Open-Source 3D Active Sample Stabilization for Fluorescence Microscopy

Open-Source 3D Active Sample Stabilization for Fluorescence Microscopy | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Open-Source 3 D Active Sample Stabilization for Fluorescence Microscopy View ORCID Profile Sanket Patil , View ORCID Profile Giuseppe Vicidomini , View ORCID Profile Eli Slenders doi: https://doi.org/10.1101/2025.01.10.631718 Sanket Patil 1 Molecular Microscopy and Spectroscopy (MMS), Italian Institute of Technology , Genoa, Italy 2 Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa , Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sanket Patil Giuseppe Vicidomini 1 Molecular Microscopy and Spectroscopy (MMS), Italian Institute of Technology , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Giuseppe Vicidomini Eli Slenders 1 Molecular Microscopy and Spectroscopy (MMS), Italian Institute of Technology , Genoa, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eli Slenders For correspondence: eli.slenders{at}iit.it Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Super-resolution microscopy has enabled imaging at nanometer-scale resolution. However, achieving this level of detail without introducing artifacts that could mislead data interpretation requires maintaining sample stability throughout the entire imaging acquisition. This process can range from a few seconds to several hours, particularly when combining live-cell imaging with super-resolution techniques. Here, we present a 3 D active sample stabilization system based on real-time tracking of fiducial markers. To ensure broad accessibility, the system is designed using readily available off-the-shelf optical and photonic components. Additionally, the accompanying software is open-source and written in Python, facilitating adoption and customization by the community. We achieve a standard deviation of the sample movement within 1 nm in both the lateral and axial direction for a duration in the range of hours. Our approach allows easy integration into existing microscopes, not only making prolonged super-resolution microscopy more accessible but also allowing confocal and widefield live-cell imaging experiments spanning hours or even days. Introduction Over the past three decades, super-resolution microscopy (SRM) techniques have emerged, enabling a spatial resolution far beyond the diffraction limit ( 1 – 5 ). Notably, in the context of single-molecule localization microscopy, resolutions at the Ångström scale have recently been achieved ( 6 – 8 ). SRM opened up the possibility to study cellular processes at the nm scale, allowing e . g ., nanoscale functional topography of chromatin and studying its role in genome integrity ( 9 , 10 ), and the direct visualization of kinesin dynamics ( 11 , 12 ). However, the performance of SRM, particularly of Single-Molecule Localization Microscopy (SMLM), depends on the inherent stability of the whole imaging system, including the sample. System drift, a pervasive challenge affecting all imaging platforms, significantly hinders the achievable resolution in SRM. This drift arises from a combination of thermal fluctuations and mechanical vibrations within both the imaging system itself and the surrounding environment. The negative impact of system drift becomes evident in the form of a declining image quality ranging from minor imperfections in low-resolution imaging to severe spatial resolution degradation, potential misinterpretation of the data, and even rendering the data unusable in super-resolution applications. Approaches to address sample drift can be broadly categorized into two methods: active stabilization ( 13 – 15 ) and post-processing corrections ( 16 – 18 ). Active stabilization focuses on real-time drift correction by continuously monitoring the sample’s position, for example, by tracking fiducial markers, and by using mechanical or piezoelectric actuators to adjust the sample’s position to counteract any drift. This approach can be implemented for all three axes or for axial direction only in scenarios where lateral drift is negligible or addressed through separate corrections ( 19 ). Post-processing corrections address drift after image acquisition. Drift estimation can be achieved either through mathematical model estimation ( 20 ) or by imaging fiducial markers alongside the sample ( 13 , 15 ). Notably, without acquiring a full 3D image stack, axial drift cannot be effectively compensated during post-processing. Indeed, axial drift leads to out-of-focus images, with inherent information loss as a consequence. Active stabilization is preferred in low-throughput SMLM techniques, such as MINFLUX, RAST-MIN, and pMINFLUX ( 21 – 24 ), where the limited number of localized molecules makes it difficult to accurately deduce drift through post-processing methods. The use of active stabilization techniques to enhance sample stability has become more widespread in recent years ( 19 , 25 – 28 ). Additionally, multiple commercial systems with axial stabilization capabilities have emerged ( 29 – 31 ). However, the adaptability of these commercial solutions is often hindered by their limited compatibility with custom-built imaging systems. This has led to the development of numerous custom approaches for sample stabilization, broadly categorized into fiducial marker-based stabilization and reflection-based stabilization. Fiducial marker-based stabilization tracks the position of pre-introduced fiducial markers, such as gold nanoparticles, gold nanorods, or fluorescent beads, within the sample. Drift is continuously monitored through a dedicated imaging pathway that tracks in real-time the position of these markers. Reflection-based stabilization makes use of the reflection of a focused beam off the coverslip. An axial movement of the sample leads to a change in the position or shape of the image of the reflected beam. In both cases, a feedback loop adjusts the sample position to actively compensate for any detected displacement. Each approach presents distinct advantages and limitations. Fiducial-based techniques offer the benefit of three-dimensional stabilization but necessitate the introduction of fiducial markers during sample preparation. Conversely, reflection-based techniques, while requiring no sample alteration, are restricted only to axial correction. Consequently, fiducial-based stabilization strategies are often the preferred choice for low throughput SMLM methods, such as MINFLUX, which require real-time 3 D stabilization. Several active 3 D stabilization systems for custombuilt microscopes currently exist ( 22 , 24 , 32 ). While these approaches yield stabilization results around 1 nm for all axes, their implementation is either hindered by the need for a costly setup, including a PC with a suitable graphics card for GPU computing, a more complex sample preparation, such as the use of functionalized polystyrene beads, a limited feedback update rate (< 20 Hz), or the lack of opensource availability. Here, we present a fast, open-source 3D active sample stabilization system that delivers similar performance but at a low cost, requiring neither complex sample preparation nor extensive hardware installation. The setup is designed as a modular, standalone add-on that is compact and can be easily integrated with any microscope. Besides the actuators to move the sample, the hardware is based on readily available commercial components, with a total implementation cost around C2500. Materials and methods A Optical Setup and sample The optical configuration is shown in Fig. 1a . An infrared laser (L780P010 - 780 nm, 10 mW, Thorlabs, New Jersey, USA) serves as the illumination source for the fiducial markers. The collimated beam is split by a 50/50 cube beam splitter (BS014, 700 - 1100 nm, Thorlabs) and coupled into the microscope with a near-infrared (NIR) short-pass dichroic mirror (AHF 745/SP BrightLine HC Shortpass Filter, F39-745). By adjusting the mirrors positioned before the beam splitter, the beam is laterally displaced from the optical axis at the back aperture of the objective, leading to highly inclined and laminated optical sheet (HiLo) illumination after passing through the tube lens and objective lens (Nikon Plan Apo VC 100 × /1.40 Oil OFN25 DIC N2). The HiLo illumination enables imaging over a large FOV (∼36 × 36 µm), which allows tracking of multiple particles simultaneously to improve robustness and precision (in our experiments, we use a concentration providing a number of particles between 5 and 15). The backscattered signal from the fiducial markers is collected by the same objective and reflected by the NIR dichroic mirror to enter the stabilization part again. Here, the beam passes through the 50/50 cube beamsplitter and enters a 4f system composed of two lenses, L 1 and L 2 (f = 200 mm each). Both the tube lens and L 1 , and L 1 and L 2 form a 4f system. In the conjugate plane between L 1 and L 2 , an image of the back focal plane is formed, and the light reflected by the cover slip is focused off-axis. An iris diaphragm (IDA25/M, Thorlabs) blocks the light reflected by the coverslip while transmitting most of the backscattered light. Finally, the signal is split with another 50/50 beam splitter and projected onto two CMOS cameras (CS165MU/M - Zelux, Thorlabs), used to independently stabilize the lateral and ax-ial direction, respectively. A cylindrical lens (f = 1000 mm) induces astigmatism into the latter beam path to enable axial position estimation. Using two cameras instead of one effectively prevents aberrations caused by the cylindrical lens from introducing errors in lateral position calculations. The captured images from both cameras are transmitted to a central computer for further processing and calculations. Drift corrections are sent to a three-axis nanopositioning piezoelectric stage (P-545.3R8S PInano, E-727 Controller, Physik