Single Cell Analysis Challenge

                 SCAP Challenge

NIH Announces Winners of Follow that Cell Phase 2!

The National Institutes of Health has named two biological engineering researchers as winners in Phase 2 of its Follow that Cell Challenge. The winners will share $400,000 in prizes awarded for the development of new tools and methods for predicting the behavior and function of a single cell in complex tissue – and how that reflects the health of the tissue. They were chosen from among several Phase 1 finalists. Nader Pourmand, Ph.D., University of California Santa Cruz, is the first place winner with a prize of $300,000. A team led by Paul Blainey, Ph.D., of the Broad Institute, Cambridge, Massachusetts, will share the second place prize of $100,000.

This $500,000 Challenge was structured in two linked phases. Phase 1 of the Follow that Cell Challenge sought novel robust methods for analysis of individual cells to detect and assess changes in cell behavior and function over time either as a result of natural state changes or when perturbed. All of the Phase 1 finalists were eligible to participate in Phase 2, Reduction to Practice, during which they were encouraged to execute their ideas in proof-of-concept studies.

FIRST PLACE PRIZE WINNER - Molecular Exam of Single Living Cells

Nader Pourmand, Ph.D., University of California Santa Cruz, Santa Cruz, California

"We have developed a novel nanopipette technology that can be used to monitor the molecular properties of single cells over time. Initially, we developed the technology to monitor changes in gene expression (genomics) in single cells. Now, in addition to single-cell sequencing technology, we have integrated methods to monitor temporal changes in the phosphorylation and activation of transcription factors. This allows us to monitor the activation of different transcription factors and the array of genes each one regulates to investigate how different signaling pathways interact in controlling gene expression in a single cell. We have also integrated methods to monitor a cell's metabolic state over time by measuring intracellular pH and glucose. Thus, we can simultaneously monitor genomics, phosphoproteomics, and metabolomics in single cells. To establish the utility of this multiplexed technology, we are employing it to study a critical biological question: How do cancer cells develop drug resistance? While the proposed studies focus on the use of our nanopipette technology to study human cancer cells in culture, the cell monitoring capabilities of the nanopipette allow it to be used for in situ analysis of single cells in tissue biopsies and slices. This technology is also adaptable to instrumentation commonly used for electrophysiological measurements of single cells."

SECOND PLACE PRIZE WINNER - Single-cell self-reporting for live-cell transcriptomics

Paul Blainey, Ph.D. (team lead), Ankur Kulshreshtha, Ph.D., Jacob Borrajo, Mohamad Najia, MIT and Broad Institute, Cambridge, Massachusetts

"Mammalian cell biology is dynamic, but today’s ‘omic approaches require destruction of cells to access their molecular contents and cannot resolve the full richness of cellular diversity in situ. The research community needs tools that can probe living cells in real tissues to produce high dimensional time-series data from cells of interest. Our technique entails export of cellular analytes via an engineered secretion pathway. We show that cells can self-report their internal transcriptional state in a quantitatively interpretable manner. We are working to render our approach a practically useful method for analyzing clinically relevant biological systems."

For additional information, read the press release here.



Phase 1 Finalists


Phase 1 finalists


  Name Institute Title
1st Place Winner Paul Blainey, Ph.D. (team leader) Broad Institute and Massachusetts Institute of Technology "Single-Cell Time-lapse Gene Expression Profiling via an Engineered Self-Reporting Pathway"
Prize Winner James Ankrum, Ph.D. University of Iowa "Self-Destructing Cellular Barcode: A Versatile Tool for Single Cell Analysis"
Prize Winner Brian Chen, Ph.D. McGill University "Measuring Protein Concentrations and Protein Translation over Time in Single Cells In Vivo"
Prize Winner James Eberwine, Ph.D. (team lead) University of Pennsylvania "BLINKER Assessed Live Cell Transcriptomics"
Prize Winner Nader Pourmand, Ph.D. University of California, Santa Cruz "Study the Longitudinal Expression of the Genome of a Single Cell"
Finalist Helen Blau, Ph.D. (team lead) Stanford University "Next Generation Automated Cell Tracking Software to Follow that Cell and Its Progeny Accurately in Complex Multicellular Environments"
Finalist Joseph Conrad, Ph.D. (team lead) Vanderbilt University "Development, Optimization, and Enhancement of Fluorescent DNA-Hairpin Functionalized Gold Nanoparticles as Imaging Tools"
Finalist Tania Konry, Ph.D. Northeastern University "Microfluidic Droplet Based Platform for Cell Co-Encapsulation and Monitoring"
Finalist Markita Landry, Ph.D. Massachusetts Institute of Technology "Synthetic Nano-Antibodies for Real-Time Monitoring of Cellular Biomarkers"
Finalist Xin Lu, Ph.D. MD Anderson Cancer Center "Single Cell Oncogenesis"
Finalist Garry Nolan, Ph.D. (team lead) Stanford University "Tracking the Phenotype of Single Cells using Multicolor Flow Cytometry Combined with Cell-Specific Barcodes"
Finalist Frederick Sachs, Ph.D. (team lead) State University of New York at Buffalo "Flow of Mechanical Information in Living Cells"
Finalist Hongjun Song, Ph.D. (team lead) Johns Hopkins University "RNA-seq"
Finalist Pak Kin Wong, Ph.D. (team lead) University of Arizona "Dynamic Tracking of Cancer Stem Cells in Native Tumor Microenvironments- Single Cell, Dynamic Monitoring of Multi-Gene Expression in Tumor Tissues"
Finalist X. Nancy Xu, Ph.D. Old Dominion University "Photostable Multiplexing NanoAssays for Real-Time Molecular Imaging of Single Live Cells"
Finalsit Zhibo Yang, Ph.D. University of Oklahoma "Development of Micro-Scale Sampling Probes for In Situ Mass Spectrometry Analysis of Secretomes from Live Single Cells"


