Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

Abstract

Background: High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms.

Methods: Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments.

Results: Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing.

Conclusions: We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

Keywords: Breast cancer; CITE-Seq; Cryopreservation; Melanoma; Prostate cancer; Single-cell RNA sequencing; Tumour heterogeneity.

Fig. 1

Cryopreservation allows for robust cell-type detection in clinical breast cancer samples. a Experimental workflow. b UMAP visualisation of 23,803, 29,828 and 24,250 cells sequenced across dissociated fresh tissue (FT; green), dissociated cryopreserved cell suspensions (CCS; orange) and solid cryopreserved tissue (CT; purple) replicates from three primary breast cancer cases (BC-P1, BC-P2 and BC-P3). UMAPs are coloured by cryopreserved replicate (top) and by cluster ID (bottom) with cell types annotations overlayed. Matched replicates were integrated using the Seurat v3 method. c Number of cells detected per cluster. Cells were downsampled to the lowest replicate size. d FeaturePlot visualisations of gene expression from BC-P1 fresh and cryopreserved replicates, showing the conservation of the housekeeping gene ACTB and heterogeneous cancer/epithelial (EPCAM), immune (PTPRC/CD45), endothelial (PECAM1/CD31) and fibroblast/perivascular (PDGFRB) clusters. e, g Distribution of silhouette scores (range −1 to + 1) (e), mixing metric (f) and local structure metrics (g) of clustering following cryopreservation. Samples were downsampled by replicate and cluster sizes and compared to the respective FT samples. Cell comparisons were performed across downsampled FT-1 vs FT-2 cells (positive control), FT vs CCS cells and FT vs CT cells. Stars represent standard deviations: e silhouette scores s.d. 0.02–0.05* and s.d. > 0.05**; f mixing metrics s.d. 2–10* and s.d. > 10**; g local structure metrics s.d. > 0.05*

Fig. 2

Cryopreservation allows for robust cell-type detection in clinical prostate cancer and melanoma samples. a UMAP visualisation of 18,331 cells sequenced across FT (green), CCS (orange) and CT (purple) from primary prostate cancer case PC-P1. UMAPs are coloured by cryopreserved replicate (top) and by cluster ID (bottom) with cell types annotations overlayed. Matched replicates were integrated using the Seurat v3 method. b UMAP visualisation as in a of 21,361 cells sequenced across FT (green), CCS (orange) and cryopreserved overnight (CO; purple) replicates from metastatic melanoma case M-P1. c Number of cells detected per cluster from PC-P1 and M-P1, highlighting the conservation of clusters detected in the FT samples following cryopreservation. Cells were downsampled to the lowest replicate size. d, e FeaturePlot visualisations of gene expression in prostate cancer (d) and melanoma (e) showing the conservation of the housekeeping gene ACTB and heterogeneous cancer/epithelial (EPCAM in d or MITF in e), immune (PTPRC/CD45), endothelial (PECAM1/CD31) and fibroblast/perivascular (PDGFRB) clusters following cryopreservation as FT, CCS and CT or CO. f–h Distribution of silhouette scores (f), mixing metric (g) and local structure metrics (h) of clustering following cryopreservation as analysed in Fig. 1e–g. Stars represent standard deviations: f silhouette scores s.d. 0.02–0.05* and s.d. > 0.05**; g mixing metrics s.d. 2–10* and s.d. > 10**; h local structure metrics s.d. > 0.05*

Fig. 3

Cryopreservation maintains the integrity and complexity of single-cell transcriptomes in clinical human cancers. a, b Number of genes (a) and UMIs (b) detected per cell across all FT, CCS, CT and CO replicates from breast (BC-P1, BC-P2 and BC-P3), prostate (PC-P1 and PC-P2) and melanoma samples (M-P1). Sequencing libraries were downsampled to equal number of mapped reads per cell using the cellranger aggregate function to account for differences in sequencing depth. Note that only one CCS replicate in M-P1 (orange) and one CT replicate in BC-P1 (purple) had significantly lower number of genes and UMIs per cell compared to their matching FT replicate. Statistical significance was determined using an unpaired Student’s t test. P values denoted by asterisks: *p < 0.05, p < 0.01, *p < 0.001 and ****p < 0.0001. c Pseudobulk gene correlations between FT cells with CCS (red line) and CT or CO (blue line) replicates. Correlation values (adjusted-R²) were computed using linear regression in R to model the log-normalised gene expression values between two replicates. In all cases, CCS replicates had higher R² values compared to CT and CO comparisons. d Cluster-level gene correlations between FT cells with CCS (circle), CT (triangle) and CO (square) replicates show similar trends with pseudobulk gene correlations. Dotted lines join corresponding clusters between different comparison methods. Plasmablasts (c18 in BC-P1 and c22 in BC-P2) were the only cell type identified in multiple cases to have significantly lower correlations

Fig. 4

Methods of human tumour cryopreservation maintain biological pathways. a Euler diagrams highlighting the overlaps between gene ontology (GO) pathways detected in FT clusters and cryopreserved replicates from CCS, CT and CO. A total of 315, 347, 368, 262, 230 and 311 pathways were assessed from the FT replicates across the BC-P1, BC-P2, BC-P3, PC-P1, PC-P2 and M-P1 cases, respectively. b–d Sensitivity of pathway enrichment scores detected in clusters across cryopreserved replicates of BC-P1 (b), PC-P1 (c) and M-P1 (d). The minimum, mean and maximum -log10 q value are plotted in the error bars of each GO pathway. All DEGs from each cluster were passed on to the ClusterProfiler package for functional enrichment with the CC sub-ontology under the human org. Hs.eg.db database. GO pathway descriptions can be found in Additional file 4

Fig. 5

Gene expression artefacts arising from cryopreservation. a–c Enrichment scores for gene ontology pathways that are unique to cryopreservation conditions: cryopreserved cell suspension (CCS; a), cryopreserved tissue (CT; b) and cryopreserved after overnight cold storage (CO; c). Comparisons were performed between all cells from each matched condition, which were first downsampled by total cell number and total number of sequencing reads. For the CCS (a) and CT (b) conditions, only pathways that were shared across multiple cases were analysed, which led to a total of 5 and 21 pathways for each condition, respectively. A total of 54 pathways were enriched in the CO (c) condition. Only the top 10 pathways based on enrichment scores are plotted for CT (b) and CO (c) conditions. DEGs from each condition (Additional file 5) were passed on to the ClusterProfiler package for functional enrichment with the CC sub-ontology under the human org.Hs.eg.db database. GO pathway descriptions can be found in Additional file 6. d–h Expression violin plots of the genes HSPA1A, HSPA1B and HSP90AA1 from cell stress response pathways (heat shock protein binding GO:0031072 and unfolded protein binding GO:0051082) that were commonly enriched across CT and CO conditions. Tumours for BC-P1 (d), BC-P2 (e), BC-P3 (f), PC-P1 (g) and M-P1 (h) are grouped by their cryopreservation conditions: fresh tissue (FT), CCS, CT or CO. Asterisk indicates significance values where adjusted p values are less than 0.05, as calculated using the MAST method

Fig. 6

Cryopreservation provides high quality immunophenotyping using CITE-Seq. a UMAP visualisation of 2621 cells sequenced from a breast cancer case cryopreserved as CT. Clusters were annotated based on canonical cell type markers by RNA expression. CITE-Seq was performed on this case using a panel of 15 canonical cell type markers. b Heatmap of rescaled antibody-derived tag (ADT) values for relevant markers for cancer/epithelial cells (EPCAM), endothelial cells (CD31/PECAM1 and CD34), perivascular cells (MCAM/CD146 and THY-1/CD90), cancer-associated fibroblasts (THY-1/CD90 and CD34), immune cells (CD45/PTPRC), T-cells (CD3, CD4, CD8, CD69 and CD103), monocytes/macrophages (CD11c and CD11d) and MHC molecules (MHC-II and MHC-I). c FeaturePlot representation of ADT protein expression values for selected markers from b highlighting the specificity of major lineage markers on RNA based clustering in a