## Main

In recent years, high-content imaging approaches have been refined for decoding the complex and dynamic orchestration of biological processes1,2,3. Fluorescence, with its high contrast, high specificity and multiple parameters, has become the reference technique for imaging4,5. Continuous improvements in fluorescence microscopes6,7,8,9 and the ever-expanding palette of genetically encoded and synthesized fluorophores have enabled the labeling and observation of a large number of molecular species10,11. This offers the potential of using multiplexed imaging to follow multiple labels simultaneously in the same specimen, but the technologies for this have fallen short of their fully imagined capabilities. Standard fluorescence microscopes collect multiple images sequentially, employing different excitation and detection bandpass filters for each label. Recently developed techniques allow for massive multiplexing by utilizing sequential labeling of fixed samples but are not suitable for in vivo imaging12,13. Unfortunately, these approaches are ill-suited to separating overlapping fluorescence emission signals and the narrow bandpass optical filters used to increase selectivity decrease the photon efficiency of the imaging (Supplementary Figs. 1 and 2) These limitations have restricted the number of imaged fluorophores per sample (usually 3–4) and risk exposing the specimen to damaging levels of exciting light. This has been a substantial obstacle for dynamic imaging, preventing in vivo and intravital imaging from reaching its full potential with a broader impact on research, from developmental biology14, cancer research15 and immunology2 to neuroimaging16.

Hyperspectral fluorescent imaging (HFI) potentially overcomes the limitations of overlapping emissions by expanding signal detection into the spectral domain17. HFI captures a spectral profile from each pixel, resulting in a hyperspectral cube (x,y and wavelength) of data, which can be processed to deduce the labels present in that pixel. Linear unmixing (LU) has been widely utilized to analyze HFI data and has performed well with bright samples emitting strong signals from fully characterized, extrinsic fluorophores, such as fluorescent proteins and dyes18,19,20. However, in vivo fluorescence microscopy is almost always limited in the number of photons collected per pixel (owing to expression levels, biophysical fluorescent properties and sensitivity of the detection system), which reduces the quality of the spectra acquired. While solutions beyond the standard LU have been proposed21, the challenge of analyzing low-intensity spectral signals remains.

A further challenge that affects the quality of spectra is the presence of multiple forms of noise in the imaging of the sample. Two examples of instrumental noise are photon noise and read noise22,23,24,25. Photon noise, also known as Poisson noise, is an inherent property related to the statistical variation of photons emission from a source and of detection. Poisson noise is inevitable when imaging fluorescent dyes and is more pronounced in the low-photon regime. It poses challenges, especially in live and timelapse imaging, where the power of the exciting laser is reduced to avoid photo-damage to the sample, decreasing the amount of fluorescent signal. Read noise arises from voltage fluctuations in microscopes operating in analog mode during the conversion from photon to digital levels intensity and commonly affects fluorescence imaging acquisition. Most biological samples used for in vivo microscopy are labeled using extrinsic signals from fluorescent proteins or probes but often include intrinsic signals (autofluorescence). Autofluorescence contributes to photons that are undesired, difficult to identify and to account for in LU. The cumulative presence of noise inevitably leads to a degradation of acquired spectra during imaging. As a result, the spectral separation by LU is often compromised and the signal-to-noise ratio (SNR) of the final unmixing is often reduced by the weakest of the signals detected19. Increasing the amount of laser excitation can partially overcome these challenges, but the higher energy deposition in the sample causes photo-bleaching and photo-damage, affecting both the integrity of the live sample and the duration of the observation. Traditional unmixing strategies such as LU are computationally demanding, requiring long analyses and often slowing the experiment. Combined, these compromises have reduced both the overall multiplexing capability and the adoption of HFI multiplexing technologies.

We have developed HyU as an answer to the challenges that have limited the wider acceptance of HFI for in vivo imaging. HyU combines our previous phasor hyperspectral approach26 merged with traditional unmixing algorithms to untangle the fluorescent signals more rapidly and more accurately from multiple exogenous and endogenous labels. The phasor approach26 is a popular dimensionality reduction approach for the analysis of both fluorescence lifetime and spectral image analysis27,28,29. In HyU, the phasor approach provides spectral compression, denoising and computational reduction that simplifies both pre-processing30 and unmixing31,32,33 of HFI datasets. Standard phasor analysis26,27,34,35,36,37,38,39,40,41 is fully supervised and requires a manual selection of regions or points on the phasor plot, a graphical representation of the transformed spectra. In contrast, HyU utilizes phasor processing as an encoder to aggregate similar spectra onto the phasor plot, reducing even the largest volumetric datasets so that unmixing algorithms, such as LU, can be applied on a far smaller number of elements (the number of pixels on the phasor plot). Furthermore, HyU provides unsupervised analysis of the HFI data, simplifying data processing and removing user subjectivity. Our results show that HyU offers three key advantages: (1) improved unmixing over conventional LU, especially for low-intensity images, down to five photons per spectra; (2) simplified identification of independent spectral components; and (3) dramatically faster processing of large datasets, overcoming the typical unmixing bottleneck for in vivo fluorescence microscopy.