Blainey Challenge Team Finalists

Single-Cell Time-lapse Gene Expression Profiling via an Engineered Self-Reporting Pathway

Paul Blainey,1,2 Jacob Borrajo1,2, Atray Dixit1,3, and Alex Shalek1,3,4,5

1Broad Institute of MIT and Harvard, Cambridge, MA
2Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA
3Harvard-MIT Division of Health, Science, and Technology, Cambridge, MA
4Institute for Medical Engineering & Science and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA
5Ragon Institute of MGH, MIT, & Harvard, Cambridge, MA

Single-cell gene expression (SCGE) profiling is an important analytical technique for studying mammalian cells. The ability to obtain high-resolution molecular phenotypes directly from individual cells is transforming the way in which we define cell states, understand cell circuitry, and study cellular responses to environmental cues. Nevertheless, there is tremendous interest in moving beyond static snapshots of SCGE in cell suspensions to understand how SCGE profiles change through time. Technologies that report the internal state and functional history of cells within tissues would enable novel insights into dynamic biological processes. Here, we outline a technology that allows cells to self-report their internal states in time-lapse measurements by barcoding and secreting a portion of their mRNA. We also highlight methods for integrating this approach with optical microscopy to collect complementary single cell information on protein levels, morphology, and spatial context. Our method is scalable and limited only by the throughput of next-generation sequencing (NGS). We propose to test our method by tracking the emergence of drug resistance in a BRAF mutant melanoma model. 

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Ankrum Challenege finalist

Self-Destructing Cellular Barcode: A Versatile Tool for Single Cell Analysis

James Ankrum

Fraternal Order of Eagles Diabetes Research Center, Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa

The role of single cells in physiological and pathological environments has become critical to the study of stem cell niches, tumor biology, and regenerative medicine. While traditional biology tools provide a picture of biological processes, the picture is actually an average of the cell population and does not represent the diversity of phenotypes present in that population. Single cell analysis platforms, including single-cell PCR, flow cytometry, and single-cell microwells have begun to reveal the true diversity that is present in biology. However, the ability to fully characterize single cells across multiple platforms and in multicellular environments is limited by our inability to uniquely identify and track single cells. In this proposal, we provide a solution that is capable of uniquely labeling thousands of single cells across multiple cell generations, is stable for weeks, can be read repeatedly, and self-destructs when a cell dies. The proposed technology relies only on routine fluorescence microscopy and is applicable to a diverse array of cell types. This technology will enable full single cell characterization of cells alone or in multicellular environments.

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Chen Challenge Finalist

Measuring Protein Concentrations and Protein Translation Over Time in Single Cells In Vivo

Brian Chen

Centre for Research in Neuroscience, Research Institute of the McGill University Health Centre, Montréal, Québec

Accurate measurement of the amount of a given protein a cell produces is important for our understanding of the basic molecular processes of the cell.  Protein expression within cells is a tightly regulated process where too little or too much of a single protein can cause disease.  Understanding how protein expression affects cellular phenotypes is an important step in understanding how diseases such as cancer, neurodegenerative diseases, or autism spectrum disorders occur.  Surprisingly, the current methods to measure protein levels have remained relatively the same over 30 years, and are crude, unreliable, and inaccurate.  We will develop a technique to track and measure protein production in single cells in the living animal.  Our technique will allow researchers the ability to measure the real-time dynamics of endogenous or exogenous protein production in cells within their native environment with unprecedented resolution, to determine how protein translation quantitatively relates to cellular phenotypes and diseases.  This new technique will significantly aid cell biology and biomedical research and open the door to a wide range of experiments investigating diseases caused by abnormal regulation of protein production.

