1.IntroductionCell migration and single cell metabolism are popular areas of investigation across immunology, cancer research, and developmental biology.1 However, a significant bottleneck exists in 3D imaging of metabolic features in moving cells throughout a live model organism, such as the zebrafish. Current methods require sample destruction (e.g., mass spectrometry, flow cytometry, and histology) that removes 3D context and prevents time-course studies that follow the fate of the same cell, or fluorescent reporters that require sample manipulation.2 Additionally, fluorescent reporters often provide a binary on/off indicator of expression rather than a continuous variable of dynamic cell state, even though most cells are known to function on a spectrum of activity. The reduced form of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] is a naturally fluorescent metabolic co-factor involved in hundreds of reactions within the cell (NADH and NADPH are optically indistinguishable and their fluorescence is collectively referred to as NAD(P)H).2–13 The fluorescence lifetime of NAD(P)H is distinct in the free (short lifetime) and protein-bound (long lifetime) conformations, so fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H provides information on protein-binding activities, preferred protein-binding partners, and other environmental factors, such as pH and oxygen, at a single cell level.2,10,12,14,15 FLIM of NAD(P)H is advantageous for imaging samples that are difficult to label with fluorescent reporters, for in vivo imaging of preclinical models, and for visualizing fast biological processes. For example, NAD(P)H FLIM can probe the temporal and spatial regulation of macrophage metabolism during tissue damage and repair in live zebrafish.11 NAD(P)H FLIM typically relies on two-photon (2P) laser scanning microscopy, which provides intrinsic optical sectioning, improved penetration depth, and high spatial resolution with minimal phototoxicity.16,17 However, autofluorescence FLIM traditionally suffers from long acquisition times and limited spatial views, partly due to the low fluorescence quantum yield ( for NAD(P)H is between 0.02 and 0.10 depending on protein binding) as well as lower molar absorption coefficient [ for NAD(P)H is ], which makes the autofluorescence signal hundreds of times dimmer than that of conventional fluorophores (e.g., the green fluorescent protein [GFP] has and ).18–20 There is a significant opportunity to develop new NAD(P)H FLIM imaging hardware to better understand 3D cellular dynamics in vitro and in vivo. Single-photon avalanche diodes (SPAD) are solid-state photodetectors with high photon counting and time-resolving capabilities.21 SPADs are semiconductor p–n junctions reverse-biased above their breakdown voltage such that a single-photon event can create an electron–hole pair and trigger a self-sustaining avalanche current. Advances in SPAD technology have led to the development of multipixel arrays with on-chip integrated timing electronics [such as time-to-digital converters (TDC)] capable of distinguishing single-photon arrival times.21 SPAD arrays have numerous applications in biophotonics, including fluorescence correlation spectroscopy,22–24 Raman spectroscopy,25–27 spectrally resolved FLIM,28 and optical tomography.29–31 Larger SPAD arrays () have also been used extensively for FLIM in combination with multifocal multiphoton excitation, providing exceptional speed advantages while maintaining the spatial and temporal resolution of traditional single-beam scanning microscopes.32–35 Other application examples include real-time widefield FLIM with time-correlated single photon counting (TCSPC) or time-gated sensing to image standard fluorescence solutions and quantum dots,36,37 fluorescently labeled cells,38,39 and fungal spores.40 High-speed time domain FLIM with SPAD arrays has been used for rapid tumor phantom margin measurements41 and to distinguish labeled vasculature42 and melanomas in live mice.43 Other geometries and biophotonics applications are detailed in the extensive review by Bruschini et al.21 Although SPAD arrays can be easily integrated into widefield microscopes, these microscopes lack optical sectioning capabilities and therefore offer poor sensitivity for NAD(P)H FLIM, which targets a weak autofluorescence signal over a high background. Light-sheet microscopy, also known as selective plane illumination microscopy (SPIM), uses a light sheet perpendicular to the imaging axis to illuminate the focal plane and achieve optical sectioning.44 Light-sheet microscopy is an attractive alternative to widefield or laser scanning systems because it provides 3D optical sectioning along with fast volumetric imaging due to the following features: first, SPIM enables higher throughput due to parallel acquisition of image pixels; second, out-of-focus sample exposure and photobleaching are eliminated; finally, SPIM provides better excitation efficiency and lower irradiance compared to scanning a diffraction-limited spot in epi-illumination geometry, in turn lowering the light dose and phototoxicity relative to single-photon confocal systems.44–46 Light-sheet FLIM has been performed on GFP-labeled cancer cell spheroids and C. elegans using microchannel plate photomultiplier tube detectors47 and live transgenic zebrafish using a frequency-domain two-tap CMOS FLIM camera.48 Gated optical image intensifiers have been used in conjunction with CMOS49 or CCD50 cameras on SPIM-FLIM systems to image GFP-labeled live zebrafish49 or canine kidney cysts,50 respectively. To our knowledge, autofluorescent NAD(P)H FLIM has not yet been performed using light-sheet systems. Here we demonstrate the first use of a commercially available SPAD array for detecting NAD(P)H autofluorescence in a light-sheet geometry. To improve image quality, the SPAD image from the FLIM arm of the light-sheet system was upscaled with the CMOS image from the intensity arm of the light-sheet system. The sensitivity of the NAD(P)H light-sheet FLIM system was confirmed with metabolic perturbations to pancreatic cancer cells with 10 s integration times, and the NAD(P)H light-sheet FLIM system was demonstrated in vivo with live neutrophil imaging in a wounded zebrafish tail, also at 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM are compared across laser scanning and light-sheet geometries, indicating a to acquisition speed advantage for the light-sheet compared to the laser scanning geometry. 