bioRxiv Science⌕ Search

Biology subjects

Laddach, A. C.

Publications and source records attributed to Laddach, A. C..

2 recordsLinked to original sources

A branching model of cell fate decisions in the enteric nervous system

How neurogenesis and gliogenesis are coordinated during development and why mature glial cells often share properties with neuroectodermal progenitors remains unclear. Here, we have used single cell RNA sequencing to map the regulatory landscape of neuronal and glial differentiation in the mammalian enteric nervous system (ENS). Our analysis indicates that neurogenic trajectories branch directly from a linear gliogenic axis defined by autonomic neural crest cells adopting sequential states as they progressively lose their strong neurogenic bias and acquire properties of adult enteric glia. We identify gene modules associated with transcriptional programs driving enteric neurogenesis and cell state transitions along the gliogenic axis. By comparing the chromatin accessibility profile of autonomic neural crest and adult enteric glia we provide evidence that the latter maintain an epigenetic memory of their neurogenic past. Finally, we demonstrate that adult enteric glia maintain neurogenic potential and are capable of generating enteric neurons in certain contexts by activating transcriptional programs employed by early ENS progenitors. Our studies uncover a novel configuration of enteric neurogenesis and gliogenesis that enables the coordinate development of ENS lineages and provides a mechanistic explanation for the ability of enteric glia to be functionally integrated into the adult intestine and simultaneously maintain attributes of early ENS progenitors.

neuroscience↗

Multimodal single cell data integration challenge: results and lessons learned

Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges arising from sparsity, technical and biological variability, and high dimensionality of the data hinder the derivation of such mechanistic insights. To promote the innovation of algorithms for analysis of multimodal single-cell data, we organized a competition at NeurIPS 2021 applying the Common Task Framework to multimodal single-cell data integration. For this competition we generated the first multimodal benchmarking dataset for single-cell biology and defined three tasks in this domain: prediction of missing modalities, aligning modalities, and learning a joint representation across modalities. We further specified evaluation metrics and developed a cloud-based algorithm evaluation pipeline. Using this setup, 280 competitors submitted over 2600 proposed solutions within a 3 month period, showcasing substantial innovation especially in the modality alignment task. Here, we present the results, describe trends of well performing approaches, and discuss challenges associated with running the competition.

bioinformatics↗