Method Article

Ultra-high Information-content Chemical Imaging with Broadband Coherent Anti-Stokes Raman and Two-photon Fluorescence Lifetime Microscopy

DOI:

10.3791/68845

⸱

October 7th, 2025

 ,  ,  ,  ,  ,  , 
1Department of Biomedical Engineering, Georgia Institute of Technology, 2School of Chemistry and Biochemistry, Georgia Institute of Technology, 3School of Electrical and Computer Engineering, Georgia Institute of Technology

In This Article

Summary

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

This article demonstrates amultimodal imaging platform combining broadband coherent anti-Stokes Raman scattering (BCARS), two-photon excitation fluorescence (TPEF), and two-photon fluorescence lifetime imaging microscopy (2p-FLIM), enabling ultra-high information-content chemical bioimaging.

Abstract

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Raman fingerprint spectroscopy and fluorescence lifetime imaging are emerging tools for studying metabolic profiles of biological specimens. While Raman fingerprint spectroscopy detects intrinsic molecular vibrations that reflect the molecular composition and chemical environment of a sample, fluorescence lifetime imaging measures changes in the excited-state lifetime of fluorophores that are sensitive to their microenvironments. Here, we present a multimodal imaging platform combining broadband coherent anti-Stokes Raman scattering (BCARS) and two-photon fluorescence lifetime imaging (2p-FLIM) microscopy that can acquire biologically relevant Raman fingerprint spectra and fluorescence lifetime signals in vivo and simultaneously. The tremendous chemical information obtained from spatially co-registered BCARS and 2p-FLIM images allows us to characterize the subtle differences between sub-cellular compartments and verify the potential false-positive results generated by fluorescence imaging alone. This is demonstrated by directly comparing the BCARS, 2p-FLIM, and two-photon excitation fluorescence (TPEF) signals simultaneously obtained from the same dye-stained organelle in the live, intact C. elegans expressing a green fluorescent protein (GFP) marker. In this work, we introduce the BCARS/2p-FLIM/TPEF setup scheme, the image acquisition steps, data processing, and representative results showing that the cross-modality imaging method enables rigorous characterization and in vivo detection at sub-cellular resolution. This protocol provides a framework for simultaneous chemical and fluorescence lifetime imaging to improve the accuracy of biological interpretation in complex living systems.

Introduction

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

One challenge in biological studies is to accurately detect biological building materials in their native states and within the intact architecture of cells and tissues. Raman spectroscopy shows unprecedented potential for detecting intrinsic metabolites and chemical species within biological specimens and has been recently recognized as an emerging tool for metabolic profiling of biological specimens1. By analyzing the peaks of various metabolic groups shown in Raman fingerprint spectra, increasing evidence shows that the fingerprint features can be used to detect the early stages of diseases such as cancer2,3,4, cardiac disorders5,6, and neurodegenerative diseases7,8 and is particularly useful for addressing fundamental biological problems9,10 including imaging intracellular structures without any labeling perturbation11, differentiating various cell states12,13, and cell type classification14,15,16. Most of the chemical information ( > 90%) encoded in the Raman spectra is in the fingerprint region (500 to 1800 cm-1), making this region by far the most useful for discriminating chemical compositions in biological samples17. However, the weak Raman signal in this frequency range makes the applications of Raman fingerprint spectroscopy to biological studies challenging, particularly for live, intact biological specimens1.

Coherent Raman imaging (CRI) approaches significantly shorten the acquisition time of Raman signals, but most of these acquire relatively narrow spectral segments and are optimized for the strong CH stretch signals between 2800 - 3100 cm-1. Narrowband CARS and stimulated Raman scattering (SRS) (Figure 1A,B) use two picosecond pulses, usually to acquire a narrow portion of the strong CH signal 18,19,20,21,22,23. Multiplex CRI approaches, such as multiplex CARS (Figure 1C), use a combination of a picosecond and ~100 fs pulses to obtain a moderate spectral range (usually ≤ 400 cm-1) usually in the CH region24,25,26,27,28,29,30. Acquiring a whole biological-relevant Raman spectrum (from 600 - 3200 cm-1) per pixel using these imaging approaches is usually prohibitive because most fingerprint signals are 10-100 times weaker than CH-stretch signals31. Unlike most CRI approaches, broadband coherent anti-Stokes Raman scattering (BCARS) is optimized for single-shot broadband acquisition that covers the fingerprint and CH-stretch regions31,32,33. BCARS uses intra-pulse interactions of a ~10 fs pulse to efficiently excite low-frequency (fingerprint) vibrations (Figure 1D), and uses the same mechanism as multiplex CARS, the combination of the fs pulse with a ps pulse to generate vibrational coherence in the CH stretch region (Figure 1C)32,33. Furthermore, the BCARS signals are intrinsically calibrated due to their interaction with the non-resonant background (NRB), resulting in a quantitative and instrument-invariant full Raman spectrum per laser pulse at each pixel31, with the capability of rapidly acquiring high-information-content data from cells, intact tissues, and live biological organisms32,34,35,36,37,38,39.

The acquired raw BCARS spectra contain a strong so-called "non-resonant background" (NRB). The NRB is the contribution from the electronic response to the laser pulses that is not chemically specific. The NRB coherently interferes with the vibrationally resonant signal, acting as a heterodyne amplifier32. The NRB also distorts the underlying Raman spectrum (Figure 2A), but the Raman spectrum can be retrieved as long as significant contiguous spectral sections are acquired. Currently, there is no post-processing method available to separate the resonant CARS signal from the non-resonant component using a narrowband CARS approach. Raman signal retrieval from the broadband CARS signal can be achieved by utilizing the time-domain Kramers-Kronig relation (or Hilbert transform)33,40 (Figure 2B), maximum entropy41,42, or machine-learning43,44,45,46,47,48 approaches. Note that the first two methods analytically retrieve the phase (the resonant Raman signal) from the raw BCARS spectra, the machine-learning approaches can be highly dependent on the instrumentation and the training models.

