Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.

Introduction: Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. Methods: A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Results: Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. Discussion: This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

Synthesis and evaluation of aromatic BDSF bioisosteres on biofilm formation and colistin sensitivity in pathogenic bacteria.

The diffusible signal factor family (DSF) of molecules play an important role in regulating intercellular communication, or quorum sensing, in several disease-causing bacteria. These messenger molecules, which are comprised of cis-unsaturated fatty acids, are involved in the regulation of biofilm formation, antibiotic tolerance, virulence and the control of bacterial resistance. We have previously demonstrated how olefinic N-acyl sulfonamide bioisosteric analogues of diffusible signal factor can reduce biofilm formation or enhance antibiotic sensitivity in a number of bacterial strains. This work describes the design and synthesis of a second generation of aromatic N-acyl sulfonamide bioisosteres. The impact of these compounds on biofilm production in Acinetobacter baumannii, Escherichia coli, Burkholderia multivorans, Burkholderia cepacia, Burkholderia cenocepacia, Pseudomonas aeruginosa and Stenotrophomonas maltophilia is evaluated, in addition to their effects on antibiotic tolerance. The ability of these molecules to increase survival rates on co-administration with colistin is also investigated using the Galleria infection model.

Synthetic white balancing for intra-operative hyperspectral imaging.

Purpose: Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially resolved, spectral information. For calibration purposes, a white reference image of a highly reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. Approach: The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using ΔE and normalized RMSE, respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modeled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Results: Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77% while maintaining good spectral and color reconstruction. Conclusions: We demonstrate the importance of in situ white referencing and present a novel synthetic referencing algorithm. This algorithm is suitable for surgery while maintaining the quality of classical data reconstruction.

Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: application to surgical imaging.

PURPOSE: Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fast acquisition speed and compact size. However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images. Most state-of-the-art demosaicking algorithms require ground-truth training data with paired snapshot and high-resolution hyperspectral images, but such imagery pairs with the exact same scene are physically impossible to acquire in intraoperative settings. In this work, we present a fully unsupervised hyperspectral image demosaicking algorithm which only requires exemplar snapshot images for training purposes. METHODS: We regard hyperspectral demosaicking as an ill-posed linear inverse problem which we solve using a deep neural network. We take advantage of the spectral correlation occurring in natural scenes to design a novel inter spectral band regularisation term based on spatial gradient consistency. By combining our proposed term with standard regularisation techniques and exploiting a standard data fidelity term, we obtain an unsupervised loss function for training deep neural networks, which allows us to achieve real-time hyperspectral image demosaicking. RESULTS: Quantitative results on hyperspetral image datasets show that our unsupervised demosaicking approach can achieve similar performance to its supervised counter-part, and significantly outperform linear demosaicking. A qualitative user study on real snapshot hyperspectral surgical images confirms the results from the quantitative analysis. CONCLUSION: Our results suggest that the proposed unsupervised algorithm can achieve promising hyperspectral demosaicking in real-time thus advancing the suitability of the modality for intraoperative use.

Hybrid confocal Raman endomicroscopy for morpho-chemical tissue characterization.

Confocal laser endomicroscopy (CLE) offers imaging of tissue microarchitecture and has emerged as a promising tool for in vivo clinical diagnosis of cancer across many organs. CLE, however, can show high inter-observer dependency and does not provide information about tissue molecular composition. In contrast, Raman spectroscopy is a label-free optical technique that provides detailed biomolecular compositional information but offers limited or no morphological information. Here we present a novel hybrid fiber-optic confocal Raman endomicroscopy system for morpho-chemical tissue imaging and analysis. The developed confocal endomicroscopy system is based on a novel detection scheme for rejecting Raman silica fiber interference permitting simultaneous CLE imaging and Raman spectral acquisition of tissues through a coherent fiber bundle. We show that this technique enables real-time microscopic visualization of tissue architecture as well as simultaneous pointwise label-free biomolecular characterization and fingerprinting of tissue paving the way for multimodal diagnostics at endoscopy.

In vitro and in vivo investigation of a zonal microstructured scaffold for osteochondral defect repair.