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Eberwine Challenge Team Finalists

BLINKER Assessed Live Cell Transcriptomics

James Eberwine1, David Cappelleri2, Junhyong Kim3, Ülo Langel4, and Jai-Yoon Sul1

1Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 2 Department of Mechanical Engineering, Purdue University, West Lafayette, IN 3 Department of Biological Sciences, University of Pennsylvania, Philadelphia, PA 4 Department of Neurochemistry, Stockholm University, Stockholm, Sweden

We propose to develop a live cell real-time RNA expression profiling methodology, called Blinker, to assess transcription in live mouse and human neurons. We will assess this in neurons that are dispersed in primary cell culture as well as the live slice preparation. This technology may also be transitioned into cortical cells of the intact mouse brain. We will integrate novel concepts in chemistry, enzymology, cell biology and imaging to exploit the live cell’s innate transcriptional machinery to assess the identity of RNAs that this machinery transcribes from the cell’s genomic DNA. In preliminary experiments, we have generated preliminary data showing the viability of this Blinker technology in vitro and after further in vitro optimization it will be transitioned into live cells. Blinker is enabled by engineering enzymes involved in transcription so that they contain a capture sequence near the site of RNA synthesis that will interact with a chemically-synthesized “transcriptional detector peptide” (TDP). This peptide is a chimera of: a peptide sequence that will bind to the capture sequence; a cell penetrating peptide (to transport it into the cell); a nuclear localization signal to move the peptide into the cell’s nucleus; and a peptide nucleic acid (PNA) cassette that will permit interaction of the TDP peptide with RNA as it is synthesized. Upon binding of the PNA sequence to RNA (in a sequence specific manner), a fluorescence resonance energy transfer (FRET) signal is produced from the fluorophores that have been incorporated in the TDP flanking the PNA sequence. Each FRET signal will be detected as a signal blink in live cells using light sheet microscopy. Because the PNA will interact with a specific nucleotide motif on the synthesized RNA, the timing between blinks will correspond to the distance between the annealing sequences in the newly synthesized RNA. The distances between blinks are unique for any particular RNA, and using a set of algorithms that we have developed, we will identify the gene that is being transcribed. Preliminary data shows that this technology has worked in an in vitro model and in so accomplishing it shows promise to permit in vitro expression profiling of 100,000s of sequences from single cells in minutes decreasing the time and cost of RNA sequencing significantly. We have enabled parts of the Blinker technology and are refining it for expanded in vitro and in vivo use. In vivo use of the technology is being implemented and evaluated as a means to assess changes in gene expression that correlate with long-term potentiation, and pharmacological responsiveness of mouse and human neurons.

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Pourmand Challenge Finalist

Study the Longitudinal Expression of the Genome of a Single Cell

Nader Pourmand

Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, California

We have developed a novel technology to measure the longitudinal genomics of single cells with spatial and temporal resolution. To do this, we use a nanopipette with a tiny tip to sample miniscule samples of a cell in a manner that does not affect the function, genomic expression or viability of the cell. Using a novel targeting system, the nanopipette tip can sample the same cell repetitively over minutes, hours, days or weeks. We also developed a powerful single cell sequencing technology to conduct WTA analysis on the small cell samples to reveal changes in gene expression as the cell ages or undergoes other phenotypic alterations. This allows us to link genomic expression changes with phenotypic changes in any given cell. We have published results using this powerful single cell genomic technology to begin to study mechanisms of drug resistance in breast cancer cells. This is important because drug resistance is a major cause of relapse of chemotherapy and causes thousands of deaths a year because of the regrowth of tumors in cancer patients. We propose to use our nanopipette/nanoGenomics technology to monitor over time genomic changes in drug resistance in breast cancer cells from parent cells to daughter cells to study the molecular basis of how resistance is maintained in growing tumors and also how resistant cells revert to drug sensitive cells to identify genes needed to maintain drug sensitivity. We believe these studies will provide fundamental information on the basis for chemotherapy resistance and sensitivity which could not be obtained using any other technology. It will have implications in the development of adjuvant therapies to prolong and increase the efficacy of chemotherapeutics to treat breast cancer and allow patients to become cancer free.

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Magnusson-Blau Challenge Team Finalists

Next Generation Automated Cell Tracking Software to Follow That Cell and Its Progeny Accurately In Complex Multicellular Environments

Helen Blau1, Klas Magnusson1,2, Andrew Ho1, Andrew Chan1, Colin Holbrook1, John Ramunas1, and Joakim Jaldén2

1Department of Microbiology and Immunology, Stanford University, Stanford, California
2KTH Royal Institute of Technology, Stockholm, Sweden