2.Methods2.1.SPAD ArrayA FLIMera SPAD array camera (Horiba Scientific) was used for NAD(P)H FLIM.51 The array consists of with dedicated TDC electronics per pixel for TCSPC. Each pixel TDC has a 12-bit quantized (4096 time bins) time axis with a temporal resolution (bin width) of 41.1 ps. The SPAD pixels have a pitch of vertically and horizontally ( sensor size), a fill factor of 13%, and median dark count rate (DCR) of 34 counts per second (cps). The instrument response function (IRF) of the device has a full-width at half-maximum (FWHM) of 380 ps. The FLIMera was controlled using Horiba EzTime Image software, with raw photon arrival time data streamed for 10 s detection periods to computer memory and subsequently saved to HDF5 files for post-processing. This photon stream saving mode provided complete raw data for custom analysis but constrained the effective multiframe acquisition speed by creating an avoidable time delay between successive frames (due to data being saved to disk in photon stream mode, requiring 32 bits per photon event, resulting in multigigabyte saved files as opposed to the equivalent 3D histogram, which would require a few megabytes). Conversely, integration times per image were constrained by the fill factor (13%) and photon detection probability (34%) of the sensor (i.e., detection efficiency). 2.2.Multidirectional Selective Plane Illumination Microscopy with SPAD ArrayThe mSPIM system is detailed in Fig. 1,52 with multidirectional capabilities maintained in the intensity arm only. Illumination was achieved using two opposing light sheets. Three water immersion objective lenses (CFI60, Nikon Instruments, Inc.) were used for illumination and detection. The “Flamingo” T-SPIM design53 was modified to incorporate a QuixX 375-70PS picosecond-pulsed diode laser (Omicron-Laserage Laserprodukte GmbH) in the right illumination arm for NAD(P)H excitation. The laser produced 90 ps pulses at a 50 MHz repetition rate with an average power of 0.4 mW at the sample. The output beam had a diameter of 1 mm which was used along with a cylindrical lens (Thorlabs) to underfill a objective lens, generating a -wide light sheet with waist thickness that matched the FLIMera sensor size (right arm, Fig. 1). A bank of continuous-wave lasers (TOPTICA) was used to generate a 1.5 mm-wide light sheet (left arm, Fig. 1) for targeting other fluorescent labels for intensity imaging using a larger sCMOS sensor () with , pitch (Panda 4.2, PCO GmbH). Brightfield trans-illumination was achieved using a red LED and imaged using the sCMOS sensor. NAD(P)H was excited at 375 nm (pulsed laser, 50 MHz, integration time = 10 s on SPAD array) and mCherry at 561 nm (CW laser, integration = 1 s on sCMOS camera). Emissions were split by a 495 nm long-pass dichroic mirror (DM); NAD(P)H was captured using a 440/80 nm bandpass filter on the SPAD array; and a lower NAD(P)H intensity signal was collected through a bandpass filter on the sCMOS camera (integration time = 1 s). Fluorescence from mCherry and brightfield images was captured through a bandpass filter on the sCMOS camera using illumination from the 561 nm CW laser light sheet or the red LED brightfield, respectively. 2.3.System Characterization and Calibration MeasurementsThe combined IRF of the pulsed laser source (Omicron QuixX 375-70PS) and the SPAD array camera (Horiba FLIMera) was measured in an epi-illuminated widefield microscope setup by imaging a retroreflector target. A fraction of the light from excitation laser pulses (with 50 MHz pulse repetition rate) was imaged onto the SPAD array. The non-zero tail of the recorded pixels IRF was used to estimate the DCR of individual pixels. Standard fluorescent samples including a saturated solution of coumarin 6 (Sigma-Aldrich) in ethanol, Fluoresbrite® Yellow-Green (YG), and Bright Blue (BB) microspheres (Polysciences) were mounted in fluorinated ethylene-propylene (FEP) tubes and imaged on the light-sheet FLIM system to test the accuracy of the FLIM measurements against reference values. 2.4.Data ProcessingThe HDF5 photon stream files were converted to 3D histograms of photon arrival times in MATLAB (MathWorks). The histograms were pre-processed to correct for the SPAD sensor artifacts, i.e., variable DCR and timing skew across the sensor pixels.54 The DCR was estimated for each pixel from the tail of the decay (i.e., the last 2 ns of the decay) as the offset variable for the lifetime fitting algorithm. The timing skew was corrected by applying a circular shift to each pixel decay histogram by a value (up to 2.5 ns) estimated from cross-correlation maximization of each pixel decay with a reference decay (measured from the coumarin solution). To improve lifetime fits, a binning factor of one (i.e., neighboring pixels) was applied to the decays. The instrument response was deconvolved from the raw decay measurements through an iterative reconvolution fitting algorithm that performs a least-squares minimization of the residuals. To determine NAD(P)H fluorescence lifetime parameters, the pixel decays were fit to a biexponential model: . Mean fluorescence lifetime was estimated as . For visualization purposes, the NAD(P)H lifetime images from the SPAD camera were upscaled using the intensity image from the sCMOS camera acquired through the bandpass filter with a 1 s integration time. For upscaling, the SPAD lifetime images were interpolated to the resolution of the CMOS image (i.e., 4 times higher pixel count) and weighted by the CMOS pixel intensities. For live in vivo imaging of mCherry-labeled neutrophils, masks were generated from the mCherry image using a binary threshold in ImageJ,55 and this mask was applied to the NAD(P)H FLIM image. 