Visual Abstract
Keywords: single cell RNA-sequencing, transcriptomics, kidney, analysis
Abstract
Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.
Background
The first description of single cell gene expression analysis on the basis of next-generation sequencing was in 1992.1 In 2015, encapsulation and barcoding-based analysis was developed.2 During the last 5 years, single cell analysis was democratized and most academic institutions have dedicated core facilities to perform single cell expression, epigenome, or other multimodal analysis. New statistical analytical methods have been developed rapidly. Several analytical platforms have also been developed, such as Seurat,3 which is written in R (https://satijalab.org/seurat/get_started.html) and Scanpy,4 written in Python (https://scanpy.readthedocs.io/en/stable/tutorials.html). Here, we review basic analytical tools and concepts. We focus on 10× Genomics data as they are more commonly used (Figure 1). The review is strongly on the basis of two case study tutorials (https://www.github.com/theislab/single-cell-tutorial and http://scrnaseqcourse.cog.sanger.ac.uk/website/index.html).5,6
Figure 1.
Workflow of renal scRNA-seq data creation and analysis. (A) Steps for preparation of kidney scRNA-seq data. (B) Typical steps for data analysis.
Data Matrix Generation and Quality Control
A key technical advance in single cell analysis has been the development of barcoding, which allows massive parallelization while keeping costs at a minimum. The barcodes are added to the RNA molecules during reverse transcription, allowing the identification of both individual cells and unique molecules. The first analytical step is the generation of a data matrix, which represents a barcode (cell) by transcript database from the raw sequencing files. For 10× Genomics data, CellRanger (Table 1, summarizes tools, methods, and databases as mentioned in the text) is the most commonly used pipeline that includes demultiplexing and alignment of the sequencing reads to the genome, annotating the aligned reads to genes, and quantifying genes. Alternatives include, for example, unique molecular identifier (UMI) tools,7 zUMIs,8 kallisto,9 STAR,10 and STARsolo (https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md).
Table 1.
Overview of software tools, methods, and databases
For a comprehensive overview of bioinformatic tools for scRNA-seq analysis see also https://www.scrna-tools.org/tools. Tools, methods, and databases are ordered as mentioned in the main text and given with a repository URL. RPCA, reciprocal PCA; t-SNE, t-distributed stochastic neighbor embedding; GSEA, gene set enrichment analysis; WGCNA, weighted correlation network analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology.
Each barcode could represent a single cell, a doublet, or an “empty” droplet containing no cells, but ambient RNA. An important issue to mention is that the standard pipeline aligns the sequencing data to the transcriptome, such as the processed mature mRNA. However, single nuclear RNA data or epigenome data (Assay for Transposase Accessible Chromatin by sequencing, ATAC-seq) should be aligned to the full genome, as the nucleus mostly contains pre-mRNA, which includes the intronic regions. Raw read counts usually also filter out genes detected in very few cells, effectively reducing data matrix size.
The next step in the analytical pipeline is quality control (QC), such as identifying the number of counts per barcode, the number of genes per barcode, and the fraction of counts from mitochondrial genes per barcode (Figure 2).11,12 Low gene numbers and a high fraction of mitochondrial reads generally indicates poor-quality cells. Some cells, however, including the kidney proximal and distal convoluted tubule cells, are very rich in mitochondria. Unusually high read and gene counts could represent doublets. Several doublet detection tools now are available including DoubletDecon,13 Scrublet,14 and DoubletFinder (Figure 2).15 However, a critical issue in doublet detection is that transitional cells containing marker genes from, for example, both epithelial and mesenchymal origin might be tagged as doublet, sometimes resulting in false-positive detection. Furthermore, these tools identify only poorly homotypic doublets, namely, doublets formed from transcriptionally similar cells that cluster among their composite cell type singlets in the gene-expression space.
Figure 2.
