Name: droplet barcoding-based single cell transcriptomics of adult mammalian tissues
Uploaded: 2019-01-10T14:30:00.000+00:00
Duration: 10 min 12 s
Description: Watch this Scientific Journal Video about Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues at JoVE.com

We acknowledge the support staff at the UCDNA Services Facility, as well as the Animal Care facility staff at the University of Calgary. We thank Matt Workentine for his bioinformatics support and Jens Durruthy for his technical support. This work was funded by a CIHR grant (R.M. and J.B.), a CIHR New Investigator Award to J.B., and an Alberta Children&#39;s Health Research Institute Fellowship (J.S.).

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This protocol demonstrates how the appropriate preparation of single cells can uncover the transcriptional heterogeneity of thousands of single cells and discriminate functional states or unique cellular identities within a tissue. The protocol does not require fluorescent reporter proteins or transgenic tools and can be applied to the isolation of single cells from various tissues of interest including those from humans; keeping in mind&#160;each tissue is unique and this protocol will require some degree of adjustment/modification.
The diverse and highly dynamic transcriptional programs within cells have emphasized the value of single-cell genomics. Aside from isolating high-quality RNA, a critical sample preparation step necessary for high quality datasets is ensuring that cells are completely released from tissue and that cells are healthy and intact. This is relatively straight forward for collecting cells that are easily released, such as circulating cells or in tissues where cells are loosely retained, such as in lymphoid tissues. But this can be challenging for other adult tissues, due to the highly-developed cellular architecture spanning large distances, surrounding extracellular matrix and the often-rigid cytoskeletal proteins involved in maintaining cell structure. Even with appropriate dissociation techniques for the full release of cells, there is potential that the rigorous and often lengthy processing required would alter mRNA quality and cell integrity. In addition, the high temperatures used for enzyme-assisted dissociation also affect transcriptional signatures29,30. The intent of the protocol is to present quality control checks, using tissues such as the myelinated adult nerve and the extracellular matrix-rich adult skin, to demonstrate how&#160;optimization can help to overcome these obstacles.
A major consideration when designing any scRNA-Seq experiment is the choice of sequencing depth. Sequencing can be highly multiplexed and read depth can vary from being very low using Drop-Seq2 to up to 5 million reads/cell14 using a full-length RNA-Seq method such as Smart-Seq. Most scRNA-Seq experiments can detect moderate-to-high expression transcripts with sequencing as low as 10,000 reads/cell, which is usually sufficient for cell type classification41,42. Shallow sequencing depth is of value to save on sequencing costs when trying to detect rare cell populations across complex tissues where thousands of cells may be needed to confidently ascribe rare populations. But shallow depth sequencing is not adequate when detailed information on gene expression and processes associated with subtle transcriptional signatures is necessary. Currently, it is estimated that the large majority of genes in a cell are detected with 500,000 reads/cell, but this can vary depending on the protocol and tissue type43,44. While full-length transcript sequencing circumvents the need for assembly and can, therefore, detect novel or rare splice variants, sequencing costs often limit scaling such approaches to examine thousands of cells comprising a complex tissue system. In contrast, 3&#39; tagged single-cell libraries such as the ones described in this protocol typically have lower complexity and require shallower sequencing. It is important to note that libraries generated using the described protocol can be sequenced on one of five supported sequencers: 1) NovaSeq, 2) HiSeq 3000/4000, 3) HiSeq 2500 Rapid Run and High Output, 4) NextSeq 500/550, and 5) MiSeq.
An alternative approach to single cell RNA-Seq, that reduces the need for delicate tissue&#160;and cell handling yet maintains some of the benefits of single cell RNA-Seq, is the analysis of RNA from single nuclei45. This approach&#160;allows more rapid processing reducing RNA degradation, and more extreme measures to ensure adequate release of nuclei, and thus likely allows for a more confident capture of the transcriptional profiles representing all cells within a given tissue. This would, of course, only provide a portion of the transcriptional activity present within a given cell, thus depending on what experimental objectives are of interest this approach may or may not be appropriate.
Besides the complete characterization of cellular identities within a given tissue, one of the most valuable analyses for&#160;scRNA-Seq datasets is the assessment of intermediate transcriptional states across &#39;defined&#39; cell populations. These intermediary states can impart insights into the lineage relationships between cells within identified populations, which was&#160;not possible with traditional bulk RNA-Seq approaches. Several scRNA-Seq bioinformatic tools have now been developed to elucidate this. Such tools can assess the processes involved in, for example, cancer cells transitioning to an oncogenic/metastatic state, stem cells maturing into diverse terminal fates or immune cells shuttling between active and quiescent states. Subtle transcriptome differences in cells may also be indicative of lineage biases that, recently developed bioinformatic tools like FateID, can infer47. Since the distinctions between transitioning cells can be difficult to ascertain given the transcriptional differences may be subtle, deeper sequencing may be necessary46. Fortunately, coverage of a shallowly sequenced library can be increased if interested in probing the dataset further by re-running the library on another flow cell.
Taken together, this protocol provides an easy-to-adapt workflow that enables users to transcriptionally profile hundreds to thousands of single-cells within one experiment. The final quality of a scRNA-Seq dataset relies on optimized cell isolation, flow cytometry, cDNA library generation, and interpretation of raw gene-barcode matrices. To this end, this protocol provides a comprehensive overview of all key steps that can be easily modified to enable studies of diverse tissue types.

