values to the stage specifications found in the manufacturer's datasheets.

## Materials and methods

### Stage drift

**Sample.** The custom-made fluorescent slide with the 1 µm diameter beads or the 4 µm beads was used (the same slide used for coregistration studies).

**Acquisition protocol.** For both stage drift and repeatability acquisitions, the microscopes were set up on active optical tables, as for all other measurements in this study. We checked the flatness of the stages by using spirit levels for WF and SDCM and by imaging a mirror in reflection mode for LSCM. The horizontality of the stage can be adjusted by inserting layers of paper or tape between the mounting frame and the stage (You et al., 2021).

The stage drift characterization is a fairly straightforward procedure to follow. A fluorescent bead is convenient, but bright-field observation of various features, like dust as a sample, is possible as well, implying that the dust is not located on the faceplate of the camera or in the optical path. This condition can be easily verified: in the presence of dust in the FOV of the microscope in bright-field mode, if the dust moves with the *xy* stage, then the dust is on the sample. Turning the camera slightly around its connecting axis in live mode allows knowing if the dust is in the optical path (in the live-image streaming the dust moves) or on the camera (in the live-image streaming the dust does not move). One should pay attention to stitching applications, where a shift of the camera angle with the *xy* stage directions requires a new calibration.

We placed the slide on the stage and the whole microscope setup was switched on at least 30 min before the experiment to equilibrate the temperature between the sample and the microscope to reduce sample drift. We centered one bead in the illumination field and adjusted the *z*-position on focus. If present, it can be very useful to activate hardware and software focus corrections for the calibration of experiments. However, to properly monitor the *xy* behavior of stages and the *z* stability, the *z* correction has to be deactivated to allow raw drift evaluation and thus maintain a sensitivity to the environmental parameters, which must be taken into account. We started *z*-stack recordings over time at a range of 15 µm. We used 60/63× high NA oil immersion objectives to stay closer to experimental conditions where a high numerical aperture makes it possible to precisely detect drifts in the three dimensions. We evaluated the long-term drift (overnight acquisitions) for a time-lapse of 15 h with an acquisition frequency every 10 min at room temperature set at 20-22°C (depending on the microscope room the defined temperature values can be slightly different).

**Metrics.** We measured (1) the stabilization time $\tau_{stab}$ of the stage, (2) the mean velocity $V_b$ of the beads in nm/min in *xyz* before the stabilization time, and (3) the subsequent velocity $V_a$ after this time. The stabilization time is the one required for the stabilization of the “sample on stage” system. This duration is measured from the start of the acquisition until the moment when any *xyz* drift is lower than a distance corresponding to the spatial resolution of the system.

**Analysis.** We used the TrackMate plugin of Fiji (Tinevez et al., 2017). The TrackMate plugin is widely used for single particle tracking purposes. The plugin finds the center of mass of the bead for each time-point in the three dimensions and it calculates the mean velocity of the bead and the 3D displacement. We exported the bead *xyz* displacement values and traced them over time. This plot is convenient to find the stabilization time. Finally, we recalculated the mean velocity for the time values before and after the bead stabilization.

**Biological imaging.** We used osteosarcoma cells (ATCC 143 B - CRL-8303) expressing the construction CD63phluorin/scarlet (Addgene pLenti-pHluorin_M153R-CD63-mScarlet/Plasmid #172118). Images were acquired with a Nikon Ti2 microscope using TIRF illumination, a Plan-Apo 100×/1.45 objective, and a Prime 95B sCMOS camera (Photometrics).

### Stage positioning repeatability

**Sample.** Any samples that can be well-distinguished in fluorescence or transmitted mode would work for this kind of measurement. In our cases, we used the 1- or 4-µm fluorescent multicolor beads of the self-made bead slide (the same slide as used for the coregistration studies).

**Acquisition protocol.** Microscope stage specifications include maximum travel range, (maximum) speed, repeatability, or accuracy. We focused on the repeatability and determined the repositioning of the *xy* stage movement by moving between multiple fixed points over time and observing the variability of the position precision. Repeatability can be performed unidirectionally (the stage returns to a given point with only one axis movement along *x* or *y*) or bidirectionally (the stage returns to a reference point coming from a previous point, involving *x* and *y* movements). The above diagonal movements are the displacements that could result in the less optimal repositioning since they involve movement in both the *x* and *y* axes.

For our acquisition protocol, the stage was first initialized (whenever this option was available in the software). The microscope with all its components was switched on at least 30 min before the experiment and 10× or 20× dry objectives were used to avoid any oil pressure/surface tension influence. The sample was firmly fixed on the mounting frame of the stage (usually the stages offer a clipping mode that fixes the mounting frames and slides securely in place). To avoid drift, the setup stayed at this state for the stabilization time found during drift characterization. A reference stage position, where a fluorescent bead sits in the center of the FOV, was recorded as $P_{ref}$. The stage was then shifted 3 mm away in both *x* and *y*-axes toward the top-right direction (i.e., a maximum of 4.2 mm diagonal movement). This position (referred to as top/right position P1) was recorded. The stage was then returned to the initial bead position, before being further shifted away in the opposite position along the *x* axis (bottom/right, 4.2 mm away from the bead position, referred to as P2). Opposite positions along the *y* axis were also recorded (referred to as P3 and P4), resulting in a total diagonal movement of 8.4 mm. Then the acquisition software of the microscope was set to acquire a 20-cycle time-lapse of the nine positions ($P_{ref}-P1-P_{ref}-P2-P_{ref}-P3-P_{ref}-P4-P_{ref}$), as shown in Fig. 1 E.

We chose a 4.2-mm diagonal distance as most often imaging of a coverslip/slide mounted sample does not involve more than
3 to 5 mm stage movements. This value has to be adapted depending on the application the setup is intended for. To fully characterize a stage for all kinds of applications, we performed repeatability measurements for two more distances along the x and y axes, for 0.3 and 30 mm when achievable. The latter distance corresponds to experiments on histological slides or multiwell plates. All experiments were carried out at room temperature set at 20-22°C (depending on the microscope room the defined temperature values can be slightly different).

**Metrics.** The x and y coordinates of the bead reference positions are calculated for each cycle (values in µm). The SDs σₓ and σᵧ of the measurements give the repeatability values along the x and y axes. The values are then compared to the repeatability value given by the manufacturer. This value usually appears in the datasheet of the stage which is a document containing all parameter values which are measured before the purchase for that item ideally or for this item category. This information is useful to properly characterize the device at installation and over time.

**Analysis.** Bead positions were analyzed using the Fiji tracking plugin TrackMate (Tinevez et al., 2017). We then calculated the SDs of the measurements. Fig. 8 A shows a time projection of the reposition experiment for a bead on the left. Cyan and magenta represent the two extreme positions of the bead. On the right, the calculated trajectory is illustrated with a red line.

**Biological imaging.** The yeast cells were W303 strain, named ySP16782. They expressed mCherry-septin protein (mCherry-cdc3::URA3). For testing the stage repositioning repeatability, a SDCM (SpinSR10, Olympus) was used with an ASI xyz stage. The acquisition was recorded through a 60×/1.5 UPLAPO objective (Olympus) and a Fusion BT camera (Hamamatsu Photonics).

# Results

## Stage drift

We characterized first qualitatively the drift by observing the time projection of the maximum intensity 3D projection of a typical bead track (Fig. 7 A). The 3D displacement (increased z drift compared to xy) decreases over time leading to stabilization (Fig. 7, B and C). The z-axis drift was lower than the axial resolution of the system, so it can be considered negligible, for standard microscopy experiments.

The stabilization time τstab (time from the start of the acquisition until the xyz drift is lower than a distance corresponding to the spatial resolution) in Fig. 7 B was 105 min. Before stabilization, the mean 3D speed Vb was 56 nm/min, and after stabilization Va = 8 nm/min. The τstab depends mainly on environmental conditions, particularly related to temperature variations that can imply large drifts (Mason and Green, 1975).

We plotted the distribution of the 3D drift speed before and after the calculated stabilization times of 20 stages. When a short stabilization time (<45 min) is observed, Vb and Va values are close. For longer stabilizations (45 min < τ < 120 min or τ > 120 min), the two-speed values are uncorrelated, showing that once the stabilization is achieved under our criterion, the whole system is stable. Finally, for one of the 20 tested setups, we found no stabilization during the complete 15-h time-lapse period.

We experimentally classified drift behavior into three categories according to the specificity of experiments and the need for precision (Table 2). When Vb and Va values are under 15 nm/min and close to each other, the drift is negligible (high precision experiments, e.g., super-resolution purposes). Accumulated over time, this amount of drift may be problematic for localization microscopy (acquisition time of minutes or hours). When 45 min < τstab < 120 min and 15 nm/min < Vb, Va < 50 nm/min, drift can be considered acceptable (e.g., time-lapses at the cell scale). Any other drift parameter combination should be earnestly examined because it can lead to failed experiments, especially when the focus is lost.

A representative example of stage drift in biological imaging is shown in Fig. 7 E. Living cells, labeled with CD63PhLuorin (CD63 is a membrane protein and PhLuorin a GFP variant), were imaged for 600 s at a time interval of 1 s. As shown in the overlay image of Fig. 7 F, which is a zoom region of Fig. 7 E, we observed a mechanical drift between the first (image in cyan) and last (image in magenta) acquired frames. The drift occurs along one single axis and is constant during the acquisitions (no stabilization achieved, and the drift velocity is 400 nm/min, in the critical range of Table 2). The reason for that drift was a damaged joystick (when at “no move” position, the stage kept moving along one axis) of the stage controller and not the stage itself.

Additionally, we investigated the drift speed fluctuations over days. Three different measurements on the same system were performed on three consecutive days. The drift values were very close and the velocity dispersion was low. However, during a month or even a year, the drift of a system can change significantly if environmental parameters are not stable, like the room temperature or even the temperature flow in the room.

## Stage positioning repeatability

We tested 25 different stages on 21 microscopes. Stage suppliers were Märzhäuser, ASI, Nikon, and Olympus, and the majority of the stages had linear two-stepper motors and 5 stages had linear encoders. The variability for the 3 mm xy-stage displacements was always below 0.4 µm (Fig. 8 B). This value is within the specifications of the manufacturers, which are usually lesser than 1 µm for stepper motors and 0.7 µm for linear encoders. A distribution analysis highlights a population with values below 0.2 µm at both axes (green zone in Fig. 8 B). For most multiposition experiments on tracking fluorescence localization within a living cell, the 0.2 µm value can be a critical one. It is near the xy-resolution limit for high NA objectives, and we define it as our tolerance value.

We examined some parameters that could influence repeatability. The stage speed, for the ranges that we tested (7-20 mm/s) did not significantly affect the values (Fig. S12, A and B). The stage acceleration did not affect the repeatability as well (Fig. S12, C and D). The waiting time was not the same for all experiments as it cannot always be controlled and adjusted via the acquisition software. Typical waiting times were in the range of 10-300 ms. The proper fixing of the sample is important for repeatability measurements, but it depends very much on the used insert type for each microscope, which is quite diverse. We
3 to 5 mm stage movements. This value has to be adapted depending on the application the setup is intended for. To fully characterize a stage for all kinds of applications, we performed repeatability measurements for two more distances along the x and y axes, for 0.3 and 30 mm when achievable. The latter distance corresponds to experiments on histological slides or multiwell plates. All experiments were carried out at room temperature set at 20-22°C (depending on the microscope room the defined temperature values can be slightly different).

**Metrics.** The x and y coordinates of the bead reference positions are calculated for each cycle (values in µm). The SDs σₓ and σᵧ of the measurements give the repeatability values along the x and y axes. The values are then compared to the repeatability value given by the manufacturer. This value usually appears in the datasheet of the stage which is a document containing all parameter values which are measured before the purchase for that item ideally or for this item category. This information is useful to properly characterize the device at installation and over time.

**Analysis.** Bead positions were analyzed using the Fiji tracking plugin TrackMate (Tinevez et al., 2017). We then calculated the SDs of the measurements. Fig. 8 A shows a time projection of the reposition experiment for a bead on the left. Cyan and magenta represent the two extreme positions of the bead. On the right, the calculated trajectory is illustrated with a red line.

**Biological imaging.** The yeast cells were W303 strain, named ySP16782. They expressed mCherry-septin protein (mCherry-cdc3::URA3). For testing the stage repositioning repeatability, a SDCM (SpinSR10, Olympus) was used with an ASI xyz stage. The acquisition was recorded through a 60×/1.5 UPLAPO objective (Olympus) and a Fusion BT camera (Hamamatsu Photonics).

# Results

## Stage drift

We characterized first qualitatively the drift by observing the time projection of the maximum intensity 3D projection of a typical bead track (Fig. 7 A). The 3D displacement (increased z drift compared to xy) decreases over time leading to stabilization (Fig. 7, B and C). The z-axis drift was lower than the axial resolution of the system, so it can be considered negligible, for standard microscopy experiments.

The stabilization time τstab (time from the start of the acquisition until the xyz drift is lower than a distance corresponding to the spatial resolution) in Fig. 7 B was 105 min. Before stabilization, the mean 3D speed Vb was 56 nm/min, and after stabilization Va = 8 nm/min. The τstab depends mainly on environmental conditions, particularly related to temperature variations that can imply large drifts (Mason and Green, 1975).

We plotted the distribution of the 3D drift speed before and after the calculated stabilization times of 20 stages. When a short stabilization time (<45 min) is observed, Vb and Va values are close. For longer stabilizations (45 min < τ < 120 min or τ > 120 min), the two-speed values are uncorrelated, showing that once the stabilization is achieved under our criterion, the whole system is stable. Finally, for one of the 20 tested setups, we found no stabilization during the complete 15-h time-lapse period.

We experimentally classified drift behavior into three categories according to the specificity of experiments and the need for precision (Table 2). When Vb and Va values are under 15 nm/min and close to each other, the drift is negligible (high precision experiments, e.g., super-resolution purposes). Accumulated over time, this amount of drift may be problematic for localization microscopy (acquisition time of minutes or hours). When 45 min < τstab < 120 min and 15 nm/min < Vb, Va < 50 nm/min, drift can be considered acceptable (e.g., time-lapses at the cell scale). Any other drift parameter combination should be earnestly examined because it can lead to failed experiments, especially when the focus is lost.

A representative example of stage drift in biological imaging is shown in Fig. 7 E. Living cells, labeled with CD63PhLuorin (CD63 is a membrane protein and PhLuorin a GFP variant), were imaged for 600 s at a time interval of 1 s. As shown in the overlay image of Fig. 7 F, which is a zoom region of Fig. 7 E, we observed a mechanical drift between the first (image in cyan) and last (image in magenta) acquired frames. The drift occurs along one single axis and is constant during the acquisitions (no stabilization achieved, and the drift velocity is 400 nm/min, in the critical range of Table 2). The reason for that drift was a damaged joystick (when at “no move” position, the stage kept moving along one axis) of the stage controller and not the stage itself.

Additionally, we investigated the drift speed fluctuations over days. Three different measurements on the same system were performed on three consecutive days. The drift values were very close and the velocity dispersion was low. However, during a month or even a year, the drift of a system can change significantly if environmental parameters are not stable, like the room temperature or even the temperature flow in the room.

## Stage positioning repeatability

We tested 25 different stages on 21 microscopes. Stage suppliers were Märzhäuser, ASI, Nikon, and Olympus, and the majority of the stages had linear two-stepper motors and 5 stages had linear encoders. The variability for the 3 mm xy-stage displacements was always below 0.4 µm (Fig. 8 B). This value is within the specifications of the manufacturers, which are usually lesser than 1 µm for stepper motors and 0.7 µm for linear encoders. A distribution analysis highlights a population with values below 0.2 µm at both axes (green zone in Fig. 8 B). For most multiposition experiments on tracking fluorescence localization within a living cell, the 0.2 µm value can be a critical one. It is near the xy-resolution limit for high NA objectives, and we define it as our tolerance value.

