Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Abstract

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

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published: 22 April 2021
doi: 10.3389/fnins.2021.591122
Edited by:
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Icahn School of Medicine at Mount
Sinai, United States
Reviewed by:
Ankush Sharma,
University of Oslo, Norway
Xun Zhu,
University of Hawaii Cancer Center,
United States
*Correspondence:
Arif Tasleem Jan
atasleem@bgsbu.ac.in
Mohammed Asger
masgerghazi@bgsbu.ac.in
†These authors have contributed
equally to this work
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Frontiers in Neuroscience
Received: 03 August 2020
Accepted: 19 March 2021
Published: 22 April 2021
Citation:
Adil A, Kumar V, Jan AT and
Asger M (2021) Single-Cell
Transcriptomics: Current Methods
and Challenges in Data Acquisition
and Analysis.
Front. Neurosci. 15:591122.
doi: 10.3389/fnins.2021.591122
Single-Cell Transcriptomics: Current
Methods and Challenges in Data
Acquisition and Analysis
Asif Adil1†, Vijay Kumar2†, Arif Tasleem Jan3*and Mohammed Asger1*
1Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, India, 2Department of Biotechnology,
Yeungnam University, Gyeongsan, South Korea, 3School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah
University, Rajouri, India
Rapid cost drops and advancements in next-generation sequencing have made
profiling of cells at individual level a conventional practice in scientific laboratories
worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has
an immense potential of uncovering the novel basis of human life. The well-known
heterogeneity of cells at the individual level can be better studied by single-cell
transcriptomics. Proper downstream analysis of this data will provide new insights
into the scientific communities. However, due to low starting materials, the SC-
RNA-seq data face various computational challenges: normalization, differential gene
expression analysis, dimensionality reduction, etc. Additionally, new methods like 10×
Chromium can profile millions of cells in parallel, which creates a considerable amount
of data. Thus, single-cell data handling is another big challenge. This paper reviews
the single-cell sequencing methods, library preparation, and data generation. We
highlight some of the main computational challenges that require to be addressed
by introducing new bioinformatics algorithms and tools for analysis. We also show
single-cell transcriptomics data as a big data problem.
Keywords: single-cell transcriptomics, Sc-RNA-seq, big data, single-cell big data, normalization, single-cell
analysis, downstream analysis
INTRODUCTION
The human body exhibits a diverse range of cells that undergo transit from one state to another in
life (development, disease, and regeneration). Though derived from the same zygote, the cell, with
its types and states, is greatly influenced by the internal processes and external factors (Song et al.,
2019). In its progression through proliferation and the differentiation states to generate multiple cell
types for organ formation, complex heterogeneities in the cellular architecture are observed. The
cellular heterogeneity in terms of morphology, function, and gene expression profiles lie between
various tissues, but has also been observed among the same cell types that allow them to perform
different roles. Dysregulation in any particular cell type (irrespective of tissues, organs, and organ-
system) influences the entire system that progresses to disorders and even severe diseases like cancer
(Macaulay et al., 2017).
Recent technological advancements have enabled biologists to profile cells at individual levels on
a variety of omics layers (genomes, transcriptomes, epigenomes, and proteomes) (Hu et al., 2016);
among these, single cell (SC) transcriptomics is widely studied. The cells of a human body, being
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heterogeneous, often show a drastic variation at the individual
level (Wang and Bodovitz, 2010;Xin et al., 2016). The SC
experiments were found much conclusive compared with bulk
cell sequencing that involves sequencing in bulk (assuming cells
of a particular type are identical) and estimating an average of
expressions. The SC transcriptomics was awarded as method
of the year by Nature in 2013 (Xue et al., 2015). With the
advent of next-generation sequencing, it becomes possible to
develop sequencing methods to probe the dynamics of the
genome and variations thereof. Of them, RNA sequencing (RNA-
seq)-mediated transcriptomic profiling revealed information of
novel RNA species that deepened our understanding of the
transcriptome dynamics (Tang et al., 2009;Wang et al., 2009;
Ozsolak and Milos, 2011). Lately, these sequencing approaches
have been extended to study intra-population heterogeneity of
SCs (Wills et al., 2013), whereby it enabled the study of cell
fates, their transition to different subtypes, and the dynamics of
gene expression masked in bulk population studies (Altschuler
and Wu, 2010;Trapnell et al., 2014). Compared with bulk
sequencing, where libraries are prepared from thousands of cells,
libraries for single-cell RNA sequencing (SC-RNA-seq) are cell-
specific towards investigating cellular functionalities of DNA
and RNA in different cellular subsets (Gross et al., 2015;Xue
et al., 2015). Though SC-RNA-seq has revealed novel findings in
different cellular backgrounds, it poses specific challenges: Pre-
processing of the SC-RNA-seq data is majorly different from
bulk RNA-seq, stricter protocols for library preparation and low
starting material. Another challenge is the lack of analytical
approaches required to accommodate large datasets generated
during SC-RNA-seq experiments. Keeping this in view, we
investigated the methods adopted in SC experiments, sequencing
approaches, and challenges thereof, as part of realizing the goal of
precision medicine.
