Effects of skin tone on photoacoustic imaging and oximetry

Significance

Photoacoustic imaging (PAI) provides contrast based on the concentration of optical absorbers in tissue, enabling the assessment of functional physiological parameters such as blood oxygen saturation (sO 2 ). Recent evidence suggests that variation in melanin levels in the epidermis leads to measurement biases in optical technologies, which could potentially limit the application of these biomarkers in diverse populations.

Aim

To examine the effects of skin melanin pigmentation on PAI and oximetry.

Approach

We evaluated the effects of skin tone in PAI using a computational skin model, two-layer melanin-containing tissue-mimicking phantoms, and mice of a consistent genetic background with varying pigmentations. The computational skin model was validated by simulating the diffuse reflectance spectrum using the adding-doubling method, allowing us to assign our simulation parameters to approximate Fitzpatrick skin types. Monte Carlo simulations and acoustic simulations were run to obtain idealized photoacoustic images of our skin model. Photoacoustic images of the phantoms and mice were acquired using a commercial instrument. Reconstructed images were processed with linear spectral unmixing to estimate blood oxygenation. Linear unmixing results were compared with a learned unmixing approach based on gradient-boosted regression.

Results

Our computational skin model was consistent with representative literature for in vivo skin reflectance measurements. We observed consistent spectral coloring effects across all model systems, with an overestimation of sO 2 and more image artifacts observed with increasing melanin concentration. The learned unmixing approach reduced the measurement bias, but predictions made at lower blood sO 2 still suffered from a skin tone-dependent effect.

Conclusion

PAI demonstrates measurement bias, including an overestimation of blood sO 2 , in higher Fitzpatrick skin types. Future research should aim to characterize this effect in humans to ensure equitable application of the technology.

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

Photoacoustic Imaging and Characterization of Bone in Medicine: Overview, Applications, and Outlook

Photoacoustic techniques have shown promise in identifying molecular changes in bone tissue and visualizing tissue microstructure. This capability represents significant advantages over gold standards (i.e., dual-energy X-ray absorptiometry) for bone evaluation without requiring ionizing radiation. Instead, photoacoustic imaging uses light to penetrate through bone, followed by acoustic pressure generation, resulting in highly sensitive optical absorption contrast in deep biological tissues. This review covers multiple bone-related photoacoustic imaging contributions to clinical applications, spanning bone cancer, joint pathologies, spinal disorders, osteoporosis, bone-related surgical guidance, consolidation monitoring, and transsphenoidal and transcranial imaging. We also present a summary of photoacoustic-based techniques for characterizing biomechanical properties of bone, including temperature, guided waves, spectral parameters, and spectroscopy. We conclude with a future outlook based on the current state of technological developments, recent achievements, and possible new directions.

Insights into S. aureus-Induced Bone Deformation in a Mouse Model of Chronic Osteomyelitis Using Fluorescence and Raman Imaging

Osteomyelitis is an infection of the bone that is often difficult to treat and causes a significant healthcare burden. Staphylococcus aureus is the most common pathogen causing osteomyelitis. Osteomyelitis mouse models have been established to gain further insights into the pathogenesis and host response. Here, we use an established S. aureus hematogenous osteomyelitis mouse model to investigate morphological tissue changes and bacterial localization in chronic osteomyelitis with a focus on the pelvis. X-ray imaging was performed to follow the disease progression. Six weeks post infection, when osteomyelitis had manifested itself with a macroscopically visible bone deformation in the pelvis, we used two orthogonal methods, namely fluorescence imaging and label-free Raman spectroscopy, to characterise tissue changes on a microscopic scale and to localise bacteria in different tissue regions. Hematoxylin and eosin as well as Gram staining were performed as a reference method. We could detect all signs of a chronically florid tissue infection with osseous and soft tissue changes as well as with different inflammatory infiltrate patterns. Large lesions dominated in the investigated tissue samples. Bacteria were found to form abscesses and were distributed in high numbers in the lesion, where they could occasionally also be detected intracellularly. In addition, bacteria were found in lower numbers in surrounding muscle tissue and even in lower numbers in trabecular bone tissue. The Raman spectroscopic imaging revealed a metabolic state of the bacteria with reduced activity in agreement with small cell variants found in other studies. In conclusion, we present novel optical methods to characterise bone infections, including inflammatory host tissue reactions and bacterial adaptation.

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Spotlight on Nerves: Portable Multispectral Optoacoustic Imaging of Peripheral Nerve Vascularization and Morphology

Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease. Based on recent improvements in optoacoustic image quality, the ability of multispectral optoacoustic tomography, to investigate the vascular environment and morphology of peripheral nerves is explored in vivo in a pilot study on healthy volunteers in tandem with ultrasound imaging (OPUS). The unique ability of optoacoustic imaging to visualize the vasa nervorum by observing intraneural vessels in healthy nerves is showcased in vivo for the first time. In addition, it is demonstrated that the label-free spectral optoacoustic contrast of the perfused connective tissue of peripheral nerves can be linked to the endogenous contrast of hemoglobin and collagen. Metrics are introduced to analyze the composition of tissue based on its optoacoustic contrast and show that the high-resolution spectral contrast reveals specific differences between nervous tissue and reference tissue in the nerve's surrounding. How this showcased extraction of peripheral nerve characteristics using multispectral optoacoustic and ultrasound imaging could offer new insights into the pathophysiology of nerve damage and neuropathies, for example, in the context of diabetes is discussed.

Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method.