We examined some parameters that could influence repeatability. The stage speed, for the ranges that we tested (7-20 mm/s) did not significantly affect the values (Fig. S12, A and B). The stage acceleration did not affect the repeatability as well (Fig. S12, C and D). The waiting time was not the same for all experiments as it cannot always be controlled and adjusted via the acquisition software. Typical waiting times were in the range of 10-300 ms. The proper fixing of the sample is important for repeatability measurements, but it depends very much on the used insert type for each microscope, which is quite diverse. We
<figure><img src="image_20.png" /><figcaption>**Figure 7.** **Stage drift data summary.** (A) Max Z projection of the time projection of a 15 h time-lapse of a fluorescent bead, and trajectory of the bead, with the initial segmented bead position shown in magenta. (B) Global 3D displacement over time. The arrow indicates the calculated stabilization time τ<sub>stab</sub>. (C) Relative displacement along the xyz axes. (D) Calculated velocity before (V<sub>a</sub>) and after (V<sub>b</sub>) stabilization of different microscope stages grouped in three categories upon τ<sub>stab</sub>. The dark green zone highlights the acceptable values. (E) xy time lapse over 600 s of living cells labeled with CD63PhLuorin (membrane labeling). (F) Overlay of the initial *t* = 0 (cyan) and final *t* = 600 s (magenta) frame of the series of E to highlight the relative drift along the x axis.</figcaption></figure>

then examined the reproducibility of the measurements by
performing acquisitions for five consecutive times on five
different stepper motor stages (Fig. 8 C). We observed that
the variability of the results was stage dependent (stages
2 and 4 showed lower variability than the rest of the stages in
Fig. 8 C).

When we compared two identical model stages on the same
setup (ASI stages with linear encoders), we observed different re-
positioning values. We identified that the first stage, which had a
z-piezo issue and had to be replaced, showed σ<sub>x</sub> = 0.05 µm and σ<sub>y</sub> =
0.27 µm (capped line of Fig. 8 B). The replacement stage showed
much better results (σ = 0.03 µm for both xy), far under the system
<figure><img src="image_23.png" /><figcaption>**Figure 7.** **Stage drift data summary.** (A) Max Z projection of the time projection of a 15 h time-lapse of a fluorescent bead, and trajectory of the bead, with the initial segmented bead position shown in magenta. (B) Global 3D displacement over time. The arrow indicates the calculated stabilization time τ<sub>stab</sub>. (C) Relative displacement along the xyz axes. (D) Calculated velocity before (V<sub>a</sub>) and after (V<sub>b</sub>) stabilization of different microscope stages grouped in three categories upon τ<sub>stab</sub>. The dark green zone highlights the acceptable values. (E) xy time lapse over 600 s of living cells labeled with CD63PhLuorin (membrane labeling). (F) Overlay of the initial *t* = 0 (cyan) and final *t* = 600 s (magenta) frame of the series of E to highlight the relative drift along the x axis.</figcaption></figure>

then examined the reproducibility of the measurements by
performing acquisitions for five consecutive times on five
different stepper motor stages (Fig. 8 C). We observed that
the variability of the results was stage dependent (stages
2 and 4 showed lower variability than the rest of the stages in
Fig. 8 C).

When we compared two identical model stages on the same
setup (ASI stages with linear encoders), we observed different re-
positioning values. We identified that the first stage, which had a
z-piezo issue and had to be replaced, showed σ<sub>x</sub> = 0.05 µm and σ<sub>y</sub> =
0.27 µm (capped line of Fig. 8 B). The replacement stage showed
much better results (σ = 0.03 µm for both xy), far under the system
<table><caption>Table 2. Drift tolerance values</caption><thead><tr><td></td><td>Standard</td><td>Acceptable</td><td>Critical</td></tr></thead><tbody><tr><td>τ<sub>stab</sub>: stabilization time</td><td>&lt;45 min</td><td>(45-120) min</td><td>&gt;120 min</td></tr><tr><td>V<sub>b</sub>: mean velocity before stabilization</td><td>&lt;15 nm/min</td><td>(15-100) nm/min</td><td>&gt;100 nm/min</td></tr><tr><td>V<sub>a</sub>: mean velocity after stabilization</td><td>&lt;15 nm/min</td><td>(15-50) nm/min</td><td>&gt;50 nm/min</td></tr></tbody></table>

The drift is negligible when the velocities before and after stabilization are identical and kept under 15 nm/min and the stabilization time τ<sub>stab</sub> is lower than 45 min. The drift becomes acceptable for velocities before and after stabilization in the range of 15-100 and 15-50 nm/min respectively and 45 < τ<sub>stab</sub> < 120 min. For higher values the drift is considered critical and not acceptable. These values are experimental and serve to guide microscopy users to apprehend the orders of magnitude for their experiments.

spatial resolution (arrow of Fig. 8 B). This example illustrates the diversity in quality that can occur while purchasing such devices.

We next examined the influence of the stage displacement distance using three different values (0.3, 3, and 30 mm) for both axes for five different stages equipped with two stepper motors from the same manufacturer (Märzhäuser) and with similar datasheets. Although there is a high variability (measurements not taken on the same microscope and the stages were not exactly the same model), we can distinguish that the repeatability deteriorated for longer displacements (Fig. 8 D). The 30-mm xy displacements correspond to typical distances used during multiposition experiments in a multiwell plate.

We examined the influence of position repeatability on biological imaging. Fig. 8 E shows a maximum intensity projection of a 3D time-lapse of a single position of two zoomed yeast cells under division. The time series lasts 30 min with an acquisition time interval of 2 min. This series of frames belongs to a 6-position time-lapse experiment using a 60×/1.4 objective with a SDCM. *Saccharomyces cerevisiae* yeast cells expressed mCherry coupled to the septin protein Cdc3 (the protein forms various filaments containing structures such as rings, as we see in Fig. 8 E when the two cells are divided). We clearly see that the repeatability is not good, as some frames are displaced along the y direction. In Fig. 8 F, we decided to visualize the frames of the 12-min time point (in cyan) and 14 min (in magenta) and show the vertical drift that is in the order of >1 µm. For the whole 30 min time-lapse, the SD of the position repeatability is calculated at 0.07 and 0.25 µm for the x and y axes respectively. The 0.25 µm value is out of the defined limit value and makes it impossible to treat the images. When we characterized the repeatability of the same stage without sample and with the correspondent protocol (20× dry objective), the values of the SD were 0.11 and 0.15 µm on the x and y axes, respectively.

A method to overcome the stage repeatability issue is to postprocess the images. We used StackReg ImageJ plugin (Thevenaz et al., 1998) and corrected the drift (bottom row of Fig. 8, E and F).

## Discussion

### Stage drift

The stage drift measurements are simple to perform, but their interpretation can be complex. Drift measurements only make sense if environmental conditions (e.g., temperature, humidity, air flow, and warming time of the stage) are kept

stable. As external changes (e.g., period of 1 yr) influence the drift results, time monitoring of the drift is necessary. We applied a straightforward protocol to measure the drift during overnight experiments. We defined three metrics and proposed their tolerance values (Table 2). Here again, these are values that we recommend and should not be considered as an absolute reference. We propose to measure stage drift once per year in the frame of preventive maintenance. For super-resolution microscopy, the frequency should be adapted to the modality.

Within the framework of a given experiment, the user must have general control over the orders of magnitude to be quantified. For example, if distances between two fluorescent signals in two different channels are measured, the relevant time domain is the acquisition delay between both channels, especially if the two signal sources drift together.

Our results showed that a stabilization time is mandatory before starting the acquisition. Furthermore, it is very important to consider that once the sample is fixed on the stage, it becomes an integral part of this device. To preserve the intrinsic heat of the stage, it is possible to leave the controller under permanent voltage, avoiding additional drift due to stage heating cycles. A z drift can also arise due to bad tightening of its drive axis, which is possible on aging microscopes (Price and Gough, 1994). For biological imaging, minimizing the stage drift is critical, especially for time-lapse experiments. As shown with the biological example of Fig. 7, E and F, drift can be problematic for live cell imaging, even for short time scales (10 min in our case). Effective software solutions exist to overcome the drifts after the acquisitions (e.g., StackReg [Thevenaz et al., 1998]), but it is always preferable to understand and master the drifts at the hardware level (Table 1 for troubleshooting).

### Positioning repeatability

Stage repeatability is directly linked to stage drift. When parameters involved in drift are controlled and the drift is characterized, the ability of the stage to reposition at the same xy location can be measured. Monitoring repeatability must then be performed after the stage stabilization time and the sample should be well secured to avoid a sample-associated source of drift. Parameters that may influence the repeatability should be controlled when possible (often the acquisition software does not give access to these parameters). We propose to measure positioning repeatability once per year.

Our experimental repeatability values were always within the manufacturers' specifications. We defined a tolerance value
<table><caption>Table 2. Drift tolerance values</caption><thead><tr><td></td><td>Standard</td><td>Acceptable</td><td>Critical</td></tr></thead><tbody><tr><td>τ<sub>stab</sub>: stabilization time</td><td>&lt;45 min</td><td>(45-120) min</td><td>&gt;120 min</td></tr><tr><td>V<sub>b</sub>: mean velocity before stabilization</td><td>&lt;15 nm/min</td><td>(15-100) nm/min</td><td>&gt;100 nm/min</td></tr><tr><td>V<sub>a</sub>: mean velocity after stabilization</td><td>&lt;15 nm/min</td><td>(15-50) nm/min</td><td>&gt;50 nm/min</td></tr></tbody></table>

The drift is negligible when the velocities before and after stabilization are identical and kept under 15 nm/min and the stabilization time $τ_{stab}$ is lower than 45 min. The drift becomes acceptable for velocities before and after stabilization in the range of 15-100 and 15-50 nm/min respectively and 45 < $τ_{stab}$ < 120 min. For higher values the drift is considered critical and not acceptable. These values are experimental and serve to guide microscopy users to apprehend the orders of magnitude for their experiments.

spatial resolution (arrow of Fig. 8 B). This example illustrates the diversity in quality that can occur while purchasing such devices.

We next examined the influence of the stage displacement distance using three different values (0.3, 3, and 30 mm) for both axes for five different stages equipped with two stepper motors from the same manufacturer (Märzhäuser) and with similar datasheets. Although there is a high variability (measurements not taken on the same microscope and the stages were not exactly the same model), we can distinguish that the repeatability deteriorated for longer displacements (Fig. 8 D). The 30-mm xy displacements correspond to typical distances used during multiposition experiments in a multiwell plate.

We examined the influence of position repeatability on biological imaging. Fig. 8 E shows a maximum intensity projection of a 3D time-lapse of a single position of two zoomed yeast cells under division. The time series lasts 30 min with an acquisition time interval of 2 min. This series of frames belongs to a 6-position time-lapse experiment using a 60×/1.4 objective with a SDCM. *Saccharomyces cerevisiae* yeast cells expressed mCherry coupled to the septin protein Cdc3 (the protein forms various filaments containing structures such as rings, as we see in Fig. 8 E when the two cells are divided). We clearly see that the repeatability is not good, as some frames are displaced along the y direction. In Fig. 8 F, we decided to visualize the frames of the 12-min time point (in cyan) and 14 min (in magenta) and show the vertical drift that is in the order of >1 µm. For the whole 30 min time-lapse, the SD of the position repeatability is calculated at 0.07 and 0.25 µm for the x and y axes respectively. The 0.25 µm value is out of the defined limit value and makes it impossible to treat the images. When we characterized the repeatability of the same stage without sample and with the correspondent protocol (20× dry objective), the values of the SD were 0.11 and 0.15 µm on the x and y axes, respectively.

A method to overcome the stage repeatability issue is to postprocess the images. We used StackReg ImageJ plugin (Thevenaz et al., 1998) and corrected the drift (bottom row of Fig. 8, E and F).

## Discussion

### Stage drift

The stage drift measurements are simple to perform, but their interpretation can be complex. Drift measurements only make sense if environmental conditions (e.g., temperature, humidity, air flow, and warming time of the stage) are kept

stable. As external changes (e.g., period of 1 yr) influence the drift results, time monitoring of the drift is necessary. We applied a straightforward protocol to measure the drift during overnight experiments. We defined three metrics and proposed their tolerance values (Table 2). Here again, these are values that we recommend and should not be considered as an absolute reference. We propose to measure stage drift once per year in the frame of preventive maintenance. For super-resolution microscopy, the frequency should be adapted to the modality.

Within the framework of a given experiment, the user must have general control over the orders of magnitude to be quantified. For example, if distances between two fluorescent signals in two different channels are measured, the relevant time domain is the acquisition delay between both channels, especially if the two signal sources drift together.

Our results showed that a stabilization time is mandatory before starting the acquisition. Furthermore, it is very important to consider that once the sample is fixed on the stage, it becomes an integral part of this device. To preserve the intrinsic heat of the stage, it is possible to leave the controller under permanent voltage, avoiding additional drift due to stage heating cycles. A z drift can also arise due to bad tightening of its drive axis, which is possible on aging microscopes (Price and Gough, 1994). For biological imaging, minimizing the stage drift is critical, especially for time-lapse experiments. As shown with the biological example of Fig. 7, E and F, drift can be problematic for live cell imaging, even for short time scales (10 min in our case). Effective software solutions exist to overcome the drifts after the acquisitions (e.g., StackReg [Thevenaz et al., 1998]), but it is always preferable to understand and master the drifts at the hardware level (Table 1 for troubleshooting).

### Positioning repeatability

Stage repeatability is directly linked to stage drift. When parameters involved in drift are controlled and the drift is characterized, the ability of the stage to reposition at the same xy location can be measured. Monitoring repeatability must then be performed after the stage stabilization time and the sample should be well secured to avoid a sample-associated source of drift. Parameters that may influence the repeatability should be controlled when possible (often the acquisition software does not give access to these parameters). We propose to measure positioning repeatability once per year.

Our experimental repeatability values were always within the manufacturers' specifications. We defined a tolerance value
A

<figure><img src="image_21.png" /></figure>

B

<figure><img src="image_22.png" /></figure>

E

<figure><img src="image_23.png" /></figure>

C

<figure><img src="image_24.png" /></figure>

D

<figure><img src="image_25.png" /></figure>

F

Figure 8. **Stage positioning repeatability data summary.** (A) Left: Time projection of the repeatability experiment for a fluorescent bead. Cyan and magenta represent the two extreme positions of the bead. Right: The calculated trajectory is shown with a red line. (B) Bidirectional stage repeatability for 3 mm displacements in both xy axes (4.2 mm diagonal). The arrow and capped line show the results of two linear stages (same model with encoders) tested on the same microscope stand. The dark green zone highlights the acceptable values. (C) Box plots summarizing the reproducibility in time (five different measurements per stage) of stage xy repeatability for five stages equipped with two stepper motors. (D) Repeatability dispersion increases for the five stages
<figure><img src="image_24.png" /><figcaption>**Figure 8.** **Stage positioning repeatability data summary.** (A) Left: Time projection of the repeatability experiment for a fluorescent bead. Cyan and magenta represent the two extreme positions of the bead. Right: The calculated trajectory is shown with a red line. (B) Bidirectional stage repeatability for 3 mm displacements in both xy axes (4.2 mm diagonal). The arrow and capped line show the results of two linear stages (same model with encoders) tested on the same microscope stand. The dark green zone highlights the acceptable values. (C) Box plots summarizing the reproducibility in time (five different measurements per stage) of stage xy repeatability for five stages equipped with two stepper motors. (D) Repeatability dispersion increases for the five stages</figcaption></figure>
of C with the xy displacement. Whiskers indicate the min/max values of the box plots of C and D and dots indicate the mean values. (E) Raw and postprocessed aligned max intensity Z projection of 30 min time-lapse of a yeast cell under division expressing mCherry-septin Cdc3 protein. The experiment included six positions with 2 min time intervals, here we highlight the time series of a single position. (F) Overlay of the 12 min (cyan) and 14 min (magenta) timepoint of E showing the mechanical drift at the raw case and the zero drift at the postprocessed series.

that is close to the spatial resolution of the system. For multi-position imaging, the user can expect positioning repeatability to be accurate at the scale of a focal volume, although this precision is not necessary for all experiments. We found that repositioning is less precise when the stage displacements increase.