## Results

### Method overview

HyU combines the best features of hyperspectral phasor analysis and LU, resulting in faster computation speeds and more reliable results, especially at low light levels. Phasor approaches reduce the computational load because they are compressive, reducing the 32 channels of an HFI spectral plot into a position on a two-dimensional (2D) histogram, representing the real and imaginary Fourier components of the spectrum (Fig. 1a,b). Different 32-channel spectra are represented as different positions on the 2D phasor plot and mixtures of the two spectra will be rendered at a position along a line connecting the pure spectra. Because the spectral content of an entire 2D or three-dimensional (3D) image set is rendered on a single phasor plot, there is a dramatic data compression, from a spectrum for each voxel in an image set (up to or even beyond gigavoxels) to a histogram value on the phasor plot (megapixels). In addition, because each ‘bin’ on the phasor plot histogram corresponds to multiple voxels with highly similar spectral profiles, the binning itself represents spectral averaging, which reduces the Poisson and instrumental noise (Fig. 1c–e). Poisson noise in the collected light is unavoidable in HFI unless the excitation is turned so high that the statistics of collected fluorescence creates hundreds or thousands of photons per spectral bin. The clear separation of the spectral phasor plot and its referenced imaging data, permits denoising algorithms to be applied to phasor plots with minimal degradation of the image resolution. LU or other unmixing approaches can be applied to the spectra on the phasor plot, offering a dramatic reduction in computational burden for large image datasets (Fig. 1d). To understand this saving, consider the conventional approach of LU applied to image data at the voxel level (Fig. 1a,f). A timelapse volumetric dataset of 512 × 768 × 17 (x, y and z) pixels, over six time points, (Supplementary Table 1), would require 40 million operations. HyU requires only ~18,000 operations to unmix each bin on the phasor plot, representing more than a thousand-fold saving (Fig. 1f,g).

### Performance evaluation

To quantitatively assess the relative performance of LU and HyU, we analyzed them on synthetic hyperspectral fluorescent datasets, created by computationally modeling the biophysics of fluorescence spectral emission and microscope performance (Fig. 2a,b and Supplementary Figs. 35). We used this synthetic dataset to evaluate LU and HyU algorithm performance quantitatively by using metrics such as mean squared error (MSE) and unmixing residual (Supplementary Fig. 6 and Methods; for both metrics, a lower value indicates better performance). In addition to the computational efficiency mentioned above, HyU analysis shows better ability to capture spatial features over a wide dynamic range of intensities, when compared to standard LU, in large part due to the denoising created by processing in phasor space (Fig. 2a,b). The improved accuracy is demonstrated by a lower MSE, in comparing the results of LU and HyU to the image ground truth. The absolute MSE for HyU is ~2× lower than that of LU, especially at low and ultralow fluorescence levels (five denoising filters and 16 photons per spectrum) (Fig. 2c). MSE can be further decreased using denoising filters on the phasor plot, resulting in superiority of HyU relative to LU for HFI at low (5–20 photons per spectrum) and ultralow (2–5 photons per spectrum) levels (Fig. 2d). To better characterize the performance in the experimental data without ground truth, we also define the unmixing residual as the difference between the original multichannel hyperspectral images and their unmixed results. Residuals provide a measure of how closely the unmixed results reconstruct the original signal (Supplementary Fig. 3 and Methods). Unmixing residuals are inversely proportional to the performance of the algorithm, with low residuals indicating high similarity between the unmixed and the original signals. Analysis of unmixing residuals in the synthetic data highlights an improved interpretation of the spectral information in HyU with an average unmixing residual reduction of 21% compared to the standard (Supplementary Fig. 5c). The reduction in both MSE and average unmixing residual for synthetic data demonstrates the superior performance of HyU and provides a baseline comparison when demonstrating performance improvements for experimental data.

We support the enhanced performance of HyU with analysis of experimental data, which reveals comparatively lower unmixing residuals and a higher dynamic range as compared to LU. Data were acquired from a quadra-transgenic zebrafish embryo Tg(ubiq:Lifeact-mRuby);Gt(cltca-citrine);Tg(ubiq:lyn-tdTomato);Tg(fli1:mKO2), labeling actin, clathrin, plasma membrane and pan-endothelial cells, respectively (Figs. 2e–l and 3, Supplementary Figs. 79 and Supplementary Video 1). HyU unmixing of the data shows minimal signal cross-talk between channels, whereas LU presents noticeable bleed-through (Fig. 2m–p). Consistent with synthetic data, we utilize the unmixing residual as the main indicator for quality of the analysis in experimental data, owing to the absence of a ground truth. The residual images (Fig. 2f,g) depict a striking difference in performance between HyU and LU. The average relative residual of HyU denotes a sevenfold improvement compared to LU (Fig. 2h) in disentangling the fluorescent spectra. We visualize the unmixed channels independently (Fig. 2i–l), zooming in on details (Fig. 2i–p) to highlight areas affected by bleed-through and which are difficult to unmix. HyU, with contrast twofold higher than standard LU, reduces bleed-through effects and produces images with sharper spatial features, leading to better interpretation of the experimental data (Fig. 2k,l, Supplementary Fig. 7 and Methods).

