henhaobio
Sign in

bioRxivNeurosciencePreprint

deCLUTTER2+ pipeline to analyze calcium traces in a novel stem cell model for ventral midbrain patterned astrocytes

Martyna M. GrochowskaiD, Federico FerraroiD, Ana Carreras MascaroiD, Domenico NataleiD, Amber B Winkelaar, Valerie Boumeester, Guido J. BreedveldiD, Vincenzo BonifatiiD, Wim J. MandemakersiD

PDFDOI

Abstract

Astrocytes are the most populous cell type of the human central nervous system and are essential for physiological brain function. Increasing evidence suggests multiple roles for astrocytes in Parkinsons disease (PD), nudging a shift in the research focus, which historically pivoted around the ventral midbrain dopaminergic neurons (vmDANs). Studying human astrocytes and other cell types in vivo remains technically and ethically challenging. However, in vitro reprogrammed human stem cell-based models provide a promising alternative. Here, we describe a novel protocol for astrocyte differentiation from human stem cell-derived vmDANs-generating progenitors. This protocol simulates the regionalization, gliogenic switch, radial migration, and final differentiation that occur in the developing human brain. We have characterized the morphological, molecular, and functional features of these ventral midbrain astrocytes with a broad palette of techniques. In addition, we have developed a new pipeline for calcium imaging data analysis called deCLUTTER2+ (deconvolution of Ca2+ fluorescent patterns) that can be used to discover spontaneous or cue-dependent patterns of Ca2+ transients. Altogether, our protocol enables the characterization of the functional properties of human ventral midbrain astrocytes under physiological conditions and in PD.

Citations
0

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISLendemeijer '24TCW '17Voulgaris '22Krohn '26Soubannier '22Kane '17
65% semantic matchNeuroscience
Bas Lendemeijer, Maurits Unkel, Hilde Smeenk+12 more·eNeuro·Sep 1, 2024· 28 citations