Scientists often sacrifice their own skin to study how mosquitos drink blood. They allow mosquitos to bite them in laboratory settings so they can observe the insects’ feeding behavior. By observing blood feeding, scientists hope to find ways to prevent deadly diseases like malaria, which is transmitted by bites from mosquitos carrying the malaria parasite. These studies are not only unpleasant for the volunteers, they also have important limitations. For example, it is too risky to use pathogen-infected mosquitos that could make the volunteers sick.

A device called the biteOscope developed by Hol et al. may give scientists and their skin a reprieve. The device has a transparent skin-like covering that attracts mosquitos and supplies them an artificial blood meal when they bite. The device captures high-resolution images of the insects’ behavior. It is small enough to fit in a backpack when disassembled, costs about $900 to $3,500 US dollars, and is suitable for use in the laboratory or in the field. Using machine-learning techniques, Hol et al. also developed an automated system for analyzing the images.

The researchers tested the device on four types of disease-transmitting mosquitos. In one set of experiments, Anopheles mosquitos were recorded interacting with a biteOscope partially coated with an insect repellent called DEET. The images captured by the biteOscope showed that the mosquitos are attracted to the warm surface and land on the part coated with DEET. But when their legs come in contact with the repellent, they leave.

The biteOscope provides scientists a new way to study blood feeding, even in mosquitos infected with dangerous pathogens. It might also be used to test new ways to prevent mosquitos from biting and spreading disease. Because the device is portable and relatively inexpensive, it may enable larger studies in a variety of settings.

Blood feeding is essential for the reproduction of many mosquito species, and in the process, mosquitoes transmit myriad pathogens to their (human) host. Yet, despite being the focal point of pathogen transmission, many aspects of blood feeding remain ill understood. The initial step in obtaining a blood meal, flying toward a host, is relatively well characterized (Dekker and Cardé, 2011; McMeniman et al., 2014; van Breugel et al., 2015). The steps that unfold after a mosquito has landed on a host, however, are much less understood. Once landed, mosquitoes exhibit exploratory bouts during which the legs and proboscis frequently contact the skin (Jones and Pilitt, 1973; De Jong and Knols, 1995; Clements, 2013). An increasing body of literature reports the presence of receptors involved in contact-dependent sensing on the legs and proboscis (Sparks et al., 2013; Matthews et al., 2019; Dennis et al., 2019), suggesting that these appendages evaluate the skin surface and thus serve an important role in bite-site selection. Yet, the role and mechanism of contact-dependent sensing in blood feeding is largely unclear (Benton, 2017). In addition to the body parts that come in contact with the skin surface, the skin piercing labrum also serves as a chemosensory organ, guiding blood feeding in currently unknown ways (Lee, 1974; Werner-Reiss et al., 1999; Jove et al., 2020).

In addition to external cues, an animal’s (internal) physiology may also affect its behavior. Nutrition, hydration, and pathogen infections, for instance, have been hypothesized to affect blood feeding behavior, for example by altering feeding avidity (i.e. number of feeding attempts) or the size of the meal taken (Rossignol et al., 1984; Choumet et al., 2012; Cator et al., 2013; Vantaux et al., 2015; Hagan et al., 2018). These topics, however, remain a matter of debate, due to a lack of (standardized) assays to measure mosquito behavior (Stanczyk et al., 2017). Quantitative mapping of Drosophila behavior provides an important perspective, suggesting that innovative experimental approaches and computational tools can fuel the acquisition of new insights (e.g. Branson et al., 2009; Kain et al., 2013; Berman et al., 2014; Corrales-Carvajal et al., 2016; Robie et al., 2017; Moreira et al., 2019). Yet, apart from olfactometers and other flight chambers, very few assays to characterize the blood-feeding behavior of mosquitoes exist (Geier and Boeckh, 1999; Verhulst et al., 2011; McMeniman et al., 2014; van Breugel et al., 2015; Murray et al., 2020). Due to this paucity of assays, studies often expose human subjects to quantify the number of landings and/or bites, or the time it takes to complete a blood meal, and score experimental outcomes by hand (Jones and Pilitt, 1973; Ribeiro, 2000; Moreira et al., 2009; DeGennaro et al., 2013; Dennis et al., 2019; Hughes et al., 2020). The use of humans as bait constrains the number and type of experiments that can be done (e.g. prohibiting the use of infected mosquitoes) and limits the type, detail, and throughput of measurements that can be made. Furthermore, the opaque nature of skin prevents the visualization of the stylets after piercing the skin leaving this aspect of blood feeding almost entirely unstudied, except for one notable study using intravital imaging of dissected mouse skin (Choumet et al., 2012) and two much earlier descriptions (Gordon and Lumsden, 1939; Griffiths and Gordon, 1952).

