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  • Self-organized patterning of crocodile head scales by compressive folding

    Self-organized patterning of crocodile head scales by compressive folding

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    Animal husbandry

    Fertilized crocodile eggs (imported from Seronera Crocodile Farm) were transported to the University of Geneva and incubated at 31 °C in moist vermiculite. All treated and non-treated crocodile embryos were fixed and stored in 10% neutral buffered formalin (NBF). All non-fluorescence imaging of embryonic crocodile samples was undertaken using the Keyence VHX 7000 digital microscope. Imaging of hatched crocodile specimens was undertaken using an overhead-mounted Nikon D800 camera. Maintenance of, and experiments with, crocodile embryos and juveniles were approved by the Geneva Canton ethical regulation authority (authorization GE10619B) and performed according to Swiss law. The sample sizes are specified in figure legends and the Supplementary Information. Randomization and blinding was not required.

    Nanoindentation

    A Piuma nanoindenter (Optics11) was used to acquire stiffness measurements (effective Young’s modulus, Pa) of embryonic crocodile skin surface. When comparing measurements in two skin samples, a change in epidermal keratinization will produce a change in surface stiffness, which is very likely to be correlated with a change of the same sign in the effective overall Young’s modulus of the whole multilayered epidermis. In other words, an increase in epidermal surface stiffness is very likely accompanied by an increased stiffness of the whole epidermis. Freshly dissected upper jaws were positioned lateral side upwards, submerged in PBS. A probe with a tip radius of 99 µm and stiffness rating of 0.48 N m−1 was used to indent at a depth of 2,000 nm. Only measurements from load-displacement curves with a Hertzian contact model fit of ≥95% were subsequently analysed. Each biological replicate for the embryonic nanoindentation series was indented 10 times (Fig. 1c) or 5 times (Fig. 2f). These indentation points were positioned in a single row with each point separated by 120 µm. Plots showing the mean effective Young’s modulus values for each biological replicate with s.d. are presented. Statistical analysis was undertaken in Prism 9 (GraphPad).

    Confocal microscopy

    Confocal microscopy was used for embryonic crocodile samples stained with the Fast Green FCF dye (Sigma-Aldrich) according to a protocol of whole-mount collagen staining25. Image acquisition was undertaken as previously described25, using the SP8 microscope (Leica Microsystems), with a ×63 oil-immersion objective (numerical aperture, 1.4). Fast Green was excited at 627 nm and the signal was detected in the range of 630–730 nm. Image reconstruction was undertaken using Imaris (Oxford Instruments).

    H&E staining

    Fixed embryonic crocodile samples were dissected and embedded in paraffin as previously described24. Paraffin sections were cut at 10 µm with a RM2255 microtome (Leica Microsystems) before staining with haematoxylin and eosin (H&E). Slides were imaged using an automated slide scanner (3DHISTECH).

    In ovo intravenous EGF injections in crocodiles

    The injection of crocodile eggs was undertaken in accordance with our previously published work20,21 (https://youtu.be/qCYWSgbffnY). Crocodile eggs were incubated until the appropriate developmental stage and then cleaned with 70% ethanol. Eggs were candled to identify a suitable vein for injection, and a detailing saw (Micromot 50/E, Proxxon) was used to remove the shell while keeping the underlying membrane intact. The eggshell was then removed using fine forceps, and mineral oil was applied to the membrane with a cotton bud, thereby increasing membrane transparency to allow clear visualization of the underlying veins. The samples were injected with either 30 µl of PBS as a control or 30 µl of PBS containing recombinant murine EGF (PeproTech). Different doses of EGF were injected (0.625 µg, 1.5 µg or 2 µg). Patent Blue was also added to the solution to enable visualization of the solution entering the vein during injection. Injections were undertaken using a Hamilton syringe attached to a micromanipulator (MM33 right, Marzhauser). Once injected, the eggs were cleaned to remove excess mineral oil, and the eggshell window was covered with adhesive tape. Treated embryos were then returned to their incubator. The samples were each injected three times over the course of 10 days for each experiment (Fig. 2a). At collection, the embryos were treated with an intravenous injection of EdU to label proliferating cells (Baseclick); embryo collection and fixation were undertaken 3 h after EdU injection. Some EGF-treated embryos were used for nanoindentation at the end of the experiment, and some others were incubated until hatching. Embryos were subsequently fixed in 10% NBF at 4 °C and imaged with a Keyence VHX 7000 digital microscope. Every embryo injected with EGF exhibited modified head-scale patterning. All of the replicates from these experiments are shown in Supplementary Fig. 4 and are summarized in Supplementary Table 1.