With the development of high throughput single cell technology1,2 and advancements in user-friendly bioinformatics tools over the last decade3, a new field of high-resolution gene expression analysis has emerged &#8211; single-cell RNA sequencing (scRNA-Seq). The study of single cell gene expression was first developed to identify heterogeneity within defined cell populations, such as in stem cells or cancer cells, or to identify rare populations of cells4,5, which were unattainable using traditional bulk RNA sequencing techniques. Bioinformatic tools have enabled the identification of novel sub-populations (Seurat)2, visualization of the order of cells along a psuedotime space (Monocle)6, definition of active signaling networks within or between populations (SCENIC)7,&#160;prediction of the assembly of single-cells in an artificial 3D space (Seurat, and more)8. With these new and exciting analyses available to the scientific community, scRNA-Seq is fast-becoming the new standard approach for gene expression analysis.
Despite the vast potential of scRNA-Seq, the technical skillsets required to produce a clean dataset and to accurately interpret results can be challenging to newcomers. Here, a basic, but comprehensive protocol, starting from the isolation of single cells from whole primary tissues to visualization and presentation of data for publication is presented (Figure 1). First, the isolation of healthy single cells can be deemed challenging, as different tissues vary in their degree of sensitivity to enzymatic digestion and subsequent mechanical dissociation. This protocol provides guidance in these isolation steps and identifies important quality control checkpoints throughout the process. Second, understanding the compatibility and requirements between single cell technology and next-generation sequencing can be confusing. This protocol provides guidelines to implement a user-friendly, droplet-based single-cell barcoding platform and perform sequencing. Finally, computer programming is an important prerequisite for analyzing single-cell transcriptomic datasets. This protocol provides resources for getting started with the R programming language and provides guidance on implementing two popular scRNA-Seq-specific R packages. Together, this protocol can guide newcomers in performing scRNA-Seq analysis for obtaining clear, interpretable results. This protocol can be adjusted to most tissues in the mouse, and importantly could be modified for use with other organisms, including human tissue. Adjustments depending on the tissue and user will be required.
There are several considerations to keep in mind while following this protocol; including, 1) Following all quality control guidelines in Steps 1 and 2 of this protocol is recommended to ensure a viable single cell suspension of all cells within the sample of interest while ensuring accurate total cell number counts (summarized in Figure 2). Once this is achieved, and if all the optimized conditions are followed, the quality control steps can be dropped (to save time - preserving RNA quality and reducing cell loss). Confirming successful isolation of high viability single cells from the tissue of interest is highly recommend before any downstream processing. 2) Since some cell types are more sensitive than others to stress, excessive dissociation techniques can inadvertently bias the population, therefore confounding downstream analysis. Gentle dissociation without unnecessary cellular shearing and digestion is critical for achieving high cellular yields and an accurate representation of tissue composition. Shear forces occur during the trituration, FACS and resuspension steps. 3) As with any RNA work, is it best to introduce as little additional RNase into the sample as possible during preparation. This will help maintain high-quality RNA. Use ribonuclease inhibitor solutions with rinsing to clean tools and any equipment that is not RNase-free but avoid DEPC-treated products. 4) Perform preparations as quickly as possible. This will help maintain high-quality RNA and reduce cell death. Depending on the tissue dissection length and animal number, consider starting multiple dissections/preparations at the same time. 5) Prepare cells on ice when possible to maintain high quality RNA, reduce cell death, and slow cell signaling and transcriptional activity. Albeit, ice-cold processing is ideal for most cell types, some cell types (e.g., neutrophils) perform better when processed at room temperature. 6) Avoid calcium, magnesium, EDTA, and DEPC-treated products during cell preparation.

