Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM

Domain: physics
Published: 2025-11-28
Editorial outcome: major_revision

Abstract
Structured illumination microscopy (SIM) is a powerful method for fast and gentle live-cell super-resolution imaging. However, its susceptibility to reconstruction artifacts from out-of-focus blur and background imposes substantial barriers to analyze the dynamics of densely packed volumetric intraorganellar ultrastructures that are typically in a size range of SIM’s spatial resolution. To address this limitation, we have developed Lock-in-SIM, an open-access two-dimensional SIM framework that eliminates background and maximizes the recovery of sub-diffraction information with the highest possible frequency extraction. By leveraging the intrinsic modulation differences of volumetric sample structures, Lock-in-SIM enables efficient optical sectioning, extends imaging depth, and improves data fidelity and quantifiability. We demonstrate the superiority of Lock-in-SIM by visualizing various challenging intraorganellar ultrastructures in live cells. Our investigations uncover mechanisms of mitochondrial fission and endoplasmic reticulum-lysosome interactions and provide insights into the intricate yet highly regulated structural remodeling of organelles.

Introduction
Fluorescence super-resolution microscopy (SRM) has substantially enhanced our understanding of biological structures, dynamics, and functions by expanding the imaging capability of conventional optical microscopy from the diffraction-limited to subcellular, intraorganellar, and even molecular levels1–4. The visualization of ultrafine cellular components requires high spatial and temporal resolution, which has been the main direction of development in optical microscopy for decades. Structured illumination microscopy (SIM)5–7 is particularly well-suited for live-cell super-resolution imaging, as it achieves a resolution doubling over conventional microscopy at a higher speed while imposing lower photodamage compared to other SRM approaches.

While SRM methods are typically compared by their maximum spatiotemporal resolution under ideal conditions, in real-world sample conditions, noise and background are major limiting factors that undermine the effective resolution, reduce image quality, and cause artifacts8–11. Although both can lead to the deterioration of the illumination light field and the overwhelming of weak high-frequency ultrastructures, the underlying mechanisms of influence are different12,13. Noise predominantly arises in the optoelectronic signal conversion processes of the detector, especially under the conditions of low exposure time or low illumination intensity, and commonly varies from pixel to pixel. By contrast, the background primarily originates from out-of-focus blur caused by epi-fluorescence illumination along the optical axis, particularly in densely packed volumetric samples, as well as light scattering, unspecific labeling, and autofluorescence. Background levels typically vary slowly across the field and are poorly modulated by the illumination pattern. While impressive advances have been made in suppressing noise in recent years14–16, especially with the emergence of machine and deep learning technologies17–22, more efforts are still required to specifically address the background issue in SRM. Furthermore, deep learning-assisted SRM methods commonly suffer from a lack of accessibility and universality due to their heavy reliance on high-quality training datasets17. This is, in part, why confocal microscopy, despite its lower acquisition speed, has become one of the gold standards for bioimaging applications, namely by effectively blocking background through a mechanical pinhole8,23.

SIM’s susceptibility to background artifacts is based on its reliance on accurate illumination patterning and the necessity for robust modulation, transmission, and demodulation of weak high-frequency signals to obtain high-quality results24–27. In addition, background from fluorescence labeling or autofluorescence might reduce the modulation contrast and interfere with the SIM imaging results28,29. Therefore, it remains challenging to investigate the ultrafast dynamics and interactions of volumetric and intraorganellar ultrastructures, such as the endoplasmic reticulum (ER) matrix30 and mitochondria inner membrane14, which play key roles in regulating biological health and disease. These ultrastructures have sizes that typically fall within the SIM resolution range and are thus prone to being obscured or misreconstructed by the presence of even slight background and motion30. Consequently, instead of the original two-beam interference two-dimensional structured illumination (2D-SIM) implementation6, other SIM modalities using total internal reflection fluorescence (TIRF-SIM)31, grazing incidence (GI-SIM)30,32, and three-beam interference (3D-SIM)33–35 have become more commonly used in practice due to their shared ability to effectively suppress background. However, the imaging depths of TIRF-SIM and GI-SIM are too limited to encompass entire cells, while 3D-SIM is typically subject to increased photobleaching and reduced temporal resolution due to the requirement of imaging z-stacks with 15 raw images (5 phases, 3 angles) per plane.

Regarding 2D-SIM algorithms, substantial advances have been made in recent years to improve either reconstruction speed, image fidelity, or background suppression with methods such as weighted linear SIM (WLR-SIM)36, Open-SIM37, Fair-SIM38, Hessian-SIM14, IM-SIM39, SP-SIM40, HiFi-SIM15, joint space and frequency reconstruction SIM (JSFR-SIM)41, principal component analysis SIM (PCA-SIM)42, background-filtering SIM (BF-SIM)43, Direct-SIM44, and Flex-SIM45. However, current state-of-the-art 2D-SIM methods still face challenges in balancing background suppression and image fidelity (i.e., ultrastructure preservation and structural integrity), especially in densely packed intraorganellar imaging. Thus, there remains an unmet need for low-background, high-quality image data in many common imaging situations, which is a prerequisite for non-biased quantitative analysis23,46,47.

To address these shortcomings, we propose a reconstruction framework for two-beam interference 2D-SIM, termed Lock-in-SIM, which achieves a signal-to-background ratio (SBR) comparable to that of TIRF-, GI-, and 3D-SIM but surpasses their limitations in temporal resolution (3D-SIM) or imaging depth (TIRF-/GI-SIM). Lock-in-SIM exploits the modulation differences of the periodic SIM illumination pattern for sample structures of varying whole-cell depths to lock, extract, and amplify the weak high-frequency signal deriving from sub-diffraction-sized sample features while minimizing the effects of unmodulated background. This process is inherent in SIM imaging and can therefore be applied to data acquired from existing SIM systems, allowing for seamless integration into established post-processing workflows without additional hardware modifications and troublesome parameter fine-tuning.

The generalized implementation of Lock-in-SIM enables the background- and artifact-free visualization of a wide range of fixed and live subcellular and intraorganellar ultrastructures with the highest resolution, fidelity, and imaging depth theoretically attainable with SIM. Through quantitative long-term live-cell imaging, we demonstrate an improvement in quantification sensitivity and a reduction in analysis bias by utilizing Lock-in-SIM reconstruction. In two-color time-lapse ER-lysosome imaging, we successfully captured the structural rearrangements of intraorganellar components, including the ER matrix and lysosome tubules, and their interactions, shedding light on the remodeling and interactions of organelles. In multi-color time-lapse mitochondria-dynamin-related protein 1 (Drp1) imaging, we reveal a mechanism of mitochondria-mediated mitochondrial fission via the transfer of Drp1, completing the classical mitochondrial fission model and shedding light on the interactions of organelles. Lock-in-SIM combines compelling reconstruction quality with universal applicability, in a user-friendly open-access package, either as MATLAB GUI, Fiji/ImageJ plugin, or executable software, that will greatly facilitate the exploration of highly intricate and dynamic biological processes.

Results
Principle and validation of the background-free Lock-in-SIM
The key aspect of background removal is to identify and separate the signal from the background by detecting their differences. In electronic circuits, a classical method for separating signals and background noise involves the use of a lock-in amplifier. The amplifier generates a known modulated signal as the reference waveform that is highly correlated with the detecting signal but not with background noise. By performing correlation analysis between the reference and detecting waveforms, the amplifier effectively suppresses background noise and enables the extraction and tracking of weak signals. In this regard, upon reconsidering the imaging process of SIM, it becomes evident that the sinusoidal periodic illumination pattern of SIM can serve as an ideal reference signal. Based on this, the possibility of applying lock-in detection to widefield-based optical SIM imaging was explored.

The intensity distribution of a raw acquired SIM image Ij(r) at a certain pattern orientation can be described by the following:
Ij(r) = [1 + m cos(k0 r + φj)] O(r) H(r) (1)
where k0 is the frequency coordinate, φj is the phase of the jth phase-shifted illumination pattern (j = 1, 2, 3), m is the modulation depth, O(r) represents the sample information, H(r) is the point spread function (PSF) of the system, and * denotes the convolution operation.

In traditional 2D-SIM reconstruction, the sample O(r) is regarded as a 2D structure that is modulated equidistantly at each lateral position by a sinusoidal illumination pattern. However, as the imaged sample and area are 3D distributed, the resulting raw image Ij(r) is actually the intensity projection of the entire 3D volume, including both the in-focused sample structure and background. Figure 1a illustrates that the illumination pattern exhibits a relatively ideal sinusoidal distribution near the focal plane, which leads to better modulation of the in-focused sample structure. At the high-depth positions, the modulation effect of the illumination stripe rapidly deteriorates due to the increasing poorly modulated and unmodulated background from scattering, autofluorescence, noise, and aberration. Therefore, Eq. (1) should be rewritten as follows:
Ij(r) = Oout(r) + [1 + m cos(k0 r + φj)] Oin(r) H(r) (2)

To address this issue, here, we further rewrite Eq. (2) as the sum of two components, the modulation-independent (DC) background Idc(r) and modulation-dependent (AC) signal Ijac(r):
Ij(r) = [Oout(r) + Oin(r) H(r)] + [m cos(k0 r + φj) Oin(r) H(r)] (3)

The DC component Idc(r) can be calculated with raw images Ij(r) at three phases (see Supplementary Note 1 for further algorithm details). The in-focused signal is then obtained by filtering out Idc(r) from the raw image Ij(r), which is subsequently used for super-resolution SIM reconstruction (Fig. 1b).