2.5.Sample Preparation: In Vitro Pancreatic Cancer CellsPANC-1 human pancreatic cancer cells (ATCC) were maintained in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. Cells were pelleted, re-suspended in culture medium supplemented with 2% agarose and loaded into FEP tubing suspended in a water bath (Fig. 1). Known metabolic perturbations were induced by exposing cells to sodium cyanide (1 mM), which binds to electron transport chain complex IV and inhibits aerobic ATP production; this in turn increases the amount of free NAD(P)H in the cell, detectable as a decrease in mean fluorescence lifetime and an increase in fluorescence intensity.56–58 NAD(P)H FLIM images of live PANC-1 cells were acquired every 3 min using the SPAD camera and a filter with an integration time of 10 s to ensure sufficient photon counts ( per pixel before binning). An NAD(P)H intensity image was also acquired using the sCMOS camera and a filter with a 1 s integration time to upscale the SPAD FLIM images for better visualization. 2.6.Sample Preparation: In Vivo Zebrafish Tail Wound ModelAnimal care and use was approved by the Institutional Animal Care and Use Committee of University of Wisconsin and strictly followed guidelines set by the federal Health Research Extension Act and the Public Health Service Policy on the Humane Care and Use of Laboratory Animal, administered by the National Institute of Health Office of Laboratory Animal Welfare. All protocols using zebrafish in this study have been approved by the University of Wisconsin-Madison Research Animals Resource Center (protocols M005405-A02). Adult zebrafishes were maintained on a 14/10 h light/dark schedule. Upon fertilization, embryos were transferred into E3 medium (5 mM NaCl, 0.17 mM KCl, 0.44 mM , 0.33 mM , 0.025 mM NaOH, and 0.0003% methylene blue) and maintained at 28.5°C. Adult “casper” fish59 and wild-type transgenic Tg(mpx:mCherry) in AB background60 were used in this study. Tg(mpx:mCherry) fishes were outcrossed to casper fish to generate transgenic fish without pigmentation. Larvae at 3 days post-fertilization (dpf) were screened for mCherry expression using a ZEISS Axio Zoom.V16 fluorescence stereo zoom microscope (EMS3/SyCoP3, Zeiss: Plan-Neofluar Z 1X:0.25 FWD 56-mm lens) and raised to breeding age. These adult fishes were used for incross breeding. Larvae at 3 dpf were screened for casper larvae with mCherry expression as a cytoplasmic marker of neutrophils, which are dynamic innate immune cells that rapidly migrate to wounds during early healing stages.60,61 3 dpf larvae were anesthetized in E3 medium containing Tricaine (ethyl 3-aminobenzoate; Sigma-Aldrich) and caudal fin transection was performed11 30 min prior to imaging. Larvae were mounted in a 0.8-mm inner diameter FEP tube in 0.4% low-melting-point agarose with Tricaine and an agarose plug. We collected brightfield (25 ms exposure sCMOS), mCherry intensity (1 s exposure sCMOS), and NAD(P)H lifetime (10 s integration SPAD) from neutrophils in quick succession (every 2 min). NAD(P)H FLIM was acquired in a -stack with four slices through the thickness of the tail fin. The sCMOS mCherry image was superimposed on the NAD(P)H fluorescence lifetime image to generate neutrophil masks, and lifetime information was extracted from single cells. 3.Results3.1.System Characterization and CalibrationThe combined IRF of the 50 MHz pulsed laser source and the SPAD array camera is shown in Fig. 2. The system IRF has an FWHM of 380 ps. Due to non-zero DCRs of the SPAD pixels, the measured IRF drops to a non-zero constant in later time bins. This tail of the measured IRF was used to estimate per-pixel DCRs. A histogram of the estimated DCRs for this SPAD camera is shown in Fig. S1 in the Supplementary Material. The median DCR of the SPAD pixels was 34 cps. A map of the inter-pixel delays across the SPAD array sensor was estimated from the coumarin 6 fluorescence lifetime image as shown in Fig. 3 using a pixel-wise cross-correlation maximization scheme. If left uncorrected, this non-uniform delay results in distortion artifacts when aggregating decays from different pixels on the sensor (e.g., for pixel binning during lifetime fit analysis). Figure 4 shows the effect of correcting for these inter-pixel delays on the aggregate decay histogram of coumarin 6. The estimated fluorescence lifetime after such correction (2.51 ns) agrees with reported values in the literature.62–64 Figures S2 and S3 in the Supplementary Material show lifetime images and aggregate decays for YG and BB fluorescent microspheres. The estimated lifetime of 2.15 ns for YG beads and 2.6 ns for BB beads also agree with reported values in the literature.65,66 3.2.Effects of Cyanide on NAD(P)H Metabolism In VitroAfter the addition of 1 mM cyanide, NAD(P)H FLIM images of PANC-1 cells were taken every 3 min (Fig. 5). We observed the expected decrease in NAD(P)H mean fluorescence lifetime [, Figs. 5(a)–5(d) and 5(i)], an increase in the free fraction of NAD(P)H [, Figs. 5(e)–5(h) and 5(j)], and an increase in the NAD(P)H fluorescence intensity [Fig. 5(k)], starting at 3 min post-exposure and continuing over time. These changes in NAD(P)H intensity and lifetime parameters were expected,4,56,58,67 and the range of NAD(P)H and also fall within published values for cells in culture.57,68 These experiments confirm the sensitivity of this system to NAD(P)H lifetimes and to physiologically relevant changes in NAD(P)H lifetimes. These studies also demonstrate that this NAD(P)H light-sheet FLIM system can perform time-course imaging of single-cell autofluorescence in a fluorescent background of culture medium. Further, we found that 10 s integration time was sufficient to capture enough NAD(P)H photons for accurate lifetime fitting analysis. 