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

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

  • 4,694
    views
  • 555
    downloads
  • 26
    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

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.