Back

Multi omics reveals mesodermal fate bias and enables predictive cell state control in human pluripotent stem cell biomanufacturing

Colter, J.; Dang, T.; Young, D.; Dufour, A.; Lewis, I.; Murari, K.; Kallos, M. S.

2026-05-29 bioengineering
10.64898/2026.05.26.727850 bioRxiv
Show abstract

Despite accelerating interest in using human induced pluripotent stem cell (hiPSC)-derived products for disease modeling and therapeutic development, there is substantial evidence that conventional culture approaches do not fully recapitulate natural embryonic nor lineage-committed states. It remains poorly understood how in vitro environmental conditions cause divergence from natural developmental trajectories, and current strategies emphasize restricted characterization of phenotype without appreciating the complexity of biology in maintaining pluripotency and driving differentiation. To address this knowledge gap, we examined hiPSC cell state during short-term culture in stirred-suspension bioprocesses under varying oxygen and agitation conditions. We profiled intracellular metabolic, transcriptional, and proteomic changes to characterize cellular responses to engineered environments and implications for cell phenotype. Using a random forest framework, we modeled population dynamics over time across metabolic and transcriptional programs and mapped those predictions onto hallmark biological signatures. This integrative approach captures and identifies environmentally reinforced programs, offering a framework to guide optimization of pluripotent cell state maintenance and differentiation.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Stem Cell Reports
130 papers in training set
Top 0.1%
17.0%
2
Advanced Science
286 papers in training set
Top 0.1%
15.0%
3
Cell Systems
201 papers in training set
Top 0.4%
7.9%
4
Nature Communications
5641 papers in training set
Top 24%
6.7%
5
Science Advances
1243 papers in training set
Top 3%
6.7%
50% of probability mass above
6
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 15%
3.4%
7
Journal of Biological Engineering
12 papers in training set
Top 0.1%
2.4%
8
iScience
1154 papers in training set
Top 11%
2.4%
9
Cell Reports Methods
165 papers in training set
Top 1%
2.4%
10
Communications Biology
993 papers in training set
Top 10%
2.1%
11
Scientific Reports
3612 papers in training set
Top 51%
1.9%
12
Biotechnology and Bioengineering
53 papers in training set
Top 0.4%
1.7%
13
Cell Reports
1498 papers in training set
Top 19%
1.7%
14
eLife
5828 papers in training set
Top 50%
1.7%
15
Nature Biomedical Engineering
47 papers in training set
Top 0.7%
1.7%
16
Genome Biology
637 papers in training set
Top 6%
1.5%
17
Cell
431 papers in training set
Top 7%
1.3%
18
EMBO Reports
263 papers in training set
Top 5%
1.1%
19
Computational and Structural Biotechnology Journal
242 papers in training set
Top 5%
1.1%
20
Nature Chemical Biology
119 papers in training set
Top 2%
1.1%
21
PLOS Computational Biology
1863 papers in training set
Top 18%
1.1%
22
PLOS ONE
5266 papers in training set
Top 59%
1.0%
23
npj Systems Biology and Applications
125 papers in training set
Top 2%
0.8%
24
BMC Methods
15 papers in training set
Top 0.2%
0.8%
25
ACS Synthetic Biology
287 papers in training set
Top 2%
0.8%
26
Biofabrication
36 papers in training set
Top 0.6%
0.8%