scholarly journals Information flow, cell types and stereotypy in a full olfactory connectome

2020 ◽  
Author(s):  
Philipp Schlegel ◽  
Alexander Shakeel Bates ◽  
Tomke Stürner ◽  
Sridhar R. Jagannathan ◽  
Nikolas Drummond ◽  
...  
Keyword(s):  
Information Flow ◽  
Olfactory System ◽  
Mushroom Body ◽  
Antennal Lobe ◽  
Large Scale ◽  
Cell Types ◽  
Neuronal Morphology ◽  
Striking Similarity ◽  
Data Set ◽  
Lateral Horn

AbstractThe hemibrain connectome (Scheffer et al., 2020) provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the most complex olfactory system studied at synaptic resolution to date, covering all first, second and third-order neurons of the olfactory system associated with the antennal lobe and lateral horn (mushroom body neurons are described in a parallel paper, (Li et al., 2020)). We develop a generally applicable strategy to extract information flow and layered organisation from synaptic resolution connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. We also leverage a second data set (FAFB, (Zheng et al., 2018)) to provide a first quantitative assessment of inter- versus intra-individual stereotypy. Complete reconstruction of select developmental lineages in two brains (three brain hemispheres) reveals striking similarity in neuronal morphology across brains for >170 cell types. Within and across brains, connectivity correlates with morphology. Notably, neurons of the same morphological type show similar connection variability within one brain as across brains; this property should enable a rigorous quantitative approach to cell typing.

scholarly journals Information flow, cell types and stereotypy in a full olfactory connectome

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Philipp Schlegel ◽  
Alexander Shakeel Bates ◽  
Tomke Stürner ◽  
Sridhar R Jagannathan ◽  
Nikolas Drummond ◽  
...  
Keyword(s):  
Information Flow ◽  
Large Scale ◽  
Cell Types ◽  
Morphological Type ◽  
Neuronal Morphology ◽  
Striking Similarity ◽  
Data Set ◽  
Lateral Horn ◽  
Olfactory Input ◽  
Descending Interneurons

The hemibrain connectome provides large scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.

scholarly journals Functional and anatomical specificity in a higher olfactory centre

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Shahar Frechter ◽  
Alexander Shakeel Bates ◽  
Sina Tootoonian ◽  
Michael-John Dolan ◽  
James Manton ◽  
...  
Keyword(s):  
Olfactory System ◽  
Mushroom Body ◽  
Cell Types ◽  
Sensory Systems ◽  
Projection Neurons ◽  
Olfactory Behavior ◽  
Population Code ◽  
Odor Coding ◽  
Sensory Stimuli ◽  
Lateral Horn

Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. In the insect olfactory system, second order projection neurons target both the mushroom body, required for learning, and the lateral horn (LH), proposed to mediate innate olfactory behavior. Mushroom body neurons form a sparse olfactory population code, which is not stereotyped across animals. In contrast, odor coding in the LH remains poorly understood. We combine genetic driver lines, anatomical and functional criteria to show that the Drosophila LH has ~1400 neurons and >165 cell types. Genetically labeled LHNs have stereotyped odor responses across animals and on average respond to three times more odors than single projection neurons. LHNs are better odor categorizers than projection neurons, likely due to stereotyped pooling of related inputs. Our results reveal some of the principles by which a higher processing area can extract innate behavioral significance from sensory stimuli.

scholarly journals Olfactory processing in the lateral horn of Drosophila

2021 ◽  
Vol 383 (1) ◽  
pp. 113-123
Author(s):  
Sudeshna Das Chakraborty ◽  
Silke Sachse
Keyword(s):  
Functional Properties ◽  
Olfactory System ◽  
Cell Types ◽  
Specific Cell ◽  
Olfactory Behavior ◽  
Lateral Horn ◽  
Behavioral Decision ◽  
Animal Phyla ◽  
Survival And Reproduction ◽  
Almost All

AbstractSensing olfactory signals in the environment represents a crucial and significant task of sensory systems in almost all organisms to facilitate survival and reproduction. Notably, the olfactory system of diverse animal phyla shares astonishingly many fundamental principles with regard to anatomical and functional properties. Binding of odor ligands by chemosensory receptors present in the olfactory peripheral organs leads to a neuronal activity that is conveyed to first and higher-order brain centers leading to a subsequent odor-guided behavioral decision. One of the key centers for integrating and processing innate olfactory behavior is the lateral horn (LH) of the protocerebrum in insects. In recent years the LH of Drosophila has garnered increasing attention and many studies have been dedicated to elucidate its circuitry. In this review we will summarize the recent advances in mapping and characterizing LH-specific cell types, their functional properties with respect to odor tuning, their neurotransmitter profiles, their connectivity to pre-synaptic and post-synaptic partner neurons as well as their impact for olfactory behavior as known so far.

Production, crystallization and preliminary X-ray analysis of the human integrin \alpha_1 I domain

1999 ◽  
Vol 55 (7) ◽  
pp. 1365-1367 ◽  
Author(s):  
Tiina A. Salminen ◽  
Yvonne Nymalm ◽  
Jussi Kankare ◽  
Jarmo Käpylä ◽  
Jyrki Heino ◽  
...  
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Diffusion Method ◽  
Scale Production ◽  
Data Set ◽  
Cell Parameters ◽  
Collagen Receptors ◽  
Large Scale Production ◽  
Peg 4000 ◽  
Reservoir Solution

Integrin α1β1 is one of the main collagen receptors in many cell types. A fast large-scale production, purification and crystallization method for the integrin α1 I domain is reported here. The α1 I domain was crystallized using the vapour-diffusion method with a reservoir solution containing a mixture of PEG 4000, sodium acetate, glycerol and Tris–HCl buffer. The crystals beong to the C2 space group, with unit-cell parameters a = 74.5, b = 81.9, c = 37.3 Å, α = γ = 90.0, β = 90.8°. The crystals diffract to 2.0 Å and a 94.2% complete data set to 2.2 Å has been collected from a single crystal with an R merge of 5.8%.

scholarly journals Neurogenetic dissection of the Drosophila lateral horn reveals major outputs, diverse behavioural functions, and interactions with the mushroom body

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Michael-John Dolan ◽  
Shahar Frechter ◽  
Alexander Shakeel Bates ◽  
Chuntao Dan ◽  
Paavo Huoviala ◽  
...  
Keyword(s):  
Mushroom Body ◽  
Cell Types ◽  
Olfactory Cues ◽  
Body Site ◽  
Olfactory Behavior ◽  
Gal4 Driver ◽  
Sensory Stimuli ◽  
Lateral Horn ◽  
Functional Understanding ◽  
Innate Behaviour

Animals exhibit innate behaviours to a variety of sensory stimuli including olfactory cues. In Drosophila, one higher olfactory centre, the lateral horn (LH), is implicated in innate behaviour. However, our structural and functional understanding of the LH is scant, in large part due to a lack of sparse neurogenetic tools for this region. We generate a collection of split-GAL4 driver lines providing genetic access to 82 LH cell types. We use these to create an anatomical and neurotransmitter map of the LH and link this to EM connectomics data. We find ~30% of LH projections converge with outputs from the mushroom body, site of olfactory learning and memory. Using optogenetic activation, we identify LH cell types that drive changes in valence behavior or specific locomotor programs. In summary, we have generated a resource for manipulating and mapping LH neurons, providing new insights into the circuit basis of innate and learned olfactory behavior.

scholarly journals Avoidance response to CO2 in the lateral horn

2018 ◽  
Author(s):  
Nélia Varela ◽  
Miguel Gaspar ◽  
Sophie Dias ◽  
Maria Luísa Vasconcelos
Keyword(s):  
Calcium Imaging ◽  
Mushroom Body ◽  
Antennal Lobe ◽  
Functional Role ◽  
Direct Evidence ◽  
Physiological Responses ◽  
Lateral Horn ◽  
The One ◽  
The Brain

ABSTRACTIn flies, the olfactory information is carried from the first relay in the brain, the antennal lobe, to the mushroom body (MB) and the lateral horn (LH). Olfactory associations are formed in the MB. The LH was ascribed a role in innate responses based on the stereotyped connectivity with the antennal lobe, stereotyped physiological responses to odors and MB silencing experiments. Direct evidence for the functional role of the LH is still missing. Here we investigate the behavioral role of the LH neurons directly, using the CO2 response as a paradigm. Our results show the involvement of the LH in innate responses. Specifically, we demonstrate that activity in two sets of neurons is required for the full behavioral response to CO2. Using calcium imaging we observe that the two sets of neurons respond to CO2 in different manners. Using independent manipulation and recording of the two sets of neurons we find that the one that projects to the SIP also outputs to the local neurons within the LH. The design of simultaneous output at the LH and the SIP, an output of the MB, allows for coordination between innate and learned responses.

scholarly journals Large scale similarity search across digital reconstructions of neural morphology

