Application Kit: EV Profiler 2

• Pipettes (p2.5, p20, and p200)
• Pipette Tips (10L, 20L, and 200 L)
• Microtubes (0.5 mL, 1 mL and 1.5 mL recommended)
• Timer(s)
• Humidity chamber e.g. Staintray™ 10 Slides Staining System (M918-1)
• Vortex mixer
• Personal protective equipment: gloves, goggles, biohazard bag
• MilliQ water
• Laboratory rocker or rotator
• Aspirator or vacuum pump (optional)
• Heat block at 37°C
• Bead slide
• Lens cleaning paper
• Objective oil

1. Purified EV sample to be tested
2. User-defined antibody against EV surface or luminal targets of interest conjugated to dSTORM-compatible fluorophores (Alexa Fluor 647® (AF647), or other far red dSTORM compatible fluorophore), in selected configurations.
3. Isotype-control antibodies conjugated to the same fluorophores, in selected configurations.

The EV Profiler 2 kit provides a fast, user-friendly system for the characterization of EVs. EV Profiler 2 uses a combination of functionalized surface chemistry, biochemical capture and detection molecules, and is compatible with the ONI Nanoimager and ONI’s cloud-based analysis platform, Collaborative Discovery software (CODI), to streamline sample preparation, imaging, and analysis.

The protocol for user-defined protein and Pan-EV Detection enables the identification of the EV population, characterization of EV size, combined CD81, CD63, and CD9 presence (1-color), and surface or luminal user-defined protein detection on human derived EVs. This protocol can also be used with either Tetraspanin Capture or phosphatidylserine (PS) Capture. The user shall provide the primary antibody for user-defined protein detection, and a matched isotype control, fluorescently labeled with dSTORM compatible fluorophore, such as AF647 or other far red dSTORM compatible fluorophore.

The protocol for Tetraspanin Detection (3-color) is designed to characterize CD81, CD63, and CD9 expression on human-derived EVs. It can be used with either tetraspanin-specific EV capture (Tetraspanin Capture) or phosphatidylserine EV capture (PS Capture).

Each lot of the EV Profiler 2 components is tested against predetermined specifications to ensure consistent product quality.

EV Profiler 2 kits are intended for Research Use Only. The kits are intended for use by professional users with training in basic laboratory techniques. Care should be taken in the handling of products. We recommend that users adhere to the MISEV guidelines provided by the International Society of Extracellular Vesicles.

• • Select anti-CD81, anti-CD63 or anti-CD9 Capture if you want to enrich in a specific tetraspanin. If the EV source is known to be enriched in an individual tetraspanin, isolating with that individual tetraspanin for capture can help isolate that EV sub-population. Otherwise, combined tetraspanin capture can be used to isolate a broad range of EVs.
  • PS Capture may be used with EVs enriched in phosphatidylserine (e.g. cancer-derived EVs). PS Capture is useful when you do not want to enrich a protein biomarker.
  • If phosphatidylserine (PS) content is unknown, we suggest comparing PS Capture to anti-CD81, anti-CD63, anti-CD9 (individual or combined) capture.
  • To select a user-defined capture molecule, ensure that the target of interest is membrane-associated and accessible. To check for compatibility with EV Profiler 2, test a lane with the selected staining protocol but without EVs: analyze with the defined analysis parameters, and confirm fewer than 100 clusters per field of view in negative control lanes. For more detailed information regarding capture molecule selection, click here.

• • Detecting with the Pan-EV (488) and Tetraspanin Trio allows for the assessment of EV sizing and EV shape, as well as characterization of tetraspanin-negative EVs. Additionally, this configuration allows for detection of protein targets in the 647 channel. Follow the provided protocol to assess surface or luminal protein cargo associated with EVs. For more detailed information regarding capture molecule selection, click here.
  • Detecting with anti-CD81 (647), anti-CD63 (561), and anti-CD9 (488) allows for the biotyping of EVs, and can aid in the understanding of EV populations. This can be useful in down-selecting EV capture and detection for future experiments, for characterizing new EV sources, or EV isolation methods.
  • To maximize reproducibility and lane coverage, we recommend agitation on a laboratory rocker or oscillatory shaker. When rocking, Assay Chips lanes should be parallel to the direction of agitation.
  • Prepared EV samples should be purified prior to deposition onto the Assay Chip. If assistance is needed regarding suggested EV purification for dSTORM imaging, please see EV Profiler 2 Protocol V4 – February 2025 oni.bio/contactour webinar on EV preparation. We suggest size exclusion chromatography purified EVs or differential centrifugation EVs.
  • The HEK EV Standard Control (800-00166) may be used as a positive control because they are known to be enriched with PS and are captured efficiently with PS Capture.
  • Dilute EVs to 108-1010 EVs / mL for deposition onto the Assay Chip.
    • HEK EV Standard Control is a 10X solution. If using HEK EV Standard Control, dilute the reconstituted EVs in Wash Buffer (e.g., 1 μL HEK EV Standard Control in 9 μL Wash Buffer).
  • PS Capture Supplement is needed:
    • If using PS Capture and user-supplied EVs PS Capture Supplement is not needed:
    • If using antibody based capture (including Tet. Trio Capture). If using PS Capture with HEK EV Standard Control reconstituted in Wash Buffer.
  • PS Capture Supplement is a 10x concentrated solution. Dilute 1:10 in your EV sample (e.g. 2.5 uL of supplement per 25 uL EVs).
    • If EVs are diluted at least 1:5 in Wash Buffer (i.g., 10uL of EVs into 40uL of Wash Buffer), there is no need to add additional capture supplement.

• • Assay Chips should be brought to room temperature before opening and must be used upon opening.
  • Do not store opened chips for later use.
  • Once the Assay Chip is removed from the packaging, incubate it in a humidity chamber. This reduces the risk of evaporation and bubble introduction during the protocol.
  • ONI recommends using a designated humidity chamber such as Simport Stain Tray M918-2™. This will provide three advantages: a humid environment to avoid evaporation from the lane inlet and outlet, protection of the sample from light, and avoiding chip movement during pipetting. Humidity chambers can be made with available lab equipment (such as slide storage boxes or dark-colored plastic freezer boxes) provided they meet the advantages listed above.
  • Liquid should always be added to the inlet hole makered “IN”.
  • To create a seal and ensure constant liquid delivery, insert the pipet tip at a 90° angle, hold the pipet vertically, and press down firmly before you begin dispensing liquid. If liquid spills out of the inlet, press the pipette tip down harder to ensure a full seal.
  • Ensure that liquid is flowing into the chip lane by watching the meniscus during liquid deposition.
  • It is important to minimize the introduction of bubbles when pipetting. To reduce bubbles, we suggest using p20 or p200 pipette tips, only pipetting to the first stop, and keeping consistent pressure until the pipette tip has been removed from the inlet (to reduce backflow).
  • As you pipet liquid into the inlet hole, the liquid will flow through the lane and exit through the outlet marked “OUT”. We suggest removing excess liquid as it flows out of the outlet using a vacuum aspirator, by placing it gently next to the outlet hole. If you do not have access to an aspirator, use a laboratory dust-free wipe to remove excess liquid. Regardless of method, ensure there is minimal spillover from one lane outlet to another.

