Connecting single-cell transcriptomes to projectomes in mouse visual cortex

Связывание одно-клеточных транскриптомов с проектомами в зрительной коре мыши
Ed S. Lein, Jack Waters, Lydia Ng, Nick Dee, Kimberly A. Smith, Chao Chen, Alex M. Henry, Staci A. Sorensen, Karla E. Hirokawa, Clare Gamlin, Jeff Goldy, Katelyn Johnson, Brian Kalmbach, Shenqin Yao, Darren Bertagnolli, Jonathan T. Ting, Qingming Luo, Tim Jarsky, Changkyu Lee, Mary McGraw, Tanya L. Daigle, Rachel Dalley, Delissa McMillen, Josef Šulc, Michael Tieu, Jim Berg, Agata Budzillo, Nathan W. Gouwens, Brian Lee, Lauren Alfiler, Katherine Baker, Kris Bickley, Jasmine Bomben, Krissy Brouner, Rebecca de Frates, Tom Egdorf, Rachel Enstrom, Colin Farrell, Amanda Gary, Kristen Hadley, Sara Kebede, Lisa Kim, Matthew Mallory, Rusty Mann, Michelle Maxwell, Alice Mukora, Lindsay Ng, Aaron Oldre, Daniel Park, Christina Alice Pom, Lydia Potekhina, Shea Ransford, David Sandman, Jessica Trinh, Grace Williams, Luke Esposito, Melissa Reding, Dillan Brown, Maggie Chvilicek, Michelle Stoecklin, Forrest Collman, Adrian Wanner, Phil Lesnar, Mike Hawrylycz, Anan Li, Hui Gong, Wei Xiong, Yun Wang, Xiuli Kuang, Hsien-Chi Kuo, Yaoyao Li, Olga Gliko, Leila Ahmadinia, Fahimeh Baftizadeh, Sarah Bannick, Phil Bohn, K. Amy Chen, Tim Dawes, Maxwell Departee, Laila El-Hifnawi, Rohan Gala, Andrew Glomb, 弘 西谷, DiJon Hill, Zili Huang, Zoe Juneau, Elizabeth Liang, Katie A. Link, Thomas Ochoa, Zoran Popovich, Ram Rajanbabu, Daniel Park, Zoran Popovich, Sara Vargas, Dave Vumbaco, Miranda Walker, Micheal Wang, Julia Wilson, Uygar Sümbül
2023-11-27

Patch-seqmorpho-electric-transcriptomic (MET) typesprojection targetssingle-cell RNA sequencingwhole brain morphology
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
1
Complete morphologies of 300 neurons were reconstructed and linked to transcriptomic MET-types using a multi-step classifier.
2
Coordinated Patch-seq and whole-brain morphology datasets of excitatory neurons in mouse visual cortex were collected and unified.
3
From Patch-seq data, 16 integrated morpho-electric-transcriptomic (MET) types of excitatory neurons were defined.
4
Transcriptomic variation combined with anatomical cell location can predict individual neurons' long-range projection targets, revealing cell-type-specific interhemispheric projections among infragranular cells.
5
Transcriptomic variations within and across MET-types correspond with distinct morphological and electrophysiological phenotypes.

Excitatory neurons in mouse visual cortex with coordinated Patch-seq, whole-brain morphology, and projection (projectome) data

Relationships between transcriptomic identities (single-cell transcriptomes/MET-types), morphological and electrophysiological phenotypes, anatomical location, and their ability to predict long-range projection targets (projectomes) of individual excitatory neurons

Publication Details
Publication Date
2023-11-27
Journal
Publisher
ISSN
Cited by
15
Access Type
Author Information
Authors
Ed S. Lein
Jack Waters
Lydia Ng
Nick Dee
Kimberly A. Smith
Chao Chen
Alex M. Henry
Staci A. Sorensen
Karla E. Hirokawa
Clare Gamlin
Jeff Goldy
Katelyn Johnson
Brian Kalmbach
Shenqin Yao
Darren Bertagnolli
Jonathan T. Ting
Qingming Luo
Tim Jarsky
Changkyu Lee
Mary McGraw
Tanya L. Daigle
Rachel Dalley
Delissa McMillen
Josef Šulc
Michael Tieu
Jim Berg
Agata Budzillo
Nathan W. Gouwens
Brian Lee
Lauren Alfiler
Katherine Baker
Kris Bickley
Jasmine Bomben
Krissy Brouner
Rebecca de Frates
Tom Egdorf
Rachel Enstrom
Colin Farrell
Amanda Gary
Kristen Hadley
Sara Kebede
Lisa Kim
Matthew Mallory
Rusty Mann
Michelle Maxwell
Alice Mukora
Lindsay Ng
Aaron Oldre
Daniel Park
Christina Alice Pom
Lydia Potekhina
Shea Ransford
David Sandman
Jessica Trinh
Grace Williams
Luke Esposito
Melissa Reding
Dillan Brown
Maggie Chvilicek
Michelle Stoecklin
Forrest Collman
Adrian Wanner
Phil Lesnar
Mike Hawrylycz
Anan Li
Hui Gong
Wei Xiong
Yun Wang
Xiuli Kuang
Hsien-Chi Kuo
Yaoyao Li
Olga Gliko
Leila Ahmadinia
Fahimeh Baftizadeh
Sarah Bannick
Phil Bohn
K. Amy Chen
Tim Dawes
Maxwell Departee
Laila El-Hifnawi
Rohan Gala
Andrew Glomb
弘 西谷
DiJon Hill
Zili Huang
Zoe Juneau
Elizabeth Liang
Katie A. Link
Thomas Ochoa
Zoran Popovich
Ram Rajanbabu
Daniel Park
Zoran Popovich
Sara Vargas
Dave Vumbaco
Miranda Walker
Micheal Wang
Julia Wilson
Uygar Sümbül
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%