Characterizing the landscape of gene expression variance in humans.

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.

Silica Aerogel-Polycaprolactone Scaffolds for Bone Tissue Engineering.

Silica aerogel is a material composed of SiO2 that has exceptional physical properties when utilized for tissue engineering applications. Poly-ε-caprolactone (PCL) is a biodegradable polyester that has been widely used for biomedical applications, namely as sutures, drug carriers, and implantable scaffolds. Herein, a hybrid composite of silica aerogel, prepared with two different silica precursors, tetraethoxysilane (TEOS) or methyltrimethoxysilane (MTMS), and PCL was synthesized to fulfil bone regeneration requirements. The developed porous hybrid biocomposite scaffolds were extensively characterized, regarding their physical, morphological, and mechanical features. The results showed that their properties were relevant, leading to composites with different properties. The water absorption capacity and mass loss were evaluated as well as the influence of the different hybrid scaffolds on osteoblasts' viability and morphology. Both hybrid scaffolds showed a hydrophobic character (with water contact angles higher than 90°), low swelling (maximum of 14%), and low mass loss (1-7%). hOB cells exposed to the different silica aerogel-PCL scaffolds remained highly viable, even for long periods of incubation (7 days). Considering the obtained results, the produced hybrid scaffolds may be good candidates for future application in bone tissue engineering.

IR780 loaded gelatin-PEG coated gold core silica shell nanorods for cancer-targeted photothermal/photodynamic therapy.

Gold core silica shell (AuMSS) nanorods present excellent physicochemical properties that allow their application as photothermal and drug delivery agents. Herein, AuMSS nanorods were dual-functionalized with Polyethylene glycol methyl ether (PEG-CH3 ) and Gelatin (GEL) to enhance both the colloidal stability and uptake by HeLa cancer cells. Additionally, the AuMSS nanorods were combined for the first time with IR780 (a heptamethine cyanine molecule) and its photothermal and photodynamic capacities were determined. The obtained results reveal that the encapsulation of IR780 (65 µg per AuMSS mg) increases the photothermal conversion efficiency of AuMSS nanorods by 10%, and this enhanced heat generation was maintained even after three irradiation cycles with a NIR (808 nm) laser. Moreover, the IR780-loaded AuMSS/T-PEG-CH3 /T-GEL presented ≈2-times higher uptake in HeLa cells, when compared to the non-coated counterparts, and successfully mediated the light-triggered generation of reactive oxygen species. Overall, the combination of photodynamic and photothermal therapy mediated by IR780-loaded AuMSS/T-PEG-CH3 /T-GEL nanorods effectively promoted the ablation of HeLa cancer cells.

Influence of ClearT and ClearT2 Agitation Conditions in the Fluorescence Imaging of 3D Spheroids.

3D tumor spheroids have arisen in the last years as potent tools for the in vitro screening of novel anticancer therapeutics. Nevertheless, to increase the reproducibility and predictability of the data originated from the spheroids it is still necessary to develop or optimize the techniques used for spheroids' physical and biomolecular characterization. Fluorescence microscopy, such as confocal laser scanning microscopy (CLSM), is a tool commonly used by researchers to characterize spheroids structure and the antitumoral effect of novel therapeutics. However, its application in spheroids' analysis is hindered by the limited light penetration in thick samples. For this purpose, optical clearing solutions have been explored to increase the spheroids' transparency by reducing the light scattering. In this study, the influence of agitation conditions (i.e., static, horizontal agitation, and rotatory agitation) on the ClearT and ClearT2 methods' clearing efficacy and tumor spheroids' imaging by CLSM was characterized. The obtained results demonstrate that the ClearT method results in the improved imaging of the spheroids interior, whereas the ClearT2 resulted in an increased propidium iodide mean fluorescence intensity as well as a higher signal depth in the Z-axis. Additionally, for both methods, the best clearing results were obtained for the spheroids treated under the rotatory agitation. In general, this work provides new insights on the ClearT and ClearT2 clearing methodologies and their utilization for improving the reproducibility of the data obtained through the CLSM, such as the analysis of the cell death in response to therapeutics administration.

Genomic Perspective on Multivariate Variation, Pleiotropy, and Evolution.

