Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

  1. Dylan Kotliar  Is a corresponding author
  2. Adrian Veres
  3. M Aurel Nagy
  4. Shervin Tabrizi
  5. Eran Hodis
  6. Douglas A Melton
  7. Pardis C Sabeti
  1. Harvard Medical School, United States
  2. Massachusetts Institute of Technology, United States
  3. Broad Institute of MIT and Harvard, United States
  4. Harvard University, United States

Abstract

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell's expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

Data availability

All of the analyzed real datasets are publicly available and the relevant GEO accession codes are included in the manuscript. All of the simulated and real data can be accessed through Code Ocean at the following URL: https://doi.org/10.24433/CO.9044782e-cb96-4733-8a4f-bf42c21399e6

The following previously published data sets were used

Article and author information

Author details

  1. Dylan Kotliar

    Department of Systems Biology, Harvard Medical School, Boston, United States
    For correspondence
    dylan_kotliar@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7968-645X
  2. Adrian Veres

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

    Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Shervin Tabrizi

    Viral Computational Genomics, Broad Institute of MIT and Harvard, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Eran Hodis

    Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Douglas A Melton

    Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1623-5504
  7. Pardis C Sabeti

    Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institute of General Medical Sciences (T32GM007753)

  • Dylan Kotliar
  • Adrian Veres
  • M Aurel Nagy
  • Eran Hodis

National Institute of Allergy and Infectious Diseases (R01AI099210)

  • Pardis C Sabeti

U.S. Food and Drug Administration (HHSF223201810172C)

  • Dylan Kotliar
  • Pardis C Sabeti

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

Reviewing Editor

  1. Alfonso Valencia, Barcelona Supercomputing Center - BSC, Spain

Version history

  1. Received: November 21, 2018
  2. Accepted: July 7, 2019
  3. Accepted Manuscript published: July 8, 2019 (version 1)
  4. Version of Record published: July 18, 2019 (version 2)

Copyright

© 2019, Kotliar 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

  • 33,954
    Page views
  • 3,054
    Downloads
  • 126
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, 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. Dylan Kotliar
  2. Adrian Veres
  3. M Aurel Nagy
  4. Shervin Tabrizi
  5. Eran Hodis
  6. Douglas A Melton
  7. Pardis C Sabeti
(2019)
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
eLife 8:e43803.
https://doi.org/10.7554/eLife.43803

Further reading

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Jennifer K Briggs, Anne Gresch ... Richard KP Benninger
    Research Article

    Diabetes is caused by the inability of electrically coupled, functionally heterogeneous -cells within the pancreatic islet to provide adequate insulin secretion. Functional networks have been used to represent synchronized oscillatory [Ca2+] dynamics and to study -cell subpopulations, which play an important role in driving islet function. The mechanism by which highly synchronized -cell subpopulations drive islet function is unclear. We used experimental and computational techniques to investigate the relationship between functional networks, structural (gap-junction) networks, and intrinsic -cell dynamics in slow and fast oscillating islets. Highly synchronized subpopulations in the functional network were differentiated by intrinsic dynamics, including metabolic activity and KATP channel conductance, more than structural coupling. Consistent with this, intrinsic dynamics were more predictive of high synchronization in the islet functional network as compared to high levels of structural coupling. Finally, dysfunction of gap junctions, which can occur in diabetes, caused decreases in the efficiency and clustering of the functional network. These results indicate that intrinsic dynamics rather than structure drive connections in the functional network and highly synchronized subpopulations, but gap junctions are still essential for overall network efficiency. These findings deepen our interpretation of functional networks and the formation of functional sub-populations in dynamic tissues such as the islet.

    1. Computational and Systems Biology
    2. Immunology and Inflammation
    David J Torres, Paulus Mrass ... Judy L Cannon
    Research Article Updated

    T cells are required to clear infection, and T cell motion plays a role in how quickly a T cell finds its target, from initial naive T cell activation by a dendritic cell to interaction with target cells in infected tissue. To better understand how different tissue environments affect T cell motility, we compared multiple features of T cell motion including speed, persistence, turning angle, directionality, and confinement of T cells moving in multiple murine tissues using microscopy. We quantitatively analyzed naive T cell motility within the lymph node and compared motility parameters with activated CD8 T cells moving within the villi of small intestine and lung under different activation conditions. Our motility analysis found that while the speeds and the overall displacement of T cells vary within all tissues analyzed, T cells in all tissues tended to persist at the same speed. Interestingly, we found that T cells in the lung show a marked population of T cells turning at close to 180o, while T cells in lymph nodes and villi do not exhibit this “reversing” movement. T cells in the lung also showed significantly decreased meandering ratios and increased confinement compared to T cells in lymph nodes and villi. These differences in motility patterns led to a decrease in the total volume scanned by T cells in lung compared to T cells in lymph node and villi. These results suggest that the tissue environment in which T cells move can impact the type of motility and ultimately, the efficiency of T cell search for target cells within specialized tissues such as the lung.