Wong, K. K. Y. | Search bioRxiv

bioRxiv2021

A neural circuit basis for bilateral olfactory input-enhanced chemosensory avoidance navigation

Our understanding of how bilaterian animals utilize parallel input channels from paired sensory organs to optimize chemosensory behavior and the underlying neural circuit mechanisms are limited. Here we developed microfluidics-based behavioral and brainwide imaging platforms to study the neural integration of binasal inputs and chemosensory avoidance in larval zebrafish. We show that larval zebrafish efficiently escape from cadaverine-carrying streams by making more frequent swim bouts and larger undirected turns. Binasal inputs are strictly required for the nasal input-dependent component of klinokinesis, while each nasal input additively enhances angular orthokinesis. Throughout brain regions, including those along the olfactory processing pathways, a distributed neural representation with a wide spectrum of ipsilateral-contralateral nasal stimulus selectivity is maintained. Nonlinear sensory information gain with bilateral signal convergence is especially prominent in neurons weakly encoding unilateral cadaverine stimulus, and associated with stronger activation of sensorimotor neurons in the downstream brain regions. Collectively, these results provide insights into how the vertebrate model sums parallel input signals to guide chemosensory avoidance behavior.

neuroscience↗

bioRxiv2021

VIA: Generalized and scalable trajectory inference in single-cell omics data

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g. cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

bioinformatics↗