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| <?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.1d1 20130915//EN" "JATS-archivearticle1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.1d1"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="hwp">eLife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn publication-format="electronic">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">04147</article-id><article-id pub-id-type="doi">10.7554/eLife.04147</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research article</subject></subj-group><subj-group subj-group-type="heading"><subject>Neuroscience</subject></subj-group></article-categories><title-group><article-title>Decoding odor quality and intensity in the <italic>Drosophila</italic> brain</article-title></title-group><contrib-group><contrib contrib-type="author" id="author-17113"><name><surname>Strutz</surname><given-names>Antonia</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-17114"><name><surname>Soelter</surname><given-names>Jan</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="other" rid="par-3"/><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-17115"><name><surname>Baschwitz</surname><given-names>Amelie</given-names></name><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-0614-1667</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-17116"><name><surname>Farhan</surname><given-names>Abu</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con6"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-5250"><name><surname>Grabe</surname><given-names>Veit</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con4"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-17117"><name><surname>Rybak</surname><given-names>Jürgen</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con5"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-2224"><name><surname>Knaden</surname><given-names>Markus</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con7"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-17118"><name><surname>Schmuker</surname><given-names>Michael</given-names></name><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-6753-4929</contrib-id><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="fn" rid="pa1">†</xref><xref ref-type="other" rid="par-3"/><xref ref-type="fn" rid="con8"/><xref ref-type="fn" rid="conf2"/></contrib><contrib contrib-type="author" id="author-1769"><name><surname>Hansson</surname><given-names>Bill S</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="other" rid="par-2"/><xref ref-type="fn" rid="con9"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" corresp="yes" id="author-3420"><name><surname>Sachse</surname><given-names>Silke</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="corresp" rid="cor1">*</xref><xref ref-type="other" rid="par-1"/><xref ref-type="fn" rid="con10"/><xref ref-type="fn" rid="conf2"/></contrib><aff id="aff1"><label>1</label><institution content-type="dept">Department of Evolutionary Neuroethology</institution>, <institution>Max Planck Institute for Chemical Ecology</institution>, <addr-line><named-content content-type="city">Jena</named-content></addr-line>, <country>Germany</country></aff><aff id="aff2"><label>2</label><institution content-type="dept">Department for Biology, Pharmacy and Chemistry</institution>, <institution>Free University Berlin, Neuroinformatics and Theoretical Neuroscience</institution>, <addr-line><named-content content-type="city">Berlin</named-content></addr-line>, <country>Germany</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Ramaswami</surname><given-names>Mani</given-names></name><role>Reviewing editor</role><aff><institution>Trinity College Dublin</institution>, <country>Ireland</country></aff></contrib></contrib-group><author-notes><corresp id="cor1"><label>*</label>For correspondence: <email>ssachse@ice.mpg.de</email></corresp><fn fn-type="present-address" id="pa1"><label>†</label><p>School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom</p></fn></author-notes><pub-date publication-format="electronic" date-type="pub"><day>16</day><month>12</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>3</volume><elocation-id>e04147</elocation-id><history><date date-type="received"><day>24</day><month>07</month><year>2014</year></date><date date-type="accepted"><day>09</day><month>11</month><year>2014</year></date></history><permissions><copyright-statement>© 2014, Strutz et al</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>Strutz et al</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife04147.pdf"/><abstract><object-id pub-id-type="doi">10.7554/eLife.04147.001</object-id><p>To internally reflect the sensory environment, animals create neural maps encoding the external stimulus space. From that primary neural code relevant information has to be extracted for accurate navigation. We analyzed how different odor features such as hedonic valence and intensity are functionally integrated in the lateral horn (LH) of the vinegar fly, <italic>Drosophila melanogaster</italic>. We characterized an olfactory-processing pathway, comprised of inhibitory projection neurons (iPNs) that target the LH exclusively, at morphological, functional and behavioral levels. We demonstrate that iPNs are subdivided into two morphological groups encoding positive hedonic valence or intensity information and conveying these features into separate domains in the LH. Silencing iPNs severely diminished flies' attraction behavior. Moreover, functional imaging disclosed a LH region tuned to repulsive odors comprised exclusively of third-order neurons. We provide evidence for a feature-based map in the LH, and elucidate its role as the center for integrating behaviorally relevant olfactory information.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.001">http://dx.doi.org/10.7554/eLife.04147.001</ext-link></p></abstract><abstract abstract-type="executive-summary"><object-id pub-id-type="doi">10.7554/eLife.04147.002</object-id><title>eLife digest</title><p>Organisms need to sense and adapt to their environment in order to survive. Senses such as vision and smell allow an organism to absorb information about the external environment and translate it into a meaningful internal image. This internal image helps the organism to remember incidents and act accordingly when they encounter similar situations again. A typical example is when organisms are repeatedly attracted to odors that are essential for survival, such as food and pheromones, and are repulsed by odors that threaten survival.</p><p>Strutz et al. addressed how attractiveness or repulsiveness of a smell, and also the strength of a smell, are processed by a part of the olfactory system called the lateral horn in fruit flies. This involved mapping the neuronal patterns that were generated in the lateral horn when a fly was exposed to particular odors.</p><p>Strutz et al. found that a subset of neurons called inhibitory projection neurons processes information about whether the odor is attractive or repulsive, and that a second subset of these neurons process information about the intensity of the odor. Other insects, such as honey bees and hawk moths, have olfactory systems with a similar architecture and might also employ a similar spatial approach to encode information regarding the intensity and identity of odors. Locusts, on the other hand, employ a temporal approach to encoding information about odors.</p><p>The work of Strutz et al. shows that certain qualities of odors are contained in a spatial map in a specific brain region of the fly. This opens up the question of how the information in this spatial map influences decisions made by the fly.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.002">http://dx.doi.org/10.7554/eLife.04147.002</ext-link></p></abstract><kwd-group kwd-group-type="author-keywords"><title>Author keywords</title><kwd>olfaction</kwd><kwd>neural circuit</kwd><kwd>lateral horn</kwd><kwd>antennal lobe</kwd><kwd>odor processing</kwd><kwd>functional imaging</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd><italic>D. melanogaster</italic></kwd></kwd-group><funding-group><award-group id="par-1"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100002347</institution-id><institution>Bundesministerium für Bildung und Forschung</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Sachse</surname><given-names>Silke</given-names></name></principal-award-recipient></award-group><award-group id="par-2"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100004189</institution-id><institution>Max-Planck-Gesellschaft</institution></institution-wrap></funding-source><principal-award-recipient><name><surname>Hansson</surname><given-names>Bill S</given-names></name></principal-award-recipient></award-group><award-group id="par-3"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001659</institution-id><institution>Deutsche Forschungsgemeinschaft</institution></institution-wrap></funding-source><award-id>Priority program SPP1392 (SCHM2474/1-1 and 1-2)</award-id><principal-award-recipient><name><surname>Schmuker</surname><given-names>Michael</given-names></name><name><surname>Soelter</surname><given-names>Jan</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta><meta-name>elife-xml-version</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>The lateral horn can be classified into three functional odor response domains that decode opposing hedonic valences and odor intensity.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type="intro" id="s1"><title>Introduction</title><p>To navigate the environment in a way that optimizes their survival and reproduction, animals have evolved sensory systems. These have three essential tasks: First, the external world has to be translated into an internal representation in the form of an accurate neural map. Second, the neural map has to be readable and interpretable, that is, the generated neural code must allow common attributes to be extracted across stimuli to enable the animal to make the best decisions. Third, the animal has to be able to adapt to environmental changes and to form a sensory memory of new stimuli. Many studies have been dedicated to unraveling the primary transformation from a stimulus into an initial neural representation within various sensory systems (<xref ref-type="bibr" rid="bib33">Manni and Petrosini, 2004</xref>; <xref ref-type="bibr" rid="bib60">Vosshall and Stocker, 2007</xref>; <xref ref-type="bibr" rid="bib47">Sanes and Zipursky, 2010</xref>) and to elucidating neuronal plasticity and sensory memory formation in higher-level processing centers (<xref ref-type="bibr" rid="bib18">Heisenberg, 2003</xref>; <xref ref-type="bibr" rid="bib41">Pasternak and Greenlee, 2005</xref>). The ability to extract features and integrate stimulus modalities have so far mainly been studied in the visual system (<xref ref-type="bibr" rid="bib31">Livingstone and Hubel, 1988</xref>; <xref ref-type="bibr" rid="bib1">Bausenwein et al., 1992</xref>; <xref ref-type="bibr" rid="bib38">Nassi and Callaway, 2009</xref>). We addressed the question of how stimulus features such as odor valence and intensity are coded and integrated within the olfactory system using the model organism <italic>Drosophila melanogaster</italic>.</p><p>The olfactory system of the vinegar fly provides an excellent model system for deciphering olfactory processing mechanisms, since it displays remarkable similarities to the mammalian system but is less complex and highly genetically tractable. Like other sensory systems, the olfactory system employs a spatio-temporal map to translate the variables in chemosensory space into neuronal activity patterns in the brain. This map emerges when the olfactory sensory neurons (OSNs) with the same chemosensory receptors converge into one exclusive glomerulus in the antennal lobe (AL) which represents the equivalent to the mammalian olfactory bulb (<xref ref-type="bibr" rid="bib19">Hildebrand and Shepherd, 1997</xref>; <xref ref-type="bibr" rid="bib61">Vosshall et al., 2000</xref>; <xref ref-type="bibr" rid="bib60">Vosshall and Stocker, 2007</xref>). Glomeruli, the functional and morphological units of the AL, are microcircuits comprising OSNs, multiglomerular local interneurons (LNs) and uniglomerular output neurons, so-called excitatory projection neurons (ePNs) (<xref ref-type="bibr" rid="bib66">Wilson and Mainen, 2006</xref>; <xref ref-type="bibr" rid="bib60">Vosshall and Stocker, 2007</xref>) that convey the olfactory information to higher brain centers, as the mushroom body calyx (MBc) and the lateral horn (LH) (<xref ref-type="bibr" rid="bib53">Stocker et al., 1997</xref>). The stringent spatial arrangement of OSNs and ePNs in the AL generates a spatial map containing characteristic combinatorial glomerular activity patterns for all odorants (<xref ref-type="bibr" rid="bib12">Fiala et al., 2002</xref>; <xref ref-type="bibr" rid="bib62">Wang et al., 2003a</xref>; <xref ref-type="bibr" rid="bib8">Couto et al., 2005</xref>; <xref ref-type="bibr" rid="bib14">Fishilevich and Vosshall, 2005</xref>). The MBc is involved in olfactory memory formation (<xref ref-type="bibr" rid="bib18">Heisenberg, 2003</xref>) and enables a contextualization of the odor space (<xref ref-type="bibr" rid="bib3">Caron et al., 2013</xref>). By exclusion, the LH is believed to be involved in innate olfactory behavior (<xref ref-type="bibr" rid="bib10">de Belle and Heisenberg, 1994</xref>; <xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>). Excitatory PNs retain the sensory information encoded in the AL and form glomerulus-dependent, stereotypic axonal terminal fields in the LH (<xref ref-type="bibr" rid="bib34">Marin et al., 2002</xref>; <xref ref-type="bibr" rid="bib68">Wong et al., 2002</xref>; <xref ref-type="bibr" rid="bib57">Tanaka et al., 2004</xref>). Compartmentalization in the LH has been observed in form of a spatial segregation of ePNs innervating specific glomerular subgroups (<xref ref-type="bibr" rid="bib57">Tanaka et al., 2004</xref>), fruit and pheromone odor information processing ePNs (<xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>) as well as ammonia and amine vs carbon dioxide coding ePNs (<xref ref-type="bibr" rid="bib35">Min et al., 2013</xref>).