Taking the guesswork out of genetic engineering
STAMPScreen pipeline helps streamline genetic studies in mammalian cells


Date:
September 27, 2021
Source:
Wyss Institute for Biologically Inspired Engineering at Harvard
Summary:
If necessity is the mother of invention, frustration is the
father. When scientists kept running into aggravating problems
with the existing tools and methods they were using to perform
genetic engineering experiments, they decided to make better
ones. They teamed up and created an integrated pipeline called
STAMPScreen that combines novel algorithms, a new gene cloning
technique, and powerful next-generation sequencing technology to
help scientists get from a database to results quickly, easily,
and frustration-free.



FULL STORY ========================================================================== Today's genetic engineers have a plethora of resources at their disposal:
an ever-increasing number of massive datasets available online, highly
precise gene editing tools like CRISPR, and cheap gene sequencing
methods. But the proliferation of new technologies has not come with
a clear roadmap to help researchers figure out which genes to target,
which tools to use, and how to interpret their results. So, a team of scientists and engineers at Harvard's Wyss Institute for Biologically
Inspired Engineering, Harvard Medical School (HMS), and the MIT Media
Lab decided to make one.


==========================================================================
The Wyss team has created an integrated pipeline for performing genetic screening studies, encompassing every step of the process from identifying target genes of interest to cloning and screening them quickly and
efficiently.

The protocol, called Sequencing-based Target Ascertainment and Modular Perturbation Screening (STAMPScreen), is described in Cell Reports
Methods, and the associated open-source algorithms are available on
GitHub.

"STAMPScreen is a streamlined workflow that makes it easy for researchers
to identify genes of interest and perform genetic screens without having
to guess which tool to use or what experiments to perform to get the
results they want," said corresponding author Pranam Chatterjee, Ph.D.,
a former graduate student at the MIT Media Lab who is now the Carlos
M. Varsavsky Research Fellow at HMS and the Wyss Institute. "It is
fully compatible with many existing databases and systems, and we hope
that many scientists are able to take advantage of STAMPScreen to save themselves time and improve the quality of their results." Frustration
is the mother of invention Chatterjee and Christian Kramme, a co-first
author of the paper, were frustrated. The two scientists were trying to
explore the genetic underpinnings of different aspects of biology --
like fertility, aging, and immunity -- by combining the strengths of
digital methods (think algorithms) and genetic engineering (think gene sequencing). But they kept running into problems with the various tools
and protocols they were using, which are commonplace in science labs.

The algorithms that purported to sift through an organism's genes to
identify those with a significant impact on a given biological process
could tell when a gene's expression pattern changed, but didn't provide
any insight into the cause of that change. When they wanted to test a
list of candidate genes in living cells, it wasn't immediately clear what
type of experiment they should run. And many of the tools available to
insert genes into cells and screen them were expensive, time-consuming,
and inflexible.



==========================================================================
"I was using methods known as Golden Gate and Gateway to clone genes into vectors for screening experiments, and it took me months and thousands
of dollars to clone 50 genes. And using Gateway, I couldn't physically
barcode the genes to identify which one got into which vector, which was
a crucial requirement for my downstream sequencing-based experimental
design. We figured there had to be a better way to do this kind of
research, and when we couldn't find one, we took on the challenge
of creating it ourselves," said Kramme, who is a graduate student at
the Wyss Institute and HMS, Kramme teamed up with co-first author and
fellow Church lab member Alexandru Plesa, who was experiencing identical frustrations making gene vectors for his project. Kramme, Plesa, and
Chatterjee then set to work outlining what would be required to make an end-to-end platform for genetic screening that would work for all of their projects, which ranged from protein engineering to fertility and aging.

From bits to the bench To improve the earliest stage of genetic research
-- identifying genes of interest to study -- the team created two new algorithms to help meet the need for computational tools that can analyze
and extract information from the increasingly large datasets that are
being generated via next-generation sequencing (NGS). The first algorithm
takes the standard data about a gene's expression level and combines
it with information about the state of the cell, as well as information
about which proteins are known to interact with the gene. The algorithm
gives a high score to genes that are highly connected to other genes
and whose activity is correlated with large, cell-level changes.

The second algorithm provides more high-level insight by generating
networks to represent the dynamic changes in gene expression during
cell-type differentiation and then applying centrality measures, such
as Google's PageRank algorithm, to rank the key regulators of the process.

"The computational part of genetic studies is like a Jenga game: if
each block in the tower represents a gene, we're looking for the genes
that make up the base of the Jenga tower, the ones that hold the whole
thing up. Most algorithms can only tell you which genes are in the same
row as each other, but ours allow you to home in on how far up or down
the tower they are, so you can quickly identify the ones that have the
biggest influence on the cell state in question," said Chatterjee.