Figure 1c shows the experimental results from the different 2D imaging methods. Although the subtle hollow structures of the ER were distinguishable after standard 2D-SIM reconstruction, they were superimposed with high background, and thus, the image contrast, i.e., effective resolution, deteriorated. In contrast, TIRF-SIM was capable of providing background-free and high-contrast results due to the subwavelength-depth illumination, but the ER structures beyond the illumination depth were lost. With our Lock-in-SIM, the background-depth trade-off was broken. Lock-in-SIM maintained both TIRF contrast and 2D-level structural integrity and therefore could facilitate new advances for cell biology research. To further illustrate the capability and biological benefits of Lock-in-SIM, we focused on the ER in thicker perinuclear regions of the cell (Supplementary Fig. 2) rather than examining only the cell margin. By merging Lock-in-SIM with standard 2D-SIM (Supplementary Fig. 2e) and TIRF-SIM (Supplementary Fig. 2f), Lock-in-SIM clearly preserved the entire ER meshwork while eliminating the background, especially around thicker, more densely packed perinuclear ER.

Lock-in-SIM provides optimal background suppression, image resolution, and fidelity
To systematically test the performance of the algorithm, we further compared Lock-in-SIM with a total of 13 existing algorithms, including Wiener-SIM6, WLR-SIM36, Open-SIM37, Fair-SIM38, Hessian-SIM14, IM-SIM39, SP-SIM40, HiFi-SIM15, JSFR-SIM41, PCA-SIM42, BF-SIM43, Direct-SIM44, and Flex-SIM45. Applied to simulated, synthetic, or experimental bead datasets, Lock-in-SIM reconstructions proved highly robust against variations in noise (Supplementary Fig. 3), field-of-view distortion (Supplementary Fig. 4), and background (Supplementary Figs. 5 and 6), while the algorithms compared against showed different degrees of susceptibility to these factors. Notably, the resolution of Lock-in-SIM results remained consistently high across varying background conditions, closely matching the theoretical SIM resolution (Supplementary Fig. 6). When applied to 3D volumetric datasets, Lock-in-SIM effectively eliminated out-of-focus background to provide optical sectioning and deliver high-quality pseudo-3D reconstructions from standard nine-image 2D-SIM acquisition (Supplementary Fig. 3 and Supplementary Movie 1).

We next validated the superiority of Lock-in-SIM for different sample types with zero-, one-, two-, or three-dimensional continuity and varying imaging conditions using both open-access (Supplementary Figs. 7–13) and our own experimental datasets (Fig. 2, Supplementary Figs. 14–20, and Supplementary Movie 2), including the mitochondrial inner and outer membranes, microtubules, actin, lysosomes, clathrin-coated pits, mitochondrial-derived vesicles, and 5-ethynyl-2′-deoxyuridine (EdU) replication-labeled nuclei. In all these scenarios, Lock-in-SIM outperformed the other algorithms by effectively removing background and retaining the in-focus ultrastructure with high integrity, achieving superior image definition and reconstruction fidelity. The global SBR (Fig. 2e, g and Supplementary Figs. 14b and 15b) and local SBR maps (Supplementary Fig. 14a) quantitatively supported the above deductions. As the background was suppressed, the effective resolution was improved (Fig. 2f, h). Accordingly, the membranes of mitochondria (Fig. 2a and Supplementary Figs. 7, 15 and 17) and the hollow structures of clathrin-coated pits (Supplementary Fig. 18) were more clearly visible after Lock-in-SIM reconstruction.

The background fluorescence may also lead to reconstruction artifacts in the existing 2D-SIM images, while these artifacts could be suppressed through the Lock-in-SIM reconstruction. When reconstructing high-background and high-density samples, existing methods often compromise between SBR and structural integrity (Supplementary Figs. 14 and 16). In contrast, Lock-in-SIM achieved optimal background suppression and structural fidelity. When inspecting the frequency distribution (Supplementary Fig. 14), the reconstructed spectra of existing 2D-SIM showed abnormal amplitude drop-offs in both the low-frequency and high-frequency bands; these correspond to the possible image artifacts in the spatial domain. In contrast, the spectrum of the Lock-in-SIM image decayed more smoothly. Lock-in-SIM had the smallest slope and the highest amplitude and cut-off frequency, which indicated higher signal strength, noise resistance, and effective resolution. Therefore, considering either the space or frequency domain under diverse imaging conditions, our Lock-in-SIM extracts weak high-frequency signals, preserves ultrastructural integrity, and extends the frequency transfer function with the highest fidelity (Supplementary Note 3).

High-fidelity whole-cell optical sectioning reconstruction using Lock-in-SIM with standard 2D-SIM acquisition
In general, 2D SRM techniques, including 2D-SIM, improve only the lateral resolution and lack optical sectioning ability due to the influence of the background. Although 3D-SIM can solve this problem, 15 raw images at each focal plane (3 angles and 5 phases), and at least ±3 adjacent planes are required for reconstructing each single plane, which results in a lower acquisition speed and stronger photobleaching35,46,50. Optical sectioning-SIM suppresses the background at the cost of lower spatial resolution24. Here, we show that Lock-in-SIM can alleviate the imaging trade-off among optical sectioning, spatial resolution, and temporal resolution.

In addition to the simulated 3D volumetric filament structure (Supplementary Fig. 3, Supplementary Movie 1), we performed axial scanning experiments on single-color microtubules, mitochondria, EdU-labeled replication sites, and two-color microtubule-lysosomes with normal nine-image 2D-SIM acquisition at each axial plane to demonstrate the optical sectioning capability of the Lock-in-SIM (Fig. 3, Supplementary Figs. 20 and 21, and Supplementary Movies 3–7). In all cases, Lock-in-SIM resulted in much greater SBR than the standard 2D Wiener-SIM reconstruction. By color-coding the pixel intensities of each stack, Lock-in-SIM provided both super-resolution and optical-sectioning depth information, whereas this information was highly obscured by the background in the standard 2D-SIM results (Fig. 3 and Supplementary Movies 3 and 4). When comparing the whole-cell-depth microtubule datasets acquired at different stages of growth (Fig. 3a–d), the cells at the telophase (Fig. 3c) extend over a greater depth range, approximately twice that of the non-telophase cell (Fig. 3a). Interestingly, by applying orientation analysis51,52 (Fig. 3n; see the next section and “Methods” for details), we found that the non-telophase microtubules tended to be uniformly aligned towards the cell migration direction, while the telophase cell exhibited a two-peak orientation distribution spaced nearly 90° apart (i.e., most microtubules were orthogonally organized); these results indicated the differences in microtubule function at different growth stages. The high-depth axial view of the telophase microtubules further strongly confirmed the improvement in the optical sectioning capability of standard 2D-SIM by using Lock-in reconstruction (Fig. 3d). The reconstruction comparison of different mitochondrial depths shown in Fig. 3g and Supplementary Movies 6 and 7 demonstrates Lock-in-SIM’s capability of efficiently filtering out the high background and retaining the depth-dependent structural features at each axial plane, while the structure and background were mixed in Wiener-SIM images. According to the two-color microtubule-lysosome data (Fig. 3h–j and Supplementary Movie 4), Lock-in-SIM also provided background-free depth information and super-resolution structural details to facilitate colocalization studies, while standard 2D-SIM only improved the resolution of the widefield images.

Intensity profile plots along adjacent fluorescent features not resolvable by widefield also display far higher peak-to-valley intensity values with Lock-in-SIM, further validating contrast and effective resolution enhancement (Fig. 3k–m). Benchmarking Lock-in-SIM against standard 3D-SIM of the same cell (Supplementary Figs. 21 and 22 and Supplementary Movie 8), both displayed highly consistent depth and 3D structural distributions, thus confirming the reconstruction effectiveness and accuracy of our Lock-in-SIM method.

In summary, our Lock-in-SIM provides a powerful tool for achieving 3D-SIM-like high-fidelity whole-cell volumetric super-resolution imaging with standard 2D-SIM acquisitions. Compared to 3D-SIM, Lock-in-SIM has similar optical sectioning capability, i.e., similar SBR and structural depth distribution, albeit without two-fold axial resolution improvement (Supplementary Figs. 21 and 22), while improving the imaging speed beyond 3-fold due to its requirement for 9 versus 15 exposures and halved z-sampling, which greatly reduces photobleaching and phototoxicity. In addition, Lock-in-SIM’s ability to achieve optical sectioning from a single-plane acquisition renders it ideal for ultrafast, background-free 2D time-lapse live-cell measurements, which are not possible with either standard 2D-SIM or 3D-SIM.