    The drug that we use here (EGF) has the remarkable property of specifically promoting epidermal growth and differentiation without exhibiting strong deleterious effects in other aspects of in vivo embryonic development. Further validation of the parameters involved in the compression-folding process of crocodile head-scale patterning will require the identification of other drugs that would specifically affect one parameter at the time. For example, it would be particularly interesting to pharmacologically perturb the 3D architecture of collagen in developing crocodile embryos to investigate the corresponding effects on skin folding of the dorsal versus lateral upper jaw surface. Unfortunately, drugs currently known to effect collagen organization (such as β-aminoproprionitrile, BAPN) are highly toxic in vivo as they strongly affect the development of multiple connective tissues such as skin, bones and blood vessels. Given the great difficulties of experimentation with crocodile embryos, the screening of drugs that could, in vivo, specifically affect one mechanical parameter at a time in the skin, could be initially performed in a more classical model (such as the chicken) with more reliable source of embryos.

    LSFM

    The upper and lower jaws of fixed embryonic crocodile samples were dissected, dehydrated into methanol, and bleached with hydrogen peroxide, before rehydration and permeabilization in PBS with Triton X-100 (Sigma-Aldrich) (PBST). For nuclear staining, the samples were incubated in either TO-PRO-3 iodide or YO-PRO-1 iodide (3:1,000, Thermo Fisher Scientific) for 6 h. EdU-positive cells (EdU+) were detected using the EdU detection kit manufacturer’s guidelines (Baseclick). The samples were then dehydrated into methanol and collagen staining was undertaken in anhydrous conditions with the same Fast Green protocol25 as for confocal microscopy (see above). Samples were then cleared according to the iDISCO+ protocol37. Upper and lower jaw samples were imaged separately using a light-sheet microscope (Ultramicroscope Blaze, Miltenyi Biotec). Selected specimens were restained with Alizarin Red in potassium hydroxide (KOH) and re-imaged to visualize the developing calcified bone matrix (Extended Data Fig. 1b). Image stacks were processed using ImageJ38, before rendering with the Redshift engine of Houdini (SideFX) and the Unreal Engine (Epic Games). A summary of replicates used for LSFM is shown in Supplementary Table 5. Each sample includes both upper and lower jaws, which we scanned separately.

    3D reconstructions of hatched crocodiles

    Using our custom-built imaging system39, combining a robotic arm, high-resolution camera and illumination basket of light-emitting diodes, we combine ‘photometric stereo’ and ‘structure from motion’ to reconstruct the precise 3D surface mesh and colour-texture of hatched crocodile heads (Fig. 5b–e and Extended Data Fig. 6a–d). To compare the polygonal scale sizes among individuals, we first compute the minimum principle curvature of the meshes. Then, the folding network of each sample is computed by applying a skeletonization algorithm40, followed by graph simplification (using MATLAB R2021a), on the negative curvature regions of the mesh. Using the colour texture of meshes, the folding networks were manually completed and cleaned using Houdini (SideFX).

    Segmentation of LSFM data

    Using TO-PRO-3, YO-PRO-1, EdU, Alizarin Red and Fast Green staining (see above), we segmented the light-sheet microscopy data to extract (in both the upper and lower jaws) the geometry of the epidermis, dermis and bone tissues (Supplementary Video 6), as well as the dominant orientations of the dermal collagen fibres, and the distribution of proliferating cells in the dermis and epidermis. The segmented data were used to build a finite element model (FEM, see below) of the crocodile head.