Repeatability can be improved when a backlash correction (backlash is the systematic error created by lost motion in the drive mechanism that appears when changing direction) is applied or when a waiting time is added and/or when the stage has linear encoders (Stuurman and Thorn, 2015).

Last but not least, our measurements were performed considering the stage as part of the microscope and under “real” conditions, which is not always in accordance with the manufacturers’ protocols and datasheet values where the stage is considered as an isolated item. In the latter case, the stage is not subjected to the conditions of temperature change in the context of live cell imaging or drifts. The way to measure stage repeatability can also influence the results. For instance, when using a dry objective, we measure the stage position repeatability. If we want to approach the biological imaging conditions, immersion objectives can deteriorate the repeatability results. In the case of the example of Fig. 8, E and F where a 60× oil immersion objective was used, the repeatability values are out of the defined experimental limit values. The microscopy acquisitions can serve only for qualitative studies (observe cell division and creation of the septin rings in that case) but not for quantifications (study the size and the structure of the septin ring during cell division or study its colocalization with other proteins for two-color imaging). In that case, postprocessing correction is necessary to correct the drifts.

## Camera noise

### Background

The last element in the acquisition chain of a light microscope is the detector. It can be a 2D array sensor chip (mainly CCD, EMCCD, or sCMOS cameras) or a point detector (mainly PMT, APD, HyD, or GaAsP). By construction, all these devices generate different noises, which can influence the contrast of the generated image, especially for the detection of weak signals, and thus determine whether the signal will be detected (Stelzer, 1998). The quality of an image depends mainly on the Nyquist sampling of the sample illuminated with the least possible luminous flux while obtaining as much contrast as possible. It is thus important to monitor the contribution of detector noises (Deagle et al., 2017). Due to the different detector technologies, it is particularly complex to assess a single protocol that would apply to all cases. In this study, we chose to focus on array sensor chips (CCD, EMCCD, and especially sCMOS cameras). Metrics for single-point detectors were proposed in the past (Murray, 2013; Zucker and Price, 1999). The European Machine Vision

Association has published standards on noise characterization of cameras (EMVA Standard 1288, 2010). In this study, we proposed a simple protocol to measure read noise-related metrics, including dark offset and dark signal non-uniformity (DSNU) values that can be crucial for further fine processing of the data (Watanabe et al., 2017).

Compared to previous studies (Ferrand et al., 2019; Mullikin et al., 1994; Watanabe et al., 2017; Zucker, 2006b; Zucker and Price, 1999), we performed measurements among a wide variety of CCD, EMCCD, and sCMOS cameras for which simple noise metrics are missing since these kinds of cameras are quite recent. These past studies also evaluate noises within the framework of a complete detection system where the whole optical path influences the captured signal. Although this method is beneficial and necessary for microscopy experiments when using actual samples, it is very dependent on microscope components. The influence of the upstream elements may hinder the ability to characterize the sensor accurately. We thus evaluated the noise of the sensor as an isolated element to have some easily reproducible values. With the proposed protocol, we do not aim to calibrate a sensor, but simply to characterize it and monitor its noise over time after the installation day.

## Materials and methods

### Sample

No sample is needed for this measurement.

### Acquisition protocol

We monitored three parameters: (1) the read noise of the camera sensor, (2) the dark offset value, and (3) the DSNU. The basic unit used by manufacturers to characterize the different sources of noise is not the gray level but the electron. The cameras are characterized by variable photon/electron conversion factors, so the use of the electron unit allows more direct comparison between cameras.

(1) The read noise is created during the conversion of photons to electrons and originates mainly from on-chip amplifiers. It is measured in dark conditions to avoid noise contributions due to uncertainties of photon arrival (Janesick, 2007). Read noise is often a technical bottleneck when a low signal is to be quantified. This sensor parameter is usually provided in the manufacturer's datasheet, mainly as "Read out noise," in electron RMS (root mean square) for CCD cameras or in electron med (median value) for sCMOS. Each pixel in CCD and sCMOS cameras accumulates a charge proportional to the intensity of the localized illumination. At the end of the exposure time, the CCD camera sequentially transfers the charge of each pixel to a common bus. The conversion of the charge into voltage takes place in the printed circuit of the camera and the read noise is therefore constant across all pixels. In a sCMOS chip, the charge-to-voltage conversion takes place in each individual pixel, resulting in pixel-to-pixel variation of the read noise. This difference in the
of C with the xy displacement. Whiskers indicate the min/max values of the box plots of C and D and dots indicate the mean values. (E) Raw and postprocessed aligned max intensity Z projection of 30 min time-lapse of a yeast cell under division expressing mCherry-septin Cdc3 protein. The experiment included six positions with 2 min time intervals, here we highlight the time series of a single position. (F) Overlay of the 12 min (cyan) and 14 min (magenta) timepoint of E showing the mechanical drift at the raw case and the zero drift at the postprocessed series.

that is close to the spatial resolution of the system. For multi-position imaging, the user can expect positioning repeatability to be accurate at the scale of a focal volume, although this precision is not necessary for all experiments. We found that repositioning is less precise when the stage displacements increase.

Repeatability can be improved when a backlash correction (backlash is the systematic error created by lost motion in the drive mechanism that appears when changing direction) is applied or when a waiting time is added and/or when the stage has linear encoders (Stuurman and Thorn, 2015).

Last but not least, our measurements were performed considering the stage as part of the microscope and under “real” conditions, which is not always in accordance with the manufacturers’ protocols and datasheet values where the stage is considered as an isolated item. In the latter case, the stage is not subjected to the conditions of temperature change in the context of live cell imaging or drifts. The way to measure stage repeatability can also influence the results. For instance, when using a dry objective, we measure the stage position repeatability. If we want to approach the biological imaging conditions, immersion objectives can deteriorate the repeatability results. In the case of the example of Fig. 8, E and F where a 60× oil immersion objective was used, the repeatability values are out of the defined experimental limit values. The microscopy acquisitions can serve only for qualitative studies (observe cell division and creation of the septin rings in that case) but not for quantifications (study the size and the structure of the septin ring during cell division or study its colocalization with other proteins for two-color imaging). In that case, postprocessing correction is necessary to correct the drifts.

## Camera noise

### Background

The last element in the acquisition chain of a light microscope is the detector. It can be a 2D array sensor chip (mainly CCD, EMCCD, or sCMOS cameras) or a point detector (mainly PMT, APD, HyD, or GaAsP). By construction, all these devices generate different noises, which can influence the contrast of the generated image, especially for the detection of weak signals, and thus determine whether the signal will be detected (Stelzer, 1998). The quality of an image depends mainly on the Nyquist sampling of the sample illuminated with the least possible luminous flux while obtaining as much contrast as possible. It is thus important to monitor the contribution of detector noises (Deagle et al., 2017). Due to the different detector technologies, it is particularly complex to assess a single protocol that would apply to all cases. In this study, we chose to focus on array sensor chips (CCD, EMCCD, and especially sCMOS cameras). Metrics for single-point detectors were proposed in the past (Murray, 2013; Zucker and Price, 1999). The European Machine Vision

Association has published standards on noise characterization of cameras (EMVA Standard 1288, 2010). In this study, we proposed a simple protocol to measure read noise-related metrics, including dark offset and dark signal non-uniformity (DSNU) values that can be crucial for further fine processing of the data (Watanabe et al., 2017).

Compared to previous studies (Ferrand et al., 2019; Mullikin et al., 1994; Watanabe et al., 2017; Zucker, 2006b; Zucker and Price, 1999), we performed measurements among a wide variety of CCD, EMCCD, and sCMOS cameras for which simple noise metrics are missing since these kinds of cameras are quite recent. These past studies also evaluate noises within the framework of a complete detection system where the whole optical path influences the captured signal. Although this method is beneficial and necessary for microscopy experiments when using actual samples, it is very dependent on microscope components. The influence of the upstream elements may hinder the ability to characterize the sensor accurately. We thus evaluated the noise of the sensor as an isolated element to have some easily reproducible values. With the proposed protocol, we do not aim to calibrate a sensor, but simply to characterize it and monitor its noise over time after the installation day.

## Materials and methods

### Sample

No sample is needed for this measurement.

### Acquisition protocol

We monitored three parameters: (1) the read noise of the camera sensor, (2) the dark offset value, and (3) the DSNU. The basic unit used by manufacturers to characterize the different sources of noise is not the gray level but the electron. The cameras are characterized by variable photon/electron conversion factors, so the use of the electron unit allows more direct comparison between cameras.

(1) The read noise is created during the conversion of photons to electrons and originates mainly from on-chip amplifiers. It is measured in dark conditions to avoid noise contributions due to uncertainties of photon arrival (Janesick, 2007). Read noise is often a technical bottleneck when a low signal is to be quantified. This sensor parameter is usually provided in the manufacturer's datasheet, mainly as "Read out noise," in electron RMS (root mean square) for CCD cameras or in electron med (median value) for sCMOS. Each pixel in CCD and sCMOS cameras accumulates a charge proportional to the intensity of the localized illumination. At the end of the exposure time, the CCD camera sequentially transfers the charge of each pixel to a common bus. The conversion of the charge into voltage takes place in the printed circuit of the camera and the read noise is therefore constant across all pixels. In a sCMOS chip, the charge-to-voltage conversion takes place in each individual pixel, resulting in pixel-to-pixel variation of the read noise. This difference in the
architecture of the reading method results in lower average photodiode read noise for sCMOS compared to CCD cameras, while for sCMOS, read noise is no longer constant across the chip (Brouk et al., 2010; Hain et al., 2007; Lambert and Waters, 2014; Mandracchia et al., 2020). This must be taken into consideration when using image analysis algorithms (e.g., single-molecule localization) on images acquired with sCMOS cameras (Babcock et al., 2019; Huang et al., 2013). Also, some quantitative image data analysis, like maximum likelihood estimation methods, requires individual pixel characteristics, such as variation of the read noise (Huang et al., 2013). In our protocol, the read noise is measured by the fluctuation of the value of a pixel along a temporal acquisition of 100 images, without any light influence. Read noise is then considered as a temporal noise. While the quantity of 100 frames may seem overestimated for CCD read noise measurement, it is a fair compromise for sCMOS read noise (obviously not to characterize every pixel's attributes; Huang et al., 2013), and DSNU measurements, as explained below. An open debate in the field is one in which noise needs to be taken into account, RMS or median. Our protocol calculates both. According to theory, for CCD and EMCCD cameras, the RMS and median model gives similar read noise values as the readout in these cameras is serial, and an identical read noise across the whole chip is expected. In sCMOS sensors, the noise distribution is not symmetrical. Thus, both approximations, Gaussian (RMS) and median, are justified. Our interest here is to follow the calculated values over time and compare those with the ones in the camera datasheet. We recommend comparing with the value that is given in the datasheet, and in any case contact the manufacturer to request missing values. The median value provides information about the general magnitude of the noise. Together with the RMS value, they indicate the spread of the read noise distribution.

(2) The dark offset value is a baseline arbitrarily adjusted by the manufacturer to allow access to the complete read noise fluctuation and thus avoid negative gray values (Lambert and Waters, 2014).

(3) The DSNU (also called Offset Fixed Pattern Noise) is measured to establish the stability and uniformity of the camera offset. It refers to the fluctuation between pixel values of the chip under the same dark condition, is measured in electrons and is the SD of all pixels' dark offset (Long et al., 2014). As seen previously, the gain of the amplifiers may vary from pixel to pixel (and column to column) and thus contribute to the DSNU. This noise is often observed to a greater extent in sCMOS cameras than in CCD cameras, although recent improvements in sCMOS design tend to reduce fixed pattern noise (El Gamal et al., 1998; Snoeij et al., 2006). EMCCDs often have additional noise distributed over the image sensor chip area (Rieger and Stallinga, 2014). For precise DSNU evaluation, thousands of frames should be acquired to eliminate the read noise. To define the optimal dataset size, we introduced a quality criterion that compares the DSNU obtained with datasets of different sizes. A threshold of 10% yielded an optimal dataset size of 100 frames (i.e., the DSNU value difference obtained with larger datasets is <10% of the DSNU value calculated with 100 frames, Fig. S14 D). Hence, we recommend using a 100 dark frames dataset for DSNU evaluation.

To resume, read noise is an inherent property of camera sensors, regardless of whether or not light reaches it. Thus, the measurements were carried out without excitation light and sample. When possible, the camera shutter was closed to avoid any unwanted light contributions. When available, the cameras were cooled according to the manufacturer's recommendations (for sCMOS 0 or -10°C, EMCCDs -70 to -80°C). The acquisition software was set to acquire a 100-cycle time-lapse. The sensor exposure time was set to short values (e.g., 10 ms) to avoid DSNU contribution, and pixel binning was not used (Orzanowski and Wachulak, 2018).

For this study, the CCD cameras were all Coolsnap HQ2 (Photometrics); the EMCCD cameras were iXon 888, iXon 897 (Andor-Oxford Instruments) and Evolve512 (Photometrics); the sCMOS cameras were Zyla 4.2 or 5.5 (Andor-Oxford Instruments); Orca Flash 4 LT, LT+, V2, V3, Fusion BT (Hamamatsu); Prime 95B (Photometrics); and Edge 4.2 (PCO). Most often we measured the read noise for the different readout modes of the cameras (30, 100, 200, or 500 MHz for sCMOS, 10 and 17 or 30 MHz for EMCCD, 10 and 20 MHz for CCD). For EMCCD cameras, we set the EM gain at the minimal value of 0, which is not the common condition that these cameras are used for, but this setting allows comparison with the manufacturers' values. When available, we also acquired data with blemish corrections to replace cold and hot pixels (defective pixels not influenced by incoming light, with constant zero value and saturated value of the sensor dynamic, respectively) with the average intensity of surrounding pixels (McNally et al., 1999). This also must be taken into consideration when using image analysis algorithms (e.g., likelihood-based analysis methods) since in this case, noise statistics are violated (Huang et al., 2013).

## Analysis

The data analysis was performed using the MetroloJ_QC plugin. The main workflow is shown in Fig. S13. The plugin generates both intensity average and SD projections across the 100 frames. The average intensity projection was used to calculate offset and DSNU values. The offset is the mean average intensity across all pixels of the average intensity projection. The DSNU is the SD of the pixel intensities of the mean average intensity projection multiplied by the electron to analog-to-digital units (ADU) conversion factor (or gain), usually provided in the manufacturer's datasheet. To find the read noise map, we calculated the SD image of the 100 frames in electrons (the conversion factor e⁻/ADU should be used). The duration of each frame was 10 ms. The root mean square (RMS) noise refers to the RMS value of all pixels read noise. The median noise is the median value of all pixels read noise (dark noise is considered equal to zero for this short acquisition time; Lambert and Waters, 2014).

