BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Somewhere over the Brainbow: super-multicolour imaging to automate
  neural circuit reconstructions - Dr Marcus Leiwe\, Kyushu University\, Ja
 pan
DTSTART:20220627T150000Z
DTEND:20220627T160000Z
UID:TALK176057@talks.cam.ac.uk
CONTACT:Dr Dervila Glynn
DESCRIPTION:Ectopic Neuroscience Seminar in person and on Zoom\n\nHosted i
 n person by Elisa Galliano and on Zoom by Cambridge Neuroscience\n\nThe br
 ain is made up of dense networks of interconnected neurons. Mapping the an
 atomy of these dense networks is one of the biggest challenges in neurosci
 ence. Electron microscopy provides the highest resolution and is used as a
  gold standard in connectomics\; however\, its data size hampers large-sca
 le circuit reconstruction at the millimetre scale. Light microscopy combin
 ed with tissue clearing is a new emerging approach for mesoscopic circuit 
 mapping. However\, the reconstruction of densely labelled circuits is chal
 lenging as its limited resolution hinders the discrimination of different 
 neurons. Stochastic multicolour labelling strategies\, such as Brainbow\, 
 utilise a combination of 3 fluorescent proteins (XFPs) to create different
  colour hues. Allowing for the reconstruction of densely labelled circuits
 . However\, these tools only produce ~20 colour hues\, which is not enough
  to reconstruct neuronal circuits at sufficient density. Moreover\, manual
  circuit tracing based on the colour hue is a rate limiting step in this s
 trategy. We aimed to solve these issues by increasing the number of colour
  hues available\, then use machine learning to automatically reconstruct n
 eurons based on their colour hue alone. Firstly\, we increased the number 
 of colour hues by stochastically expressing a combination of 7 different f
 luorescent proteins\, then separating the spectral overlap through linear 
 unmixing. Our modelling suggests that this can generate ~1\,200 different 
 colour hues. Secondly\, as our eyes are limited to trichromatic vision\, w
 e developed a pipeline to automatically recognize the combination of >3 co
 lours. This pipeline includes a newly developed unsupervised clustering al
 gorithm\, named the “Euclidean Crawler”\, which classifies data points
  in N-dimensional space purely based on the threshold Euclidean distance. 
 It holds an advantage over other distance-based clustering algorithms as w
 e do not need to specify the number of clusters (like K-means)\, nor the d
 ensity of clusters (like mean-shift clustering). As proof of concept\, fir
 st\, we successfully reconstructed densely labelled layer 2/3 neurons in S
 1 (~300 neurons). Secondly\, we automatically reconstructed long range (>2
  mm) axonal terminals of mitral and tufted cell axons in the olfactory cor
 tex. Finally\, we used our automatic circuit reconstruction pipeline to re
 gister neighbouring brain sections: this was done by identifying neurites 
 that go between sections by their colour hue\, then performing piece-wise 
 linear mapping for registration. Thus\, super multi-colour labelling is a 
 powerful tool for highly multiplexed circuit reconstruction on the mesosco
 pic scale.\n\nhttps://us02web.zoom.us/j/84601255206?pwd=S1UzMVBSbFJOQnlPUm
 ZZV1IzSmhlQT09\n\nMeeting ID: 84601255206 / Passcode: 844907
LOCATION:Hodgkin–Huxley Seminar Room Physiology Building\, Downing Site 
 CB2 3DY Cambridge
END:VEVENT
END:VCALENDAR