Quality control. Common QC metrics include (A) the number of unique genes (features) detected in each cell, (B) the total number of molecules detected within a cell, and (C) the percentage of reads that map to the mitochondrial genome. (D) DoubletFinder identifies the doublets in a single cell dataset. Doublets are shown in UMAP plot by pink. (E) SoupX highlights ambient RNA contamination. In this mock dataset, one cell cluster has relatively high ambient RNA contamination (rho=fraction of contamination), whereas other clusters demonstrate very low contamination.
It also important to control for ambient RNA contamination. Ambient RNA is RNA that is present in the single cell solution and is incorporated into the oil droplet during encapsulation. We routinely use SoupX, which estimates ambient RNA contamination from empty droplets (Figure 2).16 An alternative package is CellBender, which removes counts due to ambient RNA molecules and random barcode swapping from (raw) UMI-based single cell RNA sequencing (scRNA-seq) count matrices.17 (preprint) In a typical analysis, we consider multiple QC parameters for filtering, which we use iteratively.
Normalization
Different types and levels of normalization are needed for single cell data (Figure 1). For example, the total sequencing read count number alters the raw count number, so gene counts should be scaled to the overall count depths. A commonly used method assumes each cell had the same initial number of transcripts, simply normalizing data into counts per million. Scran uses pooling-based size factor estimation and linear regression to normalize data, and it is one of the most popular methods18 in addition to simple log normalization used by Seurat.3 Other methods have been developed, such as SCtransform,19 SCnorm,20 and BayNorm.21 After normalization, the data are log(x+1) transformed. It is common to regress out cell cycle–associated variation from the data, and it is included in the standard analysis platform in Seurat or Scanpy. The platform allows the regression of other technical or biologic variation as well.
Batch Effect Correction and Data Integration
Most often, several datasets are generated, necessitating additional batch correction and data integration methods (Figure 3). Larger datasets that contain multiple different experiments and different methods are typically integrated using nonlinear methods. In Seurat there is an option for reference-based integration, which uses the Canonical Correlation Analysis or reciprocal principal component analysis (PCA).22 Scanorama is another popular and well-performing method used in Scanpy.23 Recently, Harmony24 has gained popularity and is rapidly becoming the most commonly used integration method for single cell datasets. In a first step, the PCA-derived embeddings matrix and batch metadata are used for scaling so that each cell unit is given a length parameter. Then, cluster centroids are initialized with regular k-means clustering on the scaled data. Finally, batch effects are removed by iteratively pulling batch-specific centroid to cluster centroid until convergence. Linked Inference of Genomic Experimental Relationships25 identifies shared and dataset-specific factors through integrative non-negative matrix factorization. After normalization by the number of UMIs, gene expression is scaled but not centered. Different integration methods could possibly show different results. In general, we expect the same cell types from different experiments integrate, specifically the control cells should align from multiple experiments. The interested reader is referred to two excellent recent papers by Tran et al. 26 and Chen et al. 27 that provide some of the best evidence in favor of Harmony, Linked Inference of Genomic Experimental Relationships, and Seurat regarding batch effect correction.
Figure 3.
Batch effect correction and data integration. Batch effects are common in single cell datasets, whether they pertain to technical replicates or biologic samples. (A) Two-dimensional visualization of PCs corresponding to two separate batches analyzed in the same dataset. Note that cells clearly separate by individual batches. (B) The embedding value is a surrogate measure of similarity of the PCs. (C) After batch correction, cells overlap in the PC space and (D) embedding values are similar between the two batches, making the batches more comparable within the dataset. (E) Data integration algorithms such as Canonical Correlation Analysis (CCA) use anchors for batch integration. (F and G) The same dataset as in (A–D) is visualized in UMAP-embedded space (F) before and (G) after data integration with CCA.
Visualization and Clustering
The first step of visualization is feature selection when informative genes (1000–5000) are retained and others are filtered out, which is implemented in both Seurat and Scanpy (Figures 4 and 5). Visualization is an attempt to summarize the dataset in a low dimensional space to observe patterns. In general, dimension reduction is achieved by linear and nonlinear methods. PCA is the basis of clustering and trajectory inference and is a linear transformation that preserves the Euclidian distances between the cells in the full PCA. In the commonly used Seurat pipeline, PCA is used in the preprocessing stage. PCs can be projected into technical and biologic covariates to understand their performance. Using a permutation-test–based jackstraw method, the PCA is summarized for the top PCs and the number of PCs selected by the “elbow” heuristic method (Figure 4).
