Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (51) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (99) Apply Betzig Lab filter
- Beyene Lab (8) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (49) Apply Branson Lab filter
- Card Lab (34) Apply Card Lab filter
- Cardona Lab (44) Apply Cardona Lab filter
- Chklovskii Lab (10) Apply Chklovskii Lab filter
- Clapham Lab (13) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (8) Apply Darshan Lab filter
- Dickson Lab (32) Apply Dickson Lab filter
- Druckmann Lab (21) Apply Druckmann Lab filter
- Dudman Lab (38) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (14) Apply Espinosa Medina Lab filter
- Feliciano Lab (7) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- Fitzgerald Lab (16) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (37) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (50) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (22) Apply Hermundstad Lab filter
- Hess Lab (73) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (42) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Karpova Lab (13) Apply Karpova Lab filter
- Keleman Lab (8) Apply Keleman Lab filter
- Keller Lab (61) Apply Keller Lab filter
- Koay Lab (2) Apply Koay Lab filter
- Lavis Lab (132) Apply Lavis Lab filter
- Lee (Albert) Lab (29) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (3) Apply Li Lab filter
- Lippincott-Schwartz Lab (93) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (1) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (56) Apply Liu (Zhe) Lab filter
- Looger Lab (137) 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 (5) Apply O'Shea Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (34) Apply Pachitariu Lab filter
- Pastalkova Lab (5) Apply Pastalkova Lab filter
- Pavlopoulos Lab (7) Apply Pavlopoulos Lab filter
- Pedram Lab (4) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (45) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (31) Apply Romani Lab filter
- Rubin Lab (105) Apply Rubin Lab filter
- Saalfeld Lab (45) Apply Saalfeld Lab filter
- Satou Lab (1) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (50) Apply Schreiter Lab filter
- Shroff Lab (29) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (37) Apply Singer Lab filter
- Spruston Lab (56) Apply Spruston Lab filter
- Stern Lab (72) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (29) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (8) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (16) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (58) Apply Truman Lab filter
- Turaga Lab (36) Apply Turaga Lab filter
- Turner Lab (26) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (17) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (6) Apply Wang (Shaohe) Lab filter
- Wu Lab (8) Apply Wu Lab filter
- Zlatic Lab (26) Apply Zlatic Lab filter
- Zuker Lab (5) Apply Zuker Lab filter
Associated Project Team
- CellMap (12) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- FIB-SEM Technology (2) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (10) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (53) Apply FlyEM filter
- FlyLight (48) Apply FlyLight filter
- GENIE (42) Apply GENIE filter
- Integrative Imaging (2) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (18) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (26) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Associated Support Team
- Project Pipeline Support (3) Apply Project Pipeline Support filter
- Anatomy and Histology (18) Apply Anatomy and Histology filter
- Cryo-Electron Microscopy (33) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (15) Apply Electron Microscopy filter
- Gene Targeting and Transgenics (11) Apply Gene Targeting and Transgenics filter
- Integrative Imaging (17) Apply Integrative Imaging filter
- Invertebrate Shared Resource (40) Apply Invertebrate Shared Resource filter
- Janelia Experimental Technology (36) Apply Janelia Experimental Technology filter
- Management Team (1) Apply Management Team filter
- Molecular Genomics (15) Apply Molecular Genomics filter
- Primary & iPS Cell Culture (14) Apply Primary & iPS Cell Culture filter
- Project Technical Resources (47) Apply Project Technical Resources filter
- Quantitative Genomics (19) Apply Quantitative Genomics filter
- Scientific Computing Software (88) Apply Scientific Computing Software filter
- Scientific Computing Systems (6) Apply Scientific Computing Systems filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (57) Apply 2025 filter
- 2024 (223) Apply 2024 filter
- 2023 (162) Apply 2023 filter
- 2022 (167) Apply 2022 filter
- 2021 (175) Apply 2021 filter
- 2020 (177) Apply 2020 filter
- 2019 (177) Apply 2019 filter
- 2018 (206) Apply 2018 filter
- 2017 (186) Apply 2017 filter
- 2016 (191) Apply 2016 filter
- 2015 (195) Apply 2015 filter
- 2014 (190) Apply 2014 filter
- 2013 (136) Apply 2013 filter
- 2012 (112) Apply 2012 filter
- 2011 (98) Apply 2011 filter
- 2010 (61) Apply 2010 filter
- 2009 (56) Apply 2009 filter
- 2008 (40) Apply 2008 filter
- 2007 (21) Apply 2007 filter
- 2006 (3) Apply 2006 filter
2633 Janelia Publications
Showing 2281-2290 of 2633 resultsSingle-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.