We defined two metrics: VAR consists of measuring the variation in the percentage of the manufacturer's specification as indicated in the datasheet to the measured read noise experimental value:

$$VAR = 100 \times \frac{N_{theor}}{N_{exp}},$$

with N the read noise value (RMS or median, depending on the given theoretical value) of the camera. The second one consists
architecture of the reading method results in lower average photodiode read noise for sCMOS compared to CCD cameras, while for sCMOS, read noise is no longer constant across the chip (Brouk et al., 2010; Hain et al., 2007; Lambert and Waters, 2014; Mandracchia et al., 2020). This must be taken into consideration when using image analysis algorithms (e.g., single-molecule localization) on images acquired with sCMOS cameras (Babcock et al., 2019; Huang et al., 2013). Also, some quantitative image data analysis, like maximum likelihood estimation methods, requires individual pixel characteristics, such as variation of the read noise (Huang et al., 2013). In our protocol, the read noise is measured by the fluctuation of the value of a pixel along a temporal acquisition of 100 images, without any light influence. Read noise is then considered as a temporal noise. While the quantity of 100 frames may seem overestimated for CCD read noise measurement, it is a fair compromise for sCMOS read noise (obviously not to characterize every pixel's attributes; Huang et al., 2013), and DSNU measurements, as explained below. An open debate in the field is one in which noise needs to be taken into account, RMS or median. Our protocol calculates both. According to theory, for CCD and EMCCD cameras, the RMS and median model gives similar read noise values as the readout in these cameras is serial, and an identical read noise across the whole chip is expected. In sCMOS sensors, the noise distribution is not symmetrical. Thus, both approximations, Gaussian (RMS) and median, are justified. Our interest here is to follow the calculated values over time and compare those with the ones in the camera datasheet. We recommend comparing with the value that is given in the datasheet, and in any case contact the manufacturer to request missing values. The median value provides information about the general magnitude of the noise. Together with the RMS value, they indicate the spread of the read noise distribution.

(2) The dark offset value is a baseline arbitrarily adjusted by the manufacturer to allow access to the complete read noise fluctuation and thus avoid negative gray values (Lambert and Waters, 2014).

(3) The DSNU (also called Offset Fixed Pattern Noise) is measured to establish the stability and uniformity of the camera offset. It refers to the fluctuation between pixel values of the chip under the same dark condition, is measured in electrons and is the SD of all pixels' dark offset (Long et al., 2014). As seen previously, the gain of the amplifiers may vary from pixel to pixel (and column to column) and thus contribute to the DSNU. This noise is often observed to a greater extent in sCMOS cameras than in CCD cameras, although recent improvements in sCMOS design tend to reduce fixed pattern noise (El Gamal et al., 1998; Snoeij et al., 2006). EMCCDs often have additional noise distributed over the image sensor chip area (Rieger and Stallinga, 2014). For precise DSNU evaluation, thousands of frames should be acquired to eliminate the read noise. To define the optimal dataset size, we introduced a quality criterion that compares the DSNU obtained with datasets of different sizes. A threshold of 10% yielded an optimal dataset size of 100 frames (i.e., the DSNU value difference obtained with larger datasets is <10% of the DSNU value calculated with 100 frames, Fig. S14 D). Hence, we recommend using a 100 dark frames dataset for DSNU evaluation.

To resume, read noise is an inherent property of camera sensors, regardless of whether or not light reaches it. Thus, the measurements were carried out without excitation light and sample. When possible, the camera shutter was closed to avoid any unwanted light contributions. When available, the cameras were cooled according to the manufacturer's recommendations (for sCMOS 0 or -10°C, EMCCDs -70 to -80°C). The acquisition software was set to acquire a 100-cycle time-lapse. The sensor exposure time was set to short values (e.g., 10 ms) to avoid DSNU contribution, and pixel binning was not used (Orzanowski and Wachulak, 2018).

For this study, the CCD cameras were all Coolsnap HQ2 (Photometrics); the EMCCD cameras were iXon 888, iXon 897 (Andor-Oxford Instruments) and Evolve512 (Photometrics); the sCMOS cameras were Zyla 4.2 or 5.5 (Andor-Oxford Instruments); Orca Flash 4 LT, LT+, V2, V3, Fusion BT (Hamamatsu); Prime 95B (Photometrics); and Edge 4.2 (PCO). Most often we measured the read noise for the different readout modes of the cameras (30, 100, 200, or 500 MHz for sCMOS, 10 and 17 or 30 MHz for EMCCD, 10 and 20 MHz for CCD). For EMCCD cameras, we set the EM gain at the minimal value of 0, which is not the common condition that these cameras are used for, but this setting allows comparison with the manufacturers' values. When available, we also acquired data with blemish corrections to replace cold and hot pixels (defective pixels not influenced by incoming light, with constant zero value and saturated value of the sensor dynamic, respectively) with the average intensity of surrounding pixels (McNally et al., 1999). This also must be taken into consideration when using image analysis algorithms (e.g., likelihood-based analysis methods) since in this case, noise statistics are violated (Huang et al., 2013).

## Analysis

The data analysis was performed using the MetroloJ_QC plugin. The main workflow is shown in Fig. S13. The plugin generates both intensity average and SD projections across the 100 frames. The average intensity projection was used to calculate offset and DSNU values. The offset is the mean average intensity across all pixels of the average intensity projection. The DSNU is the SD of the pixel intensities of the mean average intensity projection multiplied by the electron to analog-to-digital units (ADU) conversion factor (or gain), usually provided in the manufacturer's datasheet. To find the read noise map, we calculated the SD image of the 100 frames in electrons (the conversion factor e⁻/ADU should be used). The duration of each frame was 10 ms. The root mean square (RMS) noise refers to the RMS value of all pixels read noise. The median noise is the median value of all pixels read noise (dark noise is considered equal to zero for this short acquisition time; Lambert and Waters, 2014).

We defined two metrics: VAR consists of measuring the variation in the percentage of the manufacturer's specification as indicated in the datasheet to the measured read noise experimental value:

$$VAR = 100 \times \frac{N_{theor}}{N_{exp}},$$

with N the read noise value (RMS or median, depending on the given theoretical value) of the camera. The second one consists
of the stability of the read noise over a long time and is defined as:

$$STAB_{\text{noise}} = 100 \times \left[ 1 - \left( \frac{N_{\text{max}} - N_{\text{min}}}{N_{\text{max}} + N_{\text{min}}} \right) \right],$$

with Nmax and Nmin the maximal and minimal read noise values. The timescales here are long-term (in correspondence to the source stabilization studies) and are on a scale of months to years.

## Biological imaging

We used yeast cells W303 strain, namely ySP16782. They expressed mCherry-septin protein (mCherry-cdc3::URA3). For testing the camera noise and offset uniformity, we used a SDCM (SpinSR10; Olympus) microscope, a 60×/1.5 UPLAPO objective, and a Fusion BT camera (Hamamatsu Photonics).

## Results

The read noise was calculated from three CCD, four EMCCD, and 25 sCMOS cameras from all main manufacturers and most often for both standard and fast camera readout modes.

We characterized the read noise maps (typical cases in Fig. 9 A or extreme cases with glow effect and offset calibration in Fig. S14 A). We observed that blemish corrections considerably decreased the measured noise RMS values (Fig. S14 B). DSNU map also gave useful information on the camera sensor chip corrections (Fig. S14 C). For each sCMOS camera, we measured the read noise and DSNU values in electrons (Fig. 9 B). The measured DSNU was never higher than the read noise, as expected from theory for this kind of cameras (Janesick, 2007).

For all types of cameras, we calculated the VAR metric for both RMS and median read noise values (Fig. 9 C). We calculated this metric for both noise values as camera manufacturers often provide a single readout noise value (either RMS or median). The majority of the VAR values were close to 100%. This shows that measured readout noise values were close to the manufacturer's specification. We found 84% of the calculated RMS values within a ±10% variation from the manufacturer's values. Almost half of the values are higher than 100% (better noise evaluation compared to specifications) and were most often observed when a blemish correction was applied. For CCD and EMCCD cameras (dots in blue and magenta), the RMS or median readout noise values are almost identical, and VAR distribution is kept in a narrow ±10% variation window. We propose then experimental tolerance values for VAR 90%.

We also monitored the read noise evolution over months for sCMOS cameras to calculate the STAB<sub>noise</sub> metric (Fig. 9, D and E). We define 97% as the limit value, which is always the case for the monitored cameras.

We studied the influence of the read noise and a non-homogeneous noise pattern on biological imaging. Fig. 9 F shows the read noise map (no illumination) in electrons of a back-illuminated sCMOS camera and the image of a biological sample (same sample as in Fig. 8 D, yeast cells, expressing mCherry coupled to septin protein). On the noise pattern, we distinguish vertical stripes (which is due to a pixel offset issue of the sensor). The overall read noise is 0.78e<sup>-</sup> (RMS value) but the inhomogeneous pattern can influence imaging. On the biological

image, especially when the signal is dim, noise variations in the FOV may hinder quantifications. In our case, we zoom on the region of a vertical stripe of the noise pattern. Relative quantification studies of the intensity signal among cells can be influenced by the different offset and noise levels among cells.

## Discussion

The 32 tested cameras proved to be particularly reliable to their respective datasheet characteristics. The calculated metrics were most often in the limit values that we defined (98% of the cases for RMS and 91% for mean read noise). Although, a visual inspection of the noise map is often necessary to understand the origin of an issue (e.g., glow effect, offset calibration, or camera sensor chip corrections shown in Fig. S14).

VAR metric values showed mainly a lower than 10% variation of the manufacturer's values, and we considered the 90% VAR value as our experimental tolerance. When applicable, on-chip blemish correction should be activated as it yields lower noise values. For deeper sensor chip characterization, it is preferable to avoid pixel correction. It can hide some information on the number of warm pixels and their evolution in time. It should be also noted that the number of warm pixels may increase with exposure time due to the dark noise influence or cosmic rays. Cameras are constantly subjected to cosmic rays, which implies an increase in the number of hot pixels blocked at the saturation values (Theuwissen, 2007; Nampoothiri et al., 2011; Niedzwiecki et al., 2019). For QC studies, it is necessary to keep the same settings during the different measurements on different dates, so the results are comparable.

We propose measuring the camera noise once per year (Table 1) as our data show very low variability.

It is important to note that camera manufacturers apply protocols to correct the homogeneity of the chip through the DSNU (column or pixel correction for sCMOS), average hot pixels, or adjust the linearization of the sensor (Mandracchia et al., 2020). Other postacquisition alternatives exist to correct compromising noises for demanding image processing tasks (Liu et al., 2017).

Even though all the studied cameras were within the noise limit values, some of them had to be replaced, sometimes twice. Indeed, the major defect that we observed within our various core-facility measurements that lead to a replacement of the camera lies in the reliability of the vacuum seal carried out to protect the sensor chip of the cameras. When the seal fails, we observe condensation forming on the faceplate protection of the sensor chip depending on atmospheric conditions, as highlighted in Fig. S15. This situation has no simple solution to our knowledge and the camera must be replaced or returned for repair.

A feature of the sCMOS cameras is the rolling shutter readout option, in which different rows of the pixel array are exposed and read at different time frames. Therefore, the generated image contains parts exposed at different absolute times. At short exposure times or for very dynamic samples, it is advisable to check that the rolling shutter effect does not affect the acquired data (Lambert and Waters, 2014).
of the stability of the read noise over a long time and is defined as:

$$STAB_{\text{noise}} = 100 \times \left[ 1 - \left( \frac{N_{\text{max}} - N_{\text{min}}}{N_{\text{max}} + N_{\text{min}}} \right) \right],$$

with $N_{max}$ and $N_{min}$ the maximal and minimal read noise values. The timescales here are long-term (in correspondence to the source stabilization studies) and are on a scale of months to years.

## Biological imaging

We used yeast cells W303 strain, namely ySP16782. They expressed mCherry-septin protein (mCherry-cdc3::URA3). For testing the camera noise and offset uniformity, we used a SDCM (SpinSR10; Olympus) microscope, a 60×/1.5 UPLAPO objective, and a Fusion BT camera (Hamamatsu Photonics).

## Results

The read noise was calculated from three CCD, four EMCCD, and 25 sCMOS cameras from all main manufacturers and most often for both standard and fast camera readout modes.

We characterized the read noise maps (typical cases in Fig. 9 A or extreme cases with glow effect and offset calibration in Fig. S14 A). We observed that blemish corrections considerably decreased the measured noise RMS values (Fig. S14 B). DSNU map also gave useful information on the camera sensor chip corrections (Fig. S14 C). For each sCMOS camera, we measured the read noise and DSNU values in electrons (Fig. 9 B). The measured DSNU was never higher than the read noise, as expected from theory for this kind of cameras (Janesick, 2007).

For all types of cameras, we calculated the VAR metric for both RMS and median read noise values (Fig. 9 C). We calculated this metric for both noise values as camera manufacturers often provide a single readout noise value (either RMS or median). The majority of the VAR values were close to 100%. This shows that measured readout noise values were close to the manufacturer's specification. We found 84% of the calculated RMS values within a ±10% variation from the manufacturer's values. Almost half of the values are higher than 100% (better noise evaluation compared to specifications) and were most often observed when a blemish correction was applied. For CCD and EMCCD cameras (dots in blue and magenta), the RMS or median readout noise values are almost identical, and VAR distribution is kept in a narrow ±10% variation window. We propose then experimental tolerance values for VAR 90%.

We also monitored the read noise evolution over months for sCMOS cameras to calculate the STAB<sub>noise</sub> metric (Fig. 9, D and E). We define 97% as the limit value, which is always the case for the monitored cameras.

We studied the influence of the read noise and a non-homogeneous noise pattern on biological imaging. Fig. 9 F shows the read noise map (no illumination) in electrons of a back-illuminated sCMOS camera and the image of a biological sample (same sample as in Fig. 8 D, yeast cells, expressing mCherry coupled to septin protein). On the noise pattern, we distinguish vertical stripes (which is due to a pixel offset issue of the sensor). The overall read noise is 0.78e⁻ (RMS value) but the inhomogeneous pattern can influence imaging. On the biological

image, especially when the signal is dim, noise variations in the FOV may hinder quantifications. In our case, we zoom on the region of a vertical stripe of the noise pattern. Relative quantification studies of the intensity signal among cells can be influenced by the different offset and noise levels among cells.

## Discussion

The 32 tested cameras proved to be particularly reliable to their respective datasheet characteristics. The calculated metrics were most often in the limit values that we defined (98% of the cases for RMS and 91% for mean read noise). Although, a visual inspection of the noise map is often necessary to understand the origin of an issue (e.g., glow effect, offset calibration, or camera sensor chip corrections shown in Fig. S14).

VAR metric values showed mainly a lower than 10% variation of the manufacturer's values, and we considered the 90% VAR value as our experimental tolerance. When applicable, on-chip blemish correction should be activated as it yields lower noise values. For deeper sensor chip characterization, it is preferable to avoid pixel correction. It can hide some information on the number of warm pixels and their evolution in time. It should be also noted that the number of warm pixels may increase with exposure time due to the dark noise influence or cosmic rays. Cameras are constantly subjected to cosmic rays, which implies an increase in the number of hot pixels blocked at the saturation values (Theuwissen, 2007; Nampoothiri et al., 2011; Niedzwiecki et al., 2019). For QC studies, it is necessary to keep the same settings during the different measurements on different dates, so the results are comparable.