    Cell nuclei staining signal enables precise segmentation of the epidermis from the dermis because the former exhibits a higher cell density (Fig. 3a). More specifically, the 3D image generated by LSFM on the basis of the TO-PRO-3/YO-PRO-1 fluorescence signal was subjected to 3D Canny’s edge detection41 in MATLAB-R2021a, generating a 3D binary image in which non-zero voxels form point clouds corresponding to two 3D surfaces: the surface of the epidermis and the epidermis–dermis boundary. For each of these two surfaces, we compute at each point the surface normal vector from the intensity gradient. The position of points and their corresponding normal vectors are then fed to a screened Poisson surface reconstruction algorithm42 in Meshlab43 to reconstruct triangular surface meshes, which effectively represent the initial point clouds in a much lighter format: 3D meshes are much easier to manipulate, for example, with the Laplacian smoothing algorithm to filter out the artifactual stair-step patterns in the original voxelized data format. The epidermis surface and the epidermis–dermis boundaries allow for computing the epidermis thickness across each control and treated sample at different developmental stages.

    Collagen network 3D architecture is likely to become instrumental in biomechanical modelling25,26 because it endows tissues with distinctive mechanical properties such as anisotropic response to homogeneous stress. Thus, we assess the orientation(s) of collagen fibres in the dermis across the face and jaws of developing crocodile embryos (Fig. 3b). To this end, we use our recently published whole-mount Fast Green staining method, which provides unmatched visualization of 3D collagen network architecture via confocal or light-sheet microscopy25. In brief, (1) the two most dominant orientation(s) of populations of collagen fibres were identified by determining the dominant 3D Fast Fourier transform coefficients in each of 13,000 homogeneously distributed dermal samples (cubic patches of 50 × 50 × 50 voxels) of 3D light-sheet images (Supplementary Note 1); (2) smoothing of the spatial variation of fibres orientations was achieved with an exact optimization procedure using a fibre axis mismatch energy functional (Supplementary Note 2); and (3) the two dominant fibre orientations, both tangential to the dermis mid-plane, were interpolated using spectral least-squares approximation (Supplementary Note 3).

    After standard EdU labelling and detection (Supplementary Video 3), we used a 3D principal curvatures approach36 (on the fluorescence signal) to segment proliferating cells in the jaws of an embryonic crocodile at E51, that is, at the onset of head-scale emergence (Fig. 3c). This approach is highly efficient for individually segmenting cells when they are grouped (that is, in contact). As the signal intensity is embedded in a 3D domain, three signal principal curvatures k1,2,3 are computed (in MATLAB) for each voxel, and voxels characterized by ks > kthreshold, where \({k}_{s}={({k}_{1}^{+}{k}_{2}^{+}{k}_{3}^{+})}^{\frac{1}{3}}\) and \({k}_{i}^{+}=\max ({k}_{i},0)\) are stored. The centroid of the connected voxels is considered as the location of an EdU+ cell. We then compute the density of EdU+ cells, separately for the dermis and the epidermis, by choosing sampling points in the corresponding segmented tissue layers. The space surrounding each sampling point is limited to a box of 80 × 80 × 80 voxels clipped by the layer boundaries. The density of EdU+ cells at a sampling point is computed as the number of cells inside the clipped box divided by its volume. In our numerical model, densities of proliferating cells are represented as a space-dependent growth function. We transfer this information to the 3D model using a spectral least-squares approximation approach to interpolate data on the spatial modes of the target mesh (details are provided in Supplementary Note 3).

    For segmenting bone tissue, we use either the 3D Canny’s edge detection of the (very strong) Alizarin Red signal or a semi-automatic procedure for samples with (weaker) Fast Green or EdU signals. In the latter case, we (1) choose several sections in the x, y and z directions and manually mark the separation between the dermis and the bone, (2) store the coordinates of all profile points as a 3D point cloud and compute their normal with Variational Implicit Point Set Surface44 and (3) use screened Poisson surface reconstruction42 from Meshlab43 to generate the mesh corresponding to the bone surface.

    A biomechanical model of head-scale emergence

    We use the segmented data to build a 3D finite-element numerical growth model. Triangular meshes were generated, both for upper and lower jaws, at the surface boundaries of the epidermis, dermis and bone of embryos before the onset of head-scale patterning (Fig. 1b and see above). The epidermis surface and the epidermis–dermis interface were smoothed to remove any artificial local deformations associated with sample preparation, including dehydration into methanol. The 3D volume of each of the three layers was represented as a tetrahedral mesh generated with TetGen45 (Extended Data Fig. 8a).

