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Drewes, R.

Publications and source records attributed to Drewes, R..

2 recordsLinked to original sources

Cellular Anatomy of the Mouse Primary Motor Cortex

An essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.

neuroscience

Super-resolution light-sheet fluorescence microscopy by SOFI.

Here we describe a new method, named LS-SOFI, that combines light-sheet fluorescence microscopy and super-resolution optical fluctuation imaging to achieve fast nanoscale-resolution imaging over large fields of view in native 3D tissues. We demonstrate the use of LS-SOFI in super-resolution analysis of neuronal structures and synaptic proteins, including cortical axons, dendritic spines, pre- and postsynaptic cytoskeletal proteins and postsynaptic AMPA receptors, in thick mouse brain sections. We also introduce an algorithm to determine the number of active fluorophore emitters detected, allowing the localization of individual molecules in LS-SOFI images. We conclude that LS-SOFI is a versatile method for fast super-resolution imaging from any tissue of the body using both commercial and custom LSFM instruments.

neuroscience