• • Lanes should contain liquid throughout the entire experiment. If there are bubbles within the lane, or liquid has been inadvertently removed, wash the lane with 100 µL of Wash Buffer to dislodge bubbles and reintroduce the intended liquid.

• • Do not vortex EVs.
  • At the beginning of the assay, all assay components (except dSTORM Imaging Buffer) can be removed from the fridge and freezer.
  • Prepare detection dilutions immediately prior to use.

• • Once reconstituted Pan-EV Detection should be stored at–20ºC for a maximum of two months.
  • We do not recommend long-term storage.

The EV Profiler 2 Assay Chips can be used with any biotinylated capture molecule. An alternative biotinylated capture molecule can be selected to isolate EV subpopulations via a capture modality not provided in the kit.

Optimization of user-defined capture molecules should include the following steps:

• • • • Assess capture molecule specificity: To determine specificity, stain tissue culture cells that express the target and a negative control (e.g., knockout or peptide blocking) with a fluorescent-labeled version of the capture molecule. Ideally, the negative controls should include less than 10% of the density of the positive control samples; however, this threshold should be predetermined by the user.
      • Assess capture molecule compatibility with EV Profiler 2 assay: When using a new biotinylated capture molecule, ensure that at least one lane with the capture molecule does not contain EVs and that less than 100 clusters are detected in the EV negative lane when stained in the same manner as EVs.
      • Optimize background: If negative control lanes with the new capture molecule result in high background, 0.5%-2.5% BSA can be included in the biotinylated capture molecule dilution.
      • Optimize capture efficiency and reproducibility: For the most efficient capture, start with 70 nM of biotinylated capture molecule and test 20% higher and lower concentrations for ideal capture efficiency. A 75 min incubation time is recommended, but 20% shorter and longer incubation times can also be tested for ideal capture efficiency and reproducibility.

Surface or luminal user-defined molecules can be assessed in the 647 channel with the Pan-EV (488) and Tetraspanin Trio (561).

Optimization of user-defined detection molecules should include the following steps:

• • • • Assess detection molecule specificity: To determine specificity, stain tissue culture cells that express the target and a negative control (e.g. knockout or peptide blocking) with a fluorescent-labeled version of the detection molecule. Ideally, the negative controls should include less than 10% of the density of the positive control samples, however, this threshold should be predetermined by the user.
      • Optimize degree of labeling: Molecules (antibodies, proteins etc.) can be purchased fluorescently labeled or can easily be labeled by the user. Use a dSTORM compatible dye that fluoresces in the 647 channel (e.g, AF647). The number of fluorophores per protein can be modulated by increasing the molar ratio of dye to protein during the conjugation. ONI recommends starting with a degree of labeling of 2-6. Please reach out to ONI if support is needed for antibody labeling.
      • Optimize concentration: For most efficient detection, start with 5 µg/mL of labeled antibody. The antibody or the detection molecule can be titrated around 1-10 µg/ mL. Use negative control lanes (no permeabilization for internal cargo, isotype controls for surface cargo) to guide detection molecule concentration.
      • Optimize laser power: The laser power recommended in this protocol was optimized for AF647. If the user-def ined detection molecule is not AF647, titrate the 647 laser power to ensure fluorophore blinking throughout the acquisition. Optimal laser power and number of frames for the user-defined molecule can be determined using the CODI analysis software. The optimal laser power will minimize the localization precision (increased precision leading to a decreased numerical value) and maximize the photon count while still capturing several localizations per frame over the entirety of the imaging period. This can be determined by observing the number of localizations per frame, which should have a steep decline in the first frames leading to a blinking steady state for the remainder of the imaging time.

All steps are performed at room temperature. All volumes provided are per lane. Pan-EV detection can be used for size evaluation with or without permeabilization. All incubation steps should be performed in a humidity chamber. When applying reagents to the inlet use a vacuum aspirator to remove the flow through from the outlet.

1. Turn on and initiate microscope temperature control to allow the microscope temperature to equilibrate prior to imaging (see image acquisition protocol).
2. Remove all kit components except for dSTORM Imaging Buffer from the fridge and freezer and place at room temperature. Place DMSO in 37ºC heat block for a minimum of 10 minutes.
3. Allow unopened Assay Chip to reach room temperature. [10 min incubation]
4. Open the chip pouch and place the Assay Chip in a humidity chamber. Set aside inlet/outlet sealing stickers until they are needed for imaging.
   Note: it is essential that chips are placed in a humidity chamber during incubations to prevent evaporation.
5. Apply 10 µL of Surface Reagent. [15 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
6. Wash with 100 µL Wash Buffer.
7. Apply 10 µL Capture Reagent. [15 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
8. Wash with 100 µL Wash Buffer.

9. Apply 10 µL EV solution (Click here for EV preparation)
   [75 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
   Note: add PS Capture Supplement (1:10) to EVs if using PS Capture
10. Wash with 100 µL Wash Buffer.
11. Apply 20 µL Fixative.
    
    [10 min incubation]
12. Wash with 100 µL Wash Buffer.

13. Apply 10 µL Staining Buffer
    
    [10 min incubation]
14. Prepare Detection Antibody dilution.
    1. Reconstitute lyophilized Pan-EV Detection reagent
       1. Centrifuge Pan-EV Detection
          
          to ensure the reagent is at the bottom of the tube
       2. Add 48 µL DMSO
          
       3. Vortex for at least 20 seconds
       4. Centrifuge to bring the reconstituted reagent to the bottom of the tube
       5. Pipet and visually inspect to ensure even distribution and no visible flakes

15. Apply 10 µL Detection Antibody dilution.
    [50 min incubation, rocking 30 RPM parallel to Assay Chip lanes]
    Note: Protect from light in an opaque or foil-covered humidity chamber.
16. Wash with 100 µL Wash Buffer.
17. Apply 20 µL Fixative. [5 min incubation]
18. Wash with 100 µL Wash Buffer. Pause point: Chips can be stored in 4ºC for a maximum of 24 hours with lanes sealed with inlet/outlet sealing stickers in a humidity chamber. Data quality will be impacted if imaging is performed after 24 hours.
    Note: If imaging more than one Assay Chip, only add dSTORM Imaging Buffer immediately prior to imaging. Preparing dSTORM Imaging Buffer – total time: 20 min
19. Remove one vial of dSTORM Imaging Buffer Part A from –20ºC storage.
    
