LINCS Virtual Symposium (November 19-20, 2020)

Video recordings of the LINCS Virtual Symposium are available on the BD2K-LINCS DCIC YouTube channel.

The Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program was launched in 2010. Its focus is perturbational biology undertaken at scale to create community resources for query and analysis. By using a multi-omic approach, LINCS investigators have built a catalog of cellular responses (signatures) from various assays (transcriptomics, proteomics, imaging) performed on a variety of different cell types following genetic, small molecule, antibody, or microenvironment perturbations.

The LINCS Virtual Symposium took place on November 19-20, 2020 beginning at 11:00AM EST each day. The symposium featured major scientific achievements and insights on:

  • Drug action and prediction of drug-related adverse events
  • Integration of multiple data types
  • Methodologies for rigorous reproducible biological research
  • Computational tools for data integration and data FAIRness
  • Future challenges in perturbational biology

The symposium also included presentations from industry and academic users of LINCS data and tools. Links to video recordings of the LINCS Virtual Symposium are now available on the BD2K-LINCS DCIC YouTube channel. You can also follow #LINCSVirtualSymposium on Twitter for the latest updates.

LINCS Virtual Symposium Agenda

Day 1: November 19, 2020

(all times are listed in US EST)

Webinar: Video recordings are available on the BD2K-LINCS DCIC YouTube channel

Session I: Undertaking Large-Scale Perturbation Studies: New Biology and Lessons
Time Topic Speaker
11:00-11:10 am US EST Welcome and Introduction Ajay Pillai and Albert Lee, NIH
11:10-11:30 am US EST Generating and mining signatures of drug-induced perturbations Peter Sorger, Harvard Medical School, HMS LINCS
11:30-11:50 am US EST Transcriptomic signatures for drug adverse event prediction in human iPSC- derived cardiomyocyte Ravi Iyengar, Mt. Sinai, DToxS
11:50-12:10 pm US EST The impact of microenvironmental signals on molecular and cellular phenotype Laura Heiser, Oregon Health & Science University, MEP-LINCS
12:10-12:30 pm US EST Using perturbational data to inform mechanism of action and drug repurposing Todd Golub, Broad Transcriptomics
12:30-12:50 pm US EST Establishing an iPSC-based multi-omics and cell based pipeline for motor neuron disease Clive Svendsen, Cedars Sinai, NeuroLINCS
12:50-1:10 pm US EST Unbiased epigenetic and phosphoproteomic profiling of APOE4 lines suggests novel biology and new potential therapeutics Maeve Bonner, MIT/Broad Proteomics
1:10-1:30 pm US EST Where to find and how to use the LINCS data and tools? Dusica Vidovic and Mario Medvedovic, DCIC
1:30-2:00 pm US EST Q&A Session Steve Finkbeiner, UCSF, NeuroLINCS
2:00-2:30 pm US EST Break  

Session II: Impact of LINCS on the Community: Short Community Vignettes
Time Topic Presenter
2:30-2:55pm US EST PredicTox Knowledge Environment: LINC-ing Data to Cardiotoxicity Rebecca Racz, FDA Pharmacologist, Division of Applied Regulatory Science
2:55-3:10pm US EST Generating hit-like molecules from L1000 profiles using artificial intelligence Oscar Méndez-Lucio, Scientist, The Janssen Pharmaceutical Companies of Johnson & Johnson
3:10-3:25pm US EST Identifying Novel Addiction Treatment Strategies through Gene Expression and LINCS Analysis Dayne Mayfield, University of Texas at Austin
3:25-3:40pm US EST Using LINCS to prioritize compounds for oncology and beyond Sikander Hayat, Senior Computational Scientist I, Bayer Pharmaceuticals
3:40-4:10pm US EST Q&A Session Mario Medvedovic, University of Cincinnati, DCIC
4:10-4:25pm US EST Break  

Session III: Hands-On Workshop/Poster Session (Parallel sessions)