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight of hyperspectral sensor characteristics on image capability in order to distinguish this effect from errors caused by image pre-processing and processing steps and improve our knowledge of errors. For these purposes, two satellite hyperspectral sensors with similar spatial and spectral characteristics (Hyperion and PRISMA) were compared with corresponding synthetic images, and the city of Venice was selected as the study area. After creating the synthetic images, the errors in the simulation of Hyperion and PRISMA images were evaluated (1.6 and 1.1%, respectively). The same spectral unmixing procedure was performed using real and synthetic images, and their accuracies were compared. The spectral accuracies in root mean square error were equal to 0.017 and 0.016, respectively. In addition, 72.3 and 77.4% of these values were related to sensor characteristics. The spatial accuracies in the mean absolute error were equal to 3.93 and 3.68, respectively. A total of 55.6 and 59.0% of these values were related to sensor characteristics, and 22.6 and 22.3% were related to co-localization and spatial resampling errors. The difference between the radiometric precision values of the sensors was 6.81 and 5.91% regarding the spectral and spatial accuracies of Hyperion image. In conclusion, the results of this study showed that the combined use of two or more real hyperspectral images with similar characteristics and their synthetic images quantifies the weight of hyperspectral sensor characteristics on their image capability and improves our knowledge regarding processing errors, and thus image capability.

Optical Detection of Heparin in Whole Blood Samples Using Nanosensors Embedded in an Agarose Hydrogel

Point-of-care quantification of the anticoagulant heparin still remains a significant clinical challenge as the reference method (colorimetric anti-factor Xa assay) cannot be performed in whole blood. Our group recently put forth the novel optical nanosensing principle using an ionic solvatochromic dye as a signal transducer. These nanosensors demonstrated significantly improved selectivity and sensitivity compared to ion-exchange-type polyion nanosensors and enabled protamine/heparin quantification in blood plasma samples. However, because the readout is absorbance-based, they are still not suitable for whole blood measurements. To overcome the background absorbance of blood, the nanosensors were here embedded in an agarose hydrogel capable of filtering out red blood cells while allowing plasma components to diffuse into the gel. Calibration curves for both protamine and heparin were successfully obtained in buffer, undiluted plasma, and undiluted whole blood using different colorimetric image analysis methods and a simple experimental setup.

Motion Compensation for 3D Multispectral Handheld Photoacoustic Imaging

Three-dimensional (3D) handheld photoacoustic (PA) and ultrasound (US) imaging performed using mechanical scanning are more useful than conventional 2D PA/US imaging for obtaining local volumetric information and reducing operator dependence. In particular, 3D multispectral PA imaging can capture vital functional information, such as hemoglobin concentrations and hemoglobin oxygen saturation (sO₂), of epidermal, hemorrhagic, ischemic, and cancerous diseases. However, the accuracy of PA morphology and physiological parameters is hampered by motion artifacts during image acquisition. The aim of this paper is to apply appropriate correction to remove the effect of such motion artifacts. We propose a new motion compensation method that corrects PA images in both axial and lateral directions based on structural US information. 3D PA/US imaging experiments are performed on a tissue-mimicking phantom and a human wrist to verify the effects of the proposed motion compensation mechanism and the consequent spectral unmixing results. The structural motions and sO₂ values are confirmed to be successfully corrected by comparing the motion-compensated images with the original images. The proposed method is expected to be useful in various clinical PA imaging applications (e.g., breast cancer, thyroid cancer, and carotid artery disease) that are susceptible to motion contamination during multispectral PA image analysis.

Building a spectral cytometry toolbox: Coupling fluorescent proteins and antibodies to microspheres

Fluorescent proteins (FPs) have become an essential tool for biological research. Since the isolation and description of GFP, hundreds of fluorescent proteins have been discovered and created with various characteristics. The excitation of these proteins ranges from ultra-violet (UV) up to near infra-RED (NIR). Using conventional cytometry with each detector assigned to each fluorochrome, great care must be taken when selecting the optimal bandpass filters to minimalize the spectral overlap. In the last 8 years, several companies have released full spectrum flow cytometers which eliminates the need to change optical filters for analyzing FPs. This addressed at least part of the problem however, the laser wavelengths in commercial instruments are generally not ideal for all fluorescent proteins yet do allow the separation of at least six FPs. Another technical challenge is to have convenient single color controls. If four different FPs are being used in an experiment, single color controls will be needed to compensate or unmix the data. In the case of cultured cells this will involve having each of the FPs expressed in cell lines separately with a parental cell line expressing none. In the case of in vivo experiments, colonies of animals may need to be maintained expressing each FP along with a wildtype animal. This represents a considerable expense and inconvenience. An appealing alternative is to produce and purify FPs and covalently couple to polystyrene microspheres. Such microspheres are ready to use and can be stored at 4°C for months or even years without any deterioration in fluorescence. The same procedure can be used to couple antibodies to these particles. Here we describe this procedure which can be executed in any lab without any special equipment or skills.

In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration

Multispectral photoacoustic imaging is a powerful noninvasive medical imaging technique that provides access to functional information. In this study, a set of methods is proposed and validated, with experimental multispectral photoacoustic images used to estimate the concentration of chromophores. The unmixing techniques used in this paper consist of two steps: (1) automatic extraction of the reference spectrum of each pure chromophore; and (2) abundance calculation of each pure chromophore from the estimated reference spectra. The compared strategies bring positivity and sum-to-one constraints, from the hyperspectral remote sensing field to multispectral photoacoustic, to evaluate chromophore concentration. Particularly, the study extracts the endmembers and compares the algorithms from the hyperspectral remote sensing domain and a dedicated algorithm for segmentation of multispectral photoacoustic data to this end. First, these strategies are tested with dilution and mixing of chromophores on colored 4% agar phantom data. Then, some preliminary in vivo experiments are performed. These consist of estimations of the oxygen saturation rate (sO2) in mouse tumors. This article proposes then a proof-of-concept of the interest to bring hyperspectral remote sensing algorithms to multispectral photoacoustic imaging for the estimation of chromophore concentration.

