Hindsight 2020 Virtual Symposium

The Allen Institute hosted a reimagined, virtual Hindsight 2020 - The Allen Institute Developmental Recording Symposium. This meeting featured the latest insights from leading researchers in the fields of cell lineage and developmental recording, chaired by Jay Shendure, M.D., Ph.D. and Michael Elowitz, Ph.D., of the Allen Discovery Center at UW Medicine.

The symposium was previously scheduled for March 3-5, 2020 at the Allen Institute in Seattle, WA. In order to accommodate the full lineup of speakers in a virtual format, the symposium was presented as series of virtual events.

The kickoff event was hosted by the Allen Institute as a Zoom Webinar on November 18, 2020.

View Kickoff Event Recording

Future seminars in the Hindsight 2020 Virtual Series will be hosted by the Allen Discovery Center at UW Medicine.

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See below for the agenda and speaker information for the Hindsight 2020 virtual series kick-off that was held on November 18, 2020.

WEDNESDAY, 11/18/2020
8:30am-12:05pm Pacific Time

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Please contact with any questions. 

Event Overview (All Times Pacific)

Wednesday, November 18:

8:20-8:30am - Virtual meeting log-in
8:30-8:40am - Kathy Richmond, The Paul G. Allen Frontiers Group and Jay Shendure & Michael Elowitz, Allen Discovery Center for Cell Lineage Tracing, Welcome and introductory remarks
8:40-9:20am - Barbara Treutlein, ETH Zürich, "Lineage dynamics during brain organoid formation"
9:20-10:00am - Allon Klein, Harvard Medical School, "Lineage reconstruction from clonal correlations"
10:00-10:15am - Break
10:15-10:55am - Cole Trapnell, University of Washington, "Massively multiplex chemical transcriptomics at single cell resolution"
10:55-11:55am - Allen Institute Cell Lineage Reconstruction DREAM Challenge Session (*see info below)
11:55am-12:05pm - Closing Remarks

View Talk Descriptions

Allen Institute Cell Lineage Reconstruction DREAM Challenge Session:

Chaired by Pablo Meyer Rojas of IBM Research, this session discusses the results of the Allen Cell Lineage Reconstruction DREAM Challenge, an open science competition for computational approaches to building accurate cell lineages in developmental biology, which was hosted by the Allen Institute and Sage Bionetworks in early 2020. A combination of DREAM Challenge organizers and participants presented the winning methods in all three subcategories.

DREAM Session Speakers

Allon Klein, Ph.D.

Harvard Medical School

"Lineage reconstruction from clonal correlations"

Abstract: To understand mechanisms of cell fate choice, a 'minimal' goal of stem cell and developmental biology is to establish the fate decisions that cells undergo, and their sequence. Differentiation is often modeled as a branching process, but cells with different developmental histories can converge onto the same mature cell identities in several tissues. How can we infer the order of fate choices, and identify convergent differentiation? Using hematopoiesis as a model system, I will present our work exploring whether stochasticity of cell fate choice during tissue development could be harnessed to infer developmental sequences using genetic lineage tracing experiments combined with single cell RNA-Seq. I will specifically address:

1. Are we measuring the state of cells sufficiently well to map the sequence of events in fate choice? In hematopoiesis, we show that fate biases depend on stable cellular properties hidden from single-cell RNA sequencing, arguing for more comprehensive descriptions of cell state.

2. Can we establish the conditions under which the final distribution of barcodes over observed cell types encodes developmental relationships? This leads us to a generalized theory of multi-type branching processes, that identifies pitfalls in analysis. We use the theory to develop a practical pipeline for clonal analysis. We detect couplings between monocytes and dendritic cells and between erythrocytes and basophils that suggest multiple pathways of differentiation for these lineages.

Bio: Throughout Dr. Allon Klein’s career, he has been interested in how cells make decisions. This is a multi-scale problem, with explanations spanning from identifying ‘molecular instabilities’ that drive regulatory networks to one of several stable states, to defining interactions across tissues and even organisms. Understanding cellular decision-making also requires determining how cells and tissues maintain discrete phenotypes over many years of life. These problems cannot be solved using any single discipline. Dr. Klein worked on these problems first as a statistical physicist in his PhD with Dr. Ben Simons (Cambridge), before transitioning into experimental biology and computational genomics during a postdoc with Dr. Marc Kirschner. To address these general questions, the Klein lab develops new assays, technologies and theoretical methods.


