Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.

1. Single-Cell RNA-seq analysis
Section 1:Overview of Single-Cell RNA-seq
Presented By Alireza Dosutmohammadi
M.Sc. in Bioinformatics, Tarbiat Modares University
Oct 2021

2. RNA-seq Vs Microarrays
RNA-seq allows profiling the transcripts in a sample in an efficient and cost-
effective way.
RNA-seq allows for an unbiased sampling of all transcripts in a sample, rather
than being limited to a pre-determined set of transcripts
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3. with bulk RNA-seq:
we can only estimate the average expression level for each gene across a
population of cells, without regard for the heterogeneity in gene expression
across individual cells of that sample. Therefore, it is insufficient for studying
heterogeneous systems, e.g. early development studies or complex tissues
such as the brain.
Unlike with the bulk approach, with scRNA-seq we can estimate a distribution
of expression levels for each gene across a population of cells.
Bulk RNA-seq Vs Single-Cell RNA-seq
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4. Bulk RNA-seq Vs Single-Cell RNA-seq
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Bulk RNA-seq Vs Single-Cell RNA-seq

6. Single-Cell RNA-seq
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7. • Human Cell Atlas (H. sapiens)
• Tabula Muris (M. musculus)
• Fly Cell Atlas (D. melanogaster)
• Cell Atlas of Worm (C. elegans)
• Arabidopsis Root Atlas (A. thaliana)
Single-cell Atlases
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8. Sample Preparation Protocols
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9. • Tissue dissection and cell dissociating to obtain a suspension of cells.
• Optionally cells may be selected (e.g. based on membrane markers,
fluorescent transgenes or staining dyes).
• Capture single cells into individual reaction containers (e.g. wells or oil
droplets).
• Extracting the RNA from each cell.
• Reverse-transcribing the RNA to more stable cDNA.
• Amplifying the cDNA (either by in vitro transcription or by PCR).
• Preparing the sequencing library with adequate molecular adapters.
• Sequencing, usually with paired-end Illumina protocols.
• Processing the raw data to obtain a count matrix of genes-by-cells
• Carrying several downstream analysis.
Sample Preparation Protocols
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10. Sample Preparation Protocols
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11. Sample Preparation Protocols
two most important aspects are: cell capture or isolation and transcript
quantification.
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12. Sample Preparation Protocols
In tissues where cell dissociation is difficult or in frozen tissue samples, instead
of isolating whole single cells it is possible to instead isolate single nuclei. Apart
from the isolation step, the protocol to prepare single-nuclei sequencing
libraries is similar to that of single-cell protocols. However, nuclear RNA usually
contains a higher proportion of unprocessed RNA, with more of the sequenced
transcripts containing introns.
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13. Sample Preparation Protocols
Cell Capturing methods:
• Microtitre-plate-based
• Microfluidic-array-based
• Microfluidic-droplet-based
The strategy determines the throughput of the experiment.
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14. Sample Preparation Protocols
Cell Capturing methods:
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15. Sample Preparation Protocols
Cell Capturing methods:
Microtitre-plate methods (well-based methods ):
• isolating cells into individual wells of the plate using, for example, pipetting,
microdissection or fluorescent activated cell sorting (FACS).
• Advantage: take pictures of the cells before library preparation.
• Can identify and discard damaged cells or find wells containing doublets
associate information such as cell size and the intensity of any used labels
with the well coordinates.
• The main drawback: they are often low-throughput.
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16. Sample Preparation Protocols
Cell Capturing methods:
Microfluidic-array platforms:
• integrated system for capturing cells and for carrying out the reactions
necessary for the library preparations.
• they provide a higher throughput than microtitre-plate-based methods.
• only around 10% of cells are captured in a microfluidic platform
• they are not appropriate if one is dealing with rare cell-types or very small
amounts of input.
• has to be taken with the cell sizes captured by the arrays, as the nanowells
are customised for particular sizes. this may therefore affect the unbiased
sampling of cells in complex tissues.
• the chip is relatively expensive.
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17. Sample Preparation Protocols
Cell Capturing methods:
Microfluidic-droplet methods:
• offer the highest throughput.
• They work by encapsulating individual cells inside a nanoliter-sized oil droplet,
together with a bead.
• The bead is loaded with enzymes and other components required to construct
the library.
• Each bead contains a unique barcode which is attached to all of the
sequencing reads originating from that cell.
• all of the droplets can be pooled, sequenced together and the reads can
subsequently be assigned to the cell of origin based on those barcodes.
• Droplet platforms have relatively cheap library preparation costs on the order of
0.05 USD/cell.
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18. Sample Preparation Protocols
Transcript Quantification:
• full-length: uniform read coverage across the whole transcript
• tag-based: only capture either the 5’ or 3’ ends
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19. Sample Preparation Protocols
Transcript Quantification:
• full-length Protocol:
• identical to what is done in bulk RNA-seq
• Although in theory full-length protocols should provide an even coverage of
transcripts, there can sometimes be biases in the coverage across the gene
body.
• Full-length protocols also allow the detection of splice variants.
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20. Sample Preparation Protocols
Transcript Quantification:
• SMART-seq2 is a popular low-throughput method, providing full-length transcript
quantification. It is ideally suited for studying a smaller group of cells in greater
detail.
• 10x Chromium is a popular high-throughput method, using UMIs for transcript
quantification (from either 3’ or 5’ ends). It is ideally suited to study highly
heterogeneous tissues and sample large populations of cells at scale.
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21. Sample Preparation Protocols
Transcript Quantification:
• tag-based protocols:
• only one of the ends (3’ or 5’) of the transcript is sequenced.
• 3’ protocols are more commonly used, many protocols now allow sequencing from
either end (e.g. 10x Chromium supports both).
• Advantage of 5’-end sequencing: obtain information about the transcription start site
(TSS), which allows to explore whether there is differential TSS usage across cells.
• Advantage: they can be combined with unique molecular identifiers (UMIs), which
can help improve the accuracy of transcript quantification.
• Unique molecular identifiers (UMIs) are a type of molecular barcoding that provides
error correction and increased accuracy during sequencing. These molecular
barcodes are short sequences used to uniquely tag each molecule in a sample
library.
• Disadvantage: being restricted to one end of the transcript only, it reduces our ability
to unambiguously align reads to a transcript, as well as making it difficult to distinguish
different isoforms.
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22. Importance of Single-Cell RNA-seq
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23. Comparing different protocols
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Comparing different protocols