Comparison of Imaging Models for Spectral Unmixing in Oil Painting

The radiation captured in spectral imaging depends on both the complex light–matter interaction and the integration of the radiant light by the imaging system. In order to obtain material-specific information, it is important to define and invert an imaging process that takes into account both aspects. In this article, we investigate the use of several mixing models and evaluate their performances in the study of oil paintings. We propose an evaluation protocol, based on different features, i.e., spectral reconstruction, pigment mapping, and concentration estimation, which allows investigating the different properties of those mixing models in the context of spectral imaging. We conduct our experiment on oil-painted mockup samples of mixtures and show that models based on subtractive mixing perform the best for those materials.

An Application of Multivariate Data Analysis to Photoacoustic Imaging for the Spectral Unmixing of Gold Nanorods in Biological Tissues

Gold nanorods (GNRs) showed to be a suitable contrast agent in photoacoustics (PA), and are able to provide a tunable absorption contrast against background tissue, while a detectable PA signal can be generated from highly localized and targeted areas. A crucial issue for these imaging techniques is represented by the discrimination between exogenous and endogenous contrast and the assessment of the real PA signal magnitude. The application of image resolution/unmixing methods was implemented and optimized to recover the relative magnitude spectra and distribution maps of image constituents of the biological sample based on multivariate analysis (multivariate curve resolution-alternating least squares, MCR-ALS) in the presence of GNRs with tunable absorption properties. The proposed data analysis methodology is demonstrated on real PA images from experimental animal models and ex-vivo preparations.

Aliasing mitigation in optical microscopy of dynamic biological samples by use of temporally modulated color illumination and a standard RGB camera

SIGNIFICANCE

Despite recent developments in microscopy, temporal aliasing can arise when imaging dynamic samples. Modern sampling frameworks, such as generalized sampling, mitigate aliasing but require measurement of temporally overlapping and potentially negative-valued inner products. Conventional cameras cannot collect these directly as they operate sequentially and are only sensitive to light intensity.

AIM

We aim to mitigate aliasing in microscopy of dynamic monochrome samples by implementing generalized sampling via the use of a color camera and modulated color illumination.

APPROACH

We solve the overlap problem by spectrally multiplexing the acquisitions and using (positive) B-spline segments as projection kernels. Reconstruction involves spectral unmixing and inverse filtering. We implemented this method using a color LED illuminator. We evaluated its performance by imaging a rotating grid and its applicability by imaging the beating zebrafish embryo heart in transmission and light-sheet microscopes.

RESULTS

Compared to stroboscopic imaging, our method mitigates aliasing with performance improving as the projection order increases. The approach can be implemented in conventional microscopes but is limited by the number of available LED colors and camera channels.

CONCLUSIONS

Generalized sampling can be implemented via color modulation in microscopy to mitigate temporal aliasing. The simple hardware requirements could make it applicable to other optical imaging modalities.

Spectral Unmixing-Based Reaction Monitoring of Transformations between Nucleosides and Nucleobases

The increased interest in (enzymatic) transformations between nucleosides and nucleobases has demanded the development of efficient analytical tools. In this report, we present an update and extension of our recently described method for monitoring these reactions by spectral unmixing. The presented method uses differences in the UV absorption spectra of nucleosides and nucleobases after alkaline quenching to derive their ratio based on spectral shape by fitting normalized reference spectra. It is applicable to a broad compound spectrum comprising more than 35 examples, offers HPLC-like accuracy, ease of handling and significant reductions in both cost and data acquisition time compared to other methods. This contribution details the principle of monitoring reactions by spectral unmixing, gives recommendations regarding solutions to common problems and applications that necessitate special sample treatment. We provide software, workflows and reference spectra that facilitate the straightforward and versatile application of the method.

Multiscale Analysis of Metal Oxide Nanoparticles in Tissue: Insights into Biodistribution and Biotransformation

Metal oxide nanoparticles have emerged as exceptionally potent biomedical sensors and actuators due to their unique physicochemical features. Despite fascinating achievements, the current limited understanding of the molecular interplay between nanoparticles and the surrounding tissue remains a major obstacle in the rationalized development of nanomedicines, which is reflected in their poor clinical approval rate. This work reports on the nanoscopic characterization of inorganic nanoparticles in tissue by the example of complex metal oxide nanoparticle hybrids consisting of crystalline cerium oxide and the biodegradable ceramic bioglass. A validated analytical method based on semiquantitative X-ray fluorescence and inductively coupled plasma spectrometry is used to assess nanoparticle biodistribution following intravenous and topical application. Then, a correlative multiscale analytical cascade based on a combination of microscopy and spectroscopy techniques shows that the topically applied hybrid nanoparticles remain at the initial site and are preferentially taken up into macrophages, form apatite on their surface, and lead to increased accumulation of lipids in their surroundings. Taken together, this work displays how modern analytical techniques can be harnessed to gain unprecedented insights into the biodistribution and biotransformation of complex inorganic nanoparticles. Such nanoscopic characterization is imperative for the rationalized engineering of safe and efficacious nanoparticle-based systems.