3.3.Light-Sheet FLIM of NAD(P)H In Vivo in Larval ZebrafishWe next sought to measure NAD(P)H lifetime in a dynamic, in vivo environment (Fig. 6). NAD(P)H is present in all cell types of a living organism, so we used a zebrafish caudal fin wound model to track mCherry-labeled neutrophils,69 innate immune cells recruited to the injury site. To demonstrate light-sheet NAD(P)H FLIM in these dynamic cells, an mCherry intensity image was acquired using light-sheet excitation from the left (CW) illumination arm whose sectioning plane was aligned to perfectly match the right (pulsed) illumination arm that excites NAD(P)H for the FLIM image. NAD(P)H FLIM was simultaneously acquired in a -stack to track the same neutrophils over time. The mCherry fluorescence was used to identify the cells of interest and create masks to isolate NAD(P)H specifically from the neutrophils, as autofluorescence signal is generated from the whole tail fin tissue in the FOV. These studies demonstrate that the NAD(P)H light-sheet FLIM system is sensitive to single cells in vivo, and that combined intensity and FLIM images can be used to isolate quickly moving cells in vivo. 4.Discussion and ConclusionDetector arrays in a widefield geometry provide faster FLIM acquisition speed (i.e., shorter integration time per frame) compared to a single-detector multiphoton or confocal laser scanning geometry. Recent developments in SPAD array sensor technology enable widefield FLIM with high TCSPC temporal resolution. These new SPAD cameras can be combined with low-cost pulsed diode lasers for rapid FLIM. Table 1 shows the theoretical advantage in integration time for the SPAD array used in this work over a standard laser scanning FLIM microscope using a single TCSPC module for acquiring an image of the same size. However, the simplest implementation of widefield FLIM, i.e., an epi-fluorescence geometry, can only be applied to bright fluorescently labeled samples due to lack of optical sectioning. In label-free applications, out-of-focus background fluorescence can overwhelm the weak autofluorescence signals from single cells. Thus widefield autofluorescence FLIM has remained impractical. Table 1Comparison of theoretical FLIM imaging speed limit between laser scanning and widefield geometries. The saturation count rate for the SPAD camera with per-pixel TDC modules working in parallel is 30× higher than the saturation count rate of a single TCSPC module in laser scanning microscopes. The speed advantage scales with the number of array pixels.
In this work, we combined multichannel detection using a SPAD array camera with light-sheet excitation to minimize the effects of out-of-focus background fluorescence. In addition to providing optical sectioning, light-sheet illumination is more efficient at exciting fluorophores than epi-illumination because photons traveling in the focus plane have a higher probability of encountering a fluorophore. The peak irradiance of a light sheet is also many orders of magnitude lower than a diffraction-limited spot in laser scanning (e.g., confocal) microscopy, which decreases phototoxicity. Artifacts are noticeable in areas of the presented lifetime images where dead “screamer” SPAD pixels (that comprise 15% of all pixels) cluster together. We also acknowledge that a secondary streaking artifact is visible in Figs. 5 and 6 due to shadowing in the light-sheet excitation path. This is caused by absorption in the sample and can be mitigated by pivoting the light sheet (also known as dithering) such that these shadows are averaged out during the integration period.52 Such artifacts could be eliminated in future designs using UV-optimized multidirectional/pivoting optics. The low quantum yield of endogenous fluorophores [e.g., NAD(P)H, FAD]18 limits the acquisition speed of autofluorescence FLIM because photodamage to cells occurs before the detector count rate saturates. As such, it is helpful to compare the light-sheet SPAD system to a laser scanning system for the imaging settings used and count rates observed for NAD(P)H FLIM in practice. This comparison is made in Table 2 for the specific 2P laser scanning microscope,67 the light-sheet SPAD system, and the PANC-1 cells used in this study. The pixel count rate and photons per pixel are reported before any pixel binning. Figure S4 in the Supplementary Material shows a representative two-photon FLIM image of PANC-1 cells. For the same field of view, number of image pixels, and targeted photon count per pixel, the light-sheet SPAD system in Fig. 1 requires less integration time to acquire the NAD(P)H image compared to a 2P laser scanning microscope. Frame rates of SPAD arrays relative to single-pixel laser scanning systems scale with the number of array pixels, when acquiring the same number of image photons. SPAD arrays are based on the same CMOS fabrication process as existing cameras, so the commercial infrastructure for inexpensive and large-scale manufacturing of high-resolution arrays exists.70 This has resulted in a rapid improvement in the capabilities of SPAD systems with improved photon detection probabilities and fill factors (e.g., using microlens arrays over SPAD pixels71,72), which can further reduce integration time per frame in FLIM or reduce excitation light dose for reduced phototoxicity over long imaging sessions, which is an important consideration given the higher phototoxicity of shorter wavelengths.45 Table 2Comparison of practical FLIM imaging speed limit between a standard 2P laser scanning system and the light-sheet system in Fig. 1. For the same field of view size, number of image pixels, and targeted number of emission photons per image pixel, the light-sheet system required 6× less integration time per image.