Multivariate quantitative genetics provides a powerful framework for understanding patterns and processes of phenotypic evolution. Quantitative genetics parameters, like trait heritability or the G-matrix for sets of traits, can be used to predict evolutionary response or to understand the evolutionary history of a population. These population-level approaches have proven to be extremely successful, but the underlying genetics of multivariate variation and evolutionary change typically remain a black box. Establishing a deeper empirical understanding of how individual genetic effects lead to genetic (co)variation is then crucial to our understanding of the evolutionary process. To delve into this black box, we exploit an experimental population of mice composed from lineages derived by artificial selection. We develop an approach to estimate the multivariate effect of loci and characterize these vectors of effects in terms of their magnitude and alignment with the direction of evolutionary divergence. Using these estimates, we reconstruct the traits in the ancestral populations and quantify how much of the divergence is due to genetic effects. Finally, we also use these vectors to decompose patterns of genetic covariation and examine the relationship between these components and the corresponding distribution of pleiotropic effects. We find that additive effects are much larger than dominance effects and are more closely aligned with the direction of selection and divergence, with larger effects being more aligned than smaller effects. Pleiotropic effects are highly variable but are, on average, modular. These results are consistent with pleiotropy being partly shaped by selection while reflecting underlying developmental constraints.

Directional selection can drive the evolution of modularity in complex traits.

Modularity is a central concept in modern biology, providing a powerful framework for the study of living organisms on many organizational levels. Two central and related questions can be posed in regard to modularity: How does modularity appear in the first place, and what forces are responsible for keeping and/or changing modular patterns? We approached these questions using a quantitative genetics simulation framework, building on previous results obtained with bivariate systems and extending them to multivariate systems. We developed an individual-based model capable of simulating many traits controlled by many loci with variable pleiotropic relations between them, expressed in populations subject to mutation, recombination, drift, and selection. We used this model to study the problem of the emergence of modularity, and hereby show that drift and stabilizing selection are inefficient at creating modular variational structures. We also demonstrate that directional selection can have marked effects on the modular structure between traits, actively promoting a restructuring of genetic variation in the selected population and potentially facilitating the response to selection. Furthermore, we give examples of complex covariation created by simple regimes of combined directional and stabilizing selection and show that stabilizing selection is important in the maintenance of established covariation patterns. Our results are in full agreement with previous results for two-trait systems and further extend them to include scenarios of greater complexity. Finally, we discuss the evolutionary consequences of modular patterns being molded by directional selection.

Insights from Systems Biology in Physiological Studies: Learning from Context.

Systems biology presents an integrated view of biological systems, focusing on the relations between elements, whether functional or evolutionary, and providing a rich framework for the comprehension of life. At the same time, many low-throughput experimental studies are performed without influence from this integrated view, whilst high-throughput experiments use low-throughput results in their validation and interpretation. We propose an inversion in this logic, and ask which benefits could be obtained from a holistic view coming from high-throughput studies-and systems biology in particular-in interpreting and designing low-throughput experiments. By exploring some key examples from the renal and adrenal physiology, we try to show that network and modularity theory, along with observed patterns of association between elements in a biological system, can have profound effects on our ability to draw meaningful conclusions from experiments.

Modularity: genes, development and evolution.

Modularity has emerged as a central concept for evolutionary biology, providing the field with a theory of organismal structure and variation. This theory has reframed long standing questions and serves as a unified conceptual framework for genetics, developmental biology and multivariate evolution. Research programs in systems biology and quantitative genetics are bridging the gap between these fields. While this synthesis is ongoing, some major themes have emerged and empirical evidence for modularity has become abundant. In this review, we look at modularity from an historical perspective, highlighting its meaning at different levels of biological organization and the different methods that can be used to detect it. We then explore the relationship between quantitative genetic approaches to modularity and developmental genetic studies. We conclude by investigating the dynamic relationship between modularity and the adaptive landscape and how this potentially shapes evolution and can help bridge the gap between micro- and macroevolution.

Costly learning: preference for familiar food persists despite negative impact on survival.

Animals often rely on events in their environment that provide information (i.e. experience) to alter their future decision-making in ways that are presumed to be beneficial. Such experience-based learning, however, does not always lead to adaptive decision-making. In this study, we use the omnivorous harvestman Heteromitobates discolor to explore the role of past diet on subsequent food choice and survival. We first tested whether a short-term homogeneous diet (rotten crickets, fresh crickets or dog food) influenced subsequent food choice (rotten cricket versus fresh cricket). We next examine the impact of diet on survival. We found that following experience with a homogeneous cricket diet, adult harvestmen displayed a learned preference for familiar food, regardless of whether it was rotten or fresh crickets; individuals experiencing dog food were equally likely to choose rotten versus fresh crickets. We additionally found that individuals that ate rotten crickets suffered shorter survival than those that ate fresh crickets. Together, our results suggest that the diet an individual experiences can lead to maladaptive food preferences-preferences that ultimately result in reduced longevity.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster , we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

Hyaluronic acid-functionalized graphene-based nanohybrids for targeted breast cancer chemo-photothermal therapy.