While BCARS microscopy allows for label-free characterization of metabolic profiling of biological specimens, the same supercontinuum laser (950 - 1200 nm for the BCARS setup presented in this work) used for BCARS signal generation can also excite a wide range of dyes, such as Rhodamine-based, Bodipy-based, Alexa Fluor-based, and cyanine-based dyes, and various fluorescent proteins, including GFP and mCherry49,50. Compared to single-photon excitation using visible continuous wave (CW) lasers, multiphoton fluorescence microscopy, such as two-photon fluorescence lifetime imaging microscopy (2p-FLIM) and two-photon excitation fluorescence microscopy (TPEF), using near infrared (NIR) light offers a deeper sample penetration depth, intrinsic 3D sectioning capability, high-contrast images, and good resolution comparable to state-of-the-art confocal microscopy, making it an attractive tool for biological research51,52. Fluorescence lifetime imaging microscopy (FLIM) using a time-correlated single photon counting (TCSPC) approach detects the fluorescence decay signals repeatedly excited by a time-modulated laser source53,54. The fluorescence spectrum of a given fluorophore may change with environmental factors such as solvent polarity, but these changes are usually subtle. On the other hand, fluorescence lifetimes often vary by factors of two or more with environmental changes such as pH values55, viscosity56, polarity57, and concentration of ionic species58,59, binding interactions, the presence of quenchers, or structural changes53,54. This unique property of fluorescence lifetime provides an additional signal contrast in fluorescence imaging and has successfully addressed the issues of autofluorescence interference in cell and tissue imaging60, unmixing multiple fluorophores using a spectrally resolved FLIM approach61, cancer diagnosis and treatment monitoring62, label-free detection of cellular heterogeneity63, and metabolic changes64,65.

In this work, we introduce a multimodal imaging platform combining BCARS, 2p-FLIM, and TPEF. To the best of our knowledge, this is the first time presenting an imaging platform allowing simultaneous BCARS and 2p-FLIM/TPEF imaging. We demonstrate the in vivo imaging capabilities of this instrument using a widely used genetically tractable model organism, Caenorhabditis elegans (C. elegans). We show that the BCARS spectra, the 2p-FLIM decays of Nile Red (a vital lipid dye), and the two-photon fluorescence GFP signals of a reported lipid marker (DHS-3::GFP) can be simultaneously acquired in vivo from Nile-Red-stained worms. A practical guide for the imaging approach presented here includes (i) the sample should be optically transparent and with a thickness typically less than 100 µm; (ii) the staining condition can be different depending on the dyes and samples. In the Nile-Red staining of C. elegans results shown here, the Nile Red concentration is at ~µM level, and staining time can range from a few hours to overnight66; (iii) a dark sample environment is required to avoid visible light leakage into the 2p-FLIM/TPEF detectors. These imaging conditions are identical to the imaging conditions of most nonlinear optical and fluorescence microscopy. In addition, our system demonstrated here can be further applied to detect other dyes and fluorescent reporters in addition to Nile Red and GFP by introducing an additional Ti:Sapphire fs laser that covers 700 -950 nm range for the two-photon fluorescence excitation. Thus, while the representative results described in this protocol are the in vivo imaging of dye-stained C. elegans expressing GFP, the ultra-high information-content chemical bioimaging platform has applications that are broad and far-reaching.

Access restricted. Please log in or start a trial to view this content.

Protocol

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

1. C . elegans sample preparation and Nile Red Vital staining

NOTE: C. elegans has been heavily used for multiple imaging techniques due to its whole-body transparency. The internal organs and tissues can be easily imaged in the intact animal without dissection. In addition, ethics approval is not required for research involving C. elegans, as they are not considered vertebrates and are not subject to the same ethical regulations as higher animals like rodents. This section describes the synchronization of C. elegans worms and the preparation of Nile Red-stained samples for fluorescence imaging. Synchronized one-day adult worms are obtained via a two-generation egg-laying protocol and stained using freshly prepared Nile Red-OP50 plates.

  1. Maintain the C. elegans strains (e.g., N2, LIU1 ldrIs1 [dhs-3p::dhs-3::GFP + unc-76(+)]) on standard Nematode Growth Medium (NGM) plates seeded with OP50 bacteria at 20 °C.
  2. Synchronize the worms
    1. Pick 10-20 gravid adult worms onto a freshly made OP50-seeded NGM plate.
    2. Incubate the plate at 20 °C for 1-3 h to allow egg laying.
    3. Remove all adult worms, leaving only the freshly laid eggs on the plate.
    4. Incubate the plate at 20 °C until worms reach the one-day adult stage.
    5. Repeat the above steps for an additional generation to improve synchronization precision.
  3. Prepare Nile Red stock solution
    1. Dissolve 15.92 mg of Nile Red in 10 mL of DMSO to a final concentration of 5 mM.
    2. Sonicate for 10-30 min if needed to aid dissolution.
  4. Prepare Nile Red-OP50 staining plates.
    1. Mix OP50 bacterial slurry thoroughly with the Nile Red stock to achieve a final Nile Red concentration of 5 µM per NGM plate. For example, mix 10 µL of the Nile Red stock with 20 µL OP50 bacterial slurry, and then immediately apply the whole mixture to an unseeded 6 cm (with ~10 mL volume) NGM agar plate to make a final Nile Red concentration of 5 µM per NGM plate.
    2. Spread the mixture evenly onto unseeded 6 cm NGM plates.
    3. Store plates in the dark at room temperature overnight to allow bacterial growth and dye absorption.
  5. Stain synchronized worms
    1. Transfer 30-50 synchronized one-day adult worms onto each prepared Nile Red-OP50 plate.
    2. Incubate the worms in the dark at 20 °C for 1-2 h before imaging.
  6. Immobilize worms for imaging
    1. Apply a drop of 100 mM sodium azide on a 2% agarose pad on a microscope slide (approximately 10-15 µL for 20 worms)
    2. Transfer stained worms to the anesthetizing agent (sodium azide) drop and cover the worms with a coverslip prior to microscopy.
      NOTE: When picking 10-20 gravid adults onto a freshly made NGM OP50 plate in the first step of egg-laying synchronization, only pick the adults, but not any stray eggs or larvae. This is to make sure that all the remaining eggs on the plates are produced by the picked worms within the 1-3 h egg-laying time window. In addition, sodium azide is classified as a hazardous material, although it requires a very small amount for nematode anesthetization. The usage and disposal of sodium azide waste must follow the guidelines of the institutional Environmental Health and Safety department.

2. Probe beam generation

NOTE: This protocol describes the generation of the picosecond (ps) probe beam using a home-built optical parametric oscillator (OPO) followed by second harmonic generation (SHG) with a home-built signal feedback loop system to stabilize the output power of the probe beam.

