Mobile microscopy, sensing and diagnostics

Transcription

1 Mobile microscopy, sensing and diagnostics Aydogan Ozcan, Ph.D. Electrical Engineering Department & Bioengineering Department & California NanoSystems Institute University of California, Los Angeles (UCLA)

2 Smart systems for personalized and connected healthcare Personalized healthcare systems connected to our everyday lives using modern communication platforms, including consumer devices and electronics. Multidisciplinary engineering approaches required, including biomedical engineering, material science, mechanical engineering, electrical engineering, computer science, chemical engineering, industrial engineering, among many others.

3 Democratization of future healthcare systems -- Can we convert patient s home into an advanced 24/7 laboratory for medical diagnosis, monitoring of patients, high-risk and aging populations, preventive & personalized medicine? -- Manage costs better, early diagnosis, better treatment, better adherence, etc.? 3

4 Democratization of future healthcare systems $6 USA Healthcare Costs (Trillion USD) $5 $4 $3 $2 $1 $0 Y1960 Y1964 Y1968 Y1972 Y1976 Y1980 Y1984 Y1988 Y1992 Y1996 Y2000 Y2004 Y2008 Y2012 Y2016 Y2020 Y2024 5% of Americans Make Up 50% of U.S. Health Care Spending

5 Democratization of future healthcare systems -- Global health & under-served communities: Can medical diagnostics and care be practiced in resource limited settings using innovative and cost-effective technologies at massive scales? Harnessing Big/Small Data for better patient outcomes 5

6 6

7 A different view of the Moore s Law

8 Smart-phones as super-computers

9 Cell phones are now everywhere: A great potential for Telemedicine needs Afghanistan 40 Canada 68 U.K. 97 Russia 162 Mexico 78 U.S. 97 Chad 24 India 45 China 56 Bolivia 72 Brazil 90 Argentina 129 Angola 44 South Africa 94 World Bank ~7 billion cell phones are being used worldwide. ~15 billion cell phones have been sold so far. UAE 232 Indonesia 69 Australia 111 ~80% are in developing countries. (International Telecommunication Union)

10 Providing a new platform for telemedicine Lensfree Microscope Tomographic Microscope DNA Analyzer Fluorescence Microscope Lensfree Super-resolution Microscope Holographic Microscope Albumin Tester Imaging Flow-Cytometer Ozcan Research UCLA

11 Providing a new platform for telemedicine Blood Analyzer Heavy metal detector Allergen detector E. coli sensor Diagnostic test reader (HIV, malaria, etc.) Well-plate ELISA Reader Antimicrobial Susceptibility Testing Giardia sensor

12

13

14

15 Providing a new platform for telemedicine Wearable Microscopy for Implantable Sensors Blood Analyzer Heavy metal detector Allergen detector E. coli sensor Google Glass based Diagnostics Chlorophyll Measurement Diagnostic test reader (HIV, malaria, etc.) Well-plate ELISA Reader Antimicrobial Susceptibility Testing Giardia sensor

16 Providing a new platform for telemedicine LUCAS: Lensless, Ultra wide-field Cell monitoring Array platform based on Shadow imaging 16

17 Incoherent Lensfree Holography Using Large Apertures: -Reduced speckle noise and multiple interference noise -Reduced cross-talk among cells -Tolerant to misalignments; No need for coupling optics -No need for lasers; LEDs would be sufficient -Significantly larger field of view

18 Cell Density: 102K [Cells/uL] Reconstruction 10X

19 Lensfree Incoherent Holography on a Chip Field of view: 24 mm 2 Effective Numerical Aperture: ~ Weight: <45 grams (<1.6 ounces) Source: LED 19

20 CD4 CD8 counts using lensfree on-chip imaging Collaborator: Revzin Group UC Davis

21 Lensfree on-chip cytometry in microfluidic channels based on surface chemistry MC=87 AC=90 CD4 T-cells 40um MC=93 AC=96 40um MC=53 AC=55 CD8 T-cells 40um MC=49 AC=51 40um 21

