The translational opportunities for stem cell research were wholeheartedly explored at the ISSCR symposium this year. By embracing a wealth of lectures by academic and clinical researches as well as industry representatives, it demonstrated the riches to be mined from the sharing of ideas at an interdisciplinary level and on an international platform. This was highlighted in the lively question and answer sessions that revealed the diverse expertise of the audience as well as the speakers.

Of particular note were the overarching themes that cut across all the sessions, partly as a result of new knowledge in stem cell research throwing up new challenges. Two familiar recurring motifs addressed in the sessions on modelling tissue development, cellular disease models, emerging technologies and drug discovery, were the themes of scalability and reproducibility. Scalability refers to the ability to manufacture enough cells to get a therapeutic response for as many patients as possible, and reproducibility is doing this over and over again with standardised cells in order to get a reliable, consistent and safe outcome.

Music to any biomedical engineers’ ears was the offer of potential solutions to these hurdles; solutions that showed potential in providing reliable platforms for drug discovery, disease modelling and delivering cell therapies to patients. These were presented as novel solutions and technologies; from the development of animal free, culture media to self-organising, chemically defined 3D organoids that tune their size and shape according to their stage of development—whether they are proliferating or differentiating. Tackling reproducibility was not only the remit of scientists working at the bench but was explored in statistical analysis of unwanted variation in omics data. In addition to identifying cross-site variation between laboratories when neural differentiation of iPSC lines were compared, the potential of a statistical protocol that normalises the data, removing unwanted variation while retaining variation associated with biological covariate analysis, was discussed ¹.

The organising committee are to be congratulated for co-ordinating a symposium with speakers that are advancing the field of stem cell research from bench-to-bedside. In addition to many of the speakers addressing the pre-clinical criteria for reliable manufacture of stem cells — which included assurance of their homogeneity and phenotype — a prospective clinical trial was introduced. This will evaluate whether embryonic stem cell derived dopaminergic neurons relieve patients’ suffering from Parkinson’s disease.

While it is exciting to see the progress of burgeoning clinical trials, the symposium was encouragingly and firmly grounded in the need to understand developmental biology. Many speakers, and indeed posters, presented previously difficult to expand functional cell types or hard-to-construct and previously unseen in vitro tissue constructs that were created by recapitulating stages of embryonic development or by identifying the integral sequence of transcription and growth factors in the developmental pathway.

It is an important reminder that successful, safe and functional stem cell therapies are built on well characterised cell phenotypes. For instance, the lack of understanding of skeletal stem cell heterogeneity might not be the only reason why most clinical trials of mesenchymal stem cells fail to progress past Phase I and II, but I believe that going back to first principles to address these hitherto unanswered questions is a great philosophy on which to push the field of stem cell translation forward and bodes well for the future. Basel, Switzerland was a beautiful location for the symposium and added to the superb hospitality and delicious food provided by our hosts at the venue.

International Society for Stem Cell Research (ISSCR) international symposium, presented by ISSCR, StemBANCC, and the Basel Stem Cell Network and held on 27^th February to 1^st March 2017 in Basel, Switzerland

References

1. Risso, D. et al. Normalization of RNA-seq data using factor analysis of control genes or samples. Nature Biotechnology 32, 896–902 (2014)

Banner Image by RebecaCuesta (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons