Filter
Associated Lab
- 43418 (27) Apply 43418 filter
- 43427 (18) Apply 43427 filter
- 43430 (52) Apply 43430 filter
- 46293 (4) Apply 46293 filter
- Ahrens Lab (39) Apply Ahrens Lab filter
- Aso Lab (33) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (97) Apply Betzig Lab filter
- Beyene Lab (3) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (40) Apply Branson Lab filter
- Card Lab (25) Apply Card Lab filter
- Cardona Lab (44) Apply Cardona Lab filter
- Chklovskii Lab (10) Apply Chklovskii Lab filter
- Clapham Lab (10) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (7) Apply Darshan Lab filter
- Dickson Lab (29) Apply Dickson Lab filter
- Druckmann Lab (21) Apply Druckmann Lab filter
- Dudman Lab (32) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (11) Apply Espinosa Medina Lab filter
- Feliciano Lab (6) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- Fitzgerald Lab (11) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (26) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (42) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (14) Apply Hermundstad Lab filter
- Hess Lab (59) Apply Hess Lab filter
- Jayaraman Lab (37) Apply Jayaraman Lab filter
- Ji Lab (32) Apply Ji Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Karpova Lab (12) Apply Karpova Lab filter
- Keleman Lab (8) Apply Keleman Lab filter
- Keller Lab (59) Apply Keller Lab filter
- Lavis Lab (107) Apply Lavis Lab filter
- Lee (Albert) Lab (27) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (1) Apply Li Lab filter
- Lippincott-Schwartz Lab (74) Apply Lippincott-Schwartz Lab filter
- Liu (Zhe) Lab (47) Apply Liu (Zhe) Lab filter
- Looger Lab (136) Apply Looger Lab filter
- Magee Lab (31) Apply Magee Lab filter
- Menon Lab (12) Apply Menon Lab filter
- Murphy Lab (6) Apply Murphy Lab filter
- O'Shea Lab (3) Apply O'Shea Lab filter
- Pachitariu Lab (21) Apply Pachitariu Lab filter
- Pastalkova Lab (5) Apply Pastalkova Lab filter
- Pavlopoulos Lab (7) Apply Pavlopoulos Lab filter
- Pedram Lab (1) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (36) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (25) Apply Romani Lab filter
- Rubin Lab (94) Apply Rubin Lab filter
- Saalfeld Lab (35) Apply Saalfeld Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (40) Apply Schreiter Lab filter
- Shroff Lab (12) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (36) Apply Singer Lab filter
- Spruston Lab (54) Apply Spruston Lab filter
- Stern Lab (58) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (15) Apply Stringer Lab filter
- Svoboda Lab (130) Apply Svoboda Lab filter
- Tebo Lab (2) Apply Tebo Lab filter
- Tervo Lab (8) Apply Tervo Lab filter
- Tillberg Lab (12) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (57) Apply Truman Lab filter
- Turaga Lab (32) Apply Turaga Lab filter
- Turner Lab (17) Apply Turner Lab filter
- Vale Lab (3) Apply Vale Lab filter
- Wu Lab (8) Apply Wu Lab filter
- Zlatic Lab (27) Apply Zlatic Lab filter
- Zuker Lab (5) Apply Zuker Lab filter
Associated Project Team
- COSEM (2) Apply COSEM filter
- Fly Descending Interneuron (7) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (13) Apply Fly Functional Connectome filter
- Fly Olympiad (4) Apply Fly Olympiad filter
- FlyEM (56) Apply FlyEM filter
- FlyLight (35) Apply FlyLight filter
- GENIE (37) Apply GENIE filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (15) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (12) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Publication Date
- 2023 (71) Apply 2023 filter
- 2022 (176) Apply 2022 filter
- 2021 (175) Apply 2021 filter
- 2020 (176) Apply 2020 filter
- 2019 (177) Apply 2019 filter
- 2018 (206) Apply 2018 filter
- 2017 (187) Apply 2017 filter
- 2016 (191) Apply 2016 filter
- 2015 (196) Apply 2015 filter
- 2014 (191) Apply 2014 filter
- 2013 (136) Apply 2013 filter
- 2012 (112) Apply 2012 filter
- 2011 (98) Apply 2011 filter
- 2010 (62) Apply 2010 filter
- 2009 (56) Apply 2009 filter
- 2008 (40) Apply 2008 filter
- 2007 (21) Apply 2007 filter
- 2006 (3) Apply 2006 filter
Type of Publication
- Remove Janelia filter Janelia
2274 Publications
Showing 2101-2110 of 2274 resultsThis paper provides a compilation of diagrammatic representations of the expression profiles of epidermal and fat body mRNAs during the last two larval instars and metamorphosis of the tobacco hornworm, Manduca sexta. Included are those encoding insecticyanin, three larval cuticular proteins, dopa decarboxylase, moling, and the juvenile hormone-binding protein JP29 produced by the dorsal abdominal epidermis, and arylphorin and the methionine-rich storage proteins made by the fat body. The mRNA profiles of the ecdysteroid-regulated cascade of transcription factors in the epidermis during the larval molt and the onset of metamorphosis and in the pupal wing during the onset of adult development are also shown. These profiles are accompanied by a brief summary of the current knowledge about the regulation of these mRNAs by ecdysteroids and juvenile hormone based on experimental manipulations, both in vivo and in vitro.
