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. 2017 Mar 1;18(1):45.
doi: 10.1186/s13059-017-1171-9.

Single-cell Transcriptome Conservation in Cryopreserved Cells and Tissues

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Free PMC article

Single-cell Transcriptome Conservation in Cryopreserved Cells and Tissues

Amy Guillaumet-Adkins et al. Genome Biol. .
Free PMC article

Abstract

A variety of single-cell RNA preparation procedures have been described. So far, protocols require fresh material, which hinders complex study designs. We describe a sample preservation method that maintains transcripts in viable single cells, allowing one to disconnect time and place of sampling from subsequent processing steps. We sequence single-cell transcriptomes from >1000 fresh and cryopreserved cells using 3'-end and full-length RNA preparation methods. Our results confirm that the conservation process did not alter transcriptional profiles. This substantially broadens the scope of applications in single-cell transcriptomics and could lead to a paradigm shift in future study designs.

Keywords: Conservation; Cryopreservation; MARS-Seq; PBMC; PDOX; Patient-derived orthotopic xenograft; Peripheral blood mononuclear cells; RNA sequencing; Single-cell genomics; Smart-seq2; Transcriptomics.

Figures

Fig. 1
Fig. 1
Comparative analyses of MARS-Seq-derived single-cell transcriptome data from fresh (red) and cryopreserved (–80 °C: blue; liquid nitrogen: green) HEK293 cells. a Mapping distribution of sequencing reads after MARS-seq library preparation. Each line represents a single cell and transcript sizes are scaled from 0 to 100. b Cumulative gene counts split by fresh and cryopreserved cells and analyzed using randomly sampled cells (average of 100 permutations). c, d Comparative analysis of the number of sequencing reads and detected transcripts (c) or genes (d) per cell using a linear model. The slope of the regression line was calculated separately for fresh and cryopreserved cells. e, f Gene expression profile variances between fresh and cryopreserved cells displayed as principal component analysis (PCA, e) or as t-distributed stochastic neighbor embedding (t-SNE) representation (f) using the 100 most variable genes
Fig. 2
Fig. 2
Correlating MARS-Seq-derived single-cell transcriptomes from fresh (red) and cryopreserved (blue) HEK293 cells identify subpopulations. a Pearson’s correlation analysis between 20 randomly selected fresh and cryopreserved cells displaying the correlation coefficient (r2). b Distribution of Pearson’s correlation coefficients (r2) within and between processing conditions. The median coefficients are indicated. c Pearson’s correlation analysis between 20 randomly selected fresh and cryopreserved HEK293 or K562 cells displaying the correlation coefficient (r2). df Linear regression model comparing average gene expression levels of (d) expressed, (e) cell cycle (G2/M checkpoint, and (f) apoptosis genes. The coefficient of determination (r2) is indicated. g Hierarchical clustering of single cells based on transcriptional programs (defined by Gene Ontology) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to condition (fresh: red; cryopreserved: blue) and clusters. h A t-SNE representation of similarities between cells using previous defined distances and cluster identity (as in g). Conditions are indicated (fresh: circle; cryopreserved: triangle). i Hierarchical cluster of single cells (as in g) displaying the 25 most variable cell cycle genes (G2/M checkpoint). Expression levels of the cell cycle signature are summarized (first panel; high: orange, low: green) and conditions (second panel; fresh: red; cryopreserved: blue) and clusters are indicated
Fig. 3
Fig. 3
Comparative analyses of Smart-seq2-derived single-cell transcriptomes from fresh (red) and cryopreserved (blue) HEK293 cells. a Sequencing read distribution following RNA library preparation of full-length transcripts. Each line represents a single cell and transcript sizes are scaled from 0 to 100. b Cumulative gene counts split by fresh and cryopreserved cells and analyzed using randomly sampled cells (average of 100 permutations). c, d Gene expression variances of single cells displayed as PCA (c) or t-SNE representation (d) using the 100 most variable genes. e Pearson’s correlation analysis between 20 randomly selected fresh and cryopreserved cells displaying the correlation coefficient (r2). f Distribution of Pearson’s correlation coefficients (r2) within and between processing conditions. The median coefficients are indicated. g Hierarchical clustering of single cells based on transcriptional programs (defined by Gene Ontology) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to conditions (fresh: red; cryopreserved: blue) and clusters. h A t-SNE representation of similarities between cells using previous defined distances and cluster identities (as in g). Conditions are indicated (fresh: circle; cryopreserved: triangle). i Hierarchical clustering (as in g) displaying the 25 most variable cell cycle genes (G2/M checkpoint). Expression levels of the cell cycle signature are summarized (first panel; high: orange, low: green) and conditions (second panel; fresh: red; cryopreserved: blue) and clusters are indicated
Fig. 4
Fig. 4