Time Topic Presenter
4:25-5:30pm US EST LINCS Transcriptomics: Data, Tools, and Workflows for Connectivity Map analysis Rajiv Narayan, Ted Natoli, Anup Jonchhe and Jacob Asiedu, Broad Transcriptomics
4:25-5:30pm US EST Proteomics: Round table discussions focused on extending usage of currently available LINCS data Andrea Matlock, Cedars Sinai, NeuroLINCS and Mike MacCoss, U Washington, Broad Proteomics
4:25-5:30pm US EST Rapid high-content measurement of perturbagen response in living cells: DyeDrop Assays, GR metrics and multi-center reproducibility in the face of confounding variables Caitlin Mills, HMS LINCS
4:25-5:30pm US EST MCF10A: Building an integrative data matrix of perturbation responses Laura Heiser, Sean Gross, and Mark Dane, OHSU, MEP-LINCS
4:25-5:30pm US EST

Poster Session:

P1: Cardiomyocytes from Healthy Human Subjects to delineate mechanisms of myocardial damage in COVID-19—Mustafa Siddiq, Mt. Sinai School of Medicine, DToxS

P2: Predicting drivers of drug response from baseline omics data across breast cancer cell lines and models—Chiara Victor, Harvard Medical School, HMS LINCS 

P3: DyeDrop: a high-throughput single cell microscopy platform for phenotyping the response of cancer cell lines to therapeutic agents—Mirra Chung, Harvard Medical School, HMS LINCS

P4: Feature Controlled Variational Autoencoder for Single Cell Image Analysis—Luke Ternes, Oregon Health & Science University, MEP-LINCS

P5: Oncostatin M induces migratory and morphological changes in mammary epithelial cells—Ian McLean, Oregon Health & Science University, MEP-LINCS

P6: Appyters: Turning Jupyter Notebooks into Data Driven Web Apps—Daniel Clarke, Mt. Sinai School of Medicine, DCIC

P7: Access to LINCS data via the LINCS Data Portal and Google Big Query—Amar Koleti, University of Miami, DCICC

P8: Cheminformatics survey of LINCS data—Tanya Kelley, University of Miami, DCIC

P9: Sig2Lead: Accelerating Drug Discovery and Re-purposing with Connectivity Enhanced Structure Activity Relationship—Jim Reigel, University of Cincinnati College of Medicine, DCIC

P10: Transcriptional footprints of signaling pathway activity with LINCS genetic loss of function signatures—Yan Ren, University of Cincinnati College of Medicine, DCIC

P11: Results from the LINCS Mechanism of Action (MoA) Prediction Challenge—Andrea Blasco, Broad Transcriptomics

P12: Identification of dysregulated disease networks in ALS—Johnny Li, NeuroLINCS

P13: Robotic Microscopy and Multidimensional Feature Analysis to Interrogate iPSC-Derived Neurons from ALS patients—Julia Kaye, NeuroLINCS

 

Day 2: November 20, 2020

(all times are listed in US EST)

Webinar: Video recordings are available on the BD2K-LINCS DCIC YouTube channel

Session IV: Integrative Data Analysis within Perturbational Studies
Time Topic Presenter
11:00-11:10 am US EST Day 2 Introduction Ajay Pillai and Albert Lee, NIH
11:10-11:30 am US EST Large-scale gene expression compendia from LINCS and Connectivity Map: Computational approaches, tools, and challenges Aravind Subramanian, Broad Transcriptomics
11:30-11 :50 am US EST A library of well-characterized 40 healthy human subject iPSC lines: Generation and Uses Christoph Schaneil and Nicole Dubois, Mt.Sinai, DToxS
11:50-12:10 pm US EST Interactions between drug, genomic and environmental perturbations Jim Korkola, OHSU, MEP-LINCS
12:10-12:30 pm US EST Data integration of multi-omic data sets to reveal disease signatures Leslie Thompson, UC Irvine, NeuroLINCS
12:30-12:50 pm US EST Integrating and mining multi-omic data on cell state and phenotype Caitlin Mills, HMS LINCS
12:50-1:10 pm US EST A mass spectrometry cloud-based pipeline enables the accurate analysis of thousands of phosphosites in P100 datasets Karen Christianson, Broad Proteomics
1:10-1:30pm US EST The many ways the community utilized the LINCS resources Avi Ma’ayan, Mt. Sinai, DCIC
1:30-2:00 pm US EST Q&A Session Eric Sobie, Mt. Sinai, DToxS
2:00-2:30 pm US EST Break  