We propose measuring the camera noise once per year (Table 1) as our data show very low variability.

It is important to note that camera manufacturers apply protocols to correct the homogeneity of the chip through the DSNU (column or pixel correction for sCMOS), average hot pixels, or adjust the linearization of the sensor (Mandracchia et al., 2020). Other postacquisition alternatives exist to correct compromising noises for demanding image processing tasks (Liu et al., 2017).

Even though all the studied cameras were within the noise limit values, some of them had to be replaced, sometimes twice. Indeed, the major defect that we observed within our various core-facility measurements that lead to a replacement of the camera lies in the reliability of the vacuum seal carried out to protect the sensor chip of the cameras. When the seal fails, we observe condensation forming on the faceplate protection of the sensor chip depending on atmospheric conditions, as highlighted in Fig. S15. This situation has no simple solution to our knowledge and the camera must be replaced or returned for repair.

A feature of the sCMOS cameras is the rolling shutter readout option, in which different rows of the pixel array are exposed and read at different time frames. Therefore, the generated image contains parts exposed at different absolute times. At short exposure times or for very dynamic samples, it is advisable to check that the rolling shutter effect does not affect the acquired data (Lambert and Waters, 2014).
<figure><img src="image_26.png" /><figcaption>A</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_30.png" /></figure>

Flash4 LT+

Zyla 4.2

<figure><img src="image_31.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_33.png" /></figure>

read noise RMS 0.78 e⁻
mean offset: 100

10 µm

biological
image

10 µm

zoom

2 µm

Figure 9. **Read noise and DSNU characterization of cameras.** (A) Read noise map in electrons of an Orca Flash4 v3 (Hamamatsu) and a zoom zone of 100 × 100 pixels. For display reasons, the maximum of the noise map is set to four electrons. (B) Read noise (RMS) and DSNU distribution as expressed in electrons of sCMOS cameras. Each color corresponds to a single camera, with different readout speed modes and the color stays the same for the read noise and DSNU representation. Total 40 datapoints for each case. (C) Read noise RMS/median variability (VAR) metric distribution for CCD (blue), EMCCD (magenta), and sCMOS cameras (black). The green zone and the red dashed line highlight the acceptable values. Total 58 points for each case. (D) Read noise monitoring over time for two sCMOS cameras. (E) Stability read noise metric (STAB) for six cameras over at least 1 yr of monitoring. The red dashed line defines the minimal
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>Figure 9. **Read noise and DSNU characterization of cameras.** (A) Read noise map in electrons of an Orca Flash4 v3 (Hamamatsu) and a zoom zone of 100 × 100 pixels. For display reasons, the maximum of the noise map is set to four electrons. (B) Read noise (RMS) and DSNU distribution as expressed in electrons of sCMOS cameras. Each color corresponds to a single camera, with different readout speed modes and the color stays the same for the read noise and DSNU representation. Total 40 datapoints for each case. (C) Read noise RMS/median variability (VAR) metric distribution for CCD (blue), EMCCD (magenta), and sCMOS cameras (black). The green zone and the red dashed line highlight the acceptable values. Total 58 points for each case. (D) Read noise monitoring over time for two sCMOS cameras. (E) Stability read noise metric (STAB) for six cameras over at least 1 yr of monitoring. The red dashed line defines the minimal</figcaption></figure>
97% VAR tolerance value. (F) Influence of noise pattern in biological imaging. Noise map of an sCMOS back-illuminated camera (Fusion BT; Hamamatsu) for read noise and offset value characterization. Biological imaging of yeast cells expressing mCherry coupled to septin protein (zoom image shows imaging of three cells on an inhomogeneous noise-offset pattern). Scale bar in intensity (digital) values.

For biological imaging, read noise inhomogeneity on the sensor can influence quantification studies for low signal applications (e.g., fluorescent protein low expression levels as is often the case for yeast cells, shown in Fig. 9 F, or for single molecule applications).

It should be mentioned that for proper camera characterization, the linearity and the non-uniformity measurements of the photon response are of great importance. Of equal importance is the measurement of the sensitivity, which implies considering the whole microscope and its components. These measurements require reference samples, uniform illumination, and a reference calibrated sensor to compare (Dixon et al., 2008). They also require costly equipment and entail skill and time commitment levels that cannot be reasonably expected from most microscope users or facility staff. We thus do not recommend these protocols in the type of study performed here.

## Conclusion

We described the essential aspects of the QC tools, the acquisition process, the analysis, and gave a vision of how helpful an image database that collects data from different laboratories could be (Fig. 10). We tried to use simple tools, as affordable as possible, such as custom-made bead slides, plastic fluorescent slides, and power meters to assess QC of light microscopes over time. Other QC tools exist, like the Argolight slide (Argolight SA, France), GATTAquant beads (Gattaquant GmbH, Germany), nanorulers, or PSFCheck slides. The drawbacks of these tools (i.e., price or guaranteed performance for a limited duration of time) influenced our choice for using simple and cost-effective tools.

We defined metrics through simple and robust acquisition protocols that do not take >10 min to acquire or launch. Nevertheless, the acquisitions require that the user is well trained to perform experiments in a reproducible manner. A future direction will be to establish a single sample slide and the adapted software for an automated acquisition, eliminating associated human error here.

Acquired data needs to be analyzed in a reproducible manner as well. We thus developed and proposed the ImageJ/Fiji plugin MetroloJ_QC. When possible, we compared the measured values with the theoretical ones, such as the resolution for PSF or the coregistration. For some other cases, we referred to manufacturers' specifications like the stage repositioning or the sensor read noise. We also associated the limit values summed up in Table 1 with the limits for biological experiments when possible. We gave recommended measurement frequency, which depends on multiple parameters like the per year occupation time of the microscope, the number of users (major difference if the system is in a core facility with dozens or hundreds of users per year or in a research team, used by the same people the whole year), the human resources that take care of the microscope, and the kind of biological experiments carried out.

The values are experimental, not fixed, and the fruit of the experience of 10 core facilities over the last years. Even more importantly, the limit values should serve as a basis for any biologists wishing to understand the orders of magnitude involved in their microscopy experiments. For instance, the QC requirements will not be the same for nucleus count imaging, subcellular protein colocalization, or single-molecule localization. We encourage researchers to contact core facilities or imaging scientists and discuss their specific QC needs, as these groups often dispose the expertise and the tools for QC measurements. When this kind of interaction is not possible, another way is to contact the microscope suppliers, who might have established their own specific QC measurements. This article will act as a starting point for the most often used QC measurements in light microscopy.

It should be noted that the datasets of that work were not acquired to compare the performance of different systems. This kind of comparison may be influenced by parameters difficult to standardize, like the microscope environment or the association of the different microscope components which are by nature different, even if they can have similar datasheets, and thus lead to different results. Our datasets and the resulting guidelines aim to characterize the microscope's health state over time, using relative and not absolute values.

We consider that the metrics that we define in that work complement the ISO 21073:2019 norm (ISO, 2019) and hopefully will be further enriched by the international initiative QUAREP-LiMi, whose role is to establish commonly accepted guidelines for quality assessment in light microscopy (Boehm et al., 2021; Nelson et al., 2021). This need for standardization accepted by the whole scientific community and principal actors from the academic and private sectors has been recently highlighted (Marx, 2022a, 2022b).

The recording of the metric values is the last link in the chain of the QC. Monitoring over time the different measured values is necessary, and a simple spreadsheet file may be sufficient. Ideally, the solution is a QC management database for processing the analysis and obtaining the metrics as quality control images and data are imported (Huisman et al., 2020; Linkert et al., 2010; Swedlow et al., 2021). Furthermore, such a database would be able to integrate these values into the metadata of the acquired images (Hammer et al., 2021). In the end, these metrics would allow an open and accessible inspection of the quality of the used microscope and of every single image, within a collection of microscopy metadata, as recently proposed by Rigano et al. (2021), with the Micro-Meta App approach, and that would be a key contribution to an automated image quality assessment method.

## Online supplemental material

Fig. S1 shows PSF variability with image processing, the image field of view, and the PSF repeatability accuracy. Fig. S2 shows PSF variability with SBR and SNR ratios, pixel size for LSCM,
97% VAR tolerance value. (F) Influence of noise pattern in biological imaging. Noise map of an sCMOS back-illuminated camera (Fusion BT; Hamamatsu) for read noise and offset value characterization. Biological imaging of yeast cells expressing mCherry coupled to septin protein (zoom image shows imaging of three cells on an inhomogeneous noise-offset pattern). Scale bar in intensity (digital) values.

For biological imaging, read noise inhomogeneity on the sensor can influence quantification studies for low signal applications (e.g., fluorescent protein low expression levels as is often the case for yeast cells, shown in Fig. 9 F, or for single molecule applications).

It should be mentioned that for proper camera characterization, the linearity and the non-uniformity measurements of the photon response are of great importance. Of equal importance is the measurement of the sensitivity, which implies considering the whole microscope and its components. These measurements require reference samples, uniform illumination, and a reference calibrated sensor to compare (Dixon et al., 2008). They also require costly equipment and entail skill and time commitment levels that cannot be reasonably expected from most microscope users or facility staff. We thus do not recommend these protocols in the type of study performed here.

## Conclusion

We described the essential aspects of the QC tools, the acquisition process, the analysis, and gave a vision of how helpful an image database that collects data from different laboratories could be (Fig. 10). We tried to use simple tools, as affordable as possible, such as custom-made bead slides, plastic fluorescent slides, and power meters to assess QC of light microscopes over time. Other QC tools exist, like the Argolight slide (Argolight SA, France), GATTAquant beads (Gattaquant GmbH, Germany), nanorulers, or PSFCheck slides. The drawbacks of these tools (i.e., price or guaranteed performance for a limited duration of time) influenced our choice for using simple and cost-effective tools.

We defined metrics through simple and robust acquisition protocols that do not take >10 min to acquire or launch. Nevertheless, the acquisitions require that the user is well trained to perform experiments in a reproducible manner. A future direction will be to establish a single sample slide and the adapted software for an automated acquisition, eliminating associated human error here.

Acquired data needs to be analyzed in a reproducible manner as well. We thus developed and proposed the ImageJ/Fiji plugin MetroloJ_QC. When possible, we compared the measured values with the theoretical ones, such as the resolution for PSF or the coregistration. For some other cases, we referred to manufacturers' specifications like the stage repositioning or the sensor read noise. We also associated the limit values summed up in Table 1 with the limits for biological experiments when possible. We gave recommended measurement frequency, which depends on multiple parameters like the per year occupation time of the microscope, the number of users (major difference if the system is in a core facility with dozens or hundreds of users per year or in a research team, used by the same people the whole year), the human resources that take care of the microscope, and the kind of biological experiments carried out.

The values are experimental, not fixed, and the fruit of the experience of 10 core facilities over the last years. Even more importantly, the limit values should serve as a basis for any biologists wishing to understand the orders of magnitude involved in their microscopy experiments. For instance, the QC requirements will not be the same for nucleus count imaging, subcellular protein colocalization, or single-molecule localization. We encourage researchers to contact core facilities or imaging scientists and discuss their specific QC needs, as these groups often dispose the expertise and the tools for QC measurements. When this kind of interaction is not possible, another way is to contact the microscope suppliers, who might have established their own specific QC measurements. This article will act as a starting point for the most often used QC measurements in light microscopy.

It should be noted that the datasets of that work were not acquired to compare the performance of different systems. This kind of comparison may be influenced by parameters difficult to standardize, like the microscope environment or the association of the different microscope components which are by nature different, even if they can have similar datasheets, and thus lead to different results. Our datasets and the resulting guidelines aim to characterize the microscope's health state over time, using relative and not absolute values.

We consider that the metrics that we define in that work complement the ISO 21073:2019 norm (ISO, 2019) and hopefully will be further enriched by the international initiative QUAREP-LiMi, whose role is to establish commonly accepted guidelines for quality assessment in light microscopy (Boehm et al., 2021; Nelson et al., 2021). This need for standardization accepted by the whole scientific community and principal actors from the academic and private sectors has been recently highlighted (Marx, 2022a, 2022b).

The recording of the metric values is the last link in the chain of the QC. Monitoring over time the different measured values is necessary, and a simple spreadsheet file may be sufficient. Ideally, the solution is a QC management database for processing the analysis and obtaining the metrics as quality control images and data are imported (Huisman et al., 2020; Linkert et al., 2010; Swedlow et al., 2021). Furthermore, such a database would be able to integrate these values into the metadata of the acquired images (Hammer et al., 2021). In the end, these metrics would allow an open and accessible inspection of the quality of the used microscope and of every single image, within a collection of microscopy metadata, as recently proposed by Rigano et al. (2021), with the Micro-Meta App approach, and that would be a key contribution to an automated image quality assessment method.

## Online supplemental material

Fig. S1 shows PSF variability with image processing, the image field of view, and the PSF repeatability accuracy. Fig. S2 shows PSF variability with SBR and SNR ratios, pixel size for LSCM,
<figure><img src="image_34.png" /><figcaption>Figure 10. **Heuristic map of light microscopy QC.** QC needs tools (blue branch), acquisition protocols (green branch), image analysis (yellow branch), and clever data storage (red branch).</figcaption></figure>

and bead size for WF. Fig. S3 shows the PSF analysis workflow with the MetroloJ_QC plugin. Fig. S4 shows the field illumination metrics using either a green chroma plastic slide or a glass slide/coverslip configuration with a fluorophore (rhodamine) layer for a spinning disk microscope. Fig. S5 shows the field illumination workflow with the MetroloJ_QC plugin. Fig. S6 shows the distribution of uniformity and centering field illumination metrics classified by the detector type (CCD/EMCCD, sCMOS, PMT). Fig. S7 shows the influence of the bead size on the coregistration accuracy calculated by the MetroloJ_QC plugin. Fig. S8 shows the coregistration workflow with the MetroloJ_QC plugin. Fig. S9 shows the warm-up time of lasers of a LSCM and a SDCM. Fig. S10 shows the short-term power stability of the illumination source and Fig. S11 shows the long-term stability for two different cases (one for lasers and another for a X-Cite light source). Fig. S12 shows the stage positioning repeatability dependence on stage speed and acceleration. Fig. S13 shows the camera noise specifications (read noise, dark offset, DSNU) workflow calculation with the MetroloJ_QC plugin. Fig. S14 shows the camera read noise and DSNU map and distributions. Fig. S15 shows three examples of condensation effects on the camera chip.

## Data availability

The download link of the plugin, direct access to its dependencies, its manual, and simple sample preparation and acquisition protocols can be found at: https://github.com/MontpellierRessourcesImagerie/MetroloJ_QC. We provide the

majority of the datasets used for this work, with the maximum of metadata available under the principle of accessibility of research open source data compatible with the FAIR principle. The data represent 360 GB of data and are accessible under the following DOI: https://doi.org/10.57889/OD_METROLOJ_QC.

## Acknowledgments

The authors thank Sebastian Beer and Bruno Emica (Hamamatsu Photonics) and Gerhard Holst (Excelitas PCO GmbH) for their valuable support and discussion on the metrics of the QC of camera sensors. We would like to thank Kees van der Oord (Nikon Europe B.V.), Christian Schulz (Leica Microsystems CMS GmbH), and Ralf Wolleschensky (Carl Zeiss Microscopy GmbH) for their helpful input on PSF issues, and Arnd Rühl (Märzhäuser Wetzlar GmbH) for his invaluable advice on the stage part of this manuscript. We thank Yea-lih Lin for the help on Fig. 1 and Azam Aslemarz, Nathalie Morin, Raissa Rathar, Daniel Bouvard, and Maritzaida Varela for providing us with the biological samples for the QC metrics validation. We thank Claire Dupont for the measurements and feedback on the stage repeatability. We are deeply indebted to Ulrike Boehm, Pierre Bon, Mark Scott, Alain Trautmann, Raphael Gaudin, and Dimitris Liakopoulos for their critical review of the manuscript. We finally thank the French RTmfm community for the fruitful exchanges throughout the past years.

