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SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

ABSTRACT: The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have quickly gained enormous popularity, novel methods for analyzing this new type of single cell multi-omics data are still in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT can be used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include 1) using confident cell type labels identified from surface protein data as guidance for cell clustering, 2) providing general annotation of confident cell types for each cell cluster, 3) fully utilizing cells with uncertain or missing cell type labels to increase performance, and 4) accurate prediction of confident cell types identified from surface protein data for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results, and our framework can be easily extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house real datasets. We believe this new method will greatly help researchers characterize novel cell types and make new biological discoveries using single cell multi-omics data.

ORGANISM(S): Homo sapiens

PROVIDER: GSE168264 | GEO | 2022/09/03

REPOSITORIES: GEO

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Project description:Understanding the cellular heterogeneity and lineage potential of human bone marrow hematopoietic progenitors is required to understand normal differentiation and the mechanisms of hematological disease. Central to this objective are protocols that enable the delineation, isolation and functional characterization or known and novel cell populations, which are traditionally empowered by flow cytometry. Recognizing the need for tackling cellular heterogeneity, we have designed a use case for compiling next-generation processing methods via existing and highly curated single-cell interrogation technologies. Multi-omics approaches, such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), enable concurrent transcriptomic and immunophenotyping at a single-cell level but necessitate precision antibody titration mixes that can be directly integrated with parallel flow cytometry strategies. To bridge this gap, we performed a molecular titration of 266 antibodies using primary human bone marrow cells spanning race and sex to approximate diversity. Through machine learning and user curation, we created an optimized titrated bone marrow CITE-seq panel of 132 antibodies. Multimodal integration of these data using a game-theory driven approach, improved our ability to resolve new stable stem, progenitor, and transitional cell populations, readily defined by unique RNA and ADT markers. Leveraging complexity-matching high-dimensional flow cytometry (pyInfinityFlow), we consolidated the findings from CITE-seq and subsequently developed novel isolation strategies for maturing granulocytes, late-stage erythroid progenitors and improved purification of megakaryocyte/erythrocyte progenitors (MEP). Finally, we integrated CITE-seq with flow cytometry data from matching donors, successfully identified 37 cell surface markers consistently expressed across donors and technologies. The compiled bone marrow cell atlas serves as an advanced digital analytical resource for progenitor analyses in health and disease.

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SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

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