SINGLE-CELL RNA SEQUENCE
PROFILING TECHNIQUES
With the first report in 2009, a surge in the SC transcriptomics
methods capable of sequencing millions of cells with great
accuracy and viability in a short span of time was observed (Tang
et al., 2009). These methods are generally different from each
other in terms of cell isolation methods, cell lysis procedure,
amplification process, cDNA generation, transcript coverage, and
Unique Molecular Identifier (UMI) tagging (at either 30end or
50end). The most critical distinction in the SC-RNA profiling
techniques is that some provide full-length transcript coverage
and some only partially sequence from either 30end or 50end of
the transcript (Chen et al., 2019). Table 1 highlights widely used
SC-RNA profiling methods in terms of different properties.
OPTIMAL METHODOLOGY OF
SINGLE-CELL TRANSCRIPTOMICS
Of the various sequencing platforms, Drop-seq, InDrop, and 10×
Chromium are well-known platforms for sequencing hundreds
TABLE 1 | Current SC-RNA-seq profiling techniques, based on transcript
coverage and UMI insertion possibility.
Method Length of
transcript
UMI insertion
possibility
References
ScNaUmi-seq Full length Yes Lebrigand et al., 2020
MATQ-seq Full length Yes Sheng and Zong, 2019
10×Chromium 30end Yes Zheng et al., 2017
CEL-seq2 30end Yes Hashimshony et al., 2016
Drop-seq 30end Yes Macosko et al., 2015
InDrop 30end Yes Klein et al., 2015
Smart-seq2 Full length No Picelli et al., 2014
STRT-seq 50end Yes Islam et al., 2014
MARS-seq 30end Yes Jaitin et al., 2014
Smart-seq Full length No Ramskold et al., 2013
SC-RNA-seq, single-cell RNA sequencing; UMI, Unique Molecular Identifier.
and thousands of cells in an unbiased manner (Kulkarni et al.,
2019). In SC transcriptomics, each cell needs to be isolated from
its originating tissue. The Droplet-based techniques, which at the
core use microfluidics to attach cells with beads containing a
unique barcode, are widely incorporated to separate cells. The
performance criteria for isolation methods are based on three
parameters: throughput, purity, and recovery (Tomlinson et al.,
2013;Gross et al., 2015). Throughput indicates the number of cells
that can be isolated per unit time, purity refers to the number
of cells collected after separation from tissue, and recovery is
the final amount of the target cells, in hand, after separation.
The morphological complexity of cells like those of the central
nervous system (CNS) makes the separation process a little
challenging. The segregation process exposes them to specific
environmental, chemical, and harsh dissociation steps that often
bias data analysis (Kulkarni et al., 2019). The dissociation of intact
cells from a frozen postmortem tissue is also challenging, as cell
membranes are prone to damage from mechanical and physical
stresses as part of the freeze–thaw process (McGann et al., 1988).
Though each cell separation methods currently in use shows an
advantage different for the above three parameters, it becomes
imperative to select a well-suited method for the isolation of a cell.
The current methodology of cell separation is broadly categorized
into two groups based on (1) cellular properties like cell density,
cell shape, cell size, etc., and (2) biological characteristics of a
cell that comprises affinity methods (Tomlinson et al., 2013).
Tables 2,3show some of the widely used methods concerning the
operational mode, throughput, advantages, and disadvantages.
Though high-throughput SC-RNA approaches such as 10×
Chromium allows analysis of cells in an unbiased manner,
it lacks in providing an in-depth information on sequence
diversity, splicing, and chimeric transcripts generated in the
process (Lebrigand et al., 2020). The problem is overcome
by performing Nanopore long-read sequencing [using a cell
barcode (cellBC) assignment to long reads] to obtain a full-
length sequence corresponding to the 10×Chromium system’s
data. As SC library preparation requires robust amplification,
chimeric cDNA generation and amplification bias issues are
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TABLE 2 | Commonly used methods for cell isolation based on biological characteristics.
Technique Mode of operation Throughput Advantages Disadvantages References
Fluorescence-
activated cell
sorting
Automatic High High rate of rare cell
sorting, high purity
Cost-intensive, high skills
required
Herzenberg et al., 2002;
Gross et al., 2015
Magnetic-activated
cell separation
Automatic High High purity, cost-efficient Cell capture is non-specific Schmitz et al., 1994;Welzel
et al., 2015
TABLE 3 | Commonly used methods for cell isolation on the bases of physical characteristics.
