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Cambridge Healthtech Institute’s Inaugural

Single Cell Analysis

Profiling to Interpretation for Emerging Diagnostic Applications

August 23-24, 2018 | Grand Hyatt Washington | Washington, DC

Single cell analysis is a rapidly evolving field with applications in cancer, PGD, immune response, and others. This method is enabling researchers to more deeply understand basic biology and disease development. As with many newer technologies, the largest challenges lie in the validation, data analysis, interpretation, and method standardization. Cambridge Healthtech Institute’s Inaugural Single Cell Analysis will bring together early researchers and champions of the field to discuss key challenges in data analysis and interpretation as well as new technologies and platforms for performing the analysis. We will also discuss cell heterogeneity, population dynamics, and future directions for using single cell analysis in a diagnostic setting.


Final Agenda

THURSDAY, AUGUST 23

10:00 am Registration

PLENARY SESSION
Constitution A&B

11:15 am Chairperson’s Remarks

11:20 am - 12:00 pm TECHNOLOGY PANEL: Disruptive Technologies in Lab Medicine

Moderator: Gregory J. Tsongalis, PhD, HCLD, CC, Professor, Pathology; Director, Laboratory for Clinical Genomics and Advanced Technology (CGAT), Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center and The Audrey and Theodor Geisel School of Medicine at Dartmouth

  • What is appropriate test utilization?
  • How do you ensure both user and patient safety?
  • How are tests implemented in the clinical setting?
  • How do they get regulated?
  • How do results get reported?
  • How do you enforce quality control in implementation?
  • How does it impact emerging applications such as liquid biopsies, infectious disease outbreaks, and POC diagnostics?

Panelists:

Nagarajan RakeshRakesh Nagarajan, MD, PhD, Chief Biomedical Informatics Officer, PierianDx


Richard_GregGreg Richard, Chief Commercial Officer, Interpace Diagnostics


Icenhour_CrystalCrystal R. Icenhour, PhD, CEO, Aperiomics, Inc.


Additional Panelists to be Announced

12:00 - 12:45 pm Changing Approaches to Sustainable Funding in Diagnostics

Moderator: Bruce Quinn, MD, PhD, Principal, Bruce Quinn Associates

Today, many innovators in the diagnostics industry struggle for funding, and yet news of large scale acquisitions or large (over $30M) funding rounds pops up regularly. How can innovators better understand the changing dynamics of the funding environment to succeed? How can investors know if they are getting exposed to the potentially best investments? Whether government or private, what drives the final difficult decisions and how can companies raise their chances of success? This session features leaders from several different channels of funding for innovators, including the NIH, other federal programs, and corporate and traditional venture investors.

Panelists:

Alex DeWinter, PhD, Managing Director, GE Ventures

Tyler Merkeley, BARDA’s CARB-X Program Manager, Health Scientist, Division of CBRN Countermeasures, BARDA

Todd Haim, PhD, Program Director, National Cancer Institute SBIR Development Center

Wouter Meuleman, PhD, Director, Venture Investments, Illumina Ventures

David Sans, PhD, MBA, F.A.A.R.M., Managing Director, Healthcare Capital Markets, THINK EQUITY (A Division of Fordham Financial Mgmt., Inc.)

12:45 pm Enjoy Lunch on Your Own

1:15 Ice Cream and Cookie Break in the Exhibit Hall with Poster Viewing (Independence Ballroom)

SINGLE CELL OMICS AND CELL HETEROGENEITY
Farragut/Lafayette

2:00 Chairperson’s Opening Remarks

Joshy George, PhD, Associate Director, Computational Sciences, Jackson Laboratory for Genomic Medicine

2:05 Dissecting Adult and Pediatric Gliomas by Single-Cell Genomics

Mario_SuvaMario Suva, MD, PhD, Assistant Professor, Pathology, Massachusetts General Hospital

I will discuss single-cell RNA-sequencing analysis of clinical samples of glioblastoma, IDH-mutant gliomas and pediatric histone-mutant gliomas, focusing both on malignant cells and on the tumor micro-environment.

2:35 Sparse Profiling of Single-Cell Genomes for Diagnosis and Detection of Tumors

Alex_KrasnitzAlexander Krasnitz, PhD, Associate Professor, Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory

Nuclear DNA copy number variation (CNV) profiles of individual cells can be determined, accurately and efficiently, by next-generation sequencing at very low coverage. Given the ubiquity of genome-wide somatic CNV in cancer, such profiling, applied to cells harvested from tissue biopsies, is a powerful diagnostic tool which facilitates reconstruction of clonal cell populations in a tumor and evaluation of its invasive potential. Sparse single-nucleus sequencing, supplemented by leukocyte depletion, can also provide a basis for sensitive, specific and affordable early detection of cancer signatures in blood of asymptomatic individuals.

