Single Cell Biology Symposium 2015
April 30, 2015, 9 AM – 5:30 PM
Houston Hall, University of Pennsylvania
ABSTRACTS
Listed alphabetically by speakers’ names (underlined)
The eDAR and SD Platform for Single-Cell Isolation and Analysis
Daniel Chiu
Dept. of Chemistry and Bioengineering, University of Washington
This presentation will describe the eDAR platform for rare cell isolation and downstream
analysis of single cells. I will discuss the SD chip for digital assays, including digital PCR
analysis of single cells."
Visualizing Epigenetic Mosaicism in a Loss of Imprinting Mutant
Paul Ginart
Dept. of Bioengineering, University of Pennsylvania
Imprinting is a classic epigenetic effect in which only the maternal or paternal copy of a gene is
expressed. Imprinting defects can lead to inappropriate expression from the normally silenced
allele, yet prior studies are limited to studying cell population averages. Here, we apply a new
fluorescence in situ hybridization method capable of measuring allele-specific expression in
single cells to explore how aberrant H19 imprinting manifests at the single cell level. We show
that mutant mouse embryonic fibroblasts (MEFS) are mosaic, comprised of two subpopulations:
one that expresses both alleles of H19, and another that expresses only the maternal copy. These
identities are heritable. We observe the same two subpopulations are present in vivo within
murine cardiac tissue. Our results establish that single cell analysis may be critical in
understanding the mechanisms governing loss of imprinting disorders and the maintenance of
DNA methylation.
CT Scans of Single Cells
Mark A. Le Gros1,2 and Carolyn A. Larabell1,2
1.
2.
Department of Anatomy, University of California San Francisco, San Francisco USA.
Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley USA.
Soft X-ray tomograpahy (SXT) is similar in concept to the well-established medical diagnostic
technique, computed axial tomography (CAT), except SXT is capable of imaging with a spatial
resolution of 50 nm or better. With SXT we can examine whole, hydrated cells between 10-15
µm thick. Cells are imaged using X-ray energies between the K shell absorption edges of carbon
Page 1 of 4 (284 eV, λ=4.4 nm) and oxygen (543 eV, λ=2.3 nm). In this energy range, photons readily
penetrate the aqueous environment while encountering significant absorption from carbon- and
nitrogen-containing organic material. Since X-ray absorption follows Beer’s Law, the
absorption of photons is linear and a function of the biochemical composition at each point in the
cell. As a result, cell structures are seen based on differences in linear absorption coefficient
(LAC) values. For example, lipid drops with high concentrations of carbon are more highly
absorbing (LAC=0.7 µm-1) than fluid-filled vesicles (LAC=0.2 µm-1). By collecting images from
multiple angles through 180 degrees of rotation, SXT reconstructions yield information with
isotropic resolution. To determine the location of specific molecules, we overlay molecular
information obtained with fluorescence tomography on the structural information obtained with
x-ray tomography – of the same cell. This approach yields 3-D views of the molecules with
respect to cell structures in the native state at isotropic resolution.
Clinical Utility of Single-cell Genome Analysis
Woong-Yang Park
Samsung Genome Institute, SungKyunKwan University
Intratumoral genetic and functional heterogeneity correlates with cancer clinical prognoses.
However, the mechanisms by which intratumoral heterogeneity impacts therapeutic outcome
remain poorly understood. RNA sequencing (RNA-Seq) of single tumor cells can provide
precise information about gene expression and single-nucleotide variations among individual
tumor cells, which could allow translating heterogeneous tumor cell functional responses into
customized anti-cancer treatments. We isolated 34 patient-derived xenograft (PDX) tumor cells
from lung adenocarcinoma (LADC) patient tumors. The observed variance in the transcriptome
reflected higher genomic heterogeneity of the PDX cells compared with conventional cancer cell
line cells. Fifty tumor-specific SNVs including KRASG12D were observed heterogeneously in the
individual PDX cells. Semi-supervised clustering based on KRASG12D mutation status and risk
score (RS) representing expression of 78 LADC-prognostic genes could classify PDX cells into
three groups; Group 1, KRASwt/low RS; Group 2, KRASG12D/low RS; and Group 3,
KRASG12D/high RS. PDX cells survived from in vitro cytotoxic drug treatment had Group 2-like
signature. Single-cell RNA-Seq for viable PDX cells could identify a tumor cell subgroup
associated with anti-cancer drug resistance. Thus, single-cell RNA-Seq is a powerful approach
for identifying unique tumor cell-specific gene expression profiles that could facilitate
optimizing clinical anti-cancer strategies.
