Computational modeling of light processing in the habenula and dorsal raphe based on laser ablation of functionally-defined cells.

BACKGROUND: The habenula is a major regulator of serotonergic neurons in the dorsal raphe, and thus of brain state. The functional connectivity between these regions is incompletely characterized. Here, we use the ability of changes in irradiance to trigger reproducible changes in activity in the habenula and dorsal raphe of zebrafish larvae, combined with two-photon laser ablation of specific neurons, to establish causal relationships. RESULTS: Neurons in the habenula can show an excitatory response to the onset or offset of light, while neurons in the anterior dorsal raphe display an inhibitory response to light, as assessed by calcium imaging. The raphe response changed in a complex way following ablations in the dorsal habenula (dHb) and ventral habenula (vHb). After ablation of the ON cells in the vHb (V-ON), the raphe displayed no response to light. After ablation of the OFF cells in the vHb (V-OFF), the raphe displayed an excitatory response to darkness. After ablation of the ON cells in the dHb (D-ON), the raphe displayed an excitatory response to light. We sought to develop in silico models that could recapitulate the response of raphe neurons as a function of the ON and OFF cells of the habenula. Early attempts at mechanistic modeling using ordinary differential equation (ODE) failed to capture observed raphe responses accurately. However, a simple two-layer fully connected neural network (NN) model was successful at recapitulating the diversity of observed phenotypes with root-mean-squared error values ranging from 0.012 to 0.043. The NN model also estimated the raphe response to ablation of D-off cells, which can be verified via future experiments. CONCLUSION: Lesioning specific cells in different regions of habenula led to qualitatively different responses to light in the dorsal raphe. A simple neural network is capable of mimicking experimental observations. This work illustrates the ability of computational modeling to integrate complex observations into a simple compact formalism for generating testable hypotheses, and for guiding the design of biological experiments.

Microfluidic compartmentalization to identify gene biomarkers of infection.

Infectious diseases caused by pathogens, such as SARS-COV, H7N9, severe fever with thrombocytopenia syndrome virus, and human immunodeficiency virus, have fatal outcomes with common features of severe fever and subsequent bacterial invasion progressing to multiorgan failure. Gene biomarkers are promising to distinguish specific infections from others with similar presenting symptoms for the prescription of correct therapeutics, preventing pandemics. While routine laboratory methods based on polymerase chain reaction (PCR) to measure gene biomarkers have provided highly sensitive and specific viral detection techniques over the years, they are still hampered by their precision and resource intensity precluding their point-of-care use. Recently, there has been growing interest in employing microfluidic technologies to advance current methods for infectious disease determination via gene biomarker measurements. Here, based on the requirement of infection detection, we will review three microfluidic approaches to compartmentalize gene biomarkers: (1) microwell-based PCR platforms; (2) droplet-based PCR; and (3) point-of-care devices including centrifugal chip, SlipChip, and self-powered integrated microfluidic point-of-care low-cost enabling chip. By capturing target genes in microwells with a small sample volume (∼µl), sensitivity can be enhanced. Additionally, with the advance of significant sample volume minimization (∼pl) using droplet technology, gene quantification is possible. These improvements in cost, automation, usability, and portability have thereby allowed point-of-care applications to decentralize testing platforms from laboratory-based settings to field use against infections.