Accurate tracking of single cells and their progeny in complex multicellular environments will be essential to realize the immense potential of the molecular probes, imaging technologies, and other tools that will spring from the Follow That Cell Challenge. However currently this fundamental task is not possible except in certain experimental models and only if users have a particularly high level of expertise. We know this limitation exists because we developed the current gold standard in cell tracking software –the Baxter Algorithms – which won the International Symposium on Bioimaging (ISBI) Cell Tracking Challenge for the past two years, and has been employed to yield high profile discoveries. Despite its position as the current--‐best cell tracking software, the Baxter Algorithms are specialized tools, require specialized skills, and most limiting, specialized experimental models. To overcome this limitation, we now propose to develop next- generation automated software that can accurately follow cells in complex multicellular environments in image sequences made using diverse probes and imaging technologies such as those that may arise from the Follow That Cell initiative. This novel and highly enabling advance will make it possible for cell tracking to be widely used in research and clinical settings. We will make the system publicly available in the form of open source software to ensure  its broad reach and impact on the biomedical  community. Because the core algorithms of our current software are the current best in the world, we are uniquely positioned to make this leap in capability and accuracy of cell tracking software, and will design and implement user--‐friendly interfaces to allow researchers with basic computer skills to carry out sophisticated cell tracking. This technology will greatly enhance the benefit of existing and future Follow That Cell technologies that depend on imaging. The methods we design will be sufficiently robust to track cells using a broad range of cell types without requiring modifications. Currently, no cell tracking tools fulfill these requirements. Our primary biological research objective for this challenge is tracking and predicting the lineage  of muscle stem cells (MuSCs) in the complex multicellular and physiologically relevant environment  of isolated muscle fibers containing the MuSC niche, the myofiber, fibroblasts, and other cell types. MuSCs constitute a highly heterogeneous cell  population  that is compromised in aging and in diseases such as Duchenne muscular dystrophy. The ability to accurately track and predict the lineage of MuSCs is critical for understanding how the composition of the cell population changes over time and  in response to different treatments (therapeutic agents), in culture  and in vivo. Our laboratory has shown that a subpopulation of MuSCs undergoes a pronounced cell--‐intrinsic decline in function during aging. When MuSCs are cultured in  vitro, they can be expanded by treatment with the  pharmacologic p38--‐MAPK inhibitor SB202190 (SB), which results in a striking increase in MuSC regenerative capacity. We propose to use the algorithms described here, together with our dynamic fluorescent reporters of MuSC fate, to track cell fate changes, and to elucidate and predict both clonal and population dynamics in the course of SB--‐induced MuSC rejuvenation using markers our laboratory has developed. We also propose to monitor the behavior of MuSCs in the context of myofiber repair. Accomplishing this challenge using a dynamic analysis of gene expression at the single--‐cell level will extend the impact of cell tracking technology to the entire biomedical research community, from basic to clinical researchers. Moreover it will advance our laboratory’s long- term goal of developing novel therapies by enabling screens for agents capable of specifically targeting and rejuvenating MuSCs compromised by aging and disease.

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Wong-Conrad Challenge Team Finalists

Development, Optimization, and Enhancement of Fluorescent DNA-Hairpin Functionalized Gold Nanoparticles as Imaging Tools

Joseph Conrad1, Alexis Wong1, Andries Zijlstra1, and David Wright1

1Vanderbilt University, Nashville, Tennessee

Convenient evaluation of gene expression in metastatic cells represents an important unmet need in translational cancer research. The pattern of gene or protein expression, the molecular signature of individual cells within a tumor, has clinical value. 1-3 Individual cells within a tumor mass exhibit unique transcriptional profiles that may influence the progression and outcome of disease.4 The diversity of cell types in the tumor microenvironment means that aggregate studies of a tumor may miss small, highly malignant cell populations that drive disease development.5 Therefore, inherent heterogeneity of gene expression among a population of cells necessitates single cell analysis in order to fully characterize a disease state. However, molecular analysis is cumbersome due to non-standard technical approaches, invasive collection of tissue, the multistep process for collection, processing, analysis of specimens, and the paucity of FDA-approved diagnostics. 3,6-9 Quantitative RT-PCR (qRT-PCR) is the gold standard for gene expression analysis for researchers, but this data does not reflect gene expression at a single cell level. Clinicians continue to rely on microscopy and histopathology for tumor diagnosis, prognosis, and prediction. These techniques cannot optically identify tumor tissue and report on functional or molecular status simultaneously. 9 Thus, readily available tools for evaluation of tumor cells or tissue do not capture the critical genotypic and phenotypic elements that define a tumor’s metastatic potential. Tools to evaluate gene expression in real time within intact cells would provide accessible molecular insight to inform research questions and clinical decisions for treatment, surveillance, or assessment of therapy. We propose development, optimization, and enhancement of fluorescent DNA-hairpin functionalized gold nanoparticles as imaging tools that can directly identify tumor cells that have metastatic potential in a live animal model. Hairpin DNA-functionalized gold nanoparticle (hAuNP) probes pioneered by our group are an ideal bioimaging platform for molecular analysis of cells and tissues in situ (Gene-specific hAuNPs: 1) are readily synthesized, 2) offer high specificity, 3) retain high signal output and narrow spectral bandwidth, 4) are modular with interchangeable imaging and targeting moieties, and 5) remain small, deliverable, and biocompatible.10,11 The hAuNPs penetrate living cells without the need for transfection agents 11 and emit quantifiable fluorescence upon intracellular hybridization with target mRNA.10,11 We have investigated several model systems, and now propose to translate these results into enhanced second generation tools for probing markers of metastasis in preclinical live animal models of tumor metastasis. We hypothesize that gene-specific hAuNPs can provide essential molecular information at a cellular level to enhance optical imaging and routine histopathology.

