detection technologies for cryo-electron microscopy...16 sensitivity • minimum detectable signal...

1

Detection Technologies for Cryo-Electron

Microscopy

S2C2 Workshop – Cryo-EM Training

Christopher Booth

Director of Product Management

Gatan Inc.

May 29, 2020

2

Detection Technologies in Cryo-EM

• Detection of Electrons

• Measuring Detector

Performance

• Energy Filtering and cryo-EM

• Framerate: Motion Correction,

CDS and Co-incidence loss

• Throughput in Cryo-EM

• Practical Considerations

3




Performance






4




Performance






5




Performance






6




Performance






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Performance






8

Detection Of Electrons

9

Key Concepts in Detecting Electrons

• CMOS and CCD

• “indirect” vs direct detection cameras

• Sensitivity

• Linearity and dynamic range

• Dynamic range

• Pixel size and field of view

• Electron counting

• Co-incidence loss

10

Recording Images In Electron MicroscopyA little bit of history

• Oldest recording medium: photographic film

• 1970: Charge coupled device (CCD) was invented

• 1976: CCD camera was used for astronomy

• 1982: 100 x 100 CCD was directly exposed to 100 kV electrons…radiation damage

• 1988: 576 x 382 CCD used with scintillator and optical coupler

• 1990: Gatan made the world’s first commercial CCD camera

• 2002: 128 x 128 direct detection camera developed

• 2008 – 2009: commercial complementary metal-oxide semiconductor (CMOS) cameras and radiation hard CMOS cameras were introduced

11

CCD vs. CMOS

Both CCD and CMOS use photo diodes to convert photons to electrons,

the difference is how they store charge and transfer it.

• CCD: Charge is transferred between neighboring cells, and read-out

• CMOS : Charge immediately converted to voltage (read out with digital output)

http://meroli.web.cern.ch/meroli/lecture_cmos_vs_ccd_pixel_sensor.html

12

Detectors in Electron Microscopy

A. Optically coupled

B. Fiber-optic coupling

C. Direct detection

D. Transmission direct detection

13

Traditional Indirect Detection

e-

Scintillator

Light transfer

Light sensitive CCD or CMOS

1. Convert

electrons to

light

2. Transfer light to

detector

3. Detect light and

convert to

signal

14

Direct Detectione-

Radiation hard CMOS

1. Convert

electrons to

light


detector

3. Detect electron

and convert to

signal

15

Transmission Direct Detectione-

Radiation hard, thinned CMOS

1. Convert

electrons to

light


detector

3. Detect electron

and convert to

signalMinimize back scattered

electrons that add noise

16

Sensitivity

• Minimum detectable signal in terms of the number of incident electrons.

• Single-electron sensitivity

• if the gain of the system is such that the output of a single incident electron is above the

noise floor

Deposited Energy

# p

ixel

s

Noise floorSignal from incident electrons

17

Linearity

• Relationship between output (image

intensity in digital units) and the input

(number of incident electrons).

• CMOS and CCDS are much more linear

than film

• Counted cameras have a special kind of

non-linearity called co-incidence loss

Journal of Microscopy, Vol. 200, Pt 1, 2000, pp. 1±13.

18

Dynamic Range

Dynamic range: The range of values that can be distinguished between a maximum level

(saturation) and zero (noise)

• Driven by combination of max allowable charge in each and noise floor

• One pixel can have 16 bit dynamic range (values between 0 – 16000)

• Used to be a very important factor for cameras, now frame rate is much more important

• A camera with only 12 bit dynamic range (0 – 4095) might accumulate 40 frames in a

second.

• 4095 x 20 = 163,800 counts of dynamic range

Journal of Microscopy, Vol. 200, Pt 1, 2000, pp. 1±13.

19

3,71

0 pi

xels

3,838 pixelsK2

,4.0

92 p

ixel

s

5,760 pixelsK3

23.6 Mpixels(94 Mpixels super-resolution)

14.4 Mpixels

How Many Pixels are Enough?

20

Rio™ 16: 4k x 4k, 9 µm

Rio 9: 3k x 3k, 9 µm

How Important is FOV?