Technique Mode of
operation
Throughput Advantages Disadvantages References
Microfluidic cell separation Automatic High Works with low starting
materials, amplification
integration
High skills required,
dissociated cells
Wyatt Shields et al., 2015
Micromanipulation manual
cell picking
Manual Low More control over cell, live
and intact cell separation
Laborious, high skills
needed
Citri et al., 2012
Laser-capture
microdissection
Manual Low Undamaged live cell
capture, highly advanced
Too complex to operate,
threat of contamination by
neighboring cells
Espina et al., 2006
Density gradient
centrifugation
Manual Low Cost-efficient Too slow and laborious, low
yield
Beakke, 1951
currently addressed by employing a 30or 50end tag-
based approach (Trombetta et al., 2015;Natarajan et al., 2019).
The sequence length method determines the quality of alignment
across the total length of a gene, while tag-based methods
integrate UMIs at either 30end or 50end of the transcript
(Kivioja et al., 2012;Smith et al., 2017;Sena et al., 2018).
The UMI addition makes it easier to identify and quantify the
individual transcripts by eliminating PCR artifacts and minimizes
false annotation of PCR-generated chimeric cDNAs as novel
transcripts. The full length-based methodology provides an all-
inclusive coverage of the reads, yet they contribute a bias for long
genes, as the genes with shorter length are often missed (Phipson
et al., 2017). Additionally, the higher sequencing error rate of
long-read sequencers and UMI problems account for a serious
issue pertaining to these platforms (Gupta et al., 2018;Lebrigand
et al., 2020;Volden and Vollmers, 2020). Despite this, the Tag-
based methods have shown a fair dominance in SC-RNA library
preparation for quantifying the transcripts in SC analysis when
cell number is large (Figure 1).
QUANTIFICATION OF EXPRESSION AND
QUALITY CONTROL
Like bulk RNA-seq, the transcripts in SC-RNA are sequenced
into reads that generate the raw fastq data. The quality of the
sequence reads generated in a sequencing method is considered
an important quality indicator of SC-RNA-seq data. As the
alignment of the transcript reads for SC-RNA-seq is same as
bulk RNA-seq, the methods and tools used for the gene or
transcript quantification for bulk RNA-seq can also be used
for quantifying transcripts generated by SC-RNA-seq (Li and
Homer, 2010;Fonseca et al., 2012). HISAT2 (Kim et al., 2019),
TopHat2 (Kim et al., 2013), and STAR (Dobin et al., 2013)
are currently the most popular alignment tools, which can
map billions of reads to a reference transcriptome with greater
accuracy and high speed. Transcriptome reconstruction can
be either de novo (for samples lacking reference genome) or
reference based, also called genome-guided assembly (Chen et al.,
2011). However, the former technique sometimes lacks accuracy
in comparison with the reference-based assembly approach
(Garber et al., 2011). For SC-RNA-seq methods that generate
data on a whole-transcriptome basis, Smart-seq2 (Picelli et al.,
2014) and MATQ-seq (Sheng and Zong, 2019) use Cufflinks,
RSEM, Stringtie, etc., for the quantification of transcripts, while
methods that incorporate the 30end UMI tagging [like Drop-seq
(Macosko et al., 2015), InDrop (Klein et al., 2015), MARS-seq
(Jaitin et al., 2014), etc.] require specific algorithms to generate
the expression count for the transcript. Another efficient tool
for the UMI-based methods was developed by Huang and
Sanguinetti (2017) for calculating the expression count of SCs
accurately. Table 4 provides information about the current tools
for read alignment and expression quantification. The SC-RNA-
seq exhibits certain limitations, which results in higher technical
noise (Kolodziejczyk et al., 2015). In SC-RNA-seq data, many
transcripts appear to be lost during reverse transcription due to
the small number and low capture efficiency of RNA molecules
in SCs (Saliba et al., 2014). Consequently, in one cell, some
transcripts are highly expressed but are missing in another cell.
This pattern is described as a “dropout” event. It has been
reported that even the most sensitive protocol for SC-RNA-seq
fails to detect some of the transcripts as part of Dropout events
(Haque et al., 2017). When the cells are dissociated or isolated,
a certain number of cells become dead or get destroyed. The
SC-RNA-seq methods generate low-quality data from these cells
(Ilicic et al., 2016). After alignment and quantification of the
transcripts, the quality control check of cells is necessary to
remove low-quality cells for an accurate downstream analysis.