Single Cell Genomics
Stephen Quake
Departments of Applied Physics and Bioengineering, Stanford University and Howard
Hughes Medical Institute, Stanford CA 94305-5012 [email protected]
An exciting emerging area revolves around the use of microfluidic tools for single-cell genomic
analysis. We have been using microfluidic devices for both gene expression analysis and for
Page 2 of 4 genome sequencing from single cells. In the case of gene expression analysis, it has become
routine to analyze hundreds of genes per cell on hundreds to thousands of single cells per
experiment. This has led to many new insights into the heterogeneity of cell populations in
human tissues, especially in the areas of cancer and stem cell biology. These devices make it
possible to perform “reverse tissue engineering” by dissecting complex tissues into their
component cell populations, and they are also used to analyze rare cells such as circulating tumor
cells or minor populations within a tissue. We have also used single-cell genome sequencing to
analyze the genetic properties of microbes that cannot be grown in culture—the largest
component of biological diversity on the planet—as well as to study the recombination potential
of humans by characterizing the diversity of novel genomes found in the sperm of an individual.
We expect that single cell genome sequencing will become a valuable tool in understanding
genetic diversity in many different contexts.
Imaging Biology at High Spatiotemporal Resolution
Hari Shroff
National Institute of Biomedical Imaging and Bioengineering
National Institutes of Health, Bethesda, MD USA
I will discuss our efforts to develop high resolution optical methods that are better suited for the
study of live, dynamic, and 3D biological samples than conventional imaging tools. Structured
illumination microscopy (SIM) doubles the spatial resolution of a light microscope, and requires
lower light intensities and acquisition times than other super-resolution techniques, but has been
mostly applied to the study of single cells. I will present alternative SIM implementations that
permit resolution doubling in live volumes > 10-20x thicker than possible with conventional
SIM, as well as hardware modifications that enable effectively ‘instant’ SIM imaging at rates 10100x faster than other SIM implementations. The second half of the talk will focus on the
development of inverted selective plane illumination microscopy (iSPIM), and subsequent
application to the noninvasive study of neurodevelopment in nematode embryos. Next, I will
discuss progress that quadruples the axial resolution of iSPIM by utilizing a second specimen
view, thus enabling imaging with isotropic spatial resolution (dual-view iSPIM, or diSPIM).
Applications of this technology will be presented, including efforts to computationally ‘untwist’
the growing worm embryo.
Single Cell Transcriptomics Analysis of Neurons and Cardiomyocytes from Live Human
Tissue
Jennifer M Singh*, Mugdha Khaladkar*, Young-Ji Na*, JaeHee Lee*, Niyatie Ammanamanchi, Thomas
Bell, Sangita Choudhury, Hannah H Dueck, Ivan J Dmochowski, Stephen A Fisher, Marcela Garcia, Jamie
Shallcross, Douglas Smith, Alexandra Ulyanova, Jinhui Wang, John Wolf, Sean Yeldell, Jai-Yoon Sul^, Bernhard
Kuhn^, Sean Grady^, Junhyong Kim^, James Eberwine^
* co-first authors
^ co-senior authors
Page 3 of 4 The Penn Single Cell Analysis Program (SCAP-T) project aims to characterize the transcriptome
landscape of electrically excitable cells from human brains and the hearts in order to understand
and manipulate excitable cell physiology in a directed manner using multigenic functional
genomics methods. In this project, live tissue samples from patients undergoing neurosurgery or
cardiac surgery were prepared by surgical teams and immediately processed for live cell
transcriptome characterization. Single cell RNAs were collected using multiple methods
including disassociation and flow sorting; adult primary cell culture; and the newly developed
Transcriptome In Vivo Analysis (TIVA) method that utilizes a photo-activatable RNA capture
reagent in cells in their natural microenvironment. Here we report on preliminary data from 125
neurons from 17 patients and 342 cardiomyocytes from 12 patients. Single cell RNAs were
amplified using the aRNA linear amplification method and RNA sequenced for an average of
27.4 million read coverage. For human neurons, we observed a broad range of expressed genes
per cell with an average of 4433 genes detected. Human cardiomyocytes also show a broad range
of expression with an average of 2300 number of genes detected at expression threshold of five
or more reads. We will detail this transcriptome complexity and describe sequence
characteristics of expressed genes in single cells of human neurons and cardiomyocytes. These
data will be used to guide our functional genomics selected phenotype transfer (TIPeR)
experiments. Through these experiments, we will gain a better understanding of the unique
functioning and control of excitable cells from different regions of the human body.
Reverse Engineering Network Crosstalk
Lani Wu
Dept. of Pharmaceutical Chemistry, University of California, San Francisco
How do complex biological networks shape highly-coordinated cellular responses? We will
describe our recent progress in using data-drive approaches for inferring rules that drive these
processes.
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