Single Cell Analysis

Despite being in its infancy, single cell analysis is a rapidly developing field. Techniques examining single cells are revolutionizing both basic biology as well as our understanding of disease. Rapid development of innovative technologies and overcoming key challenges within single cell data analysis have contributed to the growth of the industry. At Cambridge Healthtech Institute’s Inaugural Single Cell Analysis symposium, innovators and early adopters will present single cell omics case studies and therapeutic applications in genomics, transcriptomics, and proteomics. Focus will be given to cell heterogeneity, method standardization, and data analysis. Overall, this symposium will share new methods and biological insights to continue accelerating single cell omics.

Thursday, February 15

7:00 am Registration Open and Morning Coffee


8:25 Chairperson’s Opening Remarks

Richard H. Scheuermann, Ph.D., Director, La Jolla Campus, J. Craig Venter Institute

8:30 FEATURED PRESENTATION: Profiling Protein Heterogeneity with Precision Single Cell Immunoblotting

Amy_HerrAmy E. Herr, Ph.D., Lester John and Lynne Dewar Lloyd Distinguished Professor, Bioengineering Investigator, Chan Zuckerberg Biohub, Bioengineering, University of California, Berkeley

We have introduced a new class of ‘electrophoretic cytometry’ tools that increase target selectivity beyond simple immunoassays. Enhanced selectivity is essential for targets that lack high-quality immunoreagents – as is the case for most protein forms (proteoforms). In fundamental engineering and design, I discuss how the physics and chemistry accessible in microsystems allow both the “scale-down” of electrophoresis to single cells and the “scale-up” to concurrent analyses of large numbers of cells.

9:00 Considerations for High Dimensional Immunophenotyping of Clinical Specimens by Mass Cytometry

Elma_KadicElma Kadic, MSc, Senior Scientist, In Vitro Pharmacology, Merck & Co. Inc.

This talk discusses validation steps that were taken to assess applicability of highly multiplexed mass cytometry analysis of single cells, with the ultimate goal of using this technology for biomarker discovery in clinical patient specimens. Case studies of renal and colorectal carcinomas will be presented.

9:30 High-Resolution Lineage Mapping of Myogenesis by Single Cell Mass Cytometry

Ermelinda_PorpigliaErmelinda Porpiglia, Ph.D., Scientist, Microbiology and Immunology, Baxter Laboratory for Stem Cell Biology, Stanford University

During muscle regeneration, cell state and identity change dynamically over time. Here we capitalized on a transformative technology, single cell mass cytometry (CyTOF), to identify in vivo skeletal muscle stem cells and novel progenitor populations. X-shift clustering analysis paired with single cell force-directed layout visualization of the myogenic compartment, resolved the intermediate stages of myogenesis at an unprecedented level of detail, revealing the complex relationship between stem and progenitor states during regeneration.

 Nexcelom10:00 Validating and Optimizing Single Cell Sorting of FACS Using the Celigo Image Cytometer

Wenyi Chen, Ph.D., Field Application Specialist, Nexcelom Bioscience LLC

Currently, single cell sorting is validated via microscopy, but manual observation is highly tedious and time-consuming. We demonstrated a high-throughput detection method to validate single cell sorting using the Celigo. The image cytometer was used to detect a single calcein-AM stained Jurkat cell sorted into 96-well microplates. The sorting efficiencies ranged from 90 to 96%. The proposed method is important to flow core laboratories and users for confirmation of single cell in each well.

10:30 Coffee Break in the Exhibit Hall with Poster Viewing


11:15 Printed Droplet Microfluidics for On-Demand Dispensing of Picoliter Droplets and Cells

Christian Siltanen, Ph.D., Postdoctoral Scholar, Adam Abate Laboratory, Bioengineering and Therapeutic Sciences, University of California, San Francisco; Scientist, Scribe Biosciences

Although the elementary unit of biology is the cell, high-throughput methods for the microscale manipulation of cells and reagents are limited. The existing options either are slow, lack single cell specificity, or use fluid volumes out of scale with those of cells. I present printed droplet microfluidics, a technology to dispense picoliter droplets and cells with deterministic control, and discuss its recent applications in molecular barcoding for single cell sequencing.

11:45 Single Cell RNA Sequencing Advancements Support Transcriptional Profiling of Thousands of Cells from Diverse Sample Types

Zora_ModrusanZora Modrusan, Ph.D., Senior Scientist, Molecular Biology Department, Genentech

We employed a single cell RNA-seq approach to understand the composition and heterogeneity of diverse sample types. Examples of single cell RNA-seq technical assessment and analysis of the data generated with different commercial platforms (Fluidigm, Wafergen, 10x Genomics) and from conventional FACS-based single cell and single nuclei transcriptional profiling will be discussed to illustrate both the enhancements and challenges of current scRNA-seq methodology.