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FIGURE 1 | Single-cell analysis in disease and health. Starting from the dissociation of target cells from the target tissue/organ, their isolation based on
fluorescence-activated cell sorting (FACS) or other microfluidic techniques to RNA extraction. The RNA extraction is followed by cDNA synthesis by reverse
transcriptase, followed by amplification and sequencing. From the sequencing, the reads are aligned and subjected to quantification that results in a quantification
matrix or Gene Expression Matrix.
TABLE 4 | Widely used tools for read alignment and expression quantification.
Tool Function Feature URL References
Salmon Expression quantification k-mer-based read quantification https://combine-lab.github.
io/salmon/
Patro et al., 2017
Kallisto Expression quantification Pseudoalignment-based rapid read
determination
https://pachterlab.github.
io/kallisto/
Bray et al., 2016
StringTIe Expression quantification Alignment based, splice aware https://ccb.jhu.edu/
software/stringtie/
Pertea et al., 2015
HISAT2 Read alignment Alignment based, splice aware https://daehwankimlab.github.io/
hisat2/
Sirén et al., 2014
Sailfish Expression quantification k-mer-based read quantification http://www.cs.cmu.edu/
~{}ckingsf/software/sailfish/
Patro et al., 2014
RNA-Skim Expression quantification Sig-mer (a type of k-mer)-based
read quantification of transcripts
http:
//www.csbio.unc.edu/rs/
Zhang and Wang,
2014
TopHat2 Read alignment Alignment based, splice aware https:
//ccb.jhu.edu/software/
tophat/index.shtml
Kim et al., 2013
STAR Read alignment Alignment based, splice aware https://github.com/
alexdobin/STAR
Dobin et al., 2013
Bowtie Read alignment Maintains quality threshold, hence
less no. of mismatches
http:
//bowtie-bio.sourceforge.
net/index.shtml
Langmead et al.,
2009
Cufflinks Expression quantification Alignment based, splice aware https://github.com/cole-
trapnell-lab/cufflinks
Trapnell et al., 2010
CHALLENGES IMPEDING SINGLE-CELL
RNA SEQUENCE DATA ANALYSIS
Though SC-RNA-seq has deepened our understanding of the
cellular heterogeneity and molecular basis of life, it is impeded
by several technical and computational challenges. The foremost
among them is that its datasets exhibit a considerable amount of
noise attributed to meager starting materials that often causes
faulty downstream analysis and erroneous results (Brennecke
et al., 2013). The SC-RNA-seq data analysis is performed as subtle
execution in computational steps; read alignment, expression
count generation, cell quality control, normalizing the data,
and then further downstream analysis including SC clustering,
differential gene expression (DGE), pseudo-temporal analysis,
etc. In addition to low starting materials, the technical noise
in the datasets is contributed by various factors, like batch
effects (Haghverdi et al., 2018) and the low capture efficiency
of protocols (Hwang et al., 2018). A few of the analytical
steps, including read alignment and generation of count matrix,
can be resolved using already available computational methods
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designed for bulk RNA-seq. However, data processing tasks like
normalization, DGE analysis, cell imputation, and dimensionality
reduction, etc., call for the development of novel computational
techniques, algorithms, and tools for smooth execution of SC-
RNA-seq data analysis. The nature of the challenges that SC-
RNA-seq data possess, including big data problem (Costa, 2012;
Yu and Lin, 2016;Angerer et al., 2017;He et al., 2017), is
highlighted in the following subsections:
Normalization
In SC-RNA-seq, coverage of sequences between the libraries
exhibit systematic differences from experimental procedures,
dropout events, depth of the sequencing, and other technical
effects (Stegle et al., 2015). These differences must be corrected
by normalizing the data such that there is no interference in the
comparison of the gene expression between cells. Being crucial,
normalization of the SC-RNA-seq datasets eventually leads to
lucid downstream analysis, including identifying different cell
subsets and revealing differential expression of genes. In bulk
RNA-seq, expression counts from various libraries are usually
normalized by computing the fragments per kilobase of transcript
counts of per million mapped fragments (FPKM) (Mortazavi
et al., 2008), transcripts per million (TPM) (Li and Dewey,
2011), reads per kilobase of transcripts per million mapped
reads (RPKM), upper quartile (UQ) (Bullard et al., 2010),
DESeq (Love et al., 2014), removed unwanted variation (RUV)
(Risso et al., 2014), and Gamma regression model (Ding et al.,
2015). Generally, there are two types of normalization: (1)
normalization of data within the sample, and (2) normalization
of the data between the sample (Vallejos et al., 2015, 2017).