<table><tbody><tr><td>&lt;strong&gt;Products&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>RNAse out</td><td>Biosciences</td><td>786-70</td><td></td></tr><tr><td>Pentobarbital sodium</td><td>Euthanyl</td><td></td><td>50mg/kg</td></tr><tr><td>HBSS</td><td>Gibco</td><td>14175-095</td><td></td></tr><tr><td>Dispase 5U/ml</td><td>StemCell Technologies</td><td>7913</td><td>5 mg/ml</td></tr><tr><td>Collagenase-4 125 CDU/mg</td><td>Sigma-Aldrich</td><td>C5138</td><td>2 mg/ml</td></tr><tr><td>DNAse</td><td>Sigma-Aldrich</td><td>DN25</td><td>10mg/ml</td></tr><tr><td>BSA</td><td>Sigma-Aldrich</td><td>A7906</td><td></td></tr><tr><td>15 ml Narrow bottom tube VWR&amp;reg; High-Performance Centrifuge Tubes</td><td>VWR</td><td>89039-666</td><td></td></tr><tr><td>Sytox Orange Viability Dye</td><td>Molecular Probes</td><td>11320972</td><td>1.3 nM/&amp;micro;l</td></tr><tr><td>Nuc Blue Live ReadyProbes</td><td>Invitrogen</td><td>R37605</td><td></td></tr><tr><td>Agilent 2100 Bioanalyzer High senitivity DNA Reagents</td><td>Agilent</td><td>5067-4626</td><td></td></tr><tr><td>Kapa DNA Quantification Kit</td><td>Kapa Biosystems</td><td>KK4844</td><td></td></tr><tr><td>Chromium Single Cell 3&#039; reagents</td><td>10x Genomics</td><td></td><td></td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>BD FACSAria III</td><td>BD Biosciences</td><td></td><td></td></tr><tr><td>Agilent 2100 Bioanalyzer Platform</td><td>Agilent</td><td></td><td></td></tr><tr><td>Illumina&amp;reg; HiSeq 4000</td><td>Illumina</td><td></td><td></td></tr><tr><td>Illumina&amp;reg; MiSeq SR50</td><td>Illumina</td><td></td><td></td></tr><tr><td>10X Controller + accessories</td><td>10x Genomics</td><td></td><td></td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>The Cell Ranger</td><td>10x GENOMICS</td><td>support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome</td><td></td></tr><tr><td>Loupe Cell Browser</td><td>10x GENOMICS</td><td>support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest</td><td></td></tr><tr><td>R</td><td></td><td>https://anaconda.org/r/r</td><td></td></tr></tbody></table>

droplet barcoding-based single cell transcriptomics of adult mammalian tissues

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying cell composition, discrimination of functional states, and molecular pathways underlying observed tissue functions and animal behaviors. However, the isolation of intact, healthy single cells from adult mammalian tissues for subsequent downstream single cell molecular analysis can be challenging. This protocol describes the general processes and quality control checks necessary to obtain high-quality adult single cell preparations from the nervous system or skin that enabled subsequent unbiased single cell RNA sequencing and analysis. Guidelines for downstream bioinformatic analysis are also provided.