Miltenyi_Biotec  12:15 pm Methods for Sample Preparation and Single Cell Analysis of Solid Tumors

Jill_HerschledJill Herschleb, Ph.D., Staff Scientist, Sample Preparation, 10x Genomics

10x Genomics’ Chromium Solutions are the industry standard for single cell digital gene expression and cellular heterogeneity analysis. Examination of tumor microenvironments requires processing tissue samples into cellular suspensions while maximizing viability and preserving cell-surface markers. Miltenyi’s solutions rigorously and consistently address this challenge. The combination of these methods enables complex tissues to be studied by single cell genomics workflows, revealing interrelationships between molecular and cellular phenotypes within tumor microenvironments.

12:30 Session Break

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:15 Session Break


1:55 Chairperson’s Remarks

Raeka Aiyar, Ph.D., Director of Communications and Development, Stanford Genome Technology Center

2:00 FEATURED PRESENTATION: Application of Machine Learning for Quality Control and Marker Gene Selection in Single Cell and Single Nuclei RNA Sequencing Data Analysis

Richard_ScheuermannRichard H. Scheuermann, Ph.D., Director, La Jolla Campus, J. Craig Venter Institute

Next-generation sequencing of RNA from single cells or single nuclei (sc/nRNA-seq) has become a powerful approach to understand the cellular complexity and diversity of multicellular organisms and environmental ecosystems. We present our experience in using random forest machine learning methods to perform robust, unbiased quality control for data filtering and process improvement and marker gene selection for cell type clustering.

2:30 Comprehensive Single Cell Transcriptional Profiling of a Multicellular Organism by Combinatorial Indexing

Junyue_CaoJunyue Cao, Research Assistant, Jay Shendure Laboratory, Genomic Science, University of Washington

We developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei (sci-RNA-seq: Single cell Combinatorial Indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 stage, which is over 50-fold “shotgun cellular coverage” of its somatic cell composition. These data generated by sci-RNA-seq constitute a powerful resource for nematode biology, and foreshadow similar atlases for other organisms.


Cytobank3:00 Uncovering Hidden Single Cell Biomarkers with Machine Learning

Katherine DrakeKatherine Drake, Ph.D., Director, Informatics, Cytobank, Inc.

Cytobank’s intuitive informatics platform provides easy, cloud-based access to machine learning tools that can rapidly uncover novel biomarkers from single cell data. We’ll demonstrate how Cytobank’s single cell analysis tools can be applied to fluorescent and mass cytometry, high content imaging, and single-cell RNA-seq data. See how machine learning analysis methods build our understanding of disease pathology and enable you to predict therapy response in all of these applications.

3:30 Refreshment Break and Poster Competition Winner Announced in the Exhibit Hall


4:15 Microfluidic Digital Universal High Resolution Melt for Sensitive Single Cell Analysis

Stephanie_FraleyStephanie I. Fraley, Ph.D., Assistant Professor, Department of Bioengineering, University of California, San Diego

We report a microfluidic platform enabling the isolation and genetic analysis of individual cells within a heterogeneous population in under four hours. Cells or extracted cell genomes are partitioned by digital Poisson loading across 20,000 reactions. Precise thermal control and optical instrumentation accomplish universal amplification and High Resolution Melting (HRM) in each reaction. Image processing and machine learning algorithms identify genetic signatures and quantify absolute loads.

4:45 Imaging and Sequencing Single Cells

Aaron_StreetsAaron Streets, Ph.D., Assistant Professor, Bioengineering, University of California, Berkeley

Microscopy and RNA sequencing are both precise techniques that can be used to quantitatively profile single cells. However, in order to infer the relationship between gene expression and morphological phenotype, it is necessary to image and sequence the same single cell. We use a microfluidic platform to couple imaging and RNA sequencing of single cells and present recent developments in analysis of large, multimodal, single-cell datasets.

5:15 Characterizing the Complexity of Cell Populations with Sequencing and Magnetic Levitation Technologies

Raeka_AiyarRaeka Aiyar, Ph.D., Director of Communications and Development, Stanford Genome Technology Center

We have developed several single cell technologies to advance our understanding of the molecular systems driving developmental processes, tissue differentiation, immune responses, and disease onset. These include RNA-seq methods that have proven efficient in a variety of cell types and in characterizing T cell repertoires. We have also developed a novel, smartphone-compatible magnetic levitation platform capable of marker-free visualization and isolation of rare cells for downstream phenotyping and diagnostic applications.