In the former, FPKM/RPKM or TPM are used to exclude
gene-specific biases (Vallejos et al., 2017) such as guanine–
cytosine (GC) content and gene length, while in the latter,
the normalization method tunes the sample-specific differences
such as sequencing depth and capture efficiency. While ignoring
the underlying stochasticity, normalization generates a relative
expression estimate (Stegle et al., 2015), assuming the overall
processed RNA per sample is equal (AlJanahi et al., 2018;
Olsen and Baryawno, 2018). The bulk-based strategies for
normalization have been reported unsuitable for SC-RNA-seq
datasets because the datasets are highly zero-inflated and have
higher technical noise. Multiple methods have been developed for
normalizing the SC-RNA-seq data (Vallejos et al., 2015;Lun et al.,
2016;Sengupta et al., 2016;Bacher et al., 2017;Yip et al., 2017).
However, O(nlogn) is considered more efficient than others in
performing normalization of SC-RNA-seq data (Yip et al., 2017).
Dimensionality Reduction
High dimensionality is yet another challenge that SC-RNA-seq
data present. Owing to the data coming from cells showing high
dimensions, i.e., a large number of genes, it is necessary to reduce
(while optimally preserving the critical properties) the set of
random variables and work with the principle variables which
describe the data profoundly (Andrews and Hemberg, 2019). The
two most frequently used methods for dimensionality reduction
are principal component analysis (PCA) (Van Der Maaten et al.,
2009) and T-distribution stochastic neighbor embedding (t-SNE)
(Van Der Maaten and Hinton, 2008;Kobak and Berens, 2019).
PCA uses a linear process to transform a set of variables (possibly
correlated) into an uncorrelated variable known as a principal
component, while t-SNE is a non-linear probability distribution-
based approach. Both PCA and t-SNE methods of dimensionality
reduction have certain limitations (Chen et al., 2019); based on
the assumption that approximately all the data are distributed
normally, PCA does not effectively amount to the underlying
complexities in the structure of SC-RNA-seq data, and t-SNE
has a larger time complexity reaching O(n2) (Pezzotti et al.,
2017). The most recent algorithm employed for dimensionality
reduction “UMAP” (Uniform Manifold Approximation and
Projection) (McInnes et al., 2018;Becht et al., 2019) outperforms
PCA and t-SNE for SC-RNA-seq in terms of high reproducibility
and meaningful organization of cells (Becht et al., 2018). UMAP
is a non-linear graph-based algorithm that tends to identify
the closest neighbors of a data point and assigns them a
larger weight, thereby preserving the topological structure of the
data. The idea is to project a low-dimensional representation
of the data while preserving the nearest neighbours of an
individual data point (i.e., cells). This helps to group more
closely related neighbours and partly conserves the relation of
points in the “long-range” using the intermediate data points.
Although the interpretation of the distances in a reduced space
becomes difficult, UMAP has been largely able to uncover the
elusive features of the data. UMAP is computationally faster
than t-SNE, preserves the global structure, and maintains the
continuity of cell subsets (Becht et al., 2018). At the core, UMAP
assumes the subsistence of a “manifold structure” in the data.
This assumption makes it find the manifolds in the noise of
data. Since SC-RNA-seq suffers from a significant amount of
noise, it is necessary to consider it before applying UMAP
(McInnes et al., 2018).
Another method to perform dimensionality reduction is
the linear discriminant analysis (LDA). LDA is a supervised
dimensionality reduction method that tends to maximize
the separability between the predetermined classes, using
the covariance of “between-class” and “within-class.” It first
calculates the mean of the distances between the classes and then
the mean of distances within the classes. The goal is to find a
projection to maximize the ratio of between-class variability to
the lower within-class variability (Tharwat et al., 2017;Qiao and
Meister, 2020).
The SC-RNA-seq exhibits potential challenges similar to text
mining, such as polysemy and synonymy, noise, and sparsity.
Recently, a popular text mining technique, latent semantic
analysis (LSA), has been used in SC-RNA-seq dimensionality
reduction (Cheng et al., 2019). LSA at core uses a linear algebra-
based method, called singular value decomposition (SVD), to
cluster the semantically similar terms. SVD approximates a
low-rank matrix to the given cell-gene matrix, such that the
dimensions of the new matrix are much less than the original.
This approximation is made by taking a combined product of
the matrices of left-singular vector, right-singular vector, and the
diagonal singular values.