Existing light-sheet FLIM microscopes perform well for samples with bright fluorophores, but access to weaker autofluorescence lifetimes is challenging due to limitations in sensitivity and temporal resolution of current detector arrays. Here, we show that it is feasible to use a commercial SPAD array in a light-sheet microscope for NAD(P)H FLIM of single cells in vitro and in vivo. The integration time per image for this configuration is 6× faster than that of traditional laser scanning NAD(P)H FLIM microscopes. Faster acquisition speeds will be achieved as SPAD arrays continue to improve in detection efficiency and array size. The FLIM frame interval in these studies was limited by write-to-disk speed when saving data in raw photon stream mode, which imposed delays between successive frames. Specifically, the FLIMera camera can only transfer data in photon streaming mode over USB 3.0, with histogram compilation and lifetime analysis on the computer CPU. The process of transferring individual photon time tags costs both USB data transfer bandwidth and analysis time, which prohibits the acquisition of successive frames at the maximum speed of the hardware. Some SPAD array developers use on-chip histogramming with an FPGA73 to substantially reduce this data transfer and analysis bottleneck, enabling larger SPAD arrays with per-pixel TDC. Alternatively, a time-gating scheme with a user-selectable number and width of time gates can be used instead of TCSPC. This approach removes per-pixel TDC electronics and simplifies the design to provide larger SPAD arrays with better fill factors. This approach also allows the user to trade temporal resolution for imaging speed by reducing the number of time gates. For example, time gating was employed in SwissSPAD detectors with .38 Therefore, as sensor sensitivity improves, parallel improvements in data streaming and analysis will be necessary to reach the full frame-rate potential of SPAD arrays for NAD(P)H light-sheet FLIM. Overall, these NAD(P)H light-sheet FLIM systems will be an important new tool to study single cell metabolism and migration in 3D, including in vivo studies of whole model organisms. DisclosuresK.W.E. is a co-founder of OnLume Inc., a company developing fluorescence guided surgery technologies; an advisor to Bruker Technologies FM, a company developing laser scanning solutions for biological microscopy; and an advisor to Elephas Corporation, a company developing technical solutions for preserving, analyzing, and interrogating live human tumors ex vivo. A.V. is a co-founder, a shareholder, and a board member of Ubicept, which develops SPAD camera software for machine vision applications. These relationships were not involved in this study. All other authors declare they have no conflicts of interest. Data AvailabilityAll data and code used in the analyses are available for purposes of reproducing or extending the analyses through a GitHub repository ( https://github.com/skalalab/Light-sheet-SPAD-array). AcknowledgmentsWe would like to acknowledge funding from the Morgridge Institute for Research (M.C.S.); Carol Skornicka Chair of Biomedical Imaging (M.C.S.); Retina Research Foundation Daniel M. Albert Chair (M.C.S.); Melita F. Grunow Post-Doctoral Fellowship (D.E.D.); NIH [Grant Nos. R35 GM118027 (A.H.), 1K99GM138699-01A1 (V.M.), and U54 CA268069 (K.W.E. and M.C.S.)]; Retina Research Foundation Walter H. Helmerich Professor (K.W.E.); and Beckman Center for Advanced Light-Sheet Microscopy and Data Science (K.W.E. and J.H.). ReferencesA. P. Cuny et al.,
“Live cell microscopy: from image to insight,”
Biophys. Rev., 3 021302 https://doi.org/10.1063/5.0082799 1793-0480
(2022).
Google Scholar
R. Datta et al.,
“Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications,”
J. Biomed. Opt., 25 071203 https://doi.org/10.1117/1.JBO.25.7.071203 JBOPFO 1083-3668
(2020).
Google Scholar
T. M. Heaster et al.,
“Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism,”
Cancer Res., 80 5408
–5423 https://doi.org/10.1158/0008-5472.CAN-20-0831 CNREA8 0008-5472
(2020).
Google Scholar
A. T. Shah et al.,
“In vivo autofluorescence imaging of tumor heterogeneity in response to treatment,”
Neoplasia-U. S., 17 862
–870 https://doi.org/10.1016/j.neo.2015.11.006
(2015).
Google Scholar
A. J. Walsh et al.,
“Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,”
Cancer Res., 74 5184
–5194 https://doi.org/10.1158/0008-5472.CAN-14-0663 CNREA8 0008-5472
(2014).
Google Scholar
A. J. Walsh et al.,
“Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response,”
Pancreas, 45 863
–869 https://doi.org/10.1097/MPA.0000000000000543 PANCE4 0885-3177
(2016).
Google Scholar
J. T. Sharick et al.,
“Metabolic heterogeneity in patient tumor-derived organoids by primary site and drug treatment,”
Front. Oncol., 10 553 https://doi.org/10.3389/fonc.2020.00553 FRTOA7 0071-9676
(2020).
Google Scholar
A. J. Walsh et al.,
“Classification of T-cell activation via autofluorescence lifetime imaging,”
Nat. Biomed. Eng., 5 77
–88 https://doi.org/10.1038/s41551-020-0592-z
(2021).
Google Scholar
B. Chance et al.,
“Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals,”
J. Biol. Chem., 254 4764
–4771 https://doi.org/10.1016/S0021-9258(17)30079-0 JBCHA3 0021-9258
(1979).
Google Scholar
J. T. Sharick et al.,
“Protein-bound NAD(P)H lifetime is sensitive to multiple fates of glucose carbon,”
Sci. Rep., 8 5456 https://doi.org/10.1038/s41598-018-23691-x SRCEC3 2045-2322
(2018).
Google Scholar
V. Miskolci et al.,
“In vivo fluorescence lifetime imaging of macrophage intracellular metabolism during wound responses in zebrafish,”
eLife, 11 e66080 https://doi.org/10.7554/eLife.66080
(2022).
Google Scholar
R. Schmitz et al.,
“Extracellular pH affects the fluorescence lifetimes of metabolic co-factors,”
J. Biomed. Opt., 26 056502 https://doi.org/10.1117/1.JBO.26.5.056502 JBOPFO 1083-3668
(2021).