OneView®: 4k x 4k, 15 µm

Rio 16: 4k x 4k, 9 µm

K3™ : 6k x 4k, 5 µm

K2®: 4k x 4k, 5 µm

Side-mount camera

Bottom-mount camera

CCD

21

Electron Counting Makes All the Difference

Single high speed frame using conventional

CCD-style charge read-out

Same frame after counting

Counting removes the variability from scattering,

rejects the electronic read-noise, and restores the DQE.

22

Traditional Integration

Similar to indirect detection cameras, direct detectors can integrate

the total charge produced when an electron strikes a pixel.

Electron enters

Detector.

Electron signal is

scattered.

Charge collects in

each pixel.

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Counting

In counting mode, individual electron events are identified at the time

that they reach the detector.

Electron enters

Detector.

Electron signal is

scattered.

Charge collects in

each pixel.

Event reduced to

highest charge pixels.

To do this efficiently the camera must run fast enough so that individual electron

events can be identified separately.

24

Super-Resolution

The theoretical information limit defined by the physical pixel size is

surpassed when you use super-resolution mode.

Electron enters

Detector.

Electron signal is

scattered.

Charge collects in

each pixel.

Event localized to

sub-pixel accuracy.

High-speed electronics are able to recognize each electron event

(at 400 fps) and find the center of event with sub-pixel precision.

25

Measuring Detector Performance

26

PSF, MTF, NTF and DQE?

• PSF: Point spread function

• Blurring of a single point in the camera

• MTF: Modulation transfer function

• PSF as a function of spatial frequency• Most often estimated using a “knife

edge”

• NTF: Noise transfer function

• Noise power spectrum• Noise as a function of spatial

frequency

DQE s

=SNRout s

SNRin s

=MTF(s)2

ΤNPSout(s) Dosein(s)

27

DQE Challenges

• Signal challenges: Edge image non-ideality

• Charging and edge cleanliness• Scale• Edge dose• Motion• Fields• scatter

• Noise challenges:

• Fixed pattern noise• Calibration of noise power• Measurement of incoming beam level

• Counting-related challenges:

• spatial effects of coincidence loss: high-pass filtering• Non-linear counting due to coincidence loss – calibration.• background

𝐷𝑄𝐸 𝑠

=𝑆𝑁𝑅𝑜𝑢𝑡 𝑠

𝑆𝑁𝑅𝑖𝑛 𝑠

=Τ𝑆𝑃𝑆𝑜𝑢𝑡(𝑠) 𝑆𝑃𝑆𝑖𝑛(𝑠)

Τ𝑁𝑃𝑆𝑜𝑢𝑡(𝑠) 𝑁𝑃𝑆𝑖𝑛(𝑠)

=𝑀𝑇𝐹(𝑠)2

Τ𝑁𝑃𝑆𝑜𝑢𝑡(𝑠) 𝐷𝑜𝑠𝑒𝑖𝑛(𝑠)

28

Measuring MTF with a Physical Edge (1)

AuPd-coated and plasma cleaned edge with canted face mounted in the entrance aperture of a GIF Quantum®

imaged on the K2 at the end of the GIF in super-resolution mode at 200 kV. (particularly bad example – not

always this bad)

Effect of charging

Short traversal close to

surface charge

Long traversal

close to surface

charge

Canted

edge Wire

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Measuring MTF with a Physical Edge (2)

Motion – edge creepNoise

A noise-tolerant method for measuring MTF from

found-object edges in a TEM, Paul Mooney,

Microscopy and Microanalysis 15:1322-1323 CD

Cambridge University Press (2009). Figure 4:

Simulated MTF with various amounts of shot noise

added.

Good: difference

between two 20 s

edge images

showing no “motion

fringe”.

At low dose rates, need long exposures to get

enough dose → have to be careful about edge

creep.

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Other Things to Avoid with DQE Measurements

• Missing dose in a Faraday cup holder: overestimates DCE and

therefore DQE (in same proportion)

• Using the TEM screen calibration

• Drifting beam current

• Over- or under- values MTF(0) or DCE

• Leaving specimen holder inserted during MTF measurement

Charging of specimen and/or holder can move image of edge shadow.

31

Measuring Image Performance Using Thon Rings

Thon rings are a indicator of

performance.