Cole Trapnell, Ph.D.

University of Washington

"Massively multiplex chemical transcriptomics at single cell resolution"

Abstract: High-throughput chemical screens typically employ coarse assays, e.g. cell survival, limiting what can be learned about mechanisms of action, off-target effects, and heterogeneous responses. Here we introduce sci-Plex, which uses ‘nuclear hashing’ to quantify global transcriptional responses to thousands of independent perturbations at single-cell resolution. As a proof-of-concept, we applied sci-Plex to screen 3 cancer cell lines exposed to 188 compounds. In total, we profiled ~650,000 single-cell transcriptomes across ~5,000 independent samples in one experiment. Our results reveal substantial intercellular heterogeneity in response to specific compounds, commonalities in response to families of compounds, and insight into differential properties within families. In particular, our results with HDAC inhibitors support the view that chromatin acts as an important reservoir of acetate in cancer cells.

Bio: Cole Trapnell is an Associate Professor of Genome Sciences at the University of Washington. Dr. Trapnell has formal training in both computational and experimental biology, with a broad background in functional genomics and specific training in next generation sequencing and gene expression analysis.  As a graduate student with Steven Salzberg and Lior Pachter, Dr. Trapnell wrote TopHat and Cufflinks, two widely used tools for transcriptome sequencing (RNA-seq) analysis. As a postdoctoral fellow in John Rinn's lab at Harvard, Dr. Trapnell sought experimental training focused on analysis of cell differentiation, and developed “single-cell trajectory analysis”, an approach for studying cell differentiation using single-cell RNA-seq. At the University of Washington, the Trapnell lab develops single-cell genomics assays and the algorithms needed to analyze them. The lab then applies these technologies to dissect the genetic architecture that governs cell fate decisions in development, reprogramming, and disease. The Trapnell lab co-developed, along with Jay Shendure’s lab, a general, ultra-scalable workflow for single-cell genomics called “combinatorial cellular indexing”. They recently used this approach to construct a transcriptional atlas for the C. elegans nematode and profile organogenesis in the mouse at whole-embryo scale. Dr. Trapnell was the recipient of an NIH Director's New Innovator Award, an Alfred P. Sloan Fellowship, the Dale F. Frey Award, and the ISCB Overton Prize.


Barbara Treutlein, Ph.D.

ETH Zürich

"Lineage dynamics during brain organoid formation"

Abstract: Diverse regions develop within cerebral organoids generated from human induced pluripotent stem cells (iPSCs), however it has been a challenge to understand the lineage dynamics associated with brain regionalization. Here we establish an inducible lineage recording system that couples reporter barcodes, inducible CRISPR/Cas9 scarring, and single-cell transcriptomics to analyze lineage relationships during cerebral organoid development. We infer fate-mapped whole organoid phylogenies over a scarring time course, and reconstruct progenitor-neuron lineage trees within microdissected cerebral organoid regions. We observe increased fate restriction over time, and find that iPSC clones used to initiate organoids tend to accumulate in distinct brain regions. We use lineage-coupled spatial transcriptomics to resolve lineage locations as well as confirm clonal enrichment in distinctly patterned brain regions. Using long term 4-D light sheet microscopy to temporally track nuclei in developing cerebral organoids, we link brain region clone enrichment to positions in the neuroectoderm, followed by local proliferation with limited migration during neuroepithelial formation. Our data sheds light on how lineages are established during brain organoid regionalization, and our techniques can be adapted in any iPSC-derived cell culture system to dissect lineage alterations during perturbation or in patient-specific models of disease.

Bio: Barbara Treutlein performed her PhD in single-molecule biophysics at LMU Munich, Germany. During her Postdoc with Stephen Quake at Stanford University, she pioneered the use of microfluidic-based single-cell transcriptomics to dissect the cellular composition of complex tissues, and to elucidate differentiation pathways during lung development and cell reprogramming. 2015-2018, she was a Max Planck Research Group Leader at the Max Planck Institute for Evolutionary Anthropology in Leipzig and held a tenure-track assistant professorship at TU Munich. Since 2019, Barbara is Professor for Quantitative Developmental Biology at the ETH Zürich D-BSSE, Switzerland. Her group uses and develops single-cell genomics approaches in combination with stem cell based 2- and 3-dimensional culture systems to study human organogenesis. For her work, Barbara has received multiple awards including the Friedmund Neumann Prize of the Schering Foundation and the Dr. Susan Lim Award for Outstanding Young Investigator of the International Society of Stem Cell Research.


