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Homogeneous multifocal excitation for high-throughput super-resolution imaging

Super-resolution microscopies, which allow features below the diffraction limit to be resolved, have become an established tool in biological research. However, imaging throughput remains a major bottleneck in using them for quantitative biology, which requires large datasets to overcome the noise of the imaging itself and to capture the variability inherent to biological processes. Here, we develop a multi-focal flat illumination for field independent imaging (mfFIFI) module, and integrate it into an instant structured illumination microscope (iSIM). Our instrument extends the field of view (FOV) to >100×100 µm2 without compromising image quality, and maintains high-speed (100 Hz), multi-color, volumetric imaging at double the diffraction-limited resolution. We further extend the effective FOV by stitching multiple adjacent images together to perform fast live-cell super-resolution imaging of dozens of cells. Finally, we combine our flat-fielded iSIM setup with ultrastructure expansion microscopy (U-ExM) to collect 3D images of hundreds of centrioles in human cells, as well as of thousands of purified Chlamydomonas reinhardtii centrioles per hour at an effective resolution of ∼35 nm. We apply classification and particle averaging to these large datasets, allowing us to map the 3D organization of post-translational modifications of centriolar microtubules, revealing differences in their coverage and positioning.


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Super-resolution fluorescence techniques have enabled optical imaging beyond the 37 diffraction limit. Two main illumination strategies are used to achieve super-resolution: wide-38 field illumination is typically used for single-molecule localization microscopies (SMLM) 39 such as STORM 1 and PALM 2 , while patterned illumination is typically used for methods 40 such as structured illumination microscopy (SIM) 3,4 and stimulated emission depletion 41 (STED) 5 . Initial implementations of these methods were relatively slow, in part due to the 42 7 from the second flat-fielding MLA 27 , resulting in a flat wavefront and thereby ensuring that 135 the pitch of the excitation is conserved (Figure 1d, Supplemental Figure 1b). Additionally,136 adjustment of the distance between the second flat-fielding MLA and the Fourier lens can 137 be used to finely adjust the resulting periodicity of the excitation spots to match that of the 138 other optical elements (Figure 1e, Supplemental Figure 2a-d). This feature, not shared by 139 refractive beam-shapers, allows us to optimize transmission efficiency. 140 Finally, in confocal microscopy and its variants such as iSIM, maximizing the 141 achievable resolution requires focusing the excitation light to a diffraction-limited spot on the 142 sample. This imposes a limitation on the spot size that should be generated in the excitation 143 path. However, implementing a traditional Koehler integrator in the excitation path would 144 produce large excitation spots due to the nature of the partially coherent extended source 145 created by the rotating diffuser (Figure 1e-f, Supplemental Figure 1). In that case, the 146 accessible improvement in resolution would not be based on diffraction-limited 147 performance. A possible solution would be to introduce a pinhole array to mask the 148 excitation spots that are focused onto the sample, but at a significant cost to the 149 transmission efficiency. 150 In an alternative design, we find that -contrary to the typical Koehler integrator 151 where illuminating a maximal number of flat-fielding microlenses is preferred 24-26 -152 illuminating fewer microlenses offers a solution to control the excitation spot size and 153 ensure diffraction-limited excitation at the sample (Figure 1g, illumination. This configuration, which we call multifocal flat illumination for field-159 independent imaging (mfFIFI), meets all the above requirements for homogeneous 160 multifocal excitation and is shown in Figure 1i. The effective image size can be further extended by stitching together adjacent 237 FOVs. Such an approach requires homogeneous illumination to allow seamless stitching 238 (Figure 3b). The speed of iSIM imaging combined with the large FOV enabled by mfFIFI 239 allowed us to stitch together dual-color 3D stacks covering a 500x500x5 µm 3 volume with a 240 5x5 grid of FOVs, each acquired within 5 seconds. With this approach, within 2 minutes, we 241 could capture 3D images of more than 80 cells stained for the mitochondrial inner (TIM23) 242 and outer (TOM20) membrane translocases, revealing coupled geometries of the two 243 membranes (Figure 3b-c). The current limiting factor for multi-FOV imaging is the speed of 244 stage translation and synchronization between stage translation and the rest of the iSIM 245 acquisition control (Supplemental information). In contrast, the same acquisition without 246 mfFIFI resulted in varying intensity across each FOV, with pronounced dark boundaries at 247 the borders of individual FOVs (Figure 3b). The high throughput offered by mfFIFI-iSIM allowed us to collect 3D images of 329 thousands of purified expanded Chlamydomonas reinhardtii centrioles per hour 330 (Supplemental Figure 8, Supplemental Table 2). Having obtained 3D rather than 2D 331 particles allowed us to use fewer particles to perform averaging and high-resolution 332 multicolor reconstruction 44 compared to previous approaches using STORM 45 or electron 333 microscopy 46,47 . Thus, we classified particles before reconstruction to identify and average 334 only those particles sharing a high degree of similarity (ranging from 10-50% of the whole 335 dataset), and thereby achieve even higher resolution reconstructions (Figure 4f-h,  336 Supplemental information, Supplemental Figure 9). Here, we have focused on the most 337 frequently appearing class, but other classes could also be independently reconstructed. 338 For mapping multiple PTMs within the centriole, we employ dual-color labeling, using α-339 tubulin staining of the microtubule triplet wall as a reference to subsequently align all PTMs 340 (Supplemental Figure 10). 341 The resulting reconstructions revealed an unexpected shift in the tangential position 342 of the PolyE signal from the proximal to the distal end (Figure 4h), which is also sometimes 343 visible in individual raw particles (Supplemental Figure 8b inset, Figure 4e

