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A computational guide to efficient cell differentiation

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Published on Friday, 12 March 2021

A collaborative effort, led by Prof. Antonio del Sol, head of Computational Biology groups at the Luxembourg Centre for Systems Biomedicine (LCSB) and at CIC bioGUNE, a member of the Basque Research and Technology Alliance, and Prof. George Church at Harvard’s Wyss Institute for Biologically Inspired Engineering and Harvard Medical School (HMS), has developed a computational tool which significantly helps increase the efficiency of cell conversions. The team demonstrated their approach generates higher numbers of natural killer cells used in immune therapies, and of melanocytes used in skin grafts, than other methods. It also allowed to generate for the first time breast mammary epithelial cells, whose availability would be highly desirable for the repopulation of surgically removed mammary tissue.

The study is published in Nature Communications.

Reprogramming cells is still a challenge

There is a great need to generate various types of cells for use in new therapies to replace tissues that are lost due to disease or injuries, or for studies outside the human body to improve our understanding of how organs and tissues function in health and disease. Many of these efforts start with human induced pluripotent stem cells (iPSCs) that, in theory, have the capacity to differentiate into virtually any cell type in the right culture conditions. The 2012 Nobel Prize awarded to Shinya Yamanaka recognised his discovery of a strategy that can reprogram adult cells to become iPSCs by providing them with a defined set of gene-regulatory transcription factors (TFs). However, going from there to efficiently generate a wide range of cell types with tissue-specific differentiated functions for biomedical applications has remained a challenge.

While the expression of cell type-specific TFs in iPSCs is the most often used cellular conversion technology, the efficiencies of guiding iPSC to the fully functional differentiated state of, for example, a specific heart, brain, or immune cell are currently low, mainly because the most effective TF combinations cannot be easily pinpointed. This is where this new computational tool - called IRENE – comes into play. It helps significantly increase the efficiency of cell conversions by predicting highly effective combinations of cell type-specific TFs.

Bringing together a comprehensive library of TFs and an innovative algorithm

“In our group, the study naturally built on the “TFome” project, which assembled a comprehensive library containing 1,564 human TFs as a powerful resource for the identification of TF combinations with enhanced abilities to reprogram human iPSCs to different target cell types,” says George Church, lead of the Wyss Institute’s Synthetic Biology platform and Professor of Genetics at HMS. “The efficacy of this new computational algorithm will boost a number of our tissue engineering efforts, and as an open resource can do the same for many researchers in this burgeoning field.”

Several computational tools have been developed to predict combinations of TFs for specific cell conversions, but these are almost exclusively based on the analysis of gene expression patterns in many cell types. Missing in these approaches was a view of the epigenetic landscape, the organisation of the genome itself around genes and on the scale of entire chromosome sections which goes far beyond the sequence of the naked genomic DNA. 

“The changing epigenetic landscape in differentiating cells predicts areas in the genome undergoing physical changes that are critical for key TFs to gain access to their target genes. Analysing these changes can inform more accurately about gene regulatory networks and their participating TFs that drive specific cell conversions,” says co-first author Dr Evan Appleton. Appleton is a Postdoctoral Fellow in Church’s group who joined forces with Dr Sascha Jung from Del Sol’s group in the new study. “Our collaborators had developed a computational approach that integrated those epigenetic changes with changes in gene expression to produce critical TF combinations as an output which we were in an ideal position to test.”

Validating computational results in the lab

To experimentally validate TF combinations prioritised by IRENE, the researchers focused on three target cell types with clinical relevance. They chose human mammary epithelial cells (HMECs) as a first cell type. Thus far HMECs are obtained from a tissue environment, dissociated, and transplanted where breast tissue has been resected. HMECs generated from patients’ cells, via an intermediate iPSC stage, could provide a means for less invasive and more effective breast tissue regeneration. One of the combinations that was generated by IRENE enabled the team to convert 14% of iPSCs into differentiated HMECs in iPSC-specific culture media, showing that the provided TFs were sufficient to drive the conversion without help from additional factors.

The team then turned their attention to melanocytes, which can provide a source of cells in cellular grafts to replace damaged skin. Two out of four combinations were able to increase the efficiency of melanocyte conversion by 900% compared to iPSCs grown without the TFs. Finally, the researchers compared combinations of TFs prioritised by IRENE to generate natural killer (NK) cells with a state-of-the-art differentiation method based on cell culture conditions alone. Immune NK cells have been found to improve the treatment of leukemia. The researchers’ approach outperformed the standard with five out of eight combinations increasing the differentiation of NK cells with critical markers by up to 250%.

“Our research team composed of scientists from the LCSB and CIC bioGUNE has a long-standing expertise in developing computational methods to facilitate cell conversion. IRENE is an additional resource in our toolbox and one for which experimental validation has demonstrated it substantially increased efficiency in most tested cases,” explains Prof. Antonio Del Sol. “Our fundamental research should ultimately benefit the patients and we are thrilled that IRENE could enhance the production of cell sources readily usable in therapeutic applications, such as cell transplantation and gene therapies.”


Reference: Jung, S., Appleton, E., Ali, M. et al. A computer-guided design tool to increase the efficiency of cellular conversions. Nat Commun 12, 1659 (2021). https://doi.org/10.1038/s41467-021-21801-4

The study is funded by the European Union’s Horizon 2020 research and innovation programme under grant# 643417, the FunGCAT program from the Office of the Director of National Intelligence Advanced Research Projects Activity, via the Army Research Office, under award# W911NF-17-2-0089 and the EGL Charitable Foundation. Church is a co-founder of and has equity in GC Therapeutics, which commercialises the TFome project.