Guidance on Processing the 10x Genomics Single Cell Gene Expression Assay

The demand for technologies that allow the study of gene expression at single cell resolution continues to increase. One such assay was launched in 2016 by the US-based company 10x Genomics Inc. Utilizing the power of the single cell on a large scale (Zheng et al. Nat Commun 8:14049, 2017)-capturing thousands of cells at once-has shaped life sciences ever since and allowed researchers to discover new insights within their respective fields of study such as oncology, neurobiology, and immunology (among others). Obtaining high-data quality is the key to being able to make these meaningful discoveries, which in turn is directly linked to the quality of the initial cell (or nuclei) suspension that is used to load the 10x Genomics Chromium Single Cell Gene Expression assay. A successful workflow relies on a cell suspension which is fully dissociated, extremely clean, and of high viability. While the workflow itself has been detailed elsewhere (De Simone et al. Methods Mol Biol 1979:87-110, 2019), in this chapter we will focus on the importance of the quality of the initial cell suspension, as well as common mistakes that can occur while running a Single Cell Gene Expression assay. The descriptions of these tips and tricks refer to the current version of the 10x Genomics User Guide (Chromium Single Cell 3' Reagent Kits User Guide (v3.1 Chemistry Dual Index). https://support10xgenomicscom/single-cell-gene-expression/index/doc/user-guide-chromium-single-cell-3-reagent-kits-user-guide-v31-chemistry-dual-index) which can be downloaded from the Support section on the 10x Genomics website (10x Genomics website. https://www10xgenomicscom). These documents and user guides are continuously improved and updated; hence, it is important to regularly check the company's website for the most recent version.

Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has quickly become one of the most dominant techniques in modern transcriptome assessment. In particular, 10X Genomics' Chromium system, with its high throughput approach, turn key and thorough user guide made this cutting-edge technique accessible to many laboratories using diverse animal models. However, standard pre-processing, including the alignment and cell filtering pipelines might not be ideal for every organism or tissue. Here we applied an alternative strategy, based on the pseudoaligner kallisto, on twenty-two publicly available single cell sequencing datasets from a wide range of tissues of eight organisms and compared the results with the standard 10X Genomics' Cell Ranger pipeline.

RESULTS

In most of the tested samples, kallisto produced higher sequencing read alignment rates and total gene detection rates in comparison to Cell Ranger. Although datasets processed with Cell Ranger had higher cell counts, outside of human and mouse datasets, these additional cells were routinely of low quality, containing low gene detection rates. Thorough downstream analysis of one kallisto processed dataset, obtained from the zebrafish pineal gland, revealed clearer clustering, allowing the identification of an additional photoreceptor cell type that previously went undetected. The finding of the new cluster suggests that the photoreceptive pineal gland is essentially a bi-chromatic tissue containing both green and red cone-like photoreceptors and implies that the alignment and pre-processing pipeline can affect the discovery of biologically-relevant cell types.

CONCLUSION

While Cell Ranger favors higher cell numbers, using kallisto results in datasets with higher median gene detection per cell. We could demonstrate that cell type identification was not hampered by the lower cell count, but in fact improved as a result of the high gene detection rate and the more stringent filtering. Depending on the acquired dataset, it can be beneficial to favor high quality cells and accept a lower cell count, leading to an improved classification of cell types.

Single-Cell RNA Sequencing Analysis of the Drosophila Larval Ventral Cord

Drosophila provides a powerful genetic system and an excellent model to study the development and function of the nervous system. The fly's small brain and complex behavior has been instrumental in mapping neuronal circuits and elucidating the neural basis of behavior. The fast pace of fly development and the wealth of genetic tools has enabled systematic studies on cell differentiation and fate specification, and has uncovered strategies for axon guidance and targeting. The accessibility of neuronal structures and the ability to edit and manipulate gene expression in selective cells and/or synaptic compartments has revealed mechanisms for synapse assembly and neuronal connectivity. Recent advances in single-cell RNA sequencing (scRNA-seq) have further enhanced our appreciation and understanding of neuronal diversity in a fly brain. However, due to the small size of the fly brain and its constituent cells, scRNA-seq methodologies require a few adaptations. Here, we describe a set of protocols optimized for scRNA-seq analysis of the Drosophila larval ventral nerve cord, starting from tissue dissection and cell dissociation to cDNA library preparation, sequencing, and data analysis. We apply this workflow to three separate samples and detail the technical challenges associated with successful application of scRNA-seq to studies on neuronal diversity. An accompanying article (Vicidomini, Nguyen, Choudhury, Brody, & Serpe, 2021) presents a custom multistage analysis pipeline that integrates modules contained in different R packages to ensure high-flexibility, high-quality RNA-seq data analysis. These protocols are developed for Drosophila larval ventral nerve cord, but could easily be adapted to other tissues and model organisms. © 2021 U.S. Government. Basic Protocol 1: Dissection of larval ventral nerve cords and preparation of single-cell suspensions Basic Protocol 2: Preparation and sequencing of single-cell transcriptome libraries Basic Protocol 3: Alignment of raw sequencing data to indexed genome and generation of count matrices.