    During simulated growth, the deformation of tetrahedral elements is realized through finite-strain theory in which the bulk material configuration at current time t is represented as the spatial coordinates of a collection of points in the form of a vector variable x = x(X,t), where X is the spatial coordinates of these points at a reference configuration, that is, at t = 0 (Extended Data Fig. 8b). The coordinates between the current and the reference configurations are connected by the deformation gradient map, F—that is, a second-order tensor that incorporates the elastic and growth deformations. The elastic energy and the mechanical stress stored in each tetrahedral element is then calculated from the neo-Hookean material model, known to behave appropriately under large deformations30,31,46, and allowing the incorporation of anisotropic material, such as collagen fibres47 (Supplementary Note 4). The direction of fibres, as well as the spatial pattern of cell proliferation density, both inferred from LSFM data (Fig. 3b,c), are fed to the mechanical model. However, the elastic moduli, fibre stiffness and final amount of growth are considered as unknown parameters. Note that the absolute values of stiffness are irrelevant in the numerical simulations as the model key parameters are the fibre stiffness relative to the dermis and epidermis moduli, as well as the ratio of epidermis to dermis stiffnesses (Young’s moduli).

    Numerical simulations and parameter optimization

    To perform numerical simulations, the mechanical model formulation described above is discretized for tetrahedral elements using the FEM and integrated with contact and viscous forces (Supplementary Note 5). The final model is then implemented in an in-house application that uses NVIDIA GPUs for high-performance computation. For that purpose, we used the CUDA programming language to develop intensive-computation kernels, whereas C++ is used for data management, geometry processing, input/output operations and the graphical user interface. Our application integrates the following open-source libraries: Dear ImGui (https://github.com/ocornut/imgui, MIT licence) for the graphical user interface, CUDA C++ Core Libraries (https://github.com/NVIDIA/cccl, Apache-2.0, FreeBSD, BSD-3-Clause licences) for parallel algorithms, Eigen (https://gitlab.com/libeigen/eigen, MPL-2.0, BSD licences) for linear algebra and libigl (https://github.com/libigl/libigl, GPL-3.0, MPL-2.0 licences) for geometry processing. The simulation input is a tetrahedral mesh that defines the geometry of the crocodile head (epidermis, dermis and bone layers). Moreover, a set of model parameters are used: in addition to the dermal collagen fibres orientation and stiffness, we include, both for epidermis and dermis, the Young’s modulus and Poisson’s ratio, the growth rate functions and the cell proliferation pattern. The deformation of the skin is then computed and the final geometry is generated as a tetrahedral mesh.

    The mechanical model is integrated with a Bayesian optimization process (bayesopt library from MATLAB R2021a with parallel sampling), that is, a machine-learning global minimization algorithm. The optimality criterion consists of the distance between the metrics (integrating multiple topological and geometrical features, see below) of the steady-state simulated geometry versus LSFM-acquired meshes. To compute the metrics of a folding network, we first compute the minimum principle curvature of the corresponding surface mesh representing the epidermis boundary. We then segment the skin folds by applying a skeletonization algorithm40, followed by graph simplification (using MATLAB R2021a), on the negative curvature regions of the mesh. Next, we compute the following geometrical and topological features of the network: number of domains (cycles), perimeters of domains, lengths of edges, curvatures of edges and lengths of incomplete edges. The final metrics is a vector of which the components are the means of these features, normalized to the diagonal length of its bounding box. Given that components within a metrics vector may differ significantly among each other, we need to normalize them properly. For this purpose, we use LSFM data to compute the metrics of controls at E64 and treated individuals (2 μg EGF) at E64. We then compute the interindividual (that is, among all individuals) mean and s.d. of each component (Fig. 2e). We finally normalize the components of any newly computed metrics by subtracting the interindividual mean and dividing by the interindividual s.d.