To overcome these limitations, we developed the biteOscope, an open platform that allows the high-resolution and high-throughput characterization of surface exploration, probing, and engorgement by blood-feeding mosquitoes. The biteOscope consists of a rudimentary skin mimic: a substrate that attracts mosquitoes to its surface, induces them to land, pierce the surface, and engage in blood feeding. The bite substrate can be mounted in the wall of a mosquito cage allowing freely behaving mosquitoes access. By virtue of its transparent nature, the substrate facilitates imaging of mosquitoes interacting with it, including the visualization of the skin piercing mouthparts of the mosquito. We developed a suite of computational tools that automates the extraction of behavioral statistics from image sequences, and use machine learning to track the individual body parts of behaving mosquitoes. These capabilities enable a detailed characterization of blood-feeding mosquitoes. We demonstrate that the biteOscope is an effective instrument to study the behavior of several medically relevant species of mosquito and describe behavioral patterns of the two main vectors of dengue, Zika, and chikungunya virus (Aedes aegypti and Aedes albopictus), and two important malaria vectors (Anopheles coluzzii and Anopheles stephensi). The biteOscope allows detailed tracking of the complex interactions of mosquitoes with a substrate and can be used to characterize behavioral alterations in the presence of chemical surface patterns. Using this capability, we provide evidence that DEET repels Anopheles coluzzii upon contact with their legs, demonstrating the utility of body part tracking to understand behaviors mediated by contact-dependent sensing. We anticipate that the biteOscope will enable studies that increase our understanding of the sensory biology and genetics of blood feeding, and the effects external (environmental) and internal (physiology) variables have on this behavior. Given its relevance for pathogen transmission, dissecting the interplay between the mosquito sensory system and host-associated cues during blood feeding is of clear interest, and may suggest new avenues to interfere with blood feeding, and eventually curb pathogen transmission.

The biteOscope provides an alternative for current methods using human subjects or mice to study mosquito blood feeding. The elimination of the need for a human subject opens new avenues of research, for example allowing blood-feeding studies with pathogen-infected mosquitoes, enabling precise surface manipulations and characterization of the associated behavior, and facilitates the use of high-resolution imaging and machine-learning-based image analysis. Through these innovations, the biteOscope increases experimental throughput and expands the type of experiments that can be performed and measurements that can be made. We developed computational tools that allow the behavioral monitoring of mosquitoes at an unprecedented level of detail. Behavioral research on other animals, including fruit flies (Werkhoven et al., 2019; Pereira et al., 2019) and zebrafish (Marques et al., 2018; Johnson et al., 2020) shows that high spatiotemporal resolution data describing the posture of animals can be very informative to dissect behavioral trajectories and compare behavioral statistics across individuals and experimental treatments. While the details of computational approaches differ, a common theme is the two dimensional embedding of a high-dimensional representation of an animal at a given time point (e.g. body part coordinates and derived features), data points in two dimensions can subsequently be clustered to reveal behavioral classes (see Figure 4—figure supplement 1 for an illustration of this concept using tSNE to embed the data represented in Figure 4). Translating such advances in computational ethology to mosquito research is a very promising avenue for future research.

We used the biteOscope to describe behavioral patterns of four medically relevant mosquito species and anticipate that such datasets will provide a useful ‘behavioral baseline’ for future studies quantifying the effect of a mosquito’s physiology on blood feeding behavior. The role of pathogen infections is particularly interesting in this respect, as infections may alter feeding behavior, for example by affecting the structural integrity of the salivary glands or other tissues, or inducing systemic change through the immune system or infection of neural tissues (Rossignol et al., 1984; Girard et al., 2007; Cator et al., 2013; Turley et al., 2009). A quantitative understanding of such behavioral alterations, however, is lacking. Gaining such insights is of high epidemiological relevance, as mathematical models suggest that (pathogen induced) changes in bite behavior can have important implications for pathogen transmission (Cator et al., 2014; Abboubakar et al., 2016). In addition to pathogen-induced behavioral changes, there are many other promising lines of inquiry, including the behavioral influence of the microbiome (Dickson et al., 2017), which, in other insects such as Drosophila, influences locomotor behavior (Schretter et al., 2018) and food choice (Leitão-Gonçalves et al., 2017; Wong et al., 2017). Drosophila research furthermore shows interesting examples of collective behaviors mediated by for example olfaction or direct contact between animals (Schneider et al., 2012; Ramdya et al., 2015; Lihoreau et al., 2016; Ramdya et al., 2017), it would be interesting to explore if mosquitoes also take advantage of collective intelligence when searching for food or avoiding noxious stimuli. Tools enabling high-throughput behavioral monitoring may also be useful to characterize population intervention strategies aimed at curbing pathogen transmission, such as Wolbachia infected Ae. aegypti, or Anopheles genetically engineered to be refractory to P. falciparum infection. Quantifying the behavioral effects of such interventions is an important step toward assessing the competitiveness of engineered mosquitoes in the field. As the biteOscope enables novel high-throughput experiments with a variety of mosquito species, we anticipate that it will prove useful for the characterization of various behaviors relevant to pathogen transmission.

By tracking the individual body parts of An. coluzzii, we discovered that they are repelled by DEET upon leg contact—a mechanism that may work in concert with other ways in which DEET prevents anopheline mosquitoes to locate humans. Our findings regarding An. coluzzii are in agreement with observations in Ae. aegypti which are also repelled by DEET upon leg contact (DeGennaro et al., 2013; Dennis et al., 2019). However, in contrast to An. coluzzii, olfactory neurons of Ae. aegypti are activated by volatile DEET (Davis and Rebert, 1972; Boeckh et al., 1996; Stanczyk et al., 2010) and Ae. aegypti has been reported to avoid volatile DEET in recent studies (Stanczyk et al., 2013; Afify and Potter, 2020) (in contrast, an earlier study reported attraction of Ae. aegypti by DEET [Dogan et al., 1999]). Together, these observations suggest that contact-based repellency may be conserved across Anopheles and Aedes mosquitoes and thus may be a potentially interesting target for the design of new repellents. It is less clear, however, what degree of conservation exists for the olfactory modes of action, as the only study comparing the olfactory effects of volatile DEET on Anopheles and Aedes mosquitoes in the same assay, suggests that the former is not repelled at all by volatile DEET, while the latter showed moderate repulsion (these behavioral responses may be concentration dependent) (Afify and Potter, 2020). This observation, together with the observation that volatile DEET activates olfactory neurons in Ae. aegypti while it does not seem to do this in An. coluzzii, suggest that volatile DEET may modulate the response of olfactory neurons to attractive stimuli (‘scrambling of the odor code’ [Pellegrino et al., 2011]) and/or trigger repulsion in Ae. aegypti, while these mechanisms seem less appropriate for An. coluzzii. In addition to effects on olfactory signaling, DEET has also been suggested to decrease the amount of volatile odorants emanating from hosts through chemical interactions between DEET and the odorants resulting in the masking of a host (Afify et al., 2019). As in this scenario the amount of attractive odorants reaching a mosquito is reduced, it may affect the behavior of a variety of species. The observation that both Ae. aegypti and An. coluzzii avoid DEET upon leg contact, while the effects of volatile DEET may partly overlap and partly differ, may guide efforts aimed at uncovering the underlying molecular mechanisms.

