scholarly journals De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

2016 ◽  
Vol 19 (2) ◽  
pp. 266-277 ◽  
Author(s):  
Dominic Grün ◽  
Mauro J. Muraro ◽  
Jean-Charles Boisset ◽  
Kay Wiebrands ◽  
Anna Lyubimova ◽  
...  
2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Nasna Nassir ◽  
Asma Bankapur ◽  
Bisan Samara ◽  
Abdulrahman Ali ◽  
Awab Ahmed ◽  
...  

Abstract Background In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of ‘brain to behaviour’ pathogenic mechanisms, remains largely unknown. Methods We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases). Results We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10–11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10–11, oligodendrocyte, p < 1.31 × 10–09). Conclusion Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.


2019 ◽  
Vol 10 ◽  
Author(s):  
Melissa Galinato ◽  
Kristen Shimoda ◽  
Alexis Aguiar ◽  
Fiona Hennig ◽  
Dario Boffelli ◽  
...  

Cell Reports ◽  
2021 ◽  
Vol 34 (10) ◽  
pp. 108850
Author(s):  
Punn Augsornworawat ◽  
Kristina G. Maxwell ◽  
Leonardo Velazco-Cruz ◽  
Jeffrey R. Millman

2021 ◽  
Author(s):  
Peijie Zhou ◽  
Shuxiong Wang ◽  
Tiejun Li ◽  
Qing Nie

AbstractAdvances of single-cell technologies allow scrutinizing of heterogeneous cell states, however, analyzing transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique for the underlying stochastic dynamical systems that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transition, and distinguishes meta-stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using the coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. Mathematical analysis reveals consistency of the method with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms and benchmarking with seven existing tools, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.


Sign in / Sign up

Export Citation Format

Share Document