### Decreased signal cross-talk

Applying HyU to another HFI dataset further highlights HyU’s improvements in noise reduction (less bleed-through) and reconstitution of spatial features (increased spatial resolution) for low-photon unmixing (Fig. 3 and Supplementary Fig. 8). In the zoomed-in image of a single slice of the embryo skin surface, acquired in the trunk region, the HyU image correctly does not display pan-endothelial (magenta) signal in the periderm, an area which should be devoid of endothelial cells and mKO2 signal (Fig. 3c). In contrast, the result from LU shows a visually distinctive pan-endothelial signal throughout the tissue plane (Fig. 3d). This incorrect estimation of the relative contribution of mKO2 fluorescence for LU is possibly due to the presence of noise, corrupting the spectral profiles. This is further delineated in the intensity profiles of the mKO2 signal between HyU and LU with much higher individual peaks from noise demonstrated for LU (Fig. 3g, bottom left). Intensity profiles for both magnified cross-sections of the volume (Fig. 3c–f) provide visualization of the improvements of HyU. The line intensity profiles in HyU present reduced noise and represent more closely the expected distribution of signals (Fig. 3g,h). The visible micro-patterns of actin on the membrane of the periderm suggest that the improvements quantified with synthetic data are maintained in live samples’ signals and geometrical patterns of microridges42. In contrast, noise corruption and the presence of misplaced signals are characterized in the results from LU, with high-frequency intensity variations that mismatch both the labeling and biological patterns.

HyU is more accurate and results in more reliable unmixing results across the depth of sample with greatly reduced unmixing residuals. The average residual for HyU is ninefold lower than that of LU with a threefold narrower variance. (Fig. 3i and Supplementary Fig. 8). This reduction in the residual is consistent with increasing z depth, where HyU unmixing results stably maintain both lower residuals and variance on average. These reduced residuals correspond both to a mathematically more precise and more uniform decomposition of signals as illustrated by the distribution of residuals versus photons (Supplementary Figs. 8e,f and 14).

We utilized HyU’s increased sensitivity to overcome common challenges of multiplexed imaging, such as poor photon yield and spectral cross-talk and were able to visualize dynamics in a developing zebrafish embryo. We used a triple-transgenic zebrafish embryo with labeled pan-endothelial cells, vasculature and clathrin-coated pits (Tg(fli1:mKO2); Tg(kdrl:mCherry); Gt(cltca-citrine)). Multiplexing these spectrally close fluorescent proteins is enabled by HyU’s increased sensitivity at lower photon counts. The increased performance at lower SNR allowed us to maintain high quality results (Fig. 4 and Supplementary Video 2) while performing faster acquisitions and reducing photo-damage through lower excitation laser power and pixel dwell time. Decreased experimental requirements allow for tiling of larger volumes and extending the field of view, while still providing enough time resolution for developmental events, even with a high number of multiplexed fluorescent signals. The timelapses include the simultaneous acquisition of clathrin, kdrl and fli1, enabling visualization of the formation of ventral vasculo-endothelial protrusions, while tracking the development of vesicles and vasculature. HyU enables comparative quantifications of spatiotemporal features, allowing for the determination of volumetric changes over lengthy timelapses, in this case, over the course of 300 min (Fig. 4b)43,44.

### Analysis of both Intrinsic and extrinsic fluorescent signals

HyU provides the ability to combine the information from intrinsic and extrinsic signals during live imaging of samples, at both single (Fig. 5) and multiple time points (Fig. 6). The graphical representation of phasors allows identification of unexpected intrinsic fluorescence signatures in a quadra-transgenic zebrafish embryo Gt(cltca-citrine);Tg(ubiq:lyn-tdTomato;ubiq:Lifeact-mRuby;fli1:mKO2), imaged with single photon (488 and 561 nm excitation) (Fig. 5a–d). The elongated distribution on the phasor (Fig. 5c) highlights the presence of an additional, unexpected spectral signature, related to strong sample autofluorescence (Fig. 5d, blue). HyU analysis of the sample, inclusive of this additional signal, provides separation of the contributions of five different fluorescent spectra with residual 3.9 ± 0.3%. HyU allows for reduced energy load, tiled imaging of the entire embryo without perturbing its development or depleting its fluorescence signal (Fig. 5a). The higher speed, lower power imaging allows for subsequent re-imaging of the same sample, as we report in the zoomed high-resolution acquisitions of the head section (Fig. 5b,e).

With the ability to unmix low-photon signals, HyU enables imaging and decoding of intrinsic signals, which are inherently low light. Two-photon lasers are ideal for exciting and imaging blue-shifted intrinsic fluorescence from samples45,46,47,48. Here, the same quadra-transgenic sample is imaged using 740 nm excitation to access both intrinsic and extrinsic signals (Fig. 5e–g, Supplementary Note 2 and Supplementary Fig. 21). HyU enables unmixing of at least nine intrinsic and transgenic fluorescent signals (Fig. 5), recovering fluorescent intensities from labels illuminated at a suboptimal excitation wavelength (Fig. 5e). The spectra for intrinsic fluorescence were obtained from in vitro measurements and values reported in literature (Methods). For this sample the intrinsic signals arise from events related mainly with metabolic activity (nicotinamide adenine dinucleotide (NADH) and retinoids)49,50,51,52,53, tissue structure (elastin)54 and illumination (laser reflection) (Fig. 5e, Supplementary Figs. 22 and 26 and Supplementary Note 2). These results confirm our conclusion that HyU is a powerful tool for allowing the imaging and analysis of endogenous labels.

### Multiplexed timelapse acquisition

Finally, we exploited the HyU capabilities to multiplex volumetric timelapse of extrinsic and intrinsic signals by imaging the tail region of the same quadra-transgenic zebrafish embryo. We excited extrinsic labels at 488/561 nm and the intrinsic signals with 740 nm two-photon microscopy, collecting six tiled volumes over 125 min (Fig. 6, Supplementary Figs. 911, 15, Supplementary Video 3 and Supplementary Note 2). HyU unmixing in this sample allows for distinction of nine signals, separating their contributions with sufficiently low requirements to allow repeated imaging of notoriously low SNR intrinsic fluorescence.

