Periodic Reporting for period 4 - FAKIR (Focal Adhesion Kinetics In nanosurface Recognition)
Reporting period: 2020-02-01 to 2021-07-31
It is widely known that surface topography affects the behaviour of cells, however, there is a limited understanding how topographical designs are linked to cell behaviours. A better understanding of this would open for the potential of designer materials for biomedical applications. The interaction interface between the substrate and the cell is mediated through the focal adhesions. They will form the cornerstone of the project. It is known from the literature that unique distances in between anchoring points in the extra cellular matrix plays a pivotal role in the cells ability to function and perform.
The vision of the proposal was to establish a deeper understanding of the mechanisms regulating cell response to nanotopographical surfaces at the focal adhesion level and use that information to engineer future functional biomaterials. There are two driving factors behind this vision. 1) As the hypothesis of this project is based on unique distances identified in the focal adhesion proteins, a nanopatterning platform is the ideal test bed for this hypothesis. 2) Surface topography is easy to manufacture in large volumes and a very reliable manner. This provides for easier translation of the findings to applications in diagnostics and regenerative medicine. To this end, 4 objects were identified:
Objective 1. Design and fabrication of gradient cell engineering substrates.
Objective 2 – Model cell systems and FA proteins
Objective 3-Cell imaging and analysis
Objective 4 – Linking morphometric parameters with FA structure and dynamics
In summary, the major achievement in the project was the development of a generic high-content screening platform to explore cell-biomaterial interactions at the nanoscale. This culminated in the development of multiwell plates which can be completely integrated in a standard workflow of microscopy and molecular biology. This platform is now greatly diversified and not only serves as a platform for cell-topography interactions but has also been developed to handle microfluidics for organ-on-a-chip applications. The platform led to the development of materials to directly monitor forces exerted between cells and materials. We are currently exploring the commercial potential through a start-up (www.forcebiology.com). Another major milestone was the development of AI algorithms for the microscopy tools used in the project as well as to explore the biomaterial interactions. Here algorithms were developed to identify individual cell types in mixed populations – a process normally assessed by PCR and only at a population level. This led to AI assisted software interfaces to de-skill the analytical steps during image analysis. Ultimately, this led to the demonstration that using microscopy images, the genetic expression levels in single cells are directly encoded in their morphology. As a legacy of the project, research developed from the ERC CoG has already led to new charity and EU projects being funded (>25M Euros).
The vision of the proposal was to establish a deeper understanding of the mechanisms regulating cell response to nanotopographical surfaces at the focal adhesion level and use that information to engineer future functional biomaterials. There are two driving factors behind this vision. 1) As the hypothesis of this project is based on unique distances identified in the focal adhesion proteins, a nanopatterning platform is the ideal test bed for this hypothesis. 2) Surface topography is easy to manufacture in large volumes and a very reliable manner. This provides for easier translation of the findings to applications in diagnostics and regenerative medicine. To this end, 4 objects were identified:
Objective 1. Design and fabrication of gradient cell engineering substrates.
Objective 2 – Model cell systems and FA proteins
Objective 3-Cell imaging and analysis
Objective 4 – Linking morphometric parameters with FA structure and dynamics
In summary, the major achievement in the project was the development of a generic high-content screening platform to explore cell-biomaterial interactions at the nanoscale. This culminated in the development of multiwell plates which can be completely integrated in a standard workflow of microscopy and molecular biology. This platform is now greatly diversified and not only serves as a platform for cell-topography interactions but has also been developed to handle microfluidics for organ-on-a-chip applications. The platform led to the development of materials to directly monitor forces exerted between cells and materials. We are currently exploring the commercial potential through a start-up (www.forcebiology.com). Another major milestone was the development of AI algorithms for the microscopy tools used in the project as well as to explore the biomaterial interactions. Here algorithms were developed to identify individual cell types in mixed populations – a process normally assessed by PCR and only at a population level. This led to AI assisted software interfaces to de-skill the analytical steps during image analysis. Ultimately, this led to the demonstration that using microscopy images, the genetic expression levels in single cells are directly encoded in their morphology. As a legacy of the project, research developed from the ERC CoG has already led to new charity and EU projects being funded (>25M Euros).
The results were delivered through the 4 objectives:
Objective 1. Design and fabrication of gradient cell engineering substrates.
Key results: The development of nanopatterned libraries applicable for high-content screening. A number of groups have developed different platforms for exploring/screening cell-topography interactions. The vast majority of them do not have the different patterned areas physically separated which can lead to cross-chemokine interaction and thus skewing the results. To that end, we developed a platform centered on 96-well plates, one of the most common cell culture consumables, where each well represents a different pattern or functional cue. It is even possible to incorporate variance in mechanical properties in the different wells. This was published in Biofabrication 2020 (DOI: 10.1088/1758-5090/ab5d3f).
Objective 2 – Model cell systems and FA proteins
Key results: We establish model cell lines to explore the dynamics at the single cell level. Here transfection of cell lines allowed us to express reporters for either single molecule expression used for super resolution microscopy (currently on BioRxiv, doi.org/10.1101/2020.07.23.191858) or the force-sensing receptors. The result of the latter is currently being prepared for publication but also led to a start-up (www.forcebiology.com).
Objective 3-Cell imaging and analysis
Key results: Super resolution microscopy was developed to quantify the direct interactions with nanostructures (currently on BioRxiv, doi.org/10.1101/2020.07.23.191858). The unique designs of our nanostructures allow us to align super resolution imaging to topography with a resolution of better than 20 nm. For the first time, this has enabled us to directly image the dynamic interaction of single molecules and the nanotopography but also visualise the formation of focal adhesions in relation to the topography.