Instrumente). Download figure Open in new tab Fig 1. Optical Setup and Axial Calibration. (a) Optical setup of the stabilization system. The green part shows the stabilization setup, which is coupled to a microscope (partially shown in purple). The stabilization beam is coupled into the imaging path using a short-pass dichroic mirror, depicted in red in the imaging part. (b) Exemplary camera images of the XY and Z camera. The column on the right shows the astigmatism induced by the cylindrical lens, as imaged by the Z camera at different axial planes. (c) Autocorrelation of the images captured by the Z camera for the same z positions as in (b). The axial position of the sample is derived from the asymmetry of the autocorrelation function, calculated as the ratio of the summed intensities of the pixel values in H (green) and V (blue). (d) Measured autocorrelation asymmetry as a function of the z position. The calibration measurement performed at the start of each experiment covers a smaller range (from −50 nm to +50 nm, see inset), where the asymmetry can be well approximated as a linear curve (dotted line). The calibration curve and the error bars are the mean and standard deviation over ten iterations. The inset plot in the top left shows the horizontal and vertical components (shown in (c)) of the summed intensities over the green and blue boxes. To stabilize the sample, fiducial markers such as gold nanoparticles (GNPs) can be used. Here, we use GNPs with a diameter ranging from 100-150 nm. The choice of gold nanoparticles stems from their several advantageous characteristics: readily available and cost-effective at various sizes, and capable of scattering laser light across a wide range of wavelengths ( 33 ). The components required for building the stabilization are listed in Table S1 and Table S2 . B Control Software The system integrates a central computer interfacing with the CMOS cameras and the nanopositioning piezoelectric stage. The computer stabilizes the sample by imaging the fiducial markers, performing real-time 3 D locaisation of the particles, and repositioning the stage accordingly. The software is developed in Python and runs entirely on the CPU. We use multiprocessing to parallelize fiducial marker localization in the lateral direction, accelerating drift calculations. The decision to forgo GPU utilization ensures broad compatibility across a diverse range of systems, including systems equipped solely with integrated GPUs or non-NVIDIA dedicated options, where GPU utilization might not offer a performance advantage and could even impede processing speed due to data transfer overhead. Before initiating the stabilization process, the software connects to the two CMOS cameras and the nanopositioning piezoelectric stage. All three pieces of hardware require dynamic-link library (.dll) files to establish a connection using the USB 3.0 protocol. These .dll files, provided by the proprietary control software of the cameras (Thorlabs) and the piezoelectric stage (Physik Instrumente), can be installed by the user via a straightforward Windows installation wizard. After the hardware connection, the user can crop the FOV for each camera independently, Fig. 1 (b) , balancing a larger FOV to capture more particles with a smaller FOV to achieve a higher frame rate. Subsequently, the user selects a chosen number of particles within this FOV for lateral stabilization. The software then performs a z -scan using the same piezoelectric stage employed for stabilization to measure the PSF shape at various axial positions, Fig. 1 (b) . Then, depending on the selected settings, up to 10 processes start running in parallel: one process per camera for image capture, one process per camera for image analysis, one process to control the stage, one process for real-time plotting, and up to four additional processes for miscellaneous tasks, such as data saving, statistical calculations, and data logging. This implementation accommodates processes running at different speeds, allowing capturing images at a high frame rate ( e . g ., ∼120 Hz), even when stage updates occur at a lower frequency (∼20 Hz). Fig. 2 illustrates the pseudocode for the stabilization software. Of the 10 processes initiated, 5 are essential for actively stabilizing the sample, while the remaining 5 are optional and can be toggled on or off depending on experimental requirements. Disabling the non-essential processes can enhance acquisition and calculation speed when the CPU is fully utilized. When active, all 10 processes operate in parallel, utilizing a shared memory space allocated at the start of the program to facilitate simultaneous data access. In this way, the data generated by each process is shared with the other processes. Within this shared memory, data is classified into two types: new data (indicated by green arrows) generated with each process iteration and duplicate data (indicated by pink arrows), which is used for