Figure 4.
Visualization. To visualize high-dimensional single cell datasets, dimension reduction is used. (A) The jackstraw method performs association tests between known (empirical) values and estimated (theoretical) latent variables. The dashed line denotes a uniform distribution for each PC, against which the distribution of P values for each PC is compared. P values aid in choosing the number of informative PCs. (B) An elbow plot demonstrates the degree of variance explained by each individual component. Looking for the “elbow” in the plot is usually a good indication of where usefulness of additional PCs is minimal. (C) Heatmaps showing the enrichment of top genes loading on the first three PCs and PCs 25 through 30. Sharpness of separation as a surrogate of discriminatory power is decreasing with increasing PCs. (D–G) A mock kidney dataset that is projected on to tSNE, UMAP, PCA, and Diffusion map spaces to demonstrate the different visualization properties of the respective dimension reduction techniques.
Figure 5.
Basic workflow of single cell analysis in Scanpy. (A) Scanpy uses common metrics, such as the total number of molecules, the number of unique genes, and the percentage of reads mapped to the mitochondrial genome detected in each cell for quality control. (B) Scanpy finds highly variable genes within the normalized data. (C) Scanpy reduces the dimensionality of the data by running PCA, followed by the calculation of cell neighborhood graphs. (D) Leiden graph-clustering method is run on UMAP to separate cells. (E) Scanpy can run a Wilcoxon rank-sum test to calculate a ranking for the highly differential genes within each cluster, which helps identify its cell type.
Single cell data visualization mostly uses other nonlinear dimension-reduction methods, such as t-distributed stochastic neighbor embedding.28 This method is focused on capturing local similarities at the expense of global structure. The Uniform Approximation and Projection (UMAP) method has gained popularity also due to its speed.29 UMAP appears to capture underlying data structure better and can summarize data in more than two dimensions; therefore, it is now most commonly used for single cell data visualization. A key limitation of UMAP and t-distributed stochastic neighbor embedding is that they strongly depend on user-defined parameters, and the results are highly sensitive for these parameters. Most important to note is that neither visualization preserves cell-cell distances, so the resulting embedding should not be used directly by downstream analysis (Figure 4).
Cell clusters, formed on the basis of their similarities of gene expression, are the first immediate results of the analysis. Cell clustering allows inference of cell types by grouping cells on the basis of similarities of gene expression. Clustering is an unsupervised machine learning process that is on the basis of a distance matrix. The default clustering method in the community is the Louvain community detection on a single cell K-nearest neighbor approach. Cells are represented as nodes in the graph. Each cell is connected to its K most similar cells, which are typically obtained using Euclidean distances on the PC-reduced expression space. One critical issue is that the user determines the resolution in Louvain clustering, and the resolution determines the number of clusters or cell types identified in the dataset. We recommend performing subclustering, such as subsetting certain clusters from the initial dataset and then reclustering without the other cell types. This allows the emergence of finer, more granular data structure within the cell types. The Leiden community detection algorithm,30 as incorporated in the Leidenbase package, is an alternative to the Louvain algorithm and is used as default in Monocle trajectory analysis (see below). New clustering methods use neural networks and artificial intelligence, for example Deep Embedding for Single-cell Clustering uses a deep neural network, with network weights and initial clustering obtained from an autoencoder.31
Clusters do not necessarily mean cell types. This is critically important to highlight, because user-defined cluster resolution parameters determine the number of observed clusters. Annotation and clustering are strongly linked. Clustering and annotation is conducted in an iterative fashion, which is time consuming. At present, there is no consensus around optimal clustering parameters. Therefore, multiple versions of clustering and interpretation of the same data are acceptable. Wilcoxon rank-sum test is used to rank genes by difference in expression among groups.