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Konry Challenge Finalist

Microfluidic Droplet Based Platform for Cell Co-Encapsulation and Monitoring

Tania Konry

Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts

Evaluation of the subtypes, numbers and functionality of tumor and immune cells as well as the activity of immune regulators on a single cell level is a complex scientific and technological challenge. The developed herein single cell micro-technology will allow researchers and physicians to identify and isolate specific cellular phenotype and rare cells in a mixed population based on multiplex analysis of both cell surface and secretion analysis as well as allow ultra fast screening of therapy protocols to identify cellular resistance to drugs over the time. The cellular phenotype is a conglomeration of multiple cellular processes, representing varying expression levels of genes and proteins that determine the cell’s particular function in activities such as drug resistance, cellular communication and cell-cell interactions. While flow cytometry has traditionally been used to determine single fixed cell phenotypes, it cannot provide continuous measurements of proteins and secretions in the same individual live cells over time or measure the cellular phenotype during live cell-cell interaction. It is therefore different co-culture methods and immuno-assays are performed on groups of cells to study cell-cell interactions and secretions, under the assumption that all cells of a particular “type” are identical. However, recent evidence from studies of single cells reveals that this assumption is incorrect. Thus new approaches to single cell phenotype analyses and live cell-cell interaction on a single cell level are needed to uncover fundamental biological principles and ultimately improve the detection and treatment of disease. In this context, we have developed a sample-sparing assay that can conduct simultaneous multiparameter assessments of immune function that is critical for establishing a correlation between the immunological mechanism of the activation or inhibition of immune effectors and the targeted clinical response in cancer. In particular the proposed micro-technology allows to: 1) conducting sample sparing simultaneous multi-parameter analysis of both live and fixed cell surface and secretions, 2) controlled delivery of pico-liter volumes of immuno-regulatory agents and therapeutics to cell of interest to study their effect on functional phenotype of the cell, 3) monitoring of live cell-cell interactions on a single cell based level as well as secretion measurements during this interaction, and 4) fluorescenceactivated droplet sorting (FADS) for specific functional cell phynotype isolation.

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Landry Challenge Finalist

Synthetic Nano-Antibodies for Real-Time Monitoring of Cellular Biomarkers

Markita Landry

 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

The past decade has seen a rapid emergence in the use of nanomaterials for biomedical applications including the development of high-sensitivity and high-specificity optical sensors of biological activity. In particular, semiconducting single-wall carbon nanotubes (SWNT) exhibit three essential figures of merit required for their use as fluorescence-based optical sensors: 1) SWNT fluoresce with an extraordinary quantum yield, 2) are the only fluorophores to-date that have essentially infinite lifetimes, and 3) emit in an optical window where tissues, cells, blood, and other biological samples are transparent. Additionally, SWNT are small, measuring only 1 nm in diameter and are often under 100 nm in length. Because of their high aspect ratio, when bio-functionalized, SWNT can be passively uptaken into cells and tissues with more ease than conventional fluorophores and quantum dots. DNA-conjugated SWNT have even shown to passively enter the plant chloroplast, which currently have no other passive mode of entry for genetic information.

Very recently, my work has produced a platform for the production of synthetic optical sensors based on engineered functionalization of SWNT with synthetic polymers. This novel platform produces nanosensors based on the infrared fluorescence of SWNT-polymer conjugates, by producing a signature SWNT-mediated intensity and wavelength shift in the presence of an analyte molecule. While this platform has been very fruitful in the production of sensors, it relies on a screening-based approach. Such screens are labor intensive, time consuming, and limit target analytes to those that can be discovered serendipitously. If more was known about the fundamental interactions leading to selective molecular recognition, a design-based approach could be taken instead, and “synthetic sensors” could be produced to monitor any biomarker: small molecules, reactive species, and even proteins within single cells. Unfortunately, existing optical tools have insufficient overlap to study both polymers and nanomaterials simultaneously, which is becoming increasingly necessary to advance fields of research that hinge on polymer-bio interactions such as the design of SWNT-based sensors. This leaves us ill equipped to design novel high-specificity synthetic sensors for applications such as label-free detection of proteins within a single cell. The goal of this research proposal is three-fold: 1. To customize a microscopy platform that will enable the simultaneous study of polymers and multi-chirality nanotubes, to determine how to better engineer SWNT-based sensors (“synthetic sensors” and “synthetic antibodies”). 2. To apply our newly-developed design rules for the detection of biologically interesting molecules such as cell metabolites, and proteins. 3. To detect proteins in a label-free manner, and on a single-molecule scale, from live cells as they express protein targets.