data saving and logging purposes. A potential challenge arises when multiple processes attempt to access the shared memory concurrently for reading or writing, which can lead to memory corruption and disrupt the stabilization program. To address this issue, we have implemented a memory lock mechanism. The memory lock is implemented in such a way that whenever a process needs to read from or write to the shared memory buffer, the memory is locked by that particular process handler so that no other process can access that memory space. While the memory lock introduces a minor time penalty of less than 100 µs per iteration, it enables efficient data exchange between processes without the need for explicit inter-process communication (IPC) mechanisms, ensuring stable and uninterrupted program operation. Download figure Open in new tab Fig 2. Stabilzaition code workflow. Flowchart with pseudocode describing the stabilization code that controls the hardware. The code is split into 10 different processes that are categorized into two types. The first type (green) represents the essential processes required for the stabilization code. The second type (pink) represents the non-essential part of the stabilization code. Similarly, the green arrows show the essential data transfer, and the pink arrows show the non-essential data transfer. Positional deviations in 3 D are assessed by comparing measured particle locations with the reference points established at the start of the measurement. Detected deviations trigger corresponding negative feedback commands sent to the piezoelectric stage. This closed-loop control system actively returns the sample to its relative position with respect to the stabilization beam path. In more detail, the software reads the cameras and stores the last 25 frames in memory. A separate process takes, for each camera, the weighted average of these frames, with the highest weight for the latest frame and an exponentially decreasing weight for earlier frames. For lateral drift correction, the particles are localized, and their average drift from the reference points is stored in memory. A separate process checks with a fixed rate ( × 20 per second) whether or not the measured average drift exceeds a threshold of 1 nm and, if necessary, updates the lateral position of the stage. Axial drift is assessed by comparing the PSF shape in the averaged frame with the reference curve obtained during the initialization procedure. Similarly to the lateral drift, a separate process updates the axial position of the stage with a fixed rate of 20 Hz. To prevent oscillatory behavior caused by overshooting, we mul-tiply the measured drift for each dimension with a factor α (0 < α ≤ 1). This gain reduction allows approaching the set point gradually, i . e ., in multiple stage movement cycles, which improves the stability. C Position Estimation We estimate the lateral drift in the camera frame of reference d ( t ) at time point t by averaging the measured changes in the 2 D position of N fiducial markers compared to the start of the measurement: d ( t ) = ⟨x( t ) − x(0)⟩, with x( t ) the positions of the particles at time t . We use the local gradients method ( 34 ) to measure x( t ) because of its computational efficiency compared to fitting a Gaussian function. Conversion of drift from pixels to nm is done based on a calibration measurement with a reflective grid sample (R1L3S3PR, ThorLabs). Axial drift estimation is based on astigmatism introduced by the cylindrical lens positioned in front of the camera and comparing the shape of the point spread function (PSF) with respect to the start of the measurement. To estimate the relative z -position, we calculate the autocorrelation G in a user-selected region of the image, ideally containing multiple particles for enhanced robustness and precision ( Fig. 1 (c, d) ). We define a measure for the PSF shape A as the ratio A =Σ i G i / Σ j G j with i ∈ H and j ∈ V, with H and V perpendicular rectangular regions centered around the autocorrelation peak. Conversion from A to axial displacement in nm is based on a reference curve measured at the start of every measurement ( Fig. 1 (d) ). This calibration involves sampling multiple planes in both the positive (+ z ) and negative (− z ) directions with respect to the experiment plane. At each sampled plane, we calculate the corresponding A value. These values are then fitted using linear regression (see the inset of Fig. 1 (e) ). The slope of the fit represents the conversion factor Δ A/ Δ z , expressed in arbitrary units per nanometer. We typically perform the calibration procedure for a small range (−50 nm to +50 nm) with respect to the initial z position, where the calibration curve can be well approximated by a linear curve. Results and discussion First, we measured the performance of the stabilization system using the calculated particle positions with respect to the start of the measurement in the