20. Allow dSTORM Imaging Buffer Part A to reach room temperature. [10 min incubation]
21. Centrifuge dSTORM Imaging Buffer Part A for 10 seconds.
22. Add 99 µL of dSTORM Imaging Buffer Part A to a fresh microtube.
23. Remove dSTORM Imaging Buffer Part B from –20ºC and immediately proceed to step 24, returning dSTORM Imaging Buffer Part B to –20ºC storage as quickly as possible.
    
24. Add 25 µL of Part B Resuspension Buffer to the vial, then put it on ice for 5 minutes. Gently mix by pipetting up and down, avoiding bubbles or air. Do not shake or vortex the vial.
25. Add 1 µL of dSTORM Imaging Buffer Part B to the fresh tube of dSTORM Imaging Buffer Part A prepared in step 23. Carefully mix the solution by pipetting, making sure not to introduce bubbles.
    Note: dSTORM Imaging Buffer Part B is viscous so pipette slowly and avoid introducing excess liquid from the outer walls of the pipet tip.
    Note: Never flick, shake, invert, or vortex dSTORM Imaging Buffer. Introducing oxygen will affect its performance.
26. Add 20 µL of prepared dSTORM Imaging Buffer to all Assay Chip lanes and seal lanes with inlet/outlet sealing stickers.
    [10 min incubation]

All steps are performed at room temperature. All volumes provided are per lane. Incubation steps should be performed in a humidity chamber. When applying reagents to the inlet use a vacuum aspirator to remove the flow through from the outlet (see detailed instructions here).

1. Turn on and initiate microscope temperature control to allow the microscope temperature to equilibrate prior to imaging (see image acquisition protocol).
2. Remove all kit components except for dSTORM Imaging Buffer from the fridge and freezer and place at room temperature.
3. Allow unopened Assay Chip to reach room temperature. [10 min incubation]
4. Open the chip pouch and place the Assay Chip in a humidity chamber. Set aside inlet/outlet sealing stickers until they are needed for imaging (step 27).
   Note: it is essential that chips are placed in a humidity chamber during incubations to prevent evaporation.
5. Apply 10 µL of Surface Reagent. [15 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
6. Wash with 100 µL Wash Buffer.
7. Apply 10 µL Capture Reagent. [15 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
8. Wash with 100 µL Wash Buffer.
   Capture and fixation – total time: 85 min
9. Apply 10 µL EV solution (Click here for EV preparation)

   [75 min incubation, rocking 30-45 RPM parallel to Assay Chip lanes]
   Note: add PS Capture Supplement (1:10) to EVs if using PS Capture (see page 7-8).

10. Wash with 100 µL Wash Buffer.
11. Apply 20 µL Fixative. [10 min incubation]
12. Wash with 100 µL Wash Buffer.
13. Apply 10 µL Staining Buffer. [10 min incubation]
14. Prepare Detection Antibody dilution:

15. Apply 10 µL Detection Antibody dilution.
    [50 min incubation, rocking 30 RPM parallel to Assay Chip lanes]
    Note: Protect from light in an opaque or foil-covered humidity chamber.
16. Wash with 100 µL Wash Buffer.
17. Apply 20 µL Fixative. [5 min incubation]
18. Wash with 100 µL Wash Buffer.
    Pause point: Assay can be paused here. Chips can be stored in 4ºC for a maximum of 24 hours with lanes sealed with inlet/outlet sealing stickers in a humidity chamber. Data quality will be impacted if imaging is performed after 24 hours.
    Note: If imaging more than one Assay Chip, only add dSTORM Imaging Buffer immediately prior to imaging. Preparing dSTORM Imaging Buffer – total time: 20 min
19. Remove one vial of dSTORM Imaging Buffer Part A from –20ºC storage.
20. Allow dSTORM Imaging Buffer Part A to reach room temperature. [10 min incubation]
21. Centrifuge dSTORM Imaging Buffer Part A for 10 seconds.
22. Carefully mix the solution by pipetting, making sure not to introduce bubbles.
    Note: Never flick, shake, invert, or vortex dSTORM Imaging Buffer. Introducing oxygen will affect its performance.
23. Add 99 µL of dSTORM Imaging Buffer Part A to a fresh microtube.
24. Remove dSTORM Imaging Buffer Part B from –20ºC and immediately proceed to step 25, returning dSTORM Imaging Buffer Part B to –20ºC storage as quickly as possible.
25. Add 25 µL of Part B Resuspension Buffer to the vial, then put it on ice for 5 minutes. Gently mix by pipetting up and down, avoiding bubbles or air. Do not shake or vortex the vial.
    Note: dSTORM Imaging Buffer Part B is viscous so pipette slowly and avoid introducing excess liquid from the outer walls of pipet tip.
26. Add 1 µL of dSTORM Imaging Buffer Part B to the fresh tube of dSTORM Imaging Buffer Part A prepared in step 23. Carefully mix the solution by pipetting, making sure not to introduce bubbles.
27. Add 20 µL of prepared dSTORM Imaging Buffer to all Assay Chip lanes and seal lanes with inlet/outlet sealing stickers.
    [10 min incubation]

AutoEV is a CODI application that connects the ONI Nanoimager to CODI cloud-based analysis platform, enabling Extracellular Vesicle (EV) characterization using the ONI Application KitTM: EV Profiler 2. AutoEV consists of several advanced features that allow users to perform super-resolution imaging and analysis of an entire 4-lane EV Profiler 2 chip with reduced hands-on time and a robust pipeline.

Placing the chip holder on the Nanoimager
AutoEV comes with a chip holder that makes the Nanoimager feel like it was made for EV Profiler 2 chips. It also allows AutoEV to know where each lane is without any specific calibration.

To run the EV Profiler 2 kit, you will need to register to ONI’s CODI software platform, download the CODI System App and use AutoEV app.

Navigate to https://alto.codi.bio to access the CODI platform. It is important to sign in and log in (see [1] on the image below) to CODI prior to starting AutoEV installation. If this is the first time creating an account in CODI, contact ONI to get the AutoEV app activated. Once logged in, go to Application apps [3], and select the AutoEV app.