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Single Cell Oncogenesis

Xin Lu

MD Anderson Cancer Center, Houston, Texas

It is believed that cancer originates from a single cell that has gone through generations of evolution of genetic and epigenetic changes that associate with the hallmarks of cancer. In some cancers such as various types of leukemia, cancer is clonal. Yet in other cancers like glioblastoma (GBM), there is tremendous tumor heterogeneity that is likely to be caused by simultaneous evolution of multiple subclones within the same tissue. It is obvious that understanding how a single cell develops into a clonal tumor upon genetic alterations, at molecular and cellular levels, holds the key to the real appreciation of tumor etiology and ultimate solution for therapeutics. However, surprisingly very little is known about the process of spontaneous tumorigenesis from single cells in human or vertebrate animal models. The main reason is the lack of technology to track the natural process of single cell changes from a homeostatic state to a progressively cancerous state. Recently, we developed a patented compound, photoactivatable ("caged") tamoxifen analogue 4-OHC and associated technique called optochemogenetic switch (OCG switch), which we believe opens the opportunity to address this urgent biological as well as clinical question about cancer. We propose to combine OCG switch with multiallelic genetically engineered mouse models (GEMM) of head and neck squamous cell carcinoma (HNSCC) and high grade astrocytoma (including GBM) to study how single cells, when transformed through acute loss of tumor suppressor genes PTEN and TP53 and gain of oncogenic KRAS (for modeling HNSCC), can develop into tumor colonies with cellular and molecular heterogeneity in these tissues. Specifically, we hypothesize that individual somatic cells of the same lineage and residing in the same tissue can display significant behavioral variations in terms of growth and invasiveness, after initial transformation by genetic manipulation, and furthermore this cellular behavioral heterogeneity can be correlated with accumulation of different additional genetic and epigenetic changes. To validate our hypothesis, we will perform doxycyclineinduced lineage tracing and photoactivation-mediated single cell transformation of the tongue and brain tissue followed by longitudinal intravital imaging (IVM) with multi-photon microscopy (MPM). Next we will collect single cell-derived tumors as well as subsets within the same tumor colony using 3D intravital microdissection and perform genomic and transcriptomic profiling with next generation sequencing (NGS). We propose to apply mathematical modeling to the data on cellular behavioral variations to address theoretical questions associated with clonal evolution and cellular heterogeneity. More importantly, we aim to correlate the cellular heterogeneity with gene and pathway changes detected by NGS. Comparing conclusions drawn from two organ systems will generate interesting insights on tissue-specificity of oncogenic evolution. All together, we anticipate that our approaches with such novelty and comprehension will pave roads for paradigm-shifting new knowledge about single cell biology, oncogenesis, and clonal evolution of cancer, as well as generating potential new ideas on cancer therapy.

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Zunder-Nolan Challenge Team Finalist

Tracking the Phenotype of Single Cells Using Multicolor Flow Cytometry Combined With Cell-Specific Barcodes

Garry Nolan1, Nikolay Samusik1, Eli Zunder1, and Nima Aghaeepour1

1Department of Microbiology and Immunology, Stanford University, Stanford, California

Monitoring the phenotype of individual cells over time in heterogeneous tissues is an important goal to facilitate improved understanding of normal development and disease pathologies such as the clonal evolution of cancer. Fluorescent flow cytometry has been widely applied to measure the phenotypes of single living cells by staining the surface markers with fluorescent antibodies. However, currently only snapshot measurements are possible. Even though the laser-based flow cytometry procedure is sufficiently non-invasive so that the cells can be cultured or reintroduced into the organism after measurement, there is no way to track the identity of individual cells between the measurements. We propose a novel genetic barcoding scheme that uses unique combinations of epitope tags that are expressed on the cell surface. This system can be used to track individual cells over time in several biological systems. Here we describe our plan to track the progression of leukemia between the flow cytometry measurements. At each time point, barcoded leukemic cells will be sampled out of the bloodstream, stained with barcode-specific as well as phenotype-specific antibodies, measured in the flow cytometer, collected and returned into the bloodstream of the animal. Since our barcoding scheme allows over 1,000,000 unique individual combinations, this setup will enable us to track the surface marker repertoire of a large number single cells over time in vivo. We will apply this strategy to track the development of clonal phenotypic heterogeneity in a mouse model of AML, identify and perform phenotypic characterization of leukemia stem cells (LSC) and assess the sensitivity of individual leukemia clones to the chemotherapy drugs in vivo.

