ArticleFrontiers in zoology2022
Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material.
Article in Frontiers in zoology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
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- Starve-Feed Cycles Direct Quiescence to Proliferation Transitions inbioRxiv : the preprint server for biology · 2026Article
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- Single-nucleus RNA sequencing reveals the distinct heterogeneity of primary pulmonary sarcomas (PPS) and pulmonary sarcomatoid carcinoma (PSC).Clinical and translational medicine · 2026Article
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- Single-cell analysis of matched FFPE and frozen tissue samples reveals comparable resolution of intratumoural heterogeneity.Frontiers in genetics · 2026Article
- Cell type-specific in vivo proteomes with a multicopy mutant methionyl tRNA synthetase mouse line.Lab animal · 2025Article
- From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology.Molecular ecology · 2025Review
- Investigative needle core biopsies support multimodal deep-data generation in glioblastoma.Nature communications · 2025Article
- Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.International journal of molecular sciences · 2024Review
- A single cell genomics atlas of the Drosophila larval eye reveals distinct photoreceptor developmental timelines.Nature communications · 2023Article
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4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundRecent technological advances opened the opportunity to simultaneously study gene expression for thousands of individual cells on a genome-wide scale. The experimental accessibility of such single-cell RNA sequencing (scRNAseq) approaches allowed gaining insights into the cell type composition of heterogeneous tissue samples of animal model systems and emerging models alike. A major prerequisite for a successful application of the method is the dissociation of complex tissues into individual cells, which often requires large amounts of input material and harsh mechanical, chemical and temperature conditions. However, the availability of tissue material may be limited for small animals, specific organs, certain developmental stages or if samples need to be acquired from collected specimens. Therefore, we evaluated different dissociation protocols to obtain single cells from small tissue samples of Drosophila melanogaster eye-antennal imaginal discs.
resultsWe show that a combination of mechanical and chemical dissociation resulted in sufficient high-quality cells. As an alternative, we tested protocols for the isolation of single nuclei, which turned out to be highly efficient for fresh and frozen tissue samples. Eventually, we performed scRNAseq and single-nuclei RNA sequencing (snRNAseq) to show that the best protocols for both methods successfully identified relevant cell types. At the same time, snRNAseq resulted in less artificial gene expression that is caused by rather harsh dissociation conditions needed to obtain single cells for scRNAseq. A direct comparison of scRNAseq and snRNAseq data revealed that both datasets share biologically relevant genes among the most variable genes, and we showed differences in the relative contribution of the two approaches to identified cell types.
conclusionWe present two dissociation protocols that allow isolating single cells and single nuclei, respectively, from low input material. Both protocols resulted in extraction of high-quality RNA for subsequent scRNAseq or snRNAseq applications. If tissue availability is limited, we recommend the snRNAseq procedure of fresh or frozen tissue samples as it is perfectly suited to obtain thorough insights into cellular diversity of complex tissue.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.