Defining diurnal fluctuations in mouse choroid plexus and CSF at high molecular, spatial, and temporal resolution

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Abstract

Transmission and secretion of signals via the choroid plexus (ChP) brain barrier can modulate brain states via regulation of cerebrospinal fluid (CSF) composition. Here, we developed a platform to analyze diurnal variations in male mouse ChP and CSF. Ribosome profiling of ChP epithelial cells revealed diurnal translatome differences in metabolic machinery, secreted proteins, and barrier components. Using ChP and CSF metabolomics and blood-CSF barrier analyses, we observed diurnal changes in metabolites and cellular junctions. We then focused on transthyretin (TTR), a diurnally regulated thyroid hormone chaperone secreted by the ChP. Diurnal variation in ChP TTR depended on Bmal1 clock gene expression. We achieved real-time tracking of CSF-TTR in awake Ttr ^mNeonGreen mice via multi-day intracerebroventricular fiber photometry. Diurnal changes in ChP and CSF TTR levels correlated with CSF thyroid hormone levels. These datasets highlight an integrated platform for investigating diurnal control of brain states by the ChP and CSF.

Abstract

The choroid plexus (ChP) modulates cerebrospinal fluid (CSF) composition and the blood-CSF barrier. Here the authors show that the ChP is a critical circadian component with time-of-day variations in translation, barrier, and metabolism to alter CSF composition.

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Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Kenneth Livak, Thomas D. Schmittgen (2001)

The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Author and article information

Contributors

Maria K. Lehtinen:

ORCID: http://orcid.org/0000-0002-7243-2967

maria.lehtinen@childrens.harvard.edu

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 22 June 2023

Publication date PMC-release: 22 June 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 3720

Affiliations

[1 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, , Boston Children’s Hospital and Harvard Medical School, ; Boston, MA 02115 USA

[2 ]GRID grid.38142.3c, ISNI 000000041936754X, Graduate Program in Neuroscience, , Harvard Medical School, ; Boston, MA 02115 USA

[3 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard/MIT MD-PhD Program, , Harvard Medical School, ; Boston, MA 02115 USA

[4 ]GRID grid.38142.3c, ISNI 000000041936754X, Graduate Program in Biophysics, , Harvard University, ; Cambridge, MA 02138 USA

[5 ]GRID grid.2515.3, ISNI 0000 0004 0378 8438, Department of Neurology and the F.M. Kirby Neurobiology Center, , Boston Children’s Hospital, ; Boston, MA 02115 USA

[6 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Sleep Medicine, , Harvard Medical School, ; Boston, MA 02115 USA

[7 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Brain and Cognitive Sciences, MIT, ; Cambridge, MA USA

[8 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Picower Institute for Learning and Memory, ; Cambridge, MA USA

[9 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of MIT and Harvard, ; Cambridge, MA USA

[10 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Pulmonary and Critical Care Medicine, Department of Medicine, , Washington University, ; St. Louis, MO 63110 USA

[11 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, , Beth Israel Deaconess Medical Center, ; Boston, MA 02115 USA

[12 ]GRID grid.168010.e, ISNI 0000000419368956, Present Address: Department of Neurosurgery, , Stanford University, ; Stanford, CA 94305 USA

[13 ]Present Address: Lyterian Therapeutics, South San Francisco, 94080 CA USA

Author information

Ryann M. Fame http://orcid.org/0000-0002-8244-2624

Peter N. Kalugin http://orcid.org/0000-0003-2549-5815

Boryana Petrova http://orcid.org/0000-0001-9996-9353

Huixin Xu http://orcid.org/0000-0003-4190-4244

Michael J. Holtzman http://orcid.org/0000-0001-8750-3716

Myriam Heiman http://orcid.org/0000-0002-6365-8673

Mark L. Andermann http://orcid.org/0000-0002-9882-933X

Naama Kanarek http://orcid.org/0000-0002-2068-3908

Jonathan O. Lipton http://orcid.org/0000-0002-9648-0541

Maria K. Lehtinen http://orcid.org/0000-0002-7243-2967

Article

Publisher ID: 39326

DOI: 10.1038/s41467-023-39326-3

PMC ID: 10287727

PubMed ID: 37349305

SO-VID: 1983151e-786c-43e4-9572-ca5e89ce9a3e

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 12 December 2022

Date accepted : 7 June 2023

Funding

Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);

Award ID: R01 NS088566

Award ID: RF1 DA048790

Award Recipient : Maria K. Lehtinen

Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)

Funded by: FundRef https://doi.org/10.13039/100003194, New York Stem Cell Foundation (NYSCF);

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ScienceOpen disciplines: Uncategorized

Keywords: circadian regulation,blood-brain barrier,molecular neuroscience

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ScienceOpen disciplines: Uncategorized

Keywords: circadian regulation, blood-brain barrier, molecular neuroscience

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Defining diurnal fluctuations in mouse choroid plexus and CSF at high molecular, spatial, and temporal resolution

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Nanoparticles to cross the blood-brain barrier (BBB)

Most cited references 115

Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

STAR: ultrafast universal RNA-seq aligner.

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