Back

Vectorchip: Microfluidic platform for highly parallel bite by bite profiling of mosquito-borne pathogen transmission

Kumar, S.; Hol, F. J.; Pujhari, S.; Ellington, C.; Narayanan, H. V.; Li, H.; Rasgon, J. L.; Prakash, M.

2020-10-19 bioengineering
10.1101/2020.10.19.345603 bioRxiv
Show abstract

Mosquito bites transmit a number of human pathogens resulting in potentially fatal diseases including malaria, dengue, chikungunya, West Nile encephalitis, and Zika. Although female mosquitoes transmit pathogens via salivary droplets deposited during blood feeding on a host, very little is known about the genomic content of these nanoliter scale droplets, including the transmission dynamics of live pathogens. Here we introduce Vectorchip, a low-cost, scalable microfluidic platform for molecular interrogation of individual mosquito bites in a high-throughput fashion. An ultra-thin PDMS membrane coupled to a microfluidic chip acts as a biting interface, through which freely-behaving mosquitoes deposit saliva droplets by biting into isolated arrayed micro-wells enabling molecular interrogation of individual bites. By modulating membrane thickness, the device enables on-chip comparison of biting capacity and provides a mechanical filter allowing selection of a specific mosquito species. Utilizing Vectorchip, we show on-chip simultaneous detection of mosquito DNA as well as viral RNA from Zika infected Aedes aegypti mosquitoes - demonstrating multiplexed high-throughput screening of vectors and pathogens. Focus-forming assays performed on-chip quantify number of infectious viral particles transmitted during mosquito bites, enabling assessment of active virus transmission. The platform presents a promising approach for single-bite-resolution laboratory and field characterization of vector pathogen communities, to reveal the intricate dynamics of pathogen transmission, and could serve as powerful early warning artificial "sentinel" for mosquito-borne diseases.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.