Our results highlight the use of body part tracking in assigning roles to the various sensory appendages the mosquito body has. The recent surge in genetic tools available to manipulate mosquitoes is shedding light on the genetic elements that mediate pathogen transmission relevant behaviors (Matthews et al., 2019; Ingham et al., 2020; Raji et al., 2019; Greppi et al., 2020). Combining such molecular level insights with detailed behavioral tracking and chemical surface patterning, may enable a deep understanding of how contact-dependent sensing drives blood feeding, and other important phenotypes such as insecticide resistance and egg laying preferences.

When studying animal behavior in the lab a trade-off exists between the level of experimental control and detail of observation on the one hand, and an accurate representation of natural conditions and behaviors on the other. In case of the biteOscope, an engineered bite substrate opens up a variety of possibilities including surface modifications and high-resolution imaging impossible on human skin, yet the bite substrate does not offer the full set of cues (and thus behavioral responses) a human host would. It would therefore be interesting to add more human-associated cues, for instance using materials that resemble the texture of skin, or by coating the bite substrate with attractive human odorants (Okumu et al., 2010). In addition to more closely mimicking human hosts by presenting olfactory stimuli, surface coatings could be used to dissect the role of contact-dependent gustatory behaviors on the skin surface in bite site selection. It is important to note that many of the factors that may change behavior mentioned above (e.g. infections/nutritional status or components of the microbiome) are best assessed in a relative manner, for example comparing non-infected to infected individuals. Comparing cohorts of mosquitoes undergoing different experimental treatments puts less emphasis on the absolute attractiveness of the bite substrate and thus mitigates potential issues related to the fact that a synthetic bite substrate is likely less attractive than a real live host.

We took advantage of the possibility to elicit engorgement on a transparent meal to facilitate imaging. It seems feasible to add a dye to the meal to provide visual cues to the mosquito without interfering with image quality. Using whole blood, however, is challenging in the current system. It would therefore be worthwhile to explore the use of microfluidics to incorporate blood flow into the bite substrate while maintaining optical access. A recent study took advantage of the biteOscope to quantify stylet contact with artificial meals (Jove et al., 2020), combining such efforts with artificial vasculature presents exciting opportunities to characterize the role of the stylets in the search for blood.

The biteOscope is designed with a variety of possible users in mind. It has a relatively modest price tag (900–3500 USD depending on the configuration), uses readily available materials and components, and when disassembled fits in a backpack—characteristics we hope will facilitate adoption. Beyond the lab, we foresee interesting applications of the behavioral tracking of mosquitoes in (semi-)field settings, and expect that innovative tools that provide high-quality quantitative data will enable discoveries in this space. We anticipate that the techniques and computational tools presented here will provide a fresh perspective on mosquito behaviors that are relevant to pathogen transmission, and enable researchers to gain a detailed understanding of blood feeding without having to sacrifice their own skin.

Source data files for Figures 2 and 3 are provided as Supplementary Files, code to generate figures is available from Github (under GNU GPLv3 license): https://github.com/felixhol/biteOscope (copy archived at https://github.com/elifesciences-publications/biteOscope).

1. 1. Abboubakar H
   2. Buonomo B
   3. Chitnis N

   (2016) Modelling the effects of malaria infection on mosquito biting behaviour and attractiveness of humans

   Ricerche Di Matematica 65:329–346.

   https://doi.org/10.1007/s11587-016-0293-9

   • Google Scholar
2. 1. Afify A
   2. Betz JF
   3. Riabinina O
   4. Lahondère C
   5. Potter CJ

   (2019) Commonly used insect repellents hide human odors from anopheles mosquitoes

   Current Biology 29:3669–3680.

   https://doi.org/10.1016/j.cub.2019.09.007

   • PubMed
   • Google Scholar
3. 1. Afify A
   2. Potter CJ

   (2020) Insect repellents mediate species-specific olfactory behaviours in mosquitoes

   Malaria Journal 19:1–10.

   https://doi.org/10.1186/s12936-020-03206-8

   • PubMed
   • Google Scholar
4. Software

   1. Allan D
   2. Caswell T
   3. Keim N
   4. van der Wel C

   (2016) trackpy: Trackpy , version v0. 3.2

   Zenodo.

   https://doi.org/10.5281/zenodo.3492186
5. 1. Benton R

   (2017) The neurobiology of gustation in insect disease vectors: progress and potential

   Current Opinion in Insect Science 20:19–27.

   https://doi.org/10.1016/j.cois.2017.02.003

   • PubMed
   • Google Scholar
6. 1. Berman GJ
   2. Choi DM
   3. Bialek W
   4. Shaevitz JW