To be able to run the EV Profiler 2 kit, the CODI System App (CSA) needs to be downloaded. The CSA operates in the background to connect the Nanoimager and light engine to CODI, this connection is crucial to operate AutoEV. 

If this is the first time downloading the CODI System App, click “download” as shown in the popup message. Follow the instructions to install the CODI System App.

Setting a default Nanoimager temperature

Ithe top right corner of the CODI page [2], click on the temperature icon with a blue background, which will bring the System Status App, where users can set the desired temperature. This will be saved as the default temperature for the next time the CODI System App is launched. For EV Profiler 2 kit, the default recommended is 32°C. Whenever the CODI System App is launched, the Nanoimager will automatically start heating to 32°C and it takes 60 minutes to stabilize and be ready for imaging.

Do not start imaging before the temperature is stable. Any acquisition done during the heating process, will result in a lot of drift. See channel mapping section for more details.

AutoEV automatically uploads the data to the selected CODI account and saves it locally in the C:/Data/CODI/ folder. AutoEV saves the acquisition data in subfolders with names corresponding to the Experiment and Lane titles inputted on this screen.

AutoEV has a couple of options to simplify the process of applying settings to all lanes.

Start by inputting settings in lane 1, then use the buttons at the bottom of that lane to:

While running channel mapping before the experiment is recommended, if it has been performed recently and that the Nanoimager temperature is stable, AutoEV will allow imaging without performing channel mapping, for instance, when imaging more than one chip at once.

The camera of the Nanoimager is split in two regions (left and right). Depending on the configuration of the Nanoimager being used, channel signal will be sent to the left camera region (like the 488 or 561 channel), or the right region (like for the 640 channel). Data acquired with CODI System App aligns channels from the 2 different camera regions before being uploaded to CODI. Channel mapping is used to allow the correction of chromatic aberration that results in image distortion. Specifically, the localizations in the 488 and 561 channels are mapped up to the right camera region where the 640 channel is acquired such that they overlap. The mapping correction file comes from the channel mapping calibration done with beads prior to the experiment, which is why this step is such a crucial step.

For AutoEV to be successful at imaging of EV Profiler 2 Assay Chip, it is essential to ensure that the chip is ready to image prior to starting the super-resolution acquisition.

System calibration is a critical step that automatically finds the imaging surface of the EV Profiler 2 chip, locks the focus, and determines the optimal illumination angle with AutoTIRF.

When ready, click the Calibrate focus & illumination button [3] to start the calibration. Do not run the calibration in a negative control lane.

Once sample calibration has been successfully completed, the widget will update to display the calibrated focal plane and TIRF angle  (bottom right). Use sample pre-check to assess the TIRF angle and modify it if needed (see details below).

Click Start sample pre-check to begin the sample check, and AutoEV will start scanning each lane. It can then be stopped this by using the ◽️ button.

The autofocus tool is an automatic function to find optimal sample focus in cases where the FOV has good a Signal to Noise ratio (SNR) and there are at least 3 objects (such as EVs or beads) when calibration is performed. Autofocus may fail or look poor if there are bubbles, low/no signal, etc.

AutoTIRF is an automatic function that finds the optimal angle for which the SNR is reduced. If the signal and the surrounding background change from sample to sample or lane to lane the AutoTIRF might provide different values every time it is run. This is because it is calculated based on the SNR of the FOV selected during the system calibration step.

An optimal TIRF angle is required to achieve accurate quantitation. Manually inspect each lane for poor TIRF alignment. This will be visible in all lanes. A good TIRF angle will result in an even FOV illumination, low background noise, and high signal from the particles. A poor TIRF angle will result in a lack of signal in the bottom or top of the field of view. If the TIRF angle in the sample check is poor, proceed to the optimizing and setting the TIRF manually.

Sometimes focus found during System calibration might not be optimal, and refining focus might be needed.

In the sample pre-check step, select Check Focus in the lane title if AutoEV thinks manual refinement is needed, or Completed if the focus is good. If Check Focus is displayed, select the Refine Focus button.

Use the “up” and “down” arrows to move the Z-offset and find the optimal focus. Click the “Next FOV” button to move to the next FOV in that lane and get a view of the focus without bleaching the sample.

When ready, click the Save & Re-run button to save the optimal focus and acquire a new sample check image.

In this section FOVs will be selected for image acquisition in each lane of the chip. Click the Select 6 default FOVs per lane button (top left) once to automatically select 6 default FOVs in all lanes. These are chosen to provide a good statistical distribution of EVs. The image above shows the default 6 FOVs, optimal for most experiments as they are centered in the sample lanes and are evenly distributed.

Unchecking this box will only save the localization data to disk, saving precious disk space for long EV acquisitions.

Each FOV corresponds to a single dataset. Multiple datasets within the same lane can be generated by acquiring multiple FOVs. Six FOVs per lane should provide a good statistical power.

AutoEV will show a warning if either of these conditions are not met. The yellow warning on the banner can be clicked to explore the step to be fixed.

Now that the AutoEV acquisition is set up, click the Start Auto-EV Acquisition button ([1] in the image below). The EV Profiler 2 Kit chip will be scanned from Lane 1 to Lane 4, and the acquisition in each lane will follow the settings input in the Experiment Setup page.

A montage, report and CSV data can be generated even if a subset of datasets is acquired, or if the analysis of some of the datasets is not yet completed or failed by clicking  Select datasets [4]. Select only the datasets for which the analysis is completed [5], and use the Data export widget [6] to generate the files.

Generate a montage to visually inspect the size and morphology of the EV sample and the analysis outcome.

This feature allows the creation and saving of a montage of random EVs selected and ordered based on positivity and size (see image below). This feature is particularly useful not only to visualize the positivity results but also to assess assay quality.

At the end of an acquisition, a montage from the 4-lanes chip or a montage from individual FOVs can be generated.

To print or save the report after generating it, use the 🖨️ button. The link to the report can be saved on CODI by copying or favoriting the URL directly.

Once the summary of the acquisition is viewed and the report generated, more data from the same chip may be acquired. AutoEV makes this really easy with the Re-Run button, which shows the experiment info page, where the number of FOVs per lane can be increased/decreased, or acquisition settings changed.

Note: if re-running a chip with more FOVs, the FOV annotation will start again from 1. Changing the experiment title is recommended, for example, adding “run 2”, so that FOVs acquired in each run can be distinguished, e.g., FOV 1 acquired during the first run and FOV 1 acquired in the second run.