camera frame of reference, as measured by the stabilization system itself during closed-loop stabilization ( Fig. 3 (a) ). For all axes, we found a normal distribution with a mean position around 0 and a standard deviation below 1 nm for a 1000 s measurement. Since the nanopositioning piezo system has a closed-loop resolution of 1 nm, and our stabilization software uses a 1 nm threshold for stage updates, we cannot expect a better precision. Note that, due to differences in the frame rate of the cameras and the update rate of the stage, the measured drift may be higher than the 1 nm threshold for multiple consecutive frames, as shown in the insets in Fig. 3 (a) . The corresponding stage position ( Fig. 3(b) ), shows about 100 and 200 nm drift in the x, y direction, respectively, and about 150 nm drift in the axial direction over 1000 s. Comparison of the power spectral density curves of the stage and sample position ( Fig.3(c) ) shows that drift and low-frequency vibrations are suppressed by the stabilization software by more than one order of magnitude in all dimensions. The repositioning of the stage amplifies some vibrations, as can be seen around 1 Hz for the x -direction and 2.5 Hz for the z -direction. However, the amplitudes of these vibrations are low enough to keep the overall sample movement below 1 nm standard deviation in each direction. Download figure Open in new tab Fig 3. Stabilization results. (a) Sample position as monitored by the stabilization system and the corresponding histograms showing the distribution of the sample position across time. The parameter σ is the standard deviation of the Gaussian fit to the histogram. (b) Corresponding position of the piezoelectric stage for each axis. (c) Power spectral density (PSD) of the stabilized sample position (coloured) and the stage position (black). As a control measurement, we performed long-term confocal imaging of the gold particle sample with the stabilization system running. Specifically, we removed the bandpass filter from our custom confocal and image scanning microscope to record reflection images in descanned mode using a single-photon avalanche diode (SPAD) array detector ( 35 ). Subsequently, we conducted time-series experiments utilizing our microscope control software, BrightEyes-MCS ( 36 ). We calculated the lateral drift in the confocal images by phase correlating each image with a reference image taken at the start of the measurement. With the stabilization software running, we observed a drift of (−0.8 ± 1.2) nm and (−0.6 ± 0.9) nm (mean ± standard deviation) in the x and y direction, respectively, over the course of 4 hours. Although these values exceed the sub-nm precision calculated by the stabilization system, these results may be expected given the additional uncertainty introduced by the galvanometric scan system or other drift components in sections of the beam path that are not common with the stabilization path. With the stabilization system turned off, we measured a lateral drift of more than 500 nm on the x-axis and more than 900 nm on the y-axis, with additional drift in the axial direction, moving the sample out of focus ( Fig. 4 (b) & (d) ). Download figure Open in new tab Fig 4. Confocal images acquired with and without active stabilization. (a) Subset of a time series taken with the active stabilization on. The sample consisted of gold nanoparticles attached to the coverslip surface. Images were acquired every minute for four hours. With the stabilization system on, there is no visible drift. (b) The same measurement with the stabilization system off shows a clear drift in both the lateral and axial directions over the course of four hours. (c, d) easured drift in each image with respect to the start of the time-lapse, calculated by phase-correlation, with the stabilization system on (c) and off (d) For both lateral and axial drift calculations, we prioritized lightweight algorithms over slower, more precise alternatives ( 34 ). Consequently, the update speed of our system is not limited by the software but by the rate at which the piezo stage controller can process movement commands, here 20 Hz for each axis. For applications where speed and accuracy are less critical, such as time-lapse measurements in confocal or widefield microscopy, alternative algorithms may be preferable. For instance, the local gradients method is well-suited to sparse particles whose images are radi-ally symmetric. However, for denser particle distributions or larger, non-radially symmetric structures, other methods, such as phase correlation, will perform better. Similarly, when only axial stabilization is required, analyzing the reflection of a beam off a cover slip can be advantageous, as it eliminates the need for fiducial markers in the sample. Although we have not implemented these alternatives here, the open-source code can be easily adapted to accommodate these custom scenarios. In summary, we present an active 