</p><p>Like many other sensory networks, the olfactory circuit of the fly contains spatially distinct pathways to the higher brain, namely the inner, middle and outer antennocerebral tract (iACT, mACT and oACT) (<xref ref-type="bibr" rid="bib54">Stocker et al., 1990</xref>). Notably, the mACT projects from the AL to the LH exclusively and consists of inhibitory PNs (iPNs), which exhibit also uniglomerular but mainly multiglomerular AL innervations (<xref ref-type="bibr" rid="bib20">Ito et al., 1997</xref>; <xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>; <xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>; <xref ref-type="bibr" rid="bib39">Okada et al., 2009</xref>; <xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>). Both PN populations have been attributed different coding properties: Although both PN populations exhibit odor responses to overlapping odor ligands, iPNs seems to be broader tuned than ePNs (<xref ref-type="bibr" rid="bib63">Wang et al., 2014</xref>). Furthermore, while ePNs encode rather odor identity (<xref ref-type="bibr" rid="bib62">Wang et al., 2003a</xref>; <xref ref-type="bibr" rid="bib67">Wilson et al., 2004</xref>; <xref ref-type="bibr" rid="bib50">Silbering et al., 2008</xref>), iPNs have been shown to enhance innate discrimination of closely related odors (<xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>). Together, these PN populations process information on dual olfactory pathways (<xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib63">Wang et al., 2014</xref>), as do processing mechanisms in other sensory modalities (<xref ref-type="bibr" rid="bib38">Nassi and Callaway, 2009</xref>), and most likely accomplish different olfactory behaviors. The mainly multiglomerular AL pattern of iPNs suggests that these neurons extract characteristic stimulus features from the AL code and re-integrate this information into the LH to mediate innate odorant-guided behavior. This assumption is further supported by two recent studies showing that the inhibitory input from the AL to the LH is module-specific, that is, selective for food odors and pheromones (<xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib13">Fisek and Wilson, 2014</xref>), while the connectivity in the MBc is rather probabilistic (<xref ref-type="bibr" rid="bib36">Murthy et al., 2008</xref>; <xref ref-type="bibr" rid="bib3">Caron et al., 2013</xref>).</p><p>However, it still remains open if and how different odor features as hedonic valence or intensity are functionally coded and integrated in the LH. In this study, we characterized and dissected the iPN olfactory processing pathway regarding the coding of odor quality and intensity at morphological, functional and behavioral levels. By linking odor-evoked activity patterns in the LH to odor-guided behavior, we provide evidence that iPNs mediate odor attraction. Furthermore, our data demonstrate a feature-based, spatially segregated activity map in the LH comprised of iPNs and third-order neurons and thus expand its role as a center for integrating behaviorally relevant olfactory information.</p></sec><sec sec-type="results" id="s2"><title>Results</title><sec id="s2-1"><title>Dendrites of iPNs innervate two-thirds of olfactory glomeruli</title><p>Cell bodies of iPNs are exclusively located in the ventral cell cluster which consists of ∼50 iPNs (<xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>) that project via the mACT to the LH, thereby bypassing the MBc (<xref ref-type="bibr" rid="bib20">Ito et al., 1997</xref>) (<xref ref-type="fig" rid="fig1">Figure 1A,B</xref>). In contrast, ePN somata are located anterodorsally and laterally of the AL, and their axons project through the iACT or oACT to the MBc and the LH (<xref ref-type="bibr" rid="bib53">Stocker et al., 1997</xref>; <xref ref-type="bibr" rid="bib34">Marin et al., 2002</xref>; <xref ref-type="bibr" rid="bib68">Wong et al., 2002</xref>; <xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>). To analyze the innervation patterns of iPNs and ePNs, we labeled both PN populations simultaneously in vivo using the enhancer trap lines <italic>GH146-QF</italic> and <italic>MZ699-GAL4</italic> that label the majority of ePNs (60%) and iPNs (86%), respectively (<xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>). Double-labeling shows that both PN types innervate overlapping regions in the AL and the LH, while a small posterior-lateral LH area is targeted only by ePNs (<xref ref-type="fig" rid="fig1">Figure 1A</xref>, <xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). In GH146-positive (GH146+) PNs, immunolabeling reveals GABA production in all ∼6 PNs of the ventral cell cluster (<xref ref-type="bibr" rid="bib65">Wilson and Laurent, 2005</xref>), whereas ePNs of this line are exclusively cholinergic (<xref ref-type="bibr" rid="bib49">Shang et al., 2007</xref>). For the ∼45 MZ699-positive (MZ699+) iPNs (<xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>), GAD1 (glutamic acid decarboxylase) in situ hybridizations imply GABA synthesis (<xref ref-type="bibr" rid="bib39">Okada et al., 2009</xref>), which was recently verified via immunostaining (<xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>). The polarity of both PN populations has been studied in detail, showing that both possess dendritic regions in the AL, indicating the AL as their cholinergic input site, while the LH represents their major output site (<xref ref-type="bibr" rid="bib21">Jefferis et al., 2001</xref>; <xref ref-type="bibr" rid="bib39">Okada et al., 2009</xref>; <xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>).<fig-group><fig id="fig1" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.003</object-id><label>Figure 1.</label><caption><title>Detailed glomerular innervations of excitatory and inhibitory projection neurons in the AL.</title><p>(<bold>A</bold>) Simultaneous labeling of inhibitory projections neurons (iPNs, labeled by <italic>MZ699-GAL4;G-CaMP</italic>) and excitatory projection neurons (ePNs, labeled by <italic>GH146-QF;tdTomato</italic>) in vivo reveals distinct projections to the lateral horn (LH). All iPNs bypass the mushroom body calyx (MBc) and innervate the LH exclusively. The MZ699 line labels a few ventrolateral protocerebral neurons (vlPr neurons) projecting via the posterior lateral fascicle (plF) from the ventrolateral protocerebrum (vlPr) to the LH. (<bold>B</bold>) Schematic of the PN connectivity relay from the antennal lobe (AL) to higher brain centers (ePNs in magenta, iPNs in green, and vlPr neurons in orange). (<bold>C</bold>) Above, complete glomerular assignment of the AL neuropil (right AL), labeled with elav-<italic>n</italic>-synaptobrevin:DsRed (END1-2). Below, glomerular innervations of both PN populations related to in vivo images in <xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2</xref>. Depicted are the ventral level (∼−40 µm), the medial level (∼−20 µm) and the dorsal view onto the AL. Color annotation: blue glomeruli are not innervated by any of the used GAL4-lines; green glomeruli are innervated by MZ699+ iPNs and magenta by GH146+ ePNs; white glomeruli are innervated by both enhancer trap lines. Scale bar, 20 µm.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.003">http://dx.doi.org/10.7554/eLife.04147.003</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f001"/></fig><fig id="fig1s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.004</object-id><label>Figure 1—figure supplement 1.</label><caption><title>Characterization of excitatory and inhibitory projection neurons.</title><p>Overlap of ePNs (QUAS-tdTomato) and iPNs (UAS-GCaMP3.0) in the LH area. The circle indicates the posterior lateral region, which is sparsely innervated by iPNs and dominated by ePN axonal terminal fields. Scale bar, 20 µm.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.004">http://dx.doi.org/10.7554/eLife.04147.004</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs001"/></fig><fig id="fig1s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.005</object-id><label>Figure 1—figure supplement 2.</label><caption><title>Glomerular innervations of ePNs and iPNs.</title><p>(<bold>A</bold>) Representative in vivo images of glomerular innervations. <italic>MZ699-</italic> and <italic>GH146-GAL4</italic> lines have been reconstructed with the END1-2 background staining (two upper planes) and dual labeling via the Q-system and the GAL4-UAS expression system (lowest plane). Scale bar, 20 µm. (<bold>B</bold>) Detailed glomerular AL innervation. Green filled cells indicate innervation by <italic>MZ699-GAL4</italic>, magenta <italic>GH146-GAL4</italic> innervation, respectively and grey, no innervation by the indicated line. Bottom rows, total number of innervated glomeruli with percentage share indicated below. Merge column: white filled with ‘x’ indicates glomeruli innervated by both lines, grey only one line. Blue filled rows are glomeruli labeled by none of the enhancer trap lines.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.005">http://dx.doi.org/10.7554/eLife.04147.005</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs002"/></fig></fig-group></p><p>To further characterize PNs labeled by <italic>MZ699-GAL4</italic> and <italic>GH146-GAL4</italic>, we analyzed their precise glomerular innervation to unravel how selectively they acquire information in the AL. To allow glomerulus identification in vivo, we employed a transgenic fly carrying elav-n-synaptobrevin:DsRed (END1-2) to express the presynaptically targeted fusion protein under the control of the neuron-specific elav promotor (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2A</xref>) (<xref ref-type="bibr" rid="bib15">Grabe et al., 2014</xref>). The reconstruction and identification of all AL glomeruli provided 53 glomeruli, of which 75% were innervated by MZ699+ iPNs (40) while 70% (37) were covered by GH146+ ePNs (<xref ref-type="fig" rid="fig1">Figure 1C</xref>, <xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2B</xref>). 55% of all glomeruli were innervated by both lines. Notably, dendritic <italic>MZ699-GAL4</italic> innervation density was not homogeneous. Certain glomeruli were densely innervated (e.g., DM2, DM4 and DM5), while others did not reveal any postsynaptic sites (e.g., DL1, DL4 and DL5). Hence MZ699+ iPNs target specific glomerular subsets selectively, which suggests that these neurons have a particular function within the olfactory network.</p></sec><sec id="s2-2"><title>Calcium signals in the lateral horn spatially segregate into distinct response domains</title><p>Probabilistic synaptic density maps of GH146+ PNs predicted a regionalized neuronal activity in the LH (<xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>). Do iPNs functionally segregate in a comparable way? To address this question, we expressed the Ca<sup>2+</sup>-sensitive reporter G-CaMP3.0 (<xref ref-type="bibr" rid="bib37">Nakai et al., 2001</xref>; <xref ref-type="bibr" rid="bib58">Tian et al., 2009</xref>) in iPNs using <italic>MZ699-GAL4</italic> and performed functional imaging in the LH (<xref ref-type="fig" rid="fig2">Figure 2A–C</xref>). We initially tested three odors with potential relevance for <italic>Drosophila</italic> at different concentrations: acetoin acetate, an attractive byproduct of the yeast fermentation process, balsamic vinegar, an attractive natural odor mixture, and benzaldehyde, a well-known fly repellant (<xref ref-type="bibr" rid="bib32">Magee and Kosaric, 1987</xref>; <xref ref-type="bibr" rid="bib23">Keene et al., 2004</xref>; <xref ref-type="bibr" rid="bib48">Semmelhack and Wang, 2009</xref>). We observed that odor evoked Ca<sup>2+</sup> responses separate in certain regions of the LH in an odor-specific and concentration-dependent manner (<xref ref-type="fig" rid="fig2">Figure 2C</xref>). Acetoin acetate and balsamic vinegar evoked Ca<sup>2+</sup> activity in spatially similar regions. At higher concentrations, an additional region was recruited. Benzaldehyde elicited no response at very low concentrations, but induced clear activity at median and high concentrations in a third region, which was completely separate from the regions activated by the other two odors. Observed patterns were highly reproducible within one animal and stereotypic among different individuals, as shown for the stimulation with 1-octen-3-ol (<xref ref-type="fig" rid="fig2">Figure 2D</xref>) as well as other odors (<xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>).<fig-group><fig id="fig2" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.006</object-id><label>Figure 2.</label><caption><title>Odors evoke specific and stereotypic calcium responses in the LH subdivided into three distinct odor response domains.</title><p>(<bold>A</bold>) Schematic of the olfactory circuit with the investigated area highlighted. (<bold>B</bold>) RAW image of the LH (top picture) depicting the recorded area of figures (<bold>C</bold>–<bold>E</bold>) and the false color image (bottom picture) during the solvent application. The ΔF/F scale bar applies for all false color-coded pictures; the alpha-bar for the pixel participation x<sub>k</sub> of the indicated colors applies for (<bold>E</bold>–<bold>F</bold>). (<bold>C</bold>) Representative LH Ca<sup>2+</sup> responses (ΔF/F%) of acetoin acetate, balsamic vinegar and benzaldehyde at three concentrations. Numbers in the lower right corner indicate individual maxima. (<bold>D</bold>) Odor-evoked Ca<sup>2+</sup> responses (ΔF/F%) are exemplarily depicted for 1-octen-3ol- at three concentrations in four animals. (<bold>E</bold>) NNMF-extracted LH odor response domains (ORD) of four representative animals: three LH ORDs were fully reproducible after being extracted from all measured animals. Domains classified as identical are similarly color-coded: the green ORD is located in the <underline>p</underline>osterior-<underline>m</underline>edial region of the <underline>LH</underline> (LH-PM); blue, in the <underline>a</underline>nterior-<underline>m</underline>edial (LH-AM), and red in the <underline>a</underline>nterior-<underline>l</underline>ateral LH area (LH-AL). The alpha-bar for green, blue and red shades is placed in (<bold>B</bold>). (<bold>F</bold>) Left, schematic outlines of the LH with indicated ORDs. Right, median activity traces of all odors at three concentrations are depicted for each colored ORD. Shadows represent lower and upper quartiles (n = 6–7).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.006">http://dx.doi.org/10.7554/eLife.04147.006</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f002"/></fig><fig id="fig2s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.007</object-id><label>Figure 2—figure supplement 1.</label><caption><title>Odor-evoked activity patterns in the LH are reproducible and stereotypic.