Multiplex fatty acid imaging inside cells by Raman microscopy

Visualizing intracellular fatty acids (including free and esterified form) is very useful for understanding how and where such molecules are incorporated, stored, and metabolized within cells. However, techniques of imaging multiple intracellular fatty acids have been limited by their small size, making it difficult to label and track without changing their biological and biophysical characteristics. Here, we present a new method for simultaneously visualizing up to five atomically labeled intracellular fatty acid species. For this, we utilized the distinctive Raman spectra depending on the labeling patterns and created a new, extensible opensource software to perform by-pixel analysis of extracting original spectra from mixed ones. Our multiplex imaging method revealed that fatty acids with more double bonds tend to concentrate more efficiently at lipid droplets. This novel approach contributes to reveal not only the spatial dynamics of fatty acids, but also of any other metabolites inside cells.

Autofluorescence in Plants

Plants contain abundant autofluorescent molecules that can be used for biochemical, physiological, or imaging studies. The two most studied molecules are chlorophyll (orange/red fluorescence) and lignin (blue/green fluorescence). Chlorophyll fluorescence is used to measure the physiological state of plants using handheld devices that can measure photosynthesis, linear electron flux, and CO2 assimilation by directly scanning leaves, or by using reconnaissance imaging from a drone, an aircraft or a satellite. Lignin fluorescence can be used in imaging studies of wood for phenotyping of genetic variants in order to evaluate reaction wood formation, assess chemical modification of wood, and study fundamental cell wall properties using Förster Resonant Energy Transfer (FRET) and other methods. Many other fluorescent molecules have been characterized both within the protoplast and as components of cell walls. Such molecules have fluorescence emissions across the visible spectrum and can potentially be differentiated by spectral imaging or by evaluating their response to change in pH (ferulates) or chemicals such as Naturstoff reagent (flavonoids). Induced autofluorescence using glutaraldehyde fixation has been used to enable imaging of proteins/organelles in the cell protoplast and to allow fluorescence imaging of fungal mycelium.

Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing

The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.

Synthetic data framework to estimate the minimum detectable concentration of contrast agents for multispectral optoacoustic imaging of small animals

The concentrations of contrast agents for optoacoustic imaging of small animals must usually be optimized through extensive pilot experiments on a case-by-case basis. The present work describes a streamlined approach for determining the minimum detectable concentration (MDC) of a contrast agent given experimental conditions and imaging system parameters. The developed Synthetic Data Framework (SDF) allows estimation of MDCs of various contrast agents under different tissue conditions without extensive animal experiments. The SDF combines simulated optoacoustic signals from exogenously administered contrast agents with in vivo experimental signals from background tissue to generate realistic synthetic multispectral optoacoustic images. In this paper, the SDF is validated with in vivo measurements and demonstrates close agreement between SDF synthetic data and experimental data in terms of both image intensity and MDCs. Use of the SDF to estimate MDCs for fluorescent dyes and nanoparticles at different tissue depths and for imaging lesions of different sizes is illustrated.

A UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases

Efficient reaction monitoring is crucial for data acquisition in kinetic and mechanistic studies. However, for conversions of nucleosides to their corresponding nucleobases, as observed in enzymatically catalyzed nucleoside phosphorylation reactions, the current analytical arsenal does not meet modern requirements regarding cost, speed of analysis and high throughput. Herein, we present a UV/Vis spectroscopy-based assay employing an algorithm for spectral unmixing in a 96-well plate format. The algorithm relies on fitting of reference spectra of nucleosides and their bases to experimental spectra and allows determination of nucleoside/nucleobase ratios in solution with high precision. The experimental procedure includes appropriate dilution of a sample into aqueous alkaline solution, transfer to a multi-well plate, measurement of a UV/Vis spectrum and subsequent in silico spectral unmixing. This enables data collection in a high-throughput fashion and reduces costs compared to state-of-the-art HPLC analyses by approximately 5-fold while being 20-fold faster and offering comparable precision. Additionally, the method is robust regarding dilution and sample transfer errors as it only considers spectral form and not absolute intensity. It can be applied to all natural nucleosides and nucleobases and even unnatural ones as demonstrated by several examples.

pyMCR: A Python Library for MultivariateCurve Resolution Analysis with Alternating Regression (MCR-AR)

pyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.

A Dye-Free Analog to Retinal Angiography Using Hyperspectral Unmixing to Retrieve Oxyhemoglobin Abundance

Purpose

Retinal angiography evaluates retinal and choroidal perfusion and vascular integrity and is used to manage many ophthalmic diseases, such as age-related macular degeneration. The most common method, fluorescein angiography (FA), is invasive and can lead to untoward effects. As an emerging replacement, noninvasive OCT angiography (OCTA) is used regularly as a dye-free substitute with superior resolution and additional depth-sectioning abilities; however, general trends in FA as signified by varying intensity in images are not always reproducible in the fine structural detail in an OCTA image stack because of the source of their respective signals, OCT speckle decorrelation versus fluorescein emission.

Methods

We present a noninvasive/dye-free analog to angiography imaging using retinal hyperspectral imaging with a nonscanning spectral imager, the image mapping spectrometer (IMS), to reproduce perfusion-related data based on the abundance of oxyhemoglobin (HbO₂) in the retina. With a new unmixing procedure of the IMS-acquired spectral data cubes (350 × 350 × 43), we produced noninvasive HbO₂ maps unmixed from reflectance spectra.

