Top Tips for Enhancing Single-Cell Sequencing
How To Guide
Last Updated: July 1, 2024
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Published: June 6, 2024
Credit: iStock
Single-cell sequencing is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level, yielding fresh insights into the composition, dynamics and regulatory mechanisms of cell states in development and disease.
However, despite substantial advancements in this field, there are still significant challenges associated with sample quality, data interpretation and experimental complexity.
This guide explores the nuances of these challenges and presents promising strategies designed to overcome them, enabling researchers to access the full spectrum of cellular diversity and function in health and disease.
Download this guide to discover:
- The latest advancements in single-cell analysis techniques
- How to optimize experimental workflows from cell isolation to sequencing and data analysis
- Methods for enhancing accuracy and reliability of single-cell data
ARTICLE
Top tips for elevating your
single-cell sequencing experiments.
Single-cell sequencing is a rapidly evolving approach
to delve into the genomic, transcriptomic, epigenomic,
and proteomic landscapes of single cells. Compared to
traditional bulk sequencing methods, which often fail to
capture the rich variability present among individual cells,
single-cell sequencing offers the opportunity to investigate
cellular heterogeneity across a wide range of tissues and
cell populations.
By providing fresh insights into the complexity of cellular
populations, single-cell sequencing can elucidate rare
subpopulations, dynamic cellular states, and the molecular
determinants underlying cellular diversity, developmental
processes, and disease pathogenesis.
Over the past decade, significant advancements in
technology have facilitated the widespread adoption of
single-cell sequencing methodologies. These technological
innovations have not only increased the throughput and
sensitivity of single-cell analysis but have also significantly
reduced the cost and complexity associated with
experimental procedures.
By delving into the molecular intricacies of single cells,
researchers can uncover predictive biomarkers and
therapeutic targets with clinical implications for a wide range
of diseases. However, despite these remarkable strides,
challenges remain in the analysis and interpretation of
single-cell data.
In this article, we explore some of the prevalent challenges
within single-cell studies and discuss emerging strategies to
overcome them.
Four key challenges associated with
single-cell studies include:
1. Analytical burden and inefficient data interpretation
Streamlining the data analysis process is vital to
conserve valuable resources and enhance accurate
data extraction.
2. Low sample viability
Enhancing sample viability is paramount to improve the
robustness and reproducibility of downstream analyses.
3. RNA dropout
Overcoming RNA dropouts is crucial to enhance the
accuracy of single-cell analysis.
4. Inaccurate nuclei counting
Ensuring accurate nuclei counting is essential for
reliable downstream analyses.
Top tips for elevating your single-cell sequencing experiments.
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1. Preventing analytical burden and inefficient data interpretation
TOP TIP
Empower researchers with
preliminary analysis
TECHNOLOGY SOLUTION
BioLegend’s Multi-omics Analysis Software
Empowering researchers to independently identify
cell populations of interest before involving
bioinformatics experts for detailed analysis can
streamline the process and avoid unnecessary
rounds of investigation.
BioLegend’s cloud-based Multi-omics Analysis Software
(MAS) serves as an accessible software solution to
streamline single-cell multi-omics analysis. The software’s
user-friendly interface enables biologists to independently
explore, iterate, and draw conclusions from their data,
putting scientists back in the driver’s seat of their analyses.
MAS equips scientists with the tools to confidently perform
various downstream assessments, including normalization,
cell annotation (gating or clustering), multiplet removal,
and differential protein and gene expression investigations.
Whether researchers have robust bioinformatics support
or are operating autonomously, MAS emerges as an
indispensable tool for gaining a head start in single-cell
analysis and prevent unnecessary rounds of investigation.
“Multi-omics Analysis Software is not designed to
replace the bioinformaticist; rather, it is designed
to supplement and supercharge the ability of
the bioinformaticist and biologist to interface,
accelerating time to results.”