Remove the chip carefully and place the next one. Close the EV experiment tab and open a new one from the Acquisition apps. Load the saved experiment setting with the correct laser power that was optimized for the Nanoimager. All the next steps will be identical to the above. Only run channel mapping again if prompted by the system.

If at any time you experience a software issue, contact ONI through the Help Center (oni.bio/contact). Click on the question mark displayed in AutoEV and CODI and go to “report an issue”.

Visit the ONI Service desk to learn about common AutoEV troubleshooting.

Each EV Profiler 2 kit needs to be acquired with a specific optimised power to ensure proper blinking of the fluorophores. Follow these instructions to find the recommended power in the table below.

1. Place an ONI Bead slide or ONI 4-lane Assay Chip on the Nanoimager
2. Go to channel mapping page
3. Click “Microscope controls”
4. Turn on one laser at the time [1]:
   1. Using the slider [2], change the power percentage value until it reaches the desired mW power [3]. Specific percentage values can also be typed in the box.
   2. Change the power in percentage in the experiment settings page.
   3. Repeat with all channels and powers indicated in the table below. Use the table to record the values for the Nanoimager system in use.
   4. Once completed, save the current settings.

The next time the AutoEV is run, go to “Load Settings” in the Experiment settings page and select the settings saved. This operation needs to be done for every user account that will use the AutoEV, and repeated once a month to ensure that mW power and % values are always calibrated in the case the laser specs drop.

AutoEV uses the “Light Program” feature from NimOS to acquire data, which means that all of the lasers will be in “standby” mode. This ensures the lasers can be turned on/off very quickly and accurately, but may result in a small dose of laser excitation even when the laser is off or at 0%.

Manual System Calibration allows the user to manually inspect the sample and create the illumination and per-lane focus calibration that is required to automatically and accurately acquire FOVs across all lanes. 

The Manual System Calibration page can be accessed any time during an automated imaging workflow, and is conveniently located before the required steps of the workflow.

When creating a manual calibration, the user can easily follow which step of the workflow they were on by looking for the icon with an upper outline in yellow.

Once the manual calibration is saved and being used for the experiment, the manual calibration page will appear with a green underline, reminding the user that they are using a manual calibration.

While the Manual System Calibration allows the user to manually find and optimize these values and save them for use during automated acquisition, the automated System Calibration step performs several automated routines to find the optimal focus and illumination angle. Read the “Creating an experiment calibration” section below for more details.

Manual System Calibration can be used to set the “Acquisition Settings” from the System Info page with the correct values based on the current sample on the microscope.

• Starting focus position for autofocus used in channel mapping, automated system calibration, and automated acquisition
• Laser powers used during the automated system calibration step
• Manual TIRF angle

It can be helpful to quickly inspect these values to ensure they’re coherent and consistent across acquisitions. For example, while the number of localizations per channel will vary based on EV concentration and staining, it should generally be similar for the FOVs in the same lane. 

Notably, if the number of localizations is very low for a channel where there is expected to be a strong signal, that might be indicative of an issue during the data acquisition.

AutoEV app allows users to sequentially image 647, 561 and 488 channels, for a total of around 2 minutes per single field of view. During this time, the sample can drift in the order of a few tens of nanometers due to temperature change or physical sample movement. ONI’s drift correction algorithm allows correction for this.

A standard Drift Correction at Minimum Entropy (DME) algorithm is applied to all the SMLM channels, with the last frame used as reference (see CODI user guide for more information).

Each SMLM localization is characterized by sigma, precision and p-value. Minimum and maximum thresholds for these characteristics were optimized based on positive controls and negative controls. The analysis apps automatically filter out localizations that don’t meet these optimized thresholds as they are identified as noise.

These filters were very carefully defined for each of the EV Profiler 2 kit variants and acquisition on the Nanoimager to ensure the best quality results of the entire EV Profiling analysis tool. Notably, strict filters are applied to the frame range (where the first 50 frames of each channel are removed while the sample begins to blink) and the localization precision to ensure noisy localizations are removed from the analysis.

An essential part of SMLM analysis of EVs is grouping adjacent localizations together so that they can be identified as single EVs through a process called clustering.

DBSCAN is a clustering algorithm which groups localizations into clusters by linking points that are densely packed within a distance threshold, while filtering out noise. This makes it well-suited for EV analysis, where it reveals nanoscale molecular assemblies of arbitrary shape with the need for only a single parameter, epsilon (ε), which defines the maximum distance between two points for them to be considered neighbors. 

Choosing ε carefully is critical, as it determines how tightly localizations must pack together to form a biologically meaningful EV-associated cluster. CODI’s EV Profiling analysis app uses 85nm as a default value, which is generally a good starting point for most typically labeled EV samples. For very sparsely labeled EVs, or for very densely captured samples, it is recommended to adjust the epsilon distance to optimize clustering.

Furthermore, it is possible to select which channels are used for the clustering process, by clicking the “…” menu and selecting or de-selecting any of the channels in the dataset. Use the “merge selected channels” option to aggregate all localizations from the selected channels while clustering. 

Additional parameters can be adjusted in the “…” menu of the clustering tool, including:

• “min samples”, which relates to the minimum density of the cluster
• “min size”, which refers to the minimum number of localizations required for a cluster 

More information can be found on the ONI Service desk

Once the clustering of the localizations of an EV is completed, it is possible to refine the selection of the clusters to filter down to only those which meet the criteria of what an EV is expected to be. 

The filtering parameters are based on the spatial characteristics of the clusters:

• Area (nm2): the area of the cluster. This can be used to remove clusters which are too small, and may represent unspecific binding of fluorophores to the surface, or clusters which are too large and might represent aggregates of EVs, which would ideally be excluded from single EV analysis.
• Circularity: a measure of the roundness of the clusters. In general, the expectation is that clusters which are truly EVs are likely round
• Radius of Gyration (nm): a measure of the size of the cluster. While this is not exactly the radius of the cluster, it is an indication of the size of the cluster.
• Density (per nm^2): the localization density within the cluster. Clusters with lower density have fewer localizations per unit area than those with higher density.

Once the settings of the counting tool have been finalized, such as the counting radius and positivity thresholds for each channel, pressing the “Play” button in the summary report widget generates several CSV files containing the results of the EV Profiling analysis:

• A positivity report contains an overview of the biomarker positivity information of the datasets
• A results file contains information about each individual cluster and its biomarker positivity, and is the most useful for statistical analysis
• A parameters file contains information about the biomarker counting thresholds used in the analysis.

Once the parameters for a particular dataset have been finalized, clicking the “>>” button allows the user to save these settings for later analysis.

CODI makes it easy to apply these same settings to other datasets that have already been acquired via batch analysis, or datasets that will be acquired in the future with AutoEV by using those analysis settings directly in AutoEV.

