Technique to recover lost single-cell RNA-sequencing information
Boosting the efficiency of single-cell RNA-sequencing helps reveal subtle differences between healthy and dysfunctional cells

Date:
October 13, 2020
Source:
Massachusetts Institute of Technology
Summary:
Researchers have greatly boosted the amount of information that
can be obtained using Seq-Well, a technique for rapidly sequencing
RNA from single cells. This advance should enable scientists to
learn more about the critical genes expressed in each cell, and
to discover subtle differences between healthy and diseased cells
for designing new preventions and cures.



FULL STORY ========================================================================== Sequencing RNA from individual cells can reveal a great deal of
information about what those cells are doing in the body. MIT researchers
have now greatly boosted the amount of information gleaned from each of
those cells, by modifying the commonly used Seq-Well technique.


==========================================================================
With their new approach, the MIT team could extract 10 times as much information from each cell in a sample. This increase should enable
scientists to learn much more about the genes that are expressed in each
cell, and help them to discover subtle but critical differences between
healthy and dysfunctional cells.

"It's become clear that these technologies have transformative potential
for understanding complex biological systems. If we look across a range
of different datasets, we can really understand the landscape of health
and disease, and that can give us information as to what therapeutic
strategies we might employ," says Alex K. Shalek, an associate professor
of chemistry, a core member of the Institute for Medical Engineering
and Science (IMES), and an extramural member of the Koch Institute for Integrative Cancer Research at MIT.

He is also a member of the Ragon Institute of MGH, MIT and Harvard and
an institute member of the Broad Institute.

In a study appearing this week in Immunity, the research team demonstrated
the power of this technique by analyzing approximately 40,000 cells from patients with five different skin diseases. Their analysis of immune
cells and other cell types revealed many differences between the five
diseases, as well as some common features.

"This is by no means an exhaustive compendium, but it's a first step
toward understanding the spectrum of inflammatory phenotypes, not just
within immune cells, but also within other skin cell types," says Travis Hughes, an MD/PhD student in the Harvard-MIT Program in Health Sciences
and Technology and one of the lead authors of the paper.

Shalek and J. Christopher Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering and a member of the Koch Institute
and Ragon Institute, are the senior authors of the study. MIT graduate
student Marc Wadsworth and former postdoc Todd Gierahn are co-lead
authors of the paper with Hughes.



========================================================================== Recapturing information A few years ago, Shalek, Love, and their
colleagues developed a method called Seq-Well, which can rapidly
sequence RNA from many single cells at once. This technique, like other high-throughput approaches, doesn't pick up as much information per cell
as some slower, more expensive methods for sequencing RNA.

In their current study, the researchers set out to recapture some of
the information that the original version was missing.

"If you really want to resolve features that distinguish diseases,
you need a higher level of resolution than what's been possible," Love
says. "If you think of cells as packets of information, being able to
measure that information more faithfully gives much better insights into
what cell populations you might want to target for drug treatments,
or, from a diagnostic standpoint, which ones you should monitor."
To try to recover that additional information, the researchers focused
on one step where they knew that data was being lost. In that step,
cDNA molecules, which are copies of the RNA transcripts from each
cell, are amplified through a process called polymerase chain reaction
(PCR). This amplification is necessary to get enough copies of the DNA
for sequencing. Not all cDNA was getting amplified, however. To boost
the number of molecules that made it past this step, the researchers
changed how they tagged the cDNA with a second "primer" sequence, making
it easier for PCR enzymes to amplify these molecules.

Using this technique, the researchers showed they could generate much
more information per cell. They saw a fivefold increase in the number
of genes that could be detected, and a tenfold increase in the number
of RNA transcripts recovered per cell. This extra information about
important genes, such as those encoding cytokines, receptors found on
cell surfaces, and transcription factors, allows the researchers to
identify subtle differences between cells.



==========================================================================
"We were able to vastly improve the amount of per cell information
content with a really simple molecular biology trick, which was easy to incorporate into the existing workflow," Hughes says.

Signatures of disease Using this technique, the researchers analyzed
19 patient skin biopsies, representing five different skin diseases -- psoriasis, acne, leprosy, alopecia areata (an autoimmune disease that
causes hair loss), and granuloma annulare (a chronic degenerative skin disorder). They uncovered some similarities between disorders -- for
example, similar populations of inflammatory T cells appeared active in
both leprosy and granuloma annulare.

They also uncovered some features that were unique to a particular
disease. In cells from several psoriasis patients, they found that cells
called keratinocytes express genes that allow them to proliferate and
drive the inflammation seen in that disease.

The data generated in this study should also offer a valuable resource to
other researchers who want to delve deeper into the biological differences between the cell types studied.

"You never know what you're going to want to use these datasets for,
but there's a tremendous opportunity in having measured everything,"
Shalek says.

"In the future, when we need to repurpose them and think about particular surface receptors, ligands, proteases, or other genes, we will have
all that information at our fingertips." The technique could also be
applied to many other diseases and cell types, the researchers say. They
have begun using it to study cancer and infectious diseases such as tuberculosis, malaria, HIV, and Ebola, and they are also using it to
analyze immune cells involved in food allergies. They have also made
the new technique available to other researchers who want to use it or
adapt the underlying approach for their own single-cell studies.

The research was funded by the Koch Institute Support (core) Grant
from the National Institutes of Health, the Bridge Project of the Koch Institute and the Dana-Farber/Harvard Cancer Center, the Food Allergy
Science Initiative at the Broad Institute, the National Institutes of
Health, a Beckman Young Investigator Award, a Sloan Research Fellowship
in Chemistry, the Pew-Stewart Scholar Award, and the Bill and Melinda
Gates Foundation.


========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by Anne
Trafton. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Travis K. Hughes, Marc H. Wadsworth II, Todd M. Gierahn, Tran
Do, David
Weiss, Priscila R. Andrade, Feiyang Ma, Bruno J. De Andrade
Silva, Shuai Shao, Lam C. Tsoi, Jose Ordovas-Montanes, Johann
E. Gudjonsson, Robert L.

Modlin, J. Christopher Love, Alex K. Shalek. Second-Strand
Synthesis- Based Massively Parallel scRNA-Seq Reveals Cellular
States and Molecular Features of Human Inflammatory Skin
Pathologies. Immunity, 2020 DOI: 10.1016/j.immuni.2020.09.015 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201013134300.htm

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