ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data
- King's College London, United Kingdom
- Université Paris Descartes, France
- University College London, United Kingdom
Abstract
High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: 1, data import and quality control; 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework.
Data availability
The liquid mass cytometry dataset is avaiable from FlowRepository (http://flowrepository.org/id/FR-FCM-Z244).Imaging and flow cytometry datasets have been deposited to Dryad under the following DOIs: 10.5061/dryad.gf1vhhmpr, 10.5061/dryad.4b8gthtcf, 10.5061/dryad.3n5tb2rhd
-
Imaging mass cytometry data: Head and neck squamous cell carcinoma tissue sectionDryad Digital Repository, 10.5061/dryad.gf1vhhmpr.
-
Flow cytometry data: healthy donor bone marrow taken during hip surgeryDryad Digital Repository, 10.5061/dryad.4b8gthtcf.
-
Imaging mass cytometry data: Diffuse large B-cell lymphoma lymph node sectionDryad Digital Repository, 10.5061/dryad.3n5tb2rhd.
Article and author information
Author details
Funding
Cancer Research UK (A29283)
- Jessica A Timms
- Shahram Kordasti
Aplastic Anemia and MDS International Foundation
- Jessica A Timms
- Shahram Kordasti
Blood Cancer UK
- Jessica A Timms
- Shahram Kordasti
H2020 European Research Council (335326)
- James W Opzoomer
- James N Arnold
Medical Research Council (MR/N013700/1)
- James W Opzoomer
Medical Research Council (Doctoral Training Partnership in Biomedical Sciences)
- James W Opzoomer
Rosetrees Trust (M117-F2)
- Sedigeh Kareemaghay
- Mahvash Tavassoli
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Formalin-fixed paraffin-embedded (FFPE) DLBCL tumor tissue was obtained from King's College Hospital, in accordance with the Declaration of Helsinki and approved by the UK National Research Ethics Committee (reference 13/NW/0040).Head and neck squamous cell carcinoma (HNSCC) tissue was obtained from King's College Hospital, consent was attained by the King Guy's & St Thomas' Research Biobank, within King's Health Partners Integrated Cancer Centre.The non-interventional study which collected bone marrow samples from elderly healthy donors was approved by the ethical committee of Cochin-Port Royal Hospital (Paris, France) (CLEP Decision N{degree sign}: AAA-2020-08039).
Copyright
© 2021, Opzoomer 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
-
- 4,152
- views
-
- 429
- downloads
-
- 19
- 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)
ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data
eLife 10:e62915.
https://doi.org/10.7554/eLife.62915
Further reading
-
- Computational and Systems Biology
The RAS-MAPK system plays an important role in regulating various cellular processes, including growth, differentiation, apoptosis, and transformation. Dysregulation of this system has been implicated in genetic diseases and cancers affecting diverse tissues. To better understand the regulation of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements in cells stimulated with EGF: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis allows for model-free assessment of the timing, direction, and strength of the information flow regulating the system response. We detected significant amounts of TE in both directions between SOS and RAF, indicating feedback regulation. Importantly, the amount of TE did not simply follow the input dose or the intensity of the causal reaction, demonstrating the uniqueness of TE. TE analysis proposed regulatory networks containing multiple tracks and feedback loops and revealed temporal switching in the reaction pathway primarily responsible for reaction control. This proposal was confirmed by the effects of an MEK inhibitor on TE. Furthermore, TE analysis identified the functional disorder of a SOS mutation associated with Noonan syndrome, a human genetic disease, of which the pathogenic mechanism has not been precisely known yet. TE assessment holds significant promise as a model-free analysis method of reaction networks in molecular pharmacology and pathology.
-
- Computational and Systems Biology
- Genetics and Genomics
Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.
