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Guan, Y. L.

Publications and source records attributed to Guan, Y. L..

3 recordsLinked to original sources

GPR88 localization to primary cilia in neurons is cell-type specific.

GPR88 is an orphan G protein-coupled receptor that regulates dopamine neurotransmission and is a target for neuropsychiatric disorders. In addition to the somatic membrane, GPR88 can localize to the primary cilium, a membrane microdomain known for dynamically enriching receptors and signaling molecules. However, the distribution of GPR88 in neuronal primary cilia remains uncharacterized. Here we characterize GPR88 distribution at primary cilia in two brain areas. We show that in the striatum, GPR88 localizes both to somatodendritic and primary cilia compartments on inhibitory GABAergic medium spiny neurons. In contrast, in the somatosensory cortex, GPR88 localizes to somatodendritic and nuclear compartments of excitatory spiny stellate neurons which possess primary cilia that exclude GPR88. Additionally, we found that cilia formation and length were similar between GPR88 knockout and wild-type animals. Together, we provide key evidence for neuronal cell-type specific regulation of GPR88 localization to primary cilia, suggesting neuron subtype specific regulatory mechanisms govern receptor ciliary targeting in the brain.

neuroscience↗

D2Sim: A Computational Simulator for NanoporeSequencing based DNA Data Storage

DNA data storage has gained significant attention because of its high storage density and durability. However, errors during the storage and reading processes compromise data integrity, prompting research into error correction strategies. Researchers have explored physical (data copies) and logical redundancy (added redundancy in error-correcting codes) to mitigate errors. Evaluating these designs and reconstruction methods typically involves time-consuming and costly trials. To streamline this process, we designed a computational channel simulator, namely D2Sim, for Nanopore sequencing-based DNA data storage. This simulator mimics real experiments, generating data with certain redundancy and errors at the receiver. Integrated with DeepSimulator, D2Sim outputs signals closely resembling actual signals of Nanopore-based DNA storage. Comparative analysis reveals that the proposed simulator yields 16.7% to 88.7% lower sample difference deviations than signals from DeepSimulator alone. This cost-effective and time-efficient tool facilitates the assessment of physical and logical redundancy for data reconstruction in DNA data storage without the need for real-time experiments.

bioengineering↗

Modelling, characterization of data-dependent and process-dependent errors in DNA data storage

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process. Contactpoh.chuehloo@nus.edu.sg

bioinformatics↗