Josh Croteau, Program Manager,
Multi-omics, BioLegend
The challenge
Single-cell multi-omics experiments generate large amounts
of high-dimensional data that require advanced tools and
expertise to process and interrogate. Even with highly
skilled bioinformaticians available, defining relevant and
biologically meaningful cell populations for subsequent
analyses remains challenging due to the complexity inherent
to the data. This can lead to unnecessary rounds of
analysis, consuming precious time and resources; ultimately
delaying the answers investigators require.
Top tips for elevating your single-cell sequencing experiments.
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2. Ensuring high sample viability for sensitive and meaningful data
TOP TIP
Use of dead cell removal kits for
improved viability
TECHNOLOGY SOLUTION
BioLegend’s magnetic cell separation
system MojoSort
Dead cell removal kits can be used to selectively
remove dead and dying cells from a cell population.
By employing negative selection of live cells, the
impact of low cell viability can be mitigated before
samples are loaded into a single-cell workflow.
BioLegend’s MojoSort™ serves as a technological solution
to enhance sample viability in single-cell workflows. The
magnetic cell separation system can be used to remove
dead and dying cells, addressing the challenge of low
viability. MojoSort also provides researchers with the
flexibility to employ positive or negative selection strategies,
allowing for the additional removal of unwanted cells
and/or enrichment of cells of interest from heterogenous
populations.
This dual functionality not only improves viability but also
offers the flexibility to isolate and purify specific cell subsets
for single-cell workflows, contributing to more accurate and
reliable data generation.
“One of the fundamental challenges in single-cell
sequencing lies in the viability and quality of the
samples entering the workflow. Our dead cell
removal kits serve as a proactive solution to
mitigate the presence of dead or dying cells prior
to loading samples into a single-cell workflow.”
Ashley Cornett, Senior Manager,
Scientific Applications, BioLegend
The challenge
Ensuring high sample cell viability is a critical factor in
the success of single-cell workflows. Many single-cell
sequencing platforms recommend a minimum viability
threshold of 80% or above to ensure that the generated
data is a true representation of the cell population. Low
cell viability jeopardizes data relevance due to sample
degradation and wastes resources by committing
sequencing to low-quality samples. Ultimately, this
compromises the utility of single-cell work. Meticulous
attention to sample handling and viability optimization is
therefore essential to overcome this challenge and improve
the utility of single-cell sequencing experiments.
% Live cell yield
Competitor A
MojoSort™ Human Dead Cell Removal Kit
0
20
40
60
80
100
Top tips for elevating your single-cell sequencing experiments.
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3. Reducing the impact of RNA dropout
The challenge
Single-cell RNA-sequencing (scRNA-seq) offers insights into
cellular heterogeneity and function by quantifying gene
expression at the individual cell level. However, scRNA-seq
faces a challenge known as ‘RNA dropout’ where lowly
expressed genes with limited transcript numbers in the cell
are missed by the chosen RNAseq approach. These RNA
dropouts hinder the analysis, resulting in an incomplete
representation of the transcriptome for a given cell,
impacting data quality.
TECHNOLOGY SOLUTION
BioLegend’s TotalSeq reagents
TOP TIP
Implement CITE-seq for
comprehensive profiling
Incorporating protein detection to experimental
strategies to overcome the challenge of RNA
dropouts and enhance the accuracy of analyses.
One useful approach is CITE-seq (Cellular Indexing
of Transcriptomes and Epitopes by Sequencing),
which combines gene expression profiling with
the measurement of protein markers on individual
cells. This dual-modality profiling enables a
comprehensive understanding of both the
transcriptomic and proteomic profiles of individual
cells simultaneously.
The incorporation of oligonucleotide-labeled antibodies,
such as BioLegend’s TotalSeq™ reagents, into workflows
like CITE-seq serve as a technological solution to reduce
an experiments vulnerability to RNA dropout by adding
protein information to help identify cells and understand
their biology. TotalSeq reagents provide a method for
measuring protein and RNA simultaneously in single cells.