3 D stabilization system for microscopy imaging, utilizing real-time monitoring of infrared back-scattering from fiducial markers within the sample. We capture and analyze widefield images at a rate of about 60 Hz and update the stage position at a rate of 20 Hz. Our approach yields sub-nanometer precision across all three spatial dimensions for several hours. The optical system is compact and portable, facilitating easy installation and transfer between different microscopes. The software is open-source and runs entirely on the CPU, removing the need for specialized hardware, such as a high-end GPU-equipped computer. We anticipate that these advantages will make super-resolution microscopy, as well as time-lapse confocal and widefield microscopy experiments, more accessible. Code availability The Python code has been deposited on GitHub: https://github.com/VicidominiLab/Active_stabilization . Supplementary Information View this table: View inline View popup Download powerpoint Table S1. List of essential components required for building the stabilization system View this table: View inline View popup Download powerpoint Table S2. List of additional (optional) components that may be used for building the stabilization system Acknowledgements This research was supported by: the European Research Council, BrightEyes No. 818699 (S.P., G.V., E.S.); the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 890923 (SMSPAD) (G.V., E.S.); PNRR MUR project PE00000023 – NQSTI and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 881603 Graphene Flagship (G.V.). Footnotes https://github.com/VicidominiLab/Active_stabilization Bibliography 1. ↵ M. G. L. Gustafsson . Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy . Journal of Microscopy , 198 ( 2 ): 82 – 87 , 2000 . ISSN 1365-2818 . doi: 10.1046/j.1365-2818.2000.00710.x . _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2818.2000.00710.x . OpenUrl CrossRef PubMed Web of Science 2. Michael J. Rust , Mark Bates , and Xiaowei Zhuang . Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM ). Nat Methods , 3 ( 10 ): 793 – 796 , October 2006 . ISSN 1548-7105 . doi: 10.1038/nmeth929 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed Web of Science 3. Eric Betzig , George H. Patterson , Rachid Sougrat , O. Wolf Lindwasser , Scott Olenych , Juan S. Bonifacino , Michael W. Davidson , Jennifer Lippincott-Schwartz , and Harald F. Hess . Imaging Intracellular Fluorescent Proteins at Nanometer Resolution . Science , 313 ( 5793 ): 1642 – 1645 , September 2006 . doi: 10.1126/science.1127344 . Publisher: American Association for the Advancement of Science . OpenUrl Abstract / FREE Full Text 4. Stefan W. Hell and Jan Wichmann . Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy . Opt. Lett., OL , 19 ( 11 ): 780 – 782 , June 1994 . ISSN 1539-4794 . doi: 10.1364/OL.19.000780 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed Web of Science 5. ↵ Mike Heilemann , Sebastian van de Linde , Mark Schüttpelz , Robert Kasper , Britta Seefeldt , Anindita Mukherjee , Philip Tinnefeld , and Markus Sauer . Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes . ANGEWANDTE CHEMIEINTERNATIONAL EDITION , 47 ( 33 ), 2008 . ISSN 1433-7851 . 6. ↵ Michael Weber , Henrik von der Emde , Marcel Leutenegger , Philip Gunkel , Sivakumar Sambandan , Taukeer A. Khan , Jan Keller-Findeisen , Volker C. Cordes , and Stefan W. Hell . MINSTED nanoscopy enters the Ångström localization range . Nat Biotechnol , 41 ( 4 ): 569 – 576 , April 2023 . ISSN 1546-1696 . doi: 10.1038/s41587-022-01519-4 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 7. Michael Weber , Marcel Leutenegger , Stefan Stoldt , Stefan Jakobs , Tiberiu S. Mihaila , Alexey N. Butkevich , and Stefan W. Hell . MINSTED fluorescence localization and nanoscopy . Nat. Photonics , 15 ( 5 ): 361 – 366 , May 2021 . ISSN 1749-4893 . doi: 10.1038/s41566-021-00774-2 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 8. ↵ Susanne C. M. Reinhardt , Luciano A. Masullo , Isabelle Baudrexel , Philipp R. Steen , Rafal Kowalewski , Alexandra S. Eklund , Sebastian Strauss , Eduard M. Unterauer , Thomas Schlichthaerle , Maximilian T. Strauss , Christian Klein , and Ralf Jungmann . Ångströmresolution fluorescence microscopy . Nature , 617 ( 7962 ): 711 – 716 , May 2023 . ISSN 1476-4687 . doi: 10.1038/s41586-023-05925-9 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 9. ↵ Fena Ochs , Gopal Karemore , Ezequiel Miron , Jill Brown , Hana Sedlackova , Maj-Britt Rask , Marko Lampe , Veronica Buckle , Lothar Schermelleh , Jiri Lukas , and Claudia Lukas . Stabilization of chromatin topology safeguards genome integrity . Nature , 574 ( 7779 ): 571 – 574 , October 2019 . ISSN 1476-4687 . doi: 10.1038/s41586-019-1659-4 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 10. ↵ Ezequiel Miron , Roel Oldenkamp , Jill M. Brown , David M. S. Pinto , C. Shan Xu , Ana R. Faria , Haitham A. Shaban , James D. P. Rhodes , Cassandravictoria