Google Scholar
T. Qian et al.,
“Label-free imaging for quality control of cardiomyocyte differentiation,”
Nat. Commun., 12 4580 https://doi.org/10.1038/s41467-021-24868-1 NCAOBW 2041-1723
(2021).
Google Scholar
J. R. Lakowicz et al.,
“Fluorescence lifetime imaging of free and protein-bound NADH,”
Proc. Natl. Acad. Sci. U. S. A., 89 1271
–1275 https://doi.org/10.1073/pnas.89.4.1271
(1992).
Google Scholar
A. J. Walsh and M. C. Skala,
“An automated image processing routine for segmentation of cell cytoplasms in high-resolution autofluorescence images,”
Proc. SPIE, 8948 89481M https://doi.org/10.1117/12.2040644 PSISDG 0277-786X
(2014).
Google Scholar
Y. Qin and Y. Xia,
“Simultaneous two-photon fluorescence microscopy of NADH and FAD using pixel-to-pixel wavelength-switching,”
Front. Phys., 9 642302 https://doi.org/10.3389/fphy.2021.642302
(2021).
Google Scholar
W. R. Zipfel, R. M. Williams and W. W. Webb,
“Nonlinear magic: multiphoton microscopy in the biosciences,”
Nat. Biotechnol., 21 1369
–1377 https://doi.org/10.1038/nbt899 NABIF9 1087-0156
(2003).
Google Scholar
K. A. Kasischke et al.,
“Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis,”
Science, 305 99
–103 https://doi.org/10.1126/science.1096485 SCIEAS 0036-8075
(2004).
Google Scholar
T. G. Scott et al.,
“Synthetic spectroscopic models related to coenzymes and base pairs. V. Emission properties of NADH. Studies of fluorescence lifetimes and quantum efficiencies of NADH, AcPyADH, [reduced acetylpyridineadenine dinucleotide] and simplified synthetic models,”
J. Am. Chem. Soc., 92 687
–695 https://doi.org/10.1021/ja00706a043 JACSAT 0002-7863
(1970).
Google Scholar
A. Follenius-Wund et al.,
“Fluorescent derivatives of the GFP chromophore give a new insight into the GFP fluorescence process,”
Biophys. J., 85 1839
–1850 https://doi.org/10.1016/S0006-3495(03)74612-8 BIOJAU 0006-3495
(2003).
Google Scholar
C. Bruschini et al.,
“Single-photon avalanche diode imagers in biophotonics: review and outlook,”
Light Sci. Appl., 8 87
–87 https://doi.org/10.1038/s41377-019-0191-5
(2019).
Google Scholar
A. P. Singh et al.,
“The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy,”
Opt. Express, 21 8652
–8652 https://doi.org/10.1364/OE.21.008652 OPEXFF 1094-4087
(2013).
Google Scholar
X. Michalet et al.,
“Detectors for single-molecule fluorescence imaging and spectroscopy,”
J. Mod. Opt., 54 239
–281 https://doi.org/10.1080/09500340600769067 JMOPEW 0950-0340
(2007).
Google Scholar
M. Vitali et al.,
“A single-photon avalanche camera for fluorescence lifetime imaging microscopy and correlation spectroscopy,”
IEEE J. Sel. Top. Quantum Electron., 20 344
–353 https://doi.org/10.1109/JSTQE.2014.2333238 IJSQEN 1077-260X
(2014).
Google Scholar
F. Madonini and F. Villa,
“Single photon avalanche diode arrays for time-resolved Raman spectroscopy,”
Sensors, 21 4287
–4287 https://doi.org/10.3390/s21134287 SNSRES 0746-9462
(2021).
Google Scholar
J. Kostamovaara et al.,
“Fluorescence suppression in Raman spectroscopy using a time-gated CMOS SPAD,”
Opt. Express, 21 31632
–31632 https://doi.org/10.1364/OE.21.031632 OPEXFF 1094-4087
(2013).
Google Scholar
I. Nissinen et al.,
“A 16 × 256 SPAD line detector with a 50-ps, 3-bit, 256-channel time-to-digital converter for Raman spectroscopy,”
IEEE Sens. J., 18 3789
–3798 https://doi.org/10.1109/JSEN.2018.2813531 ISJEAZ 1530-437X
(2018).
Google Scholar
G. O. S. Williams et al.,
“Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution,”
Nat. Commun., 12 6616 https://doi.org/10.1038/s41467-021-26837-0 NCAOBW 2041-1723
(2021).
Google Scholar
M. G. Tanner et al.,
“Ballistic and snake photon imaging for locating optical endomicroscopy fibres,”
Biomed. Opt. Express, 8 4077
–4077 https://doi.org/10.1364/BOE.8.004077 BOEICL 2156-7085
(2017).
Google Scholar
F. Stuker et al.,
“Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,”
IEEE Trans. Med. Imaging, 30 1265
–1273 https://doi.org/10.1109/TMI.2011.2112669 ITMID4 0278-0062
(2011).
Google Scholar
A. D. Mora et al.,
“Towards next-generation time-domain diffuse optics for extreme depth penetration and sensitivity,”
Biomed. Opt. Express, 6 1749
–1749 https://doi.org/10.1364/BOE.6.001749 BOEICL 2156-7085
(2015).
Google Scholar
S. P. Poland et al.,
“Time-resolved multifocal multiphoton microscope for high speed FRET imaging in vivo,”
Opt. Lett., 39 6013 https://doi.org/10.1364/OL.39.006013 OPLEDP 0146-9592
(2014).
Google Scholar
S. P. Poland et al.,
“A high speed multifocal multiphoton fluorescence lifetime imaging microscope for live-cell FRET imaging,”
Biomed. Opt. Express, 6 277 https://doi.org/10.1364/BOE.6.000277 BOEICL 2156-7085
(2015).