For more information about how to run batch analysis, visit the ONI Service desk

Note: The EV batch report is for a single chip and up to 4 lanes, i.e., it is not possible to generate a single analysis report for datasets acquired from different chips. Additionally, CODI does not support analyses and reports across different chips. To generate cross-chips reports, ONI provides EVP2 Axis, an offline python-based code tool. For more information on the EVP2 Axis, visit the ONI EVP2 Axis webpage.

System Info is an app that provides the status of the microscope in real time and where users can control settings for calibration. It is accessed by clicking on the temperature icon in the top right corner of CODI, or by going to “Acquisition apps”.

Remember to click Save Settings once modified. Settings can always be reset to default.

Note: These settings are stored in the local storage of the web browser, and will reset to defaults when using a different browser profile or delete browser caches. Double check these values prior to running any experiment.

Since AutoEV is a CSA app, uploading to CODI will commence immediately after data acquisition, as long as the laptop is connected to the internet. If upload is not completed, it will begin automatically next time the AutoEV is opened on CSA as long as an internet connection is available. In the Nanoimager App, the dataset upload status widget displays information about the upload progress. This widget will show how many datasets are in the upload queue, as well as important information if that queue is stalled, for example, if the internet connection is unreliable or unavailable.

Please refer to the AutoEV and AutoLNP Troubleshooting page on the ONI Service Desk for common issues and workarounds.

Use the ONI Service desk to submit bug reports or any feature requests, using the AutoEV category. To ensure that the ONI team can properly address any bugs reported, we kindly ask that the latest “experiment_server*.log” and “hal*.log” logs are provided. These are located in the following directory: C:\Users\ONI\AppData\Local\ONI\OHM

AutoEV is an application in CODI, ONI’s cloud-based analysis platform, that enables Extracellular Vesicle (EV) characterization through an end-to-end workflow using the ONI Application KitTM: EV Profiler 2, Aplo Scope and CODI. AutoEV consists of several advanced features that allow users to perform super-resolution image acquisition, analysis, and reporting of an entire 4-lane EV Profiler 2 Assay Chip with reduced hands-on time and a robust pipeline.

AutoEV requires active internet connection on Aplo Scope PC.

Placing the chip holder on the Aplo Scope
AutoEV comes with a chip holder that makes the Aplo Scope feel like it was made for EV Profiler 2 Assay Chips. It also allows AutoEV to know where each lane is without any specific calibration.

To run the EV Profiler 2 kit, first register to ONI’s CODI software platform, download the CODI System App and use AutoEV app.

Navigate to https://alto.codi.bio to access the CODI platform. It is important to sign in and log in (see [1] on the image below) to CODI prior to starting AutoEV installation. If this is the first time creating an account in CODI, contact ONI to get the AutoEV app activated. Once logged in, go to Application apps [3], and select the AutoEV app.

To be able to run the EV Profiler 2 kit, the CODI System App (CSA) needs to be downloaded. The CSA operates in the background to connect the Aplo Scope and light engine to CODI, this connection is crucial to operate AutoEV. If this is the first time downloading the CODI System App, click “download” as shown in the popup message. Follow the instructions to install the CODI System App.

Setting a default Aplo Scope temperature

In the top right corner of the CODI page [2], click on the temperature icon with a blue background, which will bring the System Status App, where users can set the desired temperature. This will be saved as the default temperature for the next time the CODI System App is launched. For EV Profiler 2 kit, the default recommended is 32°C. Whenever the CODI System App is launched, the Aplo Scope will automatically start heating to 32°C. This typically takes 2 hours to stabilize and be ready for imaging.

Do not start imaging before the temperature is stable. Any acquisition done during the heating process, will result in a lot of drift. See channel mapping section for more details.

AutoEV automatically uploads the data to the selected CODI account and saves it locally in the C:/Data/CODI/ folder. AutoEV saves the acquisition data in subfolders with names corresponding to the Experiment and Lane titles inputted on this screen.

AutoEV has a couple of options to simplify the process of applying settings to all lanes.

Each EV Profiler 2 kit needs to be acquired with a specific optimized power to ensure proper blinking of the fluorophores. Find the recommended laser power using the table below:

The Aplo Scope contains an internal laser power sensor, which is calibrated out of the factory to estimate the laser power at the sample. AutoEV leverages this internal sensor to find the optimal power for the EV Profiler 2 Kit.

While running channel mapping before the experiment is recommended, if it has been performed recently and that the Aplo Scope temperature is stable, AutoEV will allow imaging without performing channel mapping, for instance, when imaging more than one chip at once.

For AutoEV to be successful at imaging the EV Profiler 2 Assay Chip, it is essential to ensure that the chip is ready to image prior to starting the super-resolution acquisition.

System calibration is a critical step that automatically finds the imaging surface of the EV Profiler 2 Assay Chip, locks the focus, and determines the optimal illumination angle with AutoTIRF.

When ready, click the Calibrate focus & illumination button [3] to start the calibration. Do not run the calibration in a negative control lane. Calibration can take from 2 to 3 minutes to complete. The process can be stopped at any time using the ◽️ button.

Click Start sample pre-check to begin the sample check, and AutoEV will start scanning each lane. It can then be stopped this by using the ◽️ button.

The autofocus tool is an automatic function to find optimal sample focus in cases where the FOV has good a Signal to Noise ratio (SNR) and there are at least 3 objects (such as EVs or beads) when calibration is performed. Autofocus may fail or look poor if there are bubbles, low/no signal, etc.

AutoTIRF is an automatic function that finds the optimal angle for which the SNR is reduced. If the signal and the surrounding background change from sample to sample or lane to lane the AutoTIRF might provide different values every time it is run. This is because it is calculated based on the SNR of the FOV selected during the system calibration step. 

An optimal TIRF angle is required to achieve accurate quantitation. Manually inspect each lane for poor TIRF alignment. This will be visible in all lanes. A good TIRF angle will result in an even FOV illumination, low background noise, and high signal from the particles. A poor TIRF angle will result in a lack of signal in the bottom or top of the field of view. If the TIRF angle in the sample check is poor, proceed to the optimizing and setting the TIRF manually.

Sometimes focus found during System calibration might not be optimal, and refining focus might be needed. 

In the sample pre-check step, select Check Focus in the lane title if AutoEV thinks manual refinement is needed, or Completed if the focus is good. If Check Focus is displayed, select the Refine Focus button.

Use the “up” and “down” arrows to move the Z-offset and find the optimal focus. Click the “Next FOV” button to move to the next FOV in that lane and get a view of the focus without bleaching the sample. 