## Discussion

Our results reveal the advantages of HyU over more-conventional LU in performing complex multiplexing experiments. HyU overcomes the considerable challenges of separating multiple fluorescent and autofluorescent labels with overlapping spectra while minimizing sample exposure to excitation light.

The chief advantage of HyU is its multiplexing capability when imaging in the presence of biological and instrumental noise, especially at low signal levels. HyU increased sensitivity improves multiplexing in photon limited applications (Fig. 2f–l), in deeper volumetric acquisitions (Fig. 3i and Supplementary Fig. 23) and in signal-starved imaging of autofluorescence (Figs. 5e and 6). This improvement is demonstrated through increased contrast and reduced residuals. Our simulation results (Fig. 2) demonstrate that HyU improves unmixing of spatially and spectrally overlapping fluorophores excited simultaneously. The increased robustness at low-photon imaging conditions reduces the imaging requirements for excitation levels and detector integration time, allowing for imaging with reduced photo-toxicity. Live imaging on multicolor samples performed at high sampling frequency enables improved tiling to increase the field of view (Figs. 3 and 4) while maximizing the usage of the finite fluorescent signals over time. Two-photon imaging of intrinsic and extrinsic signals suggests the ability of HyU to multiplex signals with large dynamic range differences (Fig. 5) extending multiplexed volumetric imaging into the time dimension (Fig. 6). Although improved, images with particularly low signals still present corruption (Supplementary Fig. 4), setting a reasonable range of utilization above eight photons per spectrum.

Simplicity of use and versatility are other key advantages of HyU, inherited from both the phasor approach35 and traditional unmixing algorithms. Phasors here operate as a spectral encoder, reducing computational load and integrating similar spectral signatures in histogram bins of the phasor plot. This representation simplifies identification of independent spectral signatures (Fig. 5 and Supplementary Note 1) through both phasor plot selection and phasor residual mapping (Supplementary Fig. 11), accounting for unexpected intrinsic signals (Figs. 5 and 6, Supplementary Fig. 12 and Supplementary Note 2) in a semi-automated manner, while still allowing fully automated analysis by means of spectral libraries.

The simplicity of this approach is especially helpful in live imaging, where identifying independent spectral components remains an open challenge, owing to the presence of intrinsic signals (Supplementary Fig. 12 and Supplementary Note 1). Intrinsic signals are notoriously low in emitted photons, leading to an inability to unmix using traditional unmixing algorithms. High-SNR reference spectra can be derived from other experimental data or identified directly on the phasor. Selection of portions on the phasor plot allows for visualization of the corresponding spectra in the wavelength domain (Fig. 5c,d,f,g and Supplementary Fig. 27). This intuitive versatility allows for identification of both the number of unexpected signatures and their spectra, a task previously difficult to perform due to noise and lack of global visualization tools. In single-photon imaging (Fig. 5a–d), the HyU phasor allowed identification of a fifth distinct spectral component arising from a general autofluorescent background, thereby improving the unmixed results. In two-photon imaging, HyU enabled identification and multiplexing of eight highly overlapping signals possessing a wide dynamic range of intensities, between intrinsic and extrinsic markers (Fig. 5f,g). Combination of single- and two-photon imaging increased the number of multiplexed fluorophores to nine (Fig. 6), considering some of the extrinsic labels being excited with two-photon microscopy. Multiplexing of signals may be further improved by implementing HyU on fluorescent dyes.

HyU performs better than standard algorithms both in the presence and absence of phasor noise reduction filters35. Compared to LU, the unmixing enhancement when such filters35 are applied is demonstrated by a decrease of the MSE of up to 21% (Fig. 2c), with a reduction of the average amount of residuals by sevenfold. Even in the absence of phasor denoising filters, HyU performs up to 7.3% better than the standard (Fig. 2d) based on MSE of synthetic data unmixing. This base improvement is due to the averaging of similarly shaped spectra in each phasor histogram bin, which reduces the statistical variability within the spectra used for the unmixing calculations (Fig. 1e). This averaging strategy works well for general fluorescence spectra owing to their broad and mostly unique spectral shape.

In the absence of noise, for example in the ground-truth simulations, LU produces an MSE that is sixfold lower than HyU (Supplementary Figs. 5b,c and 6g). In these noiseless conditions, the binning and averaging of spectra in the phasor histogram, without denoising, provides statistically indifferent values of error respect to LU, suggesting results of similar quality.

HyU can interface with different unmixing algorithms, adapting to existing experimental pipelines. We successfully tested hybridization with iterative approaches such as non-negative matrix factorization55, fully constrained and non-negative least squares56 (Methods). Speed tests with iterative fitting unmixing algorithms demonstrate a speed increase of up to 500-fold when the HyU compressive strategy is applied. (Supplementary Fig. 13 and Supplementary Note 3). Due to the initial computational overhead for encoding spectra in phasors, there is a twofold speed reduction for HyU in comparison to standard LU; however, this may be improved with further optimizations of HyU implementation or by implementing different types of encoding.