</title><p>Odor-evoked Ca<sup>2+</sup> responses (ΔF/F%) in the LH for the three odors acetoin acetate, balsamic vinegar and benzaldehyde in four animals are shown as false-color coded images. Two measurements in each animal are given to reveal that the activity patterns are highly reproducible within one animal. Comparison between the patterns among individuals shows that the activity regions are stereotypic. Numbers in the lower right corner indicate individual maxima. The ΔF/F scale bar is shown at the bottom.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.007">http://dx.doi.org/10.7554/eLife.04147.007</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs003"/></fig><fig id="fig2s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.008</object-id><label>Figure 2—figure supplement 2.</label><caption><title>Odor-evoked activity patterns in the LH can be reconstructed with five components.</title><p>Above, Odor-evoked Ca<sup>2+</sup> responses (ΔF/F%) in the LH for the three odors acetic acid, balsamic vinegar and 1-octen-3-ol in three animals are shown as false-color coded images. The ΔF/F scale bar is shown at the bottom. Middle, activity patterns were reconstructed using NNMF with five components. Below, residue of the pattern reconstructions with five components (as shown in the middle panel) revealing that no stimulus related activity remained.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.008">http://dx.doi.org/10.7554/eLife.04147.008</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs004"/></fig><fig id="fig2s3" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.009</object-id><label>Figure 2—figure supplement 3.</label><caption><title>Odor-evoked activity patterns in the LH cluster into three components.</title><p>Hierachichal clustering (UMPGA) of the odor response spectra of the NNMF components with a reliable stimulus response (trial-to-trial correlation >0.7, that is, 28 out of 35 components in seven animals). The response spectra segregate into three distinct clusters according to their stimulus response spectra. The corresponding response areas (left pictures) are located in similar regions of the LH.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.009">http://dx.doi.org/10.7554/eLife.04147.009</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs005"/></fig></fig-group></p><p>Due to the lack of morphological landmarks in the LH, functional data were analyzed using the pattern recognition algorithm Non-Negative Matrix Factorization (NNMF) (<xref ref-type="bibr" rid="bib29">Lee and Seung, 1999</xref>), which automatically extracts spatial areas possessing a common distinct time-course, further termed LH <underline>o</underline>dor <underline>r</underline>esponse <underline>d</underline>omains (ORDs). The NNMF analysis extracted three clearly reproducible and spatially robust ORDs (<xref ref-type="fig" rid="fig2">Figure 2E</xref>, see NNMF part in the ‘Materials and methods’ section). Notably, ORDs occupying common temporal kinetics exhibited highly stereotypic spatial patterns. We termed the ORDs LH-PM (<underline>LH</underline>-<underline>p</underline>osterior-<underline>m</underline>edial), LH-AM (<underline>LH</underline>-<underline>a</underline>nterior-<underline>m</underline>edial) and LH-AL (<underline>LH</underline>-<underline>a</underline>nterior-<underline>l</underline>ateral) according to their anatomical positions. To validate our observations, we extended our stimulus array to 11 additional odorants and applied each at three concentrations. Odorants were chosen according to chemical classes, hedonic valence and biological value. Hence, the odor set included acids, lactones, terpenes, aromatics, alcohols, esters, ketones and the natural blend, balsamic vinegar. Remarkably, analysis of the additional odorants revealed neuronal activity exclusively within the three described ORDs (<xref ref-type="fig" rid="fig2">Figure 2F</xref>, <xref ref-type="fig" rid="fig2s2 fig2s3">Figure 2—figure supplements 2,3</xref>). Furthermore, median NNMF-extracted Ca<sup>2+</sup> response traces with indicated statistical quartiles illustrate very low variability and highly reproducible LH signals. The LH-PM area chiefly revealed robust odor-evoked responses across concentrations, while the LH-AM and LH-AL were mainly activated at very high odor concentrations by distinct odorants. The global responsiveness within separate ORDs in the LH substantiates our finding of a relatively broad AL input to MZ699+ iPNs which converges into three spatially regionalized and stereotypic LH activity domains.</p></sec><sec id="s2-3"><title>iPNs can be divided into two morphological classes</title><p>We next investigated if the spatially regionalized odor-evoked response patterns are reflected in the axonal terminal fields of MZ699+ iPNs in the LH. To analyze these neurons at the single neuron level, we performed neural tracing by employing a genetically encoded photoactivatable GFP (PA-GFP) (<xref ref-type="bibr" rid="bib42">Patterson and Lippincott-Schwartz, 2002</xref>; <xref ref-type="bibr" rid="bib9">Datta et al., 2008</xref>; <xref ref-type="bibr" rid="bib45">Ruta et al., 2010</xref>). The photoconversion of all MZ699+ neurons leaving the AL confirmed the homogeneous distribution of iPN neurites in the LH and the sparse innervation of the posterior-lateral region as mentioned above (<xref ref-type="fig" rid="fig3">Figure 3A</xref>). Next we illuminated PA-GFP in single somata to selectively label individual MZ699+ iPNs from the soma up to the farthest axonal terminals in the LH. Individual iPNs were reconstructed and transformed into a reference brain using the END1-2 background (<xref ref-type="bibr" rid="bib15">Grabe et al., 2014</xref>) to align neurons of different individuals. Based on their innervation pattern in the LH, MZ699+ iPNs could be assigned to two major morphological classes (<xref ref-type="fig" rid="fig3">Figure 3B,C</xref>). As expected from the extracted ORDs, one iPN group diverged to the LH-PM region (8/25 of iPNs), while a second group extended their axonal terminations within the LH-AM area (10/25 of iPNs). In order to statistically substantiate our observation, we performed a cluster analysis based on a similarity score (<xref ref-type="bibr" rid="bib25">Kohl et al., 2013</xref>) of the target areas of all terminals of each iPN in the LH (for details see ‘Materials and methods’). The dendrogram of morphological similarity between each individual iPN shows that all, except one iPN, could be clustered according to their target region in either the LH-AM or LH-PM area (<xref ref-type="fig" rid="fig3">Figure 3D</xref>) which confirms the classification into two major categories. Additionally, we performed a principal component analysis based on the distances of the similarity scores showing that both neuronal classes possess significantly different target areas in the LH (<xref ref-type="fig" rid="fig3s1">Figure 3—figure supplement 1A</xref>; p < 0.001, one-way ANOSIM).<fig-group><fig id="fig3" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.010</object-id><label>Figure 3.</label><caption><title>iPNs can be classified according to their projection pattern in three distinct LH zones.</title><p>(<bold>A</bold>) Complete population of MZ699+ iPNs labeled using PA-GFP (left image), the posterior-lateral LH region is encircled, arrowhead indicates the final common projection point of iPN axons. Middle image: photoactivation of all vlPr neurons of the MZ699-GAL4 line that project from the LH to the vlPr via the plF. Right image: exemplary single iPN, labeled by photoconverting PA-GFP in a single soma (arrow). Scale bar, 20 µm. (<bold>B</bold>) Framed images: neuronal reconstructions of all iPNs projecting to the LH-PM zone (n = 8) with outlined olfactory neuropils. View from dorsal (left) and lateral (right). Right part represents two exemplary registered individual iPNs. (<bold>C</bold>) Neuronal reconstructions of all iPNs projecting into the LH-AM zone (n = 10), images are arranged as in (<bold>B</bold>). (<bold>D</bold> and <bold>E</bold>) Cluster analyses based on the target areas of all terminals of each iPN in the LH (<bold>D</bold>) or based on the innervated glomeruli in the AL (<bold>E</bold>). The dendrograms are split into colored subclusters. Below each dendrogram, each individual iPN is specified according to the labels in <xref ref-type="fig" rid="fig3s2">Figure 3—figure supplement 2</xref>. Note, that iPNs can be morphologically clustered according to their target or input regions. (<bold>F</bold>) Neuronal reconstruction of vlPr neurons projecting through the plF to the LH-AL zone. (<bold>G</bold>) Combination of all registered neurons. (<bold>H</bold>) Dual combinations of all registered neurons with their projections in the LH.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.010">http://dx.doi.org/10.7554/eLife.04147.010</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f003"/></fig><fig id="fig3s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.011</object-id><label>Figure 3—figure supplement 1.</label><caption><title>iPNs can be morphologically segregated according to their target and input region.</title><p>(<bold>A</bold>) Principal component analysis based on the distances of the similarity scores of all terminal points of each individual iPN in the LH (for details see ‘Materials and methods’). LH-AM iPNs (blue) and LH-PM iPNs (green) form significantly distinct clusters (***p < 0.001, One-Way ANOSIM, Bray–Curtis). (<bold>B</bold>) Principal component analysis based on the glomerular innervations of each individual iPN in the AL. Again, LH-AM iPNs (blue) and LH-PM iPNs (green) form significantly distinct clusters (***p < 0.001, One-Way ANOSIM, Bray–Curtis).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.011">http://dx.doi.org/10.7554/eLife.04147.011</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs006"/></fig><fig id="fig3s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.012</object-id><label>Figure 3—figure supplement 2.</label><caption><title>Glomerular innervations of individual iPNs.</title><p>Binary innervation patterns of 25 individually labeled MZ699+ iPNs using PA-GFP. Columns represent innervation patterns of individual neurons which have been grouped according to their innervation properties; rows represent 51 glomeruli in the AL along with the innervation in the specific odor response domains in the LH (LH-PM, LH-AM) and/or the mushroom body calyx (MBc). Glomeruli have been sorted according to their iPN innervation. Grey, innervated; white, not innervated.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.012">http://dx.doi.org/10.7554/eLife.04147.012</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs007"/></fig></fig-group></p><p>We did not observe any clear panglomerular innervations of individual MZ699+ iPNs that spanned the entire AL, consistent with <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref>. Instead, MZ699+ iPNs develop mainly oligoglomerular patterns innervating on average 5.4 ± 3.9 glomeruli (mean ± SD), which are not necessarily in close proximity. It is important to note here, that the glomerular innervations of iPNs are rather sparse in comparison to the innervation of ePNs which complicates the identification of truly innervated glomeruli. After classifying all registered neurons according to their LH zones along with their glomerular innervations, we noted a spatial subdivision of MZ699+ iPN dendritic fields in the AL (<xref ref-type="fig" rid="fig3s2">Figure 3—figure supplement 2</xref>). Whereas LH-PM iPNs extended dendrites mainly into glomeruli from the ventro- or dorsomedial area of the AL (e.g., DM4, DM2, VM7, VM5d), iPNs targeting the LH-AM zone innervated glomeruli ranging from the ventro- and dorsoanterior to the dorsocentral region (e.g., DC3, VC1, VA6, VL1). We observed that a glomerulus is typically innervated by only LH-PM iPNs or LH-AM iPNs. However, we also found a few cases where a glomerulus can be innervated by both iPN types (e.g., glomeruli D and DC2). In order to analyze whether the two categories of iPNs can also be statistically separated according to their glomerular innervations in the AL, we performed a cluster analysis based on the glomeruli innervated by each individual iPN (<xref ref-type="fig" rid="fig3">Figure 3E</xref>). Notably, the two iPN classes could be clearly clustered into two groups due to their specific AL innervations. This finding is further supported by a principal component analysis showing that iPNs targeting the LH-PM region innervate a significant different glomerular subset than iPNs that send their axonal terminals to the LH-AM area (<xref ref-type="fig" rid="fig3s1">Figure 3—figure supplement 1B</xref>; p < 0.001, one-way ANOSIM). In accordance with our finding of two major iPN categories is the study by <xref ref-type="bibr" rid="bib26">Lai et al. (2008)</xref> who observed several different stereotyped projection patterns of multiglomerular MZ699+ single-cell clones that could be broadly categorized into two groups based on the dendritic and axonal projection patterns. While we observed corresponding innervated areas in the AL, the described target areas in the LH seem to differ. However, due to the lack of 3D reconstruction of the single-cell clone data, the innervation patterns cannot be compared in detail.</p><p>In addition to the oligoglomerular iPNs, we observed a few uniglomerular MZ699+ iPNs innervating either glomerulus DA1 or VL1 (4/25 of iPNs), consistent with <xref ref-type="bibr" rid="bib26">Lai et al. (2008)</xref>, which target the LH-AM region (<xref ref-type="fig" rid="fig3s2">Figure 3—figure supplement 2</xref>). Moreover, we identified three other MZ699+ neurons that did not innervate the AL and sent their axons through the mACT to the LH and/or the MBc.</p><p>Since the <italic>MZ699-GAL4</italic> line labels also neurons connecting the LH and the ventrolateral protocerebrum (vlPr) (<xref ref-type="bibr" rid="bib20">Ito et al., 1997</xref>; <xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>), we illuminated a small fraction of the posterior lateral fascicle (plF) to target these putative third-order neurons (<xref ref-type="fig" rid="fig3">Figure 3A</xref>). The plF comprised axons of ventrolateral protocerebral neurons (vlPr neurons), which bifurcated within the LH-AL (<xref ref-type="fig" rid="fig3">Figure 3F</xref>). Combinations of all registered neuron types within the assigned zones revealed that iPNs of the LH-AM area and vlPr neurons of the LH-AL region intermingle (<xref ref-type="fig" rid="fig3">Figure 3G,H</xref>).