2021 ◽  
Author(s):  
Bengt Ljungquist ◽  
Masood A Akram ◽  
Giorgio A Ascoli
Keyword(s):  
Similarity Search ◽  
Large Scale ◽  
Principal Component ◽  
Software Tool ◽  
Cell Types ◽  
Brain Regions ◽  
Morphological Comparison ◽  
Data Set ◽  
Qualitative Performance ◽  
The Impact

Most functions of the nervous system depend on neuronal and glial morphology. Continuous advances in microscopic imaging and tracing software have provided an increasingly abundant availability of 3D reconstructions of arborizing dendrites, axons, and processes, allowing their detailed study. However, efficient, large-scale methods to rank neural morphologies by similarity to an archetype are still lacking. Using the NeuroMorpho.Org database, we present a similarity search software enabling fast morphological comparison of hundreds of thousands of neural reconstructions from any species, brain regions, cell types, and preparation protocols. We compared the performance of different morphological measurements: 1) summary morphometrics calculated by L-Measure, 2) persistence vectors, a vectorized descriptor of branching structure, 3) the combination of the two. In all cases, we also investigated the impact of applying dimensionality reduction using principal component analysis (PCA). We assessed qualitative performance by gauging the ability to rank neurons in order of visual similarity. Moreover, we quantified information content by examining explained variance and benchmarked the ability to identify occasional duplicate reconstructions of the same specimen. The results indicate that combining summary morphometrics and persistence vectors with applied PCA provides an information rich characterization that enables efficient and precise comparison of neural morphology. The execution time scaled linearly with data set size, allowing seamless live searching through the entire NeuroMorpho.Org content in fractions of a second. We have deployed the similarity search function as an open-source online software tool both through a user-friendly graphical interface and as an API for programmatic access.

scholarly journals Structural and Functional Plasticity in the Regenerating Olfactory System of the Migratory Locust

2020 ◽  
Vol 11 ◽  
Author(s):  
Gerd Bicker ◽  
Michael Stern
Keyword(s):  
Olfactory Receptor ◽  
Olfactory System ◽  
Mushroom Body ◽  
Antennal Lobe ◽  
Second Order ◽  
Neural Regeneration ◽  
Current Evidence ◽  
Olfactory Pathway ◽  
Nerve Crush ◽  
Migratory Locust

Regeneration after injury is accompanied by transient and lasting changes in the neuroarchitecture of the nervous system and, thus, a form of structural plasticity. In this review, we introduce the olfactory pathway of a particular insect as a convenient model to visualize neural regeneration at an anatomical level and study functional recovery at an electrophysiological level. The olfactory pathway of the locust (Locusta migratoria) is characterized by a multiglomerular innervation of the antennal lobe by olfactory receptor neurons. These olfactory afferents were axotomized by crushing the base of the antenna. The resulting degeneration and regeneration in the antennal lobe could be quantified by size measurements, dye labeling, and immunofluorescence staining of cell surface proteins implicated in axonal guidance during development. Within 3 days post lesion, the antennal lobe volume was reduced by 30% and from then onward regained size back to normal by 2 weeks post injury. The majority of regenerating olfactory receptor axons reinnervated the glomeruli of the antennal lobe. A few regenerating axons project erroneously into the mushroom body on a pathway that is normally chosen by second-order projection neurons. Based on intracellular responses of antennal lobe output neurons to odor stimulation, regenerated fibers establish functional synapses again. Following complete absence after nerve crush, responses to odor stimuli return to control level within 10–14 days. On average, regeneration of afferents, and re-established synaptic connections appear faster in younger fifth instar nymphs than in adults. The initial degeneration of olfactory receptor axons has a trans-synaptic effect on a second order brain center, leading to a transient size reduction of the mushroom body calyx. Odor-evoked oscillating field potentials, absent after nerve crush, were restored in the calyx, indicative of regenerative processes in the network architecture. We conclude that axonal regeneration in the locust olfactory system appears to be possible, precise, and fast, opening an avenue for future mechanistic studies. As a perspective of biomedical importance, the current evidence for nitric oxide/cGMP signaling as positive regulator of axon regeneration in connectives of the ventral nerve cord is considered in light of particular regeneration studies in vertebrate central nervous systems.

scholarly journals Biological data annotation via a human-augmenting AI-based labeling system

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Douwe van der Wal ◽  
Iny Jhun ◽  
Israa Laklouk ◽  
Jeff Nirschl ◽  
Lara Richer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Microscopic Analysis ◽  
Image Data ◽  
Cell Types ◽  
Biological Data ◽  
Data Sets ◽  
Data Set ◽  
Data Annotation ◽  
Labeling System

AbstractBiology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

Sign in / Sign up

Export Citation Format

Share Document