Articular cartilage is comprised of zones that vary in architecture, extracellular matrix composition, and mechanical properties. Here, we designed and engineered a porous zonal microstructured scaffold from a single biocompatible polymer (poly [ϵ-caprolactone]) using multiple fabrication strategies: electrospinning, spherical porogen leaching, directional freezing, and melt electrowriting. With this approach we mimicked the zonal structure of articular cartilage and produced a stiffness gradient through the scaffold which aligns with the mechanics of the native tissue. Chondrocyte-seeded scaffolds accumulated extracellular matrix including glycosaminoglycans and collagen II over four weeks in vitro. This prompted us to further study the repair efficacy in a skeletally mature porcine model. Two osteochondral lesions were produced in the trochlear groove of 12 animals and repaired using four treatment conditions: (1) microstructured scaffold, (2) chondrocyte seeded microstructured scaffold, (3) MaioRegen™, and (4) empty defect. After 6 months the defect sites were harvested and analyzed using histology, micro computed tomography, and Raman microspectroscopy mapping. Overall, the scaffolds were retained in the defect space, repair quality was repeatable, and there was clear evidence of osteointegration. The repair quality of the microstructured scaffolds was not superior to the control based on histological scoring; however, the lower score was biased by the lack of histological staining due to the limited degradation of the implant at 6 months. Longer follow up studies (e.g., 1 yr) will be required to fully evaluate the efficacy of the microstructured scaffold. In conclusion, we found consistent scaffold retention, osteointegration, and prolonged degradation of the microstructured scaffold, which we propose may have beneficial effects for the long-term repair of osteochondral defects.

Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstruction.

Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a real-time hyperspectral imaging system for surgical decision-making possible. However, optimal exploitation of the captured data requires solving an ill-posed demosaicking problem and applying additional spectral corrections. In this work, we propose a supervised learning-based image demosaicking algorithm for snapshot hyperspectral images. Due to the lack of publicly available medical images acquired with snapshot mosaic cameras, a synthetic image generation approach is proposed to simulate snapshot images from existing medical image datasets captured by high-resolution, but slow, hyperspectral imaging devices. Image reconstruction is achieved using convolutional neural networks for hyperspectral image super-resolution, followed by spectral correction using a sensor-specific calibration matrix. The results are evaluated both quantitatively and qualitatively, showing clear improvements in image quality compared to a baseline demosaicking method using linear interpolation. Moreover, the fast processing time of 45 ms of our algorithm to obtain super-resolved RGB or oxygenation saturation maps per image for a state-of-the-art snapshot mosaic camera demonstrates the potential for its seamless integration into real-time surgical hyperspectral imaging applications.

Recent Developments in the Practical Application of Novel Carboxylic Acid Bioisosteres.

BACKGROUND: The carboxylic acid moiety is an important functional group which features in the pharmacophore of some 450 drugs. Unfortunately, some carboxylic acid-containing drugs have been withdrawn from market due to unforeseen toxicity issues. Other issues associated with the carboxylate moiety include reduced metabolic stability or limited passive diffusion across biological membranes. Medicinal chemists often turn to bioisosteres to circumvent such obstacles. OBJECTIVE: The aim of this review is to provide a summary of the various applications of novel carboxylic acid bioisosteres which have appeared in the literature since 2013. RESULTS: We have summarised the most recent developments in carboxylic acid bioisosterism. In particular, we focus on the changes in bioactivity, selectivity or physicochemical properties brought about by these substitutions, as well as the advantages and disadvantages of each isostere. CONCLUSION: The topics discussed herein highlight the continued interest in carboxylate bioisosteres. The development of novel carboxylic acid substitutes which display improved pharmacological profiles is a testament to the innovation and creativity required to overcome the challenges faced in modern drug design.

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy.

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Image-guided Raman spectroscopy probe-tracking for tumor margin delineation.

SIGNIFICANCE: Tumor detection and margin delineation are essential for successful tumor resection. However, postsurgical positive margin rates remain high for many cancers. Raman spectroscopy has shown promise as a highly accurate clinical spectroscopic diagnostic modality, but its margin delineation capabilities are severely limited by the need for pointwise application. AIM: We aim to extend Raman spectroscopic diagnostics and develop a multimodal computer vision-based diagnostic system capable of both the detection and identification of suspicious lesions and the precise delineation of disease margins. APPROACH: We first apply visual tracking of a Raman spectroscopic probe to achieve real-time tumor margin delineation. We then combine this system with protoporphyrin IX fluorescence imaging to achieve fluorescence-guided Raman spectroscopic margin delineation. RESULTS: Our system enables real-time Raman spectroscopic tumor margin delineation for both ex vivo human tumor biopsies and an in vivo tumor xenograft mouse model. We then further demonstrate that the addition of protoporphyrin IX fluorescence imaging enables fluorescence-guided Raman spectroscopic margin delineation in a tissue phantom model. CONCLUSIONS: Our image-guided Raman spectroscopic probe-tracking system enables tumor margin delineation and is compatible with both white light and fluorescence image guidance, demonstrating the potential for our system to be developed toward clinical tumor resection surgeries.