Nanomaterials' application in cancer therapy has been driven by their ability to encapsulate chemotherapeutic drugs as well as to reach the tumor site. Nevertheless, nanomedicines' translation has been limited due to their lack of specificity towards cancer cells. Although the nanomaterials' surface can be coated with targeting ligands, such has been mostly achieved through non-covalent functionalization strategies that are prone to premature detachment. Notwithstanding, cancer cells often establish resistance mechanisms that impair the effect of the loaded drugs. This bottleneck may be addressed by using near-infrared (NIR)-light responsive nanomaterials. The NIR-light triggered hyperthermic effect generated by these nanomaterials can cause irreversible damage to cancer cells or sensitize them to chemotherapeutics' action. Herein, a novel covalently functionalized targeted NIR-absorbing nanomaterial for cancer chemo-photothermal therapy was developed. For such, dopamine-reduced graphene oxide nanomaterials were covalently bonded with hyaluronic acid, and then loaded with doxorubicin (DOX/HA-DOPA-rGO). The produced nanomaterials showed suitable physicochemical properties, high encapsulation efficiency, and photothermal capacity. The in vitro studies revealed that the nanomaterials are cytocompatible and that display an improved uptake by the CD44-overexpressing breast cancer cells. Importantly, the combination of DOX/HA-DOPA-rGO with NIR light reduced breast cancer cells' viability to just 23 %, showcasing their potential chemo-photothermal therapy.

Reduced graphene oxide-reinforced tricalcium phosphate/gelatin/chitosan light-responsive scaffolds for application in bone regeneration.

Bone is a mineralized tissue with the intrinsic capacity for constant remodeling. Rapid prototyping techniques, using biomaterials that mimic the bone native matrix, have been used to develop osteoinductive and osteogenic personalized 3D structures, which can be further combined with drug delivery and phototherapy. Herein, a Fab@Home 3D Plotter printer was used to promote the layer-by-layer deposition of a composite mixture of gelatin, chitosan, tricalcium phosphate, and reduced graphene oxide (rGO). The phototherapeutic potential of the new NIR-responsive 3D_rGO scaffolds was assessed by comparing scaffolds with different rGO concentrations (1, 2, and 4 mg/mL). The data obtained show that the rGO incorporation confers to the scaffolds the capacity to interact with NIR light and induce a hyperthermy effect, with a maximum temperature increase of 16.7 °C after under NIR irradiation (10 min). Also, the increase in the rGO content improved the hydrophilicity and mechanical resistance of the scaffolds, particularly in the 3D_rGO4. Furthermore, the rGO could confer an NIR-triggered antibacterial effect to the 3D scaffolds, without compromising the osteoblasts' proliferation and viability. In general, the obtained data support the development of 3D_rGO for being applied as temporary scaffolds supporting the new bone tissue formation and avoiding the establishment of bacterial infections.

From GWAS to signal validation: An approach for estimating genetic effects while preserving genomic context.

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

PEDOT-Coated PLA Fibers Electrospun from Solutions Incorporating Fe(III)Tosylate in Different Solvents by Vapor-Phase Polymerization for Neural Regeneration.

Therapeutic solutions for injuries in the peripheral nervous system are limited and not existing in the case of the central nervous system. The electrical stimulation of cells through a cell-supporting conductive scaffold may contribute to new therapeutic solutions for nerve regeneration. In this work, biocompatible Polylactic acid (PLA) fibrous scaffolds incorporating Fe(III)Tosylate (FeTos) were produced by electrospinning a mixture of PLA/FeTos solutions towards a rotating cylinder, inducing fiber alignment. Fibers were coated with the conductive polymer Poly(3,4 ethylenedioxythiophene) (PEDOT) formed by vapor-phase polymerization of EDOT at 70 °C for 2 h. Different solvents (ETH, DMF and THF) were used as FeTos solvents to investigate the impact on the scaffold's conductivity. Scaffold conductivity was estimated to be as high as 1.50 × 10-1 S/cm when FeTos was dissolved in DMF. In vitro tests were performed to evaluate possible scaffold cytotoxicity, following ISO 10993-5, revealing no cytotoxic effects. Differentiation and growth of cells from the neural cell line SH-SY5Y seeded on the scaffolds were also assessed, with neuritic extensions observed in cells differentiated in neurons with retinoic acid. These extensions tended to follow the preferential alignment of the scaffold fibers.