    Finding optimal parameter values for control and treated targets is performed in two steps. First, we use an E64 control target mesh and perform optimization on the six-dimensional parameter space, including epidermis Young’s modulus, Eepidermis (keeping Edermis = 1); epidermis and dermis Poisson’s ratios, vepidermis/dermis; dermis tangential growth values, \({G}_{T,{\rm{dermis}}}^{+/-}\) (keeping \({G}_{T,{\rm{epidermis}}}^{+/-}\) at 80% of the dermis values); and the fibre stiffness, k1 (k2 being set to 0). Second, using a 2 μg EGF-treated target, we perform another optimization on the three-dimensional parameter space including epidermis-related parameters, that is, Eepidermis, vepidermis and \({\lambda }_{T,{\rm{epidermis}}}^{{\rm{EGF}}}\) (additional epidermal tangential growth induced by EGF). See Supplementary Notes 4 and 6 for the definitions of parameters and Supplementary Table 4 for the complete list of parameter values. To minimize the distance between the metrics vectors of the simulated versus LSFM target geometry (control or treated), we use a Gaussian process (that is, a generalization of the multivariate normal distribution to infinite dimensions) in the optimization loop to approximate posterior mean and variance functions from which the objective function is sampled (Extended Data Fig. 8d). The posterior functions are updated at each iteration according to Bayesian inference and this information is then used to compute the expectation of the improvement function, which measures the chance of observing an objective (that is, the distance between simulation and observation) smaller than the minimum objective observed so far (Supplementary Note 7). The optimization process, which typically takes a few thousand iterations, continues until no more improvement is observed in the last 500 iterations.

    Reporting summary

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

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  • X-ray method takes a 3D fingerprint of materials

    X-ray method takes a 3D fingerprint of materials

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  • In situ analysis reveals the TRiC duty cycle and PDCD5 as an open-state cofactor

    In situ analysis reveals the TRiC duty cycle and PDCD5 as an open-state cofactor

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    Cell culture

    HEK (HEK Flp-In T-Rex 293, Invitrogen) cells were cultured in DMEM (Sigma-Aldrich) supplemented with 10% fetal bovine serum (Gibco) under standard tissue culture conditions (37 °C, 5% CO2). HEK293F (Thermo Fisher Scientific) cells were cultured in Freestyle medium (Thermo Fisher Scientific) at 37 °C, 8% CO2 and 120 rpm. Cells were negative for mycoplasma contamination.

    Native PAGE

    For immunoblotting, when HEK cells were at about 80% confluency, they were washed twice with ice-cold PBS and scraped in PBS, pelleted by centrifugation for 5 min at 1,000g, 4 °C and resuspended in modified native lysis buffer (50 mM HEPES pH 7.4, 50 mM KCl, 1.5 mM MgCl2, 10% glycerol, 0.1% NP-40, 1 mM PMSF, complete EDTA-free protease inhibitor cocktail and 1 mM DTT). Lysis buffer was also supplemented with 30 U ml−1 benzonase to remove DNA. Lysis was performed on ice for 20 min and the lysates were clarified by centrifugation for 10 min at 12,000g at 4 °C. The protein concentration was determined using a BCA assay (Thermo Fisher Scientific). 4× NativePAGE sample buffer (Thermo Fisher Scientific) was added to a final concentration of 1×. Then, 15 µg of each sample was resolved on 3–12% Bis-Tris NativePAGE gels (Thermo Fisher Scientific). NativePAGE was soaked in 0.1% SDS buffer for 15 min, then transferred to 0.45 µM PVDF membranes presoaked in methanol for 30 s. The membranes were blocked with 5% molecular biology grade BSA (Millipore Sigma) in Tris-buffered saline supplemented with 0.1% Tween-20 (TBST) for 1 h at room temperature, then probed with specific primary antibodies 4 °C for overnight. Primary antibodies was diluted in 1% BSA/TBST as follows: 1:10,000 rabbit anti-CCT5 (Abcam, ab129016). The secondary antibody was diluted 1:10,000 in TBST. Total protein was detected with Revert total protein stain. Fluorescence signal detection was performed using Li-Cor Odyssey infrared imager.

    PDCD5 knockdown

    HEK cells (5 × 105) were seeded into six-well plates. Then, 24 h after plating, 25 pmol siRNA (Thermo Fisher Scientific, s17467) were added with Lipofectamine RNAiMAX Transfection Reagent (Invitrogen). Cells were collected with ice-cold PBS after 48 h and then immunoblotting was run for further analysis.

