An official website of the United States government
Abstract The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross‐validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large‐scale manufacturing of regenerative medicines and cell therapies.
more »
« less
Stochastic biological system-of-systems modelling for iPSC culture
Abstract Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.
more »
« less
- Award ID(s):
- 2100442
- PAR ID:
- 10484933
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Induced pluripotent stem cells can utilize lactate as a metabolic substrate to support proliferationAbstract Human‐induced pluripotent stem cells (iPSCs) hold the promise to improve cell‐based therapies. Yet, to meet rising demands and become clinically impactful, sufficient high‐quality iPSCs in quantity must be generated, a task that exceeds current capabilities. In this study, K3 iPSCs cultures were examined using parallel‐labeling metabolic flux analysis (13C‐MFA) to quantify intracellular fluxes at relevant bioprocessing stages: glucose concentrations representative of initial media concentrations and high lactate concentrations representative of fed‐batch culture conditions, prior to and after bolus glucose feeds. The glucose and lactate concentrations are also representative of concentrations that might be encountered at different locations within 3D cell aggregates. Furthermore, a novel method was developed to allow the isotopic tracer [U‐13C3] lactate to be used in the13C‐MFA model. The results indicated that high extracellular lactate concentrations decreased glucose consumption and lactate production, while glucose concentrations alone did not affect rates of aerobic glycolysis. Moreover, for the high lactate cultures, lactate was used as a metabolic substrate to support oxidative mitochondrial metabolism. These results demonstrate that iPSCs have metabolic flexibility and possess the capacity to metabolize lactate to support exponential growth, and that high lactate concentrations alone do not adversely impact iPSC proliferation.more » « less
-
Abstract The clinical translation of mesenchymal stem cells (MSCs) is limited by population heterogeneity and inconsistent responses to engineered signals. Specifically, the extent in which MSCs respond to mechanical cues varies significantly across MSC lines. Although induced pluripotent stem cells (iPSCs) have recently emerged as a novel cell source for creating highly homogeneous MSC (iMSC) lines, cellular mechanosensing of iMSCs on engineered materials with defined mechanics is not well understood. Here, we tested the mechanosensing properties of three human iMSC lines derived from iPSCs generated using a fully automated platform. Stiffness-driven changes in morphology were comparable between MSCs and iMSCs cultured atop hydrogels of different stiffness. However, contrary to tissue derived MSCs, no significant changes in iMSC morphology were observed between iMSC lines atop different stiffness hydrogels, demonstrating a consistent response to mechanical signals. Further, stiffness-driven changes in mechanosensitive biomarkers were more pronounced in iMSCs than MSCs, which shows that iMSCs are more adaptive and responsive to mechanical cues than MSCs. This study reports that iMSCs are a promising stem cell source for basic and applied research due to their homogeneity and high sensitivity to engineered mechanical signals.more » « less
-
Abstract Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.more » « less
-
The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network–based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group–specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell–tissue and cell–cell metabolic communications.more » « less
