Scalable tools for consistent identification of neuronal cell types in mouse and human
Scalable tools for consistent identification of neuronal cell types in mouse and human
批准号:
10365216
负责人:
Staci A Sorensen
金额:
$108.14万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-16 至 2024-09-15
关键词:
AcuteAddressAgreementAlgorithmsAnatomyAxonBiologicalBrainBrain imagingBrightfield MicroscopyCellsCharacteristicsCollectionCommunitiesComputer softwareComputing MethodologiesCoupledDataData SetDescriptorEffectivenessElectrophysiology (science)Functional disorderFundingGenetic TranscriptionGoldHumanImageIndividualInstitutesJointsLabelLearningLiteratureLocationManualsMeasurementMethodsModalityModernizationMolecularMolecular ProfilingMorphologyMusNervous system structureNeuronsNeurosciencesPerformancePhenotypePhysiologicalPopulationPreparationPropertyQuality ControlReproducibilityResearchResearch PersonnelScienceShapesSliceSoftware ToolsSpeedSupervisionTestingTimeTrainingTranslatingUncertaintyVisionVisualizationWorkartificial neural networkautoencoderautomated analysisautomated segmentationbasebrain dysfunctioncell typecloud basedcloud platformcomputational pipelinescomputerized toolscostdata repositoryexperimental studyflexibilityimage archival systemimprovedinnovationlarge datasetslight microscopymachine learning algorithmmachine learning methodmicroscopic imagingmultimodalitymultiple datasetsneural networkpatch sequencingprogramsreconstructionsuccesstooltranscriptomics
中文摘要
项目总结
英文摘要
Project Summary
The proposed work will address a critical gap in our understanding of neuronal phenotypes and cell types by
developing machine learning algorithms and cloud-based software for the integration of multiple modality
characterizations large and growing datasets of cortical neurons in mouse and human. Through optimal and
innovative use of potentially incomplete data and emphasis on automated morphological characterization, the
proposed tools will enable richer and consistent characterizations of neurons from transcriptomic, anatomical, or
electrophysiological profiling.
While large-scale, BICCN-funded cell type research programs rely on the notion of unique neuronal identity
which determines the cell’s phenotype across different observation modalities, overarching agreements across
physiological, anatomical and molecular characterizations remain elusive. Although these large-scale programs
succeeded in generating extensive multiple modality datasets, the lack of principled, accurate and widely
available computational alignment and inference tools presents a roadblock to the success of the overall
program. A second issue is that anatomical characterization, despite being the classical approach to
understanding cell types, lags significantly behind molecular and physiological methods in terms of throughput.
The research proposed here aims to address the alignment problem by building on the coupled autoencoder
approach, which presents an efficient optimization framework centered on the ubiquity of neuronal identity.
Importantly, the proposed software can utilize incompletely characterized data points, which is common in
practice, to produce unified visualization and analysis of abstract neuronal identity. This tool will be both flexible
(e.g., the feature set can be changed) and extensible (e.g., more observation modalities can be added for joint
alignment). The aligned representations enable consistent clustering of the neuronal population across the
different observation modalities, which is a pressing problem in modern neuroscience.
We propose to address the anatomical throughput issue with an end-to-end computational pipeline, from the raw
image of local neuronal arbors to the anatomical descriptor that can be readily aligned and interpreted by the
coupled autoencoder software. By utilizing our extensive gold-standard manual reconstructions, we will train
supervised deep artificial neural networks to segment neuronal arbors in sparse labeling scenarios. The rich set
of training examples, together with algorithmic innovations, will endow superior generalizability of this automated
segmentation tool, accelerating science for light microscopy-based studies.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
Regulation /Dendritic Architecture in Nucleus Laminaris
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批准号:6933833
-
项目类别:
-
资助金额:$3.2万
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财政年份:2004
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负责人:Staci A Sorensen
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依托单位:
Regulation /Dendritic Architecture in Nucleus Laminaris
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批准号:6835414
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项目类别:
-
资助金额:$3.2万
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财政年份:2004
-
负责人:Staci A Sorensen
-
依托单位:
海外基金