The repertoire of open source packages designed to analyze scRNA-Seq datasets has increased dramatically40&#160;with the majority of these packages use&#160;R-based languages3. Here, representative results using two of these packages are presented: assessing unsupervised grouping of single cells based on gene expression, and ordering single cells along a trajectory in order to resolve cell heterogeneity and deconstruct biological processes.
Figure 4 illustrates the use of Seurat for pre-processing quality checks and downstream bioinformatics analysis. First, filtration and removal of deviant cells from analysis is essential for quality checking. This was done using violin (Figure 4a) and scatter plots (Figure 4b) to visualize the percentage of mitochondrial genes, number of genes (nGene), and number of UMI (nUMI) to identify cell doublets and outliers. Any cell with a clear outlier number of genes, UMI, or percentage of mitochondrial genes was removed using Seurat&#39;s FilterCells function. Since Seurat uses principal component (PC) analysis scores to clusters cells, determining statistically significant PCs to include is a critical step. Elbow plots (Figure 4c) were used for PC selection, in which PCs beyond the plateau of the &#39;standard deviation of PC&#39; axis were excluded. The resolution of clustering was also manipulated demonstrating that the number of clusters can be changed, ranging from 0.4 (low resolution leading to fewer cell clusters, Figure 4d) to 4 (high resolution leading to higher cell clusters, Figure 4e). At low resolution, it is likely that each cluster represents a defined cell type, whereas at high resolution this may also represent subtypes or transitional states of a cell population. In this instance, low-resolution cluster settings were used for further analyzing expression heatmaps (using Seurat&#39;s DoHeatmap function) to identify the most highly expressed genes in a given cluster (Figure 4f). In this instance, the most highly expressed genes were identified by assessing differential expression in a given cluster versus all other clusters combined, demonstrating that each cluster was uniquely represented by defined genes. Additionally, individual candidate genes can be visualized on tSNE plots using Seurat&#39;s FeaturePlot function (Figure 4g). This allowed for deciphering whether there were clusters that represented macrophages. Using FeaturePlot, we found that both cluster 2 and 4 were expressing Cd68 - a pan-macrophage marker.
The Monocle package was used for corroborating cell clusters identified in Seurat, and for building cell trajectories, or pseudotemporal ordering, to recapitulate biological processes (Figure 5). Pseudotemporal ordering can be used for samples where single-cell expression profiles are expected to follow a biological time course. Cells can be ordered along a pseudotemporal continuum to resolve intermediate states, bifurcation points of two alternative cell fates, and identify gene signatures underlying acquisition of each fate. Firstly, similar to Seurat&#39;s filtration, poor-quality cells were removed such that the distribution of mRNA across all cells was log normal and fell between upper and lower bounds as identified in Figure 5a. Then, using Monocle&#39;s newCellTypeHierarchy function, single cells were classified and counted using known lineage marker genes (Figure 5b, 5c). For example, cells expressing PDGF receptor alpha or Fibroblast Specific Protein 1 were assigned to Cell Type #1 to create a criterion for defining fibroblasts. Next, this population (Cell Type #1) was assessed to decipher fibroblast trajectories. To do this, Monocle&#39;s differential GeneTest function was utilized, which compared the cells representing the extreme states within the population and found differential genes for ordering the remaining cells in the population (Figure 5d). By applying manifold learning methods (a type of non-linear dimensionality reduction) across all cells, a coordinate along a pseudotemporal path was assigned. This trajectory was then visualized by cell state (Figure 5e) and pseudotime (Figure 5f).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58709/58709fig1v3.jpg" /> 
Figure 1: Flow chart. Steps from whole animal preparation to analyzing single cell RNA-Seq datasets to submitting final datasets to a publicly available repository. Gel beads in Emulsion (GEMs) refer to beads with barcoded oligonucleotides which encapsulate thousands of single cells.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58709/58709fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58709/58709fig2v3.jpg" /> 
