Multiplexing Phenotype and Function for More Biologically Relevant Insights - 28

Related Article from
Yet another difficulty is the need to limit the potential for data bias, such as that
which arises when positive controls are used. In high-throughput screening (HTS),
positive controls are needed at multiple steps, such as assessment of plate quality,
or optimization of experimental design. Historically, controls have been regarded
as little more than a technical issue, but developers are starting to appreciate the
essential roles of several types of controls at multiple steps of the discovery process.
Finally, high-content screening needs to continue incorporating machine learning
algorithms, which are expected to become routine in the earliest steps of drug
discovery. "Artificial intelligence will enable us to integrate a huge mass of information," predicts Dr. Stephan.
He points out, however, that information that is accumulated or examined piecemeal may be inaccurate or capture too little biological complexity. Comprehensive
approaches, in contrast, would be more powerful. They could even streamline
screening efforts.
Deep neural networks could help preselect some compounds in a first phase of
the screening. "Then we can go into an enriched set of compounds," explains Dr.
Stephan, "and use a more complex model to rapidly identify compounds that have a
chance to be successful."
Spheroid Optical Clearing
"We developed a high-throughput pipeline for spheroid optical clearing, fluorescent
high-content confocal imaging, and nuclear segmentation," says Molly E. Boutin,
Ph.D., biologist at the National Institute of Health's National Center for Advancing
Translational Sciences. This work is helping to refine 3D cell culture models, which
are already contributing to drug discovery.
Many of the 3D models that have been used to interrogate biology have been
conducted with spheroids. In spheroids, however, tasks such as imaging and data
analysis still pose challenges in HTS.
"Imaging through layers of cells generates a lot of light scattering," points out
Dr. Boutin. Optical clearing protocols were developed to allow imaging deeper
in tissues, but only a limited number of studies have explored them in a highthroughput context. "These are very simple analysis techniques," she continues.
28

| January, 2019

"They usually don't look at where the cells are three dimensionally within the
spheroid."
For example, to predict the cytotoxicity of a drug, the size of a sphere in a bright field
microscopy image would be used as an indicator of cell death. "But that does not
indicate whether the cells that are dying are on the outside or on the inside of the
sphere, or what exactly is going on," says Dr. Boutin.
Dr. Boutin and colleagues recently developed a high-throughput spheroid optical
clearing and nuclear segmentation pipeline and described how it was used to
examine about 558,000 image files from 3000 spheroids derived from breast
carcinoma and primary glioblastoma cell lines. Using this automated protocol, the
scientists could image a 384-well plate in 1-2.5 hours. Also, the platform allowed the
scientists to customize post-segmentation analyses based on individual users' needs.
In this proof-of-concept study, Dr. Boutin and colleagues demonstrated the ability
of the segmentation algorithm to identify several subpopulations of fluorescently
labeled cells within individual spheroids.
"One of the fields that is growing at present is that of machine learning algorithms,
which allow users to train a program to learn what a dataset is like," informs Dr. Boutin.
"From the learning process, one can predict what an unknown dataset would be."
For example, using a control and treatment image dataset, one may train the
program to determine whether an unknown treatment would cause a specific
phenotype. An advantage of machine learning is the introduction of less user bias
for steps where a threshold is manually chosen. "We did not include any machine
learning analyses in our algorithm," admits Dr. Boutin, "but there are a few parts of
the analysis where we would like to do that."
Directed Differentiation
"Our lab is interested in how different genetic and environmental factors contribute
to disease progression and how we can find drugs that can rescue the defects," says
Shuibing Chen, Ph.D., associate professor of surgery and biochemistry, Weill Cornell
Medical College. In a recent study, Dr. Chen and colleagues developed a differentiation protocol to examine the role of Glis3, a gene associated with type 1 and type 2
diabetes, in the biology of human pancreatic beta cells.


http://www.genengnews.com

Multiplexing Phenotype and Function for More Biologically Relevant Insights

Table of Contents for the Digital Edition of Multiplexing Phenotype and Function for More Biologically Relevant Insights

Contents
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 1
Multiplexing Phenotype and Function for More Biologically Relevant Insights - Contents
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 3
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 4
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 5
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 6
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 7
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 8
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 9
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 10
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 11
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 12
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 13
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 14
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 15
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 16
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 17
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 18
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 19
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 20
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 21
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 22
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 23
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 24
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 25
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 26
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 27
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 28
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 29
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 30
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 31
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 32
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 33
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 34
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 35
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 36
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 37
Multiplexing Phenotype and Function for More Biologically Relevant Insights - 38
https://www.nxtbookmedia.com