When ready, click the Save & Re-run button to save the optimal focus and acquire a new sample check image.

In this section FOVs will be selected for image acquisition in each lane of the chip. Click the Select 6 default FOVs per lane button (top left) once to automatically select 6 default FOVs in all lanes. These are chosen to provide a good statistical distribution of EVs. The image above shows the default 6 FOVs, optimal for most experiments as they are centered in the sample lanes and are evenly distributed.

Unchecking this box will only save the localization data to disk, saving precious disk space for long EV acquisitions. 

Each FOV corresponds to a single dataset. Multiple datasets within the same lane can be generated by acquiring multiple FOVs. Six FOVs per lane should provide a good statistical power.

AutoEV will show a warning if either of these conditions are not met. The yellow warning on the banner can be clicked to explore the step to be fixed.

Now that the AutoEV acquisition is set up, click the Start Auto-EV Acquisition button ([1] in the image below). The EV Profiler 2 Kit chip will be scanned from Lane 1 to Lane 4, and the acquisition in each lane will follow the settings input in the Experiment Setup page.

A montage, report and CSV data can be generated even if a subset of datasets is acquired, or if the analysis of some of the datasets is not yet completed or failed by clicking  Select datasets [4]. Select only the datasets for which the analysis is completed [5], and use the Data export widget [6] to generate the files.

Generate a montage to visually inspect the size and morphology of the EV sample and the analysis outcome. 

This feature allows the creation and saving of a montage of random EVs selected and ordered based on positivity and size (see image below). This feature is particularly useful not only to visualize the positivity results but also to assess assay quality.

At the end of an acquisition, a montage from the 4-lane Assay Chip or a montage from individual FOVs can be generated.

To print or save the report after generating it, use the  🖨️ button. The link to the report can be saved on CODI by copying or favoriting the URL directly.

Once the summary of the acquisition is viewed and the report generated, more data from the same chip may be acquired. AutoEV makes this really easy with the Re-Run button, which shows the experiment info page, where the number of FOVs per lane can be increased/decreased, or acquisition settings changed.

Note: if re-running a chip with more FOVs, the FOV annotation will start again from 1. Changing the experiment title is recommended, for example, adding “run 2”, so that FOVs acquired in each run can be distinguished, e.g., FOV 1 acquired during the first run and FOV 1 acquired in the second run.

Remove the chip carefully and place the next one. Close the EV experiment tab and open a new one from the Acquisition apps. Load the saved experiment setting with the correct laser power that was optimized for the Aplo Scope. All the next steps will be identical to the above. Only run channel mapping again if prompted by the system.

If at any time you experience a software issue, contact ONI through the Help Center (oni.bio/contact). Click on the question mark displayed in AutoEV and CODI and go to “report an issue”.

Visit the ONI Service desk to learn about common AutoEV troubleshooting.

Manual System Calibration allows the user to manually inspect the sample and create the illumination and per-lane focus calibration that is required to automatically and accurately acquire FOVs across all lanes. 

The Manual System Calibration page can be accessed any time during an automated imaging workflow, and is conveniently located before the required steps of the workflow.

When creating a manual calibration, the user can easily follow which step of the workflow they were on by looking for the icon with an upper outline in yellow.

Once the manual calibration is saved and being used for the experiment, the manual calibration page will appear with a green underline, reminding the user that they are using a manual calibration.

While the Manual System Calibration allows the user to manually find and optimize these values and save them for use during automated acquisition, the automated System Calibration step performs several automated routines to find the optimal focus and illumination angle. Read the “Creating an experiment calibration” section below for more details.

3. If this occurs, use the Up/Down/Left/Right FOV buttons to scan the stage until the imaging area is outside of the bubble.
4. The best practice is to turn off the laser during this process
5. Once in a good FOV, click the lock button to re-engage the z-lock. It is OK if it is slightly out of focus.
   4. Use the up/down arrows in the experiment calibration widget to refine the focus until it is optimal for the designated lane.
   5. Click “Set Lane Z-Offset” to set the z-offset for this lane to the experiment calibration
   6. Repeat this process for all lanes. Note that a z-offset must be set for each lane

• Save the experiment calibration. 
  1. Once the TIRF angle and z-offsets for each lane have been set, the “Save & Return to Experiment” button will turn blue.
  2. Click this button to save the calibration and use it for the active experiment.

• Continue to sample check, select FOVs, and imaging
  1. The AutoEV experiment will now use the values manually set during Manual System Calibration.
  2. The automated acquisition will now use the manually defined TIRF angle and apply the z-offset specified for each lane while imaging FOVs in the corresponding lane.

In some cases, it might be necessary to manually adjust the laser powers to ensure the sample is blinking correctly.
Manual System Calibration makes this process very simple:

1. Once focus and TIRF angle have been optimized, turn on one of the lasers that will be used for imaging and adjust the power until optimal blinking is achieved for that fluorophore. This will depend on the fluorophore, sample type and labeling density. Contact ONI support for more information on how to determine the optimal power.
2. Click the “Set laser powers” button, which will now indicate the active laser wavelength and power. This will save the currently active laser power to be used for all lanes of the current experiment.
3. Repeat this for each laser that will be used for the experiment.
4. When returning to the Experiment Setup page, note that the laser power for all lanes has been updated.

Manual System Calibration can be used to set the “Acquisition Settings” from the System Info page with the correct values based on the current sample on the microscope.

• Starting focus position for autofocus used in channel mapping, automated system calibration, and automated acquisition
• Laser powers used during the automated system calibration step
• Manual TIRF angle

1. Find the focus of the selected slide type using the focus controls, and calibrating and engaging the z-lock [1]. Click “Set as initial focus for ONI 4-lane Assay Chip” to save the current stage z-position as the global default initial position when using autofocus in the system calibration, sample check and automated FOV acquisition.
2. Find the lowest laser power where fluorescence with a good signal to noise can still easily be detected, and where the sample is not blinking. This may be anywhere from 3% to 25%, depending on the laser and the system. Click the “Set as laser power for calibration” to save this power as the power used for the autofocus and autoTIRF during automated system calibration.
3. With the laser set to a typical power used for data acquisition (generally around 50%), use the illumination angle slider to find an angle where the sample has the highest signal to noise ratio.
4. Click the “Set as manual TIRF angle” to save this as the default manual TIRF angle. Note this does not set the system to manual TIRF mode. To do so, navigate to the system info page and swap from “auto” TIRF mode to manual.