</p></sec><sec id="s2-4"><title>Odor response domains contain the activity of distinct neuronal populations</title><p>To illustrate higher-order connectivity, we labeled the three major neuron types, that is, MZ699+ iPNs, GH146+ ePNs and vlPr neurons, targeting the LH within the olfactory circuitry using PA-GFP (<xref ref-type="fig" rid="fig4">Figure 4A</xref>). Since our observed Ca<sup>2+</sup> responses in the LH-AL region might reflect activity from vlPr neurons rather than iPNs, we dissected the neuronal contributions within each extracted ORD by conducting transection experiments using two-photon laser-mediated microdissection (<xref ref-type="fig" rid="fig4">Figure 4B</xref>). By transecting the mACT, we aimed at abolishing LH-responses deriving from MZ699+ iPNs, while cutting the plF connection should eliminate potential odor-evoked vlPr neuron activity. To achieve unambiguous and comparable results, functional imaging was performed in both brain hemispheres simultaneously. Immediately after the intact brain areas were imaged, the tracts were selectively transected on one brain side each (<xref ref-type="fig" rid="fig4">Figure 4C</xref>) and the imaging procedure was repeated. We applied a reduced odor set that elicited activity in all ORDs and performed NNMF for pre- and post-lesion recordings. Transecting the mACT significantly reduced responses in the LH-PM and LH-AM region, whereas LH-AL responses were significantly abolished by plF-ablation (<xref ref-type="fig" rid="fig4">Figure 4D</xref>). Notably, we observed that LH-AL responses to some odors were significantly increased after mACT transection as a consequence of the suppression of iPN inhibition of vlPr neurons confirming the study by <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref>. Hence, activity in the LH-PM and LH-AM domain can be assigned to MZ699+ iPNs, while LH-AL activity is mainly evoked by vlPr neurons (<xref ref-type="fig" rid="fig4">Figure 4E</xref>).<fig id="fig4" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.013</object-id><label>Figure 4.</label><caption><title>Distinct odor response domains in the LH constitute neuronal activity of iPNs and vlPr neurons.</title><p>(<bold>A</bold>) Representation of all ePNs (magenta) and iPNs (green) labeled by <italic>GH146-GAL4</italic> and <italic>MZ699-GAL4</italic> using PA-GFP, respectively. Photoactivation of vlPr neurons (orange, MZ699-GAL4) connecting the LH and the vlPr via the plF. The overlay image depicts a pseudo-merge image of the different GAL4-driver lines. (<bold>B</bold>) Schematic of the olfactory circuit with integrated layout of the transection experiment. After simultaneous Ca<sup>2+</sup> imaging of bilateral LHs, the ipsilateral plF and contralateral mACT was transected (red zigzag line) with an infrared laser (dashed red arrow). (<bold>C</bold>) Projection images of a 7 µm stack of the LH area prior and post transection. Left images, mACT transected; right image, plF transected. The ablated region is indicated by the dashed red arrow. Scale bar, 20 µm. (<bold>D</bold>) Median time traces displaying percental change of ΔF/F values for indicated ORDs prior to post transection of the mACT (green, left) and the plF (orange, right) for different odorants. Significant changes of odor-evoked Ca<sup>2+</sup> signals due to transection are shown in the column SIG difference. Differences were tested with a two-tailed paired Student's <italic>t</italic> test (p < 0.05). Color codes are indicated by the corresponding scale bar below, n = 4–5. Transecting the mACT eliminates Ca<sup>2+</sup> signals in the LH-PM and LH-AM domain, while lesioning the plF significantly abolishes LH-AL responses. Notably, the LH-AL domain is significantly stronger activated after mACT transection following application of 1-octen-3-ol and γ-butyrolactone. (<bold>E</bold>) Summarized cartoon of the neuron populations contributing to ORD activity prior and post transection of axons of iPNs or vlPr neurons.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.013">http://dx.doi.org/10.7554/eLife.04147.013</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f004"/></fig></p></sec><sec id="s2-5"><title>iPN activity in the lateral horn mediates flies' attraction to odors</title><p>We next addressed the behavioral relevance of MZ699+ iPN activity in the LH for innate odor-guided behavior. To precisely target iPN function, we expressed an RNAi construct against glutamic acid decarboxylase 1 (GADi) to selectively knock-down the GABA synthesis in MZ699+ iPNs (<xref ref-type="fig" rid="fig5">Figure 5A</xref>). We confirmed the reduction in GABA production via immunostaining (<xref ref-type="fig" rid="fig5">Figure 5B</xref>). Since vlPr neurons are not GABAergic, they were not affected by the RNAi expression (<xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>; <xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>). Using wild-type flies and parental controls, we conducted T-maze assays (<xref ref-type="bibr" rid="bib59">Tully and Quinn, 1985</xref>; <xref ref-type="bibr" rid="bib4">Chakraborty et al., 2009</xref>) with nine of the odorants applied in functional imaging experiments at medium and high concentrations. Notably, flies with silenced MZ699+ iPN GABA production revealed a neutral or aversive behavioral response to attractive odors, while repellent odors evoked an even stronger aversion (<xref ref-type="fig" rid="fig5">Figure 5C</xref>). To compare the T-maze data more accurately, we calculated the average change of behavioral response indices (RIs) between GADi flies and parental controls (<xref ref-type="fig" rid="fig5">Figure 5D</xref>). Indeed, all responses changed in a negative direction, indicating MZ699+ iPNs play a crucial role in mediating attraction behavior. The sole exception involved high concentrations of the most repulsive odor, acetophenone, since this odor had already induced maximum aversion. Overall, these experiments reveal a crucial function of MZ699+ iPNs in mediating attraction behavior by releasing GABA in the LH.<fig id="fig5" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.014</object-id><label>Figure 5.</label><caption><title>iPN GABA release in the LH mediates odor attraction behavior.</title><p>(<bold>A</bold>) Experimental layout: iPN GABA production was selectively silenced via GADi expression in MZ699+ iPNs; ePN and vlPr neuron activity remained unaffected. (<bold>B</bold>) Immunostaining against GABA and GFP within AL somata (left) and LH neurites (right) of iPNs with intact (top) and silenced GABA production (bottom). GADi flies show GABA signals in somata of iPNs labeled by <italic>GH146-GAL4</italic> only (arrowhead). The arrow head points to an exemplary GABA-positive bouton in the LH. Scale bar, 20 µm. (<bold>C</bold>) Averaged behavioral response indices (RIs) determined with a T-maze assay for wild-type flies (dark blue), parental controls (light blue) and experimental animals (magenta) for nine odorants at two concentrations. Empty boxes display no response (Wilcoxon signed-rank test). Dunn's Multiple Comparison Test was used for global differences in the dataset followed by a posthoc test for selected pairs (p* < 0.05; **p < 0.01; ***p < 0.001). Error bars represent SEM. (<bold>D</bold>) RI differences between GADi flies and averaged parental controls. RI differences are negative for all but one odor indicating that GADi expression shifts odor-guided behavior towards aversion. Error bars indicate SEM.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.014">http://dx.doi.org/10.7554/eLife.04147.014</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f005"/></fig></p></sec><sec id="s2-6"><title>The lateral horn integrates hedonic valence and odor intensity into separate domains</title><p>The behavioral effect of the iPN knock-down suggests that MZ699+ iPNs encode positive hedonic valences. To correlate the complete ORD pattern array with innate behavioral preferences, we assigned behavioral RIs for all odors at median and high odor concentrations using the T-maze assay as in our previous experiment (<xref ref-type="fig" rid="fig6">Figure 6A</xref>). Since extremely low concentrations rarely evoked any behavioral response, we excluded the 10<sup>−6</sup> concentration in this analysis. It is important to note here, that different behavioral assays for testing olfactory preferences in flies might lead to contradictory results. However, the majority of odors used here was also tested in two other behavioral paradigms, the trap assay (<xref ref-type="bibr" rid="bib55">Stökl et al., 2010</xref>; <xref ref-type="bibr" rid="bib24">Knaden et al., 2012</xref>) and the FlyWalk (<xref ref-type="bibr" rid="bib51">Steck et al., 2012</xref>) (pers. comm. M Knaden) and yielded similar results (see <xref ref-type="fig" rid="fig6s1">Figure 6—figure supplement 1</xref>). When we plotted median odor-evoked activity in a three-dimensional space defined by the three ORDs, we saw a clear clustering of responses evoked by aversive and attractive odorants (<xref ref-type="fig" rid="fig6">Figure 6B</xref>). The LH-AL domain, constituted mainly by vlPr neurons, is coding aversive odors, while attractive odors activated only the LH-PM and LH-AM domains that derive from MZ699+ iPNs. This result is in accordance with our finding that iPNs mediate odor attraction.<fig-group><fig id="fig6" position="float"><object-id pub-id-type="doi">10.7554/eLife.04147.015</object-id><label>Figure 6.</label><caption><title>Integration of hedonic valence and odor concentration into ORDs.</title><p>(<bold>A</bold>) Response indices of wild type flies for all odors at median and high concentrations. Odors are sorted from highly aversive (−1, red) to highly attractive (+1, green). (<bold>B</bold>) 3D-scatter plot of median Ca<sup>2+</sup> responses of all odors based on the three ORDs. Odor-dots are labeled due to their RI shown in (<bold>A</bold>). Same odors at different concentrations are connected with a line: the dot at the end depicts 10<sup>−2</sup>, the centered dot 10<sup>−4</sup>, and the end of the line 10<sup>−6</sup>. Attractive and aversive odor representations form separate clusters. (<bold>C</bold> and <bold>D</bold>) Left, schematic LH outlines with colored ORDs corresponding to data on the right. Correlation score r (upper right corner) between median activity and measured RI in T-maze experiments or odor concentration, respectively, with significance denoted below. Student's <italic>t</italic> test, *p < 0.05, ***p < 0.001. (<bold>E</bold>) Complete correlation matrices for Ca<sup>2+</sup> response patterns of OSNs in the AL (left) and iPNs in the LH (right). The odors are arranged according to single linkage clustering of the LH activity patterns. Heatmap color-code refers to the correlation distance scale bar on the right. Correlation distance is defined as 1 − r, where r is the Pearson correlation coefficient between the response patterns of two odorants. Odor letters are color-coded according to hedonic valence; 10<sup>−6</sup> RI values are labeled in grey (complete list right hand).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.015">http://dx.doi.org/10.7554/eLife.04147.015</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147f006"/></fig><fig id="fig6s1" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.016</object-id><label>Figure 6—figure supplement 1.</label><caption><title>Odor valences determined with three different behavioral assays.</title><p>Odor-evoked behavioral responses of wild type flies for the 14 odors used in this study determined by T-maze assay, trap assay and the FlyWalk. The color denotes an attractive (green), aversive (red) or a neutral (light yellow) behavioral response. N/T, not tested. The majority of odors yielded similar results independent of the behavioral assay used. In a few cases an attractive odor evoked a neutral response (i.e., no response), but never induced an aversive response in another assay.</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.016">http://dx.doi.org/10.7554/eLife.04147.016</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs008"/></fig><fig id="fig6s2" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.017</object-id><label>Figure 6—figure supplement 2.</label><caption><title>Calcium responses of OSNs.</title><p>(<bold>A</bold>) Representative glomerular Ca<sup>2+</sup>-responses of OSNs in the AL for a subset of odorants at three concentrations. Scale bar to the right. Control (mineral oil) recordings are shown additionally as full false-color coded images. (<bold>B</bold>) Glomerular AL atlas used for glomerular identification. (<bold>C</bold>) Median Ca<sup>2+</sup>-activity traces of all glomeruli for all odorants at the three indicated concentrations. Scale bar and control measurement in the center. Odor application is indicated by the grey bar below the heatmaps (n = 6–7).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.017">http://dx.doi.org/10.7554/eLife.04147.017</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs009"/></fig><fig id="fig6s3" position="float" specific-use="child-fig"><object-id pub-id-type="doi">10.7554/eLife.04147.018</object-id><label>Figure 6—figure supplement 3.</label><caption><title>Correlation matrices for odor-evoked responses in the AL and LH.</title><p>Complete correlation matrices for calcium activity patterns of OSNs in the AL (left) and iPNs in the LH (right). The odors are arranged according to single linkage clustering of the AL activity patterns. Heatmap color-code refers to the correlation distance scale bar below each matrix. Odor letters are color-coded according to hedonic valence; 10<sup>−6</sup> RI values are labeled in grey (complete list right hand).</p><p><bold>DOI:</bold> <ext-link ext-link-type="doi" xlink:href="10.7554/eLife.04147.018">http://dx.doi.org/10.7554/eLife.04147.018</ext-link></p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="elife04147fs010"/></fig></fig-group></p><p>We next correlated ORD activity to odor valence separately for all ORDs. This evaluation enabled us to analyze iPN and vlPr neuron coding properties apart from each other (<xref ref-type="fig" rid="fig6">Figure 6C</xref>). As expected, the analysis revealed a significant correlation between positive valence and the LH-PM domain, whereas Ca<sup>2+</sup> responses in the LH-AL were strongly negatively correlated to hedonic valence. The LH-AM domain exhibited a positive but not significant correlation for odor valence. Remarkably, activity within the LH-PM was totally independent of concentration, whereas activity in both anterior domains was significantly correlated to odor intensity (<xref ref-type="fig" rid="fig6">Figure 6D</xref>). Hence, MZ699+ iPNs integrate odor attraction information into the LH-PM domain independent of odor intensity, confirming behavioral experiments. Intensity coding is in turn conducted separately by distinct iPNs within the LH-AM domain. In contrast, putative third-order vlPr neurons projecting into the LH-AL area code both negative valence and odor intensity.