   (2014) Mapping the stereotyped behaviour of freely moving fruit flies

   Journal of the Royal Society Interface 11:20140672.

   https://doi.org/10.1098/rsif.2014.0672

   • PubMed
   • Google Scholar
7. 1. Boeckh J
   2. Breer H
   3. Geier M
   4. Hoever F-P
   5. Krüger B-W
   6. Nentwig G
   7. Sass H

   (1996) Acylated 1,3-Aminopropanols as Repellents against Bloodsucking Arthropods

   Pesticide Science 48:359–373.

   https://doi.org/10.1002/(SICI)1096-9063(199612)48:4<359::AID-PS490>3.0.CO;2-Z

   • Google Scholar
8. Software

   1. Bradski G

   (2000) The opencv library, version 5.0

   Dr Dobb’s J. Software Tools.

   https://www.drdobbs.com/open-source/the-opencv-library/184404319
9. 1. Branson K
   2. Robie AA
   3. Bender J
   4. Perona P
   5. Dickinson MH

   (2009) High-throughput ethomics in large groups of Drosophila

   Nature Methods 6:451–457.

   https://doi.org/10.1038/nmeth.1328

   • PubMed
   • Google Scholar
10. 1. Calhoun AJ
    2. Pillow JW
    3. Murthy M

    (2019) Unsupervised identification of the internal states that shape natural behavior

    Nature Neuroscience 22:2040–2049.

    https://doi.org/10.1038/s41593-019-0533-x

    • PubMed
    • Google Scholar
11. 1. Cator LJ
    2. George J
    3. Blanford S
    4. Murdock CC
    5. Baker TC
    6. Read AF
    7. Thomas MB

    (2013) 'Manipulation' without the parasite: altered feeding behaviour of mosquitoes is not dependent on infection with malaria parasites

    Proceedings of the Royal Society B: Biological Sciences 280:20130711.

    https://doi.org/10.1098/rspb.2013.0711

    • PubMed
    • Google Scholar
12. 1. Cator LJ
    2. Lynch PA
    3. Thomas MB
    4. Read AF

    (2014) Alterations in mosquito behaviour by malaria parasites: potential impact on force of infection

    Malaria Journal 13:164.

    https://doi.org/10.1186/1475-2875-13-164

    • PubMed
    • Google Scholar
13. 1. Choumet V
    2. Attout T
    3. Chartier L
    4. Khun H
    5. Sautereau J
    6. Robbe-Vincent A
    7. Brey P
    8. Huerre M
    9. Bain O

    (2012) Visualizing non infectious and infectious anopheles gambiae blood feedings in naive and saliva-immunized mice

    PLOS ONE 7:e50464.

    https://doi.org/10.1371/journal.pone.0050464

    • PubMed
    • Google Scholar
14. Book

    1. Clements AN

    (2013)

    The Physiology of Mosquitoes: International Series of Monographs on Pure and Applied Biology: Zoology

    Elsevier.

    • Google Scholar
15. 1. Corfas RA
    2. Vosshall LB

    (2015) The cation channel TRPA1 tunes mosquito thermotaxis to host temperatures

    eLife 4:e11750.

    https://doi.org/10.7554/eLife.11750

    • PubMed
    • Google Scholar
16. 1. Corrales-Carvajal VM
    2. Faisal AA
    3. Ribeiro C

    (2016) Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off

    eLife 5:e19920.

    https://doi.org/10.7554/eLife.19920

    • PubMed
    • Google Scholar
17. 1. Crocker JC
    2. Grier DG

    (1996) Methods of digital video microscopy for colloidal studies

    Journal of Colloid and Interface Science 179:298–310.

    https://doi.org/10.1006/jcis.1996.0217

    • Google Scholar
18. 1. Davis EE
    2. Rebert CS

    (1972) Elements of olfactory receptor coding in the yellowfever mosquito

    Journal of Economic Entomology 65:1058–1061.

    https://doi.org/10.1093/jee/65.4.1058

    • PubMed
    • Google Scholar
19. 1. De Jong R
    2. Knols BG

    (1995) Selection of biting sites on man by two malaria mosquito species

    Experientia 51:80–84.

    https://doi.org/10.1007/BF01964925

    • PubMed
    • Google Scholar
20. 1. DeGennaro M
    2. McBride CS
    3. Seeholzer L
    4. Nakagawa T
    5. Dennis EJ
    6. Goldman C
    7. Jasinskiene N
    8. James AA
    9. Vosshall LB

    (2013) Orco mutant mosquitoes lose strong preference for humans and are not repelled by volatile DEET

    Nature 498:487–491.

    https://doi.org/10.1038/nature12206

    • PubMed
    • Google Scholar
21. 1. DeGennaro M

    (2015) The mysterious multi-modal repellency of DEET

    Fly 9:45–51.

    https://doi.org/10.1080/19336934.2015.1079360

    • PubMed
    • Google Scholar
22. 1. Dekker T
    2. Cardé RT

    (2011) Moment-to-moment flight manoeuvres of the female yellow fever mosquito (Aedes aegypti L.) in response to plumes of carbon dioxide and human skin odour

    Journal of Experimental Biology 214:3480–3494.

    https://doi.org/10.1242/jeb.055186

    • PubMed
    • Google Scholar
23. 1. Dennis EJ
    2. Goldman OV
    3. Vosshall LB

    (2019) Aedes aegypti mosquitoes use their legs to sense DEET on contact

    Current Biology 29:1551–1556.