3:05 Classical Blood Monocytes May Predict Response to Anti-PD-i Immunotherapy

Carsten_KreigCarsten Krieg, PhD, Assistant Professor, Microbiology & Immunology, Medical University of South Carolina

Anti-PD-i therapy is clinically effective against a wide range of cancers, but only a fraction of patients responds. Therefore, biomarkers to predict response are desperately needed. We combined high-dimensional single cell mass cytometry and a bioinformatics pipeline for the in-depth characterization of immune cells in the peripheral blood of metastatic melanoma patients before and after 3 months of anti-PD-i immunotherapy. A strong predictor of progression free and overall survival in response to anti-PD-i was the frequency of CD14tCDi6.HLADRb1 classical monocytes, and we propose this as a novel predictive biomarker for therapy decisions in the clinic.

3:35 Single-Cell Proteomics by Mass Spectrometry: From Embryo to Nervous System

Aparna_BaxiAparna B. Baxi, Graduate Student, Chemistry & Biochemistry, Anatomy and Regenerative Biology, University of Maryland–College Park, George Washington University

Understanding cell-to-cell heterogeneity necessitates specialized technologies capable of single-cell sensitivity. Detection of functional molecules like proteins remains a challenge due to a lack of sensitive technologies. To alleviate this challenge, we developed single-cell proteomics technologies using high-resolution mass spectrometry. In this presentation, we demonstrate the applicability of our platform to characterize hundreds of different proteins in single embryonic cells and small populations of neurons.

4:05 Networking Refreshment Break (Independence Foyer)

ADDRESSING PRE-ANALYTICAL AND ANALYTICAL CHALLENGES IN SCA
Farragut/Lafayette  

4:30 Challenges in the Design and Analysis of Single Cell RNA-Seq Experiments

Joshy_GeorgeJoshy George, PhD, Associate Director, Computational Sciences, Jackson Laboratory for Genomic Medicine

Recent advances in technology enable us to profile the expression levels of individual cells and provide us the capacity to identify novel cell-types present in a tissue. The ability to detect a novel cell-type depends on a number of parameters, including the number of cells sampled, the differences between the cell-types and the methods to analyze the dataset. In this talk, I will present an empirical approach to help solve this problem.

5:00 Using Neural Networks to Represent, Query and Retrieve Single-Cell RNA-Seq Data

Ziv_Bar-JoZiv Bar-Joseph, PhD, FORE Systems Professor of Computational Biology and Machine Learning, Computational Biology, Carnegie Mellon University

We developed methods based on neural networks (NN) to analyze scRNA-Seq data. NNs improve upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of hundreds of thousands of single cell profiles. Such database queries (which can be performed using our web server) enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.

5:30 Dimensional Reduction and Calibration Methods of Single-Cell High Throughput Data

Yuval_KlugerYuval Kluger, PhD, Associate Professor, Pathology, Yale University School of Medicine

Bulk sequencing technologies provide only an overview of the aggregate of numerous cells. We provide several algorithmic solutions for some challenges arising in the analysis of large single cell datasets. Specifically, we developed several efficient methods for dimensional reduction of massive single cell datasets, for removal of batch effects in high throughput experiments, and for unsupervised learning by Deep Learning techniques.

6:00 Close of Day

6:00 Dinner Short Course Registration (Independence Foyer)


6:30 - 9:00 pm Recommended Dinner Short Course*

SC11: Single-Cell RNA-Seq: Differential Expression Analysis and Quality Control

Michael Steinbaugh, PhD, Research Associate, Biostatistics, Harvard T.H. Chan School of Public Health

*Separate registration required

FRIDAY, AUGUST 24

8:00 am Registration and Morning Coffee (Independence Foyer)

TECHNOLOGIES FOR CELL SEPARATION AND ANALYSIS
Farragut/Lafayette 

8:25 Chairperson’s Remarks

Carsten Krieg, PhD, Assistant Professor, Microbiology & Immunology, Medical University of South Carolina


8:30 FEATURED PRESENTATION: High-Throughput Full-Length Single-Cell mRNA-Seq of Rare Cells

Shan_WangShan X. Wang, PhD, Professor, Materials Science and Engineering, and Electrical Engineering, Stanford University

Single-cell techniques are often limited to the analysis of small volumes of single cell suspensions with cell densities <~10^7 per mL. We demonstrate a cell separation platform (magnetic sifter) with Smart-seq2 protocol that enables rapid single-cell sequencing of rare cells in complex biological systems and envision this method would facilitate time-sensitive studies in areas such as immune or cancer biology, and liquid biopsies in diagnostics and immuno-therapy.

9:00 High- and Deep-Imaging Flow Cytometry: A Potential Diagnostic Tool for Hematological Disorders

Minh_DoanMinh Doan, MD, PhD, Assay Developer, Imaging Platform, Broad Institute

Flow cytometry has long been used routinely in clinical diagnoses, especially in hematological disorders. The introduction of imaging flow cytometry (IFC) brings even greater diagnostic potential thanks to its high content and high-throughput capability. We utilized both classical machine learning and deep learning to examine high-dimensional features extracted from IFC images. We demonstrate here the success of this framework in a variety of clinical studies.

