Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
- Heidelberg University, Germany
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.
-
Multiplexing droplet-based single cell RNA-sequencing using genetic barcodesGene Expression Omnibus GSE96583.
-
Spatial multi-omic map of human myocardial infarctionHuman Cell Atlas Data Portal, e9f36305-d857-44a3-93f0-df4e6007dc97.
-
Cells of the Adult Heartad98d3cd-26fb-4ee3-99c9-8a2ab085e737.
-
The Reference of the Transcriptional Landscape of Human End-Stage Heart FailureZenodo, doi: 10.5281/zenodo.3797044.
-
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathiescellxgene, e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb.
Article and author information
Author details
Ricardo Omar Ramirez Flores
Julio Saez-Rodriguez
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
-
- 4,708
- views
-
- 558
- 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)
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
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.
-
- Computational and Systems Biology
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.