Figure 2: Creating viable single cell suspension from nerve tissue. (a) Cartoon overview of quality control checks. (b) Cells and debris with cells still incorporated in debris (red arrows). (c) Cells released from debris (red arrows). (d) Cell isolation by FACS. P0: debris fraction; P1: cell-like fraction; P3: exclusion of duplets; P4: viability dye (Sytox Orange) negative fraction. (e) No viability dye control. (f) Image of P0 fraction representing isolated debris. (g) Image of P4 fraction representing isolated viable cells (red arrows).&#160;(b)(c)(f) and (g) had nuclear dye added 20 minutes before imaging. Scale Bars: 80 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58709/58709fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58709/58709fig3v3.jpg" /> 
Figure 3: Shallow sequencing predicts the number of recovered cells in 10X processed samples. (a) An example (Sample 1.6) of MiSeq-generated csv listing cell barcodes and its corresponding UMI counts as determined by confidently mapped reads. (b) Barcode rank plot for Sample 1.6 shows one significant drop in UMI count as a function of cell barcodes. The dashed and solid lines represent the cutoff between cells and background as determined by visual inspection. (c) Cell barcodes observed&#160;using the Cell Ranger pipeline post-HiSeq reveals shallow sequencing accurately approximated the number of cells for Sample 1.6. (d) An example of a flow-cell set-up based on shallow sequencing derived cell estimates. For Sample 1.6, since shallow sequencing predicted 3480 cells, 1.17 lanes were assigned to ensure &#62;100,000 reads per cell sequencing coverage in HiSeq. Note: All lanes must add to 100%. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58709/58709fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58709/58709fig4v3.jpg" /> 
Figure 4: Quality control and bioinformatics of single-cell RNA-Seq dataset using Seurat R package. (a) Plots of quality control metrics which include number of genes, number of unique molecular identifiers (UMIs), and the percentage of transcripts mapping to the mitochondrial genome. (b) Sample gene plots detecting cells with deviant levels of mitochondrial transcripts and UMIs. (c) Sample elbow plot used for ad hoc determination of statistically significant PCs. The dashed and dot-dashed lines represent the cutoff where a clear &#34;elbow&#34; becomes apparent in the graph. PC dimensions before this elbow are&#160;included in downstream analysis. (d, e) Graph-based cell clusters visualized at two different resolutions in a low-dimensional space using a tSNE plot. (f) Top marker genes (yellow) for each cluster visualized on an expression heatmap using Seurat&#39;s DoHeatmap function. (g) Visualizing marker expression of, for example, Cd68 gene representing macrophages (purple) using Seurat&#39;s FeaturePlot function. This suggests that cluster 2 and 4 (in panel d) of this dataset represents macrophages. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58709/58709fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58709/58709fig5v3.jpg" /> 
Figure 5: Cell categorization and ordering along peudotemporal trajectory using Monocle toolkit. (a) Inspecting the distribution of mRNA (inferred from UMI counts) across all cells in a sample. Only cells with mRNA between 0 - ~20,000 were used for downstream analysis. (b, c) Assigning and counting cell types based on known lineage cell markers. For example, cells expressing PDGF receptor alpha or Fibroblast Specific Protein 1 were assigned to Cell Type #1 representing pan-fibroblasts using Monocle&#39;s newCellTypeHierarchy function. Number of different cell types can be visualized as a pie chart (b) and as a table (c). (d) Using Cell Type #1 (fibroblasts) as an example, the genes used for ordering cells can be visualized using a scatter plot that demonstrates gene dispersion vs. mean expression. The red curve shows the cutoff for genes used for ordering calculated by the mean-variance model using Monocle&#39;s estimateDispersions function. Genes that meet this cutoff were used for downstream pseudotime ordering. (e, f) Visualization of cell trajectories in a reduced two-dimensional space colored by cell&#39;s &#34;State&#34; (e), and by Monocle-assigned &#34;Pseudotime&#34; (f). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58709/58709fig5v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues at JoVE.com