  1. Switch on the 1030 nm OPO pump laser (Figure 3A or the near 1ps, ~3 W OPO pump laser shown in Figure 4) and allow at least 2 h for thermal equilibration. Stable output is critical for OPO operation.
  2. Guide the output to the OPO cavity
    1. Select the appropriate poling period on the OPO PPLN (Figure 3A) crystal to match the target signal wavelength.
    2. Direct the beam into the first curved mirror (CM) (Figure 3A), forming part of a linear cavity.
    3. Ensure the beam passes through the OPO periodically poled Lithium Niobate (PPLN) crystal (Figure 3A) to facilitate optical parametric down-conversion (Figure 3B).
    4. Ensure the beam reflects off a second curved mirror (CM) and then off the end mirror (EM) (Figure 3A) to form the cavity loop.
    5. Configure the output coupler (OC) (Figure 3A) to extract the generated signal beam from the cavity.
  3. Adjust the temperature of the OPO PPLN crystal using the temperature controller (TC) (Figure 3A) to select the signal wavelength. A typical signal wavelength is 1530 nm, corresponding to an output power of approximately 0.8 W when pumped with 3 W at 1064 nm.
  4. Optimize OPO alignment
    1. Adjust the position and tilt of two CMs and EM to maximize output power and beam quality.
    2. Adjust the cavity length to shift the signal spectrum to the desired frequency.
  5. Route the OPO signal beam through a set of collimating and focusing optics and guide it into the SHG stage.
    1. Select the appropriate poling period on the SHG PPLN crystal (Figure 3A) to match the target signal wavelength.
    2. Precisely control the crystal temperature using a temperature controller to maximize SHG efficiency.
    3. Direct the beam into the SHG PPLN crystal for second harmonic generation, converting 1530 nm OPO signal to 765 nm (Figure 3C). The typical SHG conversion efficiency is approximately 30% under optimal alignment conditions.
    4. Insert a 1300 nm shortpass filter (SPF in Figure 3A, specifications in Table of Materials) after the SHG PPLN to remove residual 1530 nm fundamental light.
    5. Collimate the resulting 765 nm beam using an appropriate lens (as L in Figure 3A).
  6. Implement power stabilization
    1. Use a wedge window (WW) to sample the SHG beam and direct it to a photodiode (PD).
    2. Ensure a feedback loop compares the photodiode signal (PD in Figure 3A) to a reference value and actuates the Picomotor on the end mirror (EM) to adjust the OPO cavity length in real-time, thereby stabilizing the SHG output power.
      NOTE: To determine the reference value, first scan the EM position using the Picomotor while recording the SHG output power. Identify the peak intensity in this profile, then set the reference value to 90% of the peak. This choice ensures a clear direction of feedback correction, enabling the system to compensate for both increasing and decreasing trends in power and effectively stabilizes the output power over time.

3. Supercontinuum (SC) generation for BCARS

NOTE: The simplified scheme of the BCARS-based multimodal system is shown in Figure 4. This protocol describes the generation of the SC beam.

  1. Adjust the pre-chirp (Group Delay Dispersion, GDD) to the femtosecond (fs) pulse output (1030 nm, 150 fs, ~1 W) to obtain a wide enough SC bandwidth covering the fingerprint region signals (SC bandwidth ≥ 1800 cm−1). Achieve this by adjusting the separation of a grating pair (see Figure 4), allowing for precise control over the dispersion.
    NOTE: The application of pre-compensated GDD is crucial for generating the desired pulse and optimizing SC generation. The required GDD value may vary depending on the specific characteristics of the laser system used.
  2. Incorporate a beam telescope consisting of two lenses with the same focal lengths to adjust the divergence of the pump beam.
    NOTE: When the beam travels over meters before entering the broadening fiber, precise control of the beam divergence becomes essential. Maintaining a collimated beam during this distance is critical to ensure efficient SC generation.
  3. Set up the auto-aligner (Figure 4) using kinematic mirror mounts equipped with piezo actuators for beam steering. Combine these with optical wedges and a Position Sensing Detector (PSD) to actively monitor and correct beam alignment.
  4. Position the components such that the distance from the wedge to the first mirror is equal to the distance from the wedge to the first PSD. This symmetric configuration ensures beam-pointing stability into the broadening fiber.
    NOTE: Active alignment is essential for maximizing spectral broadening efficiency and minimizing the risk of fiber input damage.
  5. Use a half-wave plate (HWP) and a polarizing beam splitter (PBS) to control the laser power before coupling into the fiber. Rotate the HWP to adjust the polarization angle relative to the PBS, thereby tuning the transmitted power. While these components are not shown in Figure 4, they are critical for safe alignment and power control.
  6. Couple approximately 1 W of laser power into a 10 cm Large-Mode-Area Polarization Maintaining (LMA-PM-5) photonic crystal fiber (PCF) using a fiber coupler. The coupling procedure is as follows:
    1. First, reduce the laser power to ~20-50 mW during the initial alignment to avoid damage to the fiber.
    2. Direct the collimated beam into the fiber, ensuring some light emerges from the other side. Optimize the beam path by adjusting its position to maximize the output power.
    3. Insert the aspherical lens (Figure 4), fine-tune the beam again to maximize the output power.
    4. Adjust the z-position of the aspherical lens to ensure precise focus on the fiber facet.
    5. Repeat the alignment and focus adjustments iteratively to maximize coupling efficiency.
    6. Increase the laser power to 1 W for operation.
  7. Collimate the resulting broadband supercontinuum (SC) output from the PCF using an off-axis parabolic (OAP) mirror with focal length f = 15 mm, as shown in Figure 4.
    NOTE: Reflective optics, such as the OAP mirror, are preferred for broadband beams to minimize chromatic aberration introduced by refractive elements.
  8. Guide the collimated broadband supercontinuum (SC) output into a prism compressor (Figure 4) consisting of a pair of Schott Flint (SF10) prisms.
    1. Direct the SC beam into the apex of the first prism to initiate spectral dispersion.
    2. Position the second prism such that the dispersed beam enters through its apex.
    3. Mount both prisms on a rail system to enable coarse adjustment of the prism separation. Translate the second prism along the rail to adjust the optical path length and overall compression. The separation for this system is 44 cm.
    4. Mount the first prism on a precision linear translation stage. Adjust the insertion depth of the first prism to fine-tune the dispersion compensation.
    5. Iteratively adjust both the prism separation and the insertion length of the second prism to achieve optimal temporal compression of the SC pulse. Determine the optimal temporal compression by matching the experimentally observed degenerate four-wave mixing (DFWM) signal to simulated results that account for group delay dispersion (GDD) and third-order dispersion (TOD). This approach involves performing a simulation of the supercontinuum generation process, incorporating the effects of GDD and TOD, which provides a reference for the expected temporal and spectral characteristics of the optimally compressed SC pulses. Then, iteratively adjust the prism compressor parameters to compensate for the GDD and TOD present in the system until the experimental DFWM signal matches the simulation predictions. A typical result of the spectral profile of the SC is shown in Figure 5.
      NOTE: Proper dispersion compensation enhances the spectral coherence of the SC. Under optimal alignment, the SC output has a power of approximately 150 mW, a center wavelength around 1060 nm, and spans a spectral range from 950 to 1200 nm (corresponding to 1800 cm−1). These values may vary slightly depending on alignment and fiber coupling efficiency.
  9. Combine the probe and supercontinuum beams using a dichroic mirror (see DM in Figure 4). Align them with a pair of pinholes to ensure both beams are collinear. Focus the combined beams onto a high-frequency photodiode (1 GHz bandwidth). For optimal signal quality, both beams must be overlapped in both the spatial and temporal domains. The process is as follows:
    1. Direct the output from the photodiode to a high-frequency oscilloscope (500 MHz bandwidth). Use the laser trigger as the reference clock.
    2. Block the probe beam and measure the SC pulses. Record the temporal differences between the SC pulses and the laser reference.
    3. Block the SC beam and measure the probe pulses. Adjust the mechanical delay line (as shown in Figure 4) such that the probe pulses overlap with the SC pulses in time. Ensure that the two beams reach their maximum simultaneously.
    4. Remove the photodiode, and let the beam proceed into the microscope.
      NOTE: The above steps describe a coarse adjustment for beam overlap. Further optimization for more precise alignment is covered in steps 6.1 to 6.9.