The rapid adoption of high-throughput next generation sequence data in biological research is presenting a major challenge for sequence alignment tools—specifically, the efficient alignment of vast amounts of short reads to large references in the presence of differences arising from sequencing errors and biological sequence variations. To address this challenge, we developed a short read aligner for high-throughput sequencer data that is tolerant of errors or mutations of all types—namely, substitutions, deletions, and insertions. The aligner utilizes a multi-stage approach in which template-based indexing is used to identify candidate regions for alignment with dynamic programming. A template is a pair of gapped seeds, with one used with the read and one used with the reference. In this article, we focus on the development of template families that yield error-tolerant indexing up to a given error-budget. A general algorithm for finding those families is presented, and a recursive construction that creates families with higher error tolerance from ones with a lower error tolerance is developed.
A wide variety of biological experiments rely on the ability to express an exogenous gene in a transgenic animal at a defined level and in a spatially and temporally controlled pattern. We describe major improvements of the methods available for achieving this objective in Drosophila melanogaster. We have systematically varied core promoters, UTRs, operator sequences, and transcriptional activating domains used to direct gene expression with the GAL4, LexA, and Split GAL4 transcription factors and the GAL80 transcriptional repressor. The use of site-specific integration allowed us to make quantitative comparisons between different constructs inserted at the same genomic location. We also characterized a set of PhiC31 integration sites for their ability to support transgene expression of both drivers and responders in the nervous system. The increased strength and reliability of these optimized reagents overcome many of the previous limitations of these methods and will facilitate genetic manipulations of greater complexity and sophistication.
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.
Classical studies have related the spiking of selected neocortical neurons to behavior, but little is known about activity sampled from the entire neural population. We recorded from neurons selected independent of spiking, using cell-attached recordings and two-photon calcium imaging, in the barrel cortex of mice performing an object localization task. Spike rates varied across neurons, from silence to >60 Hz. Responses were diverse, with some neurons showing large increases in spike rate when whiskers contacted the object. Nearly half the neurons discriminated object location; a small fraction of neurons discriminated perfectly. More active neurons were more discriminative. Layer (L) 4 and L5 contained the highest fractions of discriminating neurons (\~{}63% and 79%, respectively), but a few L2/3 neurons were also highly discriminating. Approximately 13,000 spikes per activated barrel column were available to mice for decision making. Coding of object location in the barrel cortex is therefore highly redundant.
Complete reconstructions of vertebrate neuronal circuits on the synaptic level require new approaches. Here, serial section transmission electron microscopy was automated to densely reconstruct four volumes, totaling 670 μm(3), from the rat hippocampus as proving grounds to determine when axo-dendritic proximities predict synapses. First, in contrast with Peters’ rule, the density of axons within reach of dendritic spines did not predict synaptic density along dendrites because the fraction of axons making synapses was variable. Second, an axo-dendritic touch did not predict a synapse; nevertheless, the density of synapses along a hippocampal dendrite appeared to be a universal fraction, 0.2, of the density of touches. Finally, the largest touch between an axonal bouton and spine indicated the site of actual synapses with about 80% precision but would miss about half of all synapses. Thus, it will be difficult to predict synaptic connectivity using data sets missing ultrastructural details that distinguish between axo-dendritic touches and bona fide synapses.
