Predicting cell fates: Researchers develop AI solutions for next-gen biomedical research

Date:
February 2, 2022
Source:
Helmholtz Zentrum Mu"nchen - German Research Center for
Environmental Health
Summary:
Data is not only the answer to numerous questions in the business
world; the same applies to biomedical research. In order to develop
new therapies or prevention strategies for diseases, scientists
need more and better data, faster and faster. However, the quality
is often very variable and the integration of different data sets
often almost impossible.



FULL STORY ==========================================================================
Data is not only the answer to numerous questions in the business
world; the same applies to biomedical research. In order to develop
new therapies or prevention strategies for diseases, scientists need
more and better data, faster and faster. However, the quality is often
very variable and the integration of different data sets often almost impossible. With the Computational Health Center at Helmholtz Munich,
one of Europe's largest research centers for artificial intelligence in
medical science is now being established under the direction of Fabian
Theis. In close cooperation with the Technical University of Munich
(TUM), more than one hundred scientists are using artificial intelligence
and machine learning to discover solutions to precisely these problems,
thus enabling medical innovations for a healthier society. In the latest
issue of the journal Nature Methods, they present three articles with groundbreaking new solutions.


========================================================================== According to Fabian Theis, Head of the Computational Health Center at
Helmholtz Munich and Professor for Mathematical Modelling of Biological
Systems at TUM: "It's been a crazy 4 weeks, with many of our scientific
stories and methods coming to fruition in that same time window. Our
research groups focuses on using single cell genomics to understand the
origin of disease in a mechanistic fashion -- for this we leverage and
develop machine learning approaches to better represent this complex
data. In the three new paper, we worked on single cell data integration, trajectory learning and spatial resolution, respectively. Besides the applications shown in the papers, we expect to support the next generation
of single-cell research towards disease understanding." Here are the
latest solutions developed by Helmholtz Munich and TUM researchers:
Solving the data integration challenge To see whether an observation one
makes in a single dataset can be generalized, you can check whether the
same can be observed in other datasets of the same system. In single-cell
data, so-called batch effects complicate combining datasets in this
manner. These are differences in the molecular profiles between samples
as they were generated at a different time, in a different place, or
from a different person. Overcoming these effects is a central challenge
in single-cell genomics with more than 50 proposed solutions. But which
one is the best? A group of researchers around Malte Lu"cken carefully
curated 86 datasets and compared 16 of the most popular data integration methods on 13 tasks. After over 55,000 hours of computation time and
a detailed evaluation of 590 results, they built a guide for optimized
data integration.

This allows for improved observations on disease processes across datasets
at a population scale.

Predicting cell states with open-source software Many questions in biology revolve around continuous processes like development or regeneration. For
any cell in such a process, single-cell RNA-sequencing measures gene expression. The method, however, is destructive to cells and scientists
obtain only static snapshots. Thus, many algorithms have been developed
to reconstruct continuous processes from snapshots of gene expression. A
common limitation: These algorithms cannot tell us anything about the
direction of the process. To overcome this limitation, Marius Lange
and colleagues developed a new algorithm called CellRank. It estimates
directed cell-state trajectories by combining previous reconstruction approaches with RNA velocity, a concept to estimate gene up- or down-regulation. Across in- vitro and in-vivo applications, CellRank
correctly inferred fate outcomes and recovered previously known genes. In
a lung regeneration example, CellRank predicted novel intermediate
cell states on a dedifferentiation trajectory whose existence was
validated experimentally. CellRank is an open-source software package
that is already used by biologists and bioinformaticians around the
world to analyze complex cellular dynamics in situations like cancer, reprogramming or regeneration.

Visualizing spatial omics analysis Recent years have seen a growing
development of technologies to measure gene expression variation in
tissue. The advantage of such technologies is that scientists can see
cells in their context, thus being able to investigate principles of
tissue organization and cellular communication. Researchers need flexible computational frameworks in order to store, integrate and visualize the
growing diversity of such data. To tackle this challenge, Giovanni Palla, Hannah Spitzer, and colleagues developed a new computational framework,
called Squidpy. It enables analysts and developers to handle spatial
gene expression data. Squidpy integrates tools for gene expression and
image analysis to efficiently manipulate and interactively visualize
spatial omics data. Squidpy is extensible and can be interfaced with a
variety of machine learning tools in the python ecosystem. Scientists
around the world are already using it to analyze spatial molecular data.

========================================================================== Story Source: Materials provided by Helmholtz_Zentrum_Mu"nchen_-_German_Research_Center_for
Environmental_Health. Note: Content may be edited for style and length.


========================================================================== Journal References:
1. Malte D. Luecken, M. Bu"ttner, K. Chaichoompu, A. Danese,
M. Interlandi,
M. F. Mueller, D. C. Strobl, L. Zappia, M. Dugas,
M. Colome'-Tatche', Fabian J. Theis. Benchmarking atlas-level data
integration in single-cell genomics. Nature Methods, 2021; 19 (1):
41 DOI: 10.1038/s41592-021-01336- 8
2. Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard
Reuter,
Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Janine Schniering,
Herbert B. Schiller, Dana Pe'er, Fabian J. Theis. CellRank for
directed single- cell fate mapping. Nature Methods, 2022; DOI:
10.1038/s41592-021-01346-6
3. Giovanni Palla, Hannah Spitzer, Michal Klein, David Fischer, Anna
Christina Schaar, Louis Benedikt Kuemmerle, Sergei Rybakov,
Ignacio L.

Ibarra, Olle Holmberg, Isaac Virshup, Mohammad Lotfollahi,
Sabrina Richter, Fabian J. Theis. Squidpy: a scalable framework
for spatial omics analysis. Nature Methods, 2022; DOI:
10.1038/s41592-021-01358-2 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220202111804.htm

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