However, they are a system

test and really hard to

compare quantitatively.

32

Energy Filtering and cryo-EM

33

Inelastic scattering (EELS)

TEM Beam-Specimen Interactions and Signals

Elastic scattering

(Diffraction)

X rays

(EDS)Auger

electrons

BSE

Secondary

electrons

TEM specimen

EBIC

Light

(CL)

34

Atomic-Scale View of Electron Energy Loss in TEM

• Primary electron transfers

both energy and

momentum (angle)

• Primary electron is then

measured in the EELS

system

• Both final energy and

scattering angle are

important

Incident beam electron

E0 (60 to 300 keV)

Excited specimen electron

E = ΔE

Scattered beam electron

E0 - ΔE

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Features in an Energy-Loss Spectrum

1000

2000

3000

4000

0 50 100 150

Energy Loss (eV)

x10

Cou

nts

Zero-loss peak

“Fine structure”

37

K edges L2,3 edges M2,3 edges M4,5 edges N4,5 edges O2,3 edges

Which Edges Can be Used?

x 1

0^3

eV

0

10

20

30

40

50

x 1

0^3

1800 1900 2000 2100 2200 2300 2400 2500 2600

eV

Si

Bkgd

Edge

x 1

0^3

eV

0

20

40

60

80

100

120

140

160

x 1

0^3

700 720 740 760 780 800

eV

Fe

Bkgd

Edge

eV

0

1000

2000

3000

4000

5000

6000

1200 1300 1400 1500 1600 1700

eV

Ge

Bkgd

Edge

eV

0

5000

10000

15000

20000

25000

30000

900 950 1000 1050 1100

eV

Ce

Bkgd

Edge

Slide thanks

to G. Kothleitner

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Relative Intensities of EELS Signals

Ni M2,3

O KNi L2,3

Feature

Zero loss peak

Plasmon peak

Ni M edge

O K edge

Ni L edge

Integrated

counts

8.9 x 1010

6.5 x 1010

4.3 x 109

1.1 x 108

9.9 x 107

Inte

nsity (

CC

D c

ou

nts

)

C K (contamination)

39

EFTEM analysis of the Visual Cortex of the Occipital Brain – contrast

enhancement

ZLP

Low loss Core loss

ZLP C K-edge

Unfiltered Zero Loss

filteredPre-Carbon

• Filtering enhances the contrast in the TEM image.

• All Bio Quantum or Bio Continuum are sold for zero loss filtered imaging

40

EFTEM analysis of the Visual Cortex of the Occipital Brain

Ca elemental map P elemental map N elemental map

• Elemental maps taken using EFTEM SI

41

The Zero-Loss Signal

• Specimen thickness

• Energy reference

• Intensity reference

• Beam current

1000

2000

3000

4000

Be EELS

0 50 100 150

Energy Loss (eV)

x10

Co

un

ts

42

In-column filterPost-column filter

Basic Principles of TEM Energy Filters

43

Details of a Post-Column Spectrometer

Electron

Detector

Electrically

isolated

drift tube

44

Electron Detection

Direct Detection

scintillator:

~2000 photons

one 100 keV

electron

fiber optic plate:

~200 photons reach CCD

CMOS or CCD detector:

~60 electron-hole

pairs produced

electronics:

measure and digitize

charge signal

one 300 keV

electron

Support:

Back thinned

CMOS detector:

~electron-hole

pairs produced by primary

Incident electron

electronics:

measure and digitize

charge signal

Indirect Detection

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Final EELS

readout

Energy loss spectrum at camera

Spectrum mode

46


Unfiltered EFTEM

47


Zero-loss image projected

onto CCD detector

Zero-loss EFTEM

Energy-selecting

slit inserted

48

Energy Filtering helps improve 2D crystallography contrast

50

Energy filtering improves image contrast even for (small) particles

• Filtered Image (left)

• Unfiltered Image (right)