The need for optical sectioning in bio-imaging has amongst others led to the development of the two-photon scanning microscopy. However, this comes with some intrinsic fundamental limitations in the temporal domain as the focused spot has to be scanned mechanically in the sample plane. Hence for a large number of biological applications where imaging speed is a limiting factor, it would be significantly advantageous to generate widefield excitations with an optical sectioning comparable to the two-photon scanning microscopy. Recently by using the technique of temporal focusing it was shown that high axial resolution widefield excitation can be generated in picosecond time scales without any mechanical moving parts. However the achievable axial resolution is still well above that of a two-photon scanning microscope. Here we demonstrate a new ultrafast widefield two-photon imaging technique termed Multifocal Temporal Focusing (MUTEF) which relies on the generation of a set of diffraction limited beams produced by an Echelle grating that scan across a second tilted diffraction grating in picosecond time scale, generating a widefield excitation area with an axial resolution comparable to a two-photon scanning microscope. Using this method we have shown widefield two-photon imaging on fixed biological samples with an axial sectioning with a FWHM of 0.85 μm.
Drosophila brains contain numerous neurons that form complex circuits. These neurons are derived in stereotyped patterns from a fixed number of progenitors, called neuroblasts, and identifying individual neurons made by a neuroblast facilitates the reconstruction of neural circuits. An improved MARCM (mosaic analysis with a repressible cell marker) technique, called twin-spot MARCM, allows one to label the sister clones derived from a common progenitor simultaneously in different colors. It enables identification of every single neuron in an extended neuronal lineage based on the order of neuron birth. Here we report the first example, to our knowledge, of complete lineage analysis among neurons derived from a common neuroblast that relay olfactory information from the antennal lobe (AL) to higher brain centers. By identifying the sequentially derived neurons, we found that the neuroblast serially makes 40 types of AL projection neurons (PNs). During embryogenesis, one PN with multi-glomerular innervation and 18 uniglomerular PNs targeting 17 glomeruli of the adult AL are born. Many more PNs of 22 additional types, including four types of polyglomerular PNs, derive after the neuroblast resumes dividing in early larvae. Although different offspring are generated in a rather arbitrary sequence, the birth order strictly dictates the fate of each post-mitotic neuron, including the fate of programmed cell death. Notably, the embryonic progenitor has an altered temporal identity following each self-renewing asymmetric cell division. After larval hatching, the same progenitor produces multiple neurons for each cell type, but the number of neurons for each type is tightly regulated. These observations substantiate the origin-dependent specification of neuron types. Sequencing neuronal lineages will not only unravel how a complex brain develops but also permit systematic identification of neuron types for detailed structure and function analysis of the brain.
Changes in behavioral state modify neural activity in many systems. In some vertebrates such modulation has been observed and interpreted in the context of attention and sensorimotor coordinate transformations. Here we report state-dependent activity modulations during walking in a visual-motor pathway of Drosophila. We used two-photon imaging to monitor intracellular calcium activity in motion-sensitive lobula plate tangential cells (LPTCs) in head-fixed Drosophila walking on an air-supported ball. Cells of the horizontal system (HS)–a subgroup of LPTCs–showed stronger calcium transients in response to visual motion when flies were walking rather than resting. The amplified responses were also correlated with walking speed. Moreover, HS neurons showed a relatively higher gain in response strength at higher temporal frequencies, and their optimum temporal frequency was shifted toward higher motion speeds. Walking-dependent modulation of HS neurons in the Drosophila visual system may constitute a mechanism to facilitate processing of higher image speeds in behavioral contexts where these speeds of visual motion are relevant for course stabilization.
To study the complex synaptic interactions underpinning dendritic information processing in single neurons, experimenters require methods to mimic presynaptic neurotransmitter release at multiple sites with no physiological damage. We show that laser scanning systems built around large-aperture acousto-optic deflectors and high numerical aperture objective lenses provide the sub-millisecond, sub-micron precision necessary to achieve physiological, exogenous synaptic stimulation. Our laser scanning systems can produce the sophisticated spatio-temporal patterns of synaptic input that are necessary to investigate single-neuron dendritic physiology.