Predictive bioinformatic platforms for direct cellular reprogramming: Mogrify Ltd (cont’d)
My previous blog post presented a brief understanding of recent progress in direct cellular reprogramming and the reason for the development of two software platforms by Mogrify Ltd. This blog post is about further details of the two platforms, MOGRIFY and epiMOGRIFY.
About MOGRIFY
MOGRIFY was first co-developed by two scientists, Julian Gough (Professor of Computer Science in the University of Bristol) and Owen Rackham (PhD student of Julian Gough in the University of Bristol), in 2011. They began the development of the platform by using the data from the FANTOM 5 consortium, an international consortium launched to provide gene expression data in virtually all cell types across the human body.1 In 2014, they collaborated with Jose Polo from Monash University in Australia to test cell conversions in order to validate the prediction power of MOGRIFY. In 2015, based on the prediction results from MOGRIFY, the co-founders filed a patent application for the algorithm used by the computation platform, with over 30 direct cell conversions listed as example proof of what the algorithm can achieve.2 The predictions and cell conversion results were then published in 2016.3
According to the paper, predictions from MOGRIFY have been applied on 173 human cell types and 134 tissues. In order to assess the predictive power of MOGRIFY, the performance of the algorithm was first compared against well-known, previously-published human cell conversions. MOGRIFY correctly predicted the transcription factors (enzyme proteins that bind to a particular DNA sequence and regulate the expression of genes) used in previously-published direct cell conversions from human fibroblasts (the most common type of cell found in connective tissue) into induced pluripotent stem cells,4,5,6 and from B cells/ fibroblasts into macrophage-like cells.7,8 In addition, MOGRIFY’s prediction list included four of the five transcription factors (or similar factors) used in the conversion of human dermal fibroblasts into cardiomyocytes (cells responsible for generating contractile force in the intact heart).9
The predictive capabilities of MOGRIFY® were also experimentally examined using human cells. Two cell conversions, human fibroblasts to keratinocyte cells (cell within the epidermis) and adult human keratinocyte cells to micro-vascular endothelial cells (cells isolated from small vessels within skin tissue), were performed using prediction results from MOGRIFY. By examining the morphological and molecular characterization of the reprogrammed cells, the conversions were found to be successful.3
The results showed that MOGRIFY provides a practical and efficient mechanism to facilitate the reprogramming of human cells.
However, since the publication of the results in 2016, no further reports of cell conversions using predictions from MOGRIFY, have been published, or at least I was not able to find any after extensive searches.
About EpiMOGRIFY
Once the cells are converted from other cell types, it is important to maintain the reprogrammed cells’ identity, as the converted cells tend to revert back to what they were before. Difficulty maintaining the differentiated cells makes a large-scale production of these differentiated cells hard to achieve. This can be a hurdle for the development of cell therapies.
EpiMOGRIFY was developed with the aim of identifying optimal culture conditions (with suggested small molecules and chemical compounds) required to maintain cell identity or induce cell conversion in chemically-defined media. The computation algorithm incorporated gene-regulatory information data and cell epigenetic landscapes for more than 100 human cells and tissues, from the ENCODE and Epigenome Roadmap consortia, to define culture conditions that can maintain the phenotype and function of the converted cell, or that can be used to induce cell conversion.10
The EpiMOGRIFY platform was co-developed by Duke-NUS Medical School (Professors Enrico Pettreto and Owen Rackham), Monash University (Professor Jose Polo) and Mogrify Ltd. Owen Rackham and Jose Polo were the co-developers of MOGRIFY.11
Experiments validating the computational prediction in cell culture conditions for cell maintenance and differentiation have recently been published.12 Using the suggestion factors predicted from EpiMOGRIFY, astrocytes (cells commonly found in the brain) and cardiomyocytes (cells that make up heart muscle) were able to grow as effectively, in terms of cell numbers and proliferation rate, as they are in the culture conditions that have long been used. Moreover, these cells appeared to better maintain their morphology, in terms of the biological structure and the presence of key markers for the cell types, when growing in the culture conditions with biological factors suggested from EpiMOGRIFY. These results provide a fundamental proof for EpiMOGRIFY’s predictive power to suggest culture conditions that are able to maintain a stable phenotype.12
Furthermore, using the cell conversion conditions predicted from EpiMOGRIFY, neural progenitors and embryonic stem cells were able to differentiate to astrocyte cells and cardiomyocyte cells, respectively. The differentiation efficiency was as good as that of existing differentiation protocols.12 These results indicate that EpiMOGRIFY is highly accurate in identifying biological factors for cell conversion as well.