Non-biased quantitative live-cell imaging and analysis using Lock-in-SIM
One of SIM’s most important applications is live-cell super-resolution imaging, which benefits from its gentle illumination intensity and high acquisition speed compared to other SRM approaches. However, the background is more prone to interfere with live-cell SIM imaging results. To overcome the effect of a high level of unmodulated bright background, one may need to increase the dynamic range by increasing laser power and exposure time, albeit at the cost of higher photobleaching. In addition, quantitative biological analysis, which is an increasingly important aspect in elucidating structure-dynamic-function relationships, is also susceptible to background noise23,46,47. Here we show that, benefiting from more efficient background subtraction, Lock-in-SIM can extend sample viability and provide higher-quality reconstruction results for quantitative live-cell imaging and analysis.

We first tested Lock-in-SIM with Tubulin-488-labeled living U2OS cells by recording long-term microtubule dynamics over 7 min (Fig. 4a–f, Supplementary Figs. 23 and 24 and Movie 9). Compared to Wiener-SIM, Lock-in-SIM eliminated the background effectively and thus facilitated detailed dynamic observation. We then mapped the orientation distribution (θ angle in Fig. 4b), which is a valuable quantification metric for reporting the cell polarity51,52 (see “Methods” for algorithm details), of the microtubules to determine the spatial organization of the cell. As shown in Fig. 4b, the microtubule orientation, especially around the dense cell region, was difficult to extract based on the standard 2D-SIM result due to the influence of the background, while Lock-in-SIM reconstruction was able to generate unambiguous, high-precision θ mapping. Figure 4c–e shows the time-lapse intensity and orientation images of representative microtubules; these results demonstrate the reconstruction robustness of Lock-in-SIM at different structural density levels. In contrast, the standard 2D-SIM image could lead to inaccurate analysis results due to the influence of the background (Supplementary Fig. 23). By plotting the evolution curve of the θ angle, we found that the cell presented different orientation values and remodeling behaviors in different cell regions in response to the corresponding cellular functions (Fig. 4f). Specifically, the orientation of the dense cell region (green curve in Fig. 4f) varied more significantly than that of the sparser regions. In addition, the θ value gradually decreased in some regions (orange curves in Fig. 4f), while in others, it fluctuated around a certain baseline (blue curve in Fig. 4f).

When observing living mitochondria (Fig. 4g–l), Lock-in-SIM again achieved high-fidelity reconstruction with more clearly discriminated inner membrane structures. Based on the Lock-in-SIM images, the orientation evolution of mitochondria could be visualized and analyzed at the single cristae level. However, this information was mixed with the background in standard 2D-SIM results (Fig. 4h), resulting in quantification errors (Fig. 4k). Tracking a single mitochondrion, despite deforming distinctly in shape, its global orientation remained stable (Fig. 4l). In summary, our Lock-in-SIM reaches the full SBR, fidelity, sensitivity, and accuracy potential of both image reconstruction and quantification algorithms and thus greatly promotes the application of SIM towards more quantitative and dynamic live-cell studies.

Visualization of the intraorganellar ultrastructures, dynamics, and interactions using Lock-in-SIM
After establishing the effectiveness of Lock-in-SIM in live-cell imaging, we moved on to more challenging biological imaging tasks. The densely and minutely packed matrices of the ER have long been misinterpreted as flat sheets by standard 2D-SIM due to insufficient spatial and temporal resolution, as well as 3D-projected background blurring30. With Lock-in demodulation, we showed that the ER matrix structure and dynamics can be clearly visualized in a 2D context in the presence of high background (Supplementary Figs. 25 and 26, and Supplementary Movies 10 and 11). Thanks to the highly effective spatiotemporal resolution of Lock-in-SIM, we were able to perform long-term tracking of the intricate, drastic, and rapid morphological distortion of both the outer and inner structures of the lysosomes. During movement, the lysosome could either maintain a rounded shape (Supplementary Movie 11) or evolve in a wide variety of forms, such as fusiform, drop-shaped, wheel-like, double-ring, figure-eight, and furcate (Supplementary Fig. 27), to timely adapt to changes in cell function. Additionally, two-color time-lapse Lock-in-SIM images of lysosomes and ER showed that the lysosomes could regulate not only ER tubule53 but also the dense ER matrix and that lysosomes moved at lower speed than the commonly reported movements in non-matrix tubular regions (Supplementary Fig. 28 and Supplementary Movie 11).

The tubules of the lysosomes (tubular structures that grow from the lysosome), which play crucial roles in lysosomal trafficking and vesicle sorting54,55 (Supplementary Figs. 25, 29 and 31 and Movies 10 and 11), are characterized by their relatively fine diameter and high dynamics; thus, they are also challenging to image with conventional imaging techniques. By clearly imaging the lysosome tubule, two-color time-lapse Lock-in-SIM validated the phenomenon of ER-mediated lysosome tubule fission54 and further revealed that the tubules were detached from whole lysosomes at different contact sites by their interactions with the ER tubules (Supplementary Figs. 25, 30 and 31, and Movie 11). Quantitative analysis indicated that the lysosome tubule fission tended to occur at the root and middle locations for the short (mean length = 0.65 μm) and long tubules (mean length = 7.85 μm), respectively (Supplementary Fig. 25e). After fission, the detached tubules moved ~4 times faster (mean velocity = 0.81 μm/s) than both the other part and the original lysosomes (Supplementary Fig. 25f).

Lock-in-SIM finally helps unveil insights into the dynamic behavior of mitochondria. Time-lapse imaging of the inner mitochondrial membrane captured the dynamics of mitochondrial cristae structures, such as mitochondrial fusion and fission, as well as inner membrane fusion and fission, with unprecedented image definition (Fig. 5, Supplementary Fig. 32, and Supplementary Movie 12). Interestingly, when observing two independent mitochondria in close proximity for an extended period, the super-resolution Lock-in-SIM images showed the existence of intricate membrane connections between them (Fig. 5b, Supplementary Fig. 33, and Supplementary Movie 13). This observation indicated that mitochondria potentially exchanged materials across these fine membrane junctions.

Mitochondrial fission is a vital bioprocess closely linked to proper cellular functioning56,57 and has been previously described as being regulated only by other types of organelles or cytoskeletons, mainly the ER32,58 and actin59. However, based on the long-term dynamic Lock-in-SIM imaging of PK mito DEEP RED-labeled living mitochondria, we discovered mitochondria-mediated mitochondrial fission (MMF), a mechanism distinct from the classical mitochondrial fission model. In the MMF mechanism, mitochondrial fission occurred via interactions between the mitochondria themselves (Fig. 5e, f, Supplementary Figs. 34 and 35 and Movie 13) rather than through interactions between different subcellular components (Fig. 5d). To further investigate the MMF mechanism and understand the underlying bioprocesses, we performed dual-color live-cell Lock-in-SIM imaging of the Tomm20-EFGP-labeled mitochondrial outer membrane and mCherry-labeled Drp1 oligomers. The results showed that during the MMF process, one mitochondrion first contacted another mitochondrion and initiated its fission. Thereafter, two Drp1 oligomers, located separately on the outer membrane of the two mitochondria, moved towards the contact sites simultaneously and then merged to drive the final scission (Fig. 5g, Supplementary Fig. 36, and Supplementary Movie 14). These results improved the prevailing mitochondrial fission model by uncovering the MMF mechanism consisting of several kinetically distinct steps: the contact of two mitochondria, the transfer of Drp1 from the non-dividing mitochondrion to the dividing mitochondrion (along with the contact site-directed movement of Drp1 located on the dividing mitochondrion), and the fusion of the Drp1 oligomers to produce eventual mitochondrial division (Fig. 5h). Lock-in-SIM tracked the entire self-interaction processes of the mitochondria at high spatio-temporal resolution and elucidated the key role of Drp160 in MMF. To our knowledge, this is the first report to demonstrate that mitochondrial fission can also be regulated by the mitochondria themselves, providing insights into mitochondrial and cellular function. Taken together, the presented results demonstrate the usefulness of Lock-in-SIM for advanced cell biology investigations.

Methods
Cell lines
U2OS cells (Human osteosarcoma cell line) were cultured in McCoy’s 5A medium (Gibco) supplemented with 10% (v/v) fetal bovine serum (FBS; Gibco); COS-7 and mouse C127 epithelial cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium (Gibco), supplemented with 10% FBS and 1% penicillin-streptomycin (Beyotime). Cell lines were purchased from Procell Life Science & Technology Co., Ltd. or the American Type Culture Collection. All cells were cultured at 37 °C and 5% CO2.

Sample preparation
For low-background bead sample preparation, 100 nm diameter yellow-green fluorescent microspheres (ThermoFisher) were diluted 1:2000 in phosphate-buffered saline (PBS) solution. For high-background bead sample preparation, the beads were diluted 1:2000 in PBS solution containing 2 μM Fluorescein 5-isothiocyanate (FITC, ThermoFisher). Bead solutions were then added to 35-mm glass-bottom dishes (Cellvis) for imaging.

For live-cell microtubule labeling in U2OS cells, we followed a well-established protocol65. Briefly, cells were co-incubated with mixLPV and 5 μM Tubulin-Atto488 at 37 °C for 1 h. The cells were then washed three times with culture medium and incubated for another hour at 37 °C and 5% CO2. Before imaging, the medium was replaced with phenol red-free McCoy’s 5A medium.

