Author
Shi Yin Foo
Bio: Shi Yin Foo is an academic researcher from Harvard University. The author has contributed to research in topics: Antibody & Small hairpin RNA. The author has an hindex of 1, co-authored 1 publications receiving 1630 citations.
Papers
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TL;DR: A screen based on high-content imaging was developed to identify genes required for mitotic progression in human cancer cells and applied to an arrayed set of 5,000 unique shRNA-expressing lentiviruses that target 1,028 human genes, providing a widely applicable resource for loss-of-function screens.
1,760 citations
TL;DR: In this article , the frequency of anti-DSG2 antibodies in a population of post COVID-19 patients was determined by electrochemiluminescent-based immunoassay utilizing the extracellular domain of DSG2.
10 citations
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TL;DR: This paper identified the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes.
7,192 citations
TL;DR: The first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler is described, which can address a variety of biological questions quantitatively.
Abstract: Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
4,578 citations
TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
Abstract: Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.
4,119 citations
Broad Institute1, Harvard University2, Novartis3, Brigham and Women's Hospital4, Jikei University School of Medicine5, Boston Children's Hospital6, University of North Carolina at Chapel Hill7, University of Texas Southwestern Medical Center8, Nagoya City University9, Autonomous University of Barcelona10, University Health Network11, Cornell University12, Beth Israel Deaconess Medical Center13, Memorial Sloan Kettering Cancer Center14, University of Pennsylvania15, University of Michigan16, University of Washington Medical Center17, Howard Hughes Medical Institute18, Massachusetts Institute of Technology19
TL;DR: It is demonstrated that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival, and a large majority of SCNAs identified in individual cancer types are present in several cancer types.
Abstract: A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-kappaBeta pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types.
3,375 citations
TL;DR: In this paper, a small-molecule bromodomain inhibitor, JQ1, was used to identify BET proteins as regulatory factors for c-Myc oncoprotein.
2,500 citations