Integrated photodynamic Raman theranostic system for cancer diagnosis, treatment, and post-treatment molecular monitoring.

Theranostics, the combination of diagnosis and therapy, has long held promise as a means to achieving personalised precision cancer treatments. However, despite its potential, theranostics has yet to realise significant clinical translation, largely due the complexity and overriding toxicity concerns of existing theranostic nanoparticle strategies. Methods: Here, we present an alternative nanoparticle-free theranostic approach based on simultaneous Raman spectroscopy and photodynamic therapy (PDT) in an integrated clinical platform for cancer theranostics. Results: We detail the compatibility of Raman spectroscopy and PDT for cancer theranostics, whereby Raman spectroscopic diagnosis can be performed on PDT photosensitiser-positive cells and tissues without inadvertent photosensitiser activation/photobleaching or impaired diagnostic capacity. We further demonstrate that our theranostic platform enables in vivo tumour diagnosis, treatment, and post-treatment molecular monitoring in real-time. Conclusion: This system thus achieves effective theranostic performance, providing a promising new avenue towards the clinical realisation of theranostics.

In vivo biomolecular imaging of zebrafish embryos using confocal Raman spectroscopy.

Zebrafish embryos provide a unique opportunity to visualize complex biological processes, yet conventional imaging modalities are unable to access intricate biomolecular information without compromising the integrity of the embryos. Here, we report the use of confocal Raman spectroscopic imaging for the visualization and multivariate analysis of biomolecular information extracted from unlabeled zebrafish embryos. We outline broad applications of this method in: (i) visualizing the biomolecular distribution of whole embryos in three dimensions, (ii) resolving anatomical features at subcellular spatial resolution, (iii) biomolecular profiling and discrimination of wild type and ΔRD1 mutant Mycobacterium marinum strains in a zebrafish embryo model of tuberculosis and (iv) in vivo temporal monitoring of the wound response in living zebrafish embryos. Overall, this study demonstrates the application of confocal Raman spectroscopic imaging for the comparative bimolecular analysis of fully intact and living zebrafish embryos.

Multiplexed polarized hypodermic Raman needle probe for biostructural analysis of articular cartilage.

In this Letter, we report a multiplexed polarized hypodermic Raman needle probe for the biostructural analysis of articular cartilage. Using a custom-developed needle probe with a sapphire ball lens, we measure polarized Raman spectra of cartilage. By imaging two polarizations simultaneously on the charge-coupled device (CCD) and binning them separately, we capture both biochemical and structural tissue information in real time. Here, we demonstrate that polarized Raman spectroscopy can distinguish between different collagen fibril alignment orientations in a cartilage explant model system, supporting its capacity for diagnosing the hallmark collagen alignment changes occurring in the early stages of osteoarthritis (OA). Accordingly, this work shows that needle-based polarized Raman spectroscopy has great potential for the monitoring and diagnosis of early OA.

Molecular imaging of extracellular vesicles in vitro via Raman metabolic labelling.

