bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/101048

Stem cell differentiation is a stochastic process with memory

Stumpf, P. S.·Smith, R. C. G.·Lenz, M.·Schuppert, A.·Muller, F.-J.·Babtie, A.·Chan, T. E.·Stumpf, M. P. H.·Please, C. P.·Howison, S. D.·Arai, F.·MacArthur, B. D.
View original source ↗Export metadata ↓

Abstract

Pluripotent stem cells are able to self-renew indefinitely in culture and differentiate into all somatic cell types in vivo. While much is known about the molecular basis of pluripotency, the molecular mechanisms of lineage commitment are complex and only partially understood. Here, using a combination of single cell profiling and mathematical modeling, we examine the differentiation dynamics of individual mouse embryonic stem cells (ESCs) as they progress from the ground state of pluripotency along the neuronal lineage. In accordance with previous reports we find that cells do not transit directly from the pluripotent state to the neuronal state, but rather first stochastically permeate an intermediate primed pluripotent state, similar to that found in the maturing epiblast in development. However, analysis of rate at which individual cells enter and exit this intermediate metastable state using a hidden Markov model reveals that the observed ESC and epiblast-like macrostates conceal a chain of unobserved cellular microstates, which individual cells transit through stochastically in sequence. These hidden microstates ensure that individual cells spend well-defined periods of time in each functional macrostate and encode a simple form of epigenetic memory that allows individual cells to record their position on the differentiation trajectory. To examine the generality of this model we also consider the differentiation of mouse hematopoietic stem cells along the myeloid lineage and observe remarkably similar dynamics, suggesting a general underlying process. Based upon these results we suggest a statistical mechanics view of cellular identities that distinguishes between functionally-distinct macrostates and the many functionally-similar molecular microstates associated with each macrostate. Taken together these results indicate that differentiation is a discrete stochastic process amenable to analysis using the tools of statistical mechanics.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stumpf, P. S., Smith, R. C. G., Lenz, M., Schuppert, A., Muller, F.-J., Babtie, A., Chan, T. E., Stumpf, M. P. H., Please, C. P., Howison, S. D., Arai, F., MacArthur, B. D.. 2017-01-17. Stem cell differentiation is a stochastic process with memory. https://doi.org/10.1101/101048

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution

Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.

systems biology↗
bioRxiv2026

Amino Acid Metabolic Signature Links Pediatric Environmental Enteric Dysfunction to Maternal-Fetal Growth Restriction Across Two Independent Cohorts

Environmental Enteric Dysfunction (EED) is a leading contributor to childhood stunting. Most EED literature derives from postnatal pediatric cohorts, leaving unclear how much of the metabolic signature reflects acquired intestinal pathology versus growth restriction originating earlier. We applied genome-scale metabolic modeling to explore whether metabolic features of pediatric EED overlap with those of maternal plasma proteomes associated with adverse early-life growth. Duodenal transcriptomics from the Study of Environmental Enteropathy and Malnutrition (SEEM; 52 children with EED, 24 controls) and maternal plasma proteomics from the Alliance for Maternal and Newborn Health Improvement (AMANHI; n = 2,485 pregnant women) were integrated with the Human1 model using Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe), followed by flux sampling and Linear Discriminant Analysis, across five growth-related proxies (small-for-gestational-age, low birth weight, preterm birth, stunting, and wasting). Amino acid and nucleotide metabolism were prominent among the top discriminating reactions in EED and most proxies. Of the 50 reactions distinguishing EED from control, 24 (48%) were among the top reactions in at least one proxy, and 19 (38%) with low birth weight, the largest of any proxy. These exploratory findings suggest shared metabolic pathways across tissues and life stages and motivate longitudinal studies.

systems biology↗
bioRxiv2026

Network-guided identification of candidate biomarkers for COPD-associated comorbidities using deep learning and real-world transcriptomic data

Chronic obstructive pulmonary disease (COPD) frequently coexists with infections, metabolic disease, cardiovascular disease, inflammatory lung injury and lung cancer. Molecular markers derived by comparing a comorbidity with healthy controls need not identify the transition from an established COPD state to that comorbidity. We therefore developed a two-stage computational strategy that asks a narrower question: which proteins can distinguish COPD samples from samples annotated with a specific COPD-associated comorbidity? First, a Human Protein Network (HPN; 17,302 proteins and 492,195 relationships) was encoded by 20 graph-derived features. A convolutional-neural-network ensemble, termed the functional-distance model (FDM), was trained on known drug-target-indication relationships. Stored training-set predictions yielded an apparent balanced accuracy of 0.811 and Matthews correlation coefficient (MCC) of 0.627; these values describe resubstitution performance and are not an independent estimate of generalization. The COPD-centred network search evaluated 2,645 nearby proteins and retained 224 candidates using a COPD FDM score above 0.50. Candidate expression was then tested in independently assembled public patient-derived transcriptomic data using simple one-feature classifiers and cross-validation. The resulting exploratory panel contained GTF2I for bacterial infection, UBXN7 for type 2 diabetes, LIFR for hypertension, SRGN for lung neoplasm and FGF4 for pneumonitis. Across the saved cross-validation predictions, balanced accuracy ranged from 0.838 to 0.970 and MCC from 0.726 to 0.941. SRGN has direct experimental support in non-small-cell lung cancer, whereas the remaining candidates have varying indirect mechanistic support and should be regarded as hypotheses rather than established disease biomarkers. This work demonstrates an orthogonal discovery framework in which network-based functional prioritization and patient-derived molecular discrimination constrain one another. Its main contribution is methodological: it provides a reusable strategy for generating comorbidity-specific biomarker hypotheses from an index-disease state. Prospective longitudinal cohorts are required before claims of progression prediction or clinical utility can be made.

systems biology↗