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Comparing different protocols

26. • Sensitivity: how many genes are detected per cell
• accuracy (e.g. compared to bulk RNA-seq)
• recover all cell types present in a sample.
• low-throughput methods have higher sensitivity compared to high-
throughput methods, such as 10x Chromium.
• low-throughput methods did not capture some of the rarer cell types in their
samples, leading to an incomplete characterisation of the cell population.
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Comparing different protocols

27. • if one is interested in characterizing the composition of a heterogeneous
tissue, then a droplet-based method is more appropriate, as it allows a very
large number of cells to be captured in a mostly unbiased manner.
• Full-length transcript quantification will be more appropriate if one is
interested in studying different isoforms, since tagged protocols are much
more limited in this regard. By contrast, UMIs can only be used with tagged
protocols and they can improve gene-level quantification.
• If one is interested in rare cell types (for which known markers are not
available), then more cells need to be sequenced, which will increase the
cost of the experiment.
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What Protocol Should I Choose?

28. • take into account when performing scRNA-seq experiments Factors such as:
the cost per cell, how many cells one needs, or how much to sequence
each cell.
• Care has to be taken to avoid biases due to batches being processed at
different times.
Important !
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29. How many cells do we need to sample so that we see at least n cells of each
type?
• This depends on the number of cell type present and the diversity, i.e. the
entropy.
• Assume that there are 10 rare cell types, each one present at a fraction of
2% of the total population. If we want to be 95% confident that our sample
contains at least 5 cells from each of those cell types, we need to sample
at least 619 cells in total.
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Important !

30. • The main difference between bulk and single cell RNA-seq is that each
sequencing library represents a single cell, instead of a population of cells.
• Another important aspect to take into account are batch effects. These
can be observed even when sequencing the same material using different
technologies, and if not properly normalised, can lead to incorrect
conclusions.
Data challenges
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Data challenges

32. • if planning an experiment to compare healthy and diseased tissues
from 10 patients each, if only 10 samples can be processed per day, it
is best to do 5 healthy + 5 diseased together each day, rather than
prepare all healthy samples one day and all diseased samples in
another.
• Another consideration is to ensure that there is replication of tissue
samples. For example, when collecting tissue from an organ, it may be
a good idea to take multiple samples from different parts of the organ.
Or consider the time of day when samples/replicates are collected
(due to possible circadian changes in gene expression).
• all the common best practices in experimental design should be taken
into account.
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Data challenges

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Data challenges