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Sachs Challenge Finalist

Flow of Mechanical Information in Living Cells

Frederick Sachs1, Philip Gottlieb1, Thomas Suchyna1, Zonglu Susan Hua1, and Fanjie Meng1

1State University of New York at Buffalo, Buffalo, New York

Cells have only three sources of free energy: chemical, electrical and mechanical potentials and life involves regulating the flow of energy between them. Understanding cell mechanics is crucial, not only to basic biology, but to pathology since all diseases produce changes in cell shape, that result from changes in force. An elementary example is the ability of mechanical forces to affect stem cell differentiation. We don’t know very much about the role of mechanical forces in specific proteins within living cells since, until recently, there has not been any tools to measure those forces. We invented these probes and our initial results emphasized the inhomogeneous distribution of forces within cells, in neighboring cells, different cell types, and in the larger scale, in transgenic animals. This proposal spans biomechanics in a range of scales from individual proteins, to cells and tissues to intact organisms. Our force probes are optical transducers so any preparation that can be imaged can reveal the distribution of stress in specific structural proteins in space and in real time. The probes are genetically coded so they can be expressed in all types of cells. The proposed experiments combine the most advanced technologies of AFM, NSOM, optical imaging and patch clamp electrophysiology to measure how forces are distributed and transmitted within and between cells, and how that information is transduced into other forms of free energy.

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Shin-Song Challenge Team Finalists


Hongjun Song1 and Jaehoon Shin1

1Johns Hopkins University, Baltimore, Maryland

Single cells are the most basic functional, structural and biological units of a multicellular organism. Achieving in vivo dynamics of single cells is one of the fundamental goals of modern biology to provide foundation for complete understanding of complex biological systems. However, vast majority of molecular and cellular studies try to describe biological phenomena using a few population-level snap shots of molecular characteristics, largely ignoring not only the cellular heterogeneity, but also the continuity of the biological processes. There are only a few modalities describing in vivo dynamics at the single cell resolution, but these modalities are inherently low throughput, invasive, and limited to regions close to the surface of living organisms. We herein propose a conceptually different approach, analogous to the “shot-gun” method that defined the human genome project (Fig. 1). The key concept of the approach is following: instead of following the same cell over time, we perform simultaneous snapshot analysis of individual cells at different stages of the continuous process in vivo and apply a bioinformatics approach to reconstruct the whole biological process to reveal the underlying dynamics. Recent advances in single cell RNA-seq allowed for the first time to provide genome-wide molecular profiles, which is comprehensive enough to capture various aspects of the transition process, thereby allowing linking the biologically contiguous snapshots based on their similarity. Thus, if we achieve numerous enough single cell snapshots to seamlessly cover continuous in vivo processes, we can reconstruct the whole biological process. Once the whole process is reconstructed, we can comprehensively investigate the dynamic transition of single cells throughout the continuous process without temporal or spatial limitations. We herein propose a suite of algorithm called Waterfall to reconstruct and investigate the in vivo dynamic processes using single cell -omics data. Using this novel approach, we sought to investigate the comprehensive dynamic features underlying development of various somatic stem cells from three germ layers, which we will fully address at the Phase 2 challenge “Reduction to Practice”. Specific Aim 1. To build a suite of algorithms generally applicable for single cell -omics data to reconstruct and investigate continuous in vivo processes. Specific Aim 2. To reconstruct and characterize in vivo adult neural stem cell differentiation process using single cell RNA-seq data. Specific Aim 3. To reconstruct and characterize in vivo differentiation processes of somatic stem cells from three germ layers, including hair follicle stem cells, intestinal stem cells, and hematopoietic stem cells.

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Dynamic Tracking of Cancer Stem Cells in Native Tumor Microenvironments - Single Cell, Dynamic Monitoring of Multi-Gene Expression in Tumor Tissues

Pak Kin Wong1 and Donna Zhang2

1Department of Aerospace and Mechanical Engineering, The University of Arizona, Tucson, AZ
2Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ

Tumor heterogeneity is a hallmark of cancer. In particular, cancer cells with distinct phenotypes are hierarchically and functionally organized in the tumor microenvironment. The cancer subpopulations, including the cancer stem cells, collectively regulate carcinogenesis, metastasis, and chemoresistance of the tumor. Ironically, current approaches for characterizing tumor heterogeneity are based on physical isolation and invasive manipulation of cancer cells to identify phenotypic functions, such as immunohistochemistry, in situ hybridization and flow cytometry analysis of surface biomarkers. The hierarchical organization, cell-cell coordination and cancer niche are inherently lost in the standard practice. More importantly, the dynamic response of the cancer stem cells under external perturbations (e.g., chemotherapeutics) cannot be studied. The abilities to identify cancer stem cells in native tumor microenvironments and monitor their dynamic responses to chemotherapy will revolutionize the current practice in cancer research. By developing a gold nanorod-locked acid (GNR-LNA) complex that allows real-time monitoring of multiple gene expressions in single cells, we propose a bold approach to decipher cancer heterogeneity by directly studying the behaviors of individual cancer cells in their native tumor microenvironments. In particular, we culture intact, living tumor tissues, perturb cancer cells using chemotherapeutics, and monitor the chemoresistance responses of individual cancer cells. This approach will enable new opportunities in combating cancer chemoresistance.