    https://doi.org/10.1016/j.cub.2019.04.004

    • PubMed
    • Google Scholar
24. 1. Dickson LB
    2. Jiolle D
    3. Minard G
    4. Moltini-Conclois I
    5. Volant S
    6. Ghozlane A
    7. Bouchier C
    8. Ayala D
    9. Paupy C
    10. Moro CV
    11. Lambrechts L

    (2017) Carryover effects of larval exposure to different environmental Bacteria drive adult trait variation in a mosquito vector

    Science Advances 3:e1700585.

    https://doi.org/10.1126/sciadv.1700585

    • PubMed
    • Google Scholar
25. 1. Dogan EB
    2. Ayres JW
    3. Rossignol PA

    (1999) Behavioural mode of action of deet: inhibition of lactic acid attraction

    Medical and Veterinary Entomology 13:97–100.

    https://doi.org/10.1046/j.1365-2915.1999.00145.x

    • PubMed
    • Google Scholar
26. 1. Duvall LB
    2. Ramos-Espiritu L
    3. Barsoum KE
    4. Glickman JF
    5. Vosshall LB

    (2019) Small-Molecule agonists of Ae. aegypti neuropeptide Y receptor block mosquito biting

    Cell 176:687–701.

    https://doi.org/10.1016/j.cell.2018.12.004

    • PubMed
    • Google Scholar
27. 1. Galun R
    2. Avi-Dor Y
    3. Bar-Zeev M

    (1963) Feeding response in aedes aegypti: stimulation by Adenosine triphosphate

    Science 142:1674–1675.

    https://doi.org/10.1126/science.142.3600.1674

    • PubMed
    • Google Scholar
28. 1. Galun R
    2. Koontz LC
    3. Gwadz RW

    (1985) Engorgement response of anopheline mosquitoes to blood fractions and artificial solutions

    Physiological Entomology 10:145–149.

    https://doi.org/10.1111/j.1365-3032.1985.tb00029.x

    • Google Scholar
29. 1. Geier M
    2. Boeckh J

    (1999) A new Y-tube olfactometer for mosquitoes to measure the attractiveness of host odours

    Entomologia Experimentalis Et Applicata 92:9–19.

    https://doi.org/10.1046/j.1570-7458.1999.00519.x

    • Google Scholar
30. 1. Girard YA
    2. Schneider BS
    3. McGee CE
    4. Wen J
    5. Han VC
    6. Popov V
    7. Mason PW
    8. Higgs S

    (2007) Salivary gland morphology and virus transmission during long-term cytopathologic west nile virus infection in culex mosquitoes

    The American Journal of Tropical Medicine and Hygiene 76:118–128.

    https://doi.org/10.4269/ajtmh.2007.76.118

    • PubMed
    • Google Scholar
31. 1. Gordon RM
    2. Lumsden WHR

    (1939) A study of the behaviour of the Mouth-Parts of mosquitoes when taking up blood from living tissue; Together with some observations on the ingestion of microfilariae

    Annals of Tropical Medicine & Parasitology 33:259–278.

    https://doi.org/10.1080/00034983.1939.11685071

    • Google Scholar
32. 1. Greppi C
    2. Laursen WJ
    3. Budelli G
    4. Chang EC
    5. Daniels AM
    6. van Giesen L
    7. Smidler AL
    8. Catteruccia F
    9. Garrity PA

    (2020) Mosquito heat seeking is driven by an ancestral cooling receptor

    Science 367:681–684.

    https://doi.org/10.1126/science.aay9847

    • PubMed
    • Google Scholar
33. 1. Griffiths RB
    2. Gordon RM

    (1952) An apparatus which enables the process of feeding by mosquitoes to be observed in the tissues of a live rodent; together with an account of the ejection of saliva and its significance in malaria

    Annals of Tropical Medicine & Parasitology 46:311–319.

    https://doi.org/10.1080/00034983.1952.11685536

    • PubMed
    • Google Scholar
34. 1. Hagan RW
    2. Didion EM
    3. Rosselot AE
    4. Holmes CJ
    5. Siler SC
    6. Rosendale AJ
    7. Hendershot JM
    8. Elliot KSB
    9. Jennings EC
    10. Nine GA
    11. Perez PL
    12. Rizlallah AE
    13. Watanabe M
    14. Romick-Rosendale LE
    15. Xiao Y
    16. Rasgon JL
    17. Benoit JB

    (2018) Dehydration prompts increased activity and blood feeding by mosquitoes

    Scientific Reports 8:6804.

    https://doi.org/10.1038/s41598-018-24893-z

    • PubMed
    • Google Scholar
35. 1. Healy TP
    2. Copland MJ
    3. Cork A
    4. Przyborowska A
    5. Halket JM

    (2002) Landing responses of anopheles gambiae elicited by oxocarboxylic acids

    Medical and Veterinary Entomology 16:126–132.

    https://doi.org/10.1046/j.1365-2915.2002.00353.x

    • PubMed
    • Google Scholar
36. Software

    1. Hol FJH

    (2020) biteOscope, version 293f4fe

    GitHub.

    https://github.com/felixhol/biteOscope
37. 1. Hughes A
    2. Foster GM
    3. Guy A
    4. Matope A
    5. Abe M
    6. Towers D
    7. McCall PJ

    (2020) Quantifying late-stage host-seeking behaviour of Anopheles gambiae at the insecticidal net interface using a baited-box bioassay

    Malaria Journal 19:1–11.

    https://doi.org/10.1186/s12936-020-03213-9

    • PubMed
    • Google Scholar
38. 1. Ingham VA
    2. Anthousi A
    3. Douris V
    4. Harding NJ
    5. Lycett G
    6. Morris M
    7. Vontas J
    8. Ranson H