    Expression and purification of recombinant PDCD5 and its mutants

    PDCD5 mutants were obtained using site-directed mutagenesis. A 6× His-tag was added to the C terminus of PDCD5. Plasmids containing WT and mutant PDCD5 were transformed into Escherichia coli Rosetta DE3 competent cells for expression. PDCD5 was expressed and purified as previously reported33. In brief, cell lysates were first passed through a nickel column, then PDCD5 bound to the nickel resin was eluted in high imidazole buffer, and pure PDCD5 was obtained by passing the elution twice through a Superdex 200 size-exclusion column. Proteins were concentrated by centrifugation and then quantified using the BCA colorimetric assay.

    TRiC ATPase activity

    The assay was performed as previously described48. In brief, stock solutions of 0.05% (w/v) quinaldine red, 2.32% (w/v) polyvinyl alcohol, 5.72% (w/v) ammonium heptamolybdate tetrahydrate in 6 M HCl and water were mixed in a 2:1:1:2 ratio to prepare the quinaldine red reagent fresh before each experiment. Then, 300 nM TRiC was diluted in ATPase buffer (50 mM Tris-HCl pH 7.4, 100 mM KCl, 5 mM MgCl2, 10% glycerol, 1 mM TCEP; 30 μl total reaction volume), preheated to 37 °C and added to 3 μl water or 10 mM ATP to start the reaction, then incubated for the indicated durations in the presence or absence of 3 µM PDCD5. The reactions were stopped by the addition of 5 μl of 60 mM EDTA in a Corning 96-well opaque non-sterile polystyrene plate (Sigma-Aldrich, CLS3992) on ice. After samples at all timepoints were collected, the reactions were developed by adding 80 μl quinaldine red reagent for 10 min, then quenched by adding 10 μl 32% (w/v) sodium citrate. The fluorescence intensity was measured (excitation, 430 nm; emission, 530 nm) using the CLARIOstar plate reader (BMG Labtech). Analysis was performed by fitting a phosphate standard curve with a one-phase decay function, and we derived the parameters for calculating the amount of phosphate released from CCT complexes.

    PDCD5 binding to TRiC

    To probe the binding affinity of PDCD5 for TRiC, increasing amounts of recombinant PDCD5 variants were incubated with a fixed concentration of TRiC (300 nM) for 20 min at 25 °C in ATPase buffer (50 mM Tris-HCl pH 7.4, 100 mM KCl, 5 mM MgCl2, 10% glycerol, 1 mM TCEP), in the absence of ATP. The reactions were run in native gels and immunoblotted using PDCD5 or CCT8 antibodies, as described above. To test whether PDCD5 binds to the TRiC open or closed conformations, 3 μM of WT or mutant PDCD5 was incubated with 300 nM TRiC for 20 min at 25 °C in ATPase buffer containing 1 mM of different nucleotides and ATP analogues. The reactions were run in native gels and immunoblotted using PDCD5 or CCT8 antibodies, as described above. To obtain insights about the binding kinetics of PDCD5 variants to TRiC, 3 μM of WT or mutant PDCD5 was incubated with 300 nM TRiC in ATPase buffer at 25 °C for 10, 15, 20 and 30 min. The reactions were run in native gels and immunoblotted using PDCD5 (Proteintech, 12456-1-AP, 1:1,000) and CCT8 (Santa Cruz Biotechnology, sc-377261, 1:250) antibodies, as described above.

    Co-IP

    For PDCD5–Flag co-IP, PDCD5-Flag constructs (GenScript) were transiently expressed in HEK293F for 48 h after transfection. Cells were washed with PBS before collection by centrifugation and frozen in liquid nitrogen. HEK293F cells were lysed in lysis buffer (PBS pH 7.4, 0.1% IGEPAL CA-630, 5 mM MgCl2, freshly added 0.6 mM phenylmethylsulphonyl fluoride and protease inhibitors), triturated through a 24-gauge needle ten times and incubated on ice for 5 min. After lysate clearing by centrifugation, 500 μg clarified protein extract was mixed with 20 µl packed anti-Flag M2 beads (Sigma-Aldrich) and incubated for 1 h at 4 °C. After three washes with lysis buffer, bound proteins were eluted by boiling in LDS sample buffer (Invitrogen). For western blotting, input and eluate (IP) samples were loaded onto 4–12% Bis-Tris gels (Invitrogen) and subsequently transferred to nitrocellulose membranes (Bio-Rad).

