Abstract

Enhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.

Data availability

Sequencing data had been deposited in GEO under accession code GSE136802

The following data sets were generated

Article and author information

Author details

  1. Sinisa Hrvatin

    Department of Neurobiology, Harvard Medical School, Boston, United States
    For correspondence
    shrvatin@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Christopher P Tzeng

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. M Aurel Nagy

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4608-1152
  4. Hume Stroud

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Charalampia Koutsioumpa

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Oren F Wilcox

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Elena G Assad

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Jonathan Green

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Christopher D Harvey

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Eric C Griffith

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Michael E Greenberg

    Department of Neurobiology, Harvard Medical School, Boston, United States
    For correspondence
    Michael_Greenberg@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1380-2160

Funding

National Institutes of Health (RF1MH11408101)

  • Michael E Greenberg

National Institutes of Health (5T32AG000222-23)

  • Sinisa Hrvatin

National Institutes of Health (T32GM007753)

  • M Aurel Nagy

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

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#IS00000074-3) of Harvard Medical School. All surgery was performed under isoflurane anesthesia, and every effort was made to minimize suffering.

Reviewing Editor

  1. Anne E West, Duke University School of Medicine, United States

Publication history

  1. Received: April 30, 2019
  2. Accepted: September 16, 2019
  3. Accepted Manuscript published: September 23, 2019 (version 1)
  4. Version of Record published: October 3, 2019 (version 2)

Copyright

© 2019, Hrvatin et al.

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

Metrics

  • 9,332
    Page views
  • 1,245
    Downloads
  • 38
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sinisa Hrvatin
  2. Christopher P Tzeng
  3. M Aurel Nagy
  4. Hume Stroud
  5. Charalampia Koutsioumpa
  6. Oren F Wilcox
  7. Elena G Assad
  8. Jonathan Green
  9. Christopher D Harvey
  10. Eric C Griffith
  11. Michael E Greenberg
(2019)
A scalable platform for the development of cell-type-specific viral drivers
eLife 8:e48089.
https://doi.org/10.7554/eLife.48089

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Aaron W Jurrjens, Marcus M Seldin ... Anna C Calkin
    Review Article

    Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

    1. Genetics and Genomics
    2. Medicine
    Joshua K Park, Shantanu Bafna ... Ron Do
    Research Article

    Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.

    Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2,600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p < 0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels.

    Results: Our results identified 7 disease traits reaching Bonferroni-corrected significance in both the discovery (p < 1.92 × 10-5) and replication analyses (p < 2.26 × 10-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p = 2.47 × 10-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p < 0.05), identifying plasma TG levels as a novel potential risk factor for 9 non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p = 1.17 × 10-5).

    Conclusions: Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies.

    Funding: RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).