9:30 NEW: Targeted Analysis for Rare Cells: Highly Sensitive, Multiplexed Molecular Characterization from Whole Blood

Paul SmithPaul Smith, Chief Executive Officer, ANGLE Biosciences Inc.

Circulating tumor cells (CTCs) represent a potentially rich, non-invasive source of information for cancer diagnostics and patient management. Targeted molecular profiling of CTCs may assist in the diagnosis, staging, prognosis, and treatment of a wide range of cancers. This talk will highlight the coupling of novel separation and detection technologies to enable enrichment and profiling of CTCs in a format suitable for cost effective, routine laboratory testing.

10:00 Rare Single Cell and Tissue Micro-ROI TCR Sequencing and Transcriptomics Using the RareCyte Platform

Uren_LanceLance U’Ren, DVM, PhD, Principal Scientist, RareCyte

The RareCyte platform provides integrated imaging and retrieval capabilities that allow identification of rare cells and regions of interest for genomic and transcriptomic assays. We validate the use of the platform for single cell TCR sequencing and expression analysis from rare antigen-specific T cells and demonstrate the platform can also be utilized for RNAseq of micro-ROI retrieved from tissues.

10:30 Networking Coffee Break (Constitution Foyer)

11:00 Single-Cell Mass Spectrometry of Targeted Subpopulations Selected by Fluorescence Microscopy

Akos_VertesAkos Vertes, PhD, DSc, Professor of Chemistry, Professor of Biochemistry and Molecular Biology, Department of Chemistry, George Washington University

Fluorescence microscopy has long been the method of choice to distinguish cellular subpopulations based on their immunological properties. Emerging methods of single-cell mass spectrometry enable the analysis of metabolites and lipids in individual cells. This presentation focuses on various combinations of these two techniques to reveal the composition and heterogeneity of cells in particular stages of mitosis, subcellular localization of peptides, and the selective analysis of cells infected by bacteria.

11:30 Metabolic Labeling/Ultra-High Resolution FTICRMS for Quantitative Neutron Encoded Lipidomics and Proteomics of Any Organism

Jeffrey_AgarJeffrey N. Agar, PhD, Associate Professor, Chemistry and Pharm. Sci., Northeastern University

Metabolic labeling methods have been developed and proven effective for either proteins or for lipids, but not both. Here isotopic labeling is accomplished with inexpensive deuterated water, and differences expression are measured by ultra-high resolution MALDI-mass spectrometry. This technology is combined with genetically encoded, cell-specific fluorescence to enable single-neuron analysis in situ.

12:00 pm Sponsored Presentation (Opportunity Available)

12:30 Enjoy Lunch on Your Own

1:00 Session Break

MICROFLUIDIC PLATFORMS FOR SINGLE CELL ANALYSIS
Constitution B

1:30 Chairperson’s Remarks

Suman Bose, PhD, Postdoctoral Fellow, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology


1:35 KEYNOTE PRESENTATION: Cell-based Point-of-Care Oncology Tool (POCOT) for Precision Medicine

John-McDevittJohn T. McDevitt, PhD, Chair, Department of Biomaterials, New York University College

This talk features the development, optimization and validation of the first cell-based point-of-care oncology tool (POCOT) for precision medicine. Using single-cell data collected non-invasively from cytology samples of prospectively recruited patients with gold-standard-confirmed diagnoses, a series of predictive models were developed and validated resulting in a “continuous numerical risk score”. Model development consisted of: (1) training binary classification models for each diagnostic class pair, (2) pairwise coupling to obtain diagnostic class probabilities, and (3) a weighted aggregation to obtain a final risk score on a continuous scale.

2:05 Ultralow-Input Microfluidic Assays for Epigenomic Analysis

Chang Lu, PhD, Fred W. Bull Professor, Chemical Engineering, Virginia Tech

Epigenome dictates turning on and off genes in a highly dynamic fashion during normal development and diseases, forming another layer of regulation on top of gene sequence. In this talk, I will discuss our efforts on using microfluidics as a versatile platform for profiling epigenomes based on a low number of cells in the context of precision medicine.

2:35 A Microfluidic Platform for High-Throughput Micro-RNA Profiling of Single Cells

Suman_BoseSuman Bose, PhD, Postdoctoral Fellow, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology

MicroRNAs are non-coding RNAs that function in gene regulation and are a robust biomarker for many diseases. In this talk, I will present a microfluidic platform that enables enrichment of miRNA from single cells and processes them for sequencing. Single cells are lysed within droplets and a magnetic tweezer is used to enrich for Ago2-bound miRNA, which are then barcoded and processed for sequencing. Finally, I will discuss the application of this in single cell miRNA profiling of mouse ES cells.

3:05 End of Summit



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