Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. Not only are tumors from different patients highly variable, but cellular heterogeneity and clonal diversity even within a single tumor can lead to increased therapeutic resistance, metastasis, and tumor progression. A variety of technologies such as quantitative PCR, mass cytometry and DNA/RNA sequencing methods have yielded important insights into single-cell heterogeneity and its role in the progression and treatment of cancer. Nonetheless, most platforms are limited in the number of single cells that can be automatically captured and processed in parallel, such as 96 cells for Fluidigm's C1 Auto Prep System. To study cancer biology using genetic perturbations, available techniques such as electroporation and viral vectors are highly toxic and inefficient due to the lack of precision in delivery, and are mostly applicable to bulk populations rather than single cells. The proposed research will develop a new nanoneedle-based 3D (x,y,z) actuation technology for manipulating and analyzing single cells on a massively parallel scale. The long term objective of this project is to develop a platform that will increase the throughput of single-cell manipulation and analysis by at least 1000-fold while significantly decreasing costs. The current effort will focus on identifying, manipulating, and characterizing single tumor cells based on their gene expression profiles, and leveraging the technology to perform non-viral based single-cell CRISPR-Cas9 genome editing with high transduction efficiency and minimal invasiveness. Metastatic cell diversity will first be profiled based on high-dimensional gene expression measurements. This will be in conjunction with targeting transcription factors for siRNA knockdowns to benchmark the platform's sensitivity for detecting differential target gene expression. In addition, the proposed technology will use the CRISPR-Cas9 system to deliver sgRNAs with Cas9 into single T cells, cancerous or normal; the goal will be to determine the efficacy of single-cell CRISPR genome editing and investigate the effect on global gene expression, cell function and drug responses. Importantly, these results will be relevant to immunotherapy approaches to treating cancer with engineered T cells. Parameters such as target efficiency, sensitivity/specificity of manipulation and delivery, device functionality after repetitive transduction, uniformity of manipulation across different single cells, as well as precision, reproducibility, hysteresis and stability of the motion of the microrobotic actuator will be optimized. The silicon-based microrobotic actuator is designed such that it can accurately track and target desired positions within single cells under an open loop control without a position feedback sensor, thus avoiding complicated control system electronics. Overall, this technology will provide an unprecedented level of throughput and precision in the manipulation of single and rare cells, accelerating the study of tumor cell heterogeneity.

Public Health Relevance

Cancers are very complex diseases that often prove challenging to treat because of how diverse the single cells in a given tumor are in terms of genetics, malignancy, and response to drugs. New technologies to separate and analyze single cells have revealed how important this diversity is for understanding cancer in individual patients and why certain treatments are ineffective, but these technologies are limited in throughput. This project will engineer a new silicon-based, cost-effective, precise, mechanical technology to perform massively parallel gene knockdowns, CRISPR-Cas9 gene editing, and molecular analysis of single cells, providing a crucial tool for cancer researchers to capture the variability in tumor cells and use it to design better treatments.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21CA212731-02
Application #
9433630
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Knowlton, John R
Project Start
2017-02-15
Project End
2019-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Genetics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304