</p><p>Finally, we wondered if this valence-specific LH representation is already reflected at the primary level of olfactory processing. The odor-evoked responses in iPNs are generally similar to those in OSNs (<xref ref-type="bibr" rid="bib63">Wang et al., 2014</xref>), indicating a straight forward transduction of cholinergic OSN responses. We therefore performed functional imaging of odor-evoked Ca<sup>2+</sup> dynamics at the AL input level by expressing G-CaMP3.0 in OSNs using Orco-GAL4 (<xref ref-type="bibr" rid="bib28">Larsson et al., 2004</xref>) (<xref ref-type="fig" rid="fig6s2">Figure 6—figure supplement 2</xref>). In order to compare the activity patterns at both processing levels, we calculated correlation distances for all pair-wise combinations of odor-evoked response patterns and plotted these with respect to maximal ORD pattern similarity in the LH (<xref ref-type="fig" rid="fig6">Figure 6E</xref>). As expected, odor representations in the LH clearly clustered within three separated parts of the matrix, reflecting our observed ORDs. However, this coding similarity could not predict AL activity patterns, even if the correlation matrix was sorted with respect to pattern similarity in the AL (<xref ref-type="fig" rid="fig6s3">Figure 6—figure supplement 3</xref>).</p></sec></sec><sec sec-type="discussion" id="s3"><title>Discussion</title><p>We augment our present understanding of the <italic>Drosophila</italic> olfactory circuitry by elucidating the coding properties of a parallel and behaviorally relevant higher-order processing pathway to the LH. Morphological, functional and behavioral approaches provide strong evidence for a functional subdivision of iPNs into neurons coding either odor attraction or odor intensity. Our behavioral experiments reveal that inhibitory properties of iPNs are necessary for innate odor-guided attraction. In addition, we characterize a third neural pathway coding odor repellence.</p><p>Do MZ699+ iPNs fulfill anatomical requirements to constitute a distinct processing channel in addition to ePNs? A remarkable anatomical feature of MZ699+ iPNs is their glomerular innervation pattern in the AL. Whereas GH146+ ePNs are uniglomerular and retain the topographic code in their axonal arrangement (<xref ref-type="bibr" rid="bib34">Marin et al., 2002</xref>; <xref ref-type="bibr" rid="bib68">Wong et al., 2002</xref>; <xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>), most MZ699+ iPNs possess oligoglomerular innervations suggesting that these neurons might not convey precise odor-identity information. In addition, MZ699+ iPNs in the AL diverge only into specific glomerular subsets, and so might be pre-determined to selectively extract common features of distinct odors. We have previously shown that the AL map at the PN level exhibits a spatial segregation of valence representation (<xref ref-type="bibr" rid="bib24">Knaden et al., 2012</xref>). Certain glomeruli, which have been classified as aversion coding at the GH146+ ePN level, are omitted by MZ699+ iPNs, whereas most glomeruli classified as attraction coding are particularly densely innervated. These results suggest that within the MZ699+ iPN population, mainly positive odor traits are extracted, whereas the odor information of negative valence is neglected. This conclusion is consistent with the recent finding that one type of LH neurons is receiving input from PNs that mainly innervate glomeruli coding fruity-smelling acetates (<xref ref-type="bibr" rid="bib13">Fisek and Wilson, 2014</xref>) which represent attractive odor cues (<xref ref-type="bibr" rid="bib24">Knaden et al., 2012</xref>). We furthermore demonstrate that the MZ699+ iPN population is split into two major morphological classes possessing a clear spatial segregation in the AL which is strictly maintained within the LH. It has to be kept in mind that we do not cover all iPNs by using <italic>MZ699-GAL4</italic>. Further experiments characterizing the ∼6 missing MZ699– iPNs, which are labeled by <italic>GH146-GAL4</italic> (<xref ref-type="bibr" rid="bib65">Wilson and Laurent, 2005</xref>; <xref ref-type="bibr" rid="bib26">Lai et al., 2008</xref>), will elucidate if our assumptions apply for the whole iPN population.</p><p>So far only a handful neuroanatomical studies targeting GH146+ ePNs have dealt with the question of how olfactory information is integrated and read out by higher brain structures, in particular the LH (<xref ref-type="bibr" rid="bib34">Marin et al., 2002</xref>; <xref ref-type="bibr" rid="bib68">Wong et al., 2002</xref>; <xref ref-type="bibr" rid="bib57">Tanaka et al., 2004</xref>; <xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>). A recent study that traced the projection pattern of PNs coding ammonia and amines as attractive stimuli and carbon dioxide and acids as repulsive signals suggests that sensory stimuli of opposing valence are represented in spatially distinct areas within the LH (<xref ref-type="bibr" rid="bib35">Min et al., 2013</xref>). In addition the study by <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref> showed that MZ699+ iPNs selectively suppress the activity of vlPr neurons to food odors, while pheromone responses were not affected verifying the assumption that different odor features are processed separately. However, functional evidence for a feature-based, spatially segregated activity map in the LH was so far missing.</p><p>To unravel the coding properties of MZ699+ iPNs within the LH, we conducted Ca<sup>2+</sup> imaging experiments of MZ699+ iPNs in the LH to odorants having different hedonic valences and intensities, and could classify the LH into three functional ORDs. Our neuronal tracing and transection experiments validated the LH segmentation into two medial domains that derive from MZ699+ iPNs, and the LH-AL domain formed by vlPr neurons. In line with our observations are morphological studies on ePNs and third-order LH neurons revealing a similarly tight constriction into three zones within the LH (<xref ref-type="bibr" rid="bib57">Tanaka et al., 2004</xref>), while single-cell labeling combined with image registration resulted in five ePN target zones (<xref ref-type="bibr" rid="bib22">Jefferis et al., 2007</xref>). However, the ePN terminal zones do not necessarily correspond to the target domains of iPNs, since it has recently been shown that MZ699+ iPNs do not inhibit odor responses of GH146+ ePNs (<xref ref-type="bibr" rid="bib30">Liang et al., 2013</xref>) and that the presynaptic sites of iPNs are spatially separated from those of ePNs (<xref ref-type="bibr" rid="bib63">Wang et al., 2014</xref>). Hence both PN populations represent parallel processing pathways that most likely accomplish distinct processing tasks analogous to the honeybee olfactory system which possesses dual olfactory pathways to the higher brain that accomplish parallel processing of similar odors (<xref ref-type="bibr" rid="bib2">Brill et al., 2013</xref>).</p><p>Silencing MB function revealed that the LH alone is sufficient for basic olfactory behavior (<xref ref-type="bibr" rid="bib10">de Belle and Heisenberg, 1994</xref>; <xref ref-type="bibr" rid="bib7">Connolly et al., 1996</xref>; <xref ref-type="bibr" rid="bib17">Heimbeck et al., 2001</xref>). Our behavioral results demonstrate that selectively silencing MZ699+ iPNs severely reduced the flies' odor attraction behavior. Hence our results suggest that MZ699+ iPNs are capable of extracting specific features from the combinatorial code emerging in the AL. A behavioral study revealed that silencing MBc neurons impairs odor attraction but not repulsion (<xref ref-type="bibr" rid="bib64">Wang et al., 2003b</xref>). The authors drew the conclusion that the LH is involved in mediating innate repulsion rather than attraction. These results are not necessarily contradictory to ours since some ePNs might activate the LH-AL domain exclusively (i.e., vlPr neurons). On the other hand, <xref ref-type="bibr" rid="bib64">Wang et al. (2003b)</xref> did not include highly concentrated attractive odors. Therefore it is possible that in their experiments, the odor detection threshold was simply reduced, so that only highly concentrated odors, which induced odor aversion, could be distinguished. Our behavioral results, in contrast, revealed the constant influence of the MZ699+ iPNs in mediating attraction for odorants over a range of concentrations.</p><p>Our data suggests that odors with opposing hedonic valences are encoded by an interplay of distinct processing pathways. The study by <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref> showed that GABA release from MZ699+ iPNs directly inhibits responses of vlPr neurons to food odors as mentioned above. This finding fits well to our observations that iPNs are activated mainly by attractive odors while vlPr neurons are not, likely due to the inhibitory input from iPNs. VlPr neurons are, on the other hand, almost solely activated by repellent odors, which do hardly activate iPNs and therefore do not induce a strong inhibition to vlPr neurons. Repellent odors most likely activate vlPr neurons via ACh release of ePNs which is supported by immunostainings with pre- and postsynaptic markers indicating that vlPr neurons receive input in the LH, while the vlPr represents their major output region (<xref ref-type="bibr" rid="bib40">Parnas et al., 2013</xref>). The vlPr is supposedly also a target of visual neurons from the optic lobe (<xref ref-type="bibr" rid="bib57">Tanaka et al., 2004</xref>) implying that a certain integration of different sensory modalities takes place at this central processing relay. Given that iPNs are inhibitory neurons, the underlying mechanism of odor attraction behavior might therefore be an inhibition of aversive neuronal circuits from the LH to the vlPr that are mainly composed of vlPr neurons. However, this assumption needs to be verified with further experiments elucidating if vlPr neurons are sufficient and necessary to mediate odor aversion.</p><p>What is known about odor coding in the LH in other insect species? Notably, in locusts it has been shown that LH neurons receiving convergent PN input appeared to encode stimulus intensity in their net firing rates and in the phases of their spikes (<xref ref-type="bibr" rid="bib16">Gupta and Stopfer, 2012</xref>). Hence these results support the idea that within the LH, general stimulus features such as odor intensity are extracted, which is well in line with our observation of the anterior LH domains whose activity is also significantly correlated to odor intensity. Also in line with our results is a study from honeybees which shows that the representation of different pheromone types is spatially segregated in the LH (<xref ref-type="bibr" rid="bib44">Roussel et al., 2014</xref>), indicating that odors eliciting specific behaviors are coded according to their biological values.</p><p>In conclusion, our study provides an important step in unraveling higher olfactory processing mechanisms that are crucial for mediating innate behaviors in <italic>Drosophila</italic>. We provide functional evidence for a feature-based spatial arrangement of the LH decoding opposing hedonic valences and odor intensity. The role of the LH as a center for integrating biological values towards innate decisions by computing conveyed information of two processing pathways is thus expanded.</p></sec><sec sec-type="materials|methods" id="s4"><title>Materials and methods</title><sec id="s4-1"><title><italic>Drosophila</italic> stocks</title><p>All fly stocks were maintained on conventional cornmeal-agar-molasses medium under L:D 12:12, RH = 70% and 25°C. For wild-type controls <italic>D. melanogaster</italic> of the Canton-S strain was used. Transgenic lines were obtained from Bloomington Stock Center (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://flystocks.bio.indiana.edu/">http://flystocks.bio.indiana.edu/</ext-link>) and Vienna RNAi stock center (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.vdrc.at">http://www.vdrc.at</ext-link>). Other fly stocks were kindly provided by Kei Ito (<italic>MZ699-GAL4</italic>) and Maria Luisa Vasconcelos (<italic>UAS-C3PA</italic>). The END1-2 fly line is published in <xref ref-type="bibr" rid="bib15">Grabe et al. (2014)</xref>.</p></sec><sec id="s4-2"><title>Immunohistochemistry</title><p>Whole-mount immunofluorescence staining was carried out as described (<xref ref-type="bibr" rid="bib27">Laissue et al., 1999</xref>; <xref ref-type="bibr" rid="bib61">Vosshall et al., 2000</xref>). Initially brains were dissected in Ringer's solution (130 mM NaCl, 5 mM KCl, 2 mM MgCl<sub>2</sub>, 2 mM CaCl<sub>2</sub>, 36 mM saccharose, 5 mM HEPES, [pH 7.3]) (<xref ref-type="bibr" rid="bib11">Estes et al., 1996</xref>) and fixed in 4% PFA in PBS-T (PBS, 0.2–1% Triton-X). After washing with PBS-T brains were blocked with PBS-T, 5% normal goat serum (NGS). Primary antibodies were diluted in blocking solution or PBS-T and incubated at 4°C for 2–3 days. Secondary antibody incubation lasted 1–2 days. Brains were mounted in VectaShield (Vector Labs, Burlingame, CA). The following primary antibodies were used: rabbit α-GABA (1:500) (Sigma), mouse α-GFP (1:500) (Invitrogen). The following secondary antibodies were used: Alexa Fluor 488, goat anti-mouse (1:500); Alexa Fluor 546, goat anti-rabbit (1:500); (all IgG Invitrogen).