4. Setting up the fluorescence detection for 2p-FLIM and TPEF and introducing an additional Ti:Sapphire fs laser

  1. Turn on the Ti:Sapphire laser and allow it to reach thermal equilibrium. Wait approximately 2 h to ensure stable output power and beam pointing.
    NOTE: In this study, TPEF and FLIM imaging were performed using the SC source, which provides broad spectral coverage and sufficient power for fluorophores such as GFP and Nile Red. The Ti:Sapphire laser is included in the system as an optional excitation source for fluorophores with two-photon absorption peaks below 950 nm, which may not be efficiently excited by the SC.
  2. Activate the mode-locking mechanism on the Ti:Sapphire laser. Confirm stable mode-locking via the built-in monitor.
  3. Pre-chirp the Ti:Sapphire pulses for optimal temporal compression at the sample using a prism compressor. Arrange the prisms in a configuration similar to that described in step 3.8 and adjust the insertion length of the second prism to fine-tune the pulse duration.
    NOTE: Proper temporal compression ensures peak intensity and efficient two-photon excitation at the sample plane.
  4. (Optional) Couple the Ti:Sapphire beam into a polarization-maintaining single-mode fiber if the laser is located on a separate optical table. Adjust the input coupling optics and monitor the fiber output to minimize coupling losses.
  5. Combine the Ti:Sapphire beam with the supercontinuum (SC) and probe beams using a dichroic mirror (see the combining point at PBS shown in Figure 3). Align the beams carefully to ensure spatial overlap at the dichroic and co-propagation toward the microscope.
  6. Guide the combined beams to the microscope and fine-tune their spatial overlap. Use a fluorescent sample (e.g., a slide with 1 µm fluorescent polystyrene beads) to verify the alignment of the Ti:Sapphire beam relative to the BCARS excitation path. Adjust the Ti:Sapphire alignment mirrors to ensure that the fluorescence emission is co-localized with the BCARS focal point within the same imaging field. Confirm the colocalization by visualizing the overlap between the BCARS and TPEF images that are acquired simultaneously, where the spatial offset should be less than one pixel.
    NOTE: Achieving spatial overlap between fluorescence and BCARS signals is essential for multimodal image registration.
  7. Insert a dichroic mirror before the microscope input to reflect back-scattered fluorescence toward the detection path. Select a dichroic with an appropriate cutoff wavelength to efficiently separate fluorescence from the excitation beams.
  8. Insert a second dichroic mirror downstream in the emission path to split the fluorescence signal into two detection branches:
    1. Transmit wavelengths ≥ 550 nm to the FLIM detector.
    2. Reflect wavelengths ≤ 550 nm toward a photomultiplier tube (PMT) for analog two-photon excited fluorescence (TPEF) detection.
      NOTE: Install bandpass filters in each detection branch to block residual excitation light and to isolate specific emission bands.
  9. Connect the PMT output to a preamplifier and feed the amplified analog signal into a data acquisition (DAQ) device. Share the global synchronization trigger from the field programmable gate array (FPGA) with the DAQ to ensure temporal alignment across imaging modalities (Figure 7).
  10. Connect the FLIM detector output to a time-correlated single-photon counting (TCSPC) module.
    1. Connect the FLIM detector signal to the start input of the TCSPC module.
    2. Connect the Ti:Sapphire laser's (or BCARS SC's) synchronization output to the reference input of the TCSPC module.
    3. Connect the global camera trigger (generated by the FPGA) to the frame sync input of the TCSPC module to synchronize with line-scanned image acquisition.

5. Setting up the BCARS detection path

NOTE: This protocol describes setting up a descanned detection path for line-scanning hyperspectral imaging. The 1D galvo mirror scans the excitation beam along the y-axis with 2-4 ms dwell time per pixel for sufficient signal integration. After each line scan, the sample stage advances one step along the x-axis. Descanning using a synchronized secondary galvo mirror is essential to maintain consistent spatial registration of the spectral signal on the camera during fast beam scanning, eliminating signal crosstalk between adjacent pixels and preserving spatial-spectral fidelity39.