Unfiltere

d

Filtered

51

Energy filtering improves image contrast even for (small) particles

• Red = filtered Images

• Blue = unfiltered Images

• Few % improvement in

contrast even with TMV

52

Framerate: Motion Correction, CDS and

Coincidence Loss

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How Frame Alignment Works

Raw counted frame Final aligned image

54

+ + ... +

* ... **

=

=

Motion correction with raw frames is hard

Motion correction with sub-frames is very good

1 sub-frame

1 final image

55

MotionCor2 -InMrc Stack.mrc -OutMrc CorrectedSum.mrc -Patch 5 5 -FtBin 1.2 -Iter 10 -FmDose 1.2 -bft 1.1 -Tol 0.5

MotionCor2 on the K3

56

Better Data Through Motion Correction

• Sample damage increases

during the exposure

• The first frames have the

least damage but the most

drift

57

Better Data Through Motion Correction

• Sample damage increases during the

exposure

• The first frames have the least

damage but the most drift

• Today the first 2 –3 frames are

excluded

• The K3 should be fast enough to let us

use frames 1 – 3 !

58

K3 lowers Read Noise with Correlated Double Sampling (CDS)

59

Standard mode (K2 and K3)

Reset…expose… Signal read

CDS mode (K3 only)

Reset…Reference read…expose…Signal read… subtract


60

Standard mode (K2 and K3)

Reset…expose… Signal read

CDS mode (K3 only)

Reset…Reference read…expose…Signal read… subtract


61

Readout noise Complete absorption of electron by detector (only for low E electrons or very large pixels)

Traversing electrons

False negativesFalse positives

Improved Noise Also Allows us to Improve Electron Countability

Deposited Energy

# p

ixel

s

62

Co-incidence loss is when it is impossible to tell if a signal comes from one

electron or more

This is one electron event

What about this?

or this?

63

Coincidence Loss, Framerate and Throughput

0

30

60

90

120

150

180

0 30 60 90 120 150 180

Measure

d d

ose r

ate

(e

lectr

ons/p

ixel/second)

Input dose rate (electrons/pixel/second)

K3

250 fps camera

Perfect Detection

DQE = 1 with no coincidence loss

• Co-incidence loss is the inability of counting direct electron detectors to detect electrons that

strike the detector near the same location

64

Electron Counting Requires that Electrons Don’t Overlap on the Sensor

Faster frame

rate

Lower beam

intensity

Short Exposure

Time

Long Exposure

Time

65

Throughput In cryo-EM

66

Cryo-EM and Structural Biology

• Cryo-EM is still growing at an

exponential pace

• Efficient use of each

microscope is that much

more important

2019*201620132010200720042001199819951992

2019*201620132010200720042001199819951992

NMR X-ray EM

67

Improving Throughput: Larger Sensors

• One chance to expose a specimen area

• If the pixel quality is high, larger sensors

reduce the number of images needed

K3

K2

68

Image Shift To Improve Throughput

K2: 86 Mpix/hole K3: 122 Mpix/hole

69

Automation with Coma-correction

Mode Low Mag High Mag Focus

Mode 1 Stage Stage Stage

Mode 2 Stage Stage Optical

Mode 3 Stage Optical Optical

Mode 4 Optical Optical Optical

70

How much area is imaged per hour with a camera

Imaging

efficiency=

Area per

image

Images per

hourX Area imaged per hour=

0

50

100

150

200

250

0 2 4 6 8 10 12 14

Ima

gin

g A

rea

(u

m2/h

r)

Coincidence Loss (%)

56 e-Å-2

As electron dose increases,

imaging efficiency increases

and so does the

coincidence loss

30 e-Å-2

K3

Increasing Quality

Imaging Efficiency

71

How much area is imaged per hour with a camera

Imaging

efficiency=

Area per

image

Images per

hourX

10.6 x

Area imaged per hour=

250 fps camera

at coincidence loss of 3.9%

Imaging

efficiency= =

images per

hourXµm20.167 86 µm2/hr14.4

Imaging

efficiency ==images per

hourXµm20.167 150*at coincidence loss of 7.1% µm2/hr25.1

Imaging

efficiency = =images per

hourXµm20.235 650ϕK3

at coincidence loss of 3.9%µm2/hr152.7

Φ Experimental data from Ametek Gatan, Inc.