For live-cell inner mitochondrial membrane labeling in U2OS cells, cells were seeded in a 35-mm glass-bottom dish at 37 °C with 5% CO2 for 12–16 h. Then, the cells were co-incubated with 200 nM PK mito DEEP RED or 500 nM MitoTracker Green FM for approximately 30 min. The PK mito DEEP RED or MitoTracker Green FM dyes were then removed, and the cells were washed three times with PBS. Afterward, the cells were cultured for another 30 min at 37 °C and 5% CO2. Before imaging, the medium was replaced with phenol red-free McCoy’s 5A medium.

To label ER, mitochondria, lysosomes, and Drp1 for live-cell imaging, U2OS cells were first seeded in a 24-well plate. After 12 h, cells were transfected with 300 ng of the indicated plasmid (Sec61β-EGFP for ER, Tomm20-EGFP for mitochondria, Rab7-EGFP for lysosome, or mCherry-Drp1 for Drp1) using 1 μl of Lipofectamine 2000 in OPTI-MEM medium. Twenty-four hours post-transfection, the cells were detached with 0.25% trypsin-EDTA, re-seeded onto 35-mm glass-bottom dishes, and cultured for an additional 24 h at 37 °C and 5% CO2 prior to imaging.

For live-cell (Sec61β-DY549) ER and (Rab7-AF488) lysosomes dual-color labeling, U2OS cells were seeded in a 24-well plate. After 12 h, the cells were transfected with OPTI-MEM medium (Invitrogen) containing 1 μl of Lipofectamine 2000 (Invitrogen) and 300 ng of the indicated plasmids (Sec61β-SNAP for ER and Rab7-Halo for lysosomes). Twenty-four hours post-transfection, the cells were detached with 0.25% trypsin-EDTA (Gibco), re-seeded onto 35-mm glass-bottom dishes (Cellvis), and cultured for an additional 24 h at 37 °C and 5% CO2 prior to imaging. For fluorescent labeling, the cells were then co-incubated with mixLPV along with 5 μM SNAP-DY549 and 5 μM Halo-AF488 for 1 h at 37 °C. Following incubation, the cells were washed three times with culture medium and then rested for another hour. Finally, prior to imaging, the medium was replaced with phenol red-free McCoy’s 5A medium. All incubations were performed at 37 °C with 5% CO2.

For mitochondria and mitochondrial-derived vesicles labeling, U2OS cells were seeded in 35-mm glass-bottom dishes (Cellvis). After 12 h, the cells were transfected with OPTI-MEM medium (Invitrogen) containing 1 μl of Lipofectamine 2000 (Invitrogen) and 300 ng Tomm20-Halo. Twenty-four hours post-transfection, the cells were then co-incubated with mixLPV along with 5 μM Halo-AF488 for 1 h at 37 °C. Following incubation, the cells were washed three times with culture medium and then rested for another hour. Finally, prior to imaging, cells were fixed with 2% glutaraldehyde.

For fixed-cell EdU replication labeling, mouse C127 epithelial cells were grown on #1.5H high precision coverslips and incubated for 15 min with 10 μM 5-ethynyl-2′-deoxyuridine before fixation with 3% formaldehyde/0.1% Triton X-100/PBS for 10 min. After washing cells with PBS/0.05% Tween (PBST), cells were blocked with BlockAid (ThermoFisher) before click detection of incorporated EdU with AF488-azide using the Click-iT kit (ThermoFisher) and mounting in EverBrite medium (Biotium) following thorough washing with PBST.

For fixed-cell microtubule labeling, U2OS cells were seeded in a 35-mm glass-bottom dish at 37 °C with 5% CO2 for 12–16 h. Before fixation, the cells were washed three times with PBS, followed by treatment with fixative buffer (3% paraformaldehyde, 0.1% glutaraldehyde, and 0.2% Triton X-100) for 15 min. The cells were then incubated with 0.2% Triton X-100 for another 15 min and blocked with blocking buffer (3% bovine serum albumin and 0.05% Triton X-100) for 20 min at room temperature. Following this, the cells were incubated overnight at 4 °C with anti-alpha-tubulin antibody (ab7291, Abcam, 1:500 dilution), after which the primary antibody was removed, and the cells were washed twice with PBS. The cells were then incubated with Alexa Fluor 488-conjugated secondary antibody (ab150113, Abcam, 1:600 dilution) for another 2 h at room temperature. Finally, the antibody was removed, and the cells were washed three times with PBS before imaging.

For fixed-cell mitochondrial or lysosomal labeling, the pre-sample preparation steps were the same as those described above for microtubule sample preparation. Specifically, the cells were incubated overnight at 4 °C with either an anti-Tomm20 antibody (ab232589, Abcam, 1:500 dilution) or an anti-Rab7 antibody (ab137029, Abcam, 1:500 dilution). The primary antibody was then carefully removed, and the cells were washed twice with PBS. Afterward, the cells were incubated with Alexa Fluor 488-conjugated secondary antibody (Abcam, 1:600 dilution) for 2 h at room temperature. Finally, the cells were washed three times with PBS.

To label the ER in fixed cells, U2OS cells were initially transfected with Sec61β-EGFP using Lipofectamine 2000 according to the standard protocol. Subsequently, the cells were cultured at 37 °C with 5% CO2 for 24 h. Prior to imaging, the cells were fixed with 2% glutaraldehyde for 20 min.

For fixed-cell two-color labeling of microtubules and lysosomes, the cells were fixed with 2% glutaraldehyde for 20 min. The cells were then incubated with 0.2% Triton X-100 for another 15 min and blocked with blocking buffer (following the above microtubule protocol) for 20 min at room temperature. Following this, the cells were incubated with mouse anti-alpha-tubulin antibody (ab7291, Abcam, 1:500 dilution) and rabbit anti-Rab7 antibody (ab137029, Abcam, 1:500 dilution) overnight at 4 °C, after which the primary antibodies were removed, and the cells were washed twice with PBS. The cells were then incubated with goat anti-mouse Alexa Fluor 488 antibody and goat anti-rabbit Cy3 antibody (ab6939, Abcam, 1:800 dilution) for another 2 h at room temperature. Finally, the antibodies were removed, and the cells were washed three times with PBS before imaging.

Experimental SIM data acquisition. The SIM datasets were mainly acquired using a custom-built high-speed SIM system equipped with 2D-SIM, TIRF-SIM, and 3D-SIM imaging modalities66,67, and commercial SIM systems (GE DeltaVision OMX SR; CSR Biotech, HIS-SIM; and NanoInsights-Tec, Multi-SIM). The main hardware components of the custom-built system included the following: multi-line lasers (488 nm, 561 nm, 594 nm, and 637 nm); scanning galvanometer sets (Cambridge Technology, CTI8310k) for the incident angle and azimuth changes; electro-optic modulators (Thorlabs, EO-PM-NR-C4) for fast phase shifting; pizza-type half-wave plate (Union Optics, WPA2210-450-650) for polarization control; 100×/1.49 NA oil immersion objective; and sCMOS camera (Hamamatsu, C13440-20CU). The pixel size of the raw image was 65 nm. The typical exposure times for the fixed-cell and live-cell experiments were 50 ms and 20–30 ms, respectively. Time intervals of 2–3 s could be set for longer-duration live-cell acquisition. More details, including the other hardware components, software control, and resolution validation regarding our galvanometer-based SIM system, can be found in our published works66,67. In addition, the open-source datasets (Supplementary Figs. 7–13) were also used for comparison and validation, demonstrating the universality and advances of our Lock-in-SIM approach.

For most experiments, single slices were recorded in 2D-SIM mode, in which the data were acquired by manually focusing on the plane with the highest image contrast, i.e., the plane with the most structure and least background. For the optical sectioning 2D-SIM experiments (Fig. 3, Supplementary Figs. 20–22), the sample thickness and focus position are labeled in the corresponding figures. For comparison, the optical sectioning 2D-SIM data were typically acquired with the same axial step size as the 3D-SIM data (125 nm). The number of steps depended on the imaging depth. Note that the axial step size can also be set to ~250 nm to improve the acquisition speed of the whole stack, because although our Lock-in-SIM method minimizes the background and improves the optical sectioning ability of standard 2D-SIM, its axial resolution is still limited to the 2D-SIM level (Supplementary Fig. 21).

Simulated and synthetic SIM data generation. The simulated data in Fig. 1 and Supplementary Fig. 3 were generated in MATLAB. The ground-truth structures of the crossed filaments and radiative lines were multiplied by the sinusoidal SIM pattern and convoluted with the PSF. The simulation parameters were set to match the experimental conditions: λ = 488 nm, numerical aperture = 1.49, and pixel size = 65 nm. Gaussian noise with different standard deviations and contrast with different levels could be added for robustness tests. The synthetic data in Supplementary Fig. 5 were generated by superimposing the widefield images on the raw SIM images at different levels.