    CCT3 co-IP was performed with non-transfected HEK293F cells subjected to in vivo cross-linking with 1.5 mM dithiobis(succinimidyl propionate) (DSP; Thermo Fisher Scientific) at 37 °C for 10 min. The cross-linking reaction was quenched by the addition of Tris (pH 8.0) to a final concentration of 160 mM and cells were collected and lysed as described above. Then, 2 mg of clarified protein extract was mixed with 10 μg rabbit anti-CCT3 antibody (Proteintech, 10571-1-AP) or rabbit control IgG (Proteintech, 30000-0-AP) as mock IP for 1 h at 4 °C, followed by addition of 50  μl equilibrated Protein G Magnetic Beads (Thermo Fisher Scientific) and incubation for 1 h at 4 °C. The samples were washed, eluted and evaluated using SDS–PAGE as described above.

    The percentage of IP efficiency was calculated by normalizing the measured intensities and the respective dilution factor of the loaded sample for western blotting (1% for the input sample and 5% for the IP sample), followed by IP/input. For the quantification, the mean ± s.d. values were as follows: PDCD5–flag (42.70 ± 16.16), CCT1 (86.66 ± 41.01), CCT2 (45.54 ± 15.25), CCT3 (45.57 ± 12.47), CCT4 (61.12 ± 15.08), CCT5 (98.98 ± 27.74), CCT6 (53.74 ± 21.34), CCT7 (65.99 ± 38.51), CCT8 (135.49 ± 64.48) and GAPDH (0.03 ± 0.06), with n representing the number of biologically independent experiments (n = 4). For the quantification of PDCD5 mutation experiments, the mean ± s.d. values were as follows: WT (100 ± 0), RKK (133.65 ± 59.63) and IL (11.04 ± 9.68), with n representing the number of biologically independent experiments (n = 4).

    To induce TRiC closure during co-IP, beads bound with TRiC–PDCD5–Flag (from co-IP, see above) were incubated in ATP/AlFx buffer (lysis buffer supplemented with 5 mM Al(NO3)3, 30 mM NaF and 1 mM ATP) for 1 h at 37 °C, followed by three washes with ATP/AlFx buffer. As a control, the beads bound with TRiC–PDCD5–Flag (from co-IP, see above) were incubated and washed in lysis buffer without the ATP/AlFx. For western blotting, 1% of input, 25% of released proteins after ATP/AlFx incubation and 25% of eluates (denoted as beads) were loaded.

    Without adding ATP in the TRiC sample before plunge freezing, around 100% TRiC particles are at open conformation based on the single-particle analysis13,14,19. With extra ATP/AlFx in TRiC solution before plunge freezing, a portion of TRiC particles were closed, although different papers show different closed/open ratios with ATP/AlFx at different conditions. Closed/open ratio: ~1.7 in buffer (1 mM ATP, 5 mM MgCl2, 5 mM Al (NO3)3 and 30 mM NaF) from ref. 13; ~5.1 in buffer (1 mM ATP, 1 mM Al3(NO3)3, 6 mM NaF, 10 mM MgCl2 50 mM KCl) from ref. 21; ~0.6 in buffer with ATP-AlFx from ref. 14; and ~2.2 in buffer (1 mM ATP, 5 mM MgCl2 and AlFx (5 mM Al(NO3)3 and 30 mM NaF) from ref. 16. In our experimental settings (Extended Data Fig. 7), we used the conditions from ref. 13 (1 mM ATP, 5 mM MgCl2, 5 mM Al (NO3)3 and 30 mM NaF).

    For the quantification in Extended Data Fig. 7, the mean ± s.d. values were as follows: PDCD5 (ATP/AlFx) (0.09 ± 0.05); PDCD5 (control) (0.10 ± 0.04); CCT1 (ATP/AlFx) (1.53 ± 0.51); and CCT1 (control) (0.38 ± 0.06); with n representing the number of biologically independent experiments (n = 4).

