5)
9b3f4190e86ceb3f4ae9104925a020f2
Name
Modelling_Descriptors.xlsx
Description
Topographical descriptors
Size
110.2 KB
Format
Microsoft Excel XML
Checksum (MD5)
8b99994b760fa64d37f290df3d4b30cf