358
To summarize, we designed a flat-fielding method for efficient multifocal excitation, 359 accompanied by a wave optics simulator which ensures optimal resolution performance. 360 While the Koehler integrator has spot generating properties when used with coherent 361 illumination, it introduces a strong wavelength dependence of the periodicity, making it 362 unsuitable as such for multi-color applications 26 . mfFIFI provides a largely wavelength-363 independent solution for generating homogeneous multi-focal excitation, while benefiting 364 from the homogenizing properties of the Koehler integrator. We implemented the mfFIFI 365 module into an iSIM microscope, since its fast acquisition speeds and resolution doubling 366 give it good potential for high-throughput super-resolution imaging. However, the mfFIFI 367 module could be extended to any multi-focal excitation microscope such as a spinning disk 368 confocal microscope 6 using a Nipkow disk configuration, but not with other flat-fielding 369 solutions using diffractive optical elements 20 , spatial light modulators (SLM) 21 or beam 370 splitters 23 . Furthermore, combining mfFIFI with phase masks required for donut beam 371 shaping could extend its application to parallelized STED and RESOLFT microscopes. 372 Flat-fielding enables multi-field of view imaging, which is a powerful way to maintain 373 resolution while increasing the effective image size to span more length scales. This is 374 important for sample spanning multiple fields of view, as in the case of tissues, or expanded 375 samples. ExM sacrifices the effective FOV to increase resolution, and stitching provides a 376 way to maintain throughput, allowing cellular features or structures to be imaged in situ, as 377 we demonstrate by imaging hundreds of human centrioles in cells. 378 Particle averaging and reconstruction is traditionally used in ultrastructural biology, 379 and leverages large datasets to capture the intrinsic variability within particles through 380 classification before reconstruction, while averaging out the noise. In the case of ExM, 381 different gels can have slightly different expansion factors, but this issue is circumvented by 382 our iSIM setup since thousands of particles can be acquired from a single gel section (~5x5 383 mm 2 ). In our case, the collected datasets were sufficiently large to allow particle 384 classification prior to averaging, an approach which improves the resolution of the 385 reconstruction and which, to our knowledge, has not been previously used for fluorescence 386 super-resolution. In addition to a 100-1000-fold improvement in throughput of iSIM/U-ExM 387 over HT-STORM 10,45 or easySLM-STED 32 , expanded samples offer enhanced accessibility 388 of antibodies to bind proteins in crowded assemblies. Beyond particle averaging, high-389 content or machine learning approaches rely on large datasets for training or screening, 390 and can benefit from the combination of throughput and resolution described here. 391 Our previous attempts to image centriolar microtubules with STORM proved largely 392 unsuccessful, restricting previous studies to the more accessible epitopes 45 . Here, we 393 reveal previously unobserved molecular organization, showing high coverage of 394 polyglutamylation in human centrioles, as well as precise localization to microtubule triplets 395 along the Chlamydomonas reinhardtii centriole. We find that polyglutamylation appears to 396 shift from the A-microtubule proximally to the C-microtubule more distally. It is possible that 397 such a glutamylation shift reflects a centriolar tubule code that defines the spatial 398 boundaries of the various elements that compose it, which could determine the positioning 399 of different structural elements along the length of the organelle. To obtain the data shown in Figure 1e, h, we used the characterization platform to 497 test the parameters affecting the spot size and pitch. A Vernier caliper was used to 498 measure the iris opening diameter, which was varied methodically to control the size of the 499 beam which illuminates the MLAs. Images of the beam at different iris opening diameters 500 were analyzed using a MATLAB script, which allowed us to fit each excitation point to a 2D 501 Gaussian and measure the multifocal points' mean spot FWHM as function of iris diameter. 502 In order to study the variation in the pitch of the multifocal points, the distance between the 503 Fourier lens and the second flat-fielding MLA was varied by displacing the flat-fielding 504 MLAs. A MATLAB script is used to measure the mean pitch at each distance, so that the 505 relationship between this distance and the pitch can be determined. 506