Morphological integration during postnatal ontogeny: implications for evolutionary biology.

How covariance patterns of phenotypes change during development is fundamental for a broader understanding of evolution. There is compelling evidence that mammalian cranium covariance patterns change during ontogeny. However, it is unclear to what extent variation in covariance patterns during ontogeny can impact the response to selection. To tackle this question, we explored: (a) the extent to which covariance patterns change during postnatal ontogeny; (b) in which ontogenetic stages covariance patterns differ the most; and (c) the extent to which the phenotypic covariance pattern at different ontogenetic stages can be explained by the same processes determining additive genetic covariance. We sampled the postnatal ontogenetic series for both marsupials and placentals. Within each ontogenetic series, we compared covariance matrices (P-matrices) at different ontogenetic stages. Furthermore, we compared these P-matrices to two target matrices [adult P-matrix and an additive genetic covariance matrix (G-matrix)]. Our results show that for all ontogenetic series, covariance patterns from weaning onward are conserved and probably shaped by the same processes determining the G-matrix. We conclude that irrespective of eventual differences in how selection operates during most of the postnatal ontogeny, the net response to such pressures will probably not be affected by ontogenetic differences in the covariance pattern.

Sulfobetaine methacrylate-coated reduced graphene oxide-IR780 hybrid nanosystems for effective cancer photothermal-photodynamic therapy.

Nanomaterials with near infrared light absorption can mediate an antitumoral photothermal-photodynamic response that is weakly affected by cancer cells' resistance mechanisms. Such nanosystems are commonly prepared by loading photosensitizers into nanomaterials displaying photothermal capacity, followed by functionalization to achieve biological compatibility. However, the translation of these multifunctional nanomaterials has been limited by the fact that many of the photosensitizers are not responsive to near infrared light. Furthermore, the reliance on poly(ethylene glycol) for functionalizing the nanomaterials is also not ideal due to some immunogenicity reports. Herein, a novel photoeffective near infrared light-responsive nanosystem for cancer photothermal-photodynamic therapy was assembled. For such, dopamine-reduced graphene oxide was, for the first time, functionalized with sulfobetaine methacrylate-brushes, and then loaded with IR780 (IR780/SB/DOPA-rGO). This hybrid system revealed a nanometric size distribution, optimal surface charge and colloidal stability. The interaction of IR780/SB/DOPA-rGO with near infrared light prompted a temperature increase (photothermal effect) and production of singlet oxygen (photodynamic effect). In in vitro studies, the IR780/SB/DOPA-rGO per se did not elicit cytotoxicity (viability > 78 %). In contrast, the combination of IR780/SB/DOPA-rGO with near infrared light decreased breast cancer cells' viability to just 21 %, at a very low nanomaterial dose, highlighting its potential for cancer photothermal-photodynamic therapy.

Simple preparation of POxylated nanomaterials for cancer chemo-PDT/PTT.

Near infrared (NIR) light-responsive nanomaterials hold potential to mediate combinatorial therapies targeting several cancer hallmarks. When irradiated, these nanomaterials produce reactive oxygen species (photodynamic therapy) and/or a temperature increase (photothermal therapy). These events can damage cancer cells and trigger the release of drugs from the nanomaterials' core. However, engineering nanomaterials for cancer chemo-photodynamic/photothermal therapy is a complex process. First, nanomaterials with photothermal capacity are synthesized, being then loaded with photosensitizers plus chemotherapeutics, and, finally functionalized with polymers for achieving suitable biological properties. To overcome this limitation, in this work, a novel straightforward approach to attain NIR light-responsive nanosystems for cancer chemo-photodynamic/photothermal therapy was established. Such was accomplished by synthesizing poly(2-ethyl-2-oxazoline)-IR780 amphiphilic conjugates, which can be assembled into nanoparticles with photodynamic/photothermal capabilities that simultaneously encapsulate Doxorubicin (DOX/PEtOx-IR NPs). The DOX/PEtOx-IR NPs presented a suitable size and surface charge for cancer-related applications. When irradiated with NIR light, the DOX/PEtOx-IR NPs produced singlet oxygen as well as a smaller thermic effect that boosted the release of DOX by 1.7-times. In the in vitro studies, the combination of DOX/PEtOx-IR NPs and NIR light could completely ablate breast cancer cells (viability ≈ 4 %), demonstrating the enhanced outcome arising from the nanomaterials' chemo-photodynamic/photothermal therapy.