==========================================================================
Once the target genes have been identified, the STAMPScreen protocol
moves from the laptop to the lab, where experiments are performed to
disrupt those genes in cells and see what effect that perturbation has
on the cell. The team of researchers systematically evaluated multiple
gene perturbation tools including complementary DNA (cDNA) and several
versions of CRISPR in human induced pluripotent stem cells (hiPSCs),
the first known head-to-head comparisons performed entirely in this
highly versatile yet challenging cell type.

They then created a new tool that allows CRISPR and cDNA to be used within
the same cell to unlock synergies between the two methods. For example,
CRISPR can be used to turn off expression of all isoforms of a gene,
and cDNA can be used to sequentially express each isoform individually, allowing more nuanced genetic studies and greatly reducing background expression of off-target genes.

Scanning library barcodes The next step in many genetic experiments is generating a screening library for introducing genes into cells and
observing their effects. Typically, gene fragments are inserted into
bacterial plasmids (circular pieces of DNA) using methods that work well
for small pieces of DNA, but are cumbersome to use when inserting larger
genes. Many of the existing methods also rely on a technique called
Gateway, which uses a process called lambda phage recombination and the production of a toxin to kill off any bacteria that did not receive a
plasmid with the gene of interest. The toxin in these plasmids is often cumbersome to work with in the lab, and can be inadvertently inactivated
when a "barcode" sequence is added to a vector to help researchers
identify which gene-bearing plasmid the vector received.

Kramme and Plesa were working with Gateway when they realized that these problems could be solved if they eliminated the toxin and replaced it with short sequences on the plasmid that would be recognized and cut by a type
of enzyme called meganucleases. Meganuclease recognition sequences do not appear in the genes of any known organism, thus ensuring that the enzyme
will not accidentally cut the inserted gene itself during cloning. These recognition sequences are naturally lost when a plasmid receives a gene of interest, making those plasmids immune to meganuclease. Any plasmids that
do not successfully receive the gene of interest, however, retain these recognition sequences and are cut to pieces when a meganuclease is added, leaving only a pure pool of plasmids containing the inserted gene. The
new method, which the researchers dubbed MegaGate, had a cloning success
rate of ??99.8% and also allowed them to barcode their vectors with ease.

"MegaGate not only solves many of the problems that we kept running into
with older cloning methods, it is also compatible with many existing
gene libraries like the TFome and hORFeome. You can essentially take
Gateway and meganucleases off the shelf, put them together with a
library of genes and a library of barcoded destination vectors, and
two hours later you have your barcoded genes of interest. We've cloned
nearly 1,500 genes with it, and have yet to have a failure," said Plesa,
who is a graduate student at the Wyss Institute and HMS.

Finally, the researchers demonstrated that their barcoded vectors could
be successfully inserted into living hiPSCs, and pools of cells could
be analyzed using NGS to determine which delivered genes were being
expressed by the pool.

They also successfully used a variety of methods, including RNA-Seq,
TAR-Seq, and Barcode-Seq, to read both the genetic barcodes and the
entire transcriptomes of hiPSCs, enabling researchers to use whichever
tool they are most familiar with.

The team anticipates that STAMPScreen could prove useful for a wide
variety of studies, including pathway and gene regulatory network
studies, differentiation factor screening, drug and complex pathway characterizations, and mutation modeling. STAMPScreen is also modular,
allowing scientists to integrate different parts of it into their own workflows.

"There's a treasure trove of information housed in publicly available
genetic datasets, but that information will only be understood if we
use the right tools and methods to analyze it. STAMPScreen will help researchers get to eureka moments faster and speed up the pace of
innovation in genetic engineering," said senior author George Church,
Ph.D., a Wyss Core Faculty member who is also a Professor of Genetics
at HMS and Professor of Health Sciences and Technology at Harvard and MIT.

"At the Wyss Institute we aim for impactful 'moonshot' solutions to
pressing problems, but we know that to get to the moon, we have to first
build a rocket.

This project is a great example of how our community innovates on-the-fly
to enable scientific breakthroughs that will change the world for the
better," said Wyss Founding Director Don Ingber, M.D., Ph.D., who is also
the Judah Folkman Professor of Vascular Biology at HMS and the Vascular
Biology Program at Boston Children's Hospital, as well as Professor
of Bioengineering at Harvard John A. Paulson School of Engineering and
Applied Sciences.

Additional authors of the paper include Helen Wang, Bennett Wolf, Merrick Smela, Xiaoge Guo, Ph.D., and Richie Kohman, Ph.D. from the Wyss Institute
and HMS.

========================================================================== Story Source: Materials provided
by Wyss_Institute_for_Biologically_Inspired_Engineering_at
Harvard. Original written by Lindsay Brownell. Note: Content may be
edited for style and length.


========================================================================== Journal Reference:
1. Christian Kramme, Alexandru M. Plesa, Helen H. Wang, Bennett Wolf,
Merrick Pierson Smela, Xiaoge Guo, Richie E. Kohman, Pranam
Chatterjee, George M. Church. An integrated pipeline for mammalian
genetic screening.

Cell Reports Methods, 2021 DOI: 10.1016/j.crmeth.2021.100082 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210927110454.htm

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