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Differential Gene Expression Analysis
The expression of genes is stochastic in a cell; expression
values thus observed are quite heterogeneous at the individual
level among seemingly similar cells. The DGE analysis helps
to understand the innate cellular processes and stochasticity of
gene expressions (McDavid et al., 2013). The problem faced in
DGE analysis is identifying genes that are largely expressed in
a group of cells without any or no preliminary information of
primary cell subtypes (Stegle et al., 2015). Additionally, gene
expressions in individual cells show multimodality (Kippner
et al., 2014). As expression variability of genes between cells of the
same type indicates transcriptional heterogeneity (Johnson et al.,
2015;Angermueller et al., 2016), it needs robust computational
approaches to detect the true heterogeneity. In addition to
multimodality, the sparsity due to—but not limited to—dropout
events brings irregularities in the data, consequent of which the
differential genes are difficult to detect. Various parametric as
well as non-parametric approaches like Single-cell Differential
Expression, Model-based Analysis of Single-cell Transcriptome
(MAST), D3E, scDD, SigEMD, and DEsingle (Kharchenko et al.,
2014;Finak et al., 2015;Delmans and Hemberg, 2016;Korthauer
et al., 2016;Miao et al., 2018;Wang and Nabavi, 2018) have
been developed/proposed for the DGE analysis in the SC-RNA-
seq data. However, these tools try to manage either the gene
dropouts or multimodality (Wang et al., 2019). For the subtle
DGE analysis, these two crucial challenges need to be taken
care of together.
Cluster Analysis
Cluster analysis of SC-RNA-seq data is required to identify both
known and unknown rare cell types (Menon, 2018). Along with
the technical dropout events, the cells show a huge variation in
gene expression levels even from the same set. As mentioned
above, SC-RNA-seq suffers from massive inflation of zeros.
There are three reasons for the observation of zeros in data:
(1) the transcript was absent explicitly, hence a “true zero”;
(2) the depth of sequencing was very low, and the transcript
was present but not accounted for; and (3) at the time of
library preparation, the transcript could not be captured or
failed to amplify. The measurements from the latter two are
considered to be the “false zeros.” The concentration of too
many zeros in the data brings in irregularities. These technical
and biological factors lead to significant noise, due to which
cluster analysis becomes challenging. For this, methods like
Seurat, DropClust, and SCANPY (Satija et al., 2015;Ntranos
et al., 2016;Yip et al., 2017;Sinha et al., 2018) have been
proposed for clustering of SCs. There are certain limitations
associated with these as well. Seurat and SCANPY work well
with large datasets but underperforms when the dataset is
smaller (Kiselev et al., 2019). The anticipated complexity in
data and the rate of generation of SC data will be a challenge
for all these tools. UMAP is yet another method for cluster
identification of SC-RNA-seq data; however, as UMAP tends to
preserve the local-topological structure, it is rather difficult to
establish a relationship between clusters when the underlying cell
subtypes are unknown.
In addition to the sparsity in data, SC-RNA-seq data suffer
from a huge level of noise from faulty experimental designs
usually referred to as “batch-effects.” The noise in the data may
contribute to the overfitting of the data. The overfitting can
be avoided using regularization. Regularization is a process of
restricting or reducing the features at the time of modeling.
So far, the clustering methods cluster the cells as per the
transcription similarity, but the biological annotation of cell
clusters remains a challenge. A possible solution could come from
the generation of the data itself, as the more data are accumulated,
the more can unknown clusters be matched with the previously
known clusters. Another popular approach for cluster annotation
is to use Gene Ontology (GO) analysis of the marker genes
(Ashburner et al., 2000).
Single-Cell Spatial Transcriptomics and
RNA Velocity
Spatial transcriptomics (ST) gives measurement of gene
expression changes with reference to geographical coordinates of
the cells in tissues. It allows measurements of the transcripts with
an advantage of conserving the spatial information, providing
an additional analytical edge (Burgess, 2019). ST conform to
in situ methods like seqFISH (Shah et al., 2016), seqFISH+(Eng
et al., 2019), FISSEQ (Fluorescence in situ Sequence) (Lee et al.,
2015), MERFISH (Chen et al., 2015), and SC-RNA-seq-based
methods like slide-seq (Rodriques et al., 2019) and Niche-seq
(Medaglia et al., 2017). In situ labeling of the transcripts in
tissues is advantageous for visualizing the location; however, a
chance of molecular overcrowding results in fluorescence signal
overlap. This overcrowding can be overcome by using SC spatial
RNA-seq; however, the dissociation of cells prior to sequencing
makes it difficult to link the transcriptomes back to their original
locations (Burgess, 2019). These complementary strengths and
limitations make it necessary to integrate the datasets generated
by each technology.
In ST, a pair of images are generated, one containing whole
tissue with fairly visible spots and the other having clearly
visible fluorescence array spots (Wong et al., 2018). To leverage
the ST, the image data from ST need to be integrated with
the SC-RNA-seq data. As the principle challenges in both ST
and SC-RNA-seq are the sparsity of the data and noise from
technical and biological sources, an accurate data normalization
and transformation is necessary before any downstream analysis
(Wagner et al., 2016). Few tools have been developed to
determine the cell types with respect to their spatial identities
(Edsgärd et al., 2018;Svensson et al., 2018;Dries et al., 2019;
Queen et al., 2019). These tools lack interactive processing of
images and fails in providing a comprehensive three-dimensional
view of the tissue. Recently, STUtility (Bergenstråhle et al.,
2020b)—an R package using non-negative matrix factorization
(NMF) for reducing the dimensions, spatial correlation (based
on Pearson correlation), and K-means clustering—was found
capable of providing a holistic view of the expression in tissues.
