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Article

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

1
Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
2
Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
3
Center for Applied Biomedical Research (CRBA), University of Bologna, 40138 Bologna, Italy
4
IGA Technology Services, 33100 Udine, Italy
*
Authors to whom correspondence should be addressed.
These Authors contributed equally to this work.
Academic Editor: Fernando Albericio
Methods Protoc. 2021, 4(2), 28; https://doi.org/10.3390/mps4020028
Received: 9 April 2021 / Revised: 3 May 2021 / Accepted: 4 May 2021 / Published: 6 May 2021
(This article belongs to the Section Omics and High Throughput)
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. View Full-Text
Keywords: CITE-seq; neuroblastoma; single-cell; transcriptomics; unsupervised learning; gene regulatory networks CITE-seq; neuroblastoma; single-cell; transcriptomics; unsupervised learning; gene regulatory networks
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MDPI and ACS Style

Mercatelli, D.; Balboni, N.; Giorgio, F.D.; Aleo, E.; Garone, C.; Giorgi, F.M. The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow. Methods Protoc. 2021, 4, 28. https://doi.org/10.3390/mps4020028

AMA Style

Mercatelli D, Balboni N, Giorgio FD, Aleo E, Garone C, Giorgi FM. The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow. Methods and Protocols. 2021; 4(2):28. https://doi.org/10.3390/mps4020028

Chicago/Turabian Style

Mercatelli, Daniele, Nicola Balboni, Francesca D. Giorgio, Emanuela Aleo, Caterina Garone, and Federico M. Giorgi. 2021. "The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow" Methods and Protocols 4, no. 2: 28. https://doi.org/10.3390/mps4020028

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