The Montpellier Ressources Imagerie (BCM), PICT (Institut Curie), Bordeaux Imaging Center and IMAG'IC (Institut Cochin)
<figure><img src="image_27.png" /><figcaption>Figure 10. **Heuristic map of light microscopy QC.** QC needs tools (blue branch), acquisition protocols (green branch), image analysis (yellow branch), and clever data storage (red branch).</figcaption></figure>

and bead size for WF. Fig. S3 shows the PSF analysis workflow with the MetroloJ_QC plugin. Fig. S4 shows the field illumination metrics using either a green chroma plastic slide or a glass slide/coverslip configuration with a fluorophore (rhodamine) layer for a spinning disk microscope. Fig. S5 shows the field illumination workflow with the MetroloJ_QC plugin. Fig. S6 shows the distribution of uniformity and centering field illumination metrics classified by the detector type (CCD/EMCCD, sCMOS, PMT). Fig. S7 shows the influence of the bead size on the coregistration accuracy calculated by the MetroloJ_QC plugin. Fig. S8 shows the coregistration workflow with the MetroloJ_QC plugin. Fig. S9 shows the warm-up time of lasers of a LSCM and a SDCM. Fig. S10 shows the short-term power stability of the illumination source and Fig. S11 shows the long-term stability for two different cases (one for lasers and another for a X-Cite light source). Fig. S12 shows the stage positioning repeatability dependence on stage speed and acceleration. Fig. S13 shows the camera noise specifications (read noise, dark offset, DSNU) workflow calculation with the MetroloJ_QC plugin. Fig. S14 shows the camera read noise and DSNU map and distributions. Fig. S15 shows three examples of condensation effects on the camera chip.

## Data availability

The download link of the plugin, direct access to its dependencies, its manual, and simple sample preparation and acquisition protocols can be found at: https://github.com/MontpellierRessourcesImagerie/MetroloJ_QC. We provide the

majority of the datasets used for this work, with the maximum of metadata available under the principle of accessibility of research open source data compatible with the FAIR principle. The data represent 360 GB of data and are accessible under the following DOI: https://doi.org/10.57889/OD_METROLOJ_QC.

## Acknowledgments

The authors thank Sebastian Beer and Bruno Emica (Hamamatsu Photonics) and Gerhard Holst (Excelitas PCO GmbH) for their valuable support and discussion on the metrics of the QC of camera sensors. We would like to thank Kees van der Oord (Nikon Europe B.V.), Christian Schulz (Leica Microsystems CMS GmbH), and Ralf Wolleschensky (Carl Zeiss Microscopy GmbH) for their helpful input on PSF issues, and Arnd Rühl (Märzhäuser Wetzlar GmbH) for his invaluable advice on the stage part of this manuscript. We thank Yea-lih Lin for the help on Fig. 1 and Azam Aslemarz, Nathalie Morin, Raissa Rathar, Daniel Bouvard, and Maritzaida Varela for providing us with the biological samples for the QC metrics validation. We thank Claire Dupont for the measurements and feedback on the stage repeatability. We are deeply indebted to Ulrike Boehm, Pierre Bon, Mark Scott, Alain Trautmann, Raphael Gaudin, and Dimitris Liakopoulos for their critical review of the manuscript. We finally thank the French RTmfm community for the fruitful exchanges throughout the past years.

The Montpellier Ressources Imagerie (BCM), PICT (Institut Curie), Bordeaux Imaging Center and IMAG'IC (Institut Cochin)
are part of the National Infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04). This work was supported by the French technological network RTmfm of the CNRS "Mission pour les Initiatives Transverses et Interdisciplinaires".

The authors declare no competing financial interests.

Author contributions: O. Faklaris and T. Guilbert conceptualized the study. O. Faklaris, A. Dauphin, B. Monterroso, P. Frère, D. Geny, T. Manoliu, D. Schapman, and T. Guilbert designed the protocols. O. Faklaris, L. Bancel-Vallée, A. Dauphin, B. Monterroso, P. Frère, D. Geny, T. Manoliu, S. de Rossi, D. Schapman, and T. Guilbert collected the data. O. Faklaris and T. Guilbert performed data analysis. F.P. Cordelières developed the original software analysis tool and J. Cau developed it further for this study, integrating the proposed metrics and automating the analysis. O. Faklaris, A. Dauphin, J. Cau, and T. Guilbert designed the figures. O. Faklaris wrote the original draft and supervised the project. All authors reviewed the manuscript. O. Faklaris, A. Dauphin, R. Nitschke, and T. Guilbert revised the manuscript.

Submitted: 15 July 2021

Revised: 4 April 2022

Accepted: 31 August 2022

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Hain, R., C.J. Kähler, and C. Tropea. 2007. Comparison of CCD, CMOS and intensified cameras. *Exp. Fluids*. 42:403-411. https://doi.org/10.1007/s00348-006-0247-1

Hammer, M., M. Huisman, A. Rigano, U. Boehm, J.J. Chambers, N. Gaudreault, A.J. North, J.A. Pimentel, D. Sudar, P. Bajcsy, et al. 2021. Towards community-driven metadata standards for light microscopy: Tiered specifications extending the OME model. *Nat. Methods*. 18: 1427-1440. https://doi.org/10.1038/s41592-021-01327-9

Heddleston, J.M., J.S. Aaron, S. Khuon, and T.-L. Chew. 2021. A guide to accurate reporting in digital image acquisition - can anyone replicate your microscopy data? *J. Cell Sci.* 134:jcs254144. https://doi.org/10.1242/jcs.254144

Hell, S.W., and E.H.K. Stelzer. 1995. Lens aberrations in confocal fluorescence microscopy. *In Handbook of Biological Confocal Microscopy*. J.B. Pawley, editor. Springer US, Boston, MA. 347-354. https://doi.org/10.1007/978-1-4757-5348-6_20

Hell, S., G. Reiner, C. Cremer, and E.H.K. Stelzer. 1993. Aberrations in confocal fluorescence microscopy induced by mismatches in refractive
are part of the National Infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04). This work was supported by the French technological network RTmfm of the CNRS "Mission pour les Initiatives Transverses et Interdisciplinaires".

The authors declare no competing financial interests.

Author contributions: O. Faklaris and T. Guilbert conceptualized the study. O. Faklaris, A. Dauphin, B. Monterroso, P. Frère, D. Geny, T. Manoliu, D. Schapman, and T. Guilbert designed the protocols. O. Faklaris, L. Bancel-Vallée, A. Dauphin, B. Monterroso, P. Frère, D. Geny, T. Manoliu, S. de Rossi, D. Schapman, and T. Guilbert collected the data. O. Faklaris and T. Guilbert performed data analysis. F.P. Cordelières developed the original software analysis tool and J. Cau developed it further for this study, integrating the proposed metrics and automating the analysis. O. Faklaris, A. Dauphin, J. Cau, and T. Guilbert designed the figures. O. Faklaris wrote the original draft and supervised the project. All authors reviewed the manuscript. O. Faklaris, A. Dauphin, R. Nitschke, and T. Guilbert revised the manuscript.

Submitted: 15 July 2021

Revised: 4 April 2022

Accepted: 31 August 2022

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global view of standards for open image data formats and repositories. *Nat. Methods*. 18:1440–1446. https://doi.org/10.1038/s41592-021-10113-7

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Supplemental material

<figure><img src="image_35.png" /><figcaption>Figure S1. PSF variability with image processing, acquisition repeatability, and FOV. (A) PSF repeatability accuracy for wide-field (WF), SDCM, and LSCM along the x, y, and z axis. PSF were acquired five consecutive times. Error bars represent SD. (B) One z plane image of a 175 nm bead sample, slightly out of focus to observe the symmetry of the PSF pattern. Beads near the corners of the FOV show a significant asymmetry. Image taken on a WF-TIRF microscope (TiE Nikon) at zero angle mode, with the 488 nm laser, with a 100×/1.49 objective and use of an additional lens 1.5× to respect the Nyquist criterion (final pixel size, 73 nm), with a Prime95B Photometrics camera (FOV 87.6 × 87.6 µm). Scale bar is 2 µm. In blue square: the central area of the FOV that is taken into account for the PSF calculations for this objective. (C and D) PSF profiles along xy, yz, and xz as extracted by the MetroloJ_QC plugin (square root intensity) of the beads in dashed orange squares in B. Although along the z axis the FWHM is similar, along the xy axis the FWHM is significant different and higher for beads far from the center of the image. (E) Calculated FWHM with the SD for three repeatability studies (processing, acquisition, FOV).</figcaption></figure>
Supplemental material

<figure><img src="image_29.png" /><figcaption>Figure S1. PSF variability with image processing, acquisition repeatability, and FOV. (A) PSF repeatability accuracy for wide-field (WF), SDCM, and LSCM along the x, y, and z axis. PSF were acquired five consecutive times. Error bars represent SD. (B) One z plane image of a 175 nm bead sample, slightly out of focus to observe the symmetry of the PSF pattern. Beads near the corners of the FOV show a significant asymmetry. Image taken on a WF-TIRF microscope (TiE Nikon) at zero angle mode, with the 488 nm laser, with a 100×/1.49 objective and use of an additional lens 1.5× to respect the Nyquist criterion (final pixel size, 73 nm), with a Prime95B Photometrics camera (FOV 87.6 × 87.6 µm). Scale bar is 2 µm. In blue square: the central area of the FOV that is taken into account for the PSF calculations for this objective. (C and D) PSF profiles along xy, yz, and xz as extracted by the MetroloJ_QC plugin (square root intensity) of the beads in dashed orange squares in B. Although along the z axis the FWHM is similar, along the xy axis the FWHM is significant different and higher for beads far from the center of the image. (E) Calculated FWHM with the SD for three repeatability studies (processing, acquisition, FOV).</figcaption></figure>
<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

Figure S2. PSF variability with SBR, SNR ratios, pixel size for LSCM, and bead size. (A and B) Effect of the SBR and SNR (for 12-bit dynamics) ratio on FWHM estimation precision for a wide-field microscope. The same three FOVs of 29 175-nm one-color beads were acquired for different exposures and excitation intensities on an upright Carl Zeiss Microscopy WF microscope, GFP channel, 63×/1.4 objective. The SBR ratio is estimated using the segmented bead mean intensity value (Signal) and the mean value of a 1-µm thick background annulus around the segmented bead (Background) as calculated by the plugin MetroloJ_QC. As SBR is increasing, the FWHM estimation accuracy gets better. For very low SBR the FWHM values decrease. We distinguish three tolerance zones. The green zone is the one presenting the lowest errors and the best fit (R2 equal to 1). The orange zone presents a higher error and a worse fit (0.93 < R2 < 0.99). The red zone presents both high error and bad fit (R2 < 0.9). (C) Lateral FWHM dependence on the sampling rate on a LSCM microscope (LSM880, Carl Zeiss Microscopy GmbH) with pinhole at 1 A.U. The normality of the distributions for each condition was first confirmed with Shapiro-Wilk test. Statistical significance was determined using Dunnett's multicomparison test, with the 80 nm setting chosen as the control value. The P value was non-significant (ns), *, P < 0.05; **, P < 0.005; ***, P < 0.0005, or ****, P < 0.0001. Data points varied from 8 to 22. (D) PSF dependence on microsphere size for WF and LSCM microscopes (WF in A and B and LSCM in C). WF-100 and WF-175 stand for WF PSFs for 100 and 175 nm beads, respectively. Distribution normality was tested with Shapiro-Wilk test and showed a not normal distribution (P < 0.0001). Statistical significance was determined applying a Kolmogorov-Smirnov test for each pair and each condition (one pair: 100 and 175 nm beads). The P value for each pair was <0.0001. The data points were 87 and 156 for 100 and 175 nm beads respectively. All measurements in C and D were carried out with a 63×/1.4 lens, at the GFP channel (525 nm emission).
<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

Figure S2. PSF variability with SBR, SNR ratios, pixel size for LSCM, and bead size. (A and B) Effect of the SBR and SNR (for 12-bit dynamics) ratio on FWHM estimation precision for a wide-field microscope. The same three FOVs of 29 175-nm one-color beads were acquired for different exposures and excitation intensities on an upright Carl Zeiss Microscopy WF microscope, GFP channel, 63×/1.4 objective. The SBR ratio is estimated using the segmented bead mean intensity value (Signal) and the mean value of a 1-µm thick background annulus around the segmented bead (Background) as calculated by the plugin MetroloJ_QC. As SBR is increasing, the FWHM estimation accuracy gets better. For very low SBR the FWHM values decrease. We distinguish three tolerance zones. The green zone is the one presenting the lowest errors and the best fit (R2 equal to 1). The orange zone presents a higher error and a worse fit (0.93 < R2 < 0.99). The red zone presents both high error and bad fit (R2 < 0.9). (C) Lateral FWHM dependence on the sampling rate on a LSCM microscope (LSM880, Carl Zeiss Microscopy GmbH) with pinhole at 1 A.U. The normality of the distributions for each condition was first confirmed with Shapiro-Wilk test. Statistical significance was determined using Dunnett's multicomparison test, with the 80 nm setting chosen as the control value. The P value was non-significant (ns), *, P < 0.05; **, P < 0.005; ***, P < 0.0005, or ****, P < 0.0001. Data points varied from 8 to 22. (D) PSF dependence on microsphere size for WF and LSCM microscopes (WF in A and B and LSCM in C). WF-100 and WF-175 stand for WF PSFs for 100 and 175 nm beads, respectively. Distribution normality was tested with Shapiro-Wilk test and showed a not normal distribution (P < 0.0001). Statistical significance was determined applying a Kolmogorov-Smirnov test for each pair and each condition (one pair: 100 and 175 nm beads). The P value for each pair was <0.0001. The data points were 87 and 156 for 100 and 175 nm beads respectively. All measurements in C and D were carried out with a 63×/1.4 lens, at the GFP channel (525 nm emission).
<figure><img src="image_40.png" /></figure>

D Resolution table:

<table><thead><tr><th>Channel</th><th>Sig/Backnd ratio</th><th>Dimension</th><th>Measured FWHM (μm)</th><th>theory (μm)</th><th>Fit Goodness</th><th>Mes./theory ratio</th></tr></thead><tbody><tr><td rowspan="3">Channel 0 (em. 525.0nm)</td><td rowspan="3">16.3</td><td>X</td><td>0.262</td><td>0.191</td><td>1.0</td><td>1.37</td></tr><tr><td>Y</td><td>0.265</td><td>0.191</td><td>1.0</td><td>1.39</td></tr><tr><td>Z</td><td>0.592</td><td>0.718</td><td>0.98</td><td>0.82</td></tr></tbody></table>

E Lateral asymmetry ratios:

<table><thead><tr><th>Channel</th><th>Ratio</th></tr></thead><tbody><tr><td>Channel 0 (em. 525.0nm)</td><td>0.99</td></tr></tbody></table>