  1. Introduce the supercontinuum (SC) and probe beams into a one-dimensional (1D) galvo scanner (as shown in Figure 4 and more details in reference39). Position the galvo mirror conjugate to the back aperture of the focusing objective to enable accurate beam scanning across the field of view.
  2. Expand each beam using a reflective beam expander (as shown in Figure 4) to match the back aperture of the excitation objective. This ensures optimal focusing and maximum spatial resolution at the sample plane.
    NOTE: Use reflective optics to minimize chromatic aberration and maintain the broadband spectral content of the SC beam.
  3. Place an alignment target at the back aperture plane of the excitation objective. Adjust the SC and probe beams so that both pass through the center of the target, ensuring co-alignment along the optical axis.
    NOTE: Precise spatial overlap maximizes BCARS signal strength and ensures maximum spatial resolution at the focal point.
  4. Focus the beams onto the sample using a high-NA excitation objective (e.g., 1.2 NA, 60x water-immersion objective) mounted on an inverted microscope.
  5. Prepare an alignment sample by sandwiching a drop of water between two coverslips to create a sealed microscope slide. Place the prepared slide onto the microscope stage and adjust the focus to bring the sample into view.
  6. Collect the generated anti-Stokes signal in transmission using a collection objective placed opposite the excitation path.
  7. Implement a 4f descanning system (as shown in Figure 4) to preserve spatial mapping of the anti-Stokes signal onto fixed rows of the detector.
    1. Relay the back focal plane (BFP) of the collection objective onto the axis of the descanning galvo mirror using a 4f optical system. This consists of two precision tube lenses (see Table of Materials), arranged such that:
      1. The first lens is placed one focal length (200 mm) after the BFP of the collection objective.
      2. The second lens is placed one focal length before the galvo mirror.
    2. Synchronize the descanning galvo with the scanning galvo using an FPGA-controlled waveform generator.
    3. Integrate a 7-bit digital potentiometer into the FPGA control system to allow adjustment of the descanning galvo amplitude.
      NOTE: The descanning galvo mirror compensates for the angular deflections introduced by the primary scanning galvo. By dynamically adjusting its angle in synchrony, it counteracts beam movement, ensuring that the anti-Stokes signal remains aligned along the optical axis leading into the spectrometer. This stabilization is essential to maintain spatial registration on the detector and prevent lateral displacement of the spectral image during scanning.
  8. Configure the FPGA system (see Table of Materials) to synchronize galvo scanning and camera triggering.
    1. Generate analog outputs for the scanning and descanning galvos.
    2. Accept an input stage trigger to mark the start of each line scan.
    3. Output frame capture trigger signals to the scientific complementary metal-oxide-semiconductor (sCMOS) camera at each scan position.
    4. Independently control the descanning amplitude to accommodate different collection objectives.
      NOTE: A typical FPGA pulse train includes a scanning galvo drive signal, a descanning galvo drive signal, a Stage trigger input, and a pixel clock for synchronized frame timing.
  9. Pass the collimated anti-Stokes signal through two short-pass filters to block residual pump and SC excitation light.
  10. Focus the filtered anti-Stokes signal through the spectrometer entrance slit and onto the sCMOS detector. Adjust the angles of both the scanning and descanning galvos to ensure that the beam is scanned parallel to the slit's long axis, aligning the optical image plane with the slit. This precise alignment maximizes spectral resolution and minimizes lateral signal crosstalk across the detector rows.
  11. Configure the spectrometer to disperse the anti-Stokes spectrum vertically along the camera rows. Select an appropriate grating (e.g., 300 lines/mm, blazed at 700 nm) for optimal spectral coverage.

6. Signal optimization and spectral resolution calibration

  1. Launch the home-built acquisition software and begin real-time signal monitoring from the sCMOS camera. The acquisition software is home-built in Python 3.8+ (Windows 10) with dependencies including Hamamatsu DCAM-API and Measurement Computing Universal Library drivers, outputting data in HDF5 format. Software is available upon reasonable request from the corresponding author.
  2. Prepare a spectral calibration sample by placing a drop of benzonitrile between two coverslips to form a sealed slide. Place the slide onto the microscope stage and bring it into focus using the excitation objective.
  3. Locate the BCARS signal on the sCMOS camera. Adjust the z-position of the collection objective to maximize anti-Stokes signal intensity.
  4. Adjust the lateral position of the collection objective to ensure that the anti-Stokes light is correctly focused through the entrance slit of the spectrometer. This alignment ensures optimal coupling into the spectrometer and improved spectral fidelity.
  5. Optimize the spatial overlap between the supercontinuum (SC) and probe beams at the sample plane. Use mirrors in the probe beam path to finely adjust its lateral position and incident angle.
  6. Translate the optical delay line in the probe beam path to ensure temporal overlap with the SC pulses. Monitor the signal intensity and adjust until the anti-Stokes signal is maximized, confirming proper temporal synchronization.
  7. Identify a strong, sharp Raman peak in benzonitrile (e.g., 1002 cm-1) on the sCMOS image.
  8. Record the probe wavelength and its corresponding pixel position on the camera. Use the Rayleigh line (0 cm-1 at the probe wavelength) and known benzonitrile Raman peaks at 1002 cm-1 and 3073 cm-1 to establish a linear pixel-to-wavenumber mapping across the sCMOS detector. The Rayleigh line provides the zero-wavenumber reference, while the benzonitrile peaks determine the wavenumber increment per pixel.
    NOTE: Since the analysis requires only relative wavenumber information (mapping between pixel position and Raman shift), absolute wavelength calibration using emission lamps is not necessary.
  9. Input the pixel-wavenumber mapping into the acquisition software. This calibration should remain valid unless changes are made to the optical path, grating setting in the spectrometer, or alignment.

7. Signal acquisition

  1. Load the C. elegans sample prepared in steps 1.1 to 1.6 onto the microscope stage. Locate a region of interest and bring it into focus using the excitation objective.
  2. Identify a region on the slide that lacks Raman-active signal (water background or a bare glass coverslip) to serve as the nonresonant background (NRB) reference.
  3. Locate the BCARS signal on the sCMOS detector. Fine-tune the z-position of the collection objective to maximize the anti-Stokes signal intensity.
  4. Adjust the lateral position of the collection objective to ensure that the anti-Stokes light is properly aligned through the spectrometer entrance slit.
  5. Start the synchronized scanning and descanning galvo mirrors using the FPGA. Adjust the amplitude of the descanning galvo using the digital potentiometer until the angular deflection from the primary scan is fully compensated. The anti-Stokes signal should remain confined within ~12 rows on the camera across the entire scan range.
  6. Define a temporal acquisition sequence
    1. Acquire a cross-correlation signal between the SC and probe pulses by delaying the probe pulse. To acquire this, plot the integrated three-color BCARS signal vs. the time delay of the probe pulse. The strongest signal will appear at time 0 when the SC and probe pulses are temporally overlapped.
      NOTE: Since the FLIM and TPEF detection systems share the same trigger as BCARS, their signals are simultaneously acquired during this time point.
    2. Record at a later delay of the probe beam (e.g., 1-2 ps) for BCARS and a later delay of the SC beam for nonresonant background (NRB spectra in Figure 6A). When a time delay is applied to the narrowband pulse (probe), the NRB in the three-color CARS signal can be effectively suppressed, and thus the signal-to-noise ratio can be improved67.
    3. Acquire a dark image with no temporal overlap between the SC and the probe to capture the baseline detector noise (dark reference).
    4. Save all the BCARS, NRB, and dark images, including the metadata within a single h5 file.
  7. BCARS Signal Processing Protocol
    1. Open the CRIkit2 software68.
    2. Load all necessary datasets using Open HDF tool as shown in Figure 6B: Load BCARS hyperspectral data, NRB reference, and dark reference. Select frequency region within BCARS signal generation bandwidth using the Freq Window tool (from 400 cm-1 to ≥ 3200 cm-1 for the BCARS system demonstrated here).
    3. Perform preprocessing steps:
      1. Use the Anscombe tool (as shown in Figure 6B) to variance-stabilize the BCARS and NRB datasets.
      2. Use the De-noise tool to perform Singular Value Decomposition (SVD): Retain principal components based on visual inspection. Preserve components exhibiting spatial distributions matching the morphology of the sample and discard components dominated by unstructured or speckle like noise. This denoising step improves signal-to-noise ratio (SNR), particularly for low-intensity spectral features.
      3. Use the Inverse Anscombe tool to restore the data to its original domain.
        NOTE: These steps may be skipped if performing real-time processing on acquisition.
    4. Retrieve Raman-like spectra using the Kramers-Kronig (KK) transform:
      1. Open the KK tool (as shown in Figure 6B).
      2. Enable Norm to NRB to ensure appropriate spectral reference normalization.
      3. Set parameters including NRB bias, pad factor, and edge pixel averaging.
      4. Execute the KK transform to obtain retrieved Raman spectra.
    5. Perform post-KK error corrections
      1. Correct phase errors using Savitzky-Golay smoothing: Open the phase error correction tool (Figure 6B). Adjust the smoothing window size and polynomial order. Inspect the original vs. corrected spectra using the preview panel.
      2. Correct scale errors: Open the scale error correction tool (Figure 6B). Normalize spectra to correct relative intensity scaling across the dataset. Ensure consistency across spatial and spectral dimensions.
    6. Calibrate spectral axis (optional, if misaligned): Open the calibration tool (Figure 6B). Compare the measured wavenumbers to the known reference peaks. Apply the corrected slope/intercept to realign the spectral axis.
    7. Define and extract data from regions of interest (ROIs)
      1. Use the ROI Spect. or Pt Spect. tool (Figure 6B) to mark spatial regions of interest or points of interest in the BCARS image.
      2. Visualize and compare the spectral content of different ROIs.
      3. Export retrieved spectra for further quantitative or chemometric analysis.