SBR calculation. To evaluate the background-removing capability of each algorithm quantitatively, we calculated SBR values with the following definition68:
SBR = 10 log10 [max(signal) - mean(background)] / s.d.(background) (4)
where max(signal), mean(background), and s.d.(background) represent the maximum intensity of the in-focused signal, the average intensity of the background, and the standard deviation of the background, respectively. Note that the background value used in the SBR calculation was determined from all pixels without a structural signal. Therefore, the SBR includes the contribution of noise as well.

Orientation analysis. The quantitative, pixel-wise orientation values (θ) of fibrous structures, such as microtubules and mitochondria, were calculated with a weighted vector summation algorithm51,52 consisting of five steps. First, a typical window size of 7×7 was applied to the image. Forty-eight vectors were defined from the central pixel to the surrounding pixels. Second, the first weight factor F1 for each vector was calculated as the inverse of its length. Third, the second weight factor F2 for each vector was calculated as the intensity difference among the three pixels along the vector:
F2 = 1/3 * Σ (Intensityi - Intensity)2 (5)
Fourth, the two weight factors were summed to obtain the average direction of the central pixel. Finally, the summed direction was corrected with prior before converting the orientation angle to be between 0° and 180°.

Image processing and visualization. Wiener-SIM reconstructions were performed using a custom software based on standard Wiener-SIM developed by Gustafsson6. Fair-SIM reconstructions were performed using the open-access Fair-SIM Fiji/ImageJ plugin. WLR-SIM reconstructions were performed by the original authors. Open-SIM, Hessian-SIM, IM-SIM, SP-SIM, HiFi-SIM, JSFR-SIM, PCA-SIM, BF-SIM, Direct-SIM, and Flex-SIM reconstructions were performed using corresponding open-access MATLAB codes with their default or recommended parameters (Supplementary Table 2). Some of the reconstructed results from the existing methods with their own datasets were, where available, either directly picked from their open-access files or reproduced with the provided parameter-optimized codes, such as BF-SIM, Direct-SIM, and Flex-SIM. The processing speed of Lock-in-SIM (~8.68 s for a single-frame 512×512 2D-SIM dataset on an Intel i5-13500 2.50 GHz CPU with 64 GB RAM) is at a moderate level among the tested algorithms (Supplementary Table 2), which can be further improved through hardware optimization, software integration, and GPU acceleration to facilitate the real-time, high-SBR live-cell imaging69. Because of the possible intensity variations among different frames, either optical-sectioning z stack or time-lapse t stack, we may correct the intensity variation after reconstruction with the ImageJ plugin (https://imagej.net/plugins/bleach-correction). SIM images might also be adjusted and smoothed with a 2D Gaussian filter of 0.5–0.6 pixels for visualization purposes. 3D volumetric data were mainly rendered and visualized with the ImageJ Volume Viewer plugin and Imaris software.