One restriction of HyU derives from the mathematics of LU, where linear equations representing the unmixed channels need to be solved for the unknown contributions of each analyzed fluorophore. To obtain a unique solution from these equations and to avoid an underdetermined equation system, the maximum number of spectra for unmixing may not exceed the number of channels acquired57, which is generally 32 for commercial microscopes. This number could be increased; however, due to the broad and photon-starved nature of fluorescence spectra, acquisition of a larger number of channels could negatively affect the sample, imaging time and intensities. Depending on the number of labels in the specimen of interest, extending the number of labels to simultaneously unmix beyond 32 will likely require spectral resolution upsampling strategies.

HyU improvement is related to the presence of various types of signal disruption and noise in microscopy images, such as stochastic emission, Gaussian, Poisson and digital, as well as unidentified sources of spectral signatures that affect SNR in a variety of ways (Supplementary Fig. 5b,c and Supplementary Figs. 6g and 28). In the multiplexing of fluorescent signals, HyU offers improved performance, quality and speed in the low-signal regime. HyU is an improvement compared to previously published phasor analysis (Supplementary Figs. 24 and 25 and Supplementary Note 4) and the current gold standard LU under multiple experimental conditions of low SNR (Supplementary Figs. 16 and 17) reduced number of channels (Supplementary Figs. 18 and 19) in the case of fluorescent signals as well as combination of multiple fluorescent and autofluorescent signals (Supplementary Fig. 20). HyU is poised to be used in the context of in vivo imaging, collecting information from samples labeled at an endogenous level even in scattering mammal samples (Supplementary Figs. 29 and 30).

HyU is fully compatible with any commercial and common microscopes capable of spectral detection, facilitating access to the technology. Our analysis demonstrates HyU’s robustness, simplicity and improvement in identifying both new and known spectral signatures and vastly improved unmixing outputs, providing a much-needed tool for delving into the many questions still surrounding studies with live imaging.

## Methods

### Zebrafish lines

Adult fish were raised and maintained as described58 in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals by the University of Southern California, where the protocol was approved by the Institutional Animal Care and Use Committee (permit no. 12007 USC). Upon crossing appropriate adult lines, the embryos obtained were raised in Egg Water (60 μg ml−1 Instant Ocean and 75 μg ml−1 CaSO4 in Milli-Q water) at 28.5 °C with addition of 0.003% (w/v) 1-phenyl-2-thiourea around 18 hours post fertilization (h.p.f.) to reduce pigment formation.

Transgenic Gt(cltca-citrine)ct116a line is a genetrap of clathrin, heavy polypeptide a, labeling transport vesicles with heightened expression in the vasculature59. Tg(kdrl:mCherry) labels the vasculature and was a kind gift from C.-L. Lien (Children’s Hospital Los Angeles). Tg(fli1:mKO2)ct641ca labels pan-endothelial cells in both blood vessels and lymphatics as previously reported30. Tg(ubiq:lyn-tdTomato) labels all cell membrane by expression of lyn-tdTomato from the ubiquitin promoter, whereas Tg(ubiq:Lifeact-mRuby) labels actin by expression of LifeAct-mRuby fusion from the ubiquitin promoter.

The mpv17a9/a9;mitfaw2/w2 (casper) line was purchased from the Zebrafish International Resource Center and csf1rj4e1/j4e1 (panther) line60 was a kind gift from D. Parichy (University of Virginia). We crossed casper with panther to produce triple heterozygote mpv17a9/+;mitfaw2/+;csf1rj4e1/+ F1 generation fish, which were subsequently incrossed to produce F2 generation with 27 combinations of mutational state of these genes. As csf1rj4e1 phenotype was not clear in F2 adult with casper phenotype, we outcrossed these fish with panther fish to determine the zygosity of csf1rj4e1 mutation based on the frequency of larva with xanthophores (the heterozygote and homozygote produced a 50% and 0% fraction of xanthophore-positive larva, respectively) by fluorescence microscopy. The casper;csf1rj4e1/j4e1 line is viable and reproducible; we outcrossed either casper;csf1rj4e1/j4e1 line or casper;csf1rj4e1/+ line with other fluorescent transgenic lines over several generations to obtain fish harboring multiple transgenes on the casper background either in the presence or absence of xanthophores.

### Mouse lines

Mice imaging was approved by the Institutional Animal Care and Use Committee of University of Southern California, protocol no. 20847. Experimental research on vertebrates complied with institutional, national and international ethical guidelines. Animals were kept on a 12-h light–dark cycle. Animals were breathing double filtered air, temperature in the room was kept at 70–73 °F, with humidity at 50% and cage bedding was changed biweekly. All these factors contributed to minimize intra- and inter-experiment variability. Adult Balb-c mice (Charles River Laboratories) were killed via overdose of isoflurane followed by cervical dislocation. Organs were quickly collected from the mice, washed in phosphate-buffered saline (PBS) and cut longitudinally alongside the mid-section to expose the inner part of the organ. The two halves of the organ were arranged onto a microscope slide for imaging.

### Zebrafish sample preparation

Transgenic zebrafish lines were intercrossed over multiple generations to obtain embryos with multiple combinations of the transgenes. All lines were maintained as heterozygous for each transgene. Embryos were screened using a fluorescence stereo microscope (Axio Zoom, Carl Zeiss) for expression patterns of individual fluorescence proteins before imaging experiments. A confocal microscope (LSM 780, Carl Zeiss) was used to isolate Tg(ubiq:Lifeact-mRuby) lines from Tg(ubiq:lyn-tdTomato) lines by distinguishing spatially and spectrally overlapping signals.