Extracellular vesicles (EVs) are biologically-derived nanovectors important for intercellular communication and trafficking. As such, EVs show great promise as disease biomarkers and therapeutic drug delivery vehicles. However, despite the rapidly growing interest in EVs, understanding of the biological mechanisms that govern their biogenesis, secretion, and uptake remains poor. Advances in this field have been hampered by both the complex biological origins of EVs, which make them difficult to isolate and identify, and a lack of suitable imaging techniques to properly study their diverse biological roles. Here, we present a new strategy for simultaneous quantitative in vitro imaging and molecular characterisation of EVs in 2D and 3D based on Raman spectroscopy and metabolic labelling. Deuterium, in the form of deuterium oxide (D2O), deuterated choline chloride (d-Chol), or deuterated d-glucose (d-Gluc), is metabolically incorporated into EVs through the growth of parent cells on medium containing one of these compounds. Isolated EVs are thus labelled with deuterium, which acts as a bio-orthogonal Raman-active tag for direct Raman identification of EVs when introduced to unlabelled cell cultures. Metabolic deuterium incorporation demonstrates no apparent adverse effects on EV secretion, marker expression, morphology, or global composition, indicating its capacity for minimally obstructive EV labelling. As such, our metabolic labelling strategy could provide integral insights into EV biocomposition and trafficking. This approach has the potential to enable a deeper understanding of many of the biological mechanisms underpinning EVs, with profound implications for the design of EVs as therapeutic delivery vectors and applications as disease biomarkers.

In vivo biocompatibility and immunogenicity of metal-phenolic gelation.

In vivo forming hydrogels are of interest for diverse biomedical applications due to their ease-of-use and minimal invasiveness and therefore high translational potential. Supramolecular hydrogels that can be assembled using metal-phenolic coordination of naturally occurring polyphenols and group IV metal ions (e.g. TiIV or ZrIV) provide a versatile and robust platform for engineering such materials. However, the in situ formation and in vivo response to this new class of materials has not yet been reported. Here, we demonstrate that metal-phenolic supramolecular gelation occurs successfully in vivo and we investigate the host response to the material over 14 weeks. The TiIV-tannic acid materials form stable gels that are well-tolerated following subcutaneous injection. Histology reveals a mild foreign body reaction, and titanium biodistribution studies show low accumulation in distal tissues. Compared to poloxamer-based hydrogels (commonly used for in vivo gelation), TiIV-tannic acid materials show a substantially improved in vitro drug release profile for the corticosteroid dexamethasone (from <1 day to >10 days). These results provide essential in vivo characterization for this new class of metal-phenolic hydrogels, and highlight their potential suitability for biomedical applications in areas such as drug delivery and regenerative medicine.

Sulfonamide-based diffusible signal factor analogs interfere with quorum sensing in Stenotrophomonas maltophilia and Burkholderia cepacia.

Aim:Stenotrophomonas maltophilia (Sm) and Burkholderia cepacia complex (BCC) are Gram-negative bacterial pathogens, which are typically multidrug resistant and excellent biofilm producers. These phenotypes are controlled by quorum sensing (QS) systems from the diffusible signal factor (DSF) family. We aim to interfere with this QS system as an alternative approach in combatting such difficult-to-treat infections. Materials & methods: A library of sulfonamide-based DSF bioisosteres was synthesized and tested against the major phenotypes regulated by QS. Results & conclusion: Several analogs display significant antibiofilm activity while the majority increase the action of the last-resort antibiotic colistin against Sm and BCC. Most compounds inhibit DSF synthesis in the Sm K279a strain. Our results support the strategy of interfering with QS communications to combat multidrug resistance.

Single Particle Automated Raman Trapping Analysis.

Enabling concurrent, high throughput analysis of single nanoparticles would greatly increase the capacity to study size, composition and inter and intra particle population variance with applications in a wide range of fields from polymer science to drug delivery. Here, we present a comprehensive platform for Single Particle Automated Raman Trapping Analysis (SPARTA) able to integrally analyse nanoparticles ranging from synthetic polymer particles to liposomes without any modification. With the developed highly controlled automated trapping process, single nanoparticles are analysed with high throughput and sensitivity to resolve particle mixtures, obtain detailed compositional spectra of complex particles, track sequential functionalisations, derive particle sizes and monitor the dynamics of click reactions occurring on the nanoparticle surface. The SPARTA platform opens up a wide range of new avenues for nanoparticle research through label-free integral high-throughput single particle analysis, overcoming key limitations in sensitivity and specificity of existing bulk analysis methods.

Characterisation of minimalist co-assembled fluorenylmethyloxycarbonyl self-assembling peptide systems for presentation of multiple bioactive peptides.