Pablo Meyer Rojas, Ph.D.

IBM Research

Presenting an introduction to the Allen Institute Cell Lineage Reconstruction DREAM Challenge

Bio: Dr. Meyer is a research staff member and team leader at IBM Research, Director of DREAM Challenges and chair of the RSG/DREAM Conference. He finds himself in the intersection between modeling, data analysis and wet lab. He is overall interested in how events at the molecular level and gene circuits determine mesoscopic or higher order phenomena from enzymatic reactions to circadian behaviors in flies, influences on apoptosis and prediction of olfactory responses from molecular structures. He has been actively involved in DREAM Challenges since 2010 and has led challenges addressing critical questions in Systems Biology such as parameter estimation in gene networks and across multiple biomedical fields, including infectious disease and developmental biology.


Alejandro Granados, Ph.D.

California Institute of Technology

Presenting an introduction to Subchallenge 1 and Best Performers

Bio: Alejandro is currently a computational postdoctoral scholar at Caltech, working with Dr. Michael Elowitz. After finishing a Bachelor's degree in Bioinformatics at the National Autonomous University of Mexico (UNAM) in 2012, he pursued a Ph.D. degree at Imperial College London and Edinburgh University, working in collaboration with Dr. Peter Swain and Dr. Reiko Tanaka. His main publications and projects are related to cellular signaling, computational biology, information theory, and reconstruction of lineage histories both from synthetic recording and single-cell RNA sequencing data.


Renata Retkute, Ph.D.

University of Cambridge

Best Performer Presentation Subchallenge 1: A machine learning approach for cell lineage reconstruction

Bio: Renata Retkute is a Research Associate in Epidemiology and Modelling Group, Department of Plant Sciences. She is working on developing epidemiological models for surveillance and management of infectious diseases. Her research has focused on modelling of biological systems at different spatial and temporal scales: DNA replication, photosynthesis, neglected tropical diseases. She is interested in Bayesian inference and Machine Learning approaches.


Wuming Gong, Ph.D.

University of Minnesota

Introduction to Subchallenge 2 & 3 and Best Performer Presentation

Bio: Wuming Gong is an Assistant Professor in the Medical School, University of Minnesota. Wuming has a broad background in developmental biology, molecular biology, bioinformatics and computational biology. Wuming was trained in genetics and genomics, and has more than ten years of experience on bioinformatics and computational biology, including siRNA design, peptide array design, database construction and predicting development related genes. As an Assistant Professor in Medical School of University of Minnesota, Wuming mainly focuses on developing algorithms for single cell RNA-seq analysis including tools such as dpath (prediction of cell differentiation), TCM (visualization of temporal scRNA-seq data) and DrImpute (imputing dropout events in the scRNA-seq data), as well as inferring gene regulatory networks from multi-dimensional omics- data. In addition, Wuming collaborates with other researchers on characterizing novel genes, microRNAs and long noncoding RNAs (lncRNAs) that have important functions in various biological processes.


Matt Jones, Ph.D. Student

University of California, San Francisco and UC Berkeley

Best Performer Presentation Subchallenge 1

Bio: Matthew Jones is a Ph.D. Candidate at the University of California, San Francisco and University of California, Berkeley working in the labs of Jonathan Weissman and Nir Yosef. He holds a degree in Computer Science from UC Berkeley. Currently, his work focuses on developing algorithms for single-cell lineage tracing technologies, and using these approaches to learn new biology about non-small-cell lung cancer tumor development and metastatic behavior. Previously, he has worked on developing computational tools for analyzing high-dimensional single-cell RNA-seq assays and simulating complex population genetic events. Recently, he spent time as a research intern at Google Health where he created deep learning models to predict patient disease risk with Electronic Health Record data. He is currently a UCSF Discovery Fellow.