22 Patient#1 Number Of CD8 Cells per spot PATIENT#1 CD8 count Microscope LUCAS Error Total number of CD8 Cells Total CD8 Cells Micros cope LUCAS 947 Error CD4/CD8 Number of CD4 Cellsper spot PATIENT#1 CD4 count Microscope LUCAS Error Total Number of CD4 Cells Total CD4 Count Micros cope LUCAS 2906 Error Microscope LUCAS Error

23 Patient#2 Number Of CD8 Cells per spot PATIENT#2 CD8 count Microscope LUCAS Error Total number of CD8 Cells Total CD8 Cells Micros cope LUCAS 5074 Error CD4/CD8 Number of CD4 Cellsper spot PATIENT#2 CD4 count Microscope LUCAS Error Total Number of CD4 Cells Total CD4 Count Micros cope LUCAS 4617 Error Microscope LUCAS Error

24 x Measured x 10-5 Intensity -1 Reconstructed Amplitude Recovered Phase 10X objective microscope 40X objective microscope Scale bar - holograms: 50um Scale bar - all others: 5um x x Test feature 1 Test feature 2 Test feature 3 amplitude -0.5 x (d) Reconstructed Phase microns x phase [rad] x 10-5 x Field of view: 24 mm Effective Numerical Aperture: ~ Weight: <45 grams (<1.6 ounces) x x x x Lensfree Incoherent Holography on a Chip Source: LED microns 0 1 normalized amplitude 0.5 normalized amplitude microns Sperms Phase Image normalized amplitude microns microns x µm 24

25 Super-resolution in lensfree on-chip imaging 25

26 microcontroller 23 LEDs 23 fibers colour filter sample tray CMOS sensor

27 microcontroller 23 LEDs 23 fibers colour filter sample tray CMOS sensor

28 A numerical aperture of ~0.9 and a half-pitch resolution of <250 nm is confirmed Regular in-line hologram Reconstructed Amplitude Super-resolution hologram Reconstructed Amplitude Reconstructed Phase 40X (NA=0.65) 5mm

29 LUCAS 2.5 (NA=0.65) 5 Megapixel CMOS imager 10 mm 10 mm 10 mm radians radians radians Plasmodium falciparum

30 Malaria-infected blood sample 10 µm 30

31 Lensfree imaging of histopathology slides Science Translational Medicine - AAAS (2014) 31

32 Science Translational Medicine - AAAS (2014)

33 (a) Lensfree FOV Lensfree reconstructed amplitude (colored) Pap Smear (b) 50um (c) 50um (d) (e) 40X FOV Microscope 40X 0.75NA 1mm 50um 50um Lensfree reconstructed amplitude Abnormal Pap Smear (f) (g) (h) (i) 50um 20um 20um 20um (j) (k) (l) (m) Microscope 40X 0.75NA 50um 20um 20um 20um 33

34

35 Field-of-view Ozcan Lab - Nature Methods (2012) 35 Sample Volume Resolution

36 Wide-field on-chip imaging of single viruses and nanoparticles using self-assembled nano-lenses Nature Photonics (2013)

37 Nano-Catenoid Lenses for Imaging of Single Nano-particles Nano-catenoid lenses Nature Photonics (2013) Two Rings in Soap Solution Form a Catenoid i.e., a Minimal Surface that is selfassembled to have Zero Mean Curvature. (L. Euler )

38 Vapor condensed Tunable Nano-lenses ACS Nano (2014) & ACS Nano (2015)

39 Vapor condensed Tunable Nano-lenses ACS Nano (2014) & ACS Nano (2015)

40 Vapor condensed Tunable Nano-lenses Sizing accuracy ~11 nm

41 Computational sensing of herpes simplex virus (HSV) using a cost-effective on-chip microscope Scientific Reports (2017)

42 Air pollution is a life threatening global problem WHO: 7 million die annually from pre-mature death due to the health effects of air pollution (2012) 4.3M from indoor air pollution (e.g., cooking stoves in rural Africa, India) 3.7M from outdoor air pollution (e.g., industrial emissions) Indoor air pollution related deaths: 4.3 M Outdoor air pollution related deaths: 3.7 M Total deaths: 7 M Air pollution accounts for 2M deaths in China, India and Pakistan

