Cited by 106Single-cell and spatial transcriptomicsInnovative Microfluidic and Catalytic Techniques InnovationMolecular Biology Techniques and Applications

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DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing

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Wohnhaas, Christian T., Leparc, Germán G., Fernandez-Albert, Francesc, Kind, David, Gantner, Florian, Viollet, Coralie, Hildebrandt, Tobias, Baum, Patrick·2019·Scientific Reports

DOI 10.1038/s41598-019-46932-zPMC PMC6650608

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Abstract

Combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has recently become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomic changes associated with cell preservation and how they may bias further downstream analysis remain unknown. Here, we present a side-by-side droplet-based scRNA-seq analysis, comparing the gold standard – fresh cells – to three different cell preservation workflows: dimethyl sulfoxide based cryopreservation, methanol fixation and CellCover reagent. Cryopreservation proved to be the most robust protocol, maximizing both cell integrity and low background ambient RNA. Importantly, gene expression profiles from fresh cells correlated most with those of cryopreserved cells. Such similarities were consistently observed across the tested cell lines (R ≥ 0.97), monocyte-derived macrophages (R = 0.97) and immune cells (R = 0.99). In contrast, both methanol fixation and CellCover preservation showed an increased ambient RNA background and an overall lower gene expression correlation to fresh cells. Thus, our results demonstrate the superiority of cryopreservation over other cell preservation methods. We expect our comparative study to provide single-cell omics researchers invaluable support when integrating cell preservation into their scRNA-seq studies.

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Figure 1 Cell integrity and cell impurity of fresh and preserved cells. Cell integrity (a) and cell impurity (b) were determined for the fresh human/mouse cell mixture and cells preserved using DMSO cryopreservation (DMSO), methanol fixation (MeOH) and CellCover reagent at 4 °C (CC4) and −20 °C (CC20). Cells were stored for one (W01) and 15 weeks (W15). Cell integrity is represented by the percentage of undamaged cells as determined by live/dead staining. Cell impurity reflects the fraction of cross-species transcripts per cell barcode including contamination by ambient RNA as well as co-encapsulated cells.CC BY · PubMed Central

Figure 2 Quality control parameters of fresh and preserved human cells. Distribution and median gene count per cell (a), unique molecular identifier (UMI) count per cell (b) and percentage of mitochondrial transcripts per cell (c) detected for fresh cells and after DMSO cryopreservation (DMSO), methanol fixation (MeOH) and storage in CellCover reagent at 4 °C (CC4) and −20 °C (CC20). Cells were stored for one (W01) and 15 weeks (W15). Data shown are derived from human HEK293 cells of the species mixing experiment.CC BY · PubMed Central

Figure 3 Cluster analysis of fresh and preserved species mixing samples. Cell clustering identified seven cell clusters (a) and was mainly driven by murine (Mm) and human (Hs) origin and the preservation method. DMSO cryopreserved (DMSO) cells clustered together with the fresh cells whereas the cells preserved by CellCover reagent at 4 °C (CC4) and −20 °C (CC20) as well as methanol fixation (MeOH) formed separate clusters for murine and human cells respectively (b,d). The fraction of cross-species transcripts per cell (“cell impurity”) was variable across the different protocols and species (c). W01, storage for one week; W15, storage for 15 weeks.CC BY · PubMed Central

Figure 4 Pseudo-bulk gene expression correlation of fresh and preserved cells. Scatter plots show pairwise correlation of pseudo-bulk gene expression profiles from fresh cells and DMSO cryopreserved (DMSO) cells (a), methanol fixed (MeOH) cells (b) and cells preserved by CellCover reagent (c) at 4 °C (CC4) and −20 °C (CC20). Cells were stored for one week (W01) and 15 weeks (W15). Pearson correlation coefficient (R) indicates the degree of correlation. Data are shown for human HEK293 cells derived from the species mixing experiment. Axes represent log2 (UMI + 1) counts.CC BY · PubMed Central

Figure 5 Comparison of fresh and preserved monocyte-derived macrophages. Comparison of cell integrity (a), gene count per cell (b), unique molecular identifier (UMI) count per cell (c), percentage of mitochondrial transcripts per cell (d) and pearson correlation of pseudo-bulk gene expression profiles (log2 (UMI + 1) counts) from fresh and preserved cells (e,f). Cells were preserved by DMSO cryopreservation (DMSO), methanol fixation (MeOH) and CellCover reagent at −20 °C (CC20). The capture of cDNA from methanol preserved MDMs and processing of MDMs stored in CellCover at 4 °C was not feasible, therefore the analysis of these preservation methods is not presented. Error bars indicate the standard deviation.CC BY · PubMed Central

Figure 6 Comparison of fresh and preserved immune cells. Comparison of cell integrity (a), gene count per cell (b), unique molecular identifier (UMI) count per cell (c) and percentage of mitochondrial transcripts per cell (d) of fresh immune cells isolated from rat liver and after storage using the DMSO cryopreservation protocol. Scatter plots show correlation of pseudo-bulk gene expression profiles with axes representing log2 (UMI + 1) counts (e–h). Cells were isolated from two negative (NC) and positive control (PC) animals, respectively. Error bars indicate the standard deviation.CC BY · PubMed Central

Figure 7 Immune cell populations in fresh and cryopreserved samples. Cluster analysis identified 13 cell clusters in immune cells isolated from rat liver (a). Cell clusters represent macrophages/Kupffer cells, natural killer/natural killer T cells (NK(T) cells), T cells, B cells, neutrophils, plasmacytoid dendritic cells (pDCs), cycling cells (Cell cycle) and cells with increased expression of mitochondrial transcripts (Mito. high, b). The relative abundance per cell population is shown for fresh and DMSO cryopreserved (DMSO) samples per negative control (NC) and positive control (PC) animal, respectively (c).CC BY · PubMed Central

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Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
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Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
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Young, M. D., & Behjati, S. (2018) · doi:10.1101/303727 · Cited by 1,648
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Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619
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Srinivasan, M., Sedmak, D. D., & Jewell, S. D. (2002) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)64472-0 · Cited by 1,320
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Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., et al. (2017) · BMC Biology · doi:10.1186/s12915-017-0383-5 · Cited by 218
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Authors: Wohnhaas, C. T., Leparc, G. G., Fernandez-Albert, F., Kind, D., Gantner, F., Viollet, C., Hildebrandt, T., & Baum, P.
Affiliations: Boehringer Ingelheim (Germany) (DE)
Corresponding author: Baum, Patrick
ORCID: Wohnhaas, Christian T.
Leparc, Germán G.
Viollet, Coralie
Baum, Patrick
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2019-07-23
Volume: 9
Issue: 1
Pages: 10699-10699
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-019-46932-z
PMC: PMC6650608
OpenAlex: W2964240868
Cited by: 105
References: 31
Citations by year: 2026: 5 · 2025: 14 · 2024: 10 · 2023: 17 · 2022: 23 · 2021: 21 · 2020: 15
FWCI: 4.97
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
KOPS (University of Konstanz) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: Universität Konstanz
National Institutes of Health (HEK293)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Innovative Microfluidic and Catalytic Techniques Innovation
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
Keywords: cell cryopreservation, dimethyl sulfoxide, cell preservation, methanol fixation, gene expression correlation, cell integrity, monocyte-derived macrophages, immune cells, cell lines
MeSH terms: Dimethyl Sulfoxide, Humans, Cryopreservation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis

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DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing

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