For in vivo imaging, 5–6 zebrafish embryos at 18–72 h.p.f. were immobilized and placed into 1% UltraPure low-melting-point agarose (catalog no. 16520-050, Invitrogen) solution prepared in 30% Danieau (17.4 mM NaCl, 210 M KCl, 120 M MgSO47H2O, 180 M Ca(NO3)2 and 1.5 mM HEPES buffer in water, pH 7.6) with 0.003% 1-phenyl-2-thiourea and 0.01% tricaine in an imaging dish with no. 1.5 coverglass bottom (catalog no. D5040P, WillCo Wells). Following solidification of agarose at room temperature (1–2 min), the imaging dish was filled with 30% Danieau solution and 0.01% tricaine at 28.5 °C.

One fluorescent silica beads solution (Nanocs) labeled with Cy3 (Si500-S3-1, 0.5 ml, 0.5 μm, 1% solid, lot no. 1608BRX5) was characterized in its spectral fluorescence emission and physical size.

A 10× dilution in PBS of the beads was placed on a no. 1.5 imaging coverglass and spectrally characterized using spectral mode on a Zeiss LSM 780 laser confocal scanning microscope equipped with a 32-channel detector using 40×/1.1 W LD C-Apochromat Korr UV-VIS-IR lens utilizing a two-photon laser at 740 nm to excite fluorescence from the beads, using a 690 nm lowpass filter to separate excitation and fluorescence. Spectra obtained from multiple beads with the same label were averaged, producing the reference spectrum reported in Supplementary Fig. 30g (dashed line). Fluorescent silica bead size and concentration were determined via nanoparticle tracking analysis on the NanoSight NS300 (Malvern Panalytical). Samples were run five times and results averaged for final size and concentration values reported.

### Mouse sample preparation

For autofluorescent measurements, mouse organ samples were collected from Balb-c mice. Following euthanasia, organs were resected and washed in PBS to remove residual blood and kept in PBS until imaging preparation. Organs were sectioned to image the internal architecture and mounted on a glass imaging dish with sufficient PBS to avoid dehydration of the sample. Following imaging, all samples were fixed in a 10% neutral buffered formalin solution at 4 °C.

For ex vivo bead characterization in tissue, mouse organ samples were collected from Balb-c mice. Following euthanasia, organs were resected and washed in PBS followed by incubation for at least 24 h in 10% buffered formalin. The kidney was then removed from the fixative and sectioned into smaller ~5 × 5 × 5 mm pieces for imaging. A fluorescent silica beads working solution (Nanocs) labeled with Cy3 (Si500-S3-1, 0.5 ml, 0.5 μm, 1% solid, lot no. 1608BRX5) and previously characterized was prepared using a 10× dilution of the fluorescent beads from their stock concentration. Beads were injected in the sample using 50 μl of the solution loaded into a 0.5 ml syringe with a 28 g needle. The kidney sections were then placed in imaging dishes with a small volume of PBS to keep the samples hydrated before imaging.

### Image acquisition

Images were acquired on a Zeiss LSM 780 laser confocal scanning microscope equipped with a 32-channel detector using 40×/1.1 W LD C-Apochromat Korr UV-VIS-IR lens at 28 °C.

Samples of Gt(cltca-citrine), Tg(ubiq:lyn-tdTomato), Tg(fli1:mKO2) and Tg(ubiq:Lifeact-mRuby) were simultaneously imaged with 488 nm and 561 nm laser excitation, for citrine, tdTomato, mKO2 and mRuby. A narrow 488 nm/561 nm dichroic mirror was used to separate excitation and fluorescence emission. Samples were imaged with a two-photon laser at 740 nm to excite autofluorescence, using a 690 nm lowpass filter to separate excitation and fluorescence.

Samples of mouse kidney tissue were imaged with two-photon excitation at 740 nm or 850 nm with a 690+ nm lowpass filter, at 37 °C incubation.

For all samples, detection was performed at the full available range (410.5–694.9 nm) with 8.9 nm spectral binning.

Supplementary Table 1 provides the detailed description of the imaging parameters used for all images presented in this work.

### Hyperspectral fluorescence image simulation

The model simulates spectral fluorescent emission by generating a stochastic distribution of photons with profile equivalent to the pure reference spectra (as described in Supplementary Note 1). The effect of photon starvation, commonly observed on microscopes, is synthetically obtained by manually reducing the number of photons in this stochastic distribution. Detection, Poisson and signal transfer noises are then added to produce 32-channel fluorescence emission spectra that closely resemble those acquired on microscopes. The simulations include accurate integration of dichroic mirrors and imaging settings.

### Simulation types

#### Biologically comparable simulations

To quantify the performance of HyU versus LU for microscopy data acquired experimentally, we generated synthetic data where each input spectra were organized with intensity distributions taken from experimental data of fluorescently labeled biological samples. We calibrated the analog-(DLs)-to-photon counting rate based on existing literature61,62. Experimental data were discretized to photons to produce biologically relevant photon masks with distributions of signals highly resembling those of the samples. This provided intensities and ratios that closely resemble those acquired from a confocal microscope, while allowing control over the effects of photon starvation.