Small-molecule fluorophores, such as fluorescein and rhodamine derivatives, are critical tools in modern biochemical and biological research. The field of chemical dyes is old; colored molecules were first discovered in the 1800s, and the fluorescein and rhodamine scaffolds have been known for over a century. Nevertheless, there has been a renaissance in using these dyes to create tools for biochemistry and biology. The application of modern chemistry, biochemistry, molecular genetics, and optical physics to these old structures enables and drives the development of novel, sophisticated fluorescent dyes. This critical review focuses on an important example of chemical biology-the melding of old and new chemical knowledge-leading to useful molecules for advanced biochemical and biological experiments. Expected final online publication date for the Annual Review of Biochemistry Volume 86 is June 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.
In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.
The spatial distribution of diverse biomolecules in multicellular organisms is essential for their physiological functions. High-throughput in situ mapping of biomolecules is crucial for both basic and medical research, and requires high scanning speed, spatial resolution, and chemical sensitivity. Here, we developed a Tissue Expansion method compatible with matrix-assisted laser desorption/ionization Mass spectrometry Imaging (TEMI). TEMI reaches single-cell spatial resolution without sacrificing voxel throughput and enables the profiling of hundreds of biomolecules, including lipids, metabolites, peptides (proteins), and N-glycans. Using TEMI, we mapped the spatial distribution of biomolecules across various mammalian tissues and uncovered metabolic heterogeneity in tumors. TEMI can be easily adapted and broadly applied in biological and medical research, to advance spatial multi-omics profiling.
In many reptiles, including the red-eared slider turtle (), sex is determined by ambient temperature during embryogenesis. We previously showed that the epigenetic regulator is elevated at the male-producing temperature and essential to activate the male pathway. In this work, we established a causal link between temperature and transcriptional regulation of We show that signal transducer and activator of transcription 3 (STAT3) is phosphorylated at the warmer, female-producing temperature, binds the locus, and represses transcription, blocking the male pathway. Influx of Ca, a mediator of STAT3 phosphorylation, is elevated at the female temperature and acts as a temperature-sensitive regulator of STAT3 activation.
During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.
During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.
During development, regulatory factors appear in a precise order to determine cell fates over time. To investigate complex tissue development, one should not just label cell lineages but further visualize and manipulate cells with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labelling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation/inactivation of reporters/effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.One-Sentence Summary Gaining sequential genetic access to vertebrate cell lineages.Competing Interest StatementThe authors have declared no competing interest.
A complex nervous system requires precise numbers of various neuronal types produced with exquisite spatiotemporal control. This striking diversity is generated by a limited number of neural stem cells (NSC), where spatial and temporal patterning intersect. Drosophila is a genetically tractable model system that has significant advantages for studying stem cell biology and neuronal fate specification. Here we review the latest findings in the rich literature of temporal patterning of neuronal identity in the Drosophila central nervous system. Rapidly changing consecutive transcription factors expressed in NSCs specify short series of neurons with considerable differences. More slowly progressing changes are orchestrated by NSC intrinsic temporal factor gradients which integrate extrinsic signals to coordinate nervous system and organismal development.