Results

Here, we present 15 HbO₂ maps from seven healthy and eight diseased retinas and compare these maps with corresponding FA and OCTA results with a discussion of each technique.

Conclusions

Our maps showed visual agreement with hypo- and hyperfluorescence trends in venous phase FA images, suggesting that our method provides a new use for hyperspectral imaging as a noninvasive angiography-analog technique and as a complementary technique to OCTA.

Translational Relevance

The application of hyperspectral imaging and spectral analysis can potentially improve/broaden retinal disease screening and enable a noninvasive technique, which complements OCTA.

Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. We describe an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation, and the Grüneisen parameter from in silico three-dimensional phantom images for different radiance approximations. The scattering coefficient is assumed to be homogeneous and known a priori.

Quantitative in vivo dual-color bioluminescence imaging in the mouse brain

Bioluminescence imaging (BLI) is an optical imaging method that can be translated from the cell culture dish in vitro to cell tracking in small animal models in vivo. In contrast to the more widely used fluorescence imaging, which requires light excitation, in BLI the light is exclusively generated by the enzyme luciferase. The luciferase gene can be engineered to target and monitor almost every cell and biological process quantitatively in vitro and even from deep tissue in vivo. While initially used for tumor imaging, bioluminescence was recently optimized for mouse brain imaging of neural cells and monitoring of viability or differentiation of grafted stem cells. Here, we describe the use of bright color-shifted firefly luciferases (Flucs) based on the thermostable x5 Fluc that emit red and green for effective and quantitative unmixing of two human cell populations in vitro and after transplantation into the mouse brain in vivo. Spectral unmixing predicts the ratio of luciferases in vitro and a mixture of cells precisely for cortical grafts, however, with less accuracy for striatal grafts. This dual-color approach enables the simultaneous visualization and quantification of two cell populations on the whole brain scale, with particular relevance for translational studies of neurological disorders providing information on stem cell survival and differentiation in one imaging session in vivo.

Quantitative FRET measurement based on spectral unmixing of donor, acceptor and spontaneous excitation-emission spectra

The spontaneous excitation-emission (ExEm) spectrum is introduced to the quantitative mExEm-spFRET methodology we recently developed as a spectral unmixing component for quantitative fluorescence resonance energy transfer measurement, named as SPEES-FRET method. The spectral fingerprints of both donor and acceptor were measured in HepG2 cells with low autofluorescence separately expressing donor and acceptor, and the spontaneous spectral fingerprint of HEK293 cells with strong autofluoresence was measured from blank cells. SPEES-FRET was performed on improved spectrometer-microscope system to measure the FRET efficiency (E) and concentration ratio (R _C ) of acceptor to donor vales of FRET tandem plasmids in HEK293 cells, and obtained stable and consistent results with the expected values. Moreover, SPEES-FRET always obtained stable results for the bright and dim cells coexpressing Cerulean and Venus or Cyan Fluorescent Protein (CFP)-Bax and Yellow fluorescent protein (YFP)-Bax, and the E values between CFP-Bax and YFP-Bax were 0.02 for healthy cells and 0.14 for the staurosporine (STS)-treated apoptotic cells. Collectively, SPEES-FRET has very strong robustness against cellular autofluorescence, and thus is applicable to quantitative evaluation on the protein-protein interaction in living cells with strong autofluoresence.

Environment-specific spectral modeling: A new tool for the analysis of biological specimens

The recent discovery of fluorescent dyes for improving pathologic tissues identification has highlighted the need of robust methods for performance validation especially in the field of fluorescence-guided surgery. Optical imaging of excised tissue samples is the reference tool to validate the association between dyes localization and the underlying histology in a controlled environment. Spectral unmixing may improve the validation process discriminating dye from endogenous signal. Here, an innovative spectral modeling approach that weights the spectral shifts associated with changes in chemical environment is described. The method is robust against spectral shift variations and its application leads to unbiased spectral weights estimates as demonstrated by numerical simulations. Finally, spectral shifts values computed pixel-wise from spectral images are used to display additional information with potential diagnostic value.

Application of unsupervised learning to hyperspectral imaging of cardiac ablation lesions

Atrial fibrillation is the most common cardiac arrhythmia. It is being effectively treated using the radiofrequency ablation (RFA) procedure, which destroys culprit tissue and creates scars that prevent the spread of abnormal electrical activity. Long-term success of RFA could be improved further if ablation lesions can be directly visualized during the surgery. We have shown that autofluorescence-based hyperspectral imaging (aHSI) can help to identify lesions based on spectral unmixing. We show that use of k -means clustering, an unsupervised learning method, is capable of detecting RFA lesions without a priori knowledge of the lesions' spectral characteristics. We also show that the number of spectral bands required for successful lesion identification can be significantly reduced, enabling the use of increased spectral bandwidth. Together, these findings can help with clinical implementation of a percutaneous aHSI catheter, since by reducing the number of spectral bands one can reduce hypercube acquisition and processing times, and by increasing the spectral width of individual bands one can collect more photons. The latter is of critical importance in low-light applications such as intracardiac aHSI. The ultimate goal of our studies is to help improve clinical outcomes for atrial fibrillation patients.

Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant

Hyperspectral unmixing, which decomposes mixed pixels into endmembers and corresponding abundance maps of endmembers, has obtained much attention in recent decades. Most spectral unmixing algorithms based on non-negative matrix factorization (NMF) do not explore the intrinsic manifold structure of hyperspectral data space. Studies have proven image data is smooth along the intrinsic manifold structure. Thus, this paper explores the intrinsic manifold structure of hyperspectral data space and introduces manifold learning into NMF for spectral unmixing. Firstly, a novel projection equation is employed to model the intrinsic structure of hyperspectral image preserving spectral information and spatial information of hyperspectral image. Then, a graph regularizer which establishes a close link between hyperspectral image and abundance matrix is introduced in the proposed method to keep intrinsic structure invariant in spectral unmixing. In this way, decomposed abundance matrix is able to preserve the true abundance intrinsic structure, which leads to a more desired spectral unmixing performance. At last, the experimental results including the spectral angle distance and the root mean square error on synthetic and real hyperspectral data prove the superiority of the proposed method over the previous methods.

Spectrally and spatially resolved laser-induced photobleaching of endogenous flavin fluorescence in cardiac myocytes

Naturally occurring endogenous fluorescence of flavins, arising in response to excitation by visible light, offers broad opportunity to investigate mitochondrial metabolic state directly in living cells and tissues, including in clinical settings. However, photobleaching, the loss of the autofluorescence intensity following prolonged exposure to light is an inherent phenomenon occurring during the fluorescence acquisition, which can have a negative impact on the recorded data, particularly in the context of measurement of metabolic modulations in pathophysiological conditions. In the presented study, we present a detailed analysis of endogenous flavins fluorescence photobleaching arising in living cardiac cells during spectrally‐resolved confocal imaging. We demonstrate significant nonuniform photobleaching related to different bleaching rates of individual flavin components, resolved by linear spectral unmixing of the recorded signals. Induced photodamage was without effect on the cell morphology, but lead to significant modifications of the cell responsiveness to metabolic modulators and its contractility, suggesting functional metabolic alterations in the recorded cells. These findings point to the necessity of inducing limited photobleaching during metabolic screening in all studies involving visible light excitation and fluorescence acquisition in living cells. © 2018 The Authors Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry

Estimating relative chromophore concentrations from multiwavelength photoacoustic images using independent component analysis

Independent component analysis (ICA) is an unmixing method based on a linear model. It has previously been applied in in vivo multiwavelength photoacoustic imaging studies to unmix the components representing individual chromophores by assuming that they are statistically independent. Numerically simulated and experimentally acquired two-dimensional images of tissue-mimicking phantoms are used to investigate the conditions required for ICA to give accurate estimates of the relative chromophore concentrations. A simple approximate fluence correction was applied to reduce but not completely remove the nonlinear fluence distortion, as might be possible in practice. The results show that ICA is robust against the residual effect of the partially corrected fluence distortion. ICA is shown to provide accurate unmixing of the chromophores when the absorption coefficient is within a certain range of values, where the upper absorption threshold is comparable to the absorption of blood. When the absorption is increased beyond these thresholds, ICA abruptly fails to unmix the chromophores accurately. The ICA approach was compared to a linear spectroscopic inversion (SI) with known absorption spectra. In cases where the mixing matrix with the specific absorption spectra is ill-conditioned, ICA is able to provide accurate unmixing when SI results in large errors.

Preoperative measurement of cutaneous melanoma and nevi thickness with photoacoustic imaging

Photoacoustic imaging (PAI) is an emerging biomedical imaging technology, which can potentially be used in the clinic to preoperatively measure melanoma thickness and guide biopsy depth and sample location. We recruited 27 patients with pigmented cutaneous lesions suspicious for melanoma to test the feasibility of a handheld linear-array photoacoustic probe in imaging lesion architecture and measuring tumor depth. The probe was assessed in terms of measurement accuracy, image quality, and ease of application. Photoacoustic scans included single wavelength, spectral unmixing, and three-dimensional (3-D) scans. The photoacoustically measured lesion thickness gave a high correlation with the histological thickness measured from resected surgical samples (r=0.99, P<0.001 for melanomas, r=0.98, P<0.001 for nevi). Thickness measurements were possible for 23 of 26 cases for nevi and all (6) cases for melanoma. Our results show that handheld, linear-array PAI is highly reliable in measuring cutaneous lesion thickness in vivo, and can potentially be used to inform biopsy procedure and improve patient management.

Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology

A key feature of optoacoustic imaging is the ability to illuminate tissue at multiple wavelengths and therefore record images with a spectral dimension. While optoacoustic images at single wavelengths reveal morphological features, in analogy to ultrasound imaging or X-ray imaging, spectral imaging concedes sensing of intrinsic chromophores and externally administered agents that can reveal physiological, cellular and subcellular functions. Nevertheless, identification of spectral moieties within images obtained at multiple wavelengths requires spectral unmixing techniques, which present a unique mathematical problem given the three-dimensional nature of the optoacoustic images. Herein we discuss progress with spectral unmixing techniques developed for multispectral optoacoustic tomography. We explain how different techniques are required for accurate sensing of intrinsic tissue chromophores such as oxygenated and deoxygenated haemoglobin versus extrinsically administered photo-absorbing agents and nanoparticles. Finally, we review recent developments that allow accurate quantification of blood oxygen saturation (sO₂) by transforming and solving the sO₂ estimation problem from the spatial to the spectral domain.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

Accurate extraction of physical and biochemical parameters from optoacoustic images is often impeded due to the use of unrigorous inversion schemes, incomplete tomographic detection coverage, or other experimental factors that cannot be readily accounted for during the image acquisition and reconstruction process. For instance, inaccurate assumptions in the physical forward model may lead to negative optical absorption values in the reconstructed images. Any artifacts present in the single wavelength optoacoustic images can be significantly aggravated when performing a two-step reconstruction consisting in acoustic inversion and spectral unmixing aimed at rendering the distributions of spectrally distinct absorbers. We investigate a number of algorithmic strategies with non-negativity constraints imposed at the different phases of the reconstruction process. Performance is evaluated in cross-sectional multispectral optoacoustic tomography recordings from tissue-mimicking phantoms and in vivo mice embedded with varying concentrations of contrast agents. Additional in vivo validation is subsequently performed with molecular imaging data involving subcutaneous tumors labeled with genetically expressed iRFP proteins and organ perfusion by optical contrast agents. It is shown that constrained reconstruction is essential for reducing the critical image artifacts associated with inaccurate modeling assumptions. Furthermore, imposing the non-negativity constraint directly on the unmixed distribution of the probe of interest was found to maintain the most robust and accurate reconstruction performance in all experiments.

