1. Computational and Systems Biology

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

  1. Ricardo Omar Ramirez Flores  Is a corresponding author
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez  Is a corresponding author
  1. Heidelberg University, Germany
https://doi.org/10.7554/eLife.93161
Share this article
Cite this article
  1. Ricardo Omar Ramirez Flores
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez
(2023)
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161
  2. Download BibTeX
  3. Download .RIS

Abstract

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

Data availability

The datasets and computer code produced in this study are available in the following databases:-All scripts related to this manuscript can be consulted here: https://github.com/saezlab/MOFAcell.-The R package implementing multicellular factor analysis can be found in:https://github.com/saezlab/MOFAcellulaR-The python implementation of multicellular factor analysis is available here:https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html-A Zenodo entry containing data associated to this manuscript can be accessed here: https://zenodo.org/record/8082895.

The following previously published data sets were used

Article and author information

Author details

  1. Ricardo Omar Ramirez Flores

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    For correspondence
    roramirezf@uni-heidelberg.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0087-371X

  2. Jan David Lanzer

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.

  3. Daniel Dimitrov

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.

  4. Britta Velten

    Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8397-3515

  5. Julio Saez-Rodriguez

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    For correspondence
    pub.saez@uni-heidelberg.de
    Competing interests
    Julio Saez-Rodriguez, reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer and Grunenthal..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8552-8976

Funding

DFG CRC 1550 (464424253)

  • Ricardo Omar Ramirez Flores
  • Julio Saez-Rodriguez

Informatics for Life

  • Jan David Lanzer
  • Julio Saez-Rodriguez

EU ITN Marie Curie StrategyCKD (860329)

  • Daniel Dimitrov
  • Julio Saez-Rodriguez

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

Copyright

© 2023, Ramirez Flores 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

  • 4,031
    views
  • 518
    downloads
  • 16
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Ricardo Omar Ramirez Flores
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez
(2023)
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161

Share this article

https://doi.org/10.7554/eLife.93161

Categories and tags

Research organism

Further reading

    1. Cancer Biology
    2. Computational and Systems Biology

    Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility

    Rosalyn W Sayaman, Masaru Miyano ... Mark A LaBarge
    Research Article Updated

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.

    1. Computational and Systems Biology

    Emergence of power law distributions in protein-protein interaction networks through study bias

    David B Blumenthal, Marta Lucchetta ... Martin H Schaefer
    Research Article Updated

    Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

    1. Computational and Systems Biology
    2. Immunology and Inflammation

    A new pipeline SPICE identifies novel JUN-IKZF1 composite elements

    Peng Li, Sree Pulugulla ... Warren J Leonard
    Short Report

    Transcription factor partners can cooperatively bind to DNA composite elements to augment gene transcription. Here, we report a novel protein-DNA binding screening pipeline, termed Spacing Preference Identification of Composite Elements (SPICE), that can systematically predict protein binding partners and DNA motif spacing preferences. Using SPICE, we successfully identified known composite elements, such as AP1-IRF composite elements (AICEs) and STAT5 tetramers, and also uncovered several novel binding partners, including JUN-IKZF1 composite elements. One such novel interaction was identified at CNS9, an upstream conserved noncoding region in the human IL10 gene, which harbors a non-canonical IKZF1 binding site. We confirmed the cooperative binding of JUN and IKZF1 and showed that the activity of an IL10-luciferase reporter construct in primary B and T cells depended on both this site and the AP1 binding site within this composite element. Overall, our findings reveal an unappreciated global association of IKZF1 and AP1 and establish SPICE as a valuable new pipeline for predicting novel transcription binding complexes.