Innocent , Sara de Ornellas , Harald F. Hess , Veronica Buckle , and Lothar Schermelleh . Chromatin arranges in chains of mesoscale domains with nanoscale functional topography independent of cohesin . Science Advances , 6 ( 39 ): eaba8811 , September 2020 . doi: 10.1126/sciadv.aba8811 . Publisher: American Association for the Advancement of Science . OpenUrl FREE Full Text 11. ↵ Takahiro Deguchi , Malina K. Iwanski , Eva-Maria Schentarra , Christopher Heidebrecht , Lisa Schmidt , Jennifer Heck , Tobias Weihs , Sebastian Schnorrenberg , Philipp Hoess , Sheng Liu , Veronika Chevyreva , Kyung-Min Noh , Lukas C. Kapitein , and Jonas Ries . Direct observation of motor protein stepping in living cells using MINFLUX . Science , 379 ( 6636 ): 1010 – 1015 , March 2023 . doi: 10.1126/science.ade2676 . Publisher: American Association for the Advancement of Science . OpenUrl CrossRef PubMed 12. ↵ Jan O. Wirth , Lukas Scheiderer , Tobias Engelhardt , Johann Engelhardt , Jessica Matthias , and Stefan W. Hell . MINFLUX dissects the unimpeded walking of kinesin-1 . Science , 379 ( 6636 ): 1004 – 1010 , March 2023 . doi: 10.1126/science.ade2650 . Publisher: American Association for the Advancement of Science . OpenUrl CrossRef PubMed 13. ↵ Sang Hak Lee , Murat Baday , Marco Tjioe , Paul D. Simonson , Ruobing Zhang , En Cai , and Paul R. Selvin . Using fixed fiduciary markers for stage drift correction . Opt. Express, OE , 20 ( 11 ): 12177 – 12183 , May 2012 . ISSN 1094-4087 . doi: 10.1364/OE.20.012177 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed 14. Michael J. Mlodzianoski , John M. Schreiner , Steven P. Callahan , Katarina Smolková , Andrea Dlasková , Jitka Šantorová , Petr Ježek , and Joerg Bewersdorf . Sample drift correction in 3D fluorescence photoactivation localization microscopy . Opt. Express, OE , 19 ( 16 ): 15009 – 15019 , August 2011 . ISSN 1094-4087 . doi: 10.1364/OE.19.015009 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed Web of Science 15. ↵ Ryan McGorty , Daichi Kamiyama , and Bo Huang . Active microscope stabilization in three dimensions using image correlation . Optical Nanoscopy , 2 ( 1 ): 3 , April 2013 . ISSN 2192-2853 . doi: 10.1186/2192-2853-2-3 . OpenUrl CrossRef 16. ↵ Renmin Han , Liansan Wang , Fan Xu , Yongdeng Zhang , Mingshu Zhang , Zhiyong Liu , Fei Ren , and Fa Zhang . Drift correction for single-molecule imaging by molecular constraint field, a distance minimum metric . BMC Biophysics , 8 ( 1 ): 1 , January 2015 . ISSN 2046-1682 . doi: 10.1186/s13628-014-0015-1 . OpenUrl CrossRef PubMed 17. Yina Wang , Joerg Schnitzbauer , Zhe Hu , Xueming Li , Yifan Cheng , Zhen-Li Huang , and Bo Huang . Localization events-based sample drift correction for localization microscopy with redundant cross-correlation algorithm . Opt. Express, OE , 22 ( 13 ): 15982 – 15991 , June 2014 . ISSN 1094-4087 . doi: 10.1364/OE.22.015982 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed 18. ↵ Claudia Geisler , Thomas Hotz , Andreas Schönle , Stefan W. Hell , Axel Munk , and Alexander Egner . Drift estimation for single marker switching based imaging schemes . Opt. Express, OE , 20 ( 7 ): 7274 – 7289 , March 2012 . ISSN 1094-4087 . doi: 10.1364/OE.20.007274 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed 19. ↵ L. Silvestri , M. C. Müllenbroich , I. Costantini , A. P. Di Giovanna , G. Mazzamuto , A. Franceschini , D. Kutra , A. Kreshuk , C. Checcucci , L. O. Toresano , P. Frasconi , L. Sacconi , and F. S. Pavone . Universal autofocus for quantitative volumetric microscopy of whole mouse brains . Nat Methods , 18 ( 8 ): 953 – 958 , August 2021 . ISSN 1548-7105 . doi: 10.1038/s41592-021-01208-1 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 20. ↵ Adrian Przybylski , Björn Thiel , Jan Keller-Findeisen , Bernd Stock , and Mark Bates . Gpufit: An open-source toolkit for GPU-accelerated curve fitting . Sci Rep , 7 ( 1 ): 15722 , November 2017 . ISSN 2045-2322 . doi: 10.1038/s41598-017-15313-9 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 21. ↵ Francisco Balzarotti , Yvan Eilers , Klaus C. Gwosch , Arvid H. Gynnå , Volker Westphal , Fernando D. Stefani , Johan Elf , and Stefan W. Hell . Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes . Science , 355 ( 6325 ): 606 – 612 , February 2017 . doi: 10.1126/science.aak9913 . Publisher: American Association for the Advancement of Science . OpenUrl Abstract / FREE Full Text 22. ↵ Klaus C. Gwosch , Jasmin K. Pape , Francisco Balzarotti , Philipp Hoess , Jan Ellenberg , Jonas Ries , and Stefan W. Hell . MINFLUX nanoscopy delivers 3D multicolor nanometer resolution in cells . Nat Methods , 17 ( 2 ): 217 – 224 , February 2020 . ISSN 1548-7105 . doi: 10.1038/s41592-019-0688-0 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 23. Luciano A. Masullo , Florian Steiner , Jonas Zähringer , Lucía F. Lopez , Johann Bohlen , Lars Richter , Fiona Cole , Philip Tinnefeld , and Fernando D. Stefani . Pulsed Interleaved MINFLUX . Nano