F Summary

<table><thead><tr><th>Channel</th><th>X</th><th>Y</th><th>Z</th></tr></thead><tbody><tr><td>Channel 0</td><td>0.264 +/- 0.005 μm<br>16.0 beads<br>(0.191 μm)<br>mean R2: 1.0<br>mean SBR: 13.08</td><td>0.268 +/- 0.004 μm<br>16.0 beads<br>(0.191 μm)<br>mean R2: 1.0<br>mean SBR: 13.08</td><td>0.6 +/- 0.014 μm<br>16.0 beads<br>(0.718 μm)<br>mean R2: 0.98<br>mean SBR: 13.08</td></tr></tbody></table>

Figure S3. **PSF analysis workflow with the MetroloJ_QC. (A and B) PSF or PSF batch icon (A) and a "beadOverlay" image (B) is generated taking into account the declared user parameters (ROI size, prominence value). Beads that are taken into account for the FWHM calculation are in the green squares. Yellow square means that the beads are too close. (C) Square root PSF image of one bead that helps visualizing the PSF and detecting aberrations. (D) Resolution table: the measured FWHM along x, y, and z of the bead in C. Theoretical values are provided, along with the calculated fit goodness (R²) and the ratio FWHM measured/FWHM theoretical. If values are within specs they are highlighted in green; if not, they are highlighted in red. (E) xy asymmetry is monitored by the LAR. (F) Summary table for the multibead acquisition presenting the mean FWHM with the SD for the three axes, the number of beads taken into account, the mean R², and the mean SBR value.
<figure><img src="image_34.png" /></figure>

D Resolution table:

<table><thead><tr><th>Channel</th><th>Sig/Backnd ratio</th><th>Dimension</th><th>Measured FWHM (μm)</th><th>theory (μm)</th><th>Fit Goodness</th><th>Mes./theory ratio</th></tr></thead><tbody><tr><td rowspan="3">Channel 0 (em. 525.0nm)</td><td rowspan="3">16.3</td><td>X</td><td>0.262</td><td>0.191</td><td>1.0</td><td>1.37</td></tr><tr><td>Y</td><td>0.265</td><td>0.191</td><td>1.0</td><td>1.39</td></tr><tr><td>Z</td><td>0.592</td><td>0.718</td><td>0.98</td><td>0.82</td></tr></tbody></table>

E Lateral asymmetry ratios:

<table><thead><tr><th>Channel</th><th>Ratio</th></tr></thead><tbody><tr><td>Channel 0 (em. 525.0nm)</td><td>0.99</td></tr></tbody></table>

F Summary

<table><thead><tr><th>Channel</th><th>X</th><th>Y</th><th>Z</th></tr></thead><tbody><tr><td>Channel 0</td><td>0.264 +/- 0.005 μm<br>16.0 beads<br>(0.191 μm)<br>mean R2: 1.0<br>mean SBR: 13.08</td><td>0.268 +/- 0.004 μm<br>16.0 beads<br>(0.191 μm)<br>mean R2: 1.0<br>mean SBR: 13.08</td><td>0.6 +/- 0.014 μm<br>16.0 beads<br>(0.718 μm)<br>mean R2: 0.98<br>mean SBR: 13.08</td></tr></tbody></table>

Figure S3. **PSF analysis workflow with the MetroloJ_QC. (A and B)** PSF or PSF batch icon (A) and a "beadOverlay" image (B) is generated taking into account the declared user parameters (ROI size, prominence value). Beads that are taken into account for the FWHM calculation are in the green squares. Yellow square means that the beads are too close. **(C)** Square root PSF image of one bead that helps visualizing the PSF and detecting aberrations. **(D)** Resolution table: the measured FWHM along x, y, and z of the bead in C. Theoretical values are provided, along with the calculated fit goodness (R²) and the ratio FWHM measured/FWHM theoretical. If values are within specs they are highlighted in green; if not, they are highlighted in red. **(E)** xy asymmetry is monitored by the LAR. **(F)** Summary table for the multibead acquisition presenting the mean FWHM with the SD for the three axes, the number of beads taken into account, the mean R², and the mean SBR value.
<figure><img src="image_41.png" /><figcaption>Figure S4. Field illumination using either a green chroma plastic slide or a glass coverslide/coverslip configuration with a rhodamine layer for a spinning disk Dragonfly microscope (Andor-Oxford Instruments). (A) Field illumination images at 488 nm excitation for a 10× and 63× with a plastic and rhodamine slide, detected with an EMCCD iXon888 camera. Artifacts shown on these images come from a synchronization artifact between Nipkow disk rotation and camera acquisition. The image projected on the sensor chip results from the integration of multiple individual scans by the Nipkow disk. A short exposure time can result in these artifacts, as explained in (Chong et al., 2004). (B) Centering and uniformity metrics for A. The acquisition was set at the plastic/glass or rhodamine/coverslip interface, or 10/30 µm deep within the plastic/rhodamine layer. (C) Effect of focal plane depth on emission intensity using either a plastic slide or thin/thick layers of fluorescent rhodamine dye (PLAN APO 63×/1.4 objective, ex. 488 nm).</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>Figure S4. Field illumination using either a green chroma plastic slide or a glass coverslide/coverslip configuration with a rhodamine layer for a spinning disk Dragonfly microscope (Andor-Oxford Instruments). (A) Field illumination images at 488 nm excitation for a 10× and 63× with a plastic and rhodamine slide, detected with an EMCCD iXon888 camera. Artifacts shown on these images come from a synchronization artifact between Nipkow disk rotation and camera acquisition. The image projected on the sensor chip results from the integration of multiple individual scans by the Nipkow disk. A short exposure time can result in these artifacts, as explained in (Chong et al., 2004). (B) Centering and uniformity metrics for A. The acquisition was set at the plastic/glass or rhodamine/coverslip interface, or 10/30 µm deep within the plastic/rhodamine layer. (C) Effect of focal plane depth on emission intensity using either a plastic slide or thin/thick layers of fluorescent rhodamine dye (PLAN APO 63×/1.4 objective, ex. 488 nm).</figcaption></figure>
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<table><thead><tr><th colspan="6">Channel1 centres'</th></tr><tr><th>Channel</th><th>Type</th><th>Image Center</th><th>Center of intensity/mass</th><th>Last maximum intensity pixel</th><th>Center of the 90.0-100% reference zone</th></tr></thead><tbody><tr><td rowspan="2">Channel 1</td><td>Coordinates in pixels</td><td>(512.0, 512.0)</td><td>(515.3, 530.5)</td><td>(555.0, 690.0)</td><td>(558.4, 646.4)</td></tr><tr><td>Distance to image centre in</td><td>-</td><td>3.777</td><td>36.803</td><td>28.572</td></tr></tbody></table>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<table><thead><tr><th colspan="5">F Uniformity & Centering Accuracy:</th></tr><tr><th>Channel</th><th>Uniformity (%)</th><th>Field Uniformity (%)</th><th>Centering Accuracy (%)</th><th>Image</th></tr></thead><tbody><tr><td>Channel0 (em. 525.0 nm)</td><td>59.5</td><td>90.1</td><td>90.3</td><td>C1-Composite</td></tr><tr><td>Channel1 (em. 460.0 nm)</td><td>44.5</td><td>88.2</td><td>80.4</td><td>C2-Composite</td></tr></tbody></table>

Figure S5. **Field illumination workflow with the MetroloJ_QC.** (A) Field illumination single and batch icon. (B) Intensity scale bar. (C) Normalized intensity profile of channels 0 and 1. (D) Intensity profile along horizontal/vertical/diagonal lines going through the image geometrical center. (E) The location of the image center (geometrical center), the center of intensity (Center of Mass of the page channel), the maximum intensity pixel for the page channel) and the reference zone are provided, along with the distances to the geometrical image center. (F) Uniformity and centering metrics for both channels of C. Centering accuracy is computed using the 90-100% zone as reference rather than the maximum intensity pixel position. If values are within specs they are highlighted in green if not in red. Specs can be modified by the user.
<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

E
Channel1 centres'

<table><thead><tr><th>Channel</th><th>Type</th><th>Image Center</th><th>Center of intensity/mass</th><th>Last maximum intensity pixel</th><th>Center of the 90.0-100% reference zone</th></tr></thead><tbody><tr><td rowspan="2">Channel 1</td><td>Coordinates in pixels</td><td>(512.0, 512.0)</td><td>(515.3, 530.5)</td><td>(555.0, 690.0)</td><td>(558.4, 646.4)</td></tr><tr><td>Distance to image centre in</td><td>-</td><td>3.777</td><td>36.803</td><td>28.572</td></tr></tbody></table>

F
Uniformity & Centering Accuracy:

<table><thead><tr><th>Channel</th><th>Uniformity (%)</th><th>Field Uniformity (%)</th><th>Centering Accuracy (%)</th><th>Image</th></tr></thead><tbody><tr><td>Channel0 (em. 525.0 nm)</td><td>59.5</td><td>90.1</td><td>90.3</td><td>C1-Composite</td></tr><tr><td>Channel1 (em. 460.0 nm)</td><td>44.5</td><td>88.2</td><td>80.4</td><td>C2-Composite</td></tr></tbody></table>

Figure S5. Field illumination workflow with the MetroloJ_QC. (A) Field illumination single and batch icon. (B) Intensity scale bar. (C) Normalized intensity profile of channels 0 and 1. (D) Intensity profile along horizontal/vertical/diagonal lines going through the image geometrical center. (E) The location of the image center (geometrical center), the center of intensity (Center of Mass of the page channel), the maximum intensity pixel for the page channel) and the reference zone are provided, along with the distances to the geometrical image center. (F) Uniformity and centering metrics for both channels of C. Centering accuracy is computed using the 90-100% zone as reference rather than the maximum intensity pixel position. If values are within specs they are highlighted in green if not in red. Specs can be modified by the user.
<figure><img src="image_48.png" /><figcaption>Figure S6. Distribution of uniformity (U) and centering (C) field illumination flatness metrics classified by the detector type. (A) U–C distribution. The tolerance area is in dark green. Representative examples are shown. Inside the blue dashed circle are three values measured with an EMCCD camera. The magenta dashed circle highlights a subpopulation of low U/high C cases, only associated with sCMOS camera images. (B) Box plots summarizing all the U–C distributions of A. Whiskers show the min/max values, the dot in the box shows the mean value, and the box shows the SD. We found that the sCMOS cameras show a higher dispersion compared to the CCD-EMCCD sensor chips. We indeed observed that low U/high C accuracy combinations were mostly associated with larger sensor sizes (magenta circle in A). The only low U and C combinations (blue circle in A) were associated with misaligned TIRF microscopes, on which proper laser alignment had to be manually set before each experiment. The reference is the GFP channel, although many of the measurements were performed for both the DAPI-GFP channels or for all of the basic four channels.</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Figure S7. Influence of the bead size on the coregistration accuracy calculated by MetroloJ_QC. (A–C) Co-registration ratio distributions for 4, 1, 0.5, and 0.2 µm multicolor beads. For all cases, more than five beads per condition and two FOV were examined, for a 100×/1.4 objective, on an upright WF microscope (Axiolmager Z2, Carl Zeiss Microscopy GmbH). We observe that 0.2 µm beads present a high dispersion, with a coefficient of variation higher than 65% for all channel combinations. The 1 µm beads show the smallest dispersion for all cases and are thus the most adapted beads to use for this kind of measurements.</figcaption></figure>
<figure><img src="image_39.png" /><figcaption>**Figure S6.** Distribution of uniformity (U) and centering (C) field illumination flatness metrics classified by the detector type. (A) U–C distribution. The tolerance area is in dark green. Representative examples are shown. Inside the blue dashed circle are three values measured with an EMCCD camera. The magenta dashed circle highlights a subpopulation of low U/high C cases, only associated with sCMOS camera images. (B) Box plots summarizing all the U–C distributions of A. Whiskers show the min/max values, the dot in the box shows the mean value, and the box shows the SD. We found that the sCMOS cameras show a higher dispersion compared to the CCD-EMCCD sensor chips. We indeed observed that low U/high C accuracy combinations were mostly associated with larger sensor sizes (magenta circle in A). The only low U and C combinations (blue circle in A) were associated with misaligned TIRF microscopes, on which proper laser alignment had to be manually set before each experiment. The reference is the GFP channel, although many of the measurements were performed for both the DAPI-GFP channels or for all of the basic four channels.</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>**Figure S7.** Influence of the bead size on the coregistration accuracy calculated by MetroloJ_QC. (A–C) Co-registration ratio distributions for 4, 1, 0.5, and 0.2 µm multicolor beads. For all cases, more than five beads per condition and two FOV were examined, for a 100×/1.4 objective, on an upright WF microscope (Axiolmager Z2, Carl Zeiss Microscopy GmbH). We observe that 0.2 µm beads present a high dispersion, with a coefficient of variation higher than 65% for all channel combinations. The 1 µm beads show the smallest dispersion for all cases and are thus the most adapted beads to use for this kind of measurements.</figcaption></figure>
<figure><img src="image_50.png" /></figure>

<table><thead><tr><th></th><th>Channel 0</th><th>Channel 1</th><th>Channel 2</th><th>Channel 3</th></tr></thead><tbody><tr><th>Channel 0</th><td></td><td>0.603</td><td>0.751</td><td>1.195</td></tr><tr><th>Channel 1</th><td>0.603</td><td></td><td>0.65</td><td>1.036</td></tr><tr><th>Channel 2</th><td>0.751</td><td>0.65</td><td></td><td>0.539</td></tr><tr><th>Channel 3</th><td>1.195</td><td>1.036</td><td>0.539</td><td></td></tr><tr><th>Resolutions (µm)</th><td>0.168<br>0.168<br>0.629</td><td>0.191<br>0.191<br>0.718</td><td>0.222<br>0.222<br>0.835</td><td>0.24<br>0.24<br>0.903</td></tr><tr><th>Bead<br>centres'coord. (µm)</th><td>24.5<br>24.0<br>16.0</td><td>23.5<br>23.5<br>16.0</td><td>24.5<br>21.5<br>15.5</td><td>24.5<br>20.5<br>15.5</td></tr></tbody></table>

<table><thead><tr><th></th><th>Channel 0</th><th>Channel 1</th><th>Channel 2</th><th>Channel 3</th></tr></thead><tbody><tr><th>Channel 0</th><td></td><td>0.39 +/- 0.149<br>(n=7.0)</td><td>1.02 +/- 0.3<br>(n=7.0, 42.0% failed)</td><td>1.073 +/- 0.277<br>(n=7.0, 42.0% failed)</td></tr><tr><th>Channel 1</th><td>0.39 +/- 0.149<br>(n=7.0)</td><td></td><td>0.592 +/- 0.03<br>(n=5.0)</td><td>0.783 +/- 0.17<br>(n=7.0, 14.0% failed)</td></tr><tr><th>Channel 2</th><td>1.02 +/- 0.3<br>(n=7.0, 42.0% failed)</td><td>0.592 +/- 0.03<br>(n=5.0)</td><td></td><td>0.39 +/- 0.137<br>(n=7.0)</td></tr><tr><th>Channel 3</th><td>1.073 +/- 0.277<br>(n=7.0, 42.0% failed)</td><td>0.783 +/- 0.17<br>(n=7.0, 14.0% failed)</td><td>0.39 +/- 0.137<br>(n=7.0)</td><td></td></tr></tbody></table>

