The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow.

Daniele Mercatelli,Caterina Garone,Emanuela Aleo,Federico Manuel Giorgi,Francesca De Giorgio,Nicola Balboni

doi:10.3390/mps4020028

Daniele Mercatelli, Caterina Garone + Show 4 more

Open Access

https://doi.org/10.3390/mps4020028

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Abstract

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.

Highlights

MethodsBy adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, reducing costs of scRNAseq by performing it at the same time on multiple barcoded samples in a single run
The information on hashtag counting derived from the CITE-seq experiment, hashtag 1 and 2, is already present in counting derived from the CITE-seq experiment, hashtag 1 and 2, is already present in this raw counts matrix, as the two hashtags were counted alongside other genes, on a this raw counts matrix, as the two hashtags were counted alongside other genes, on a cellcell-by-cell basis
Even within the homogeneity of the SY5Y cell line, distinct populations independent from cell cycle phases, and characterized by different stress and metabolic states

Summary

Methods

By adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, reducing costs of scRNAseq by performing it at the same time on multiple barcoded samples in a single run. We illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. Cell hashing was performed following manufacturer’s instructions (TotalSeqTM-A Cell Hashing and TotalSeqTM-A Antibodies Protocol for Simultaneous Proteomics and Transcriptomics with 10X Single Cell 3’ Reagent Kit v2; BioLegend, San Diego, CA, USA). Exploratory analysis of of toptop varying andand most stable genes across the dataset. Most average expressed genes (x-axis) with lowest variance average (y-axis) varianceare (y-axis) are indicated in (B)

Results

Discussion

Conclusion