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Mueller, A. K.

Publications and source records attributed to Mueller, A. K..

2 recordsLinked to original sources

Generation of human appetite-regulating neurons and tanycytes from stem cells

The balance between energy intake and expenditure is controlled by the hypothalamus, a small brain region characterised by high neuronal diversity. Specifically, the arcuate nucleus (ARC) and ventromedial hypothalamus (VMH) are key hypothalamic nuclei controlling appetite through behavioural response to circulating humoral signals. Yet, despite their physiological importance, the cellular and functional characteristics of this highly specialised neural region has been studied mainly in animals due to a lack of human models. Here, we fine-tuned the differentiation of human pluripotent stem cells toward the ARC and VMH hypothalamic nuclei and identified key subtype-specific progenitor markers of these subregions. We demonstrate that the timing for initiation and termination of bone morphogenetic protein (BMP) signalling is essential for controlling subregional specification of tuberal hypothalamic progenitors along the anterior-posterior axis, balancing VMH versus ARC fates. A particular population of SHH-/NKX2.1+/FGF10high/RAXhigh/TBX3high posterior tuberal progenitors was identified as the source for generation of ARC-associated agouti-related peptide (AGRP) neurons and tanycytes whilst anterior tuberal SHH+/NKX2.1+/FGF10low/RAXlow/TBX3low progenitors generated VMH phenotypes including NR5A1 neurons. Upon maturation in vitro and in xenografts, ARC-patterned progenitors gave rise to key appetite-regulating cell types including those producing AGRP, prepronociceptin (PNOC), growth hormone-releasing hormone (GHRH), thyrotropin-releasing hormone (TRH) and pro-opiomelanocortin (POMC), as well as tanycyte glial cells. Differentiated ARC cultures showed high transcriptomic similarity to the human ARC and displayed evidence of functionality by AGRP secretion and responsiveness to leptin and fibroblast growth factor 1 (FGF1). In summary, our work provides insights into the developmental lineages underlying hypothalamic subregional specification and enables access to highly characterised human ARC and VMH cultures, which will provide novel opportunities for investigating the cellular and molecular pathways triggered by obesity-associated genetic variants and weight-regulating stimuli.

neuroscience↗

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset.

Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/504502v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@1c6579forg.highwire.dtl.DTLVardef@a2bbecorg.highwire.dtl.DTLVardef@1dfa41aorg.highwire.dtl.DTLVardef@18ee67f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