#### Spatially and spectrally overlapping simulations

We also included simulations to quantify the performance of HyU versus LU with respect to the number of spectral combinations and of end-members. The results are summarized in Supplementary Figs. 1619 in matrices of spectral overlap (0–100%, steps of 10%, x axis) by number of end-members (2–8 end-members, y axis) representing the relative MSE (Supplementary Methods; Performance quantification). Each relative MSE value reported in a matrix is the average of analysis of a 1,024 × 1,024 pixel image simulation with a spectral dimension of 32 channels matching the spectral range and bandwidth of the detectors in commercial confocal microscopes (LSM 780, Carl Zeiss). These simulations were created with artificial intensity distributions so that a simulation with x% overlap and n fluorophores would have x% of pixels with a randomized ratio of n input spectra. As an example, for a simulation with six fluorophores and 50% overlap, the simulated dataset would have 50% of the pixels contain a randomized combination of the six fluorophores, while the remaining pixels contain a single fluorophore. This allowed us to investigate the effects of an increasing number of spectral combinations on the compressive nature of the phasor method for HyU.

### Image analysis

#### Independent spectral signatures

Independent spectral fingerprints can be obtained through samples, solutions, literature or spectral viewer websites (Thermo Fisher, BD Spectral Viewer and Spectra Analyzer). Fluorescent signals used in this paper were obtained by imaging single-labeled samples in areas morphologically and physiologically known to express the specific fluorescence (Supplementary Fig. 21). For each dataset a phasor plot was computed. The 32-channel spectral fingerprint was extracted from the phasor bin at the count-weighted average position of the phasor cluster. Those fingerprints were compared to literature fingerprints and manually corrected to reduce noise. Further descriptions for how to identify new components can be found in Supplementary Note 1 and Supplementary Figs. 11 and 27.

For AF signals, the spectrum for elastin was obtained experimentally and compared to the literature54. Spectra for NADH-free, NADH-bound, retinoic acid, retinol and flavin adenine dinucleotide (FAD) were acquired from in vitro solutions using the microscope. NADH-free was B-NAD (Sigma-Aldrich, 43420) in PBS solution. NADH-bound was B-NAD and L-lactic dehydrogenase (Sigma-Aldrich, 43420, L3916) in PBS. Retinoic acid was a solution of retinoic acid (Sigma-Aldrich, R2625) in dimethylsulfoxide. Retinol was a solution of synthetic retinol (Sigma-Aldrich, R7632) in dimethylsulfoxide. FAD was FAD disodium salt hydrate (Sigma-Aldrich, F6625) in PBS.

#### Phasor analysis

For each pixel in a dataset, the Fourier coefficients of its normalized spectra define the coordinates $$\left( {G\left( n \right),\,S\left( n \right)} \right)$$ in the phasor plane, where:

$$G\left( n \right) = \frac{{\mathop {\sum }\nolimits_{\lambda _{\mathrm{s}}}^{\lambda _{\mathrm{f}}} I\left( \lambda \right){{{\mathrm{cos}}}}(n\omega \lambda )\Delta \lambda }}{{\mathop {\sum }\nolimits_{\lambda _{\mathrm{s}}}^{\lambda _{\mathrm{f}}} I\left( \lambda \right)\Delta \lambda }}$$
(1)
$$S\left( n \right) = \frac{{\mathop {\sum }\nolimits_{\lambda _{\mathrm{s}}}^{\lambda _{\mathrm{f}}} I\left( \lambda \right){{{\mathrm{sin}}}}(n\omega \lambda )\Delta \lambda }}{{\mathop {\sum }\nolimits_{\lambda _{\mathrm{s}}}^{\lambda _{\mathrm{f}}} I\left( \lambda \right)\Delta \lambda }}$$
(2)
$$\omega = \frac{{2\pi }}{c}$$
(3)

Where λs and λf are starting and ending wavelengths respectively; I is the measured intensity; c is the number of spectral channels (32 in our case) and n is the harmonic number63. In this work, we utilized the first harmonic (n = 1) for the autofluorescent signals and the second harmonic (n = 2) for fluorescent signals based on the sparsity of independent spectral components. A 2D histogram with dimensions (S and G) is applied to the phasor coordinates to group pixels with similar spectra within a single square bin. We define this process as phasor encoding.

#### LU

The hypothesis for LU in this work is that given i independent spectral fingerprints (fp), each collected spectrum (I(λ)) is a linear combination of fp and the sum of each fp contribution (R) is 1:

$$I\left( \lambda \right) = W_1R_1fp_1 + W_2R_2fp_2 + \ldots + W_iR_ifp_i + N$$
(4)
$$\Sigma R_i = 1$$
(5)

where Ri is the ratio, Wi the weight and N the noise. The acquired spectra are collected in the original spectral cube with shape (t,z,c,y,x), with t as time, c as channel and x,y,z spatial dimensions.