Classic cell type annotations use an external dataset, which is considered ground truth. The growing number of external datasets for kidney cell type annotation include Susztak Lab,32–35 Humphreys Lab,36,37 Tabula Muris,38 Human Cell Atlas,39 Renal Epithelial Cell Ontology webpage,40–42 and ImmGen Consortium.43 Recently, automated cell annotations have been developed, such as Garnett,44 (preprint) SingleR,45 CHETAH,46 and MOANA,47 which offer a more holistic and probabilistic method of cell identity annotation. Marker genes for the same cell types may differ between datasets.
Cell-level Analysis: Cell Fraction Changes, Decomposition, and Trajectory Analysis
Changes of cell fractions (proportions of each cell type in the dataset) show strong association with disease state, which is one of the most simplistic outputs of the single cell analysis. These numbers can provide relative estimates between conditions, but cell fractions inferred from single cell data might be inaccurate, due to bias in cell capture of the single cell library preparation. Also, the proportion of, for example, proximal tubule cells will be higher in samples obtained from the kidney cortex compared with samples taken from the medulla. To infer cell type composition of bulk RNA-seq data, MuSiC48 is a recently developed method for bulk tissue cell type deconvolution with single cell expression data as reference. MuSiC uses weighted non-negative least squares regression to estimate cell type proportions.49 Alternative methods include CIBERSORT,50 BSEQ-sc,51 and BisqueRNA.52 Statistical tests over changes in the proportion of a cell identity cluster between samples are dependent on one another, and, because as the proportion of one cell identity cluster changes, the proportions of all others will have changed as well. Alternatively, a permutation-based statistical testing approach could be used for differential proportion analysis, in which cluster proportions are compared with a random proportion of total cells.53
Cellular diversity cannot sufficiently be described by a discrete classification system such as clustering. Trajectory analysis captures the salient characteristics of cells during transitions, such as during organ development along several time points, or between disease states, cellular history, or topological information. The biologic processes that drive the observed heterogeneity are continuous.54 Thus, capturing transitions between cell identities, branching differentiation processes or gradual, unsynchronized changes in biologic function requires dynamic models of gene expression. Monocle is a machine learning method to reconstruct the sequence of gene expression changes each cell must execute as it transitions from one state to another.55–57 It is on the basis of reverse-graph embedding, a highly scalable nonlinear manifold learning technique. After the method learns the transition path, or trajectory, it places each cell at the correct position along it, which is called pseudotime, a measure of how far a cell has moved through biologic progress. A newer method to analyze cell history is implemented in the recently developed RNA velocity analysis, as in the package velocyto.57 RNA velocity is the time derivative of the gene expression state and can be directly estimated by distinguishing between unspliced and spliced mRNAs in common scRNA-seq protocols.57,58 RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. TradeSeq,59 which is on the basis of a prior method called Slingshot,60 outperforms other methods for simple trajectory analysis. Another useful package is PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points.61,62 Inferred trajectories do not necessarily have to represent a biologic process and further sources of evidence should be collected to interpret a trajectory derived from these methods (Figure 6).
Figure 6.
Downstream analyses. (A) Gene expression values are compared across cell cluster identities to identify similarities or differences between cell clusters. In this example, cells are grouped into respective clusters along the y-axis and gene expression of selected marker genes on the x-axis is shown. Dot size denotes the percentage of cells expressing the marker gene, whereas the color corresponds to the average expression level in each cluster. (B) Heatmap demonstrating SCENIC-derived regulon activity, a measure that is derived by cis-regulatory analysis predicting target genes of low-expression transcription factors. (C) Slingshot-derived trajectory in diffusion maps embedding space. (D) Corresponding Monocle-derived trajectory in UMAP embedding space. The color scale corresponds to increasing pseudotime. (E) Circos plot quantifying the number of cell-cell interactions of kidney cell clusters, as derived by CellphoneDB analysis.
