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Your search keyword '"Tien Phan-Everson"' showing total 2 results
Start Over Author "Tien Phan-Everson" Topic biotechnology
2 results on '"Tien Phan-Everson"'

1. A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics

Author

Felipe Segato Dezem, Maycon Marção, Bassem Ben-Cheikh, Nadya Nikulina, Ayodele Omotoso, Destiny Burnett, Priscila Coelho, Judith Hurley, Carmen Gomez, Tien Phan-Everson, Giang Ong, Luciano Martelotto, Zachary R. Lewis, Sophia George, Oliver Braubach, Tathiane M. Malta, and Jasmine Plummer

Subjects
Single cell, Spatial, Machine learning, Cancer stem, Proteomic, Transcriptomic, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

Published
2023
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2. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging

Author

Shanshan He, Ruchir Bhatt, Carl Brown, Emily A. Brown, Derek L. Buhr, Kan Chantranuvatana, Patrick Danaher, Dwayne Dunaway, Ryan G. Garrison, Gary Geiss, Mark T. Gregory, Margaret L. Hoang, Rustem Khafizov, Emily E. Killingbeck, Dae Kim, Tae Kyung Kim, Youngmi Kim, Andrew Klock, Mithra Korukonda, Alecksandr Kutchma, Zachary R. Lewis, Yan Liang, Jeffrey S. Nelson, Giang T. Ong, Evan P. Perillo, Joseph C. Phan, Tien Phan-Everson, Erin Piazza, Tushar Rane, Zachary Reitz, Michael Rhodes, Alyssa Rosenbloom, David Ross, Hiromi Sato, Aster W. Wardhani, Corey A. Williams-Wietzikoski, Lidan Wu, and Joseph M. Beechem

Subjects
Biomedical Engineering, Molecular Medicine, Bioengineering, Applied Microbiology and Biotechnology, Biotechnology
Abstract

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

Published
2022
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