Anne E Carpenter, Ph.D. | Broad Institute of Harvard and MIT
"Using images as data to accelerate drug discovery"
Abstract: Microscopy images contain tremendous information about the state of cells, tissues, and organisms. Image-based profiling goes beyond measuring individual phenotypes that biologists already know are relevant to a particular disease. Instead, we stain many cellular components and extract thousands of morphological features from each cell’s image using an assay called Cell Painting. We then harvest similarities in these “profiles” to identify how diseases, drugs, and genes affect cells, which can uncover small molecules’ mechanism of action, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles, and identify novel therapeutics.
Bio: Dr. Carpenter is an Institute Scientist and Merkin Fellow at the Broad Institute of Harvard and MIT. Her research group develops algorithms and strategies for large-scale experiments involving images. The team’s open-source CellProfiler software is used by thousands of biologists worldwide (www.cellprofiler.org). Carpenter is a pioneer in image-based profiling, the extraction of rich, unbiased information from images for a number of important applications in drug discovery and functional genomics.
Carpenter focused on high-throughput image analysis during her postdoctoral fellowship at the Whitehead Institute for Biomedical Research and MIT’s CSAIL (Computer Sciences/Artificial Intelligence Laboratory). Her Ph.D. is in cell biology from the University of Illinois, Urbana-Champaign. Carpenter has been named an NSF CAREER awardee, an NIH MIRA awardee, a Massachusetts Academy of Sciences fellow (its youngest at the time), a Genome Technology “Rising Young Investigator”, and is listed in Deep Knowledge Analytics’ top-100 AI Leaders in Drug Discovery and Advanced Healthcare.
"Inference of causality in molecular pathways from live cell images"
Abstract: Defining cause-effect relations among molecular components is the holy grail of every cell biological study. It turns out that for many of the molecular pathways this task is far from trivial. Many of the pathways are characterized by functional overlap and nonlinear interactions between components. In this configuration, perturbation of one component may result in a measureable shift of the pathway output – commonly referred to as a phenotype – but it is strictly impossible to interpret the phenotype in terms of the roles the targeted component plays in the unperturbed system. This caveat of perturbation approaches applies equally to genetic perturbations, which lead to long-term adaptation, and more acute pharmacological and optogenetic approaches, which often induce short-term adaptation. For nearly two decades my lab has been devoted to circumventing this problem by exploiting basal fluctuations of unperturbed systems to establish cause-and-effect cascades between pathway components. Inspired by the field of econometrics, where predictive models of information flows are built entirely from passive observation of fluctuations in individual financial markets, we have developed a novel mathematical framework to determine nonlinear and transient interactions between molecular components. We are particularly interested in pathways that regulate cell morphogenesis. These pathways are intrinsically organized in information flows between components that are distributed not only in time but also in space. Therefore, we had to develop in parallel to fluctuation analysis a quantitative imaging workflow that would allow us to extract meaningful fluctuation series from live cell movies at the appropriate time and length scales. We have finally arrived at the point we believe the technology begins to work. This lecture will highlight some of the cornerstones in bioimage informatics and spatiotemporal fluctuation analysis methods we have put in place to map out the functional hierarchy and redundancy among pathways components that regulate cell protrusion.
Bio: Since July 2015, Gaudenz Danuser has been appointed as the inaugural chair of the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center. He also holds the Patrick E. Haggerty Distinguished Chair in Basic Biomedical Science and is a Scholar of the Cancer Prevention and Research Institute of Texas (CPRIT). Before moving to UTSW, Danuser directed research laboratories at ETH Zurich, at The Scripps Research Institute in La Jolla, and at Harvard Medical School.
Trained as an engineer (geodetic and electrical engineering/computer science), he entered the field of cell biology as a postdoctoral fellow in the Program for Architectural Dynamics of Living Cells at the MBL in Woods Hole. Since then, he has focused his research on the question of how chemical and mechanical signals integrate in the regulation of cytoskeleton dynamics and membrane trafficking. With his CPRIT recruitment he has redirected his efforts towards understanding the implications of mechanical and chemical cell shape regulation in migration and survival of the metastatic cell, including the roles mechanical cues play in conferring what his lab calls ‘mechanical drug resistance’. To address these questions his lab develops innovative quantitative imaging methods to experimentally probe these processes and uses mathematical modeling to compile the data in mechanistic systems analyses.
