Random Forests; computational modeling; donor characteristics; human mesenchymal stromal cell; in vitro cell expansion
Abstract :
[en] BACKGROUND: Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics. METHODS: In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics. RESULTS: Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups. DISCUSSION: In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Mehrian, Mohammad
Lambrechts, Toon
Marechal, Marina
Luyten, Frank P.
Papantoniou, Ioannis
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Language :
English
Title :
Predicting in vitro human mesenchymal stromal cell expansion based on individual donor characteristics using machine learning.
Publication date :
2020
Journal title :
Cytotherapy
ISSN :
1465-3249
eISSN :
1477-2566
Publisher :
Taylor & Francis, United Kingdom
Volume :
22
Issue :
2
Pages :
82-90
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 772418 - INSITE - Development and use of an integrated in silico-in vitro mesofluidics system for tissue engineering
Funders :
ERC - European Research Council EC - European Commission
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