Figure S8. **Co-registration workflow with the MetroloJ_QC.** (A) Co-registration single and batch icon. (B) A "beadOverlay" image is generated when more than one bead is in the FOV, taking into account the declared user parameters (bead size, ROI size, bead position on z stack). (C) For each color/channel combination profile view images are composed of three maximum intensity projections, xy, xz, yz (side views) are generated. Crosses indicate the respective position of the green channel (first channel declared using the stack order) and the red channel. This is done for all channel combinations. (D) Calculation of the reference distance $r_{ref}$. Left: Centers of objects (A and B) are drawn as red and green spheres, respectively. The PSF is schematized in light yellow, while the first Airy volume appears in dark yellow. The former width, height, and depth define the resolution along the three axes. Middle: A and B are not colocalized as $r > r_{ref}$. Right: A and B are colocalized as $r \le r_{ref}$. Illustration from Cordelières and Bolte, ImageJ User and Developer Conference Proceedings, 2008, Luxembourg. (E) A ratio table is generated for each bead indicating the measured coregistration ratios of all channel combinations, the theoretical resolution for each channel, and the bead position coordinates. (F) If ratio values are within specs they are highlighted in green; if not, they are highlighted in red, when more than one bead is in the FOV, a ratio table gives the mean ratio values with their SD values. The number of beads taken into account for each channel is also given.
<figure><img src="image_41.png" /></figure>

<table><thead><tr><th></th><th>Channel 0</th><th>Channel 1</th><th>Channel 2</th><th>Channel 3</th></tr></thead><tbody><tr><th>Channel 0</th><td></td><td>0.603</td><td>0.751</td><td>1.195</td></tr><tr><th>Channel 1</th><td>0.603</td><td></td><td>0.65</td><td>1.036</td></tr><tr><th>Channel 2</th><td>0.751</td><td>0.65</td><td></td><td>0.539</td></tr><tr><th>Channel 3</th><td>1.195</td><td>1.036</td><td>0.539</td><td></td></tr><tr><th>Resolutions (µm)</th><td>0.168<br>0.168<br>0.629</td><td>0.191<br>0.191<br>0.718</td><td>0.222<br>0.222<br>0.835</td><td>0.24<br>0.24<br>0.903</td></tr><tr><th>Bead<br>centres'coord. (µm)</th><td>24.5<br>24.0<br>16.0</td><td>23.5<br>23.5<br>16.0</td><td>24.5<br>21.5<br>15.5</td><td>24.5<br>20.5<br>15.5</td></tr></tbody></table>

<table><thead><tr><th></th><th>Channel 0</th><th>Channel 1</th><th>Channel 2</th><th>Channel 3</th></tr></thead><tbody><tr><th>Channel 0</th><td></td><td>0.39 +/- 0.149<br>(n=7.0)</td><td>1.02 +/- 0.3<br>(n=7.0, 42.0% failed)</td><td>1.073 +/- 0.277<br>(n=7.0, 42.0% failed)</td></tr><tr><th>Channel 1</th><td>0.39 +/- 0.149<br>(n=7.0)</td><td></td><td>0.592 +/- 0.03<br>(n=5.0)</td><td>0.783 +/- 0.17<br>(n=7.0, 14.0% failed)</td></tr><tr><th>Channel 2</th><td>1.02 +/- 0.3<br>(n=7.0, 42.0% failed)</td><td>0.592 +/- 0.03<br>(n=5.0)</td><td></td><td>0.39 +/- 0.137<br>(n=7.0)</td></tr><tr><th>Channel 3</th><td>1.073 +/- 0.277<br>(n=7.0, 42.0% failed)</td><td>0.783 +/- 0.17<br>(n=7.0, 14.0% failed)</td><td>0.39 +/- 0.137<br>(n=7.0)</td><td></td></tr></tbody></table>

Figure S8. **Co-registration workflow with the MetroloJ_QC.** (A) Co-registration single and batch icon. (B) A "beadOverlay" image is generated when more than one bead is in the FOV, taking into account the declared user parameters (bead size, ROI size, bead position on z stack). (C) For each color/channel combination profile view images are composed of three maximum intensity projections, xy, xz, yz (side views) are generated. Crosses indicate the respective position of the green channel (first channel declared using the stack order) and the red channel. This is done for all channel combinations. (D) Calculation of the reference distance $r_{ref}$. Left: Centers of objects (A and B) are drawn as red and green spheres, respectively. The PSF is schematized in light yellow, while the first Airy volume appears in dark yellow. The former width, height, and depth define the resolution along the three axes. Middle: A and B are not colocalized as $r > r_{ref}$. Right: A and B are colocalized as $r \le r_{ref}$. Illustration from Cordelières and Bolte, ImageJ User and Developer Conference Proceedings, 2008, Luxembourg. (E) A ratio table is generated for each bead indicating the measured coregistration ratios of all channel combinations, the theoretical resolution for each channel, and the bead position coordinates. (F) If ratio values are within specs they are highlighted in green; if not, they are highlighted in red, when more than one bead is in the FOV, a ratio table gives the mean ratio values with their SD values. The number of beads taken into account for each channel is also given.
<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

Figure S9. Warming up time of lasers of two different systems. (A and B) Warm up of four different laser types on a (A) LSCM microscope (SP5 – Leica of Fig. 6 F) equipped with an argon laser (488), HeNe laser (633), a diode laser (405), and a DPSS laser (561), and a (B) SDCM microscope with diode (405) and DPSS lasers. It is to be mentioned that all sources for both systems pass through an AOTF device. The LSCM setup shows (1) some decrease of the DPSS 561 nm laser and (2) high fluctuations of the power of the argon 488 nm line. These observations were a warning for the facility team, who after contacting the service technician of the microscope manufacturer and after a visit on-site, managed to identify and correct the problem. A damaged fiber and aging of the lasers were identified as the main reasons for these poor performances. The SDCM setup in B shows an extremely long warm-up time for the 488 nm laser.

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

Figure S10. Illumination laser power short-term stability. (A and B) Short-term stability monitoring for two SDCM equipped with four diode lasers. Although the setup (B) shows higher fluctuations than (A), the calculated STAB metric stays relatively high, in the range of 98 < STAB < 99. (C) Laser power stability STAB versus SD is normalized to "1" laser power values over 5 min. The black dashed circle contains the metrics measurements of the four laser lines of B. With low stability, the lasers come from the same microscope. The red open arrows point to two DPSS lasers of the same WF-TIRF microscope pointed out with red arrows in Fig. 6 D. The orange closed arrows point to three DPSS lasers coming from the same SDCM microscope. The blue dashed magenta closed arrows point to the blue and magenta arrow laser of Fig. 6 D, respectively.
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

Figure S9. Warming up time of lasers of two different systems. (A and B) Warm up of four different laser types on a (A) LSCM microscope (SP5 – Leica of Fig. 6 F) equipped with an argon laser (488), HeNe laser (633), a diode laser (405), and a DPSS laser (561), and a (B) SDCM microscope with diode (405) and DPSS lasers. It is to be mentioned that all sources for both systems pass through an AOTF device. The LSCM setup shows (1) some decrease of the DPSS 561 nm laser and (2) high fluctuations of the power of the argon 488 nm line. These observations were a warning for the facility team, who after contacting the service technician of the microscope manufacturer and after a visit on-site, managed to identify and correct the problem. A damaged fiber and aging of the lasers were identified as the main reasons for these poor performances. The SDCM setup in B shows an extremely long warm-up time for the 488 nm laser.

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

Figure S10. Illumination laser power short-term stability. (A and B) Short-term stability monitoring for two SDCM equipped with four diode lasers. Although the setup (B) shows higher fluctuations than (A), the calculated STAB metric stays relatively high, in the range of 98 < STAB < 99. (C) Laser power stability STAB versus SD is normalized to "1" laser power values over 5 min. The black dashed circle contains the metrics measurements of the four laser lines of B. With low stability, the lasers come from the same microscope. The red open arrows point to two DPSS lasers of the same WF-TIRF microscope pointed out with red arrows in Fig. 6 D. The orange closed arrows point to three DPSS lasers coming from the same SDCM microscope. The blue dashed magenta closed arrows point to the blue and magenta arrow laser of Fig. 6 D, respectively.
<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

Figure S11. Illumination source power long-term stability. (A) Long-term stability of four laser sources on a TIRF microscope (setup of Fig. 6 B). All four lasers are coupled in an optical fiber. The system was out of maintenance contract and the source alignment was regularly done by the facility staff. The blue arrow shows the optical fiber replacement and total realignment of the lasers. (B) Long-term stability of X-Cite source. A single replacement of the bulb took place, represented by a drop in the bulb hours (orange line) and a decrease in the emitted signal (green line). As expected other changes in some light-path components (dichroic mirror, fiber) influenced the measured signal as well.
<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

Figure S11. Illumination source power long-term stability. (A) Long-term stability of four laser sources on a TIRF microscope (setup of Fig. 6 B). All four lasers are coupled in an optical fiber. The system was out of maintenance contract and the source alignment was regularly done by the facility staff. The blue arrow shows the optical fiber replacement and total realignment of the lasers. (B) Long-term stability of X-Cite source. A single replacement of the bulb took place, represented by a drop in the bulb hours (orange line) and a decrease in the emitted signal (green line). As expected other changes in some light-path components (dichroic mirror, fiber) influenced the measured signal as well.
<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

Figure S12. Stage positioning repeatability dependence on stage speed and acceleration. (A and B) Bi-directional stage repeatability along the x (A) and y (B) axis for one stage for two different stage speeds (7 mm/s and 20 mm/s) for two different xy displacements (3 and 60 mm). The horizontal line of the floating bar shows the mean value for three consecutive repeatability measurements. The 60 mm xy value corresponds to movements from the center to the extremities of a multiwell plate. The studied stage was an IX3-SSU Olympus Ultrasonic Stage with linear encoders. (C and D) Bi-directional stage repeatability along the x (C) and y (D) axis for one stage for two different stage accelerations (0.1 m²/s and 0.3 m²/s), for a speed of 10 mm/s, for three different xy displacements (3, 30, and 60 mm). The horizontal line of the floating bar shows the mean value for five consecutive repeatability measurements. The studied stage was a Märzhäuser SCANplus IM 130 × 85 two stepper stage on an inverted WF microscope (Olympus, IX83). We observe small differences between the x and y axes, but globally the applied stage speeds and accelerations do not influence the xy repeatability.
<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

Figure S12. Stage positioning repeatability dependence on stage speed and acceleration. (A and B) Bi-directional stage repeatability along the x (A) and y (B) axis for one stage for two different stage speeds (7 mm/s and 20 mm/s) for two different xy displacements (3 and 60 mm). The horizontal line of the floating bar shows the mean value for three consecutive repeatability measurements. The 60 mm xy value corresponds to movements from the center to the extremities of a multiwell plate. The studied stage was an IX3-SSU Olympus Ultrasonic Stage with linear encoders. (C and D) Bi-directional stage repeatability along the x (C) and y (D) axis for one stage for two different stage accelerations (0.1 m²/s and 0.3 m²/s), for a speed of 10 mm/s, for three different xy displacements (3, 30, and 60 mm). The horizontal line of the floating bar shows the mean value for five consecutive repeatability measurements. The studied stage was a Märzhäuser SCANplus IM 130 × 85 two stepper stage on an inverted WF microscope (Olympus, IX83). We observe small differences between the x and y axes, but globally the applied stage speeds and accelerations do not influence the xy repeatability.
<figure><img src="image_62.png" /><figcaption>Figure S13. Camera noise specifications (read noise, dark offset, DSNU) workflow calculation with the MetroloJ_QC. (A) Camera tool icon. (B) Noise map in electrons calculated for an sCMOS camera after generating an average intensity projection image and an SD of the intensity projection image of a time-lapse of 100 images in the dark. The average intensity projection image is also used to calculate the DSNU and offset in D. (C) Noise distribution of B. (D) Calculated offset and noise specifications of B.</figcaption></figure>
<figure><img src="image_53.png" /><figcaption>Figure S13. Camera noise specifications (read noise, dark offset, DSNU) workflow calculation with the MetroloJ_QC. (A) Camera tool icon. (B) Noise map in electrons calculated for an sCMOS camera after generating an average intensity projection image and an SD of the intensity projection image of a time-lapse of 100 images in the dark. The average intensity projection image is also used to calculate the DSNU and offset in D. (C) Noise distribution of B. (D) Calculated offset and noise specifications of B.</figcaption></figure>
<figure><img src="image_63.png" /><figcaption>Figure S14. Camera read noise and DSNU map and distributions. (A) Read noise map in electrons for a back-illuminated sCMOS camera (Fusion BT; Hamamatsu) after generating a SD of the intensity projection image of a timelapse of 100 images in the dark. Vertical stripes are apparent (due to pixel offset issue) and a horizontal bright line at the bottom of the image, due to a glow effect for a full chip acquisition. (B) Read noise distribution for a Flash4 v3 camera (Hamamatsu) and comparison of blemish correction on read noise for pixel correction ON (magenta) or OFF (blue). Inset table: measured values of read noise (RMS) and DSNU for both cases. (C) DSNU map (dark offset of 100 images averaged) for two different sCMOS Andor cameras in the central part (500 x 500 pixels). Different patterns are distinguished, depending on the sensor corrections (column, pixel) and the vertical juxtaposition of the two matrices of this kind of sCMOS. Scale bar in intensity (digital) values. (D) Distribution of DSNU values calculated from different dark image dataset sizes for a sCMOS zyla 4.2 camera (Andor). Using a 100 frames dataset (dashed red line), the calculated DSNU is 0.438 e⁻ while for 9,000 frames the DNSU is 0.395 e⁻.</figcaption></figure>
<figure><img src="image_54.png" /><figcaption>Figure S14. Camera read noise and DSNU map and distributions. (A) Read noise map in electrons for a back-illuminated sCMOS camera (Fusion BT; Hamamatsu) after generating a SD of the intensity projection image of a timelapse of 100 images in the dark. Vertical stripes are apparent (due to pixel offset issue) and a horizontal bright line at the bottom of the image, due to a glow effect for a full chip acquisition. (B) Read noise distribution for a Flash4 v3 camera (Hamamatsu) and comparison of blemish correction on read noise for pixel correction ON (magenta) or OFF (blue). Inset table: measured values of read noise (RMS) and DSNU for both cases. (C) DSNU map (dark offset of 100 images averaged) for two different sCMOS Andor cameras in the central part (500 x 500 pixels). Different patterns are distinguished, depending on the sensor corrections (column, pixel) and the vertical juxtaposition of the two matrices of this kind of sCMOS. Scale bar in intensity (digital) values. (D) Distribution of DSNU values calculated from different dark image dataset sizes for a sCMOS zyla 4.2 camera (Andor). Using a 100 frames dataset (dashed red line), the calculated DSNU is 0.438 e⁻ while for 9,000 frames the DNSU is 0.395 e⁻.</figcaption></figure>
A

<figure><img src="image_64.png" /></figure>

B

<figure><img src="image_65.png" /></figure>

C

<figure><img src="image_66.png" /><figcaption>Figure S15. **Condensation effect on the camera chip.** (A) Condensation is present with small crystals that are present in the whole chip surface (sCMOS camera). (B) Effect of imaging a biological sample (with DIC transmitted light) with a camera presenting condensation issues (darker round zone in the middle of the image; sCMOS camera). (C) Extreme condensation case where imaging is impossible (image acquired with transmitted light with an EMCCD camera).</figcaption></figure>
A

<figure><img src="image_55.png" /></figure>

B

<figure><img src="image_56.png" /></figure>

C

<figure><img src="image_57.png" /><figcaption>Figure S15. **Condensation effect on the camera chip.** (A) Condensation is present with small crystals that are present in the whole chip surface (sCMOS camera). (B) Effect of imaging a biological sample (with DIC transmitted light) with a camera presenting condensation issues (darker round zone in the middle of the image; sCMOS camera). (C) Extreme condensation case where imaging is impossible (image acquired with transmitted light with an EMCCD camera).</figcaption></figure>