5:45 Reception in the Exhibit Hall with Poster Viewing

6:45 Close of Day

Friday, February 16

8:00 am Registration Open and Morning Coffee


8:25 Chairperson’s Remarks

Jeff Tza-Huei Wang, Ph.D., Professor, Mechanical Engineering & Biomedical Engineering Departments, Sidney Kimmel Comprehensive Cancer Center, Institute for NanoBioTechnology, Johns Hopkins University

8:30 FEATURED PRESENTATION: Microfluidic Single Cell Analysis of Mechanics and Invasion in Cancer

Sanjay_KumarSanjay Kumar, M.D., Ph.D., Professor, Departments of Bioengineering and Chemical & Biomolecular Engineering, University of California, Berkeley

Tissue behavior is often driven by a few cells within a larger population, creating a need for single cell analytical tools. We have developed technologies to probe the structure, mechanics, and motility of individual cells, with an eye towards integrating these technologies with molecular measurements. I focus on the use of microfluidics for high-throughput characterization of tumor cell mechanics and invasion and single cell micropatterning to investigate heterogeneities in cellular structural networks.

9:00 Profiling the Somatic Variation of Thousands of Single Cells Using Combinatorial Indexing

Kristof_TorkenczyKristof Torkenczy, Research Scientist, Andrew Adey Laboratory, Department of Molecular and Medical Genetics, Oregon Health & Science University

Quantification of somatic variation within heterogeneous cell populations remains challenging. Bulk approaches fail to disambiguate low-frequency mutations. Single cell genome sequencing provides an accurate means of somatic CNV detection. However, current methods are limited by throughput. To address this, we have developed a novel multiplexed single cell protocol that uses combinatorial indexing to increase throughput and applied it to find subclonal mutations in a PDAC tumor, uncovering multiple tumorigenic events.

9:30 Microfluidic Droplet Technology for Drug Testing on Single Cancer Cells

Siva_VanapalliSiva Vanapalli, Ph.D., Associate Professor, Department of Chemical Engineering, Texas Tech University

Drug assays with single cancer cells can provide insights into mechanisms of drug resistance and development of new drug targets. We report a microfluidic technology for encapsulating single cells in nanoliter-scale droplets and conducting drug assays in parallel. Single cell analysis revealed diverse uptake profiles of cancer drugs. The uptake profiles were correlated with metastatic potential and drug resistance of cancer cells.

Cellecta10:00 Targeted RNA Expression Profiling for Biomarker Discovery and Molecular Profiling of Complex Samples  

Alex_ChenchikAlex Chenchik, Ph.D., President & CSO, Cellecta, Inc.

Rapid, robust transcriptome-based methods for cellular characterization of the tumor microenvironment and biomarker discovery are required to improve prognosis and treatment of cancer. The DriverMapTM targeted RNA expression profiling assay's genome-wide set of 19,000 primer pairs combines RT-PCR sensitivity with the depth and throughput of NGS to address this need.

10:30 Coffee Break in the Exhibit Hall with Poster Viewing


11:15 Single Cell Sequencing as a Tool for Drug Discovery in the CNS

Dimitry Ofengeim, Ph.D., Lab Head of Neuroimmunology, Neuroscience, Sanofi Genzyme

Single cell RNA sequencing is an exciting technology allowing the analysis of transcriptomes from individual cells, and is ideally suited to address the inherent complexity and dynamics of the central nervous system. scRNA-seq has already been applied to the study of molecular taxonomy of the brain. scRNA-seq has great potential in the discovery of targets and biomarkers as a new approach in developing novel therapeutics for the treatment of neurodegenerative diseases.

11:45 Transcriptomic Profiling Maps Anatomically Patterned Subpopulations among Single Embryonic Cardiac Cells

Guang_LiGuang Li, Ph.D., Research Fellow, Sean Wu Laboratory, Stanford Cardiovascular Institute, Stanford University

Cardiogenesis is orchestrated by cell type- and chamber-specific transcription. We collected 2,233 single cell RNAseq samples from embryonic mouse hearts. This data resource uncovers anatomical patterns of gene expression that enable the deduction of a single cell sample’s anatomical origin, providing insight into developmental perturbations in congenital heart defect models.

12:15 pm FEATURED PRESENTATION: Accelerating Bacterial Growth Detection and Antimicrobial Susceptibility Assessment via Single Cell Abalysis in Picoliter Droplets

Jeff_WangJeff Tza-Huei Wang, Ph.D., Professor, Mechanical Engineering & Biomedical Engineering Departments, Sidney Kimmel Comprehensive Cancer Center, Institute for NanoBioTechnology, Johns Hopkins University

We present a rapid and integrated single cell biosensing platform, termed dropFAST, for bacterial growth detection and antimicrobial susceptibility assessment. dropFAST utilizes a rapid fluorescent growth assay coupled with stochastic confinement of bacteria in picoliter droplets to detect signal from growing bacteria after 1 h incubation.

12:45 Close of Symposium

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