It can be helpful to quickly inspect these values to ensure they’re coherent and consistent across acquisitions. For example, while the number of localizations per channel will vary based on EV concentration and staining, it should generally be similar for the FOVs in the same lane. 

Notably, if the number of localizations is very low for a channel where there is expected to be a strong signal, that might be indicative of an issue during the data acquisition.

AutoEV makes channel mapping simple to run by automating the entire process. First, it finds the best focus of the bead slide using the 638 laser with 673/35 filter. Next, it optimizes the laser powers for each of the channels to be mapped. This process slowly increases the laser power from 0% until the beads have a sufficient signal to noise ratio. On Aplo Scope, thanks to dedicated emission filters for each channel, laser powers are slightly increased over the minimum required power to achieve a better signal to noise ratio and higher quality mapping.

Once laser powers are optimized, the bead slide will be scanned in (X,Y) to ensure appropriate sampling of the entire field of view to create an accurate mapping between channels. During channel mapping, 10 imaging frames are acquired with 100 ms exposure time for each of the 3 illumination channels. Localizations from each channel are used to create a channel pairing matrix between all illumination channels (638-561 channels; 638-488 channels; 488-561 channels). For each pair of channels, only points that are stable across 10 frames and consistently match the respective point in the other channel are kept. The stage moves to a new FOV and continues to acquire images until at least 1000 localization pairs are found for each channel pair. These pairs of points are used to create a mapping transform which is used to ensure that all data across all channels is aligned with nanometer precision.

Each tool can be run individually by pressing the play button on the widget, or the entire workflow can be run in a single click by clicking the play button at the bottom of the workflow steps.

The entire analysis varies based on the number of localizations in the dataset, but generally takes around 5 minutes for typical acquisitions.

AutoEV app allows users to sequentially image 647, 561 and 488 channels, for a total of around 2 minutes per single field of view. During this time, the sample can drift in the order of a few tens of nanometers due to temperature change or physical sample movement. ONI’s drift correction algorithm allows correction for this.

A standard Drift Correction at Minimum Entropy (DME) algorithm is applied to all the SMLM channels, with the last frame used as reference (see CODI user guide for more information).

Each SMLM localization is characterized by sigma, precision and p-value. Minimum and maximum thresholds for these characteristics were optimized based on positive controls and negative controls. The analysis apps automatically filter out localizations that don’t meet these optimized thresholds as they are identified as noise. 

These filters were very carefully defined for each of the EV Profiler 2 kit variants and acquisition on the Aplo Scope to ensure the best quality results of the entire EV Profiling analysis tool. Notably, strict filters are applied to the frame range (where the first 50 frames of each channel are removed while the sample begins to blink) and the localization precision to ensure noisy localizations are removed from the analysis.

Clustering is applied to identify small and large EVs, as well as single EVs and aggregates. The clustering is performed on all channels acquired.

EVs are detected and segmented using an AI-based model, specifically a neural network vision model. Unlike algorithms like DBSCAN, which requires users to input multiple parameters, this is a one-click model with no parameters to fine-tune. Additional advantages of this model include: 

• Flexibility: in accommodating different EVs types, size and morphology
• Speed: fast time from acquisition to answer

Once clusters are detected, any object that has less than 3 locs per EV localizations and a diameter that is less than 35 nm will be discarded as considered background noise. These filters can be changed by going to the back of the clustering widget (three dots, top right).  

The ML model was trained using a combination of simulated data and real acquired EVs data with a variety of background noise levels. The model was validated over multiple independent EV datasets and it detects more than 95% of clusters. The clustering tool has a run time of approximately 2 minutes for a 110 µm x 110 µm FOV.

The clustering tool segments localizations into clusters [1] and noise [2]. The segmentation is displayed to the user via colour coded points in CODI. The properties of the identified clusters including size, number of localizations, and geometry are recorded, displayed in the single cluster widget [4] and as a population level summary in the AI EV Profiling workflow widget [3].

The counting tool is the last step of the EV Profiling analysis app, and is used to count the number of localizations across all channels for each cluster so that each individual EV can be assigned a biomarker positivity (single, double, or triple positive).

If the EV report was not generated at the end of an acquisition in the AutoEV app (summary page),
it can be done at any time in CODI using the “batch report” feature, which provides a report for a single chip with up to 4 lanes.
To do that, navigate to the CODI collaboration in which the data is saved and perform the following:

1. Click the Analysis Results button to view all the analysis result in this collaboration
2. Click on the “Cross Dataset Report” button
3. Select the EV Profiling analysis App, and the settings used during the acquisition.
   Note: If the user chooses to re-analyze the data acquired from AutoEV with different analysis settings,
   all of the datasets must be batch analyzed with the same analysis settings to be included in the report.
4. Select the datasets from one chip (one dataset corresponds to one FOV) to be featured in the report and click “Generate Report”.

Note: The EV batch report is for a single chip and up to 4 lanes, i.e., it is not possible to generate a single analysis report for datasets acquired from different chips. Additionally, CODI does not support analyses and reports across different chips. To generate cross-chips reports, ONI provides EVP2 Axis, an offline python-based code tool. For more information on the EVP2 Axis, visit the ONI EVP2 Axis webpage.

System Info is an app that provides the status of the microscope in real time and where users can control settings for calibration. It is accessed by clicking on the temperature icon in the top right corner of CODI, or by going to “Acquisition apps”.

The acquisition settings widget allows adjusting of some settings for the automated algorithms.

Remember to click Save Settings once modified. Settings can always be reset to default.

To obtain insights into the EV sample, data acquired on AutoEV has to be analyzed on ONI’s cloud-based software, CODI. The raw imaging data is first saved locally on the C drive on the Aplo Scope laptop and is then uploaded to CODI. Upload is automatic, but it requires a stable internet connection and for CSA to be open. 

Since AutoEV is a CSA app, uploading to CODI will commence immediately after data acquisition, as long as the laptop is connected to the internet. If upload is not completed, it will begin automatically next time the AutoEV is opened on CSA as long as an internet connection is available. In the Aplo Scope App, the dataset upload status widget displays information about the upload progress. This widget will show how many datasets are in the upload queue, as well as important information if that queue is stalled, for example, if the internet connection is unreliable or unavailable.

Please refer to the AutoEV and AutoLNP Troubleshooting page on the ONI Service Desk for common issues and workarounds.

Use the ONI Service desk to submit bug reports or any feature requests, using the AutoEV category. To ensure that the ONI team can properly address any bugs reported, we kindly ask that the latest “experiment_server*.log” and “hal*.log” logs are provided. These are located in the following directory: C:\Users\ONI\AppData\Local\ONI\OHM