* Acta Crystallogr D Struct Biol. 2020 Apr 1;76:313-325

Imaging Efficiency

https://www.ncbi.nlm.nih.gov/pubmed/32254055

72

TMEM16F

Resolution (Å) 3.15

Camera K2 summit

Total dose (e-/Å2) 56

Dose rate (e-/pix/sec) 7.8

Exposure time (sec) 8

Imaging area (µm2) 714

Coincidence loss (%) 9.3

Looking at a real example

Cell Reports 2018;28(2):567-579

https://www.cell.com/cell-reports/fulltext/S2211-1247(19)30789-2

73

4.09

26.13

-

10

20

30

Imagin

g tim

e (

hrs

)

Ion channels ~3 Å | Symm=C4

K3 (2701 images)

250 fps camera (3794 images)

0

50

100

150

200

250

0 5 10 15

Ima

gin

g A

rea

(u

m2/h

r)

Coincidence Loss (%)

6.4x boostK3

250 fps camera

Looking at a real example

74

Standard Magnification

• 49kX Mag

• 1.6 A/pixel

• 0.8 A/super-pixel

• 233 particles per frame

• 240 collected

• 2.7 A resolution

75

Low Magnification

• 39kX Mag

• 2.0 A/pixel

• 1.0 A/super-pixel

• 427 particles per frame

• 260 collected

• 3.0 A resolution

76

Solving Structures of COVID-19

At least 6 different structures of various COVID-19 components

"The K3 camera not only gave us great images…, it also prevented data collection from becoming a bottleneck, ultimately letting our team concentrate on getting the structure as quickly as possible”

Sample to publications in 3 weeks!

77

Practical Considerations in Data Collection

78

Dark Subtraction

• Removes the noise

baseline from the image

• New dark references are

often taken once a day

Dark image

79

Gain Correction

• Gain correction normalizes the response of each pixel to an electron

• This is why images are often floating point values

• In K3 we are allowing integer gain normalization

• Each electron is 32 counts

• Usually collected once per week

Gain map

80

Defect Correction

• Removes poorly performing pixels

• Hot• Dark• Unstable

• Defect pixels contribute to fixed pattern noise

• Usually updated with Gain Reference

Defect map(enlarged detail)

81

Defect map

(enlarged detail)

Uncorrected image

Corrected image

Dark image

Gain map

-

Typical Gain Correction Scheme

82

Counted Gain Correction Scheme

-

Linear Image Correction Counted Image Correction

Electron

CountingRaw

Linear

Dark

Linear

Gain

Linear

Defect

LinearUn-proc

Counted

Counted

Gain Ref

Counted

Defect

Final

Counted

Electron

Counting

83

Image: Uniform intensity FFT: White noise

Checking the Quality of Image Correction

84

Measurement of Fixed Pattern Noise (FPN)

Uniform A

Uniform B

Center of Xcorr

FPN = 0.0106

Uniform A Uniform B⊗

Uniform illumination

Common defects, dark image and gain image

Frame rate = 75 fr/s, (0.0133156 s/fr), all images.

Total dose = 14 e/pix, all images

FPN = peak pixel valueCross-correlation map

85

Dose rate on the detector

Mean number of electrons hitting

a detector pixel per unit time.

Total dose at the sample

Number of electrons that traverse a

unit area of the sample during the

exposure of this image frame.

86

At 8-bit/pixel, gain-corrected data saturates with a value of 255.

The saturation monitor reports the percentage of pixels that have reached saturation in a single frame.

Keeping track of Pixel Saturation in K3

87

Annealing Prevents Contamination Buildup

• A cold sensor is essentially a vacuum pump. Contamination builds up on its cold surface and, if left unchecked over prolonged

times, will accumulate to the point of degrade data quality

• Severe contamination may even become evident on the gain reference images, as in the example below of a K2 sensor

Without

annealing

After

annealing

88

• Regular annealing of the sensor reduces

background levels and surface

contamination

• It can also help to repair some radiation

damage

• If the camera is used for a prolonged time

without warming, the electron beam can

harden any contaminants, making the

CMOS detector difficult to clean

• Annealing should be done at the end of

every session one day long or longer

• Waiting a week is no problem, waiting 3

months is too long

• Every time you do a cryo-cycle is a good

rule of thumb

Camera Heating/Annealing

89

Thank you!

detection technologies for cryo-electron microscopy...16 sensitivity • minimum detectable signal...

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