Access restricted. Please log in or start a trial to view this content.

Results

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

Figure 5 shows the measured spectra of the narrowband probe and the broadband supercontinuum (SC) pulses used for BCARS excitation. The probe pulse, generated via second harmonic generation of a ~1530 nm signal from a home-built optical parametric oscillator (OPO, Figure 3), is centered at 764.2 nm with a full width at half maximum (FWHM) of 0.2 nm, providing the spectral resolution at ~6.8 cm-1 in the resulting Raman spectra. The SC pulse, p...

Access restricted. Please log in or start a trial to view this content.

Discussion

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

This work demonstrates the architecture of BCARS and 2p-FLIM/TPEF imaging platform in detail. While a dual-output 1030 nm laser is used in our current setup, most high-power fs 1030 nm fiber lasers can be used as a master laser source for building a BCARS microscope. By carefully choosing the length and period of OPO/SHG PPLN crystals, generating a few-ps probe pulse with a sub-ps OPO 1030 nm pump is achievable. Although a relatively high laser repetition rate (100 MHz) or a relatively lower pulse peak power is demonstra...

Access restricted. Please log in or start a trial to view this content.

Disclosures

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

The authors have nothing to disclose and have no competing financial interests.

Acknowledgements

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

W-W.C. acknowledges support from NIH (R21AG086974). M.T.C. acknowledges support from the U.S. Department of Energy (DOE BERDE-SC0022121).

Access restricted. Please log in or start a trial to view this content.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CameraHamamatsuORCA-FusionsCMOS, 2304×2304 pixels
Collection objective Olympus LUMPLFLN60XW Water immersion, 1.0 NA
DAQ Diligent MCC USB-1208HS data aquisition card
Delay line Newport DL225 Mechanical delay for probe pulse
Dichroic Semrock FF925-Di01 combining probe and SC
Dichroic Semrock FF705-Di01 detecting epi-mode fluorescence
Dichroic Thorlabs DMLP550R seperating the FLIM signal and the TPEF signal
Excitation objective Olympus UPLSAPO60XWIR Water immersion, 1.2 NA
Filter Semrock FF01-1326/SP-25 Reject the fundamental laser after SHG
Filter Semrock FF01-758/SP Reject the fundamental laser before BCARS camera
Filter Thorlabs FESH0650 Reject the fundamental laser before the fluorescence modality
FLIM PMT PicoQuant PMA Hybrid FLIM single photon detector
FPGA Redpitaya STEMlab 125-14 Scan synchronization and triggering
Galvo Thorlabs GVS101 For scanning and descanning
Grating Coherent LightSmyth 1040nm 1000 Groves/mm, pre-chirp for SC
Half-wave plate Thorlabs AHWP05M-980 Polarization control
K-cube Thorlabs KPA101 PSD driver for the auto-aligner
Laser Prospective Instruments FSX-Dual Dual-output femtosecond laser
Laser Coherent Mira-900 Femtosecond laser for fluorescence excitation
PBS Thorlabs PBS255 Beam combination for SC and probe
PCF Thorlabs LMA-PM-5 10 cm photonic crystal fiber for SC generation
PD Thorlabs PDA100A2 OPO feedback loop
Piezo mirror mount Thorlabs POLARIS-K1S2P Piezoelectric Adjuster for the auto-aligner
Pre-amp EdmundOptics 59-179 TPEF pre-amplifier
Prism Lambda Research optics EQP-30SF10 SC compression
PSD Thorlabs PDQ80A Position Sensitive Detector for the auto-aligner
Spectrometer Teledyne Princeton Instruments IsoPlane 160 300 g/mm grating, centered at 700 nm
Stage ASI MS-2000 Motorized 3-axis stage
TCSPC PicoQuant MultiHarp 150 Photon counting device for FLIM
TPEF PMT Hamamatsu H9305-03 TPEF detector
Tube Lens Thorlabs TL200-2P2 Used in descanning system

