Heterogeneity of neurons reprogrammed from spinal cord astrocytes by the proneural factors Ascl1 and Neurogenin2

Kempf, J.; Knelles, K.; Hersbach, Bob A.; Petrik, David; Riedemann, Therese; Bednarova, V.; Janjic, Aleksandar; Simon-Ebert, Tatiana; Enard, Wolfgang; Smialowski, Pawel; Götz, Magdalena; Masserdotti, Giacomo

doi:10.1016/j.celrep.2021.109409

Cited by 29 publications

(17 citation statements)

References 90 publications

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“…Thus, both cell types make attractive starting cell populations for neuronal reprogramming in vivo. molecular insights into cell trajectories during direct reprogramming in cell culture (Cates et al, 2021;Horisawa et al, 2020;Karow et al, 2018;Kempf et al, 2021;Kim et al, 2021;Stone et al, 2019;Treutlein et al, 2016;Wang et al, 2021b;Zhou et al, 2019).…”

Section: Reprogramming Trajectoriesmentioning

confidence: 99%

Reprogramming cellular identity in vivo

2022

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Cellular identity is established through complex layers of genetic regulation, forged over a developmental lifetime. An expanding molecular toolbox is allowing us to manipulate these gene regulatory networks in specific cell types in vivo. In principle, if we found the right molecular tricks, we could rewrite cell identity and harness the rich repertoire of possible cellular functions and attributes. Recent work suggests that this rewriting of cell identity is not only possible, but that newly induced cells can mitigate disease phenotypes in animal models of major human diseases. So, is the sky the limit, or do we need to keep our feet on the ground? This Spotlight synthesises key concepts emerging from recent efforts to reprogramme cellular identity in vivo. We provide our perspectives on recent controversies in the field of glia-to-neuron reprogramming and identify important gaps in our understanding that present barriers to progress.

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Section: Reprogramming Trajectoriesmentioning

confidence: 99%

Reprogramming cellular identity in vivo

2022

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“…Since both Rnd2 and Rnd3 promote RhoA inactivation, Neurog2 and Ascl1 likely create cell states with increased capacity for axon elongation (Figure 3b). And finally, (iv) bHLH factors play a key role in achieving successful cell conversion during grafting and reprogramming (Caiazzo et al, 2011;Chen et al, 2020;Chouchane et al, 2017;di Val et al, 2017;Fukuoka et al, 2021;Guo et al, 2014;Jorstad et al, 2017;Karow et al, 2018;Kempf et al, 2021;Lentini et al, 2021;Liu et al, 2013Liu et al, , 2015Masserdotti et al, 2015;Matsuda et al, 2019;Pang et al, 2011;Puls et al, 2020;Raina et al, 2020;Torper et al, 2013;Wu et al, 2020) (Table 2). Further, bHLH family members control the expression of several other molecules that were identified to affect wiring (e.g., Adcy1, Slit1, Stat3, and Tle1, expression of which was changed by Id2 overexpression) (Luo et al, 2021), and bHLH factors have been also associated with other, non bHLH molecules related to neurogenesis, neural differentiation, axon growth or sprouting (Table 2).…”

Section: Gao Et Al (2009)mentioning

confidence: 99%

Circuit formation in the adult brain

Seng

Luo

Földy

2022

Eur J of Neuroscience

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Neurons in the mammalian central nervous system display an enormous capacity for circuit formation during development but not later in life. In principle, new circuits could be also formed in adult brain, but the absence of the developmental milieu and the presence of growth inhibition and hundreds of working circuits are generally viewed as unsupportive for such a process. Here, we bring together evidence from different areas of neuroscience—such as neurological disorders, adult‐brain neurogenesis, innate behaviours, cell grafting, and in vivo cell reprogramming—which demonstrates robust circuit formation in adult brain. In some cases, adult‐brain rewiring is ongoing and required for certain types of behaviour and memory, while other cases show significant promise for brain repair in disease models. Together, these examples highlight that the adult brain has higher capacity for structural plasticity than previously recognized. Understanding the underlying mechanisms behind this retained plasticity has the potential to advance basic knowledge regarding the molecular organization of synaptic circuits and could herald a new era of neural circuit engineering for therapeutic repair.

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“…Consistent with expectations, the most similar experiments were intra-TR comparisons from the same research group, led by a pair of ASCL1 overexpression experiments (r = 0.95, 410/500 by up-regulated; GSE153823: Ali et al, 2020). As for ChIP-seq, experiments from different groups but comparable contexts showed elevated similarity, such as two experiments overexpressing Ascl1 in astrocyte-to-neuron conversions ( r = 0.60, 222/500 overlap of up-regulated genes; GSE174238: Kempf et al, 2021; GSE132674: Rao et al, 2021). We also found examples of anti-correlative patterns that aligned with the opposing roles of the perturbations, typically involving a MECP2 overexpression versus a MECP2 loss-of-function (e.g., r = -0.66, 49/500 by p-value; GSE126640: Cholewa-Waclaw et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

Morin

Chu

Sharma

et al. 2022

Preprint

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Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. While research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we demonstrate a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

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Heterogeneity of neurons reprogrammed from spinal cord astrocytes by the proneural factors Ascl1 and Neurogenin2

Cited by 29 publications

References 90 publications

Reprogramming cellular identity in vivo

Reprogramming cellular identity in vivo

Circuit formation in the adult brain

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

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