Lett ., 21 ( 1 ): 840 – 846 , January 2021 . ISSN 1530-6984 . doi: 10.1021/acs.nanolett.0c04600 . Publisher: American Chemical Society . OpenUrl CrossRef PubMed 24. ↵ Luciano A. Masullo , Alan M. Szalai , Lucía F. Lopez , Mauricio Pilo-Pais , Guillermo P. Acuna , and Fernando D. Stefani . An alternative to MINFLUX that enables nanometer resolution in a confocal microscope . Light Sci Appl , 11 ( 1 ): 199 , June 2022 . ISSN 2047-7538 . doi: 10.1038/s41377-022-00896-4 . Publisher: Nature Publishing Group . OpenUrl CrossRef PubMed 25. ↵ Amir Rahmani , Tabitha Cox , Akhila Thamaravelil Abhimanue Achary , and Aleks Ponjavic . Astigmatism-based focus stabilisation with universal objective lens compatibility, extended operating range and nanometre precision , January 2024 . Pages: 2024.01.15.575442 Section: New Results. 26. Birthe van den Berg , Robin Van den Eynde , Baptiste Amouroux , Marcel Müller , Peter Dedecker , and Wim Vandenberg . A Modular Approach to Active Focus Stabilization for Fluorescence Microscopy , January 2023 . Pages: 2020.09.22.308197 Section: New Results. 27. Shupei Lin , Yong He , Delong Feng , Marek Piliarik , and Xue-Wen Chen . Optical Fingerprint of Flat Substrate Surface and Marker-Free Lateral Displacement Detection with Angstrom-Level Precision . Phys. Rev. Lett ., 129 ( 21 ): 213201 , November 2022 . doi: 10.1103/PhysRevLett.129.213201 . Publisher: American Physical Society . OpenUrl CrossRef PubMed 28. ↵ Shih-Ya Chen , Rainer Heintzmann , and Christoph Cremer . Sample drift estimation method based on speckle patterns formed by backscattered laser light . Biomed. Opt. Express, BOE , 10 ( 12 ): 6462 – 6475 , December 2019 . ISSN 2156-7085 . doi: 10.1364/BOE.10.006462 . Publisher: Optica Publishing Group . OpenUrl CrossRef PubMed 29. ↵ The Nikon Perfect Focus System (PFS ), https://www.microscopyu.com/tutorials/the-nikon-perfect-focus-system-pfs . Nikon’s MicroscopyU , 2022 . 30. Leica DMI6000 B with Adaptive Focus Control Leica DMI6000 B with Adaptive Focus Control , https://www.leica-microsystems.com/products/light-microscopes/p/leica-dmi6000-with-adaptive-focus-control/ . 2022 . 31. ↵ TruFocus | Z-Drift Compensation System | Olympus LS , https://www.olympuslifescience.com/en/microscopes/inverted/ix83/trufocus/ . 2022 . 32. ↵ Simao Coelho , Jongho Baek , James Walsh , J. Justin Gooding , and Katharina Gaus . 3D active stabilization for single-molecule imaging . Nat Protoc , 16 ( 1 ): 497 – 515 , January 2021 . ISSN 1750-2799 . doi: 10.1038/s41596-020-00426-9 . Publisher: Nature Publishing Group . OpenUrl CrossRef 33. ↵ P. K. Jain , K. S. Lee , I. H. El-Sayed , and M. A. El-Sayed . Calculated absorption and scattering properties of gold nanoparticles of different size, shape, and composition: applications in biological imaging and biomedicine . J. Phys. Chem. B , 110 ( 14 ): 7238 – 48 , 2006 . OpenUrl CrossRef PubMed 34. ↵ Anatolii V. Kashchuk , Oleksandr Perederiy , Chiara Caldini , Lucia Gardini , Francesco Saverio Pavone , Anatoliy M. Negriyko , and Marco Capitanio . Particle Localization Using Local Gradients and Its Application to Nanometer Stabilization of a Microscope . ACS Nano , 17 ( 2 ): 1344 – 1354 , January 2023 . ISSN 1936-0851 . doi: 10.1021/acsnano.2c09787 . Publisher: American Chemical Society . OpenUrl CrossRef 35. ↵ M. Castello , G. Tortarolo , M. Buttafava , T. Deguchi , F. Villa , S. Koho , L. Pesce , M. Oneto , S. Pelicci , L. Lanzano , P. Bianchini , C. J. R. Sheppard , A. Diaspro , A. Tosi , and G. Vicidomini . A robust and versatile platform for image scanning microscopy enabling superresolution flim . Nat. Methods , 16 : 1 – 4 , 2019 . OpenUrl CrossRef PubMed 36. ↵ M. Donato , E. Slenders , A. Zunino , L. Bega , and G. Vicidomini . Brighteyes-mcs: a control software for multichannel scanning microscopy . Journal of Open Source Software , 9 ( 103 ), 2024 . View the discussion thread. Back to top Previous Next Posted January 14, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Open-Source 3D Active Sample Stabilization for Fluorescence Microscopy Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Open-Source 3 D Active Sample Stabilization for Fluorescence Microscopy Sanket Patil , Giuseppe Vicidomini , Eli Slenders bioRxiv 2025.01.10.631718; doi: https://doi.org/10.1101/2025.01.10.631718 Share This Article: Copy Citation Tools Open-Source 3 D Active Sample Stabilization for Fluorescence Microscopy Sanket Patil , Giuseppe Vicidomini , Eli Slenders bioRxiv 2025.01.10.631718; doi: https://doi.org/10.1101/2025.01.10.631718 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Biophysics Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17651) Bioengineering (13871) Bioinformatics (41882) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19867) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22476) Immunology (17714) Microbiology (40331) Molecular Biology (17148) Neuroscience (88483) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)