Comparative analyses of Smart-seq2-derived single-cell transcriptomes from fresh (red) and cryopreserved (blue) K562 cells. a Sequencing read distribution following library preparation of full-length transcripts. Each line represents a single cell and transcript sizes are scaled from 0 to 100. b Cumulative gene counts split by fresh and cryopreserved cells and analyzed using randomly sampled cells (average of 100 permutations). c, d Gene expression variances between single cells displayed as PCA (c) or t-SNE representation (d) using the 100 most variable genes. e Pearson’s correlation analysis between 20 randomly selected fresh and cryopreserved cells displaying the correlation coefficient (r2). f Distribution of Pearson’s correlation coefficients (r2) within and between processing conditions. The median coefficients are indicated. g Hierarchical clustering of single cells based on transcriptional programs (defined by Gene Ontology) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to conditions (fresh: red; cryopreserved: blue) and clusters. h A t-SNE representation of similarities between cells using previous defined distances and cluster identities (as in g). Conditions are indicated (fresh: circle; cryopreserved: triangle). i Hierarchical clustering (as in g) displaying the 25 most variable cell cycle genes (G2/M checkpoint). Expression levels of the cell cycle signature are summarized (first panel; high: orange, low: green) and conditions (second panel; fresh: red; cryopreserved: blue) and clusters are indicated
Fig. 5
Fig. 5
Correlating single-cell transcriptome data from fresh (red) and cryopreserved (blue) samples determines cell subtypes in PBMC. a, b Comparative analysis of the number of sequencing reads and detected transcripts (a) or genes (b) per cell using a linear model. The slope of the regression line was calculated separately for fresh and cryopreserved cells. c Cumulative gene counts split by fresh and cryopreserved cells and analyzed using randomly sampled cells (average of 100 permutations). d Linear regression model comparing average gene expression levels of expressed genes. The coefficient of determination (r2) is indicated. e Gene expression variances displayed as t-SNE representation using the 100 most variable genes. f Hierarchical clustering of single cells based on transcriptional programs (see “Material and methods”) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to condition (fresh: red; cryopreserved: blue) and clusters. g A t-SNE representation of similarities between cells using distances and cluster identities (as in f). Conditions are indicated (fresh: circle; cryopreserved: triangle). Cell types were annotated based on marker gene expression (BC B-cells, CytoTC cytotoxic T-cells, MemTC memory T-cells, Myd myeloid cells). h Hierarchical clustering of single cells (as in f). Displayed are the expression levels of the 25 most variable genes implicated in cluster formation. Cells are assigned to conditions (first panel: fresh: red; cryopreserved: blue) and clusters
Fig. 6
Fig. 6
Single-cell transcriptome data from fresh (red) and cryopreserved (blue) mouse colon cells. a, b Comparative analysis of the number of sequencing reads and detected transcripts (a) or genes (b) using a linear model. The slope of the regression line was calculated separately for fresh and cryopreserved cells. c Cumulative gene counts split by fresh and cryopreserved cells and analyzed using randomly sampled cells (average of 100 permutations). d Linear regression model comparing average gene expression levels of expressed genes. The coefficient of determination (r2) is indicated. e Gene expression variances displayed as t-SNE representation using the 100 most variable genes. f Hierarchical clustering of single cells based on transcriptional programs (defined by Gene Ontology) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to condition (fresh: red; cryopreserved: blue) and clusters. g A t-SNE representation of similarities between cells using distances and cluster identities (as in f). Conditions are indicated (fresh: circle; cryopreserved: triangle). Cell types were annotated based on marker gene expression [9] (TA transit amplifying, ECpr enterocytes precursors, EC enterocytes, SC secretory cells). h Hierarchical clustering of single cells (as in f). Displayed are the expression levels of the 25 most variable genes implicated in cluster formation. Cells are assigned to condition (first panel: fresh: red; cryopreserved: blue) and clusters
Fig. 7
Fig. 7
Comparative analyses of single-cell transcriptome data from fresh (red) and cryopreserved (blue) patient-derived orthotopic ovarian tumor xenograft cells. a, b Comparative analysis of the number of sequencing reads and detected transcripts (a) or genes (b) using a linear model. The slope of the regression line was calculated separately for fresh and cryopreserved cells. c Gene expression variances displayed as t-SNE representation using the 100 most variable genes. d Linear regression model comparing average gene expression levels of expressed genes. The coefficient of determination (r2) is indicated. e Hierarchical clustering of single cells based on transcriptional programs (defined by Gene Ontology) and correlating gene sets [21]. Transcriptional programs and gene clusters are summarized in aspects. Displayed are the most variable aspects (rows) and their importance (row colors). Cells are assigned to condition (fresh: red; cryopreserved: blue) and clusters. f A t-SNE representation of similarities between cells using distances and cluster identities (as in e). Conditions are indicated (fresh: circle; cryopreserved: triangle). g, h Hierarchical clustering of single cells (as in e). Displayed are the expression levels of the 25 most variable ribosomal genes (g) and genes implicated in cell cycle (G2/M checkpoint, h). Gene set expression levels are summarized (first panel: high: orange; low: green) and cells are assigned to condition (second panel: fresh: red; cryopreserved: blue) and clusters

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References

    1. ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. doi: 10.1038/nature11247. - DOI - PMC - PubMed
    1. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14. doi: 10.1016/j.cell.2015.05.002. - DOI - PMC - PubMed
    1. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015;347:1138–42. doi: 10.1126/science.aaa1934. - DOI - PubMed
    1. Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352:1586–90. doi: 10.1126/science.aaf1204. - DOI - PMC - PubMed
    1. Paul F, Arkin Y, Giladi A, Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell. 2015;163:1663–77. doi: 10.1016/j.cell.2015.11.013. - DOI - PubMed

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