Gene-level Analysis: Differential Expression, Gene Regulatory Network, Driver Pathways, and Cell-Cell Interaction
Differential expression (DE) analysis is performed on uncorrected data by including technical and biologic covariates. Seurat uses different models for DE analysis ( Figure 6). MAST uses a hurdle model to account for drop-out.63 To correlate scRNA-seq dataset information with other phenotypic variables, regression-based models can combine several samples and their associated phenotypic characteristics to correlate gene expression changes in certain cell types (such as proximal tubular cells), with a respective quantitative measured phenotype (for example GFR, albuminuria). Although DE testing tools typically allow the user the flexibility to incorporate confounders, users must be vigilant as to which variables are added to the model. For example, in most single cell experimental set-ups, the sample and condition covariates are confounded, because it is rarely possible to obtain a single sample under multiple conditions. Gene-level analysis can also be combined with gene set enrichment analysis methods, such as gene set enrichment analysis or weighted correlation network analysis.64
To interpret DE results, we typically group genes on the basis of involvement in common biologic processes. Biologic process labels are stored in databases such as MSigDB,65 the Gene Ontology,66,67 or the Kyoto Encyclopedia of Genes and Genomes68 and Reactome69 databases. Although one needs to keep in mind that enrichment for gene expression of some pathway members might not necessarily be associated with pathway activity, enrichment of annotations on the gene list can be tested using a vast array of tools, which have been reviewed and compared elsewhere.70,71
A recent development in the single cell analysis field is the use of paired gene labels to perform ligand–receptor analysis.72 Here, interaction between cell clusters is inferred from the expression of receptors and their cognate ligands. Ligand–receptor pair labels can be obtained from recent databases, such as CellPhoneDB73 or Connectome,74 (preprint) and used to interpret highly expressed genes across clusters using statistical models.75–77
Gene Regulation at Single Cell Resolution
Single nuclei Assay for Transposase Accessible Chromatin by sequencing (snATAC-seq) allows for the analysis of the epigenomic landscape in single cells by profiling chromatin accessibility (Figure 7). Multiple tools have been developed for snATAC-seq analysis. The best known are SnapATAC developed by the Ren Lab,78 (preprint) Signac developed by the Satija Lab,79 (preprint) and ArchR developed by the Greenleaf Lab.80 (preprint) We prefer SnapATAC, which is a nonlinear dimensionality reduction method. After generating the barcode-by-cell matrix in CellRanger, we preprocess the matrix by binarizing the fragments into uniformly sized cell-by-bin matrix using SnapATAC. The QC steps include filtering poor-quality cells or doublets with a read depth that is too low or too high, and removing reads in genomic blacklist regions. Important QC criteria are the enrichment of transcription start sites, fraction of reads in peaks, and the ratio of reads in promoter regions. To identify cell types in heterogeneous tissue, SnapATAC utilizes diffusion maps. The low dimensional embeddings obtained from the diffusion maps are used as inputs into Harmony to remove the batch effect. Clustering is then performed with the Louvain algorithm using selected k values from k Nearest Neighbor algorithm as input. For cluster annotation, cell-gene activity score matrices from selected kidney cell type–specific marker genes were generated. Predefined promoter regions (e.g., from the Ensembl regulatory build) or gene body + 2 kb region were used to integrate all fragments overlapped with gene transcripts. To call peaks from each cell type, all fragments obtained from the same cell types were aggregated to build a pseudo-bulk ATAC dataset and MACS2,81 conducted separately for each cell type. ArchR implements an improved method by calling peaks on independent samples and then retaining reproducible peaks.80 Fisher’s exact test in edgeR tested between cell clusters to reveal differentially accessible regions for each cell type. To identify enriched motifs in different cell types, HOMER82 or chromVAR83 can be used for transcription factor analysis for the snATAC-seq data, although the genetic background will heavily influence which transcription factor motifs are enriched. To study how open chromatin changes are associated with cell differentiation and cell fate decision, Monocle356 for trajectory analysis was used by reducing dimensions using Latent Semantic Indexing and visualizing by UMAP. To understand open chromatin and target gene expression changes, a peak-peak correlation study is conducted by analyzing the coaccessibility of two peaks implemented in Cicero.84 This strategy aggregates similar cells to obtain a set of “metacells” and addresses the issue of sparsity in the snATAC-seq data. Or, peaks can be imputed into GREAT85 to identify nearest genes.