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

This protocol describes the general processes and quality control checks necessary for preparing healthy adult mammalian single cells for droplet-based, high throughput single cell RNA-Seq preparations. Sequencing parameters, read alignment, and downstream single-cell bioinformatic analysis are also provided.

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying ...

droplet-barcoding-based-single-cell-transcriptomics-adult-mammalian

Nanyang Technological University

Research

JoVE Journal

Biology

18.4K Views.  University of Calgary. Deze methode kan helpen bij het beantwoorden van belangrijke vragen op het gebied van microbiologie, zoals het begrijpen van genexpressie veranderingen in duizenden enkele cellen na een blessure of een ziekte. Het belangrijkste voordeel van deze techniek is dat het ons in staat stelt om transcripto's van individuele cellen binnen een complex weefsel te ontleden en de functionele dynamiek binnen een bepaalde populatie beter te waarderen. Het voordeel van ons protocol is dat het uitgebreide met...

Video: Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Transcriptome Analysis of Single Cells

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in culture.1,2,3 In the case of the brain, regions such as the hippocampus contain a complex arrangement of different cell types, each with distinct mRNA profiles. The ability to harvest single cells allows for a more in depth investigation into the molecular differences between and within cell populations. We describe a simple and rapid method for harvesting cells for further processing. Pipettes often used in electrophysiology are utilized to isolate (using aspiration) a cell of interest and conveniently deposit it into an Eppendorf tube for further processing with any number of molecular biology techniques. Our protocol can be modified for the harvest of dendrites from cell culture or even individual cells from acute slices.
We also describe the aRNA amplification method as a major downstream application of single cell isolations. This method was developed previously by our lab as an alternative to other gene expression analysis techniques such as reverse-transcription or real-time polymerase chain reaction (PCR).4,5,6,7,8 This technique provides for linear amplification of the polyadenylated RNA beginning with only femtograms of material and resulting in microgram amounts of antisense RNA. The linearly amplified material provides a more accurate estimation than PCR exponential amplification of the relative abundance of components of the transcriptome of the isolated cell. The basic procedure consists of two rounds of amplification. Briefly, a T7 RNA polymerase promoter site is incorporated into double stranded cDNA created from the mRNA transcripts. An overnight in vitro transcription (IVT) reaction is then performed in which T7 RNA polymerase produces many antisense transcripts from the double stranded cDNA. The second round repeats this process but with some technical differences since the starting material is antisense RNA. It is standard to repeat the second round, resulting in three rounds of amplification. Often, the third round in vitro transcription reaction is performed using biotinylated nucleoside triphosphates so that the antisense RNA produced can be hybridized and detected on a microarray.7,8

In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in ...

Neuroscience

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Genetics

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular phenotype diversity. Single-cell experiments investigating the network dynamics of biological systems demonstrate cellular and tissue responses to various conditions at biologically meaningful resolution. Herein, we explain our methods for gathering single cells from anatomically specific locations and accurately measuring a subset of their gene expression profiles. We combine laser capture microdissection (LCM) with microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). We also use this microfluidic RT-qPCR platform to measure the microbial abundance of gut contents.

This protocol explains how to collect single neurons, microglia, and astrocytes from the central nucleus of the amygdala with high accuracy and anatomic specificity using laser capture microdissection. Additionally, we explain our use of microfluidic RT-qPCR to measure a subset of the transcriptome of these cells.

