Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

This method can help out answer key questions in the field of microbiology, such as understanding gene expression changes across thousands of single cells after an injury or a disease. The main advantage of this technique is that it allows us to dissect transcriptomes of individual cells within a complex tissue, and to better appreciate the functional dynamics within a given population. The advantage of our protocol is that it provides comprehensive methods, from isolation of single cells in primary tissues, to analysis and visualization of this data set.

Though this method starts from single-cell suspensions from the skin and the nerve, the sequencing methods that we describe can be applied to study any mammalian tissue. Demonstrating this procedure will be Elodie Labit, and Wisoo Shin, two trainees from my laboratory. To begin, dissect the sciatic nerve of a euthanized mouse by first cutting the skin away from the hind region of the back of the mouse.

Use a sterile scalpel blade to make an incision along the length of the thigh. Then use fine forceps and scissors to expose and remove the sciatic nerve. To dissect the dorsal back skin, use fine forceps and scissors to make incisions from shoulder to shoulder, across the rump, and down the back.

Wash the tissues twice with ice-cold HBSS. Under a dissecting microscope, remove unwanted connective tissue, fat deposits, or debris. For the skin only, cut the skin into thin slices, using a sterile scalpel blade.

To obtain the skin dermis, float the skin slices in dispase, in HBSS in a 10 centimeter dish, for 30 to 40 minutes at 37 degrees Celsius. Use fine forceps to peel and separate the epidermis from the dermis, and then discard the epidermis. Then, for skin and nerve, use a pair of steril scalpel blades to mince each sample into one to two millimeter pieces.

Place the tissue pieces into freshly-thawed, two grams per milliliter cold collagenase-four enzyme in a 15 milliliter conical tube. Incubate each sample in the enzyme in a 37-degree Celsius bath for 30 minutes with gentle shaking every 10 minutes. At 30 minutes, use a P1000 pipetter to triturate the tissue 20 to 30 times, and return to the water bath.

Repeat trituration every 30 minutes until the solution appears cloudy, and chunks of tissue are largely dissociated. For skin only, in the last hour of incubation, add one milligrams per milliliter DNAse to the skin sample. Since some cell types are more sensitive than others to mechanical stress, excessive dissociation techniques can bias your population.

General dissociation is critical for achieving high cell yields, and an accurate representation of tissue composition. After that, use a 40 micrometer filter on top of the 50 milliliter conical tube to filter each tissue sample twice. Rinse the filter with one percent BSA/HBSS.

After centrifuging the samples as described in the manuscript, re-suspend the cell pellet in HBSS, containing one percent BSA, using a wide-bore tip, and place on ice. The addition of viability dye will help you optimize your preparation. You should aim for at least 80 percent viable cells.

Once optimized, this and other quality-control steps can be omitted in order to hasten the dissociation process, which will better preserve RNA quality and reduce cell loss. To isolate viable and healthy cells using FACS, first prepare 15 milliliter narrow-bottom tubes with eight milliliters of ice-cold one percent BSA/HBSS for sample collections. To ensure that the interface between the surface of the liquid and the inside of the tube is moist, and to prevent static and surface tension, invert the tubes before collections.

Immediately after FACS cell sorting, once cells are collected in a 15 milliliter tube, use one milliliter of one percent BSA/HBSS to wash and push all the cells down from the side surface of the tube. Then centrifuge each sample at 260 g for eight minutes. After discarding the supernatant, re-suspend the cell pellet in one percent BSA/HBSS, and add 33.8 microliters to a tube, and keep that tube on ice.

This step is designed to be done using reagents obtained from a standardized kit. All demonstrated steps should be performed following the manufacturer's instructions. To prepare for GEM generation, place a chip in the chip holder, the prepare a cell master mix on ice, according to manufacturer's protocol.

Add 56.2 microliters of the master mix to a tube containing 33.8 microliters of self suspension. To prepare the chip, first at 50 percent glycerol to the wells that will not be used. Then add 90 microliters of the cell master mix to well one, 40 microliters of gel beads to well two, and 270 microliters of partitioning oil to well three.

Cover the chip with a gasket. To load the chip, and run in a single-cell controller, first eject the tray. Place the chip in the tray.

Retract the tray, and press play. After the complete run, collect 100 microliters of the sample, and place in a PCR tube. Place PCR tubes in a preset PCR machine, and run according to the kit.

Following the run, GEMs will include full-length, barcoded cDNA, from polyadenylated mRNA. Place the tubes at minus 20 degrees Celsius overnight. Proceed with library construction, sequencing, alignment, and analysis.

Once samples are sequenced and aligned as described in the manuscript, deposit the data on a public repository, such as NCBI's GEO. To do this, register for the submitter account. First, complete the GEO submission by downloading the metadata sheet.

Make sure to include only one metadata sheet per project submission. Place the spreadsheet in the directory. Second, add the raw data file generated from the cell-ranger count script for all libraries into the directory.

The third folder is processed data files. Place processed data files generated from cell-ranger count script for all libraries into the directory. Use the GEO submitter's FTP server credentials to transfer the directory containing all three components.

After running Cell Ranger, analysis-ready gene barcode matrices can be further processed using advanced bioinformatics. First, pre-processing quantity checks were done using the Seurat R package, which generated violin and scatter plots to visualize the number of genes, number of unique molecular identifiers, and percentage of mitochondrial genes to identify cell doublets and outliers. For selection of principal components, or PCs, elbow plots were used, in which PCs beyond the plateau of the standard deviation of PC axis, were excluded.

The resolution of clustering was also manipulated. Low resolution led to fewer cell clusters, with each cluster likely representing a defined cell type. High resolution led to higher cell probability, representing sub-types, or transitional states, of a cell population.

Low resolution cluster settings were used for further analysis of expression heat maps to identify the most highly expressed genes in a given cluster. Individual candidate genes were visualized on tSNE plots, using Seurat's feature plot function, that allowed deciphering, whether there were clusters which represented, for example, macrophages. Both cluster two and four expressed CD68, a pan-macrophage marker.

Other packages also provide tools for quality control, for example, Monocle, an R package used for building cell trajectories, removes poor-quality cells, and ensures that the distribution of mRNA across all cells is log normal, and fell between upper and lower bounds. Single cells were then classified and counted, using known lineage marker genes, and cells expressing markers of interest were assigned to cell type number one. It was determined that cell type number one were fibroblasts.

This population was assessed to build fibroblasts developmental trajectory. Following this procedure, other methods like quantitative PCR, in situ hybridization, an immunist chemistry should be performed to validate the accuracy of your gene expression results. Single-cell mRNA sequencing has now paved the way for researchers in many different fields to explore the cell-to-cell heterogeneity, and identify functional dynamics within different tissues during homeostasis, and how these may change after injury or in development of disease.