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CITEViz: Replicating the Interactive Flow Cytometry Workflow in CITE-Seq

Garth Kong, Thai Nguyen, Wesley Rosales, Anjali Panikar, John Cheney, Brittany Curtiss, �William Yashar, Theresa Lusardi PhD, Sarah Carratt, Theodore Braun MD PhD, Julia Maxson PhD

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Presentation Overview

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Background

CITE-Seq
Blood cells
Flow cytometry

Question

What is CITEViz
Why use CITEViz
When to use CITEViz

Demo

Gating

1-layer gates
2-layer gates
Back-gates
Differential expression

Other features

Feature co-expression
Quality control
Cluster visualization

Summary

Get the code
Get the data
Acknowledgements

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CITE-Seq = scRNA-Seq + Cell Surface Proteins

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Stoeckius, M., Hafemeister, C., Stephenson, W. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14, 865–868 (2017). https://doi.org/10.1038/nmeth.4380

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ADT

RNA

Cells

ADT = antibody derived tags

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Surface protein markers help classify cell types

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https://en.wikipedia.org/wiki/Haematopoiesis

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Flow cytometry is a proven method to classify cells using surface protein markers

Corces, M., Buenrostro, J., Wu, B. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat Genet 48, 1193–1203 (2016). https://doi.org/10.1038/ng.3646

CD34⁺ CD38^-CD90⁺ CD45RA^-

CD34⁺ CD38⁺CD10⁺ CD45RA⁺

CD34⁺ CD38⁺CD45RA⁺CD10^-

CD45RA⁺ CD123⁺

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Question

If we replicate the flow cytometry workflow in CITE-Seq datasets, then we can provide researchers more methods to classify cell clusters.

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>

Can we build an interactive gating program in CITE-Seq

using the R-Shiny platform?

CD8

CD4

UMAP_2

UMAP_1

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Why Use CITEViz

CITEViz helps biologists leverage their flow cytometry knowledge to analyze single-cell datasets
Users can seamlessly explore the data as a group by drawing physical gates
Provide an accessible approach to multi-omic single-cell analysis to facilitate novel hypothesis generation

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When to use CITEViz

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Experiment

Library Preparation

Bioinformatic�Processing

Processed Single-Cell Data

CITEViz

Novel Discoveries

Bench Scientist

Computational Scientist

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Example Dataset

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Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, et al. 2021. Integrated analysis of multimodal single-cell data. Cell. 184(13):3573-3587.e29. doi:10.1016/j.cell.2021.04.048.

Original Data

Peripheral Bone Mononuclear Cells (PBMC) with 8 patients x 3 time points
Integrated via Weighted Nearest Neighbors
Total of 160K cells and 228 antibodies

Test Data

Subsampled 10K cells
228 antibodies

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CITEViz Summary

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Gating |

Feature Expression |

Quality Control |

Cluster Visualization |

1-layer gates Back-gate

2-layer gates Differential Expression

Single Expression

Multi-omic co-Expression

Common QC metrics by categorical data

2-dimensional

3-dimensional (optional)

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Get the code + data + presentation

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https://gartician.github.io/CITEVizWorkshop/

Links to website

This is also available in the “WORKSHOPS & DEMOS” page in the Bioconductor website.

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Julia Maxson, PhD

Sarah Carratt, PhD

Ted Braun, MD PhD

Brittany Curtiss

Thai Nguyen�Intern at the Maxson Lab

Wesley Rosales�Intern at Providence

John Cheney�Intern at �CEDAR OHSU

Anjali Panikar�Intern at Schrodinger

BioC 2022 Short Workshop (July 27-29^th Seattle, WA) ✅
Submit CITEViz to BioConductor 3.16 (October 2022)
Implement performance patches + long-term maintenance

Future Directions

Garth Kong

Kong GL, Nguyen TT, Rosales WK, Panikar AD, Cheney JHW, Curtiss BM, Carratt SA, Braun TP, Maxson JE. 2022. CITE-Viz: Replicating the Interactive Flow Cytometry Workflow in CITE-Seq. Bioinformatics. [accessed 2022 May 23]. http://biorxiv.org/lookup/doi/10.1101/2022.05.15.491411.

Links to preprint