Image analysis 507
Plateau uniformity quantification 508 The homogeneity was quantified using the plateau uniformity definition based on the 509 FWHM of the histogram of the intensity values, according to ISO, ISO 13694:2018: Optics 510 and optical instruments -lasers and laser-related equipment -test methods for laser beam 511 power (energy) density distribution. 512

Illumination profile measurement 513
The illumination profiles used to optimize the pitch (telecentricity) and spot size of 514 the multifocal excitation were visualized using a small color CMOS camera (DCC1645C, 515 Thorlabs), placed in an intermediate image plane of the iSIM, between the second scan 516 lens and tube lens, to avoid introducing other aberrations along the whole optical path of 517 the iSIM. 518 The excitation profile at the sample was measured using a highly concentrated 519 fluorescent dye sample 32 , as previously described 9 . Briefly, sodium fluorescein (NaFITC) 520 powder was diluted in deionized water, followed by vortexing and sonication until the 521 powder was completely dissolved. A 10 µL drop was placed on a 25 mm coverslip, before 522 covering with a 12 mm coverslip. The sample was then sealed using nail polish. The same 523 dye sample was used to measure both the 488 nm and 561 nm illumination profiles. The 524 illumination spots were characterized by fitting a 2D Gaussian to the intensity image. The 525 threshold was set so that no points in the background were detected. The scanning profiles 526 were measured with a 150 pixel thick line in Fiji 50 . 527 In the case of excitation spots visualized in Figure 1f, g and the quantification in 528 centriole barrel parallel to the imaging axis). The profile was measured using a 7 pixel thick 541 line in Fiji 50 . Each peak was fitted to a Gaussian profile to localize its center, before 542 calculating the diameter as the distance between the two peaks on opposing sides of the 543 centriole ring. 544 To analyze the PTM distribution along the length of the centriole and their coverage, 545 we measured the intensity profile along the length of the centriole using a 10 pixel thick line 546 and from summed intensity projections of side view centrioles (centriole barrel orthogonal to 547 the imaging axis). We then took the width of the profile at ¼ of the maximal signal as the 548 length, to be less susceptible to noise. The coverage was then calculated by dividing the 549 length of the PTM in question, by the length of the reference label (acetylated tubulin for 550 human centrioles and α-tubulin in the case of Chlamydomonas reinhardtii centrioles). 551

Multiple field of view stitching 552
For stitching of multiple images, raw images or z-stacks were acquired with a 10% 553 overlap for both Gaussian and mfFIFI excitation. The acquired images were then combined 554 using the Grid/Collection stitching tool in Fiji 50 using the linear blending method with the 555 25 default values of 0.30 for regression threshold, 2.50 for max/avg displacement threshold 556 and 3.50 for absolute displacement threshold. 557

Wave optics simulations 558
The mfFIFI simulation platform has been adapted based on previous work 9 . All 559 simulations are based on standard numerical Fourier optics algorithms and rely on angular 560 spectrum propagation methods to simulate the propagation of an optical wave. An initial 561 simulation showing the effect of illuminating the excitation MLA with Gaussian and flat-562 fielded profiles was performed using phase masks to represent optical components. 563 The parameters used for the simulation are summarized in Supplemental Table 1. 564