New computational tool predicts cell fates and genetic perturbations


Date:
February 1, 2022
Source:
Whitehead Institute for Biomedical Research
Summary:
Researchers have built a machine learning framework that can define
the mathematical equations describing a cell's trajectory from one
state to another, such as its development from a stem cell into one
of several different types of mature cell. The framework, called
dynamo, can also be used to figure out the underlying mechanisms
-- the specific cocktail of gene activity -- driving changes in
the cell.



FULL STORY ========================================================================== Imagine a ball thrown in the air: it curves up, then down, tracing an
arc to a point on the ground some distance away. The path of the ball
can be described with a simple mathematical equation, and if you know the equation, you can figure out where the ball is going to land. Biological systems tend to be harder to forecast, but Whitehead Institute Member
Jonathan Weissman, postdoc in his lab Xiaojie Qiu, and collaborators at
the University of Pittsburgh School of Medicine are working on making
the path taken by cells as predictable as the arc of a ball. Rather than looking at how cells move through space, they are considering how cells
change with time.


========================================================================== Weissman, Qiu, and collaborators Jianhua Xing, professor of computational
and systems biology at the University of Pittsburgh School of Medicine,
and Xing lab graduate student Yan Zhang have built a machine learning
framework that can define the mathematical equations describing a cell's trajectory from one state to another, such as its development from a stem
cell into one of several different types of mature cell. The framework,
called dynamo,can also be used to figure out the underlying mechanisms
-- the specific cocktail of gene activity -- driving changes in the
cell. Researchers could potentially use these insights to manipulate
cells into taking one path instead of another, a common goal in biomedical research and regenerative medicine.

The researchers describe dynamoin a paper published in the journal Cellon February 1. They explain the framework's many analytical capabilities
and use it to help understand mechanisms of human blood cell production,
such as why one type of blood cell forms first (appears more rapidly
than others).

"Our goal is to move towards a more quantitative version of single
cell biology," Qiu says. "We want to be able to map how a cell changes
in relation to the interplay of regulatory genes as accurately as
an astronomer can chart a planet's movement in relation to gravity,
and then we want to understand and be able to control those changes."
How to map a cell's future journey Dynamouses data from many individual
cells to come up with its equations. The main information that it requires
is how the expression of different genes in a cell changes from moment
to moment. The researchers estimate this by looking at changes in the
amount of RNA over time, because RNA is a measurable product of gene expression. In the same way that knowing the starting position and
velocity of a ball is necessary to understand the arc it will follow, researchers use the starting levels of RNAs and how those RNA levels are changing to predict the path of the cell. However, calculating changes in
the amount of RNA from single cell sequencing data is challenging, because sequencing only measures RNA once. Researchers must then use clues like
RNA- being-made at the time of sequencing and equations for RNA turnover
to estimate how RNA levels were changing. Qiu and colleagues had to
improve on previous methods in several ways in order to get clean enough measurements for dynamo to work. In particular, they used a recently
developed experimental method that tags new RNA to distinguish it from
old RNA, and combined this with sophisticated mathematical modeling,
to overcome limitations of older estimation approaches.



==========================================================================
The researchers' next challenge was to move from observing cells at
discrete points in time to a continuous picture of how cells change. The difference is like switching from a map showing only landmarks to a map
that shows the uninterrupted landscape, making it possible to trace the
paths between landmarks. Led by Qiu and Zhang, the group used machine
learning to reveal continuous functions that define these spaces.

"There have been tremendous advances in methods for broadly profiling transcriptomes and other 'omic' information with single-cell
resolution. The analytical tools for exploring these data, however,
to date have been descriptive instead of predictive. With a continuous function, you can start to do things that weren't possible with just
accurately sampled cells at different states. For example, you can ask:
if I changed one transcription factor, how is it going to change the
expression of the other genes?" says Weissman, who is also a professor
of biology at the Massachusetts Institute of Technology (MIT), a member
of the Koch Institute for Integrative Biology Research at MIT, and an investigator of the Howard Hughes Medical Institute.

Dynamocan visualize these functions by turning them into math-based
maps. The terrain of each map is determined by factors like the relative expression of key genes. A cell's starting place on the map is determined
by its current gene expression dynamics. Once you know where the cell
starts, you can trace the path from that spot to find out where the cell
will end up.

The researchers confirmed dynamo's cell fate predictions by testing it
against cloned cells-cells that share the same genetics and ancestry. One
of two nearly-identical clones would be sequenced while the other clone
went on to differentiate. Dynamo's predictions for what would have
happened to each sequenced cell matched what happened to its clone.

Moving from math to biological insight and non-trivial predictions With
a continuous function for a cell's path over time determined, dynamo can
then gain insights into the underlying biological mechanisms. Calculating derivatives of the function provides a wealth of information, for
example by allowing researchers to determine the functional relationships between genes - - whether and how they regulate each other. Calculating acceleration can show that a gene's expression is growing or shrinking
quickly even when its current level is low, and can be used to reveal
which genes play key roles in determining a cell's fate very early
in the cell's trajectory. The researchers tested their tools on blood
cells, which have a large and branching differentiation tree. Together
with blood cell expert Vijay Sankaran of Boston Children's Hospital,
the Dana-Farber Cancer Institute, Harvard Medical School, and Broad
Institute of MIT and Harvard, and Eric Lander of Broad Institute,
they found that dynamoaccurately mapped blood cell differentiation and confirmed a recent finding that one type of blood cell, megakaryocytes,
forms earlier than others. Dynamoalso discovered the mechanism behind this early differentiation: the gene that drives megakaryocyte differentiation, FLI1, can self-activate, and because of this is present at relatively
high levels early on in progenitor cells. This predisposes the progenitors
to differentiate into megakaryocytes first.

The researchers hope that dynamocould not only help them understand how
cells transition from one state to another, but also guide researchers
in controlling this. To this end, dynamoincludes tools to simulate
how cells will change based on different manipulations, and a method
to find the most efficient path from one cell state to another. These
tools provide a powerful framework for researchers to predict how to
optimally reprogram any cell type to another, a fundamental challenge
in stem cell biology and regenerative medicine, as well as to generate hypotheses of how other genetic changes will alter cells' fate.

There are a variety of possible applications.

"If we devise a set of equations that can describe how genes within
a cell regulate each other, we can computationally describe how to
transform terminally differentiated cells into stem cells, or predict
how a cancer cell may respond to various combinations of drugs that
would be impractical to test experimentally," Xing says.

Dynamo moves beyond merely descriptive and statistical analyses of
single cell sequencing data to derive a predictive theory of cell fate transitions. The dynamo toolset can provide deep insights into how cells
change over time, hopefully making cells' trajectories as predictable
for researchers as the arc of a ball, and therefore also as easy to
change as switching up a pitch.

========================================================================== Story Source: Materials provided by Whitehead_Institute_for_Biomedical_Research. Original written by Greta
Friar. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Xiaojie Qiu, Yan Zhang, Jorge D. Martin-Rufino, Chen Weng, Shayan
Hosseinzadeh, Dian Yang, Angela N. Pogson, Marco Y. Hein, Kyung Hoi
(Joseph) Min, Li Wang, Emanuelle I. Grody, Matthew J. Shurtleff,
Ruoshi Yuan, Song Xu, Yian Ma, Joseph M. Replogle, Eric S. Lander,
Spyros Darmanis, Ivet Bahar, Vijay G. Sankaran, Jianhua Xing,
Jonathan S.

Weissman. Mapping transcriptomic vector fields of single
cells. Cell, 2022; DOI: 10.1016/j.cell.2021.12.045 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220201115139.htm

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