References

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,
  1. Cutshaw, G., et al. The emerging role of Raman spectroscopy as an omics approach for metabolic profiling and biomarker detection toward precision medicine. Chem Rev. 123 (13), 8297-8346 (2023).
  2. Kong, K., Kendall, C., Stone, N., Notingher, I. Raman spectroscopy for medical diagnostics - from in vitro biofluid assays to in vivo cancer detection. Adv Drug Deliv Rev. 89, 121-134 (2015).
  3. Abramczyk, H., et al. Aberrant protein phosphorylation in cancer by using Raman biomarkers. Cancers (Basel). 11 (12), 2017(2019).
  4. Contorno, S., Darienzo, R. E., Tannenbaum, R. Evaluation of aromatic amino acids as potential biomarkers in breast cancer by Raman spectroscopy analysis. Sci Rep. 11 (1), 1698(2021).
  5. John, R. V., et al. Micro-Raman spectroscopy study of blood samples from myocardial infarction patients. Lasers Med Sci. 37 (9), 3451-3460 (2022).
  6. You, A. Y. F., et al. Raman spectroscopy imaging reveals interplay between atherosclerosis and medial calcification in the human aorta. Sci Adv. 3 (12), e1701156(2017).
  7. Carlomagno, C. Human salivary Raman fingerprint as biomarker for the diagnosis of amyotrophic lateral sclerosis. Sci Rep. 10 (1), 10175(2020).
  8. Morasso, C. F., et al. Raman spectroscopy reveals biochemical differences in plasma derived extracellular vesicles from sporadic amyotrophic lateral sclerosis patients. Nanomedicine. 29, 102249(2020).
  9. Pezzotti, G. Raman spectroscopy in cell biology and microbiology. J Raman Spectrosc. 52 (12), 2348-2443 (2021).
  10. Dodo, K., Fujita, K., Sodeoka, M. Raman spectroscopy for chemical biology research. J Am Chem Soc. 144 (43), 19651-19667 (2022).
  11. Hamada, K., et al. Raman microscopy for dynamic molecular imaging of living cells. J Biomed Opt. 13 (4), 044027(2008).
  12. Ichimura, T., et al. Non-label immune cell state prediction using Raman spectroscopy. Sci Rep. 6 (1), 37562(2016).
  13. Germond, A., Panina, Y., Shiga, M., Niioka, H., Watanabe, T. M. Following embryonic stem cells, their differentiated progeny, and cell-state changes during IPS reprogramming by Raman spectroscopy. Anal Chem. 92 (22), 14915-14923 (2020).
  14. Zahn, J., Germond, A., Lundgren, A. Y., Cicerone, M. T. Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis. J Biophotonics. 15 (7), e202100274(2022).
  15. Suzuki, Y., et al. Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering. Proc Natl Acad Sci U S A. 116 (32), 15842-15848 (2019).
  16. Nitta, N., et al. Raman image-activated cell sorting. Nat Commun. 11 (1), 3452(2020).
  17. Shipp, D. W., Sinjab, F., Notingher, I. Raman spectroscopy: techniques and applications in the life sciences. Adv Opt Photon. 9 (2), 315-428 (2017).
  18. Kong, C., et al. High-contrast, fast chemical imaging by coherent Raman scattering using a self-synchronized two-colour fibre laser. Light Sci Appl. 9 (1), 25(2020).
  19. Mutlu, A. S., Duffy, J., Wang, M. C. Lipid metabolism and lipid signals in aging and longevity. Dev Cell. 56 (10), 1394-1407 (2021).
  20. Fueser, H., Pilger, C., Kong, C., Huser, T., Traunspurger, W. Polystyrene microbeads influence lipid storage distribution in C. elegans as revealed by coherent anti-stokes Raman scattering microscopy. Environ Pollut. , 294-118662 (2022).
  21. Lee, J., et al. Label-free multiphoton imaging of microbes in root, mineral, and soil matrices with time-gated coherent Raman and fluorescence lifetime imaging. Environ Sci Technol. 56 (3), 1994-2008 (2022).
  22. Sheng, M., et al. Single source CARS-based multimodal microscopy system for biological tissue imaging. Biomed Opt Express. 15 (1), 131-141 (2024).
  23. Zeng, Q., et al. Label-free Raman imaging for screening of anti-inflammatory function food. Food Chem X. 22, 101297(2024).
  24. Zhang, J., et al. High-throughput single-cell sorting by stimulated Raman-activated cell ejection. Sci Adv. 10 (50), eadn6373(2024).
  25. Yang, L., Iyer, R. R., Sorrells, J. E., Renteria, C. A., Boppart, S. A. Temporally optimized and spectrally shaped hyperspectral coherent anti-stokes Raman scattering microscopy. Opt Express. 32 (7), 11474-11490 (2024).
  26. Pegoraro, A. F., Stolow, A. Chirp modulation stimulated Raman scattering microscopy. Opt Express. 32 (18), 31297-31310 (2024).
  27. Zhang, Y., et al. Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm. Opt Lett. 48 (3), 550-553 (2023).
  28. Sorrells, J. E., et al. Programmable hyperspectral coherent anti-stokes Raman scattering microscopy. Opt Lett. 49 (9), 2513-2516 (2024).
  29. De la Cadena, A. Multiplex chemical imaging based on broadband stimulated Raman scattering microscopy. J Vis Exp. (185), e63709(2022).
  30. Zhang, S., He, Y., Yue, S. Coherent Raman scattering imaging of lipid metabolism in cancer. J Innov Opt Health Sci. 16 (3), 2230015(2023).
  31. Camp, C. H., Cicerone, M. T. Chemically sensitive bioimaging with coherent Raman scattering. Nat Photonics. 9 (5), 295-305 (2015).
  32. Camp, C. H. High-speed coherent Raman fingerprint imaging of biological tissues. Nat Photonics. 8 (8), 627-634 (2014).
  33. Camp, C. H., Lee, Y. J., Cicerone, M. T. Quantitative comparable coherent anti-Stokes Raman scattering spectroscopy: correcting errors in phase retrieval. J Raman Spectrosc. 47 (4), 408-415 (2015).
  34. Chen, W. W., et al. Spectroscopic coherent Raman imaging of Caenorhabditis elegans reveals lipid particle diversity. Nat Chem Biol. 16 (10), 1087-1095 (2020).
  35. Poorna, R., Chen, W. W., Qiu, P., Cicerone, M. T. Toward gene-correlated spatially resolved metabolomics with fingerprint coherent Raman imaging. J Phys Chem B. 127 (25), 5576-5587 (2023).
  36. Vernuccio, F., et al. Full-spectrum CARS microscopy of cells and tissues with ultrashort white-light continuum pulses. J Phys Chem B. 127 (21), 4733-4745 (2023).
  37. Murakami, Y., et al. Molecular fingerprinting of mouse brain using ultrabroadband coherent anti-stokes Raman scattering microspectroscopy empowered by multivariate curve resolution-alternating least squares. Chem Biomed Imaging. 2 (10), 689-697 (2024).
  38. Muddiman, R., Harkin, S., Butler, M., Hennelly, B. Broadband CARS hyperspectral classification of single immune cells. J Biophotonics. 18 (3), e202400382(2025).
  39. Dixon, J. Z., Chen, W. W., Xu, H., Audier, X., Cicerone, M. T. Broadband coherent anti-stokes Raman scattering microscopy for rapid, label-free biological imaging. Rev Sci Instrum. 96 (4), 043706(2025).