Session V: Impact of LINCS on the Community: Short Community Vignettes
Time Topic Presenter
2:30-2:55 pm US EST gDR: An integrative suite for processing, storing, and visualizing drug response data Marc Hafner, Scientist in Bioinformatics at Genentech
2:55-3:20 pm US EST Invasion of homogeneous and polyploid populations in nutrient-limiting environments Noemi Andor, Assistant Professor, Integrated Mathematical Oncology department, Moffitt Cancer Center
3:20-3:30 pm US EST LINCS-based approach to identify anti-atrophogenic compounds to protect skin from glucocorticoid-induced atrophy Irina Budunova, Professor, Feinberg School of Medicine
3:30-3:40 pm US EST Single-cell-driven drug repurposing in atherosclerosis Chiara Giannarelli, Assistant Professor, Mount Sinai
3:40-3:50 pm US EST Utilizing LINCS data to identify therapeutic combinations in glioblastoma Nagi Ayad, Associate Professor and Co-Director, University of Miami Brain Tumor Initiative
3:50-4:30 pm US EST Q&A Session Kathleen Jagodnik, Mt. Sinai, DCIC

Session VI: Hands-On Workshop/Poster Session (Parallel sessions)

Time Topic Presenter
4:30-5:30 pm US EST LINCS Transcriptomics: Data, Tools, and Workflows Daniel Clarke, Minji Jeon, and Avi Ma’ayan, Mt. Sinai, DCIC
4:30-5:30 pm US EST Use of iPSC Derived Cell Types for Perturbation Biology Dhruv Sareen & Arun Sharma, Cedars Sinai, NeuroLINCS; Priyanka Narayan, NIDDK; and Nicole Dubois & Mustafa Siddiq, Mt. Sinai, DToxS
4:30-5:30 pm US EST Accessing and Integrating LINCS Data with iLINCS and LINCS Data Portals Vasileios Stathias and Amar Koleti, U Miami; and Mario Medvedovic, U Cincinnati, DCIC
4:30-5:30 pm US EST LINCS Proteomics Analysis with piNET Jarek Meller and Behrouz Shamsaei, U Cincinnati, DCIC
4:30-5:30 pm US EST

Poster Session:

P1: Structural Signatures for Drug Action—Rayees Rahman, Mt. Sinai, DToxS

P2: Leave-pair-out cross-validation is the most robust method for evaluating model performance in presence of outliers and known confounders—Maulik Nariya, Harvard Medical School, HMS LINCS

P3: Reaction networks and toric geometry in single-cell, multiplex data—Shu Wang, Harvard Medical School, HMS LINCS

P4: A cloud-based pipeline for DIA data analysis enables phosphosignaling studies in genetic risk variants of Alzheimer’s Disease—Karen Christianson, Broad Proteomics

P5: Signature Commons: A Scalable Platform for Serving Diverse Biomedical Data—Erol Evangelista, Mt. Sinai School of Medicine, DCIC

P6: Deep Learning Models to Impute and Transform Gene Expression Across Platforms—Megan Wojciechowicz, Mt. Sinai School of Medicine, DCIC

P7: Automated Vertical Partitioning of Massive Co-Expression RNA-seq Data Improves Gene Function Prediction—Alexander Lachmann, Mt. Sinai School of Medicine, DCIC

P8: FAIR data management in the LINCS Consortium—Dusica Vidovic, University of Miami, DCIC

P9: Discovering existing medicines that abrogate cellular responses to SARS CoV-2 infection—Ted Natoli, Broad Transcriptomics

P10: Integrated Connectivity Analysis of Small-molecule and CRISPR signatures across Large-scale Compendia of Cellular fitness and Gene Expression—Rajiv Narayan, Broad Transcriptomics

P11: Differentially regulated PTMs and biological pathways identified in ALS neuronal cultures by capillary electrophoresis electro-spray ionization mass spectrometry (CESI-MS)—Andrea Matlock, Cedars-Sinai, NeuroLINCS

P12: Using Machine Learning to Uncover Non-linear Genomic Signatures of ALS—Leandro Lima and Julia Kaye, NeuroLINCS

P13: Transcriptomic signatures in motor neuron disease—Jenny Wu and Ryan Lim, NeuroLINCS

 

Video recordings of the LINCS Virtual Symposium are available on the BD2K-LINCS DCIC YouTube channel.

LINCS Virtual Symposium flyer

This page last reviewed on December 14, 2020