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Cell fixation and preservation for droplet-based single-cell transcriptomics
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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. 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](/paper-figures/4aa935c2aa6d46c5b986d39c8f6d6339/cell-fixation-and-preservation-for-droplet-based-single-cell-transcriptomics-figure-3.jpg)
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“First, these methods require the use of fresh tissues, which can be difficult to obtain and thus lead to logistic complexities (Alles et al., 2017; Attar et al., 2018; Wang et al., 2018; Lafzi et al., 2018).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Alles et al., 2017
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Cites
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“Stressed or broken cells lose non-mitochondrially encoded, cytoplasmically localized mRNAs [16].”
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MethodSingle Cell Genomics: Advances and Future Perspectives
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BackgroundThe Drosophila embryo at single-cell transcriptome resolution
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BackgroundMultiplexed quantification of proteins and transcripts in single cells
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Metadata
- 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
- Published
- 2017-05-19
- Volume
- 15
- Issue
- 1
- Pages
- 44-44
- ISSN
- 1741-7007 (ISSN-L)
- 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
- gold
- doi.org
- 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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