Biology subjects

Lee, M. J.

Publications and source records attributed to Lee, M. J..

42 records · Page 3Linked to original sources

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce weighted-nearest neighbor analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2020

ELP-dependent expression of MCL1 promotes resistance to EGFR inhibition in triple-negative breast cancer cells

Targeted therapies for the treatment of cancer are generally thought to exploit oncogene addiction, a phenomenon in which a single oncogene controls both the growth and survival of the tumor cell. Many well-validated examples of oncogene addiction exist; however, the utility of oncogene targeted therapies varies substantially by cancer context, even among cancers in which the targeted oncogene is similarly dysregulated. For instance, epidermal growth factor receptor (EGFR) signaling can be effectively targeted in EGFR-mutant non-small cell lung cancer (NSCLC), but not in triple-negative breast cancer (TNBC), where EGFR is activated to a similar degree. We find that EGFR controls a similar signaling/transcriptional network in TNBC and EGFR-mutant NSCLC cells, but only NSCLC cells respond to EGFR inhibition by activating cell death. To address this paradox and identify mechanisms that contribute to insensitivity to EGFR inhibition in TNBC, we performed a genome-wide CRISPR-Cas9 genetic knockout screen. Our screen identifies the Elongator (ELP) complex as a mediator of insensitivity to EGFR inhibition in TNBC. Depleting ELP proteins caused high levels of apoptotic cell death, in an EGFR inhibition-dependent manner. We find that the tRNA-modifying function of the ELP complex promotes drug insensitivity, by facilitating expression of the anti-apoptotic protein MCL1. Furthermore, pharmacological inhibition of MCL1 synergizes with EGFR inhibition across a panel of genetically diverse TNBC cells. Taken together, we find that TNBC "addiction" to EGFR signaling is masked by the ELP complex, and our study provides an actionable therapeutic strategy to overcome this resistance mechanism by co-targeting EGFR and MCL1. One sentence summaryThe Elongator Protein (ELP) Complex masks TNBC oncogene "addiction" to EGFR signaling, by promoting expression of the anti-apoptotic protein MCL1.

cancer biology↗

bioRxiv2020

Comparative analysis of primer-probe sets for the laboratory confirmation of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is newly emerging human infectious diseases, which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, also previously known as 2019-nCoV). Within two months of the outbreak, more than 80,000 cases of COVID-19 have been confirmed worldwide. Since the human to human transmission occurred easily and the human infection is rapidly increasing, the sensitive and early diagnosis is essential to prevent the global outbreak. Recently, World Health Organization (WHO) announced various primer and probe sets for SARS-CoV-2 previously developed in China, Germany, Hong Kong, Japan, Thailand, and USA. In this study, we compared the ability to detect SARS-CoV-2 RNA among the seven primer-probe sets for N gene and the three primer-probe sets for Orf1 gene. The result of the comparative analysis represented that the 2019-nCoV_N2, N3 of USA and the ORF1ab of China are the most sensitive primer-probe sets for N and Orf1 genes, respectively. Therefore, the appropriate combination from ORF1ab (China), 2019-nCoV_N2, N3 (USA), and NIID_2019-nCOV_N (Japan) sets should be selected for the sensitive and reliable laboratory confirmation of SARS-CoV-2.

microbiology↗

bioRxiv2020

Drug GRADE: an integrated analysis of population growth and cell death reveals drug- specific and cancer subtype-specific response profiles

In the pre-clinical evaluation of anti-cancer drugs, two different measurement approaches are used: relative viability, which scores an amalgam of growth arrest and cell death, and fractional viability, which more specifically scores the degree of cell killing. In this study, we directly quantify relationships between drug-induced growth inhibition and drug-induced cell death by counting live and dead cells over time using quantitative microscopy. We find that most drugs affect both growth and death, but with different proportions and with different relative timing. These features lead to a non-uniform and unpredictable relationship between the canonical relative and fractional drug response measurements. To unify these disparate measurements, we create a new data visualization and data analysis platform, called drug GRADE, which characterizes the degree to which cell death contributes to an observed reduction in population size for any given drug. Our new method reveals both drug- and genotype-specific drug responses, which are not captured using traditional pharmaco-metrics. Taken together, this study highlights the extremely idiosyncratic nature of drug-induced growth and cell death and provides a new analysis tool for quantitatively evaluating these behaviors.

systems biology↗

bioRxiv2019

Drug Combination Antagonism and Single Agent Dominance Result from Differences in Death Activation Kinetics

Therapeutic regimens for cancer generally involve drugs used in combinations. Most prior work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding sources of antagonistic interactions remains an important and understudied issue. To enrich for antagonistic interactions and reveal common features of these drug combinations, we screened all pairwise combinations of drugs characterized as canonical activators of different forms of regulated cell death. We find that this network is strongly enriched for antagonistic interactions, and in particular, enriched for an extreme form of antagonism, which we call \"single agent dominance\". Single agent dominance refers to antagonisms in which a two drug combination phenocopies one of the two agents. We find that dominance results from differences in the cell death onset time, with dominant drugs inducing death earlier and at faster rates than their suppressed counterparts. Finally, we explored the mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of PARP1. Taken together, our study reveals death activation kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

systems biology↗

bioRxiv2017

Fibroblasts from metastatic sites induce broad-spectrum drug desensitization via modulation of mitochondrial priming

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor micro-environment. Using a large-scale co-culture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not cell intrinsic, but rather a product of tumor-fibroblast interactions. Additionally, we find that fibroblast cells influence tumor drug response in two distinct and divergent manners, which were predicable based on the anatomical origin from which the fibroblasts were harvested. These divergent phenotypes result from modulation of \"mitochondrial priming\" of tumor cells, caused by secretion of inflammatory cytokines, such as IL6 and IL8, from stromal cells.

cancer biology↗