flowCL: ontology-based cell population labelling in flow cytometry

Deep Immunophenotyping of Human Whole Blood by Standardized Multi-parametric Flow Cytometry Analyses

Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease. High-throughput flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level. Here, we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood. A total of 51 surface antibodies, which are readily available and validated, were selected to identify the key immune cell populations and evaluate their functional state in a single assay. The gating strategies for effective flow cytometry data analysis are included in the protocol. To ensure data reproducibility, we provide detailed procedures in three parts, including (1) instrument characterization and detector gain optimization, (2) antibody titration and sample staining, and (3) data acquisition and quality checks. This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00092-9.

Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.

An ontology for representing hematologic malignancies: the cancer cell ontology

BACKGROUND

Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.

RESULTS

We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies.

CONCLUSION

We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.

Transcriptional and Functional Programming of Decidual Innate Lymphoid Cells

A successful pregnancy requires many physiological adaptations from the mother, including the establishment of tolerance toward the semiallogeneic fetus. Innate lymphoid cells (ILCs) have arisen as important players in immune regulation and tissue homeostasis at mucosal and barrier surfaces. Dimensionality reduction and transcriptomic analysis revealed the presence of two novel CD56^Bright decidual ILCs that express low T-bet and divergent Eomes levels. Transcriptional correlation with recently identified first trimester decidual dNKs suggests that these novel decidual ILCs might be present throughout pregnancy. Functional testing with permutation analysis revealed production of multiple factors by individual cells, with a preference for IFNγ and VEGF. Overall, our data suggests continuity of a unique decidual innate lymphocytes across pregnancy with a polyfunctional functional profile conducive for pregnancy.

Data-Driven Flow Cytometry Analysis

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

Automated cell type discovery and classification through knowledge transfer

Motivation

Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis.

Results

We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data.

Availability and Implementation

A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc.

Supplementary information

Supplementary data are available at Bioinformatics online.

Universal cell type identifier based on number theory

Cell type classification and handling is a key issue for understanding biological systems. The advent of high multiplexing technologies increased the complexity of the classification process and new tools are needed to support the organization of this knowledge. I propose a classification based on both prime numbers and the fundamental theorem of arithmetic. As a not limiting example, I show the application of this method to unambiguously define any existing cell type using the CD nomenclature established by the Human Leukocyte Differentiation Antigens Workshops. This system allows for the unique identification of any possible combination of markers hence any cell population without previous knowledge and without the need to increment the system. This method can be the future basis of any database and ontology system dealing with cell types and beyond the biological field applies to the description of any entity characterized by a list of discrete qualities. © 2018 International Society for Advancement of Cytometry.

Computational flow cytometry analysis reveals a unique immune signature of the human maternal-fetal interface

PROBLEM

Decidual immune dysregulation is thought to underlie major pregnancy disorders; however, incomplete understanding of the decidual immune interface has hampered the mechanistic investigation.

METHOD OF STUDY

Human term decidua was collected, and single-cell phenotypic information was acquired by highly polychromatic flow cytometry. Cellular identity analysis was performed with t-distributed stochastic neighbor embedding, DensVM clustering, and matched to CellOntology database.

RESULTS

Traditional analytical methods validated known cellular T and dendritic cell subsets in human term decidua. Computational analysis revealed a complex and tissue-specific decidual immune signature in both the innate and adaptive immune compartments.

CONCLUSION

Polychromatic flow cytometry with a streamlined computational analysis pipeline is a feasible approach to comprehensive immunome mapping of human term decidua. As an unbiased, standardized method of investigation, computational flow cytometry promises to unravel the immune pathology of pregnancy disorders.

Cell type discovery and representation in the era of high-content single cell phenotyping

Background

A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology.

Results

In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations.

Conclusion

The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability

BACKGROUND

The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies.

CONSTRUCTION AND CONTENT

Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning.

UTILITY AND DISCUSSION

The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs.

CONCLUSIONS

The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational-based interactive analyses

The wide possibilities opened by the developments of multi-parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi-dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi-stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi-Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi-stained samples and allows improving multi-parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry.