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Cited by 220Single-cell and spatial transcriptomicsInnovative Microfluidic and Catalytic Techniques InnovationGenomics and Phylogenetic Studies

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Cell fixation and preservation for droplet-based single-cell transcriptomics

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Alles, Jonathan, Karaiskos, Nikos, Praktiknjo, Samantha D., Grosswendt, Stefanie, Wahle, Philipp, Ruffault, Pierre-Louis, Ayoub, Salah, Schreyer, Luisa, Boltengagen, Anastasiya, Birchmeier, Carmen, Zinzen, Robert, Kocks, Christine, Rajewsky, Nikolaus·2017·BMC Biology
DOI 10.1186/s12915-017-0383-5PMC PMC5438562

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Abstract

BACKGROUND: Recent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not altered by stress or ageing. Other challenges are rare cells that need to be collected over several days or samples prepared at different times or locations. METHODS: Here, we used chemical fixation to address these problems. Methanol fixation allowed us to stabilise and preserve dissociated cells for weeks without compromising single-cell RNA sequencing data. RESULTS: By using mixtures of fixed, cultured human and mouse cells, we first showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary cells from dissociated, complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells prepared by fluorescence-activated cell sorting, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data. CONCLUSIONS: We expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single-cell resolution.

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Fig. 1 Cell preparation for droplet-based single-cell transcriptional profiling. Schematic of experimental workflow. Cultured human (HEK) and mouse (3T3) cells were dissociated, mixed and further processed to analyse the transcriptomes of either live or fixed cells by Drop-seq. Washed cells were gently resuspended in 2 volumes of ice-cold PBS, then fixed by adding 8 volumes of ice-cold methanol. Methanol-fixed cells could be stored for up to several weeks at –80 °C. Prior to Drop-seq, cells were washed before passing them through a 35- to 40-μm cell strainer. Cells were then separately encapsulated in droplets together with a single bead in a microfluidic co-flow device and single-cell transcriptomes sequenced in a highly parallel manner. Downstream analysis and systematic quantitative comparisons were subsequently made from separate experiments using live or fixed cellular input material with an R package ('dropbead') that we developed and is freely available for download
Fig. 1 Cell preparation for droplet-based single-cell transcriptional profiling. Schematic of experimental workflow. Cultured human (HEK) and mouse (3T3) cells were dissociated, mixed and further processed to analyse the transcriptomes of either live or fixed cells by Drop-seq. Washed cells were gently resuspended in 2 volumes of ice-cold PBS, then fixed by adding 8 volumes of ice-cold methanol. Methanol-fixed cells could be stored for up to several weeks at –80 °C. Prior to Drop-seq, cells were washed before passing them through a 35- to 40-μm cell strainer. Cells were then separately encapsulated in droplets together with a single bead in a microfluidic co-flow device and single-cell transcriptomes sequenced in a highly parallel manner. Downstream analysis and systematic quantitative comparisons were subsequently made from separate experiments using live or fixed cellular input material with an R package ('dropbead') that we developed and is freely available for downloadCC BY · PubMed Central
Fig. 2 Transcriptome integrities and gene expression levels are preserved in fixed cells. a Drop-seq of mixed human and mouse cells (50 cells/μl). Plots show the number of human and mouse transcripts (UMIs) associated with a cell (dot) identified as human- or mouse-specific (blue or red, respectively). Cells expressing fewer than 3500 UMIs are grey; doublets are violet. b Distribution and the median of the number of genes and transcripts (UMIs) detected per cell (>3500 UMIs). Libraries were sequenced to a median depth of ~20,500 (Live) and ~15,500 (Fixed) aligned reads per cell. c Gene expression levels from live and fixed cells correlate well. Pairwise correlations between bulk mRNA-seq libraries and Drop-seq single-cell experiments. Non-single cell bulk mRNA-seq data were expressed as reads per kilobase per million (RPKM). Drop-seq expression counts were converted to average transcripts per million (ATPM) and plotted as log2 (ATPM + 1). Upper right panels show Pearson correlation. The overlap (common set) between all five samples is high (17,326 genes). Experiments with live and fixed cells were independently repeated with similar results (unpublished)
Fig. 2 Transcriptome integrities and gene expression levels are preserved in fixed cells. a Drop-seq of mixed human and mouse cells (50 cells/μl). Plots show the number of human and mouse transcripts (UMIs) associated with a cell (dot) identified as human- or mouse-specific (blue or red, respectively). Cells expressing fewer than 3500 UMIs are grey; doublets are violet. b Distribution and the median of the number of genes and transcripts (UMIs) detected per cell (>3500 UMIs). Libraries were sequenced to a median depth of ~20,500 (Live) and ~15,500 (Fixed) aligned reads per cell. c Gene expression levels from live and fixed cells correlate well. Pairwise correlations between bulk mRNA-seq libraries and Drop-seq single-cell experiments. Non-single cell bulk mRNA-seq data were expressed as reads per kilobase per million (RPKM). Drop-seq expression counts were converted to average transcripts per million (ATPM) and plotted as log2 (ATPM + 1). Upper right panels show Pearson correlation. The overlap (common set) between all five samples is high (17,326 genes). Experiments with live and fixed cells were independently repeated with similar results (unpublished)CC BY · PubMed Central
Fig. 3 Primary, fixed cells from dissociated Drosophila embryos cluster into distinct cell populations. Drosophila embryos were collected in 2-h time periods, aged for 6 h, dissociated into single cells and fixed. Drop-seq data correspond to two independent embryo collections, with three and four technical replicates, respectively. Libraries were sequenced to a median depth of ~13,250 aligned reads per cell. Cells expressing fewer than 1000 UMIs were excluded from the analysis. a Distribution and the median of the number of genes and transcripts (UMIs) detected per cell in Drop-seq data pooled from seven Drop-seq runs, representing a total of 4873 cells. Note that violin plots are displayed on a log scale. b Clustering of 4873 fixed cells into distinct cell populations. The plot shows a two-dimensional representation (tSNE) of global gene expression relationships among all cells. Tissue associations were made by ImaGO term analysis [20] on the 50 most variable genes of each cluster (Additional file 5: Table S1), followed by inspection of publicly accessible RNA in situ staining patterns. LVM longitudinal visceral musculature. c Marker gene expression in clusters of Drosophila embryo cells (see text for explanations). Expression coloured based on normalized expression levels
Fig. 3 Primary, fixed cells from dissociated Drosophila embryos cluster into distinct cell populations. Drosophila embryos were collected in 2-h time periods, aged for 6 h, dissociated into single cells and fixed. Drop-seq data correspond to two independent embryo collections, with three and four technical replicates, respectively. Libraries were sequenced to a median depth of ~13,250 aligned reads per cell. Cells expressing fewer than 1000 UMIs were excluded from the analysis. a Distribution and the median of the number of genes and transcripts (UMIs) detected per cell in Drop-seq data pooled from seven Drop-seq runs, representing a total of 4873 cells. Note that violin plots are displayed on a log scale. b Clustering of 4873 fixed cells into distinct cell populations. The plot shows a two-dimensional representation (tSNE) of global gene expression relationships among all cells. Tissue associations were made by ImaGO term analysis [20] on the 50 most variable genes of each cluster (Additional file 5: Table S1), followed by inspection of publicly accessible RNA in situ staining patterns. LVM longitudinal visceral musculature. c Marker gene expression in clusters of Drosophila embryo cells (see text for explanations). Expression coloured based on normalized expression levelsCC BY · PubMed Central
Fig. 4 Sorted, fixed mouse brain cells allow identification of distinct neural and non-neural cell types. Hindbrains and cerebellum from newborn mice were microdissected and dissociated, and cells were sorted by FACS into methanol and stored. Drop-seq data correspond to two independent biological replicates. Libraries were sequenced to a median depth of ~7100 reads per cell. a Distribution and the median of the number of genes detected per cell (>300 UMIs) in Drop-seq data pooled from two Drop-seq runs, representing 4366 cells. Note that violin plots are displayed on a log scale. b Clustering of 4366 fixed cells into distinct cell populations marked by colour (Additional file 8: Table S2). The plot shows a two-dimensional representation (tSNE) of global gene expression relationships among all cells. Tissue associations of cell clusters were identified by assessing the 50 most variable genes in each cluster and confirmed by inspection of publicly accessible images of RNA in situ hybridizations. c Known marker gene expression in clusters of brain cells (see text for explanation). Expression coloured based on normalized expression levels
Fig. 4 Sorted, fixed mouse brain cells allow identification of distinct neural and non-neural cell types. Hindbrains and cerebellum from newborn mice were microdissected and dissociated, and cells were sorted by FACS into methanol and stored. Drop-seq data correspond to two independent biological replicates. Libraries were sequenced to a median depth of ~7100 reads per cell. a Distribution and the median of the number of genes detected per cell (>300 UMIs) in Drop-seq data pooled from two Drop-seq runs, representing 4366 cells. Note that violin plots are displayed on a log scale. b Clustering of 4366 fixed cells into distinct cell populations marked by colour (Additional file 8: Table S2). The plot shows a two-dimensional representation (tSNE) of global gene expression relationships among all cells. Tissue associations of cell clusters were identified by assessing the 50 most variable genes in each cluster and confirmed by inspection of publicly accessible images of RNA in situ hybridizations. c Known marker gene expression in clusters of brain cells (see text for explanation). Expression coloured based on normalized expression levelsCC BY · PubMed Central

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Authors
Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., Zinzen, R., Kocks, C., & Rajewsky, N.
Affiliations
Max Delbrück Center (DE)
Corresponding author
  • Kocks, Christine
  • Rajewsky, Nikolaus
ORCID
Venue
BMC Biology
Publisher
Springer Science and Business Media LLC
Published
2017-05-19
Volume
15
Issue
1
Pages
44-44
ISSN
1741-7007 (ISSN-L)
DOI
10.1186/s12915-017-0383-5
PMC
PMC5438562
OpenAlex
W2950862527
Cited by
218
References
26
Citations by year
2026: 8 · 2025: 19 · 2024: 23 · 2023: 20 · 2022: 21 · 2021: 32 · 2020: 37 · 2019: 28 · 2018: 19 · 2017: 10
FWCI
10.50
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMC Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (SFB 665, cluster of excellence Neurocure)
  • Deutsches Zentrum für Herz-Kreislaufforschung (DZHK BER1.2VD)
  • European Molecular Biology Organization (EMBO fellowship)
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 › Genomics and Phylogenetic Studies
Keywords
cell fixation, methanol fixation, cell preservation, single-cell RNA sequencing, Drop-seq, droplet microfluidics, Drosophila embryo, neuronal cell types
MeSH terms
Methanol, Animals, Cells, Cultured, Cerebellum, Drosophila, Embryo, Nonmammalian, Flow Cytometry, Humans, Rhombencephalon, RNA, Messenger, Software, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Single-Cell Analysis

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