FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis.
light-sheet
parvalbumin
somatostatin
tissue clearing
vasoactive intestinal peptide
Journal
GigaScience
ISSN: 2047-217X
Titre abrégé: Gigascience
Pays: United States
ID NLM: 101596872
Informations de publication
Date de publication:
28 12 2022
28 12 2022
Historique:
received:
20
06
2022
revised:
15
02
2023
accepted:
26
04
2023
medline:
26
5
2023
pubmed:
24
5
2023
entrez:
24
5
2023
Statut:
ppublish
Résumé
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory's use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2's CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline. For more precise alignment, we add a landmark-based atlas registration to ClearMap's functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap's threshold-based approach: Ilastik's Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells. As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
Sections du résumé
BACKGROUND
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory's use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2's CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.
FINDINGS
For more precise alignment, we add a landmark-based atlas registration to ClearMap's functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap's threshold-based approach: Ilastik's Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells.
CONCLUSIONS
As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
Identifiants
pubmed: 37222748
pii: 7176210
doi: 10.1093/gigascience/giad035
pmc: PMC10205001
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press GigaScience.
Références
Elife. 2021 Mar 19;10:
pubmed: 33739286
J Neurosci. 2023 Mar 1;43(9):1555-1571
pubmed: 36717231
Cell. 2008 Aug 8;134(3):508-20
pubmed: 18692473
Cell. 2014 Jan 16;156(1-2):17-9
pubmed: 24439367
J Neurosci. 2018 Feb 14;38(7):1622-1633
pubmed: 29326172
Gigascience. 2022 Dec 28;12:
pubmed: 37222748
Elife. 2019 Mar 18;8:
pubmed: 30883329
Front Neuroanat. 2019 Apr 30;13:45
pubmed: 31114486
Nat Neurosci. 2013 Nov;16(11):1598-607
pubmed: 24097043
J Neurosci. 2011 Jul 27;31(30):10948-70
pubmed: 21795545
IEEE Trans Med Imaging. 2010 Jan;29(1):196-205
pubmed: 19923044
Neurosci Res. 2019 Jan;138:26-32
pubmed: 30227162
Cell. 2017 Oct 5;171(2):456-469.e22
pubmed: 28985566
Brain Struct Funct. 2015;220(3):1317-37
pubmed: 24569853
Cell. 2016 Jun 16;165(7):1789-1802
pubmed: 27238021
Front Neural Circuits. 2021 Jun 02;15:687558
pubmed: 34149368
F1000Res. 2018 Jan 8;7:23
pubmed: 29375819
Nat Neurosci. 2019 Jul;22(7):1182-1195
pubmed: 31209381
Nat Neurosci. 2018 Jul;21(7):920-931
pubmed: 29915195
Nat Methods. 2020 Apr;17(4):442-449
pubmed: 32161395
Neuron. 2015 Aug 19;87(4):684-98
pubmed: 26291155
Cell Rep Methods. 2021 Jun 21;1(2):100038
pubmed: 35475238
Front Neuroinform. 2014 Jan 16;7:50
pubmed: 24474917
Cell. 2020 May 14;181(4):936-953.e20
pubmed: 32386544
Nat Neurosci. 2018 Feb;21(2):218-227
pubmed: 29358666
Cereb Cortex. 2015 Dec;25(12):4854-68
pubmed: 26420784
Cell Mol Life Sci. 2016 Oct;73(19):3677-91
pubmed: 27193323
Elife. 2017 Mar 28;6:
pubmed: 28350297
Nat Commun. 2020 Apr 20;11(1):1885
pubmed: 32313029
Neuron. 2015 Feb 18;85(4):770-86
pubmed: 25695271
Nat Methods. 2019 Dec;16(12):1226-1232
pubmed: 31570887
Brain Struct Funct. 2020 Dec;225(9):2701-2716
pubmed: 32975655
Dev Neurobiol. 2011 Jan 1;71(1):45-61
pubmed: 21154909
Cell Rep. 2015 Jan 13;10(2):292-305
pubmed: 25558063
Proc Natl Acad Sci U S A. 2020 Sep 22;117(38):23242-23251
pubmed: 32503914
Nat Commun. 2021 May 17;12(1):2859
pubmed: 34001873
Neuron. 2016 Feb 3;89(3):521-35
pubmed: 26844832
J Neurosci. 2019 May 8;39(19):3611-3626
pubmed: 30846615
Cereb Cortex. 2018 May 1;28(5):1831-1845
pubmed: 29106504
Neuroinformatics. 2021 Jul;19(3):433-446
pubmed: 33063286
J Neurosci. 2011 Sep 14;31(37):13260-71
pubmed: 21917809
Nat Rev Neurosci. 2020 Feb;21(2):61-79
pubmed: 31896771
Curr Opin Neurobiol. 2014 Jun;26:79-87
pubmed: 24440413
Science. 2019 Jan 25;363(6425):413-417
pubmed: 30679375
Prog Brain Res. 2005;147:115-24
pubmed: 15581701
eNeuro. 2019 May 7;6(2):
pubmed: 31064838
J Neurosci. 2017 May 10;37(19):4883-4902
pubmed: 28408413
Neuron. 2017 Aug 16;95(4):884-895.e9
pubmed: 28817803
Curr Opin Neurobiol. 2017 Apr;43:149-155
pubmed: 28399421
Cereb Cortex. 2015 Jul;25(7):1782-91
pubmed: 24425250
Elife. 2021 May 05;10:
pubmed: 33949949
Cell Rep. 2021 May 11;35(6):109106
pubmed: 33979609
Science. 2013 Jan 4;339(6115):70-4
pubmed: 23180771
J Neurosci. 2010 Dec 15;30(50):16796-808
pubmed: 21159951
J Comp Neurol. 2017 Jun 1;525(8):1909-1921
pubmed: 28078786
J Neurosci. 2013 Jan 30;33(5):1907-14
pubmed: 23365230
Sci Rep. 2021 Jan 18;11(1):1716
pubmed: 33462326
Front Neural Circuits. 2016 Sep 29;10:76
pubmed: 27746722
eNeuro. 2019 Sep 18;6(5):
pubmed: 31481397
Elife. 2021 Dec 01;10:
pubmed: 34851292
Nat Rev Neurosci. 2016 Jul;17(7):401-9
pubmed: 27225074
Nature. 2020 Sep;585(7824):245-250
pubmed: 32884146
J Neurosci. 2016 Mar 23;36(12):3471-80
pubmed: 27013676
Neuron. 2017 Aug 2;95(3):639-655.e10
pubmed: 28712654
Cell. 2020 Feb 20;180(4):780-795.e25
pubmed: 32059781
Neuroinformatics. 2020 Oct;18(4):611-626
pubmed: 32448958
Brain Struct Funct. 2015 Sep;220(5):2817-34
pubmed: 25056931
Cell Mol Life Sci. 2021 Mar;78(6):2517-2563
pubmed: 33263776
Cereb Cortex. 2019 Jun 1;29(6):2384-2395
pubmed: 29771284
Neuron. 2016 Jul 20;91(2):260-92
pubmed: 27477017
J Neurosci. 2012 Jul 4;32(27):9429-37
pubmed: 22764251