SpatialCPie (Bergenstråhle et al., 2020a) is another easy-to-use R
package that uses clustering at various resolutions to interactively
uncover the gene expression patterns. Elosua-Bayes et al. (2021)
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Adil et al. Era of Single-Cell Transcriptomics
FIGURE 2 | (A) There is a steep rise every year for the publications of studies addressing the big data and SC-RNA-seq. For big data papers on PubMed, we used
the query “[big data (All Fields) AND MapReduce (All Fields) AND Hadoop (All fields)].” For SC-RNA-seq and big data papers on PubMed, we used “[(scRNA-seq OR
Big Data) OR (Single-cell AND big data)].” (B,C) Numbers were collected from the Human Cell Atlas Data portal of some exemplary projects.
developed SPOTlight, which uses NMF along with non-negative
least squares (NNLS). NMF helps in dimensional reduction,
followed by selection of marker genes using seurat package
and then using NNLS to deconvolute each captured location
(Elosua-Bayes et al., 2021).
The SC-RNA measurements have advanced our
understanding of the intrinsic cellular functionalities; however,
the destruction of cells in the process ceases the possibility of
further resampling for an additional transcriptional state analysis.
A new methodology, RNA velocity, is capable of deducing the
future transcriptional state of a cell (La Manno et al., 2018). The
idea behind the study is that the transcriptional upregulation of
gene at a particular stage leads to the short-spanned abundance of
unspliced transcripts. Similarly, the downregulation of the gene
at a point of time results in a decrease of spliced transcripts. The
ratio of this variation between unspliced and spliced transcripts
is used to estimate the future state of a cell.
Single-Cell Multi-omics and Data
Integration
Biological activities in cells are perplexing, and the measurements
of these processes show contrasting variation at temporal and
histological levels. To comprehensively understand the intricate
biological process of cells and organisms, it is necessary to
investigate them at a multi-omics scale. Contingent upon the
research question, SC experiments have flexed its reach to variety
of layers, the majority of which include the following: (1) SCI-
seq for Single-cell Genome Sequencing (Vitak et al., 2017), (2)
scBS-seq for Single-cell DNA methylation (Smallwood et al.,
2014), (3) scATAC-seq for Single-cell chromatin accessibility
(Buenrostro et al., 2015), (4) CITE-seq for cell Surface Proteins
(Stoeckius et al., 2017), (5) scCHIP-seq for Histone Modifications
(Gomez et al., 2013), and (6) scGESTALT (Frieda et al., 2017)
and MEMOIR (Raj et al., 2018) for chromosomal conformation.
A universal challenge for all the SC technologies is that
the measurements from a very low starting material led to
generation of highly sparse and extremely noisy data. Hence, the
integration of this data requires a statistically sound and robust
computational framework. A primary challenge thereof remains
to find an empirical strategy to normalize, batch-effect correction
and linking the data from different sources so that the biological
meaning and inference remain uncompromised.
For the integration and analysis of the SC multi-omics
data, several methods developed for the variety of SC-mono-
omics data have been fused or extended further to fulfill the
requirement. However, each tool follows a different strategy for
the analysis, which can be categorized as follows: (1) correlation
and unsupervised cluster analysis; (2) data integration of different
samples from a single measurement type and a single experiment
type, e.g., SC-RNA-seq; (3) analysis and integration of data from
different experiments and a single measurement type across
different samples, e.g., sc-Spatial Transcriptomics; (4) integration
of data from SC population, with more than one measurement
type, different samples, and a single experiment; and (5)
integration of data across multiple cells, multiple experiments,
and multiple measurement types, e.g., combination of the SC-
RNA-seq, scATAC, scCHIP-seq, CITE-seq, etc., of different cells
collected at different time points (Stuart et al., 2019;Lähnemann
et al., 2020;Lee et al., 2020).
Computational methods and tools for integration of biological
data are evolving gradually. A number of techniques have been
developed that have been discussed in section “Cluster Analysis.”
Seurat (Butler et al., 2018) is currently at the top of integrative
analysis of SC multi-omics data, integrating the datasets based
on the second principle. Along with Seurat, mutual nearest
neighbor (MNN)-based method (Haghverdi et al., 2018) has been
exploited to analyze the data combined on the basis of the second
category. For the fourth category, analytical methods developed
for bulk cellular analysis like MOFA (Argelaguet et al., 2018),
MINT (Rohart et al., 2017a), mixOmics (Rohart et al., 2017b),
and DIABLO (Singh et al., 2019) are being utilized. Cardelino
(McCarthy et al., 2018), MATCHER (Welch et al., 2017), and
cloealign (Campbell et al., 2019) are currently the tools used for
integrative analysis under the fourth category. To our knowledge,
there are no tools available for the last category.