To be continued.
References
1. Our history. Mogrify Ltd website. https://mogrify.co.uk/about-us/
2. https://patents.google.com/patent/WO2017106932A1/en
3. O.J.L. Rackham, J. Firas, and H. Fang, et al. A predictive computational framework for direct reprogramming between human cell types. Nature Genetics, March 2016, Vol. 48, No. 3.
4. K. Takahashi, K. Tanabe, M. Ohnuki, et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell, 2007, 131, 861–872.
5. J. Yu, M.A. Vodyanik, K. Smuga-Otto. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science, 2007, 318, 1917–1920. 6. D. Huangfu, K. Osafune, R. Maehr, et al. Induction of pluripotent stem cells from primary human fibroblasts with only Oct4 and Sox2. Nat. Biotechnol., 2008, 26, 1269–1275.
7. H. Xie, M. Ye, R. Feng, & T. Graf. Stepwise reprogramming of B cells into macrophages. Cell, 2004, 117, 663–676.
8. F. Rapino, E.F. Robles, J.A. Richter-Larrea, et al. C/EBPα induces highly efficient macrophage transdifferentiation of B lymphoma and leukemia cell lines and impairs their tumorigenicity. Cell Reports, 2013, 3, 1153–1163.
9. J. D. Fu, N.R. Stone, L. Liu, et al. Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Stem Cell Reports, 2013, 1, 235–247.
10. epiMOGRIFY PLATFORM. Systematically identify the epigenetically-predicted factors required to drive and maintain cell identity. Mogrify website. https://mogrify.co.uk/science/epimogrify/
11. Our history. Mogrify Ltd website. https://mogrify.co.uk/about-us/
12. U.S. Kamaraj, J. Chen, K. Katwadi, et al. EpiMogrify models H3K4me3 data to identify signaling molecules that improve cell fate control and maintenance. Cell Systems, 11, 509–522, November 18, 2020.
My previous blog post presented a brief understanding of recent progress in direct cellular reprogramming and the reason for the development of two software platforms by Mogrify Ltd. This blog post is about further details of the two platforms, MOGRIFY and epiMOGRIFY.
About MOGRIFY
MOGRIFY was first co-developed by two scientists, Julian Gough (Professor of Computer Science in the University of Bristol) and Owen Rackham (PhD student of Julian Gough in the University of Bristol), in 2011. They began the development of the platform by using the data from the FANTOM 5 consortium, an international consortium launched to provide gene expression data in virtually all cell types across the human body.1 In 2014, they collaborated with Jose Polo from Monash University in Australia to test cell conversions in order to validate the prediction power of MOGRIFY. In 2015, based on the prediction results from MOGRIFY, the co-founders filed a patent application for the algorithm used by the computation platform, with over 30 direct cell conversions listed as example proof of what the algorithm can achieve.2 The predictions and cell conversion results were then published in 2016.3
According to the paper, predictions from MOGRIFY have been applied on 173 human cell types and 134 tissues. In order to assess the predictive power of MOGRIFY, the performance of the algorithm was first compared against well-known, previously-published human cell conversions. MOGRIFY correctly predicted the transcription factors (enzyme proteins that bind to a particular DNA sequence and regulate the expression of genes) used in previously-published direct cell conversions from human fibroblasts (the most common type of cell found in connective tissue) into induced pluripotent stem cells,4,5,6 and from B cells/ fibroblasts into macrophage-like cells.7,8 In addition, MOGRIFY’s prediction list included four of the five transcription factors (or similar factors) used in the conversion of human dermal fibroblasts into cardiomyocytes (cells responsible for generating contractile force in the intact heart).9
The predictive capabilities of MOGRIFY® were also experimentally examined using human cells. Two cell conversions, human fibroblasts to keratinocyte cells (cell within the epidermis) and adult human keratinocyte cells to micro-vascular endothelial cells (cells isolated from small vessels within skin tissue), were performed using prediction results from MOGRIFY. By examining the morphological and molecular characterization of the reprogrammed cells, the conversions were found to be successful.3
The results showed that MOGRIFY provides a practical and efficient mechanism to facilitate the reprogramming of human cells.
