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Page 1: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome
Tom Dean
BEGINNING OF KEVIN’S SLIDES
Tom Dean
START LECTURE TOM’S SLIDES
Page 2: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Map structure Measure function Manipulate circuit

Buszaki, 2004

Behavior

2 + 2 = 4

Luo et al 2009

Reverse engineering neuronal circuits

Cajal, 1904

Network-level computational model

Page 3: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Synaptic connectivity is necessary, but not sufficient, to understand the neural basis of behavior.

But, the hope is that structure tells us something about function:

Page 4: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Electron microscopy is needed for dense synaptic reconstruction

Helmstaedter M, Briggman KL, Denk W (2008) 3D structural imaging of the brain with photons and electrons. Curr Opin Neurobiol

10x10x25nm

Livet et al 2007

Page 5: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome
Tom Dean
RETURN TO TOM’S SLIDES
Page 6: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Serial section TEM (ssTEM)

Automated tape collection ultramicrotomy (ATUM)

Serial block-face SEM (SBEM)

Focused ion beam SEM (FIBSEM)

3D electron microscopy techniques

Briggman KL, Bock DD (2012) Volume electron microscopy for neuronal circuit reconstruction. Curr Opin Neurobiol.

Page 7: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Poor isotropic resolution Good isotropic resolution Good isotropic resolution Best isotropic resolution Non-destructive Destructive Non-destructive Destructive Section distortions No distortions Section distortions No distortions Large field of view Limited field of view Large field of view Large field of view

Page 8: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Tissue

Stains

Conventional heavy metal stains

Page 9: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Contrast in block face SEM

electron beam

Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome within the SEM - a technical note. Scan Electron Microsc, (Pt 2): 73-6. Denk W, Horstmann H (2004) Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol 2(11): e329.

Page 10: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

C. elegans

1 mm

Fish brain

Mouse barrel

column

10-3 mm3

102 neurons

10-2 mm3

104 neurons

10-1 mm3

105 neurons

103 mm3 108 neurons

Mouse brain

Retina

10-2 mm3

104 neurons

What SBEM volume is currently feasible?

Page 11: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome
Page 12: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome
Tom Dean
RETURN TO TOM’S SLIDES
Page 13: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Sparse, targeted reconstruction following 2-photon calcium imaging

Neuronal circuit analysis of SBEM mouse retina data / are connectomes useful?

Dense, comprehensive reconstruction

60 ȝ

m

Page 14: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

The mammalian retina

Masland (2001)

Bipolar cells

Ganglion cells

Amacrine cells

Horizontal cells

Photoreceptor cells

Page 15: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Sparse reconstruction of direction-selectivity circuitry

DS ganglion cell

Null Preferred

Linear direction-of-motion detector

Starburst amacrine cell

Motion direction triggering GABA and/or ACh release from SAC

Euler et al. (2002)

Radial direction-of-motion detector

Page 16: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Briggman KL & Euler T. (2011) Bulk electroporation and population calcium imaging of the adult mammalian retina. J Neurophys

Bulk electroporation and 2P imaging of GCs

Page 17: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome
Page 18: BEGINNING OF KEVIN’S SLIDESSTART LECTURE TOM’S SLIDES...Contrast in block face SEM electron beam Leighton, S. B. (1981). SEM images of block faces, cut by a miniature microtome

Acknowledgments

Zebrafish Collaborators

Harry Burgess (NICHD)

SBEM / Retina

Winfried Denk (MPI, Heidelberg) Moritz Helmstaedter (MPI, Munich)

Thomas Euler (U Tübingen)

Machine Learning

Viren Jain (JFRC) Srini Turaga (UCL)

Sebastian Seung (MIT)

Briggman Lab (NINDS)

Christopher Harris John Kaufhold Marta Pallotto


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