@article{KropfRoessler2018,
  author    = {Kropf, Jan and R{\"o}ssler, Wolfgang},
  title     = {In-situ recording of ionic currents in projection neurons and Kenyon cells in the olfactory pathway of the honeybee},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {1},
  doi       = {10.1371/journal.pone.0191425},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-175869},
  pages     = {e0191425},
  year      = {2018},
  abstract  = {The honeybee olfactory pathway comprises an intriguing pattern of convergence and divergence: ~60.000 olfactory sensory neurons (OSN) convey olfactory information on ~900 projection neurons (PN) in the antennal lobe (AL). To transmit this information reliably, PNs employ relatively high spiking frequencies with complex patterns. PNs project via a dual olfactory pathway to the mushroom bodies (MB). This pathway comprises the medial (m-ALT) and the lateral antennal lobe tract (l-ALT). PNs from both tracts transmit information from a wide range of similar odors, but with distinct differences in coding properties. In the MBs, PNs form synapses with many Kenyon cells (KC) that encode odors in a spatially and temporally sparse way. The transformation from complex information coding to sparse coding is a well-known phenomenon in insect olfactory coding. Intrinsic neuronal properties as well as GABAergic inhibition are thought to contribute to this change in odor representation. In the present study, we identified intrinsic neuronal properties promoting coding differences between PNs and KCs using in-situ patch-clamp recordings in the intact brain. We found very prominent K+ currents in KCs clearly differing from the PN currents. This suggests that odor coding differences between PNs and KCs may be caused by differences in their specific ion channel properties. Comparison of ionic currents of m- and l-ALT PNs did not reveal any differences at a qualitative level.},
  language  = {en}
}
@phdthesis{Kropf2018,
  author    = {Kropf, Jan},
  title     = {The Dual Olfactory Pathway in the Honeybee Brain: Sensory Supply and Electrophysiological Properties},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-108369},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {The olfactory sense is of utmost importance for honeybees, Apis mellifera. Honeybees use olfaction for communication within the hive, for the identification of nest mates and non-nest mates, the localization of food sources, and in case of drones (males), for the detection of the queen and mating. Honeybees, therefore, can serve as excellent model systems for an integrative analysis of an elaborated olfactory system. To efficiently filter odorants out of the air with their antennae, honeybees possess a multitude of sensilla that contain the olfactory sensory neurons (OSN). Three types of olfactory sensilla are known from honeybee worker antennae: Sensilla trichoidea, Sensilla basiconica and Sensilla placodea. In the sensilla, odorant receptors that are located in the dendritic arborizations of the OSNs transduce the odorant information into electrical information. Approximately 60.000 OSN axons project in two parallel bundles along the antenna into the brain. Before they enter the primary olfactory brain center, the antennal lobe (AL), they diverge into four distinct tracts (T1-T4). OSNs relay onto ~3.000-4.000 local interneurons (LN) and ~900 projection neurons (PN), the output neurons of the AL. The axons of the OSNs together with neurites from LNs and PNs form spheroidal neuropil units, the so-called glomeruli. OSN axons from the four AL input tracts (T1-T4) project into four glomerular clusters. LNs interconnect the AL glomeruli, whereas PNs relay the information to the next brain centers, the mushroom body (MB) - associated with sensory integration, learning and memory - and the lateral horn (LH). In honeybees, PNs project to the MBs and the LH via two separate tracts, the medial and the lateral antennal-lobe tract (m/lALT) which run in parallel in opposing directions. The mALT runs first to the MB and then to the LH, the lALT runs first to the LH and then to the MB. This dual olfactory pathway represents a feature unique to Hymenoptera. Interestingly, both tracts were shown to process information about similar sets of odorants by extracting different features. Individual mALT PNs are more odor specific than lALT PNs. On the other hand, lALT PNs have higher spontaneous and higher odor response action potential (AP) frequencies than mALT PNs. In the MBs, PNs form synapses with ~184.000 Kenyon cells (KC), which are the MB intrinsic neurons. KCs, in contrast to PNs, show almost no spontaneous activity and employ a spatially and temporally sparse code for odor coding. In manuscript I of my thesis, I investigated whether the differences in specificity of odor responses between m- and lALT are due to differences in the synaptic input. Therefore, I investigated the axonal projection patterns of OSNs housed in S. basiconica in honeybee workers and compared them with S. trichoidea and S. placodea using selective anterograde labeling with fluorescent tracers and confocal- microscopy analyses of axonal projections in AL glomeruli. Axons of S. basiconica-associated OSNs preferentially projected into the T3 input-tract cluster in the AL, whereas the two other types of sensilla did not show a preference for a specific glomerular cluster. T3- associated glomeruli had previously been shown to be innervated by mALT PNs. Interestingly, S. basiconica as well as a number of T3 glomeruli lack in drones. Therefore I set out to determine whether this was associated with the reduction of glomeruli innervated by mALT PNs. Retrograde tracing of mALT PNs in drones and counting of innervated glomeruli showed that the number of mALT-associated glomeruli was strongly reduced in drones compared to workers. The preferential projections of S. basiconica-associated OSNs into T3 glomeruli in female workers together with the reduction of mALT-associated glomeruli in drones support the presence of a female-specific olfactory subsystem that is partly innervated by OSNs from S. basiconica and is associated with mALT projection neurons. As mALT PNs were shown to be more odor specific, I suppose that already the OSNs in this subsystem are more odor specific than lALT associated OSNs. I conclude that this female-specific subsystem allows the worker honeybees to respond adequately to the enormous variety of odorants they experience during their lifetime. In manuscript II, I investigated the ion channel composition of mALT and lALT PNs and KCs in situ. This approach represents the first study dealing with the honeybee PN and KC ion channel composition under standard conditions in an intact brain preparation. With these recordings I set out to investigate the potential impact of intrinsic neuronal properties on the differences between m- and lALT PNs and on the sparse odor coding properties of KCs. In PNs, I identified a set of Na+ currents and diverse K+ currents depending on voltage and Na+ or Ca2+ that support relatively high spontaneous and odor response AP frequencies. This set of currents did not significantly differ between mALT and lALT PNs, but targets for potential modulation of currents leading to differences in AP frequencies were found between both types of PNs. In contrast to PNs, KCs have very prominent K+ currents, which are likely to contribute to the sparse response fashion observed in KCs. Furthermore, Ca2+ dependent K+ currents were found, which may be of importance for coincidence detection, learning and memory formation. Finally, I conclude that the differences in odor specificity between m- and lALT PNs are due to their synaptic input from different sets of OSNs and potential processing by LNs. The differences in spontaneous activity between the two tracts may be caused by different neuronal modulation or, in addition, also by interaction with LNs. The temporally sparse representation of odors in KCs is very likely based on the intrinsic KC properties, whereas general excitability and spatial sparseness are likely to be regulated through GABAergic feedback neurons.},
  subject      = {Voltage-Clamp-Methode},
  language  = {en}
}