However, since the publication of the results in 2016, no further reports of cell conversions using predictions from MOGRIFY, have been published, or at least I was not able to find any after extensive searches.
About EpiMOGRIFY
Once the cells are converted from other cell types, it is important to maintain the reprogrammed cells’ identity, as the converted cells tend to revert back to what they were before. Difficulty maintaining the differentiated cells makes a large-scale production of these differentiated cells hard to achieve. This can be a hurdle for the development of cell therapies.
EpiMOGRIFY was developed with the aim of identifying optimal culture conditions (with suggested small molecules and chemical compounds) required to maintain cell identity or induce cell conversion in chemically-defined media. The computation algorithm incorporated gene-regulatory information data and cell epigenetic landscapes for more than 100 human cells and tissues, from the ENCODE and Epigenome Roadmap consortia, to define culture conditions that can maintain the phenotype and function of the converted cell, or that can be used to induce cell conversion.10
The EpiMOGRIFY platform was co-developed by Duke-NUS Medical School (Professors Enrico Pettreto and Owen Rackham), Monash University (Professor Jose Polo) and Mogrify Ltd. Owen Rackham and Jose Polo were the co-developers of MOGRIFY.11
Experiments validating the computational prediction in cell culture conditions for cell maintenance and differentiation have recently been published.12 Using the suggestion factors predicted from EpiMOGRIFY, astrocytes (cells commonly found in the brain) and cardiomyocytes (cells that make up heart muscle) were able to grow as effectively, in terms of cell numbers and proliferation rate, as they are in the culture conditions that have long been used. Moreover, these cells appeared to better maintain their morphology, in terms of the biological structure and the presence of key markers for the cell types, when growing in the culture conditions with biological factors suggested from EpiMOGRIFY. These results provide a fundamental proof for EpiMOGRIFY’s predictive power to suggest culture conditions that are able to maintain a stable phenotype.12
Furthermore, using the cell conversion conditions predicted from EpiMOGRIFY, neural progenitors and embryonic stem cells were able to differentiate to astrocyte cells and cardiomyocyte cells, respectively. The differentiation efficiency was as good as that of existing differentiation protocols.12 These results indicate that EpiMOGRIFY is highly accurate in identifying biological factors for cell conversion as well.
To be continued.
References
1. Our history. Mogrify Ltd website. https://mogrify.co.uk/about-us/
2. https://patents.google.com/patent/WO2017106932A1/en
3. O.J.L. Rackham, J. Firas, and H. Fang, et al. A predictive computational framework for direct reprogramming between human cell types. Nature Genetics, March 2016, Vol. 48, No. 3.
4. K. Takahashi, K. Tanabe, M. Ohnuki, et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell, 2007, 131, 861–872.
5. J. Yu, M.A. Vodyanik, K. Smuga-Otto. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science, 2007, 318, 1917–1920. 6. D. Huangfu, K. Osafune, R. Maehr, et al. Induction of pluripotent stem cells from primary human fibroblasts with only Oct4 and Sox2. Nat. Biotechnol., 2008, 26, 1269–1275.
7. H. Xie, M. Ye, R. Feng, & T. Graf. Stepwise reprogramming of B cells into macrophages. Cell, 2004, 117, 663–676.
8. F. Rapino, E.F. Robles, J.A. Richter-Larrea, et al. C/EBPα induces highly efficient macrophage transdifferentiation of B lymphoma and leukemia cell lines and impairs their tumorigenicity. Cell Reports, 2013, 3, 1153–1163.
9. J. D. Fu, N.R. Stone, L. Liu, et al. Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Stem Cell Reports, 2013, 1, 235–247.
10. epiMOGRIFY PLATFORM. Systematically identify the epigenetically-predicted factors required to drive and maintain cell identity. Mogrify website. https://mogrify.co.uk/science/epimogrify/
11. Our history. Mogrify Ltd website. https://mogrify.co.uk/about-us/
12. U.S. Kamaraj, J. Chen, K. Katwadi, et al. EpiMogrify models H3K4me3 data to identify signaling molecules that improve cell fate control and maintenance. Cell Systems, 11, 509–522, November 18, 2020.
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