43 Air Quality Monitoring Using Mobile Microscopy and Machine Learning Micro-Computer Fiber-Coupled Multi-color LEDs Impactor-based Air Sampler Battery CMOS Image Sensor Air Pump 13 Liters/min Our modular design makes it easy to perform a variety of tests and integrate cutting edge computational imaging and sensing technologies

44 Air Quality Monitoring Using Mobile Microscopy and Machine Learning (a) (b) (Start) 1. Phone APP control 5. Sync Image to Server for Processing 2. Take Background Images (R,G,B) (i) (ii) (iii) 4. Take Raw Sample Images (R,G,B) Air Sampler 3. Pump Air for 30 Seconds at 13 L/min (c) 1. Receive the Image and GPS Location 5. Transfer and display results on the Phone APP 2.Differenti al Imaging Remote Server (iv) (v) (vi) 4. Sizing and Size Statistics 3. Particle Detection using Peeling & ML

45 Air Quality Monitoring Using Mobile Microscopy and Machine Learning Our modular design makes it easy to perform a variety of tests and integrate cutting edge computational imaging and sensing technologies

46 Air Quality Monitoring Using Mobile Microscopy and Machine Learning Reseda, Los Angeles Air Sampling Station (California Environmental Protection Agency) BAM: Beta Attenuation Monitoring Light: Science and Applications (2017)

47 Air Quality Monitoring Using Mobile Microscopy and Machine Learning Los Angeles International Airport (LAX)

48 Light-weight, compact and cost-effective Can easily form a network of air measurement tools Static Nodes and Deployments Air-In Button Wireless Data Air-Out Dynamic Nodes (Integration with cars, trucks ) Integration with drones is a goal. (Our quadcopter at UCLA)

49 Light: Science and Applications NPG (2014)

50 Sci. Reports NPG (2014)

51 Sci. Reports NPG (2014)

52 A different view of the Moore s Law

53 ACS Nano, 2013

54

55 ACS Nano, 2013

56 Imaging and Sizing of Single DNA Molecules on a Mobile-Phone ACS Nano, 2014

57 Imaging and Sizing of Single DNA Molecules on a Mobile-Phone ACS Nano, 2014

58 Plasmonics Enhanced Smartphone Fluorescence Microscopy Scientific Reports, 2017

59 Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy Nature Communications, 2017

60 Quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning Lab Chip, 2015

61 Lab Chip, 2015

62 Quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning Lab Chip, 2015

63 ACS Nano 2015 Cellphone-Based Hand-Held Micro-Plate Reader for Point-of-Care ELISA Testing

64

65 ACS Nano 2015

66 Mumps Measles HSV 1 HSV 2 N Analysis Type Curve Fitting M.L Curve Fitting M.L Curve Fitting M.L Curve Fitting M.L Specificity 92.70% ± 0.49% 97.37% ± 2.63% 92.09% ± 0.77% 94.56% ± 3.43% % ± 0.00% % ± 0.00% % ± 0.00% % ± 0.00% Sensitivity % ± 0.00% % ± 0.00% 98.39% ± 0.42% 99.47% ± 0.59% 97.32% ± 0.32% % ± 0.00% % ± 0.00% % ± 0.00% Agreement 97.21% ± 0.41% 99.61% ± 0.39% 96.08% ± 0.32% 98.56% ± 0.53% 97.16% ± 0.55% 99.42% ± 0.43% 98.66% ± 0.22% 99.41% ± 0.44% ACS Nano 2015

67 Automated diagnosis of antimicrobial resistance using a cellphone-based micro-plate reader Sci. Reports 2016

68 Providing a new platform for telemedicine E. coli sensor Allergen detector Heavy metal detector Google Glass based Diagnostics Diagnostic test reader (HIV, malaria, etc.) Blood Analyzer Blood Analyzer

69 Ozcan Research Group Members & Funding 69