Variation of human neural stem cells generating organizer statesin vitrobefore committing to cortical excitatory or inhibitory neuronal fates

Abstract

<jats:title>SUMMARY</jats:title><jats:p>Better understanding the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here we used RNA-sequencing, cell imaging and lineage tracing of mouse and human <jats:italic>in vitro</jats:italic> NSCs to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs developed from pluripotency <jats:italic>in vitro</jats:italic>, they first transitioned through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines varied in these early patterning states leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analysis of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon.</jats:p>

Expression bias in retinoic acid responsive genes defines variations in neural differentiation of human pluripotent stem cells

“…Our data and the recent identification of time- and lineage-specific eQTLs that influence differentiation (Cuomo et al, 2020) suggest that the interaction between constitutive and dynamic control of gene expression is central to the lineage decisions that underlie cell line-specific differentiation bias. SOX21 is highly represented in the neuroectoderm initiating pattern P15 and is differentially expressed across hPSC lines in both SR and NSB conditions (Figure 1E and S1G), suggesting it may play a key role in generating telencephalic organizer states preceding neurogenesis (Micali et al, 2020). Previously, we have shown that SOX21 is required for reprogramming to the pluripotent state and regulates the anterior-posterior identity in the adult mouse intestine (Kuzmichev et al, 2012).…”

“…Following the discovery of reprogramming factors (Takahashi and Yamanaka, 2006), functional heterogeneity in hiPSC differentiation potential has been a continuing focus of interest (Hu et al, 2010; Kanton et al, 2019; Mariani et al, 2015; Micali et al, 2020; Strano et al, 2020; Wang et al, 2020). Initial studies noted functional consequences of epigenetic memory of parental cell types in reprogrammed cells (Cahan and Daley, 2013; Kim et al, 2010; Kim et al, 2011).…”

“…The transcriptional architecture of self-organization that we define here indicates that signatures of regional embryonic organizers exist within stem cells and precede classical anatomical organizer structures. We have previously reported a regional neural fate bias spanning the dorso-ventral (D/V) telencephalic axis, which was also divergent in hiPSC lines from a single donor (Micali et al, 2020). We showed that this lineage bias in neural stem cells was the result of the differential emergence of transcriptional signatures that lead to the formation of the cortical hem and other organizing centers.…”

“…The urgency to answer this question is illustrated by recent work reporting developmental differences between hPSC lines in the generation of neural precursors and their possible implications in the etiology of neurodevelopmental disorders (Burke et al, 2020; Kanton et al, 2019; Mariani et al, 2015; Micali et al, 2020; Strano et al, 2020; Wang et al, 2020). Here we employ cellular and bioinformatic approaches to define functional variation in hPSC lines during in vitro morphogenesis as they traverse the earliest events in forebrain development.…”

Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets

“…Thus, it is of scientific interest to harness the collective discovery potential of such complex and growing data resources. For example, in biomedical research, high-dimensional gene expression and epigenetic data are produced to gain insight into cellular processes and disease mechanisms (Johnson et al, 2020; Micali et al, 2020; Clark et al, 2019; Stein-O’Brien et al, 2019). Another example is in the study of Alzheimer’s disease where recent efforts have been focusing on combining brain imaging data, genetic data, as well as clinical outcomes in predicting disease (Nathoo et al, 2019).…”