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Xu Challenge Finalist

Photostable Multiplexing NanoAssays for Real-time Molecular Imaging of Single Live Cells

X. Nancy Xu

Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, Virginia

Binding of just a few ligand molecules with its receptors on single live cells can initiate dynamic cascades of cellular signaling transduction pathways, alter cellular functions and cause diseases. Different ligand-receptor (L-R) interactions associated with the signaling pathways often work in concert to regulate and alter the cellular functions. Such signaling pathways can take minutes, hours or days. Furthermore, different types of cells express trace amounts of distinctive sets of receptors. Therefore, it is important to simultaneously and directly visualize and study different types of receptors on single live cells with single-molecule (SM) sensitivity in order to quantify various types of receptor molecules, and to directly capture how multiple types of L-R interactions work together to achieve given cellular functions. Currently, fluorescence microscopy using commercially available fluorescence imaging probes is the primary workhorse for live cell imaging. Unfortunately, fluorescence probes (fluorophor, fluorescence protein, QD) suffer intrinsic photobleaching effect, making them unable to continuously capture the dynamic events of the same single live cells over hours. Photobleaching also makes quantitative analysis over time difficult. Separation of different excitation and emission of various fluorophores leads to complex and expensive instrument and restricts their capacity of multiplexing study of multiple types of molecules simultaneously. All fluorescence imaging methods including conventional, confocal and super-resolution fluorescence microscopy suffer these same fundamental limitations. To overcome these fundamental and technological challenges, we aim to develop a novel molecular imaging platform including photostable multicolored and multifunctional single molecule nanoparticle optical biosensors (SMNOBS) and far-field photostable optical nanoscopy (PHOTON) for real-time study of functions of the same single live cells over a long desired period of time (hours-days) with high spatiotemporal resolution. To demonstrate the-proof-of-concept, we will use them to quantitatively image and molecular characterize roles and functions of multiple types of the receptors on rare subsets of colon cancer stem cells (cCSCs) in highly heterogeneous tumor populations and to study their differentiation mechanisms over time. These powerful new tools are expected to address a wide range of pressing biochemical and biomedical questions about molecular and real-time characterization of functions of single live cells and their related signaling pathways in real time. They include molecular identification and characterization of functions of rare subsets of individual cells (e.g., cCSCs) in highly heterogeneous tumor cells in situ in real time for a wide variety of applications, ranging from addressing fundamental questions in cancer research to exploring new approaches for target therapy.

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Development of Micro-scale Sampling Probes for in situ Mass Spectrometry Analysis of Secretomes from Live Single Cells

Zhibo Yang

Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma

We are proposing a first-in-class method capable of identifying, characterizing, and studying living single cells in their native biological context through mass spectrometry (MS) analysis of the secretome of single cells. Our basis for this theoretical proposal is the idea that expansive information revealing the type and status of single cells is present in the profile of biomolecules secreted by individual living cells; this extracellular readout has not been achieved and utilized. An obvious assumption for this proposal is that different single cells will interact with their extracellular environment differently. Therefore, different types of cells will excrete different populations and signatures of biomolecules, especially non-protein secretome (i. e. peptides and metabolites). The detection of the single cell secretome has not been accomplished, due to the exacting detection sensitivity and non-perturbing sampling technology needed: single cell secretome methods would need to be able to detect minute biomolecules excrete from a single cell living under native growth conditions over a time. There will be major advances in both scientific research and biomedical application if the single cell mass spectrometry (SCMS) analysis of secretome could be accomplished; the new technologies would allow for sub-populations of cells (such as stem cells) to be identified through detecting their secreted biomolecules being produced from living cells, and this capability would be especially powerful for patient-derived samples. This approach opens up a new dimension of categorizing types of cells and understanding cellular biology. The molecules excreted from individual cells could be used for intercellular communication and could serve for biomarker discovery. (1-3) This method would not require any modifications or experimental manipulations (i.e. fluorescent dyes, genetic manipulation, etc.) of the living single cells, but would simply detect changes in the secretome of cells growing in media or serum. To accomplish this theoretical goal, we will develop a new generation of SCMS sampling technology. In this proposal, we have proposed new designs of micro-scale enrichment and sampling probes that can be coupled with a mass spectrometer allowing for non-destructive time dependent measurements of extracellular biomolecules in situ, such as secretomes from live single cells. The potential ability of our novel MS techniques to perform extracellular single cell analysis could potentially be a revolutionary advancement in both fundamental studies and clinic tests.

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Challenge Finalists with Dr. Pettigrew

Follow that Cell Challenge Information

Please note: Specific eligibility requirements state Federal grantees and Federal contractors may not use federal funds (such as NIH grants) to develop Challenge submissions.

Phase 2 Solvers Informational Webinar Slides

Eligibility Requirements

Federal Register Notice

Related links:

Phase 1 Technical Evaluation Panel Members

"Follow that Cell" Challenge Press Release

Seeker Spotlight

"Follow that Cell" YouTube Video featuring Dr. Insel


This page last reviewed on July 18, 2017