A Public BCR Present in a Unique Dual-Receptor-Expressing Lymphocyte from Type 1 Diabetes Patients Encodes a Potent T Cell Autoantigen

Ahmed, Rizwan; Omidian, Zahra; Giwa, Adebola; Cornwell, Benjamin; Majety, Neha; Bell, David R.; Lee, Sangyun; Zhang, Hao; Michels, Aaron; Desiderio, Stephen; Sadegh‐Nasseri, Scheherazade; Rabb, Hamid; Gritsch, Simon; Suvà, Mario L.; Cahan, Patrick; Zhou, Ruhong; Jie, Chunfa; Donner, Thomas; Hamad, Abdel Rahim A.

doi:10.1016/j.cell.2019.05.007

Cited by 108 publications

(76 citation statements)

References 55 publications

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“…Finally, we applied our expertise in T cell-monocyte complexes detection to other types of cell-cell complexes that can be present in PBMC. A recent paper published in Cell (5) describes the discovery of a novel immune cell type expressing both T cell and B cell receptors in the peripheral blood of type I diabetes patients. These findings reminded us of our initial assumption that we had identified a novel cell population with both T cell and monocyte lineage properties.…”

Section: Resultsmentioning

confidence: 99%

“…Random galleries of DE cells identified by imaging flow cytometry with E) the OPT imaging gating and F) the CSM gating strategy. G) B cell and T cell gene scores derived from the scRNAseq data of sorted B cells, T cells and DE cells as reported in (5), and available under GEO accession number GSE129112. Data were derived from (A-C) 5 healthy subjects and (D-F) 3 healthy subjects.…”

Section: Resultsmentioning

confidence: 99%

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The challenge of distinguishing cell-cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting

Burel

Pomaznoy

Arlehamn

et al. 2020

Preprint

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AbstractOur recent work has highlighted that care needs to be taken when interpreting single cell data originating from flow cytometry acquisition or cell sorting: We found that doublets of T cells bound to other immune cells are often present in the live singlet gate of human peripheral blood samples acquired by flow cytometry. This hidden ‘contamination’ generates atypical gene signatures of mixed cell lineage in what is assumed to be single cells, which can lead to data misinterpretation, such as the description of novel immune cell types. Here, based on the example of T cell-monocyte complexes, we identify experimental and data analysis strategies to help distinguishing between singlets and cell-cell complexes in non-imaging flow cytometry and single-cell sorting. We found robust molecular signatures in both T cell-monocyte and T cell-B cell complexes that can distinguish them from singlets at both protein and mRNA levels. Imaging flow cytometry with appropriate gating strategy (matching the one used in cell sorting) and direct microscopy imaging after cell sorting were the two methods of choice to detect the presence of cell-cell complexes in suspicious dual-expressing cells. We finally applied this knowledge to highlight the likely presence of T cell-B cell complexes in a recently published dataset describing a novel cell population with mixed T cell and B cell lineage properties.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The challenge of distinguishing cell-cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting

Burel

Pomaznoy

Arlehamn

et al. 2020

Preprint

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“…Both clusters are highlighted in red circles. Existence of such PBMC types defies conventional wisdom, where CD3 + CD19 + cells and CD4 + CD8 + T cells are believed to be extremely rare [1,33]. In fact, as revealed by cell hashing, both cell types, along with many other alleged novel rare cell types discovered in this dataset, are all phony cell types.…”

Section: Introductionmentioning

confidence: 61%

Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

Xin

Yan

Jiang

et al. 2019

Preprint

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Identifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.We propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcodingundetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.

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“…The sixth cluster, B_C5_CD3D, expressed key markers of both T and B cell lineages ( Fig. 4d), thus maybe the dual expressers (DEs)-like lymphocytes 33 or doublets. The remaining B cells, falling into the seventh cluster, B_C6_LRMP, exhibited high expression of LRMP and RGS13, indicative of the identity of germinal center B cells 34 .…”

Section: Rogue-guided Analysis Identified Pure B Cell Subtypes In LIVmentioning

confidence: 99%

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Liu

et al. 2019

Preprint

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Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Z.Z. conceived this study. C.L. and B.L. designed the -model. B.L. introduced the algorithm of ROGUE, performed the benchmark testing, analyzed the data and developed the R package. Z.L. and X.R. assisted with method development. B.L., C.L. and Z.Z. wrote the manuscript with all the authors' inputs.

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A Public BCR Present in a Unique Dual-Receptor-Expressing Lymphocyte from Type 1 Diabetes Patients Encodes a Potent T Cell Autoantigen

Cited by 108 publications

References 55 publications

The challenge of distinguishing cell-cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting

The challenge of distinguishing cell-cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting

Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

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