Google Scholar
S. P. Poland et al.,
“Multifocal multiphoton volumetric imaging approach for high-speed time-resolved Förster resonance energy transfer imaging in vivo,”
Opt. Lett., 43 6057 https://doi.org/10.1364/OL.43.006057 OPLEDP 0146-9592
(2018).
Google Scholar
J. A. Levitt et al.,
“Quantitative real-time imaging of intracellular FRET biosensor dynamics using rapid multi-beam confocal FLIM,”
Sci. Rep., 10 5146 https://doi.org/10.1038/s41598-020-61478-1 SRCEC3 2045-2322
(2020).
Google Scholar
S. Burri et al.,
“Architecture and applications of a high resolution gated SPAD image sensor,”
Opt. Express, 22 17573
–17573 https://doi.org/10.1364/OE.22.017573 OPEXFF 1094-4087
(2014).
Google Scholar
D. E. Schwartz, E. Charbon and K. L. Shepard,
“A single-photon avalanche diode array for fluorescence lifetime imaging microscopy,”
IEEE J. Solid-State Circuits, 43 2546
–2557 https://doi.org/10.1109/JSSC.2008.2005818 IJSCBC 0018-9200
(2008).
Google Scholar
A. C. Ulku et al.,
“A 512 × 512 SPAD image sensor with integrated gating for widefield FLIM,”
IEEE J. Sel. Top. Quantum Electron., 25 1
–12 https://doi.org/10.1109/JSTQE.2018.2867439 IJSQEN 1077-260X
(2019).
Google Scholar
V. Zickus et al.,
“Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation,”
Sci. Rep., 10 20986 https://doi.org/10.1038/s41598-020-77737-0 SRCEC3 2045-2322
(2020).
Google Scholar
D.-U. Li et al.,
“Real-time fluorescence lifetime imaging system with a CMOS low dark-count single-photon avalanche diode array,”
Opt. Express, 18 10257
–10257 https://doi.org/10.1364/OE.18.010257 OPEXFF 1094-4087
(2010).
Google Scholar
H. L. Stewart, G. Hungerford and D. J. S. Birch,
“Characterization of single channel liquid light guide coupling and SPAD array imaging for tumour margin estimation using fluorescence lifetime,”
Meas. Sci. Technol., 31 125701 https://doi.org/10.1088/1361-6501/aba5c6 MSTCEP 0957-0233
(2020).
Google Scholar
D. D.-U. Li et al.,
“Time-domain fluorescence lifetime imaging techniques suitable for solid-state imaging sensor arrays,”
Sensors, 12 5650
–5669 https://doi.org/10.3390/s120505650 SNSRES 0746-9462
(2012).
Google Scholar
H. A. R. Homulle et al.,
“Compact solid-state CMOS single-photon detector array for in vivo NIR fluorescence lifetime oncology measurements,”
Biomed. Opt. Express, 7 1797 https://doi.org/10.1364/BOE.7.001797 BOEICL 2156-7085
(2016).
Google Scholar
J. Huisken and D. Y. R. Stainier,
“Selective plane illumination microscopy techniques in developmental biology,”
Development, 136 1963
–1975 https://doi.org/10.1242/dev.022426
(2009).
Google Scholar
M. Wagner et al.,
“Light dose is a limiting factor to maintain cell viability in fluorescence microscopy and single molecule detection,”
Int. J. Mol. Sci., 11 956
–966 https://doi.org/10.3390/ijms11030956 1422-0067
(2010).
Google Scholar
E. M. C. Hillman et al.,
“Light-sheet microscopy in neuroscience,”
Annu. Rev. Neurosci., 42 295
–313 https://doi.org/10.1146/annurev-neuro-070918-050357 ARNSD5 0147-006X
(2019).
Google Scholar
L. M. Hirvonen et al.,
“Lightsheet fluorescence lifetime imaging microscopy with wide‐field time‐correlated single photon counting,”
J. Biophotonics, 13 e201960099 https://doi.org/10.1002/jbio.201960099
(2020).
Google Scholar
C. A. Mitchell et al.,
“Functional in vivo imaging using fluorescence lifetime light-sheet microscopy,”
Opt. Lett., 42 1269
–1269 https://doi.org/10.1364/OL.42.001269 OPLEDP 0146-9592
(2017).
Google Scholar
R. Li et al.,
“Digital scanned laser light‐sheet fluorescence lifetime microscopy with wide‐field time‐gated imaging,”
J. Microsc., 279 69
–76 https://doi.org/10.1111/jmi.12898 JMICAR 0022-2720
(2020).
Google Scholar
K. Greger et al.,
“Three-dimensional fluorescence lifetime imaging with a single plane illumination microscope provides an improved signal to noise ratio,”
Opt. Express, 19 20743 https://doi.org/10.1364/OE.19.020743 OPEXFF 1094-4087
(2011).
Google Scholar
R. K. Henderson et al.,
“A 192 × 128 time correlated SPAD image sensor in 40-nm CMOS technology,”
IEEE J. Solid-State Circuits, 54 1907
–1916 https://doi.org/10.1109/JSSC.2019.2905163
(2019).
Google Scholar
J. Huisken and D. Y. R. Stainier,
“Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM),”
Opt. Lett., 32 2608
–2610 https://doi.org/10.1364/OL.32.002608 OPLEDP 0146-9592
(2007).