    (2020) A sensory appendage protein protects malaria vectors from pyrethroids

    Nature 577:376–380.

    https://doi.org/10.1038/s41586-019-1864-1

    • PubMed
    • Google Scholar
39. 1. Johnson RE
    2. Linderman S
    3. Panier T
    4. Wee CL
    5. Song E
    6. Herrera KJ
    7. Miller A
    8. Engert F

    (2020) Probabilistic models of larval zebrafish behavior reveal structure on many scales

    Current Biology 30:70–82.

    https://doi.org/10.1016/j.cub.2019.11.026

    • PubMed
    • Google Scholar
40. 1. Jones JC
    2. Pilitt DR

    (1973) Blood-feeding behavior of adult aedes aegypti mosquitoes

    The Biological Bulletin 145:127–139.

    https://doi.org/10.2307/1540353

    • PubMed
    • Google Scholar
41. 1. Jove V
    2. Gong Z
    3. Hol FJ
    4. Zhao Z
    5. Sorrells TR
    6. Carroll TS
    7. Prakash M
    8. McBride CS
    9. Vosshall LB

    (2020)

    Sensory discrimination of blood and floral nectar by aedes aegypti mosquitoes

    Neuron In press.

    • Google Scholar
42. 1. Kabra M
    2. Robie AA
    3. Rivera-Alba M
    4. Branson S
    5. Branson K

    (2013) JAABA: interactive machine learning for automatic annotation of animal behavior

    Nature Methods 10:64–67.

    https://doi.org/10.1038/nmeth.2281

    • PubMed
    • Google Scholar
43. 1. Kain J
    2. Stokes C
    3. Gaudry Q
    4. Song X
    5. Foley J
    6. Wilson R
    7. de Bivort B

    (2013) Leg-tracking and automated behavioural classification in Drosophila

    Nature Communications 4:1910.

    https://doi.org/10.1038/ncomms2908

    • PubMed
    • Google Scholar
44. 1. Katz K
    2. Naug D

    (2015) Energetic state regulates the exploration–exploitation trade-off in honeybees

    Behavioral Ecology 26:1045–1050.

    https://doi.org/10.1093/beheco/arv045

    • Google Scholar
45. 1. Lee R

    (1974)

    Structure and function of the fascicular stylets, and the la bral and cibarial sense organs of male and female Aedes aeg ypti (L.) (Diptera, Culicidae)

    Quaestiones Entomologicae 10:187–216.

    • Google Scholar
46. 1. Leitão-Gonçalves R
    2. Carvalho-Santos Z
    3. Francisco AP
    4. Fioreze GT
    5. Anjos M
    6. Baltazar C
    7. Elias AP
    8. Itskov PM
    9. Piper MDW
    10. Ribeiro C

    (2017) Commensal Bacteria and essential amino acids control food choice behavior and reproduction

    PLOS Biology 15:e2000862.

    https://doi.org/10.1371/journal.pbio.2000862

    • PubMed
    • Google Scholar
47. 1. Lihoreau M
    2. Clarke IM
    3. Buhl J
    4. Sumpter DJ
    5. Simpson SJ

    (2016) Collective selection of food patches in Drosophila

    The Journal of Experimental Biology 219:668–675.

    https://doi.org/10.1242/jeb.127431

    • PubMed
    • Google Scholar
48. 1. Maaten L
    2. Hinton G

    (2008)

    Visualizing data using t-sne

    Journal of Machine Learning Research 9:2579–2605.

    • Google Scholar
49. 1. Marques JC
    2. Lackner S
    3. Félix R
    4. Orger MB

    (2018) Structure of the zebrafish locomotor repertoire revealed with unsupervised behavioral clustering

    Current Biology 28:181–195.

    https://doi.org/10.1016/j.cub.2017.12.002

    • PubMed
    • Google Scholar
50. 1. Mathis A
    2. Mamidanna P
    3. Cury KM
    4. Abe T
    5. Murthy VN
    6. Mathis MW
    7. Bethge M

    (2018) DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

    Nature Neuroscience 21:1281–1289.

    https://doi.org/10.1038/s41593-018-0209-y

    • PubMed
    • Google Scholar
51. 1. Matthews BJ
    2. Younger MA
    3. Vosshall LB

    (2019) The ion channel ppk301 controls freshwater egg-laying in the mosquito aedes aegypti

    eLife 8:e43963.

    https://doi.org/10.7554/eLife.43963

    • PubMed
    • Google Scholar
52. 1. McMeniman CJ
    2. Corfas RA
    3. Matthews BJ
    4. Ritchie SA
    5. Vosshall LB

    (2014) Multimodal integration of carbon dioxide and other sensory cues drives mosquito attraction to humans

    Cell 156:1060–1071.

    https://doi.org/10.1016/j.cell.2013.12.044

    • PubMed
    • Google Scholar
53. 1. Moreira LA
    2. Saig E
    3. Turley AP
    4. Ribeiro JM
    5. O'Neill SL
    6. McGraw EA

    (2009) Human probing behavior of aedes aegypti when infected with a life-shortening strain of Wolbachia

    PLOS Neglected Tropical Diseases 3:e568.

    https://doi.org/10.1371/journal.pntd.0000568

    • PubMed
    • Google Scholar
54. 1. Moreira JM
    2. Itskov PM
    3. Goldschmidt D
    4. Baltazar C
    5. Steck K
    6. Tastekin I
    7. Walker SJ
    8. Ribeiro C

    (2019) optoPAD, a closed-loop optogenetics system to study the circuit basis of feeding behaviors

    eLife 8:e43924.