i spectral vectors, fpi, need to be provided to the unmixing function. It is assumed that there are identical weights for all fp and a low value for noise N. Under these conditions, we obtain Ri by applying a Jacobian matrix inversion:64

$$\begin{array}{l}\left[ {\begin{array}{*{20}{c}} {\mathop {\sum}\nolimits_x {w\left( x \right)\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}} } & {\mathop {\sum}\nolimits_x {w\left( x \right)\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}\frac{{\partial f^{\,0}}}{{\partial \alpha _2}}} } & \cdots \\ {\mathop {\sum}\nolimits_x {w\left( x \right)\frac{{\partial f^{\,0}}}{{\partial \alpha _2}}\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}} } & {\mathop {\sum}\nolimits_x {w\left( x \right)\frac{{\partial f^{\,0}}}{{\partial \alpha _2}}\frac{{\partial f^{\,0}}}{{\partial \alpha _2}}} } & \cdots \\ \vdots & \vdots & \ddots \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {\alpha _1 - \alpha _1^0} \\ {\alpha _2 - \alpha _2^0} \\ \vdots \end{array}} \right]\\ = \left[ {\begin{array}{*{20}{c}} {\mathop {\sum}\nolimits_x {w\left( x \right)\left[ {y\left( x \right) - f^{\,0}\left( x \right)} \right]\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}} } \\ {\mathop {\sum}\nolimits_x {w\left( x \right)\left[ {y\left( x \right) - f^{\,0}\left( x \right)} \right]\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}} } \\ {\mathop {\sum}\nolimits_x {w\left( x \right)\left[ {y\left( x \right) - f^{\,0}\left( x \right)} \right]\frac{{\partial f^{\,0}}}{{\partial \alpha _1}}} } \end{array}} \right]\end{array}$$
(6)

In the pixel-by-pixel LU implementation in this work, Jacobian matrix inversion is applied on the acquired spectrum in each pixel with dimensions (t,z,c,y,x). Resulting ratios for each spectral vector are assembled in the form of a ratio cube with shape (t,z,i,y,x) where x,y,z,t are the original image spatial and time dimensions, respectively and i is the number of input spectral vectors. The ratio cube (t,z,i,y,x) is multiplied with the integral of intensity over channel dimension of the original spectral cube, with shape (t,z,y,x), to obtain the final resulting dataset with shape (t,z,i,y,x).

#### Hybrid unmixing: LU

In the HyU implementation, Jacobian matrix inversion is applied on the average spectrum of each phasor bin with dimensions (c,s,g) where g and s are the phasor histogram sizes and c is the number of spectral channels acquired. The average spectrum in each bin is calculated by using the phasor as an encoder, to reference each original pixel spectra to a bin. Resulting ratios for each component channel are assembled in the form of a phasor bin-ratio cube with shape (i,s,g) where i is the number of input independent spectra fp (LU section). This phasor bin-ratio cube is then referenced to the original image shape, forming a ratio cube with shape (t,z,i,y,x) where x,y,z,t are the original image dimensions. We multiply the ratio cube with the integral of intensity over channel dimension of the original spectral cube, with shape (t,z,y,x), obtaining a final result dataset with shape (t,z,i,x,y).

#### HyU algorithm

The pseudo-code utilized for the HyU algorithm is as follows:

Input: I(x,y,c,z,t) (5D hyperspectral image)

U(i,c) (reference spectra (n spectra))

Output: I_U(x,y,i,z,t) (multichannel unmixed image)

Procedure:

HYU(I(x,y,c,z,t), U(n, c))

// Single harmonic Fourier transform

G(x,y,z,t), S(x,y,z,t) = phasor_transform(I(x,y,c,z,t))

// 2D histogram of G and S values

H(g,s)=histogram2d(G(x,y,z,t) S(x,y,z,t))

// Averaging of hyperspectral image over phasor histogram

I_H(g,s,c)=phasor_average(I(x,y,c,z,t), H(g,s))

// Linear Unmixing of averaged hyperspectral image

I_U(g,s,i) = LU(I_H(g,s,c), U(i,c))

// Reference unmixed phasor image back to original image dimensions

I_U(x,y,i,z,t) = reverse_phasor_reference(I_U(g,s,i)

return I_U(x,y,i,z,t)

#### Other unmixing algorithms

Unmixing algorithms utilized for speed comparisons with the HyU algorithm (Supplementary Fig. 13) were plugged in the unmixing step of the analysis pipeline and sourced as follows. Non-negative constrained least squares and fully constrained least squares from pysptools.abundance_maps (https://pysptools.sourceforge.io/abundance_maps.html). Robust non-negative matrix factorization55 Python implementation was obtained from (https://github.com/neel-dey/robust-nmf)

#### Data visualization

Rendering of final result datasets were performed using Imaris v.9.5–9.7. In Figs. 2 and 3, contrast settings (minimum, maximum and gamma) for each channel were set to be equal to provide reasonable comparison between HyU and LU results. Gamma was set to 1, no minimum threshold was applied and the maximum for each channel was set to one-third of the maximum intensity. The images were rendered using maximum intensity projection and for improving display, they were digitally resampled in the z direction, maintaining a fixed xy ratio to attenuate the gap generated from sparse sampling z-wise on the microscope.

#### Box plot generation

All box plots were generated using standard plotting methods. The center line corresponds to the median, the lower box border corresponds to the first quartile and the upper box border corresponds to the third quartile. The lower and upper whiskers correspond to 1.5× the interquartile range below and above the first and third quartiles, respectively.

#### Timelapse registration

A customized Python script (Supplementary Code) was first utilized to pad the number of z slices across multiple time points, obtaining equally sized volumes. The ‘Correct 3D drift’ plugin65 (https://imagej.net/Correct_3D_Drift) in FIJI66 (https://imagej.net/Fiji) was used to register the data.

#### Timelapse statistics

Box plots and line plots for timelapses were generated using ImarisVantage in Imaris v.9.5–9.7. Box plot elements follow the same guidelines as described above. Line plots are connected box plots for each time point with the solid line denoting the median values and the shaded region denoting the first and third quartiles.

### Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.