  40. Liu, Y., Lee, Y. J., Cicerone, M. T. Broadband CARS spectral phase retrieval using a time-domain Kramers-Kronig transform. Opt Lett. 34 (9), 1363-1365 (2009).
  41. Rinia, H. A., Bonn, M., Müller, M., Vartiainen, E. M. Quantitative CARS spectroscopy using the maximum entropy method: The main lipid phase transition. Chemphyschem. 8 (2), 279-287 (2007).
  42. Cicerone, M. T., Aamer, K. A., Lee, Y. J., Vartiainen, E. Maximum entropy and time-domain Kramers-Kronig phase retrieval approaches are functionally equivalent for CARS microspectroscopy. J Raman Spectrosc. 43 (5), 637-643 (2012).
  43. Houhou, R., et al. Deep learning as phase retrieval tool for CARS spectra. Opt Express. 28 (14), 21002-21024 (2020).
  44. Wang, Z., et al. Vector: Very deep convolutional autoencoders for non-resonant background removal in broadband coherent anti-stokes Raman scattering. J Raman Spectrosc. 53 (6), 1081-1093 (2022).
  45. Camp, C. H. Raman signal extraction from CARS spectra using a learned-matrix representation of the discrete Hilbert transform. Opt Express. 30 (15), 26057-26071 (2022).
  46. Junjuri, R., Saghi, A., Lensu, L., Vartiainen, E. M. Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra. Phys Chem Chem Phys. 25, 16340-16353 (2023).
  47. Vernuccio, F., et al. Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms. Sci Rep. 14 (1), 23903(2024).
  48. Yao, B., et al. Recent progress in deep learning for improving coherent anti-stokes Raman scattering microscopy. Laser Photonics Rev. 18 (11), 2400562(2024).
  49. Bestvater, F., et al. Two-photon fluorescence absorption and emission spectra of dyes relevant for cell imaging. J Microsc. 208 (2), 108-115 (2002).
  50. Drobizhev, M., Makarov, N. S., Tillo, S. E., Hughes, T. E., Rebane, A. Two-photon absorption properties of fluorescent proteins. Nat Methods. 8 (5), 393-399 (2011).
  51. Zhu, H., Fan, J., Du, J., Peng, X. Fluorescent probes for sensing and imaging within specific cellular organelles. Acc Chem Res. 49 (10), 2115-2126 (2016).
  52. Wu, L., Liu, J., Li, P., Tang, B., James, T. D. Two-photon small-molecule fluorescence-based agents for sensing, imaging, and therapy within biological systems. Chem Soc Rev. 50, 702-734 (2021).
  53. Berezin, M. Y., Achilefu, S. Fluorescence lifetime measurements and biological imaging. Chem Rev. 110 (5), 2641-2684 (2010).
  54. Becker, W. Fluorescence lifetime imaging - techniques and applications. J Microsc. 247 (2), 119-136 (2012).
  55. Lin, H. J., Herman, P., Lakowicz, J. R. Fluorescence lifetime-resolved pH imaging of living cells. Cytometry A. 52 (2), 77-89 (2003).
  56. Kuimova, M. K., Yahioglu, G., Levitt, J. A., Suhling, K. Molecular rotor measures viscosity of live cells via fluorescence lifetime imaging. J Am Chem Soc. 130 (21), 6672-6673 (2008).
  57. Wang, B., et al. Bipolar and fixable probe targeting mitochondria to trace local depolarization via two-photon fluorescence lifetime imaging. Analyst. 140, 5488-5494 (2015).
  58. Despa, S., Vecer, J., Steels, P., Ameloot, M. Fluorescence lifetime microscopy of the Na+ indicator sodium green in HeLa cells. Anal Biochem. 281 (2), 159-175 (2000).
  59. Wilms, C. D., Eilers, J. Photophysical properties of Ca2+-indicator dyes suitable for two-photon fluorescence-lifetime recordings. J Microsc. 225 (3), 209-213 (2007).
  60. König, K. Review: Clinical in vivo multiphoton FLIM tomography. Methods Appl Fluoresc. 8 (3), 034002(2020).
  61. Scipioni, L., Rossetta, A., Tedeschi, G., Gratton, E. Phasor s-FLIM: a new paradigm for fast and robust spectral fluorescence lifetime imaging. Nat Methods. 18 (5), 542-550 (2021).
  62. Ouyang, Y., Liu, J., Wang, Z. M., Liu, Z., Wu, M. FLIM as a promising tool for cancer diagnosis and treatment monitoring. Nano Micro Lett. 13 (1), 133(2021).
  63. Shirshin, E. A., et al. Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity. Proc Natl Acad Sci U S A. 119 (9), e2118241119(2022).
  64. Liu, Z. Mapping metabolic changes by noninvasive, multiparametric, high-resolution imaging using endogenous contrast. Sci Adv. 4 (3), eaap9302(2018).
  65. Georgakoudi, I., Quinn, K. P. Label-free optical metabolic imaging in cells and tissues. Annu Rev Biomed Eng. 25, 413-443 (2023).
  66. Chen, W. W., et al. Identifying lipid particle sub-types in live Caenorhabditis elegans with two-photon fluorescence lifetime imaging. Front Chem. 11, 1161775(2023).
  67. Littleton, B., Shah, P., Kavanagh, T., Richards, D. Three-color coherent anti-stokes Raman scattering with optical nonresonant background removal. J Raman Spectrosc. 50 (9), 1303-1310 (2019).
  68. Crikit2 software for BCARS data processing. , https://github.com/ccampjr/crikit2 (2025).
  69. Zhang, P., et al. Proteomic study and marker protein identification of Caenorhabditis elegans lipid droplets. Mol Cell Proteomics. 11 (8), 317-328 (2012).
  70. Hulsey-Vincent, H. J., Cameron, E. A., Dahlberg, C. L., Galati, D. F. Spectral scanning and fluorescence lifetime imaging microscopy enable separation and characterization of C. elegans autofluorescence in the cuticle and gut. Biol Open. 13 (12), bio060613(2024).
  71. Gottlieb, D., Asadipour, B., Kostina, P., Ung, T. P. L., Stringari, C. FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biol Imaging. 3, e21(2023).
  72. Weber, G. Resolution of the fluorescence lifetimes in a heterogeneous system by phase and modulation measurements. J Phys Chem. 85 (8), 949-953 (1981).
  73. Ranjit, S., Malacrida, L., Jameson, D. M., Gratton, E. Fit-free analysis of fluorescence lifetime imaging data using the phasor approach. Nat Protoc. 13 (9), 1979-2004 (2018).
  74. Smolen, J. A., Wooley, K. L. Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues. PNAS Nexus. 1 (5), pgac235(2022).
  75. Walsh, A. J., et al. Classification of T-cell activation via autofluorescence lifetime imaging. Nat Biomed Eng. 5 (1), 77-88 (2021).
  76. Vu, T., et al. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun. 13 (1), 169(2022).

Access restricted. Please log in or start a trial to view this content.

Reprints and Permissions

Request permission to reuse the text or figures of this JoVE article

Request Permission

Tags

Coherent Anti Stokes RamanFluorescence Lifetime ImagingRaman Fingerprint SpectroscopyTwo Photon MicroscopyChemical ImagingMetabolic ProfilingSub Cellular ResolutionIn Vivo ImagingC ElegansGreen Fluorescent Protein

Related Articles