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular ...

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

Cancer Research

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental biology. It has been successfully used to identify rare cell populations, discover novel marker genes, and decode spatial and temporal developmental information. The single cell method has also evolved from the microfluidic based Fluidigm C1 technology to the droplet-based solutions in the last two to three years. Here we used the heart as an example to demonstrate how to profile the mouse embryonic tissue cells using the droplet based scRNA-Seq method. In addition, we have integrated two strategies into the workflow to profile multiple samples in a single experiment. Using one of the integrated methods, we have simultaneously profiled more than 9,000 cells from eight heart samples. These methods will be valuable to the developmental biology field by providing a cost-effective way to simultaneously profile single cells from different genetic backgrounds, developmental stages, or anatomical locations.

Here we presented a multiplexed single cell mRNA sequencing method to profile gene expression in mouse embryonic tissues. The droplet-based single cell mRNA sequencing (scRNA-Seq) method in combination with multiplexing strategies can profile single cells from multiple samples simultaneously, which significantly reduces reagent costs and minimizes experimental batch effects.

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental ...

Developmental Biology

Tumor Dissociation Protocol for Single-Cell RNA Sequencing Analysis

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Human tumor samples hold a plethora of information about their microenvironment and immune repertoire. Effective dissociation of human tissue samples into viable cell suspensions is a required input for the single-cell RNA sequencing (scRNAseq) pipeline. Unlike bulk RNA sequencing approaches, scRNAseq enables us to infer the transcriptional heterogeneity in tumor specimens at the single-cell level. Incorporating this approach in recent years has led to many discoveries, such as identifying immune and tumor cellular states and programs associated with clinical responses to immunotherapies and other types of treatments. Moreover, single-cell technologies applied to dissociated tissues can be used to identify accessible chromatin regions T and B cell receptor repertoire, and the expression of proteins, using DNA barcoded antibodies (CITEseq).
The viability and quality of the dissociated sample are critical variables when using these technologies, as these can dramatically affect the cross-contamination of single cells with ambient RNA, the quality of the data, and interpretation. Moreover, long dissociation protocols can lead to the elimination of sensitive cell populations and the upregulation of a stress response gene signature. To overcome these limitations, we devised a rapid universal dissociation protocol, which has been validated on multiple types of human and murine tumors. The process begins with mechanical and enzymatic dissociation, followed by filtration, red blood lysis, and live dead enrichment, suitable for samples with a low input of cells (e.g., needle core biopsies). This protocol ensures a clean and viable single-cell suspension paramount to the successful generation of Gel Bead-In Emulsions (GEMs), barcoding, and sequencing.

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

This protocol outlines the procedure for rapidly dissociating human and mouse tumor samples for single-cell RNA sequencing.

Human tumor samples hold a plethora of information about their microenvironment and immune repertoire. Effective dissociation of human tissue samples into ...

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...

Bioengineering

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive human diseases. Considerable advances have been made to uncover the molecular signatures of homeostatic microglia as well as alterations of their gene expression in response to environmental stimuli. With the advent and maturation of single-cell genomic methodologies, it is increasingly recognized that heterogenous microglia may underlie the diverse roles they play in different developmental and pathological conditions. Further dissection of such heterogeneity can be achieved through efficient isolation of microglia from a given region of interest, followed by sensitive profiling of individual cells. Here, we provide a detailed protocol for the rapid isolation of microglia from different brain regions in a single adult mouse brain hemisphere. We also demonstrate how to use these sorted microglia for plate-based deep single-cell RNA sequencing. We discuss the adaptability of this method to other scenarios and provide guidelines for improving the system to accommodate large-scale studies.

We provide a protocol for isolation of microglia from different dissected regions of an adult mouse brain hemisphere, followed by semi-automated library preparation for deep single-cell RNA sequencing of full-length transcriptomes. This method will help to elucidate functional heterogeneity of microglia in health and disease.

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive ...

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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