Multifunctional targeted solid lipid nanoparticles for combined photothermal therapy and chemotherapy of breast cancer.

Photothermal therapy has emerged as a new promising strategy for the management of cancer, either alone or combined with other therapeutics, such as chemotherapy. The use of nanoparticles for multimodal therapy can improve treatment performance and reduce drug doses and associated side effects. Here we propose the development of a novel multifunctional nanosystem based on solid lipid nanoparticles co-loaded with gold nanorods and mitoxantrone and functionalized with folic acid for dual photothermal therapy and chemotherapy of breast cancer. Nanoparticles were produced using an economically affordable method and presented suitable physicochemical properties for tumor passive accumulation. Upon Near-Infrared irradiation (808 nm, 1.7 W cm-2, 5 min), nanoparticles could effectively mediate a temperature increase of >20 °C. Moreover, exposure to light resulted in an enhanced release of Mitoxantrone. Furthermore, nanoparticles were non-hemolytic and well tolerated by healthy cells even at high concentrations. The active targeting strategy was found to be successful, as shown by the greater accumulation of the functionalized nanoparticles in MCF-7 cells. Finally, the combined effects of chemotherapy, light-induced drug release and photothermal therapy significantly enhanced breast cancer cell death. Overall, these results demonstrate that the developed lipid nanosystem is an efficient vehicle for breast cancer multimodal therapy.

Development of Thiol-Maleimide hydrogels incorporating graphene-based nanomaterials for cancer chemo-photothermal therapy.

Nano-sized materials have been widely explored in the biomedicine field, especially due to their ability to encapsulate drugs intended to be delivered to cancer cells. However, systemically administered nanomaterials face several barriers that can hinder their tumor-homing capacity. In this way, researchers are now focusing their efforts in developing technologies that can deliver the nanoparticles directly into the tumor tissue. Particularly, hydrogels assembled using Thiol-Maleimide Michael type additions are emerging for this purpose due to their capacity to incorporate high nanoparticles' doses in a compact 3D structure as well as good chemical selectivity, biocompatibility, and straightforward preparation. Nevertheless, such hydrogels have been mostly prepared using synthetic polymers, which is not ideal due to their poor biodegradability. In this work, a novel natural polymer-based Thiol-Maleimide hydrogel was produced for application in breast cancer chemo-photothermal therapy. To obtain natural polymers compatible with this crosslinking chemistry, Hyaluronic acid was endowed with Thiol groups and deacetylated Chitosan was grafted with Maleimide groups. Parallelly, Doxorubicin loaded Dopamine-reduced graphene oxide (DOX/DOPA-rGO) was prepared for attaining Near Infrared (NIR) light responsive chemo-photothermal nanoagents. By simply mixing Hyaluronic Acid-Thiol, deacetylated Chitosan-Maleimide and DOX/DOPA-rGO, Thiol-Maleimide crosslinked hydrogels incorporating this nanomaterial could be assembled (DOX/DOPA-rGO@TMgel). When breast cancer cells were incubated with DOPA-rGO@TMgel and exposed to NIR light (photothermal therapy), their viability was reduced to about 59 %. On the other hand, DOX/DOPA-rGO@TMgel (chemotherapy) reduced cancer cells' viability to 50 %. In stark contrast, the combined action of DOX/DOPA-rGO@TMgel and NIR light decreased breast cancer cells' viability to just 21 %, highlighting its chemo-photothermal potential.

Mix-and-extrude: high-viscosity sample injection towards time-resolved protein crystallography.

Time-resolved crystallography enables the visualization of protein molecular motion during a reaction. Although light is often used to initiate reactions in time-resolved crystallography, only a small number of proteins can be activated by light. However, many biological reactions can be triggered by the interaction between proteins and ligands. The sample delivery method presented here uses a mix-and-extrude approach based on 3D-printed microchannels in conjunction with a micronozzle. The diffusive mixing enables the study of the dynamics of samples in viscous media. The device design allows mixing of the ligands and protein crystals in 2 to 20 s. The device characterization using a model system (fluorescence quenching of iq-mEmerald proteins by copper ions) demonstrated that ligand and protein crystals, each within lipidic cubic phase, can be mixed efficiently. The potential of this approach for time-resolved membrane protein crystallography to support the development of new drugs is discussed.