Literature DB >> 33472616

No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells.

Nadia R Roan1,2, Sulggi A Lee3, Christopher S McGinnis4, David A Siegel5, Guorui Xie6,7, George Hartoularos8,9, Mars Stone10,11, Chun J Ye8,12,13,14,15, Zev J Gartner4,15,16,17.   

Abstract

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures.
RESULTS: Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets.
CONCLUSIONS: We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

Entities:  

Keywords:  Alloreactivity; PBMCs; Sample multiplexing; Sample preparation; scRNA-seq

Mesh:

Year:  2021        PMID: 33472616      PMCID: PMC7816397          DOI: 10.1186/s12915-020-00941-x

Source DB:  PubMed          Journal:  BMC Biol        ISSN: 1741-7007            Impact factor:   7.364


  32 in total

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Authors:  Philip L De Jager; Nir Hacohen; Diane Mathis; Aviv Regev; Barbara E Stranger; Christophe Benoist
Journal:  Semin Immunol       Date:  2015-03-25       Impact factor: 11.130

2.  Quantity, quality, and functionality of peripheral blood cells derived from residual blood of different apheresis kits.

Authors:  Arne Knörck; Stefanie Marx; Kim S Friedmann; Sylvia Zöphel; Lisa Lieblang; Carmen Hässig; Isabelle Müller; Jan Pilch; Urban Sester; Markus Hoth; Hermann Eichler; Martina Sester; Eva C Schwarz
Journal:  Transfusion       Date:  2018-05-06       Impact factor: 3.157

3.  Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.

Authors:  Benjamin J Schmiedel; Divya Singh; Ariel Madrigal; Alan G Valdovino-Gonzalez; Brandie M White; Jose Zapardiel-Gonzalo; Brendan Ha; Gokmen Altay; Jason A Greenbaum; Graham McVicker; Grégory Seumois; Anjana Rao; Mitchell Kronenberg; Bjoern Peters; Pandurangan Vijayanand
Journal:  Cell       Date:  2018-11-15       Impact factor: 41.582

4.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

5.  Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Authors:  Andrew Butler; Paul Hoffman; Peter Smibert; Efthymia Papalexi; Rahul Satija
Journal:  Nat Biotechnol       Date:  2018-04-02       Impact factor: 54.908

6.  Massively parallel digital transcriptional profiling of single cells.

Authors:  Grace X Y Zheng; Jessica M Terry; Phillip Belgrader; Paul Ryvkin; Zachary W Bent; Ryan Wilson; Solongo B Ziraldo; Tobias D Wheeler; Geoff P McDermott; Junjie Zhu; Mark T Gregory; Joe Shuga; Luz Montesclaros; Jason G Underwood; Donald A Masquelier; Stefanie Y Nishimura; Michael Schnall-Levin; Paul W Wyatt; Christopher M Hindson; Rajiv Bharadwaj; Alexander Wong; Kevin D Ness; Lan W Beppu; H Joachim Deeg; Christopher McFarland; Keith R Loeb; William J Valente; Nolan G Ericson; Emily A Stevens; Jerald P Radich; Tarjei S Mikkelsen; Benjamin J Hindson; Jason H Bielas
Journal:  Nat Commun       Date:  2017-01-16       Impact factor: 14.919

7.  Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics.

Authors:  Marlon Stoeckius; Shiwei Zheng; Brian Houck-Loomis; Stephanie Hao; Bertrand Z Yeung; William M Mauck; Peter Smibert; Rahul Satija
Journal:  Genome Biol       Date:  2018-12-19       Impact factor: 13.583

8.  Nuclei multiplexing with barcoded antibodies for single-nucleus genomics.

Authors:  Jellert T Gaublomme; Bo Li; Cristin McCabe; Abigail Knecht; Yiming Yang; Eugene Drokhlyansky; Nicholas Van Wittenberghe; Julia Waldman; Danielle Dionne; Lan Nguyen; Philip L De Jager; Bertrand Yeung; Xinfang Zhao; Naomi Habib; Orit Rozenblatt-Rosen; Aviv Regev
Journal:  Nat Commun       Date:  2019-07-02       Impact factor: 14.919

9.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.

Authors:  Christoph Hafemeister; Rahul Satija
Journal:  Genome Biol       Date:  2019-12-23       Impact factor: 13.583

10.  Genotype-free demultiplexing of pooled single-cell RNA-seq.

Authors:  Jun Xu; Caitlin Falconer; Quan Nguyen; Joanna Crawford; Brett D McKinnon; Sally Mortlock; Anne Senabouth; Stacey Andersen; Han Sheng Chiu; Longda Jiang; Nathan J Palpant; Jian Yang; Michael D Mueller; Alex W Hewitt; Alice Pébay; Grant W Montgomery; Joseph E Powell; Lachlan J M Coin
Journal:  Genome Biol       Date:  2019-12-19       Impact factor: 13.583

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  1 in total

Review 1.  Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics.

Authors:  Livius Penter; Satyen H Gohil; Catherine J Wu
Journal:  Front Immunol       Date:  2022-01-03       Impact factor: 8.786

  1 in total

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