    https://doi.org/10.7554/eLife.43924

    • PubMed
    • Google Scholar
55. 1. Murray GPD
    2. Lissenden N
    3. Jones J
    4. Voloshin V
    5. Toé KH
    6. Sherrard-Smith E
    7. Foster GM
    8. Churcher TS
    9. Parker JEA
    10. Towers CE
    11. N'Falé S
    12. Guelbeogo WM
    13. Ranson H
    14. Towers D
    15. McCall PJ

    (2020) Barrier bednets target malaria vectors and expand the range of usable insecticides

    Nature Microbiology 5:40–47.

    https://doi.org/10.1038/s41564-019-0607-2

    • PubMed
    • Google Scholar
56. 1. Okumu FO
    2. Killeen GF
    3. Ogoma S
    4. Biswaro L
    5. Smallegange RC
    6. Mbeyela E
    7. Titus E
    8. Munk C
    9. Ngonyani H
    10. Takken W
    11. Mshinda H
    12. Mukabana WR
    13. Moore SJ

    (2010) Development and field evaluation of a synthetic mosquito lure that is more attractive than humans

    PLOS ONE 5:e8951.

    https://doi.org/10.1371/journal.pone.0008951

    • PubMed
    • Google Scholar
57. 1. Pellegrino M
    2. Steinbach N
    3. Stensmyr MC
    4. Hansson BS
    5. Vosshall LB

    (2011) A natural polymorphism alters odour and DEET sensitivity in an insect odorant receptor

    Nature 478:511–514.

    https://doi.org/10.1038/nature10438

    • PubMed
    • Google Scholar
58. 1. Pereira TD
    2. Aldarondo DE
    3. Willmore L
    4. Kislin M
    5. Wang SS
    6. Murthy M
    7. Shaevitz JW

    (2019) Fast animal pose estimation using deep neural networks

    Nature Methods 16:117–125.

    https://doi.org/10.1038/s41592-018-0234-5

    • PubMed
    • Google Scholar
59. 1. Raji JI
    2. Melo N
    3. Castillo JS
    4. Gonzalez S
    5. Saldana V
    6. Stensmyr MC
    7. DeGennaro M

    (2019) Aedes aegypti mosquitoes detect acidic volatiles found in human odor using the IR8a pathway

    Current Biology 29:1253–1262.

    https://doi.org/10.1016/j.cub.2019.02.045

    • PubMed
    • Google Scholar
60. 1. Ramdya P
    2. Lichocki P
    3. Cruchet S
    4. Frisch L
    5. Tse W
    6. Floreano D
    7. Benton R

    (2015) Mechanosensory interactions drive collective behaviour in Drosophila

    Nature 519:233–236.

    https://doi.org/10.1038/nature14024

    • PubMed
    • Google Scholar
61. 1. Ramdya P
    2. Schneider J
    3. Levine JD

    (2017) The neurogenetics of group behavior in Drosophila melanogaster

    The Journal of Experimental Biology 220:35–41.

    https://doi.org/10.1242/jeb.141457

    • PubMed
    • Google Scholar
62. 1. Ribeiro JM

    (2000) Blood-feeding in mosquitoes: probing time and salivary gland anti-haemostatic activities in representatives of three genera (Aedes, anopheles, culex)

    Medical and Veterinary Entomology 14:142–148.

    https://doi.org/10.1046/j.1365-2915.2000.00227.x

    • PubMed
    • Google Scholar
63. 1. Robie AA
    2. Hirokawa J
    3. Edwards AW
    4. Umayam LA
    5. Lee A
    6. Phillips ML
    7. Card GM
    8. Korff W
    9. Rubin GM
    10. Simpson JH
    11. Reiser MB
    12. Branson K

    (2017) Mapping the neural substrates of behavior

    Cell 170:393–406.

    https://doi.org/10.1016/j.cell.2017.06.032

    • PubMed
    • Google Scholar
64. 1. Rossignol PA
    2. Ribeiro JM
    3. Spielman A

    (1984) Increased intradermal probing time in sporozoite-infected mosquitoes

    The American Journal of Tropical Medicine and Hygiene 33:17–20.

    https://doi.org/10.4269/ajtmh.1984.33.17

    • PubMed
    • Google Scholar
65. 1. Schneider J
    2. Dickinson MH
    3. Levine JD

    (2012) Social structures depend on innate determinants and chemosensory processing in Drosophila

    PNAS 109 Suppl 2:17174–17179.

    https://doi.org/10.1073/pnas.1121252109

    • PubMed
    • Google Scholar
66. 1. Schretter CE
    2. Vielmetter J
    3. Bartos I
    4. Marka Z
    5. Marka S
    6. Argade S
    7. Mazmanian SK

    (2018) A gut microbial factor modulates locomotor behaviour in Drosophila

    Nature 563:402–406.

    https://doi.org/10.1038/s41586-018-0634-9

    • PubMed
    • Google Scholar
67. 1. Sparks JT
    2. Vinyard BT
    3. Dickens JC

    (2013) Gustatory receptor expression in the Labella and tarsi of aedes aegypti

    Insect Biochemistry and Molecular Biology 43:1161–1171.

    https://doi.org/10.1016/j.ibmb.2013.10.005

    • PubMed
    • Google Scholar
68. 1. Stanczyk NM
    2. Brookfield JF
    3. Ignell R
    4. Logan JG
    5. Field LM

    (2010) Behavioral insensitivity to DEET in aedes aegypti is a genetically determined trait residing in changes in sensillum function

    PNAS 107:8575–8580.