"Creating a state space of stem cell signatures"
Abstract: The Allen Institute for Cell Science is generating a state space of stem cell signatures. The goal is to identify cell states, understand cell organization and elucidate how cells transition from state to state. We are doing this by conjoining single cell RNAseq, high replicate 3D live cell imaging of cell lines gene-edited with GFP tagged proteins, computational analyses, and visualization. These studies not only address basic science questions but also have implications for disease studies and regenerative medicine.
Bio: Susanne Rafelski is a quantitative cell biologist and the Director of Assay Development at the Allen Institute for Cell Science. Prior to joining the Institute in 2016, Susanne was an Assistant Professor in the Department of Developmental and Cell Biology, the Department of Biomedical Engineering, and the Center for Complex Biological Systems at UC Irvine. Susanne began imaging live cells and visualizing intracellular dynamics in 3D when she was 17 and hasn’t been able to stop since. Her life-long scientific goal is to decipher the patterns and rules that transform the overwhelming complexity found inside cells into functioning units of life. She believes that to do this we must understand the organization of the structures within the cell in space and time. Susanne takes an interdisciplinary, quantitative approach to cell biology, combining live-cell image-based assays, molecular genetics, and computational methods.
"Spatiotemporal dissection of the human proteome"
Abstract: Resolving the spatial distribution of the human proteome at a subcellular level increases our understanding of human biology and disease. In the Human Protein Atlas project, we are systematically mapping the human proteome in a multitude of human cells and organs using microscopy. I will present how this set of millions of images constitute a resource for biology and various approaches for the computational interpretation of subcellular patterns in such images. In addition, I will present results from crowd-sourced efforts such as a Kaggle challenge and the citizen science effort “Project Discovery” integrated into a massively-multiplayer online game that has engaged more than 300,000 players world-wide. In summary, I will demonstrate the importance of spatial proteomics data for improved single cell biology and present how the freely available Human Protein Atlas database can be used as resource in life science.
Bio: Dr. Lundberg is Associate Professor in cell biology proteomics at KTH Royal Institute of Technology, Sweden, and Director of the Cell Atlas, part of the Human Protein Atlas program. She is currently spending two sabbatical years as visiting Associate Professor at Stanford and the Chan-Zuckerberg Biohub. In the interface between bioimaging, proteomics and artificial intelligence her research aims to define the spatiotemporal organization of the human proteome at a subcellular level, with the goal to understand how variations and deviations in protein expression patterns can contribute to cellular function and disease. Dr. Lundberg has a keen interest in citizen science, and has engaged over 300,000 gamers to help her research through the first MMO citizen science computer game.
"Large-Scale Circuit Reconstruction in the Cerebral Cortex"
Abstract: Over the past decade, new tools have emerged for studying synaptic networks in the brain, a field now known as microscale connectomics. Large-scale serial-section electron microscopy in particular is providing increasingly complete circuit reconstructions. We have recently imaged a volume of cerebral cortex encompassing a complete local circuit within the cerebral cortex, with ~100,000 neurons and 109 interconnections. I’ll discuss the technical challenges in creating this petascale data set, along with preliminary results on the highly specific interconnections between distinct cortical cell types. High-throughput electron microscopy, along with dense segmentation of the data with machine learning (here performed by our collaborators at Princeton), is rapidly increasingly our ability to reveal the structure of neuronal circuits.
Bio: Clay Reid is Senior Investigator at the Allen Institute for Brain Science, where he started a department in 2012 to study how information is encoded and processed in neural networks of the visual system. Prior to joining the Allen Institute, Reid was Professor of Neurobiology at Harvard Medical School. Throughout his career, he has used a combination of imaging and anatomical approaches to investigate how the structure of neural connections relates to the functional of cortical circuits. He has helped to pioneer new methods for simultaneously recording increasingly large ensembles of neurons to study information processing. More recently, he has developed methods to analyze connections in these ensembles using large-scale anatomical reconstructions with electron microscopy.