Figure 7.
snATAC analysis pipeline. (A) Steps for preparation of kidney snATAC-sequencing data. (B) Typical steps for snATAC data analysis. (C) Batch effect removal by Harmony. (D) Motif enrichment analysis by Homer. (E) Trajectory analysis by Monocle3. (F) Peak-peak correlation analysis by Cicero. (G) Transcription factor analysis by chromVAR.
Webtools and Datasets
A large number of human and mouse kidney datasets have been generated over the last couple of years. The raw datasets are usually available for download from GEO. Large comprehensive reference human kidney annotation will be available as part of the Human Cell Atlas project39 and the Human BioMolecular Atlas Program.86 The Kidney Precision Medicine Project87 (preprint) aims to generate datasets for a variety of human kidney disease conditions. The Rebuilding a Kidney consortium88 will analyze developing human kidney samples and in vitro differentiated kidney organoids. In addition, several investigators have generated visualization tools for small single experimental datasets. The Humphreys Lab’s KIT site allows quick visualization of their extensive data (http://humphreyslab.com/SingleCell/). The McMahon and Kim laboratories used VisCello to visualize data from developing and adult mice by also comparing differences between male and female animals. Using the VisCello89 platform, our laboratory visualized developing, adult, healthy, and disease mouse model data (http://susztaklab.com/VisCello/)35 and open chromatin epigenome data, which is also available for the same timepoints (http://susztaklab.com/developing_adult_kidney/igv/). These sites do not allow comprehensive analyses and the clustering parameters (which are somewhat subjective) are fixed, but they are extremely useful for look-ups and comparisons. Another important development in data analysis automation is the stand-alone analysis application by the Satija Lab (http://azimuth.satijalab.org/app/azimuth),90 (preprint) which allows projection of individual datasets on to a reference dataset, in which RNA and surface protein expression have been simultaneously measured in single cells. The investigator can upload locally generated datasets and the package automatically performs all steps outlined above and clusters with the human blood reference data.
Spatial and Multiomics Datasets
For the emerging fields of integration of spatial and multiomics datasets, we would like to refer to excellent current reviews91–94 and to Supplemental Materials 1–3.
Conclusions
At present, kidney diseases are grouped on the basis of their temporal course, such as acute or chronic, or histologic descriptions, defined by color and shape homologies developed several centuries ago. These descriptions are unable to capture molecular mechanisms that underlie disease-driving molecular pathways. Therefore, they are not suited for target identification and drug development.95,96 Single cell methods can resolve changes in disease states, allowing novel molecular disease classification and potential target identification.
Disclosures
K. Susztak reports consultancy agreements with AstraZeneca, Bayer, Jnana, and Maze; reports receiving research funding from Bayer, Boehringer Ingelheim, Gilead, GSK, Lilly, Merck, Novo Nordisk, and Regeneron; reports receiving honoraria from Bayer, Jnana, and Maze; and reports being a scientific advisor or membership with the editorial board of Cell Metabolism, the Journal of Clinical Investigation, JASN, Jnana, and Kidney International. All remaining authors have nothing to disclose.
Funding
Work in the Susztak Lab is supported by the National Institutes of Health grants DK076077, DK087635, and DK105821. M.S. Balzer is supported by German Research Foundation (Deutsche Forschungsgemeinschaft) grant BA6205/2-1. We thank the University of Pennsylvania Diabetes Research Center for the use of the Core (P30-DK19525).
Supplementary Material
Footnotes
Published online ahead of print. Publication date available at www.jasn.org.
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2020121742/-/DCSupplemental.
Supplemental Material 1. Spatially resolved single cell datasets.
Supplemental Material 2. Integration of multiomics datasets: epigenome, protein expression, and beyond.
Supplemental Material 3. Supplemental references.
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