    https://doi.org/10.1073/pnas.1001313107

    • PubMed
    • Google Scholar
69. 1. Stanczyk NM
    2. Brookfield JF
    3. Field LM
    4. Logan JG

    (2013) Aedes aegypti mosquitoes exhibit decreased repellency by DEET following previous exposure

    PLOS ONE 8:e54438.

    https://doi.org/10.1371/journal.pone.0054438

    • PubMed
    • Google Scholar
70. 1. Stanczyk NM
    2. Mescher MC
    3. De Moraes CM

    (2017) Effects of malaria infection on mosquito olfaction and behavior: extrapolating data to the field

    Current Opinion in Insect Science 20:7–12.

    https://doi.org/10.1016/j.cois.2017.02.002

    • PubMed
    • Google Scholar
71. 1. Tao L
    2. Ozarkar S
    3. Beck JM
    4. Bhandawat V

    (2019) Statistical structure of locomotion and its modulation by odors

    eLife 8:e41235.

    https://doi.org/10.7554/eLife.41235

    • PubMed
    • Google Scholar
72. 1. Turley AP
    2. Moreira LA
    3. O'Neill SL
    4. McGraw EA

    (2009) Wolbachia infection reduces blood-feeding success in the dengue fever mosquito, aedes aegypti

    PLOS Neglected Tropical Diseases 3:e516.

    https://doi.org/10.1371/journal.pntd.0000516

    • PubMed
    • Google Scholar
73. 1. van Breugel F
    2. Riffell J
    3. Fairhall A
    4. Dickinson MH

    (2015) Mosquitoes use vision to associate odor plumes with thermal targets

    Current Biology 25:2123–2129.

    https://doi.org/10.1016/j.cub.2015.06.046

    • PubMed
    • Google Scholar
74. 1. Vantaux A
    2. de Sales Hien DF
    3. Yameogo B
    4. Dabiré KR
    5. Thomas F
    6. Cohuet A
    7. Lefèvre T

    (2015) Host-seeking behaviors of mosquitoes experimentally infected with sympatric field isolates of the human malaria parasite Plasmodium falciparum: no evidence for host manipulation

    Frontiers in Ecology and Evolution 3:86.

    https://doi.org/10.3389/fevo.2015.00086

    • Google Scholar
75. 1. Verhulst NO
    2. Qiu YT
    3. Beijleveld H
    4. Maliepaard C
    5. Knights D
    6. Schulz S
    7. Berg-Lyons D
    8. Lauber CL
    9. Verduijn W
    10. Haasnoot GW
    11. Mumm R
    12. Bouwmeester HJ
    13. Claas FH
    14. Dicke M
    15. van Loon JJ
    16. Takken W
    17. Knight R
    18. Smallegange RC

    (2011) Composition of human skin Microbiota affects attractiveness to malaria mosquitoes

    PLOS ONE 6:e28991.

    https://doi.org/10.1371/journal.pone.0028991

    • PubMed
    • Google Scholar
76. Preprint

    1. Virtanen P
    2. Gommers R
    3. Oliphant TE
    4. Haberland M
    5. Reddy T
    6. Cournapeau D
    7. Burovski E
    8. Peterson P
    9. Weckesser W
    10. Bright J

    (2019) Scipy 1.0–fundamental algorithms for scientific computing in python

    arXiv.

    https://doi.org/10.1038/s41592-019-0686-2

    • Google Scholar
77. Preprint

    1. Werkhoven Z
    2. Bravin A
    3. Skutt-Kakaria K
    4. Reimers P
    5. Pallares LF
    6. Ayroles J
    7. De Bivort BL

    (2019) The structure of behavioral variation within a genotype

    bioRxiv.

    https://doi.org/10.1101/779363

    • Google Scholar
78. 1. Werner-Reiss U
    2. Galun R
    3. Crnjar R
    4. Liscia A

    (1999) Sensitivity of the mosquito aedes aegypti (Culicidae) labral apical chemoreceptors to phagostimulants

    Journal of Insect Physiology 45:629–636.

    https://doi.org/10.1016/S0022-1910(98)00152-8

    • PubMed
    • Google Scholar
79. 1. Wong AC
    2. Wang QP
    3. Morimoto J
    4. Senior AM
    5. Lihoreau M
    6. Neely GG
    7. Simpson SJ
    8. Ponton F

    (2017) Gut Microbiota modifies Olfactory-Guided microbial preferences and foraging decisions in Drosophila

    Current Biology 27:2397–2404.

    https://doi.org/10.1016/j.cub.2017.07.022

    • PubMed
    • Google Scholar
80. 1. Zermoglio PF
    2. Robuchon E
    3. Leonardi MS
    4. Chandre F
    5. Lazzari CR

    (2017) What does heat tell a mosquito? characterization of the orientation behaviour of aedes aegypti towards heat sources

    Journal of Insect Physiology 100:9–14.

    https://doi.org/10.1016/j.jinsphys.2017.04.010

    • PubMed
    • Google Scholar

Author details

1. Felix JH Hol

   1. Department of Bioengineering, Stanford University, Stanford, United States
   2. Insect-Virus Interactions Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
   3. Center for research and Interdisciplinarity, U1284 INSERM, Université de Paris, Paris, France

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration

   For correspondence

   felix.hol@pasteur.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8061-0826

2. Louis Lambrechts

   Insect-Virus Interactions Unit, Institut Pasteur, UMR2000, CNRS, Paris, France

   Contribution

   Resources, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5958-2138

3. Manu Prakash

   Department of Bioengineering, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8046-8388

• 6,188

  views

• 538

  downloads

• 26

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.