Using spectral decomposition of the signals from laurdan-derived probes to evaluate the physical state of membranes in live cells

Background: We wanted to investigate the physical state of biological membranes in live cells under the most physiological conditions possible.

Methods: For this we have been using laurdan, C-laurdan or M-laurdan to label a variety of cells, and a biphoton microscope equipped with both a thermostatic chamber and a spectral analyser. We also used a flow cytometer to quantify the 450/530 nm ratio of fluorescence emissions by whole cells.

Results: We find that using all the information provided by spectral analysis to perform spectral decomposition dramatically improves the imaging resolution compared to using just two channels, as commonly used to calculate generalized polarisation (GP). Coupled to a new plugin called Fraction Mapper, developed to represent the fraction of light intensity in the first component in a stack of two images, we obtain very clear pictures of both the intra-cellular distribution of the probes, and the polarity of the cellular environments where the lipid probes are localised.

Our results lead us to conclude that, in live cells kept at 37°C, laurdan, and M-laurdan to a lesser extent, have a strong tendency to accumulate in the very apolar environment of intra-cytoplasmic lipid droplets, but label the plasma membrane (PM) of mammalian cells ineffectively. On the other hand, C-laurdan labels the PM very quickly and effectively, and does not detectably accumulate in lipid droplets.

Conclusions: From using these probes on a variety of mammalian cell lines, as well as on cells from Drosophila and Dictyostelium discoideum, we conclude that, apart from the lipid droplets, which are very apolar, probes in intracellular membranes reveal a relatively polar and hydrated environment, suggesting a very marked dominance of liquid disordered states. PMs, on the other hand, are much more apolar, suggesting a strong dominance of liquid ordered state, which fits with their high sterol contents.

Using spectral decomposition of the signals from laurdan-derived probes to evaluate the physical state of membranes in live cells

Background: We wanted to investigate the physical state of biological membranes in live cells under the most physiological conditions possible. Methods: For this we have been using laurdan, C-laurdan or M-laurdan to label a variety of cells, and a biphoton microscope equipped with both a thermostatic chamber and a spectral analyser. We also used a flow cytometer to quantify the 450/530 nm ratio of fluorescence emissions by whole cells. Results: We find that using all the information provided by spectral analysis to perform spectral decomposition dramatically improves the imaging resolution compared to using just two channels, as commonly used to calculate generalized polarisation (GP). Coupled to a new plugin called Fraction Mapper, developed to represent the fraction of light intensity in the first component in a stack of two images, we obtain very clear pictures of both the intra-cellular distribution of the probes, and the polarity of the cellular environments where the lipid probes are localised. Our results lead us to conclude that, in live cells kept at 37°C, laurdan, and M-laurdan to a lesser extent, have a strong tendency to accumulate in the very apolar environment of intra-cytoplasmic lipid droplets, but label the plasma membrane (PM) of mammalian cells ineffectively. On the other hand, C-laurdan labels the PM very quickly and effectively, and does not detectably accumulate in lipid droplets. Conclusions: From using these probes on a variety of mammalian cell lines, as well as on cells from Drosophila and Dictyostelium discoideum, we conclude that, apart from the lipid droplets, which are very apolar, probes in intracellular membranes reveal a relatively polar and hydrated environment, suggesting a very marked dominance of liquid disordered states. PMs, on the other hand, are much more apolar, suggesting a strong dominance of liquid ordered state, which fits with their high sterol contents.

High-Throughput Spectral and Lifetime-Based FRET Screening in Living Cells to Identify Small-Molecule Effectors of SERCA

A robust high-throughput screening (HTS) strategy has been developed to discover small-molecule effectors targeting the sarco/endoplasmic reticulum calcium ATPase (SERCA), based on a fluorescence microplate reader that records both the nanosecond decay waveform (lifetime mode) and the complete emission spectrum (spectral mode), with high precision and speed. This spectral unmixing plate reader (SUPR) was used to screen libraries of small molecules with a fluorescence resonance energy transfer (FRET) biosensor expressed in living cells. Ligand binding was detected by FRET associated with structural rearrangements of green fluorescent protein (GFP, donor) and red fluorescent protein (RFP, acceptor) fused to the cardiac-specific SERCA2a isoform. The results demonstrate accurate quantitation of FRET along with high precision of hit identification. Fluorescence lifetime analysis resolved SERCA's distinct structural states, providing a method to classify small-molecule chemotypes on the basis of their structural effect on the target. The spectral analysis was also applied to flag interference by fluorescent compounds. FRET hits were further evaluated for functional effects on SERCA's ATPase activity via both a coupled-enzyme assay and a FRET-based calcium sensor. Concentration-response curves indicated excellent correlation between FRET and function. These complementary spectral and lifetime FRET detection methods offer an attractive combination of precision, speed, and resolution for HTS.