</p></sec><sec id="s4-3"><title>Functional imaging</title><p>Fly preparation and functional imaging of the AL was conducted as previously described (<xref ref-type="bibr" rid="bib55">Stökl et al., 2010</xref>; <xref ref-type="bibr" rid="bib56">Strutz et al., 2012</xref>). LH imaging was conducted similarly, except for the higher resolution achieved with a 60× water immersion objective (LUMPlanFl 60×/0.90 W Olympus). The optical plane was ∼30 µm below the most dorsal entrance point of the iPN tract into the LH. Binning on the CCD-camera chip resulted in a resolution of 1 pixel = 0.4 × 0.4 µm. For bilateral LH imaging during transection a 20× water immersion objective (NA 0.95, XLUM Plan FI, Olympus) was employed. All recordings lasted 10 s with a frame rate of 4 Hz. Odors included acids (propionic acid, acetic acid), lactones (γ-butyrolactone), terpenes (linalool), aromatics (acetophenone, methyl salicylate, benzaldehyde, phenylacetic acid), alcohols (1-octen-3-ol), esters (acetoin acetate, cis-vaccenyl acetate, 2-phenethyl acetate), ketones (2,3 butanedione) and balsamic vinegar diluted in mineral oil (all from Sigma Aldrich). Odors were applied during frame 8–14 (i.e., after 2 s, lasting for 2 s). Flies were imaged for up to 1 hr, with a minimum inter-stimulus interval of one minute. We selected conventional widefield Ca<sup>2+</sup> imaging as the method of choice, since we were able to obtain single bouton resolution with this technique.</p></sec><sec id="s4-4"><title>Imaging data analysis</title><p>Calcium imaging data of AL were analyzed with custom-written IDL software (ITT Visual Information Solutions) provided by Mathias Ditzen as previously described (<xref ref-type="bibr" rid="bib55">Stökl et al., 2010</xref>; <xref ref-type="bibr" rid="bib56">Strutz et al., 2012</xref>). Regarding the Ca<sup>2+</sup> imaging data in the LH, we repeated recordings of each odor at each concentration two to three times to ensure the reliability of the extracted domain information. To execute NNMF analysis (see below), at least 6–7 valid measurements, that is, animals with repeated identical recordings, were collected for each odor and employed for the analysis. Individual odor measurements were aligned using ImageJ (Fiji) to correct movement artifacts. Fluorescence changes (∆F/F) for each odor were calculated in relation to background fluorescence using frames 0–6 (i.e., 2–0.5 s before odor application). A Gaussian low-pass filter (ó = 1px) was applied to compensate for remaining movement artifacts and pixel noise. To reduce the computational load, the frame rate was averaged by two consecutive frames, and recordings were spatially down-sampled by a factor of two. The resulting concatenated time-series of the recordings is denoted as measurement matrix Y with element Y<sub>t,p</sub> being the t<sup>th</sup> observed value of pixel p.</p></sec><sec id="s4-5"><title>NNMF—Non-Negative Matrix Factorization</title><p>In contrast to the AL, which consists of highly ordered glomerular subunits, the LH comprises a mainly homogenous neuropil which does not provide spatial or functional landmarks. Therefore, we used the automatic method NNMF to extract Ca<sup>2+</sup> signals that exhibit common spatial or temporal features. NNMF, like other matrix factorization techniques (e.g., Principal Component Analysis (PCA) and Independent Component Analysis (ICA)), decompose the measurement matrix Y into <italic>k</italic> components, <inline-formula><mml:math id="inf1"><mml:mrow><mml:mtext>Y</mml:mtext><mml:mo>=</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mi>k</mml:mi></mml:msub><mml:msub><mml:mi>x</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>∗</mml:mo><mml:msubsup><mml:mi>a</mml:mi><mml:mi>k</mml:mi><mml:mi>T</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:mtext>R</mml:mtext></mml:mrow></mml:math></inline-formula>. The time-course <italic>a</italic><sub><italic>k</italic></sub> of each component contains a common underlying time-courses of all pixels and each pixel participation <italic>x</italic><sub><italic>k</italic></sub> declares how strongly each pixel is involved in this time-course. The residual matrix R contains the unexplained data. In order to perform NNMF, we implemented the HALS algorithm in Python including a spatial smoothness constraint (<italic>a</italic><sub><italic>sm</italic></sub> = 0.1) (<xref ref-type="bibr" rid="bib6">Cichocki and Phan, 2009</xref>) and an additional spatial decorrelation constraint (<italic>a</italic><sub><italic>de</italic></sub> = 0.1) (<xref ref-type="bibr" rid="bib5">Chen and Cichocki, 2005</xref>).</p><p>In PCA decomposition is performed such that either timecourses <italic>a</italic><sub><italic>k</italic></sub> or pixel participation <italic>x</italic><sub><italic>k</italic></sub> are uncorrelated, whereas ICA aims for timecourses (temporal ICA) or pixel participation (spatial ICA) to be independent. Although spatial ICA is able to segregate signals into functional similar neuropils (<xref ref-type="bibr" rid="bib43">Reidl et al., 2007</xref>), we chose the NNMF approach, because it is known to achieve even a better parts-based representation compared to the more holistic results of PCA or ICA (<xref ref-type="bibr" rid="bib29">Lee and Seung, 1999</xref>). In contrast to PCA and ICA, NNMF constrains both the extracted time-courses and pixel participations to be positive. Positive pixel participation enabled us to make a straightforward physiological interpretation, reading the participation values as the contribution strength of an underlying physiological domain. The restriction to positive time-courses reflects the fact that we did not observe any significant decrease of fluorescence in response to an odor in the original measurement data. For each animal we performed decomposition into <italic>k</italic> = 5 components. This was sufficient to explain most of the data's variance (88% + 8%, error is standard deviation across individuals). The remaining variance in the residual matrix R contained no additional domains but rather reflected remaining movement artifacts of the measurements (<xref ref-type="fig" rid="fig2s2">Figure 2—figure supplement 2</xref>). Of the five components extracted by NNMF, three stood out prominently (<xref ref-type="fig" rid="fig2s3">Figure 2—figure supplement 3</xref>): First, they were extracted in all animals at very clearly defined anatomical positions. Second, their responses to stimuli repetitions were highly reproducible in contrast to the other two components, that is, they exhibited a significant (p < 2*10<sup>−8</sup>, <italic>t</italic> test) higher trial-to-trial correlation of 0.72 ± 0.20 in contrast to 0.52 ± 0.26 for the remaining components; third, the odorant spectra of their responses were characteristic across animals.</p><p>Though we cannot completely rule out that the remaining components of the factorization are ORDs of their own, there are several indications that they are not. On the one hand, they exhibit a lower trial-to-trial correlation than the three selected components. Second, those components did not consistently appear at similar anatomical position. Third, they were spatially overlapping with the selected three components. Instead of independent ORDs, these regions might convey fluorescence changes independent of odor stimulation or an overlapping region of two of the reliable ORDs. A validation of our NNMF-based results with spatial ICA yielded very similar, but slightly worse results. Whereas the three reliable ORDs from NNMF were also extracted in spatial ICA, the two remaining components exhibited much higher variability than when obtained with NNMF. Hence, we conclude that the LH area comprising MZ699+ neurons consists of three ORDs. We labeled those three components according to the anatomical position of their pixel participation within the LH.</p></sec><sec id="s4-6"><title>Statistical analysis of imaging data</title><p>To determine the coding properties of extracted odor response domains (ORDs), we calculated the mean response of each animal within a time window of 1–4 s after stimulus onset. Hence, median responses over all animals defined the standard stimulated response <inline-formula><mml:math id="inf2"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi>O</mml:mi><mml:mi>R</mml:mi><mml:mi>D</mml:mi></mml:mrow><mml:mi>o</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> of an ORD to an odor <italic>o</italic>. Initially, regions were evaluated individually, and correlations were calculated between standard response spectra and the behavioral response index (RI), or odor concentration, respectively, using the ‘linregress’ function of the Python scipy.stat module. To analyze the combined ORD representations of odor patterns <inline-formula><mml:math id="inf3"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi>P</mml:mi><mml:mi>M</mml:mi></mml:mrow><mml:mi>o</mml:mi></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>M</mml:mi></mml:mrow><mml:mi>o</mml:mi></mml:msubsup><mml:mo>,</mml:mo><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>L</mml:mi></mml:mrow><mml:mi>o</mml:mi></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> we calculated for all odor pairs the pattern similarity as correlation distance <inline-formula><mml:math id="inf4"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>o</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. In order to visualize the correlation matrix in a comprehensible way, we then arranged odors according to the single linkage clustering of the Python scipy.cluster.hierarchy module. To compare the representation in the LH to those of the AL, we applied the same procedure to the dorsal glomerular odor activation pattern.</p></sec><sec id="s4-7"><title>2-Photon photoactivation and neuronal reconstructions</title><p>For in vivo photoactivation experiments, 1–6 day old flies (genotype: END1-2,UAS-C3PA;MZ699-GAL4) were dissected as in the imaging experiments except that tracts of the salivary glands were cut to prevent movement. Photoactivation was accomplished via continuous illumination with 760 nm for 15–25 min. After a 5-min break to permit full diffusion of the photoconverted molecules, 925 nm z-stacks of the whole brain were acquired and subsequently used for neuronal 3D-reconstruction. For all 3D reconstructions, the segmentation software AMIRA 4.1.1 & 5.3.3 (FEI Visualization Sciences Group, Burlington, MA) was used. Neurons of different individuals were embedded into the reference brain using a labelfield registration as previously described (<xref ref-type="bibr" rid="bib46">Rybak et al., 2010</xref>). Briefly, segmented labels of brain neuropils (AL, MBc, LH) were registered onto a reference brain image using affine registration followed by elastic warping. In a second step, the calculated transformation matrix was applied to the respective neuron morphology that was then aligned to the reference brain image.</p><p>For morphological analysis of reconstructed iPNs, we first determined all terminal points of each iPN in the LH area. For each combination of terminals we calculated a similarity score (<italic>s</italic>) in analogy to (<xref ref-type="bibr" rid="bib25">Kohl et al., 2013</xref>) as follows:<disp-formula id="equ1"><mml:math id="m1"><mml:mrow><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>√</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>Δ</mml:mi><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>/</mml:mo><mml:mn>2</mml:mn><mml:msup><mml:mi>σ</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>where <italic>t</italic> is the terminal position, Δ<italic>(t1,t2)</italic> is the Euclidean distance and <italic>σ</italic> is a free parameter that determines how close in space terminal points must be to be considered similar; analogue to <xref ref-type="bibr" rid="bib25">Kohl et al. (2013)</xref> we set this parameter to 3 µm. Finally we calculated the pairwise similarity score between two neurons as their average all-to-all terminal similarity scores, normalized to their self-scores as follows:<disp-formula id="equ2"><mml:math id="m2"><mml:mrow><mml:mi>S</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:msqrt><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>∗</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi>s</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msqrt></mml:mrow></mml:math></disp-formula></p><p>Effectively this quantifies the relative overlap of the target area of all pairs of iPNs. For clustering, the similarity scores were converted to distances (i.e., <italic>1-S</italic>) and a hierarchical clustering was performed using UPGMA method. Principal component analysis and one-way ANOSIM was performed using the statistical software PAST 3.x (Paleontological statistics software package for education and data analysis).</p></sec><sec id="s4-8"><title>2-Photon-mediated transection</title><p>Transections of either the plF tract or the mACT were conducted in one brain hemisphere, each of the same fly. The target area was monitored with 925 nm and chosen to be close to the LH but distant enough not to affect neurites ramifying in the LH neuropil. For both tracts, lesioned areas had an average size of 34 µm and were illuminated with short pulses of 710 nm every 40 ms for 250 ms in 60 (plF) to 80 (mACT) cycles in a single focal plane. After a fast z-stack with 925 nm to confirm complete lesion, a 5-min neuronal recovery interval followed before continuing the imaging procedure. Data were analyzed using NNMF.</p></sec><sec id="s4-9"><title>Image acquisition</title><p>Photoactivation and transection procedures as well as image acquisition following immunohistochemistry were accomplished with a 2-photon confocal laser scanning microscope (2PCLSM, Zeiss LSM 710 NLO) equipped with a 40× (W Plan-Apochromat 40×/1.0 DIC M27, Zeiss) or 20× (W N-Achroplan 20×/0.5 M27, Zeiss). The 2PCLSM was placed on a smart table UT2 (Newport Corporation, Irvine, CA, USA) and equipped with an infrared Chameleon Ultra diode-pumped laser (Coherent, Santa Clara, CA, USA). Z-stacks were performed with argon 488 nm and helium-neon 543 nm laser or the Chameleon Laser 925 nm (BP500-550 for G-CaMP and LP555 for DsRed/tdTomato) and had a resolution of 1024 or 512 square pixels. The maximum step size for immuno-preparations or single neuron projections was 1 µm and for AL reconstructions 2 µm.</p></sec><sec id="s4-10"><title>Behavioral assay</title><p>Flies carrying P[GAD1-RNAi];P[MZ699-GAL4] were crossed just before the experiment to prevent dosage compensation effects. T-maze experiments were performed as described (<xref ref-type="bibr" rid="bib52">Stensmyr et al., 2012</xref>). WT, parental controls (P[GAD1-RNAi] or P[MZ699-GAL4]) and test flies carrying both insertions were tested separately under identical conditions. The response index (RI) was calculated as (O-C)/T, where O is the number of flies in the odor arm, C is the number of flies in the control arm, and T is the total number of flies used in the trial. Hence, the RI ranges from −1 (complete avoidance) to 1 (complete attraction). Each experiment was carried out on 30 flies and was repeated 12 times. Dunn's Multiple Comparison Test was used for global differences in the dataset. Whenever the Multiple Comparison Test was significant (i.e., p < 0.05), a posthoc test for selected pairs was performed, that is, between the GADi-flies and the other three control lines as we were not interested in differences among the different control lines. All RI were tested against 0 (no response) by using the Wilcoxon-rank-sum test.