Investigation of  Drosophila fruitless  neurons that express Dpr/DIP cell adhesion molecules

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Abstract

Drosophila reproductive behaviors are directed by fruitless neurons. A reanalysis of genomic studies shows that genes encoding dpr and DIP immunoglobulin superfamily (IgSF) members are expressed in fru P1 neurons. We find that each fru P1 and dpr/DIP ( fru P1 ∩ dpr/DIP) overlapping expression pattern is similar in both sexes, but there are dimorphisms in neuronal morphology and cell number. Behavioral studies of fru P1 ∩ dpr/DIP perturbation genotypes indicate that the mushroom body functions together with the lateral protocerebral complex to direct courtship behavior. A single-cell RNA-seq analysis of fru P1 neurons shows that many DIPs have high expression in a small set of neurons, whereas the dprs are often expressed in a larger set of neurons at intermediate levels, with a myriad of dpr/DIP expression combinations. Functionally, we find that perturbations of sex hierarchy genes and of DIP-ε change the sex-specific morphologies of fru P1 ∩ DIP-α neurons.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Massively parallel digital transcriptional profiling of single cells

Grace Zheng, Jessica M. Terry, Phillip Belgrader … (2017)

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

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UpSetR: an R package for the visualization of intersecting sets and their properties

Jake R Conway, Alexander Lex, Nils Gehlenborg (2017)

Abstract Motivation: Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. Results: We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. Availability and implementation: UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/. Contact: nils@hms.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Michelle N Arbeitman:

ORCID: https://orcid.org/0000-0002-2437-4352

Michael B Eisen: Role: Reviewing Editor

Michael B Eisen: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 22 February 2021

Publication date Collection: 2021

Volume: 10

Electronic Location Identifier: e63101

Affiliations

[1 ]Department of Biomedical Sciences and Program of Neuroscience, Florida State University, College of Medicine TallahasseeUnited States

[2 ]Department of Molecular and Human Genetics, Baylor College of Medicine HoustonUnited States

[3 ]Division of Biology and Biological Engineering, California Institute of Technology PasadenaUnited States

University of California, Berkeley United States

Author notes

[‡]

Department of Biology, St. Joseph’s College, New York, United States.

[†]

These authors contributed equally to this work.

Author information

Kai Zinn http://orcid.org/0000-0002-6706-5605

Michelle N Arbeitman https://orcid.org/0000-0002-2437-4352

Article

Publisher ID: 63101

DOI: 10.7554/eLife.63101

PMC ID: 7972454

PubMed ID: 33616528

SO-VID: e1553cef-69fa-4836-8f30-bd302313cea6

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 17 September 2020

Date accepted : 22 February 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R01GM073039

Award Recipient : Savannah G Brovero Julia C Fortier Hongru Hu Pamela C Lovejoy Nicole R Newell Colleen M Palmateer Ruei-Ying Tzeng Michelle N Arbeitman

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R03NS090184

Award Recipient : Ruei-Ying Tzeng

Funded by: FundRef http://dx.doi.org/10.13039/100006597, Florida State University;

Award ID: Department of Biomedical Sciences

Award Recipient : Hongru Hu Nicole R Newell Colleen M Palmateer

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R01GM116998

Award Recipient : Savannah G Brovero Hongru Hu Pamela C Lovejoy Michelle N Arbeitman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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Author impact statement Analyses of fru P1 neurons that express dpr/DIP genes reveal roles for subsets of neurons in courtship, and the roles of Dpr/DIPs and the sex hierarchy in neuroanatomical sexual dimorphism.

ScienceOpen disciplines: Life sciences

Keywords: drosophila,courtship,single-cell rna-seq,igsf,cell adhesion molecule,reproductive behavior,d. melanogaster

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: drosophila, courtship, single-cell rna-seq, igsf, cell adhesion molecule, reproductive behavior, d. melanogaster

Investigation of Drosophila fruitless neurons that express Dpr/DIP cell adhesion molecules

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Evolutionary Cell Biology

Most cited references 72

Comprehensive Integration of Single-Cell Data

Massively parallel digital transcriptional profiling of single cells

UpSetR: an R package for the visualization of intersecting sets and their properties

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