Karyotyping human and mouse cells using probes from single-sorted chromosomes and open source software

Multispectral karyotyping analyzes all chromosomes in a single cell by labeling them with chromosome-specific probes conjugated to unique combinations of fluorophores. Currently available multispectral karyotyping systems require the purchase of specialized equipment and reagents. However, conventional laser scanning confocal microscopes that are capable of separating multiple overlapping emission spectra through spectral imaging and linear unmixing can be utilized for classifying chromosomes painted with multicolor probes. Here, we generated multicolor chromosome paints from single-sorted human and mouse chromosomes and developed the Karyotype Identification via Spectral Separation (KISS) analysis package, a set of freely available open source ImageJ tools for spectral unmixing and karyotyping. Chromosome spreads painted with our multispectral probe sets can be imaged on widely available spectral laser scanning confocal microscopes and analyzed using our ImageJ tools. Together, our probes and software enable academic labs with access to a laser-scanning spectral microscope to perform multicolor karyotyping in a cost-effective manner.

Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing

Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

Phasor analysis of multiphoton spectral images distinguishes autofluorescence components of in vivo human skin

Skin contains many autofluorescent components that can be studied using spectral imaging. We employed a spectral phasor method to analyse two photon excited autofluorescence and second harmonic generation images of in vivo human skin. This method allows segmentation of images based on spectral features. Various structures in the skin could be distinguished, including Stratum Corneum, epidermal cells and dermis. The spectral phasor analysis allowed investigation of their fluorescence composition and identification of signals from NADH, keratin, FAD, melanin, collagen and elastin. Interestingly, two populations of epidermal cells could be distinguished with different melanin content.

Emission spectra profiling of fluorescent proteins in living plant cells

BACKGROUND

Fluorescence imaging at high spectral resolution allows the simultaneous recording of multiple fluorophores without switching optical filters, which is especially useful for time-lapse analysis of living cells. The collected emission spectra can be used to distinguish fluorophores by a computation analysis called linear unmixing. The availability of accurate reference spectra for different fluorophores is crucial for this type of analysis. The reference spectra used by plant cell biologists are in most cases derived from the analysis of fluorescent proteins in solution or produced in animal cells, although these spectra are influenced by both the cellular environment and the components of the optical system. For instance, plant cells contain various autofluorescent compounds, such as cell wall polymers and chlorophyll, that affect the spectral detection of some fluorophores. Therefore, it is important to acquire both reference and experimental spectra under the same biological conditions and through the same imaging systems.

RESULTS

Entry clones (pENTR) of fluorescent proteins (FPs) were constructed in order to create C- or N-terminal protein fusions with the MultiSite Gateway recombination technology. The emission spectra for eight FPs, fused C-terminally to the A- or B-type cyclin dependent kinases (CDKA;1 and CDKB1;1) and transiently expressed in epidermal cells of tobacco (Nicotiana benthamiana), were determined by using the Olympus FluoView™ FV1000 Confocal Laser Scanning Microscope. These experimental spectra were then used in unmixing experiments in order to separate the emission of fluorophores with overlapping spectral properties in living plant cells.

CONCLUSIONS

Spectral imaging and linear unmixing have a great potential for efficient multicolor detection in living plant cells. The emission spectra for eight of the most commonly used FPs were obtained in epidermal cells of tobacco leaves and used in unmixing experiments. The generated set of FP Gateway entry vectors represents a valuable resource for plant cell biologists.

Image mapping spectrometry: calibration and characterization

Image mapping spectrometry (IMS) is a hyperspectral imaging technique that simultaneously captures spatial and spectral information about an object in real-time. We present a new calibration procedure for the IMS as well as the first detailed evaluation of system performance. We correlate optical components and device calibration to performance metrics such as light throughput, scattered light, distortion, spectral image coregistration, and spatial/spectral resolution. Spectral sensitivity and motion artifacts are also evaluated with a dynamic biological experiment. The presented methodology of evaluation is useful in assessment of a variety of hyperspectral and multi-spectral modalities. Results are important to any potential users/developers of an IMS instrument and to anyone who may wish to compare the IMS to other imaging spectrometers.

The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data

Spectral mixing is a problem inherent to remote sensing data and results in few image pixel spectra representing ″pure″ targets. Linear spectral mixture analysis is designed to address this problem and it assumes that the pixel-to-pixel variability in a scene results from varying proportions of spectral endmembers. In this paper we present a different endmember-search algorithm called the Successive Projection Algorithm (SPA). SPA builds on convex geometry and orthogonal projection common to other endmember search algorithms by including a constraint on the spatial adjacency of endmember candidate pixels. Consequently it can reduce the susceptibility to outlier pixels and generates realistic endmembers.This is demonstrated using two case studies (AVIRIS Cuprite cube and Probe-1 imagery for Baffin Island) where image endmembers can be validated with ground truth data. The SPA algorithm extracts endmembers from hyperspectral data without having to reduce the data dimensionality. It uses the spectral angle (alike IEA) and the spatial adjacency of pixels in the image to constrain the selection of candidate pixels representing an endmember. We designed SPA based on the observation that many targets have spatial continuity (e.g. bedrock lithologies) in imagery and thus a spatial constraint would be beneficial in the endmember search. An additional product of the SPA is data describing the change of the simplex volume ratio between successive iterations during the endmember extraction. It illustrates the influence of a new endmember on the data structure, and provides information on the convergence of the algorithm. It can provide a general guideline to constrain the total number of endmembers in a search.