Big Data Pertaining to Single-Cell RNA
Sequencing
The data-intensive scientific discoveries rely on three
paradigms—theory, experimentation, and simulation modeling
(Tolle et al., 2011). As big data is described with three
characteristics (volume, velocity, and variety) (Stephens
et al., 2015;Adil et al., 2016), data generated by SC-RNA-seq
are tantamount to these three quantitative characteristics
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Adil et al. Era of Single-Cell Transcriptomics
(Ivanov et al., 2013). With the introduction of new methods
in microfluidics (Zare and Kim, 2010), combinatorial indexing
procedures (Fan et al., 2015), and rapid drop in the sequencing
cost, SC assay profiling has widely become a routine practice
among biologists for analyzing millions of cells in hours, paving
the way for the accumulation of a large amount of data. The most
popular next-generation sequencing platform, Illumina HiSeq,
results in the accumulation of around 100 gigabytes of raw RNA-
seq data per study. It usually takes hours to align these raw data to
their reference genome. SC experiments generating petabytes of
data on a variety of layers contribute to the big data paradigm.
A human genome has 20,000–25,000 genes composed of 3
million base pairs, totaling to 100 gigabytes of data, equivalent to
102,400 photos1; it is expected that more or less “25 petabytes”
of genomic data will be generated annually around the globe
by the year 2030 (Khoury et al., 2020). It is anticipated that
human genomic data can potentially overtake the data produced
by online social networks (Check Hayden, 2015). The Human
Cell Atlas (HCA)—a project to prepare a reference map of each
cell in the human body at various stages, will accumulate a
massive amount of data by the end of its completion (Regev
et al., 2017). There is a need for comprehensive integration
of big data and SC-RNA-seq technologies. A large number
of publications on SC-RNA and big data have emerged lately
(Figure 2A). The datasets of 4.5 million cells are already
published in Data2, the largest of which contains more than 1.5
million CD34+hematopoietic cells of human bone marrow (Setty
et al., 2019) and 1.3 million transcriptomes of mouse brain cells
(Figures 2B,C).
Consequently, the data acquired from these experiments
constitute a data revolution in the field of SC biology
(Lähnemann et al., 2019). As SC-RNA-seq data have a greater
potential of uncovering the hidden patterns at the molecular
level, the data pertaining to it thus require an extremely parallel,
scalable, and statistically sound computational framework as
its handling tools. Big data technologies like Apache’s Hadoop
(Taylor, 2010;O’Driscoll et al., 2013) and Spark (Zaharia et al.,
2016;Guo et al., 2018) embody the required computational
parallelism and data distribution mechanisms. Hadoop uses
MapReduce technology for parallel and scalable processing
(Dean and Ghemawat, 2008) to disintegrate the larger problems
into smaller subproblems on a distributed file system called
1https://www.experfy.com/blog/intersection-genomics-big-data
2https://data.humancellatlas.org/
Hadoop Distributed File System (HDFS). Incorporating big data
technologies in the analysis of rapidly increasing SC genomics
data will help in transforming and processing it with limitless
scalability and fault tolerance at a very low cost.
CONCLUSION AND FUTURE
PERSPECTIVE
As a consequence of meager RNA capture rate, low starting
materials, and challenging experimental protocols, the SC-RNA-
seq faces computational and analytical challenges. The noise and
sparsity due to the technical (dropout events) and biological
factors make the downstream analysis of SC-RNA-seq data a
complicated task. Additionally, the rapidity in the development
of new and exciting experimental methods for SC-RNA-seq is
paving the way for a large accumulation of data. This large
agglomeration of data is nothing but the genomic face of
“big data.” These two challenges together give rise to a new
paradigm of Big Single-Cell Data Science. Although a plethora of
algorithms and computational tools have already been developed,
it is essential to address these challenges collectively and produce
a robust, accurate, parallel, and scalable framework.
AUTHOR CONTRIBUTIONS
MA and ATJ conceived the idea, edited the manuscript,
and contributed to the compilation of data for designing of
figures. AA, VK, and ATJ contributed to the writing of the
manuscript. All authors contributed to the article and approved
the submitted version.
FUNDING
ATJ is grateful to DST-SERB for financial support
(CRG/2019/004106) that helped in to establishing the
infrastructural facilities.
ACKNOWLEDGMENTS
The authors would like to thank their colleagues for the help in
improving the contents of the manuscript.
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