Statistical analysis. The Fourier domain evaluation in Supplementary Fig. 14 was performed using the SIMcheck Fiji/ImageJ plugin70. Decorrelation analysis71 and full-width at half maximum measurement were used to assess spatial resolution. The statistical data in Fig. 4l and Supplementary Figs. 6 and 25 were plotted in the box-and-whisker format. The middle line in the box corresponds to the mean value. The upper and lower boundary lines correspond to the 15th and 75th percentiles, respectively. The whiskers represent the min and max values. Two-tailed t-tests were applied to compare the groups. P values ≥ 0.05 were considered not different and labeled ns; P values < 0.05 were considered significantly different and labeled *; P values < 0.0001 were considered extremely significantly different and labeled ****.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents Lock-in-SIM, a novel background suppression method based on a spatial domain model to address out-of-focus signal artifacts in 2D Super-resolution Structured Illumination Microscopy (2D-SR-SIM). By leveraging the modulation properties of SIM, the authors propose an effective signal extraction framework that eliminates background without requiring additional hardware modifications. The method significantly enhances the signal-to-background ratio (SBR) and resolution while preserving the integrity of super-resolution structures. This approach is innovative and practical, and experimental results comprehensively validate Lock-in-SIM across various biological samples, imaging conditions, and datasets. Notably, the method demonstrates robustness in high-background environments while maintaining structural accuracy, making it particularly useful for visualizing and quantitatively analyzing intraorganellar ultrastructures.\nThe authors provide a well-documented algorithm implementation, along with functional and accessible software tools, including a MATLAB GUI, a Fiji/ImageJ plugin, and an executable program, making it easy to reproduce and apply.\nOverall, the study is well-motivated, technically sound, and experimentally thorough, with significant potential for broad application in super-resolution imaging. However, there are a few aspects that require further clarification to enhance the completeness and impact of the manuscript.\nApproximation in Lock-in-SIM\n[SI Line 96-97] In Supplementary Note 1 the final step of Lock-in demodulation does not explicitly include the modulation depth m. Clarification on the typical range of m in practical applications and why it can be omitted in this step would be solid.\nComparison with Other Methods\nWhile the paper quantitatively compares Lock-in-SIM with 13 existing methods, additional details on parameter choices and computational efficiency would help further clarify its advantages and potential limitations. Specifically, it would be useful to explain how parameters were selected for each method to ensure a fair comparison, as different settings can significantly impact reconstruction quality. Additionally, a discussion of computational efficiency—such as processing time and resource requirements—would provide valuable insight into the practical applicability of Lock-in-SIM, particularly for live-cell imaging where speed and real-time processing are important considerations.\nClarification on PSF Modeling in BF-SIM\n[SI Line 173-179] In Supplementary Note 3, the discussion of BF-SIM states that it models the PSF as a 3D distribution, but the description of the challenges involved could be clarified. BF-SIM typically generates its PSF based on system parameters such as the objective lens properties, rather than requiring experimental measurement for each specific condition. While mismatches between the modeled and actual PSF may still affect reconstruction quality, the current wording could be interpreted as suggesting that BF-SIM requires direct experimental PSF measurement or extensive per-condition adjustments. The authors may consider refining this discussion to more precisely describe the PSF modeling process in BF-SIM and the extent to which it depends on experimental conditions.\nMinor Formatting and Writing Issues\nBelow are a few minor issues related to formatting, writing, and figure legends that could be addressed for clarity and consistency:\n1. [Line 185-187] The expression and crossref seems a bit obscure. In supplementary Fig. 2, only simulated filaments are presented.\n2. [Line 265-266] Should the corresponding figure be Extend Data Fig. 4?\n3. [Fig 3 caption] In Line 902, “b(orange histogram)” should be corrected to “c(orange histogram)”.\n4. [Fig 4b, h, Supplementary Fig. 16b] The color of the legend indicating orientation should have an angular gradient, not a linear gradient.\n5. [Supplementary Fig.4 caption] In Line 243, “without (upper left) and with (bottom right) Lock-in filtering” should be corrected to “without (bottom right) and with (upper left) Lock-in filtering”.", "reviewer_2": "Liu et al. present a new computational approach for structured illumination microscopy image reconstruction that takes advantage of several tricks from conventional signal processing. They show that by using the input grating patterns as a “lock-in” signal, they can correct the background appropriately and generate some really remarkably high-quality SIM reconstructions. They go on to demonstrate using a battery of conventional targets for SIM that their system provides a equal-or-better reconstruction than nearly any other SIM reconstruction approach currently available, and even create a few nice packaged versions of the software to allow the free distribution of the technique.\nIn general, I am extremely enthusiastic about this paper. It marks the culmination of many years of work by many groups to generate a generalizable, well-rounded reconstruction approach that can be broadly distributed. They do sufficient work to demonstrate its utility across various samples, and I am very pleased to see the Fiji plugin and MATLAB GUI designed to bring it to the average user. I am particularly impressed on how well they have represented all of the competing techniques throughout the paper, allowing a new user to really see for themselves where and how this technique will help them.\nI have only one major concern and a few minor points before strongly recommending this for publication, I have detailed them below:\nMajor Point:\nOne presumable weakness of this approach is that it requires the ability to “lock-in” on the modulated (excitation) signal that comes from the patterned illumination. The authors show lots of examples of structures with one- or two-dimensional continuity, which makes this clear. I could not find any examples where the authors test this on small, sub-diffraction limited puncta, especially those moving very quickly (fast enough to blur during 2D image acquisition). Would this be predicted to be problematic? I think it would be worth demonstrating whether this was true or not—either as a caution or as an explicit endorsement of using this technique for the numerous small puncta experiments common in biology (e.g., mitochondrial derived vesicles, plasma membrane microdomains, etc.).\nThe second implication of this is that the fidelity of Lock-In SIM reconstruction is going to be dependent on the ability of the scope to produce a clean grating illumination pattern—incomplete or uneven interference patterns (as can happen when coverslips are not flat on the objective, etc.) will still cause the reconstruction to amplify artifacts (as do most of the other reconstruction algorithms, of course). Thus, I think the authors should be clear that Lock-In SIM is not an “artifact-free” approach, per se, but rather one that reduces background and improves about the reconstruction if the data is sufficiently high quality. This will be especially important for users that generate the SIM pattern by direct interferometry such as light sheet users, since slight misalignments in these systems can render the grating pattern difficult to fit. I have tried the Lock-In SIM on some data from my own group, and I notice that when the input data is clean, it works amazingly, but when the data has issues (poor interference fringes as a result of misaligned coverslip, in my case), and I do note erroneous breaking of continuous structures and amplification of noise in dark regions to generate artifactual puncta. To be clear, this is not a flaw with the technique, but I think the authors could explicitly state these limitations, at least in the supplement for experts running core facilities to watch for in their users’ data.\nMinor Points:\n1. In Extended Data Fig. 1, the authors label the bottom of the images “Nucleus.” Are the authors actually sure this is where the nucleus is (i.e. – they have some kind of DNA signal in another channel?). I ask because this is a very unusual ER structure to have under the nucleus, and looks much more like the dense “perinuclear” ER structures that happen around its edges. If they aren’t sure, I would label it more generically as “perinuclear” to avoid an allergic response from someone who studies the nuclear envelope (which does not seem to be visible here).\n2. Can the authors make some quantitative estimates from their data about the z-resolution (or effective focal depth?) in their optical sectioning experiments? They demonstrate it is improved compared to 2D SIM, but it would be nice to have a sense of to what degree or at least order of magnitude. The results on the ER in Extended Data Fig. 1 suggest a dramatic improvement in reducing the parts of the organelle sectioned, it would be nice to know how much z-depth that corresponds to."}, "author_response": "We greatly appreciate the reviewer for the high recognition of our work, as well as for the careful reading and positive, constructive feedback to improve the manuscript. In the following, we give our responses point by point to the reviewer’s comments. The revised portions are marked in blue in the revised manuscript.\nComment: Approximation in Lock-in-SIM\n[SI Line 96-97] In Supplementary Note 1 the final step of Lock-in demodulation does not explicitly include the modulation depth m. Clarification on the typical range of m in practical applications and why it can be omitted in this step would be solid.\nResponse: We thank the reviewer for the insightful comment. In the algorithm design, we tested our algorithm both with and without including modulation depth during the Step 2 Lock-in demodulation, and found no obvious difference in the results. Therefore, we chose to omit modulation depth in this step to simplify the processing. Note that the modulation depth is still included and utilized in the subsequent Step 3 Super-resolution reconstruction. The comprehensive comparison presented in the manuscript further validates the effectiveness of this processing strategy. Lock-in-SIM consistently achieves high reconstruction quality across a wide range of experimental conditions.\nTo clarify this, we have added the explanation in the revised supplementary material:\nRevision:\n(Supplementary Note 1, Page 4, Para. 2)\n“The modulation depth m is omitted at this step to simplify the processing, without compromising the reconstruction quality.”\nComment: Comparison with Other Methods\nWhile the paper quantitatively compares Lock-in-SIM with 13 existing methods, additional details on parameter choices and computational efficiency would help further clarify its advantages and potential limitations. Specifically, it would be useful to explain how parameters were selected for each method to ensure a fair comparison, as different settings can significantly impact reconstruction quality. Additionally, a discussion of computational efficiency—such as processing time and resource requirements—would provide valuable insight into the practical applicability of Lock-in-SIM, particularly for live-cell imaging where speed and real-time processing are important considerations.\nResponse: We thank the reviewer for this valuable suggestion and fully acknowledge the importance of providing these details. We have now added explanations of the parameter selection and have tested and discussed the computational speed. Please refer to the revised content below:\nRevision:\n(Methods, Image processing and visualization, Page 27, Line 747-759)\n“Image processing and visualization. Wiener-SIM reconstructions were performed using a custom software based on standard Wiener-SIM developed by Gustafsson6. Fair-SIM reconstructions were performed using open-access Fair-SIM Fiji/ImageJ plugin. WLR-SIM reconstructions were performed by the original authors. Open-SIM, Hessian-SIM, IM-SIM, SP-SIM, HiFi-SIM, JSFR-SIM, PCA-SIM, BF-SIM, Direct-SIM, and Flex-SIM reconstructions were performed using corresponding open-access MATLAB codes with their default or recommended parameters (Supplementary Table 2). Some of the reconstructed results from the existing methods with their own datasets were, where available, either directly picked from their open-access files or reproduced with the provided parameter-optimized codes, such as BF-SIM, Direct-SIM, and Flex-SIM. The processing speed of Lock-in-SIM (~8.68 s for a single-frame 512×512 2D-SIM dataset on an Intel i5-13500 2.50 GHz CPU with 64 GB RAM) is at a moderate level among the tested algorithms (Supplementary Table 2), which can be further improved through hardware optimization, software integration, and GPU acceleration to facilitate the real-time, high-SBR live-cell imaging74.”