Objective 4 – Linking morphometric parameters with FA structure and dynamics
Key results: We applied machine learning to correlate cells, materials and function (Sci Rep 2017 doi.org/10.1038/s41598-017-03780-z PLOS One 2020 doi.org/10.1371/journal.pone.0237972 and Nature Comms 2020 doi.org/10.1038/s41467-020-15114-1). The AI development led to cell phenotype identification greatly reducing the workflow in for example immune cell work. Central to our work in the identification of cells, is the free software package CellProfiler. However, the interface is technical and is a barrier for initiating cell segmentation. To that end, we developed an AI-driven plug in removing all parameters from the user and placed them in an AI optimised engine. Finally, we have demonstrated that quantitative gene expression levels are directly encoded in the cell morpheme (shape, texture, intensity, etc). This was achieved using AI optimized algorithms to link fluorescent images with gene expression level. As a consequence, it is now possible to identify expression levels at the single cell level and spatially.
Dissemination: Numerous invited presentation were given during the project. During the pandemic, the injection moulding technology was repurposed and 10.000 visors were manufactured and distributed to the local health authorities. One start-up (www.ForceBiology.com) based on technology developed in the project.
Unfortunately, COVID struck at the most critical part of the project as we were performing the concluding experiments. Lock-down and restrictions has a significant impact on the final stage of the project. However, remaining work is continued through other funded project to complete the vision the project.
Objective 1. Design and fabrication of gradient cell engineering substrates.
Key results: The development of nanopatterned libraries applicable for high-content screening. A number of groups have developed different platforms for exploring/screening cell-topography interactions. The vast majority of them do not have the different patterned areas physically separated which can lead to cross-chemokine interaction and thus skewing the results. To that end, we developed a platform centered on 96-well plates, one of the most common cell culture consumables, where each well represents a different pattern or functional cue. It is even possible to incorporate variance in mechanical properties in the different wells. This was published in Biofabrication 2020 (DOI: 10.1088/1758-5090/ab5d3f).
Objective 2 – Model cell systems and FA proteins
Key results: We establish model cell lines to explore the dynamics at the single cell level. Here transfection of cell lines allowed us to express reporters for either single molecule expression used for super resolution microscopy (currently on BioRxiv, doi.org/10.1101/2020.07.23.191858) or the force-sensing receptors. The result of the latter is currently being prepared for publication but also led to a start-up (www.forcebiology.com).
Objective 3-Cell imaging and analysis
Key results: Super resolution microscopy was developed to quantify the direct interactions with nanostructures (currently on BioRxiv, doi.org/10.1101/2020.07.23.191858). The unique designs of our nanostructures allow us to align super resolution imaging to topography with a resolution of better than 20 nm. For the first time, this has enabled us to directly image the dynamic interaction of single molecules and the nanotopography but also visualise the formation of focal adhesions in relation to the topography.
Objective 4 – Linking morphometric parameters with FA structure and dynamics
Key results: We applied machine learning to correlate cells, materials and function (Sci Rep 2017 doi.org/10.1038/s41598-017-03780-z PLOS One 2020 doi.org/10.1371/journal.pone.0237972 and Nature Comms 2020 doi.org/10.1038/s41467-020-15114-1). The AI development led to cell phenotype identification greatly reducing the workflow in for example immune cell work. Central to our work in the identification of cells, is the free software package CellProfiler. However, the interface is technical and is a barrier for initiating cell segmentation. To that end, we developed an AI-driven plug in removing all parameters from the user and placed them in an AI optimised engine. Finally, we have demonstrated that quantitative gene expression levels are directly encoded in the cell morpheme (shape, texture, intensity, etc). This was achieved using AI optimized algorithms to link fluorescent images with gene expression level. As a consequence, it is now possible to identify expression levels at the single cell level and spatially.
Dissemination: Numerous invited presentation were given during the project. During the pandemic, the injection moulding technology was repurposed and 10.000 visors were manufactured and distributed to the local health authorities. One start-up (www.ForceBiology.com) based on technology developed in the project.
Unfortunately, COVID struck at the most critical part of the project as we were performing the concluding experiments. Lock-down and restrictions has a significant impact on the final stage of the project. However, remaining work is continued through other funded project to complete the vision the project.
The main aim was to connect design with cell function. The nanopatterned substrates developed in the project were used to establish an AI based link between cell morphology and quantitative genetic expression levels. These results were published in Nature Communications in 2020 as is a significant advance of the field.
The injection moulding platform developed is based on the well-recognised (96 well) multiwell plate. This allowed us to rapidly and flexibly interchange patterns between experiments. The legacy of this platform is now penetrating into a number of new research projects (e.g. organ on a chip) as well as commercial potential.
Finally, we studied the response of pluripotent stem cells to nanotopographies and have identified new mechanisms supporting pluripotent maintenance. The results are currently being finalised for publication.
The injection moulding platform developed is based on the well-recognised (96 well) multiwell plate. This allowed us to rapidly and flexibly interchange patterns between experiments. The legacy of this platform is now penetrating into a number of new research projects (e.g. organ on a chip) as well as commercial potential.
Finally, we studied the response of pluripotent stem cells to nanotopographies and have identified new mechanisms supporting pluripotent maintenance. The results are currently being finalised for publication.