</p></sec></sec></body><back><ack id="ack"><title>Acknowledgements</title><p>We thank Silke Trautheim, Regina Stieber, Linda Gummlich and Sascha Bucks for excellent technical assistance and Emily Wheeler for editorial assistance.</p></ack><sec sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="conflict" id="conf1"><p>BSH: Vice president of the Max Planck Society, one of the three founding funders of <italic>eLife</italic>, and a member of <italic>eLife's</italic> Board of Directors.</p></fn><fn fn-type="conflict" id="conf2"><p>The other authors declare that no competing interests exist.</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>AS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con2"><p>JS, Analysis and interpretation of data, Drafting or revising the article</p></fn><fn fn-type="con" id="con3"><p>AB, Acquisition of data, Analysis and interpretation of data</p></fn><fn fn-type="con" id="con4"><p>VG, Acquisition of data, Analysis and interpretation of data</p></fn><fn fn-type="con" id="con5"><p>JR, Acquisition of data, Analysis and interpretation of data</p></fn><fn fn-type="con" id="con6"><p>AF, Performed behavioral experiments</p></fn><fn fn-type="con" id="con7"><p>MK, Supervision of Abu Farhan, Analysis and interpretation of data</p></fn><fn fn-type="con" id="con8"><p>MS, Supervision of Jan Soelter, Analysis and interpretation of data</p></fn><fn fn-type="con" id="con9"><p>BSH, Provided intellectual and financial support, Conception and design</p></fn><fn fn-type="con" id="con10"><p>SS, Conception and design, Analysis and interpretation of data, Drafting or revising the article</p></fn></fn-group></sec><ref-list><title>References</title><ref id="bib1"><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bausenwein</surname><given-names>B</given-names></name><name><surname>Dittrich</surname><given-names>AP</given-names></name><name><surname>Fischbach</surname><given-names>KF</given-names></name></person-group><year>1992</year><article-title>The optic lobe of <italic>Drosophila melanogaster</italic>. 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<country>Ireland</country></aff></contrib></contrib-group></front-stub><body><boxed-text><p>eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see <ext-link ext-link-type="uri" xlink:href="http://elifesciences.org/review-process">review process</ext-link>). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.</p></boxed-text><p>Thank you for sending your work entitled “Decoding Odor Quality and Intensity in the Drosophila brain” for consideration at <italic>eLife</italic>. Your article has been favorably evaluated by a Senior editor, Mani Ramaswami (Reviewing editor), and 2 reviewers, one of whom, Nitin Gupta, has agreed to reveal his identity.</p><p>The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.</p><p>This study examines the role of a major class of antennal lobe projection neurons, the iPNs, and their targets in the lateral horn. Several recent studies have begun to classify the function of the iPNs and have shown a role in odor discrimination, in inhibiting food odor processing, and in pheromone-mediated behaviors. This study comprehensively examines the anatomy and functional properties of iPNs and makes several important contributions, including that (1) iPNs may be functionally segregated into two different classes based on glomerular inputs, targeting region in the lateral horn and response properties; (2) they are required for olfactory attraction; and (3) they likely inhibit third order neurons that mediate aversion. Overall, this is a very rigorous, nicely executed study that makes important contributions to odor processing in the lateral horn.</p><p>Although some of its excitement has been stolen by other recent papers covering iPNs(<xref ref-type="bibr" rid="bib30">Liang et al 2013</xref>; <xref ref-type="bibr" rid="bib40">Parnas et al 2013</xref>; <xref ref-type="bibr" rid="bib63">Wang et al 2014</xref>; <xref ref-type="bibr" rid="bib13">Fisek and Wilson 2014</xref>, the current study additionally provides the most detailed, glomerulus-level mapping of iPNs in the AL, provides a description of three projection zones of iPNs in the LH, and provide some evidence that these zones correspond to behavior. Another important finding is that blocking GABA-synthesis in iPNs makes the odors more aversive. These results suggest that the inhibition from iPNs usually keeps the aversive pathways under check. This is in agreement with recent work from Liqun Luo's lab (Liang et al), which showed that vlPR neurons form an aversive pathway inhibited by iPNs. The functional imaging and microdissection experiments performed by the authors provide additional (but indirect) evidence to support the same conclusion. Thus, this work reports discoveries and contributions appropriate for publication in <italic>eLife.</italic></p><p>However, there are some issues that need to be addressed prior to publication.</p><p>1) In <xref ref-type="fig" rid="fig3">Figure 3</xref>, the LH-AL and LH-AM zones are well separated, while in <xref ref-type="fig" rid="fig4">Figure 4</xref>, they seem nearly overlapping. <xref ref-type="fig" rid="fig4">Figure 4B,C</xref>: It seems that the reconstructions shown in panels B and C were chosen based on their projections into AM or PM zones. The authors have not shown that all iPNs can be unambiguously classified as belonging to these two categories. Is it true that no iPN branches into both zones?</p><p>The authors should use their single cell clone data to rigorously test whether lateral horn projections cluster into classes.</p><p>2) The authors also comment that AL arborizations are different for the two types of iPNs without providing any analysis–this is important to do. Cluster analysis of morphology parameters coupled with PCA should be straightforward.</p><p>3) It is not always clear that some of the work in this manuscript is consistent with published studies or replicates published studies. The authors should be careful to credit previous studies.</p><p>For example:“GAD1 (glutamic acid decarboxylase) in situ hybridizations imply GABA synthesis (<xref ref-type="bibr" rid="bib39">Okada et al., 2009</xref>), which we verified via immunostaining”. GABA Ab staining is shown in Parnas 2013.</p><p>Dendritic and synaptic regions on iPNs have been published several times (okada 09, Liang 2013, Parnas 2013) but the text states: “To determine the polarity of both PN populations, we expressed UAS141Dα7:mcherry to tag postsynaptic input sites by labeling acetylcholine receptors (AChR) (<xref ref-type="fig" rid="fig1">Figure 1C,C'</xref>), and UAS- Syt:HA to label presynaptic terminals (<xref ref-type="fig" rid="fig1">Figure 1D,D'</xref>).” MZ699 synaptobrevin and Denmark staining is shown in Parnas 2013, and nSyb or syt staining in Okada 09 and Liang 13, so the polarity of iPNs is known. The polarity of ePNs is also known.</p><p>In addition, Lai 2008 did single cell MZ699 clones and noticed regionalization in the lateral horn similar to the single-cell clone studies in this manuscript.</p><p>4) <xref ref-type="fig" rid="fig1">Figure 1</xref> does not seem essential and should be justified by the authors. It would better given prior publications to begin with <xref ref-type="fig" rid="fig2">Figure 2</xref>, which also contains the summary info from previous studies.</p><p>5) “How these crucial functions are accomplished within the olfactory system remains unknown”. The authors should modify this sentence to acknowledge the large body of work in olfaction related to feature detection, both in insects and in vertebrates.</p><p>6) In figure supplement 1B, why only a small fraction of the MZ699+ somata in AL appear GABA-positive?</p><p>7) <xref ref-type="fig" rid="fig1">Figure 1D</xref>: There may be weak Syt:HA expression in the AL for mz699+ neurons. Can the authors completely rule of the possibility of synaptic release in the AL? Perhaps a more quantitative analysis would help.</p><p>8) Are the activation patterns of iPN LH projections stereotyped among individuals for the two attractive and the one aversive odor tested above? Why is the reproducibility demonstrated using an unrelated odor (1-octen-3-ol) rather than the same three odors?</p><p>9) The authors claim that “analysis of the additional odorants revealed neuronal activity exclusively within the three described 'ORDs' (<xref ref-type="fig" rid="fig3">Figure 3F</xref>)”. Does it mean that there was no activity outside the three ORD regions? If so, no data has been shown to support this claim.</p><p>10) The observation that LH-AM and LH-AL were activated only at high odor concentrations could be because these areas had a lower density of branches than LH-PM, and therefore required stronger activation to clear the threshold for detection. Can the authors rule out this possibility?</p><p>11) If the three neurons did not innervate the AL, why call them “iPNs”?</p><p>12) Benzaldehyde seems to induce the same type of preference (repulsion) for both concentrations in the figure; opposite of what the authors say in the text.</p><p>13) In GADi flies, the responses to the two concentrations are different for several odors (vinegar, propionic acid, butadione, acetophenone), so the claim that perception of intensity was impeded is not justified based on the presented data and should be removed from this section. The results are better explained by the hypothesis that GABA blockage makes odors more repulsive (and ceiling effects in behavioral tests).</p><p>14) What was the rationale for using Dunn's Selected Pairs test in some cases, when Dunn's Multiple Comparison was used in others?</p><p>15) How do the authors' findings on odor coding in lateral horn compare with the findings from other insect species such as honeybees and locusts? Locusts also appear to have odor concentration-dependent responses in the lateral horn.</p><p>16) Valence as determined by behavior, seems to be dependent on the exact behavioral assay. Thus for most odorants (except for the rare extreme cases) it could be difficult to assign a clear valence. This would make it makes difficult to establish where absolute valence is encoded in the lateral horn. The authors should consider this issue and discuss potential difficulties in unambiguously defining the “valence” of an odor, as otherwise the literature may be full of contradictory observations.</p></body></sub-article><sub-article article-type="reply" id="SA2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.04147.020</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><p><italic>1) In</italic> <xref ref-type="fig" rid="fig3"><italic>Figure 3</italic></xref><italic>, the LH-AL and LH-AM zones are well separated, while in</italic> <xref ref-type="fig" rid="fig4"><italic>Figure 4</italic></xref><italic>, they seem nearly overlapping.</italic> <xref ref-type="fig" rid="fig4"><italic>Figure 4B,C</italic></xref><italic>: It seems that the reconstructions shown in panels B and C were chosen based on their projections into AM or PM zones. The authors have not shown that all iPNs can be unambiguously classified as belonging to these two categories. Is it true that no iPN branches</italic> <italic>into both zones?</italic></p><p><italic>The authors should use their single cell clone data to rigorously test whether lateral horn projections cluster into classes</italic>.</p><p>We agree with the reviewers and addressed the concern by adding the following data:</p><p>A) We photoactivated and reconstructed 4 further iPNs, which are now included into the table in <xref ref-type="fig" rid="fig3s2">Figure 3–figure supplement 2</xref>, resulting in 8 iPNs for the LH-PM area and 10 iPNs for the LH-AM area. Hence we characterized in total 25 MZ699+ PNs.</p><p>B) We included the reconstructions of all iPNs into the reference brain in <xref ref-type="fig" rid="fig3">Figure 3 B,C</xref> instead of showing only a subgroup of neurons.</p><p>C) In order to rigorously test whether all iPNs can be classified into two morphological categories, we performed a cluster analysis based on the target areas of all terminals of each iPN in the lateral horn. To this end we first determined all terminal points of each iPN in the lateral horn area. For each combination of terminals we calculated a similarity score (<italic>s</italic>) in analogy to Kohl et al. (Cell, 2013). Finally we calculated the pairwise similarity score between two neurons as their average all-to-all terminal similarity scores, normalized to their self-scores. Effectively this quantifies the relative overlap of the target area of all pairs of iPNs. For clustering, the similarity scores were converted to distances and a hierarchical clustering was performed using UPGMA method. Using this cluster analysis all iPNs, except one, could be clustered according to their target region in either the LH-AM or LH-PM area in the lateral horn. The cluster analysis is incorporated into <xref ref-type="fig" rid="fig3">Figure 3</xref>.</p><p>D) We performed a principal component analysis on the distances and a one-way ANOSIM (<xref ref-type="fig" rid="fig3s1">Figure 3–figure supplement 1</xref>) showing that both neuronal classes are statistically different based on their target areas in the lateral horn.</p><p><italic>2) The authors also comment that AL arborizations are different for the two types of iPNs without providing any analysis–this is important to do. Cluster analysis of morphology parameters coupled with PCA should be straightforward</italic>.</p><p>We agree with the reviewers and added a cluster analysis as well as a principal component analysis showing that both categories of iPNs innervate different subsets of glomeruli in the antennal lobe and can therefore be morphologically clustered. We also performed a one-way ANOSIM revealing that this difference is highly significant between the two iPN classes. We incorporated the cluster analysis into <xref ref-type="fig" rid="fig3">Figure 3</xref> and the PCA including the ANOSIM into <xref ref-type="fig" rid="fig3s1">Figure 3–figure supplement 1</xref>.</p><p><italic>3) It is not always clear that some of the work in this manuscript is consistent with published studies or replicates published studies. The authors should be careful to credit previous studies</italic>.