\n74. Xu, F. et al. Real-time reconstruction using electro-optics modulator-based structured illumination microscopy. Opt. Express 30, 13238-13251 (2022).\nSupplementary Table 2. Reconstruction parameters and speed of the algorithms.\nParameters setting Reconstruction speed (s)a\nWLR-SIM /b\nOpen-SIM No parameter setting 44.63\nWiener parameter=0.05, APO cutoff=2, APO\nFair-SIM /c\nbend=0.9\nWiener parameter=2, Theta Ratio=1:1:1,\nHessian-SIM /d\nMu=150, Sigma=1\nIM-SIM wiener_factor=0.05 4.02\nSP-SIM wienerFactor=0.05 1.60\nattStrength=0.6-0.9, ApoFWHM=0.5, β=1,\nHiFi-SIM 9.23\nw1=1.2\nWIENER PARAMETER=0.5, AMP=1,\nJSFR-SIM /c\nSIGMA=1.5\nsub_optimization=1, Filter_size=11,\nPCA-SIM 3.79\nMask_size=3\nBF-SIM Camera Offset=100, Wiener para=1.5 44.88\nDirect-SIM α=0.8, β=0.5, AttStr.=0.3, FilterFWHM=0.5 5.23\nparams.OTFAttStr=0.999,\nparams.OTFAttwdth=0.3,\nparams.apodize = 0, params.sepOrr = 0,\nFlex-SIM 43.32e\nparams.padSz=10, params.mu = 7e-6,\nparams.regType=1, params.maxIt = 100,\nparams.stepTol = 1e-3\nLock-in-SIM Lock-in parameter=0.5-0.8 8.68\na The speed was tested on a Windows 10 desktop equipped with a 13th Gen Intel(R) Core(TM) i5-13500 2.50 GHz CPU and 64 GB RAM, using a single-frame 512*512 2D-SIM raw dataset.\nb The WLR-SIM reconstruction was performed and shared by the original authors.\nc Reconstructed speeds for Fair-SIM and JSFR-SIM were not reported here due to their separated multi-step processing. Readers can refer to the original publications11,17 for more details. Notably, JSFR-SIM is expected to offer highly increased speed due to the accelerated spatial-domain reconstruction.\nd The reconstructed speed of Hessian-SIM was not reported here due to its multi-frame input requirement.\ne The Flex-SIM speed was tested on a MacBook Pro laptop equipped with Apple M1 Pro and 16 GB RAM, due to the incomplete running on the Windows computer.\nComment: Clarification on PSF Modeling in BF-SIM\n[SI Line 173-179] In Supplementary Note 3, the discussion of BF-SIM states that it models the PSF as a 3D distribution, but the description of the challenges involved could be clarified. BF-SIM typically generates its PSF based on system parameters such as the objective lens properties, rather than requiring experimental measurement for each specific condition. While mismatches between the modeled and actual PSF may still affect reconstruction quality, the current wording could be interpreted as suggesting that BF-SIM requires direct experimental PSF measurement or extensive per-condition adjustments. The authors may consider refining this discussion to more precisely describe the PSF modeling process in BF-SIM and the extent to which it depends on experimental conditions.\nResponse: We appreciate the reviewer for highlighting the misunderstanding caused by our inappropriate wording. BF-SIM indeed used simulated PSF for reconstruction. We have refined the corresponding discussion to improve clarity:\nRevision:\n(Supplementary Note 3, Page 7, Para. 3)\n“By generating a simulated 3D PSF based on system parameters including the immersion refractive index, wavelength, numerical aperture, and pixel size, and subsequently subtracting the out-of-focus PSF contribution, BF-SIM achieves effective background suppression. However, accurately modeling an actual 3D PSF in simulation to align with complicated experimental conditions is challenging. Any mismatch, for example due to optical aberrations, may compromise the reconstruction quality.”\nComment: Minor Formatting and Writing Issues\nBelow are a few minor issues related to formatting, writing, and figure legends that could be addressed for clarity and consistency:\n1. [Line 185-187] The expression and crossref seems a bit obscure. In supplementary Fig. 2, only simulated filaments are presented.\n2. [Line 265-266] Should the corresponding figure be Extend Data Fig. 4?\n3. [Fig 3 caption] In Line 902, “b(orange histogram)” should be corrected to “c(orange histogram)”.\n4. [Fig 4b, h, Supplementary Fig. 16b] The color of the legend indicating orientation should have an angular gradient, not a linear gradient.\n5. [Supplementary Fig.4 caption] In Line 243, “without (upper left) and with (bottom right) Lock-in filtering” should be corrected to “without (bottom right) and with (upper left) Lock-in filtering”.\nResponse: We thank the reviewer again for the meticulous reading and apologize for these confusions and mistakes. All the noted issues have been corrected, and we have also conducted multiple careful reviews to identify and correct other potential writing mistakes, as well as refine the overall clarity and expression.\nFor the reviewer’s convenience, these revisions have been appended below:\nRevision:\n1. (Results, Lock-in-SIM provides optimal background suppression, image resolution, and fidelity, Page 7, Line 185-187)\nOriginal: “Applied to simulated, synthetic, and experimental bead datasets, Lock-in-SIM reconstructions proved highly robust against variations in noise (Supplementary Fig. 2) ...”\nRevised: “Applied to simulated, synthetic, or experimental bead datasets, Lock-in-SIM reconstructions proved highly robust against variations in noise (Supplementary Fig. 2) ...”\n2. (Results, High-fidelity whole-cell optical sectioning reconstruction using Lock-in-SIM with standard 2D-SIM acquisition, Page 10, Line 267-268)\nOriginal: “Moreover, we benchmarked Lock-in-SIM with standard 3D-SIM on the same data (Extended Data Fig. 3 and Supplementary Video 8).”\nRevised: “Benchmarking Lock-in-SIM against standard 3D-SIM of the same cell (Extended Data Fig. 4, Supplementary Fig. 18, and Supplementary Video 8), …”\n3. (Fig. 3 caption, Page 35, Line 908)\nOriginal: “Orientation distribution of the cell shown in a (green histogram) and in b (orange histogram).”\nRevised: “Orientation distribution of the cell shown in a (green histogram) and in c (orange histogram).”\n4. (Fig. 4b, h, Supplementary Fig. 19b, Color bar of θ)\nOriginal:\nRevised:\n5. (Supplementary Fig. 4 caption, Page 11)\nOriginal: “Widefield image and super-resolution images reconstructed without (upper left) and with (bottom right) Lock-in filtering.”\nRevised: “Widefield image and super-resolution images reconstructed without (bottom right) and with (upper left) Lock-in filtering.”\nOther revision examples:\n6. (Introduction, Page 4, Line 105-107)\nOriginal: “This process is inherent in SIM imaging and thus can be applied to data acquired from existing SIM systems and be integrated into established postprocessing workflows, without additional hardware modifications and troublesome parameter finetuning.”\nRevised: “This process is inherent in SIM imaging and can therefore be applied to data acquired from existing SIM systems, allowing for seamless integration into established post-processing workflows without additional hardware modifications and troublesome parameter fine-tuning.”\n7. (Fig. 3 caption, Page 35)\nOriginal: “Depth-coded maximum intensity projection of a 4.625-μm thick image stack of anti-Tomm20-labeled mitochondrial reconstructed using Wiener-SIM (left) and Lock-in-SIM (middle).”\nRevised: “Depth-coded maximum intensity projection of a 4.625-μm thick image stack of anti-Tomm20-labeled mitochondria reconstructed using Wiener-SIM (left) and Lock-in-SIM (middle).”\n8. (Extended Data Fig. 10 caption, Page 51)\nOriginal: “Two-color time-lapse images of Tomm20-EFGP-labeled mitochondrial outer membrane (cyan) and mCherry-labeled Drp1 oligomers (red).”\nRevised: “Two-color time-lapse images of Tomm20-EGFP-labeled mitochondrial outer membrane (cyan) and mCherry-labeled Drp1 oligomers (red).”\n9. (Supplementary Fig. 9 caption, Page 16)\nOriginal: “Reconstruction comparison of open-access simulaed and experimental data19 using different 2D-SIM algorithms.”\nRevised: “Reconstruction comparison of open-access simulated and experimental data19 using different 2D-SIM algorithms.”\n-----------------------------------------------------------------------------------------------------------------\n-----------------------------------------------------------------------------------------------------------------\nReviewer #2:\nComment: Liu et al. present a new computational approach for structured illumination microscopy image reconstruction that takes advantage of several tricks from conventional signal processing. They show that by using the input grating patterns as a “lock-in” signal, they can correct the background appropriately and generate some really remarkably high-quality SIM reconstructions. They go on to demonstrate using a battery of conventional targets for SIM that their system provides a equal-or-better reconstruction than nearly any other SIM reconstruction approach currently available, and even create a few nice packaged versions of the software to allow the free distribution of the technique.\nIn general, I am extremely enthusiastic about this paper. It marks the culmination of many years of work by many groups to generate a generalizable, well-rounded reconstruction approach that can be broadly distributed. They do sufficient work to demonstrate its utility across various samples, and I am very pleased to see the Fiji plugin and MATLAB GUI designed to bring it to the average user. I am particularly impressed on how well they have represented all of the competing techniques throughout the paper, allowing a new user to really see for themselves where and how this technique will help them.\nI have only one major concern and a few minor points before strongly recommending this for publication, I have detailed them below:\nResponse: We appreciate the reviewer for thorough reading, careful testing, and valuable feedback on our manuscript. We also greatly appreciate the reviewer’s high recognition of our work. We sincerely hope to make the meaningful contribution to the bioimaging community as highlighted by the reviewer. In the following, we give our responses point by point to the reviewer’s comments. The revised portions are marked in blue in the revised manuscript.\nComment: Major Point:\nOne presumable weakness of this approach is that it requires the ability to “lock-in” on the modulated (excitation) signal that comes from the patterned illumination. The authors show lots of examples of structures with one- or two-dimensional continuity, which makes this clear. I could not find any examples where the authors test this on small, sub-diffraction limited punc ta, especially those moving very quickly (fast enough to blur during 2D image acquisition). Would this be predicted to be problematic? I think it would be worth demonstrating whether this was true or not—either as a caution or as an explicit endorsement of using this technique for the numerous small puncta experiments common in biology (e.g., mitochondrial derived vesicles, plasma membrane microdomains, etc.).\nResponse: We appreciate the reviewer’s thoughtful suggestion to strengthen the demonstration of our method’s applicability and apologize for the lack of clarity in the previous presentation. Lock-in-SIM is indeed also suited for small puncta applications. In the original manuscript, we have shown the effective Lock-in-SIM reconstruction on biological clathrin-coated pit puncta (Supplementary Fig. 15), in addition to the common fluorescent bead experiments (Supplementary Figs. 3,5,8,9). To further enhance the puncta demonstration and provide an explicit endorsement, we conducted additional small puncta experiments, including mitochondrial-derived vesicles (Supplementary Fig. 16) and 5-ethynyl-2’-deoxyuridine (EdU) replication labeled nuclei (Supplementary Fig. 17). All together, these results highlight the reconstruction enhancement robustness of Lock-in-SIM across a variety of biologically structures.\nWe have incorporated these new datasets and added corresponding discussions into the revised manuscript. These revisions are also provided below for the reviewer’s convenience:\nRevision:\nSupplementary Fig. 16. Reconstruction comparison of Tomm20-AF488-labeled mitochondria and mitochondrial-derived vesicles. a, Widefield image and corresponding super-resolution images reconstructed using Wiener-SIM and Lock-in-SIM. b,c Magnified images of the white boxed mitochondrial-derived vesicles in a. From top to bottom, Widefield, Wiener-SIM, and Lock-in-SIM. Scale bars, 3 μm (a) and 0.1 μm (b,c).