Google Scholar
Huisken Laboratory,
“FLAMINGO PROJECT: high-end microscopy inside and outside the optics lab,”
Google Scholar
J. Nedbal et al.,
“Correction of time-resolved SPAD array measurements for accurate single-photon time-resolved biological imaging,”
Proc. SPIE, 11721 117210T https://doi.org/10.1117/12.2587755 PSISDG 0277-786X
(2021).
Google Scholar
J. Schindelin et al.,
“Fiji: an open-source platform for biological-image analysis,”
Nat. Methods, 9 676
–682 https://doi.org/10.1038/nmeth.2019 1548-7091
(2012).
Google Scholar
E. B. Randi et al.,
“Physiological concentrations of cyanide stimulate mitochondrial Complex IV and enhance cellular bioenergetics,”
Proc. Natl. Acad. Sci. U. S. A., 118 e2026245118 https://doi.org/10.1073/pnas.2026245118 PNASA6 0027-8424
(2021).
Google Scholar
A. T. Shah et al.,
“Optical metabolic imaging of treatment response in human head and neck squamous cell carcinoma,”
PLoS One, 9 e90746
–e90746 https://doi.org/10.1371/journal.pone.0090746 POLNCL 1932-6203
(2014).
Google Scholar
S. Huang, A. A. Heikal and W. W. Webb,
“Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein,”
Biophys. J., 82 2811
–2825 https://doi.org/10.1016/S0006-3495(02)75621-X BIOJAU 0006-3495
(2002).
Google Scholar
R. M. White et al.,
“Transparent adult zebrafish as a tool for in vivo transplantation analysis,”
Cell Stem Cell, 2 183
–189 https://doi.org/10.1016/j.stem.2007.11.002
(2008).
Google Scholar
S. K. Yoo et al.,
“Differential regulation of protrusion and polarity by PI3K during neutrophil motility in live zebrafish,”
Dev. Cell, 18 226
–236 https://doi.org/10.1016/j.devcel.2009.11.015 1534-5807
(2010).
Google Scholar
R. A. Houseright et al.,
“Myeloid-derived growth factor regulates neutrophil motility in interstitial tissue damage,”
J. Cell Biol., 220 e202103054 https://doi.org/10.1083/jcb.202103054 JCLBA3 0021-9525
(2021).
Google Scholar
A. S. Kristoffersen et al.,
“Testing fluorescence lifetime standards using two-photon excitation and time-domain instrumentation: rhodamine B, coumarin 6 and lucifer yellow,”
J. Fluoresc., 24 1015
–1024 https://doi.org/10.1007/s10895-014-1368-1 JOFLEN 1053-0509
(2014).
Google Scholar
C. Freymüller et al.,
“Quenched coumarin derivatives as fluorescence lifetime phantoms for NADH and FAD,”
J. Biophotonics, 14 e202100024 https://doi.org/10.1002/jbio.202100024
(2021).
Google Scholar
H. Sparks et al.,
“A flexible wide-field FLIM endoscope utilising blue excitation light for label-free contrast of tissue,”
J. Biophotonics, 8 168
–178 https://doi.org/10.1002/jbio.201300203
(2015).
Google Scholar
M. A. K. Sagar et al.,
“Optical fiber-based dispersion for spectral discrimination in fluorescence lifetime imaging systems,”
J. Biomed. Opt., 25 014506 https://doi.org/10.1117/1.JBO.25.1.014506 JBOPFO 1083-3668
(2019).
Google Scholar
J. Sytsma et al.,
“Time-gated fluorescence lifetime imaging and microvolume spectroscopy using two-photon excitation,”
J. Microsc., 191 39
–51 https://doi.org/10.1046/j.1365-2818.1998.00351.x JMICAR 0022-2720
(1998).
Google Scholar
T. M. Heaster et al.,
“Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level,”
J. Biophotonics, 11 e201600276 https://doi.org/10.1002/jbio.201600276
(2018).
Google Scholar
D. K. Bird et al.,
“Metabolic mapping of MCF10A human breast cells via multiphoton fluorescence lifetime imaging of the coenzyme NADH,”
Cancer Res., 65 8766
–8773 https://doi.org/10.1158/0008-5472.CAN-04-3922 CNREA8 0008-5472
(2005).
Google Scholar
F. Barros-Becker et al.,
“Live imaging reveals distinct modes of neutrophil and macrophage migration within interstitial tissues,”
J. Cell Sci., 130 3801
–3808 https://doi.org/10.1242/jcs.206128 JNCSAI 0021-9533
(2017).
Google Scholar
“Canon develops SPAD sensor with world-highest 3.2-megapixel count, innovates with low-light imaging camera that realizes high color reproduction even in dark environments,”
(2021). Google Scholar
J. M. Pavia, M. Wolf and E. Charbon,
“Measurement and modeling of microlenses fabricated on single-photon avalanche diode arrays for fill factor recovery,”
Opt. Express, 22 4202
–4202 https://doi.org/10.1364/OE.22.004202 OPEXFF 1094-4087
(2014).
Google Scholar
G. Intermite et al.,
“Fill-factor improvement of Si CMOS single-photon avalanche diode detector arrays by integration of diffractive microlens arrays,”
Opt. Express, 23 33777
–33777 https://doi.org/10.1364/OE.23.033777 OPEXFF 1094-4087
(2015).
Google Scholar
H. Mai et al.,
“Development of a high-speed line-scanning FLIM microscope for biological imaging,”
Opt. Lett., 48
(8), 2042
–2045 https://doi.org/10.1364/OL.482403 OPLEDP 0146-9592
(2023).
Google Scholar
|
CITATIONS
Cited by 4 scholarly publications.
Single photon avalanche diodes
Fluorescence lifetime imaging
Imaging systems
Autofluorescence
Cameras
Fluorescence
Laser scanners