</p><p><italic>For example:“GAD1 (glutamic acid decarboxylase) in situ hybridizations imply GABA synthesis (</italic><xref ref-type="bibr" rid="bib39"><italic>Okada et al., 2009</italic></xref><italic>), which we verified via immunostaining”. GABA Ab staining is shown in Parnas 2013</italic>.</p><p><italic>Dendritic and synaptic regions on iPNs have been published several times (okada 09, Liang 2013, Parnas 2013) but the text states: “To determine the polarity of both PN populations, we expressed UAS141Dα7:mcherry to tag postsynaptic input sites by labeling acetylcholine receptors (AChR) (</italic><xref ref-type="fig" rid="fig1"><italic>Figure 1C,C'</italic></xref><italic>), and UAS- Syt:HA to label presynaptic terminals (</italic><xref ref-type="fig" rid="fig1"><italic>Figure 1D,D'</italic></xref><italic>).” MZ699 synaptobrevin and Denmark staining is shown in Parnas 2013, and nSyb or syt staining in Okada 09 and Liang 13, so the polarity of iPNs is known. The polarity of ePNs is also known</italic>.</p><p><italic>In addition, Lai 2008 did single cell MZ699 clones and noticed regionalization in the lateral horn similar to the single-cell clone studies in this manuscript</italic>.</p><p>We have modified our manuscript following the reviewers’ suggestions in order to ensure that we accurately refer to previously published data. First, we removed our GABA immunostaining from the manuscript, since the <italic>in situ</italic> hybridizations performed by <xref ref-type="bibr" rid="bib39">Okada et al. (2009)</xref> showing that the majority of MZ699+ iPNs are GAD1-positive has already been verified via immunostaining by <xref ref-type="bibr" rid="bib40">Parnas et al. (2013)</xref> and <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref> as the reviewer mentioned and do not need to be confirmed another time. We refer to both studies in the Results part. Second, we completely removed our data regarding the polarity of iPNs and ePNs and cite the published data instead. Third, we mention and discuss the study by <xref ref-type="bibr" rid="bib26">Lai et al. (2008)</xref> who also observed a categorization of MZ699+ iPNs using single-cell clone data in the corresponding Results section.</p><p><italic>4)</italic> <xref ref-type="fig" rid="fig1"><italic>Figure 1</italic></xref> <italic>does not seem essential and should be justified by the authors. It would better given prior publications to begin with</italic> <xref ref-type="fig" rid="fig2"><italic>Figure 2</italic></xref><italic>, which also contains the summary info from previous studies</italic>.</p><p>We agree and therefore begin the paper now with the original <xref ref-type="fig" rid="fig2">Figure 2</xref> as the reviewers suggested.</p><p><italic>5) “How these crucial functions are accomplished within the olfactory system remains unknown”. The authors should modify this sentence to acknowledge the large body of work in olfaction related to feature detection, both in insects and in vertebrates</italic>.</p><p>We agree and therefore modified the sentence to address our main question. In order to acknowledge the various studies that have investigated feature extraction in the olfactory system, we cite and discuss them in our Discussion section.</p><p><italic>6) In figure supplement 1B, why only a small fraction of the MZ699+ somata in AL</italic> <italic>appear GABA-positive?</italic></p><p>We observed that <italic>MZ699-GAL4</italic> is not solely expressed in iPNs and vlPr neurons, but labels also neurons that innervate the subesophageal ganglion in accordance with <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref>. Since these SOG neurons are GABA-negative, whose somata are located below the iPN cluster, they appear as unlabeled somata in our GABA staining. However, our aim was to confirm the <italic>in situ</italic> hybridizations performed by <xref ref-type="bibr" rid="bib39">Okada et al. (2009)</xref> showing that on average 80% of mACT iPNs labeled by <italic>MZ699-GAL4</italic> are GAD1-positive. Since <xref ref-type="bibr" rid="bib30">Liang et al. (2013)</xref> and <xref ref-type="bibr" rid="bib40">Parnas et al. (2013)</xref> already confirmed that the majority of iPNs is GABAergic, we decided that these data do not need to be affirmed another time and removed our GABA staining.</p><p><italic>7)</italic> <xref ref-type="fig" rid="fig1"><italic>Figure 1D</italic></xref><italic>: There may be weak Syt:HA expression in the AL for mz699+ neurons. Can the authors completely rule of the possibility of synaptic release in the AL? Perhaps a more quantitative analysis would help.</italic></p><p>We agree with the reviewers that the image shows weak signals for Syt-HA. Interestingly, also <xref ref-type="bibr" rid="bib39">Okada et al. (2009)</xref> observed a weak <italic>n</italic>-syb-GFP signal of MZ699+ neurons in the AL, which is – as in our case – much weaker than in the LH. The same holds true for Liang et al. (2014) who observed Syt-HA mainly in the LH, while it was largely, not entirely, absent in the AL. To get a more precise staining we also performed vibratome immunostainings that confirm our previous experiment. Both immunos detect an HA tag, that is fused to the presynaptic protein Synaptotagmin. Since the weak signals in the AL appear to be located in the axonal bundle that projects into the mACT, but not in glomerular structures, we presume that the protein or a HA carrying precursor might be transported in the appearing region, but not specifically be targeted to synaptic boutons. However, a weak GABA release of iPNs in the AL cannot be excluded by any of the studies so far. Nevertheless, as written above, we deleted the entire <xref ref-type="fig" rid="fig1">Figure 1</xref> and cited all corresponding papers instead.</p><p><italic>8) Are the activation patterns of iPN LH projections stereotyped among individuals for the two attractive and the one aversive odor tested above? Why is the reproducibility demonstrated using an unrelated odor (1-octen-3-ol) rather than the same</italic> <italic>three odors?</italic></p><p>The odor 1-octen-3-ol was chosen as a representative example because all three ORDs are activated by this odor over the range of concentration. Moreover 1-octen-3-ol has been employed as a comparative standard odor (due to its comprehensive ORD pattern) in all imaged animals. However, we included now the activation patterns of four animals to the three additional odors acetoin acetate, balsamic vinegar and benzaldehyde as the reviewers requested (<xref ref-type="fig" rid="fig2s1">Figure 2–figure supplement 1</xref>). All three odors were highly reproducible within one animal and stereotypic among different individuals. In addition, the stereotypy for all odors is shown in <xref ref-type="fig" rid="fig2">Figure 2F</xref>, where the shadow depicts the standard deviation for the odor-evoked responses.</p><p><italic>9) The authors claim that “analysis of the additional odorants revealed neuronal activity exclusively within the three described 'ORDs' (</italic><xref ref-type="fig" rid="fig3"><italic>Figure 3F</italic></xref><italic>)”. Does it mean that there was no activity outside the three ORD regions? If so, no data has been shown to support this claim</italic>.</p><p>In the initial analysis, we performed decomposition for each animal into five components, since five components were already sufficient to explain 88% ± 8 of the data’s variance. The remaining variance contained no additional domains but rather reflected remaining movement artifacts of the measurements. We added now a supplemental figure showing that the activity patterns of three animals to three odors can be very well reconstructed by using these five components (<xref ref-type="fig" rid="fig2s2">Figure 2–figure supplement 2</xref>) without any remaining stimulus related activity. When we further analyzed these five components, we observed that three of them stood out prominently (as shown now in <xref ref-type="fig" rid="fig2s3">Figure 2–figure supplement 3</xref>). First, they were extracted in all animals at very clearly defined anatomical positions. Second, their responses to stimuli repetitions were highly reproducible in contrast to the other two components, i.e. they exhibited a significant (p<2*10<sup>-8</sup>) higher trial-to-trial correlation. Third, the odorant spectra of their responses were characteristic across animals. Although we cannot completely rule out that the two remaining components of the NNMF are ORDs of their own, there are several indications that they are not. On the one hand, they exhibit a lower trial-to-trial correlation than the three selected components. Second, those components did not consistently appear at similar anatomical position. Third, they were spatially overlapping with the selected three components. Instead of independent ORDs, these regions might convey fluorescence changes independent of odor stimulation or an overlapping region of two of the reliable ORDs. A validation of our NNMF-based results with spatial ICA also extracted the three reliable ORDs, while the two remaining components exhibited even higher variability than with NNMF.</p><p><italic>10) The observation that LH-AM and LH-AL were activated only at high odor concentrations could be because these areas had a lower density of branches than LH-PM, and therefore required stronger activation to clear the threshold for detection. Can the authors rule out</italic> <italic>this possibility?</italic></p><p>We quantified several morphological parameters of the two neuronal populations in the lateral horn, such as the neuronal volume per µm<sup>3</sup> as a measure for innervation density, the surface per µm<sup>2</sup>, the number of branches, the lengths of the neuron as well as the total number of terminals using the neuronal reconstructions in AMIRA. However, we could not find significant differences for any of these parameters between our LH-AM and LH-PM neurons indicating that the detection threshold should be equal.</p><p><italic>11) If the three neurons did not innervate the AL, why call</italic> <italic>them “iPNs”?</italic></p><p>We agree and renamed these neurons to “other MZ699+ neurons”.</p><p><italic>12) Benzaldehyde seems to induce the same type of preference (repulsion) for both concentrations in the figure; opposite of what the authors say in the text</italic>.</p><p>We apologize for the confusion here. We intended to say that benzaldehyde evoked a different strength of repulsion for the two concentrations in wildtype flies, while GADi-flies showed an equal strong repulsion independent of the benzaldehyde concentration. However, we decided in general to remove our statement that intensity perception was impeded in GADi flies (see next comment).</p><p><italic>13) In GADi flies, the responses to the two concentrations are different for several odors (vinegar, propionic acid, butadione, acetophenone), so the claim that perception of intensity was impeded is not justified based on the presented data and should be removed from this section. The results are better explained by the hypothesis that GABA blockage makes odors more repulsive (and ceiling effects in behavioral tests)</italic>.</p><p>We agree with the reviewers and have therefore removed our conclusion that GADi flies reveal an impaired intensity perception.</p><p><italic>14) What was the rationale for using Dunn's Selected Pairs test in some cases, when Dunn's Multiple Comparison was</italic> <italic>used in others?</italic></p><p>We used the Dunn’s multiple comparison test for global differences in the dataset. Whenever the multiple comparison test was significant (i.e. p < 0.05), we performed a posthoc test for selected pairs, i.e. between the GADi-flies and the other three control lines as we were not interested in differences among the different control lines. We clarified this statistical method now in more detail to avoid confusions. In addition, we verified all statistical tests once again. While doing that, we realized that an error occurred in our first version, which we have corrected now. We are therefore grateful to the reviewers for questioning the statistics.</p><p><italic>15) How do the authors' findings on odor coding in lateral horn compare with the findings from other insect species such as honeybees and locusts? Locusts also appear to have odor concentration-dependent responses in the lateral horn</italic>.</p><p>We added to the Discussion a paragraph summarizing the results from locusts and honeybees regarding odor coding in the lateral horn and compared these findings to ours.</p><p><italic>16) Valence as determined by behavior, seems to be dependent on the exact behavioral assay. Thus for most odorants (except for the rare extreme cases) it could be difficult to assign a clear valence. This would make it makes difficult to establish where absolute valence is encoded in the lateral horn. The authors should consider this issue and discuss potential difficulties in unambiguously defining the “valence” of an odor, as otherwise the literature may be full of contradictory observations</italic>.</p><p>We agree with the reviewers that different behavioral assays for testing olfactory preferences in flies sometimes lead to contradictory results. However, 12 out of the 14 odors were also tested in a trap assay (<xref ref-type="bibr" rid="bib24">Knaden et al., 2012</xref>, CellReports; Stoekl et al., 2010, CurrBiol; trap assay results for balsamic vinegar are unpublished) and 8 were analyzed using the FlyWalk paradigm (<xref ref-type="bibr" rid="bib51">Steck et al. 2012</xref>, SciReports; pers. comm. Markus Knaden). Notably, all odors, except one, induced the same valence in the trap assay, while 5 odors out of 8 evoked the same behavioral response in the FlyWalk. We added a supplemental figure comparing the odor valence in different behavioral assays (<xref ref-type="fig" rid="fig6s1">Figure 6–figure supplement 1</xref>). In those cases where we observed a difference, the behavioral response was shifted to neutral, i.e. no response was observed. However, we never ever observed that an odor was attractive in one behavioral assay and aversive in another and <italic>vice versa</italic>. We therefore think that the valence that we determined with our behavioral assay in this study seems to be representative for those odors that we have selected. We discussed this point now in our manuscript and added a figure supplement to <xref ref-type="fig" rid="fig6">Figure 6</xref> as referred to above.</p></body></sub-article></article> |