\nSupplementary Fig. 17. Optical-sectioning reconstruction comparison of EdU replication-labelled nuclei. a-c, Widefield images and corresponding super-resolution images reconstructed using Wiener-SIM and Lock-in-SIM of the sample at different sample depths. d-f, Magnified images of the white boxed regions in a-c. From top to bottom, Widefield, Wiener-SIM, and Lock-in-SIM. Scale bars, 3 μm (a-c) and 0.5 μm (d-f).\n(Results, Lock-in-SIM provides optimal background suppression, image resolution, and fidelity, Page 7, Line 196-201)\n“We next validated the superiority of Lock-in-SIM for different sample types with zero-, one-, two-, or three-dimensional continuity and varying imaging conditions using both open-access (Supplementary Figs. 6-12) and own experimental datasets (Fig.2, Extended data Figs. 2,3, Supplementary Figs. 13-18, and Supplementary Video 2), including the mitochondrial inner and outer membranes, microtubules, actin, lysosomes, clathrin-coated pits, mitochondrial-derived vesicles, and 5-ethynyl-2’-deoxyuridine (EdU) replication labeled nuclei.”\nComment: The second implication of this is that the fidelity of Lock-In SIM reconstruction is going to be dependent on the ability of the scope to produce a clean grating illumination pattern—incomplete or uneven interference patterns (as can happen when coverslips are not flat on the objective, etc.) will still cause the reconstruction to amplify artifacts (as do most of the other reconstruction algorithms, of course). Thus, I think the authors should be clear that Lock-In SIM is not an “artifact-free” approach, per se, but rather one that reduces background and improves about the reconstruction if the data is sufficiently high quality. This will be especially important for users that generate the SIM pattern by direct interferometry such as light sheet users, since slight misalignments in these systems can render the grating pattern difficult to fit. I have tried the Lock-In SIM on some data from my own group, and I notice that when the input data is clean, it works amazingly, but when the data has issues (poor interference fringes as a result of misaligned coverslip, in my case), and I do note erroneous breaking of continuous structures and amplification of noise in dark regions to generate artifactual puncta. To be clear, this is not a flaw with the technique, but I think the authors could explicitly state these limitations, at least in the supplement for experts running core facilities to watch for in their users’ data.\nResponse: We are grateful for the reviewer’s insightful comments and fully acknowledge the reviewer’s precise understanding of the reduced reconstruction quality of the SIM technique in handling raw data with low illumination pattern quality. The issue might be further amplified in Lock-in-SIM due to its stronger dependence on accurately extracting and utilizing a clean grating illumination pattern. In response, we have expanded the Discussion section to explicitly remind this and to also discuss potential mitigation strategies. Please see below the revision:\nRevision:\n(Discussion, Page 14, Line 408-418)\n“An inherent limitation of all SIM algorithms is the reduced reconstruction quality when processing raw data with degraded illumination pattern quality, due to the shared theoretical basis of structured illumination and demodulation. Such degradation can result from, for example, system imperfections, coverslip misalignment, low signal-to-noise ratio, or ultrafast motion blur. In the case of Lock-in-SIM, this issue may be further exacerbated due to its increased reliance on the accurate extraction and utilization of a clean grating illumination pattern. By precisely controlling data acquisition quality25 and further integrating advancements such as adaptive optics62,63, illumination field flatting64, and rationalized pattern-aware deep learning denoising27, the high-fidelity background suppression capability of Lock-in-SIM can be expected to be maintained even under highly challenging experimental conditions.”\n27. Qiao, C. et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat. Biotechnol. 41, 367-377 (2023).\n62. Žurauskas, M. et al. IsoSense: frequency enhanced sensorless adaptive optics through structured illumination. Optica 6, 370-379 (2019).\n63. Turcotte, R. et al. Dynamic super-resolution structured illumination imaging in the living brain. Proc. Nat. Acad. Sci. USA. 116, 9586-9591 (2019).\n64. Liang, Y. et al. Flat-field super-resolution structured illumination microscopy with joint spatial-temporal light modulation. bioRxiv (2024).\nComment: Minor Points:\n1. In Extended Data Fig. 1, the authors label the bottom of the images “Nucleus.” Are the authors actually sure this is where the nucleus is (i.e. – they have some kind of DNA signal in another channel?). I ask because this is a very unusual ER structure to have under the nucleus, and looks much more like the dense “perinuclear” ER structures that happen around its edges. If they aren’t sure, I would label it more generically as “perinuclear” to avoid an allergic response from someone who studies the nuclear envelope (which does not seem to be visible here).\nResponse: We thank the reviewer for this reminder. We have corrected the label from “Nucleus” to “Perinuclear” and also revised the figure caption and manuscript accordingly.\nRevision:\nExtended Data Fig. 1. Reconstruction comparison of the thick cell sample using different 2D-SIM methods. a-d, Experimental ER (labeled with Sec61β-EGFP) results from widefield microscopy (a), standard 2D-SIM (b), Lock-in-SIM (c), and TIRF-SIM (d). The upper-left diagram shows the corresponding illumination scheme. e,f, Superposition of Lock-in-SIM image (green) with standard 2D-SIM image (magenta) (e) and Lock-in-SIM image (green) with TIRF-SIM image (magenta) (f). The dotted curves denote the thicker and denser perinuclear region. Scale bars, 4 μm (a-f).\n(Results, Principle and validation of the background-free Lock-in-SIM, Page 6, Line 174-179)\n“To further illustrate the capability and biological benefits of Lock-in-SIM, we focused on the ER in thicker perinuclear regions of the cell (Extended Data Fig. 1) rather than examining only the cell margin. By merging Lock-in-SIM with standard 2D-SIM (Extended Data Fig. 1e) and TIRF-SIM (Extended Data Fig. 1f), Lock-in-SIM clearly preserved the entire ER meshwork while eliminating the background, especially around thicker, more densely packed perinuclear ER.”\nComment: 2. Can the authors make some quantitative estimates from their data about the z-resolution (or effective focal depth?) in their optical sectioning experiments? They demonstrate it is improved compared to 2D SIM, but it would be nice to have a sense of to what degree or at least order of magnitude. The results on the ER in Extended Data Fig. 1 suggest a dramatic improvement in reducing the parts of the organelle sectioned, it would be nice to know how much z-depth that corresponds to.\nResponse: We thank the reviewer for this constructive suggestion and apologize for the confusion. We would like to clarify that Lock-in-SIM does not improve either lateral (xy) or axial (z) resolution beyond that of standard 2D-SIM; rather, it only enhances the 2D optical sectioning capability by reducing background while preserving the same sample structure. This point has been previously explained in both the Results section “High-fidelity whole-cell optical sectioning reconstruction using Lock-in-SIM with standard 2D-SIM acquisition.” and Methods section “Experimental SIM data acquisition.”\nTo further substantiate this clarification quantitatively, we specifically acquired both z-sectioning 2D-SIM and 3D-SIM datasets in the revised EdU replication-labelled nuclei puncta experiment. We then analyzed the reconstruction results in both xy and xz views and quantified the z-resolution by calculating the axial full width at half maximum (FWHM) (Supplementary Figs. 17,18). The data indeed corroborated our statements: Lock-in-SIM effectively removes background while maintaining the same structural distribution (Supplementary Fig. 18a), and the z-resolution remains at the diffraction-limited level of approximately 600 nm for both Lock-in-SIM and Wiener-SIM, whereas only 3D-SIM improves it to super-resolved 341 nm (Supplementary Fig. 18b). These new datasets and analyses have been added to this section and are also provided below for the reviewer’s reference:\nRevision:\nSupplementary Fig. 17. Optical-sectioning reconstruction comparison of EdU replication-labelled nuclei. a-c, Widefield images and corresponding super-resolution images reconstructed using Wiener-SIM and Lock-in-SIM of the same sample at different sample depths. d-f, Magnified images of the white boxed regions in a-c. From top to bottom, Widefield, Wiener-SIM, and Lock-in-SIM. Scale bars, 3 μm (a-c) and 0.5 μm (d-f).\nSupplementary Fig. 18. 3D reconstruction comparison of EdU replication-labelled nuclei. a, Axial views of the widefield images and corresponding super-resolution images reconstructed using Wiener-SIM, Lock-in-SIM, and 3D-SIM of the same EdU sample in Supplementary Fig. 17. b, Normalized intensity profiles along the dashed line region in a and the corresponding axial full width at half maximum (FWHM) values. Orange arrows indicate the background artifacts in Wiener-SIM image. Scale bars, 2 μm (a).\nComment: Remarks on code availability:\nI have tested both the Fiji plugin and the MATLAB code on approximately 50 images collected by my own group from different SIM implementations. I'd say on 90+% of them, it worked very well. I note it did introduce some artifacts and issues if the input data quality was poor (especially when misalignment of the system caused the underlying SIM illumination pattern to be difficult to make out). All SIM reconstruction patterns have issues with this kind of data, however, so I don't see this as a substantial flaw--but I note it existed and in some cases was exacerbated in the Lock-In SIM compared to simpler Weiner filtered SIM reconstructions.\nResponse: We would like to express our sincere gratitude to the reviewer once again for recognizing our work and for helping to enhance it through meticulous testing and enlightening and insightful feedback. This comment has been addressed in the first major point above."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised version of the manuscript entitled \"Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM\", the authors have provided thoughtful and effective responses to the comments raised in the first round of review. The revisions have improved both the technical clarity and the overall presentation of the work.\nThe clarification regarding the modulation depth m has effectively removed unnecessary ambiguity in the description of the Lock-in demodulation process. Similarly, the revised explanation of PSF modeling in BF-SIM has clarified that it relies on system parameters rather than direct experimental measurements, while still noting the potential impact of PSF mismatch.\nThe additional details and discussions added to the comparison with other methods, particularly concerning parameter selection and computational efficiency, also contribute to a more balanced and informative evaluation of the proposed technique.\nMinor formatting and figure-related issues mentioned previously have largely been resolved. The authors have also amended other minor errors missed in the initial review. The overall presentation of the manuscript is now clearer and more consistent.\nIn conclusion, the authors have sufficiently addressed the points raised during the first review round, and I have no further concerns. I think the manuscript is now in good shape for publication, pending any final editorial checks.\n(Remarks on code availability)\nThe code is complete, clear, and well-documented. Using the data provided by the authors, I successfully reproduced all results which match those reported in the text. This is further facilitated by their accessible software tools, including the MATLAB GUI, Fiji/ImageJ plugin, and standalone executable, making both validation and application straightforward.", "reviewer_2": "The authors have exhaustively addressed all of my concerns, I particularly appreciate the attention to detail with the extra supplemental figures--very thoroughly done and well explained. I do not have any remaining concerns, I hope to see the paper in press soon.\n(Remarks on code availability)\nThis was all evaluated and found to be outstanding in the previous round of reviews."}, "author_response": ""}]

Decision letter