UNLABELLED: The nanofibrillar structures that underpin self-assembling peptide (SAP) hydrogels offer great potential for the development of finely tuned cellular microenvironments suitable for tissue engineering. However, biofunctionalisation without disruption of the assembly remains a key issue. SAPS present the peptide sequence within their structure, and studies to date have typically focused on including a single biological motif, resulting in chemically and biologically homogenous scaffolds. This limits the utility of these systems, as they cannot effectively mimic the complexity of the multicomponent extracellular matrix (ECM). In this work, we demonstrate the first successful co-assembly of two biologically active SAPs to form a coassembled scaffold of distinct two-component nanofibrils, and demonstrate that this approach is more bioactive than either of the individual systems alone. Here, we use two bioinspired SAPs from two key ECM proteins: Fmoc-FRGDF containing the RGD sequence from fibronectin and Fmoc-DIKVAV containing the IKVAV sequence from laminin. Our results demonstrate that these SAPs are able to co-assemble to form stable hybrid nanofibres containing dual epitopes. Comparison of the co-assembled SAP system to the individual SAP hydrogels and to a mixed system (composed of the two hydrogels mixed together post-assembly) demonstrates its superior stable, transparent, shear-thinning hydrogels at biological pH, ideal characteristics for tissue engineering applications. Importantly, we show that only the coassembled hydrogel is able to induce in vitro multinucleate myotube formation with C2C12 cells. This work illustrates the importance of tissue engineering scaffold functionalisation and the need to develop increasingly advanced multicomponent systems for effective ECM mimicry. STATEMENT OF SIGNIFICANCE: Successful control of stem cell fate in tissue engineering applications requires the use of sophisticated scaffolds that deliver biological signals to guide growth and differentiation. The complexity of such processes necessitates the presentation of multiple signals in order to effectively mimic the native extracellular matrix (ECM). Here, we establish the use of two biofunctional, minimalist self-assembling peptides (SAPs) to construct the first co-assembled SAP scaffold. Our work characterises this construct, demonstrating that the physical, chemical, and biological properties of the peptides are maintained during the co-assembly process. Importantly, the coassembled system demonstrates superior biological performance relative to the individual SAPs, highlighting the importance of complex ECM mimicry. This work has important implications for future tissue engineering studies.

GRAB is a binding partner for the Rab11a and Rab11b GTPases.

Co-ordination of Rab GTPase function has emerged as a crucial mechanism in the control of intracellular trafficking processes in eukaryotic cells. Here, we show that GRAB/Rab3IL1 [guanine nucleotide exchange factor for Rab3A; RAB3A interacting protein (rabin3)-like 1], a protein that has previously be shown to act as a GEF (guanine nucleotide exchange factor) for Rab3a, Rab8a and Rab8b, is also a binding partner for Rab11a and Rab11b, but not the closely related Rab25 GTPase. We demonstrate that exogenous expression of Rab11a and Rab11b shift GRAB's distribution from the cytoplasm onto membranes. We find that the Rab11a/Rab11b-binding region of GRAB lies within its carboxy-terminus, a region distinct from its GEF domain and Rab3a-binding region. Finally, we describe a GRAB deletion mutant (GRABΔ223-228) that is deficient in Rab11-binding ability. These data identify GRAB as a dual Rab-binding protein that could potentially link Rab3 and Rab11 and/or Rab8 and Rab11-mediated intracellular trafficking processes.

Identification and characterization of multiple novel Rab-myosin Va interactions.

Myosin Va is a widely expressed actin-based motor protein that binds members of the Rab GTPase family (3A, 8A, 10, 11A, 27A) and is implicated in many intracellular trafficking processes. To our knowledge, myosin Va has not been tested in a systematic screen for interactions with the entire Rab GTPase family. To that end, we report a yeast two-hybrid screen of all human Rabs for myosin Va-binding ability and reveal 10 novel interactions (3B, 3C, 3D, 6A, 6A', 6B, 11B, 14, 25, 39B), which include interactions with three new Rab subfamilies (Rab6, Rab14, Rab39B). Of interest, myosin Va interacts with only a subset of the Rabs associated with the endocytic recycling and post-Golgi secretory systems. We demonstrate that myosin Va has three distinct Rab-binding domains on disparate regions of the motor (central stalk, an alternatively spliced exon, and the globular tail). Although the total pool of myosin Va is shared by several Rabs, Rab10 and Rab11 appear to be the major determinants of its recruitment to intracellular membranes. We also present evidence that myosin Va is necessary for maintaining a peripheral distribution of Rab11- and Rab14-positive endosomes.