1
|
Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D. Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. LAB ON A CHIP 2018; 18:3617-3630. [PMID: 30357245 PMCID: PMC6334845 DOI: 10.1039/c8lc00672e] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries.
Collapse
Affiliation(s)
- J Ko
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - M Hemphill
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Z Yang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - E Sewell
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Y J Na
- Department of Medicine, Division of Nephrology, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - D K Sandsmark
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - M Haber
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - S A Fisher
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E A Torre
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - K C Svane
- Department of Cell Biology and Neuroscience, Rutgers, the State University of New Jersey, NJ 08854, USA
| | - A Omelchenko
- Department of Cell Biology and Neuroscience, Rutgers, the State University of New Jersey, NJ 08854, USA
| | - B L Firestein
- Department of Cell Biology and Neuroscience, Rutgers, the State University of New Jersey, NJ 08854, USA
| | - R Diaz-Arrastia
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - J Kim
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA and Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - D F Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA. and Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - D Issadore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA. and Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
2
|
Ko J, Bhagwat N, Yee SS, Ortiz N, Sahmoud A, Black T, Aiello NM, McKenzie L, O'Hara M, Redlinger C, Romeo J, Carpenter EL, Stanger BZ, Issadore D. Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes. ACS NANO 2017; 11:11182-11193. [PMID: 29019651 DOI: 10.1021/acsnano.7b05503] [Citation(s) in RCA: 151] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.
Collapse
Affiliation(s)
- Jina Ko
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Neha Bhagwat
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Stephanie S Yee
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Natalia Ortiz
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Amine Sahmoud
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Taylor Black
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Nicole M Aiello
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Lydie McKenzie
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Mark O'Hara
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Colleen Redlinger
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Janae Romeo
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Erica L Carpenter
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - Ben Z Stanger
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| | - David Issadore
- Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
- Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
| |
Collapse
|
3
|
Ko J, Bhagwat N, Yee SS, Black T, Redlinger C, Romeo J, O'Hara M, Raj A, Carpenter EL, Stanger BZ, Issadore D. A magnetic micropore chip for rapid (<1 hour) unbiased circulating tumor cell isolation and in situ RNA analysis. LAB ON A CHIP 2017; 17:3086-3096. [PMID: 28809985 PMCID: PMC5612367 DOI: 10.1039/c7lc00703e] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
The use of microtechnology for the highly selective isolation and sensitive detection of circulating tumor cells has shown enormous promise. One challenge for this technology is that the small feature sizes - which are the key to this technology's performance - can result in low sample throughput and susceptibility to clogging. Additionally, conventional molecular analysis of CTCs often requires cells to be taken off-chip for sample preparation and purification before analysis, leading to the loss of rare cells. To address these challenges, we have developed a microchip platform that combines fast, magnetic micropore based negative immunomagnetic selection (>10 mL h-1) with rapid on-chip in situ RNA profiling (>100× faster than conventional RNA labeling). This integrated chip can isolate both rare circulating cells and cell clusters directly from whole blood and allow individual cells to be profiled for multiple RNA cancer biomarkers, achieving sample-to-answer in less than 1 hour for 10 mL of whole blood. To demonstrate the power of this approach, we applied our device to the circulating tumor cell based diagnosis of pancreatic cancer. We used a genetically engineered lineage-labeled mouse model of pancreatic cancer (KPCY) to validate the performance of our chip. We show that in a cohort of patient samples (N = 25) that this device can detect and perform in situ RNA analysis on circulating tumor cells in patients with pancreatic cancer, even in those with extremely sparse CTCs (<1 CTC mL-1 of whole blood).
Collapse
Affiliation(s)
- Jina Ko
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|