These antibody-oligonucleotide conjugates carry unique
barcodes that can be associated with RNA transcripts
derived from the same cell during the sequencing process.
By integrating information about gene expression and
proteins at the single-cell level, CITE-seq enables a more
detailed characterization of cellular heterogeneity and
function, enhancing the accuracy and reliability of scRNAseq analyses.
“Part of the challenge with RNA dropout is that
a biologically relevant marker within a specific
cell type may go undetected, not because the
transcript isn’t being made, but rather due to
the lower abundance of RNA meaning it is not
captured in downstream analysis pipelines. In
contrast, protein is more abundant which allows
for more consistent detection and aides in further
characterization of subsets of cell types.”
Ashley Cornett, Senior Manager,
Scientific Applications, BioLegend
tSNE performed using top 100
dierentially expressed genes
tSNE performed using only
12 TotalSeq™ antibodies
tSNE1
tSNE2 NE2
CD4+ T cells
CD8+ T cells
B cells
CD14+ monocytes
CD16+ monocytes
NKcellstSNE performed using top 100
dierentially expressed genes
tSNE performed using only
12 TotalSeq™ antibodies
tSNE1
tSNE2 tSNE2
tSNE1
CD4+ T cells
CD8+ T cells
B cells
CD14+ monocytes
CD16+ monocytes
NK cells
pDCs
CD1c DCs
Basophils
Neutrophils
Top tips for elevating your single-cell sequencing experiments.
For a complete listing of our
global offices, visit www.revvity.com
Copyright ©2024, Revvity, Inc.
All rights reserved. For research use only. Not for use in diagnostic procedures. 1323588
Revvity, Inc.
940 Winter Street
Waltham, MA 02451 USA
www.revvity.com
TOP TIP
Embrace automated technologies for
nuclei counting
TECHNOLOGY SOLUTION
Cellaca PLX high-throughput cell and
nuclei counter
Visualization and enumeration of individual cell
nuclei can be achieved using fluorescent dyes or
markers that specifically bind to DNA. The inherent
difficulty in manual counting, especially in the
presence of cellular debris, may necessitate the
adoption of automated cell counting techniques to
enhance accuracy.
The use of automated cell counters, such as the Cellaca™
PLX high-throughput cell and nuclei counter, emerges as a
technological solution to improve nuclei counting accuracy
in single-cell workflows. Automation not only handles large
sample quantity efficiently but also removes user bias
from the cell counting process. By adopting automated
methods, researchers enhance the accuracy and reliability
of nuclei counts compared to manual counting approaches.
Moreover, automated counters enable the adoption of
standardized methods, improving reproducibility and
streamlining workflows. The Cellaca PLX also boasts
a relatively low volume requirement. This feature is
particularly beneficial when sample material is limited,
allowing users to preserve the majority of the sample for
their intended experimental purposes.
“One of the greatest challenges is ensuring samples
meet quality criteria. If a sample isn’t clean, then
why would you submit it for sequencing? This
is where our instrumentation is wonderful – it
can give scientists valuable information prior to
submitting a sample, which can save them a lot of
time and money.”
Charles Hernandez,
Field Application Scientist
Manager, Revvity
4. Increasing accurate nuclei counting for enhanced sample quality
The challenge
Accurate nuclei counting is crucial in single-cell workflows,
particularly during library preparation, to minimize
unlyzed cells and ensure debris-free samples. In addition,
downstream analysis requires reliable data on cell
population heterogeneity which can be influenced by
poor sample preparation due to low viability and large
aggregates. Automated cell counting provides a reliable
and consistent method to detect isolated cells/nuclei where
manual counting can be challenging due to their small size,
particularly when dealing with a large number of cells.
Brightfield AO/PI
Dual fluorescent imaging enables detection of viable nuclei
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about single-cell sequencing.
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