    Thermal protein profiling (heat-shock treatment of cells)

    WT (Abcam, ab255449) and PDCD5-knockout HEK293T cells (Abcam, ab266229) were used for the heart-shock assay and cultured in DMEM (Sigma-Aldrich) supplemented with 10% fetal bovine serum (Gibco) at 37 °C with 5% CO2. The experiment was conducted as described previously49,50. In brief, cells were collected and resuspended in PBS. Five aliquots were prepared and distributed into PCR tubes, each of the tubes containing 5 × 105 cells. Each tube was incubated for 3 min at various temperatures (37.0, 44.1, 49.9, 55.5 and 62.0 °C; or 56.8, 58.3, 59.5, 60.7 and 62.1 °C). The cells were then lysed in a buffer containing 1.5 Mm MgCl2, 0.8% NP-40, 0.4U μl−1 benzonase and protease inhibitor for 40 min at 4 °C. Protein aggregations were removed, and the soluble fraction was used for western blotting. For quantification of the western blotting of thermal protein profiling, the mean ± s.d. values of actin in WT cells at 37.0 °C to 62.0 °C were as follows: 100.0 ± 0.0, 85.3 ± 5.2, 73.8 ± 7.7, 46.3 ± 2.9 and 26.3 ± 9.4; the mean ± s.d. values of actin in PDCD5-knockout cells at 37.0 °C to 62.0 °C were as follows: 100.0 ± 0.0, 100.3 ± 7.0, 109.0 ± 9.7, 83.0 ± 2.0 and 57.6 ± 9.4; the mean ± s.d. values of tubulin in WT cells at 56.8 °C to 62.1 °C were as follows: 100.0 ± 0.0, 78.2 ± 4.2, 49.3 ± 5.5, 20.0 ± 4.9 and 5.4 ± 3.8; and the mean ± s.d. values of tubulin in PDCD5-knockout cells at 56.8 °C to 62.1 °C were as follows: 138.0 ± 22.3, 99.7 ± 6.4, 63.9 ± 15.9, 34.8 ± 0.4 and 8.3 ± 4.7.

    Antibodies

    Membranes from western blotting were incubated with primary antibodies (mouse anti-Flag M2 (Sigma-Aldrich, F1804, 1:2,000), rabbit anti-PDCD5 (Abcam, ab126213, 1:1,000), rabbit anti-CCT1 (Abcam, ab240903, 1:10,000), rabbit anti-CCT2 (Abcam, ab92746, 1:10,000), rabbit anti-CCT3 (Proteintech, 10571-1-AP, 1:30,000), rabbit anti-CCT4 (Proteintech, 21524-1-AP, 1:5,000), rabbit anti-CCT5 (Proteintech, 11603-1-AP, 1:3,000), rabbit anti-CCT6 (Proteintech, 19793-1-AP, 1:1,000), rabbit anti-CCT7 (Abcam, ab240566, 1:30,000), rabbit anti-CCT8 (Proteintech, 12263-1-AP, 1:2,000), rabbit anti-GAPDH (Proteintech, 10494-1-AP, 1:15,000), mouse anti-actin (Invitrogen, AM4302, 1:3,000), mouse anti-tubulin (Sigma-Aldrich, T5168, 1:3,000)), followed by incubation with HRP-conjugated secondary antibodies (anti-rabbit IgG (Cell Signaling, 7074, 1:10,000), anti-mouse IgG + IgM (Jackson ImmunoResearch, 115-035-044, 1:10,000)). Uncropped western blots are provided as Source Data.

    Grid preparation, data acquisition and tomogram reconstruction

    Cryo-ET sample preparation, data collection and tomogram reconstruction were performed essentially as described previously22. In brief, R2/2 gold grids with 200 mesh (Quantifoil) were glow discharged for 90 s and were positioned in 3.5 cm cell culture dishes (MatTek). Then, 2 ml HEK Flp-In T-Rex 293 cell suspension, with a concentration of 175,000 cells per ml, was added to the dish. For untreated samples, cells were cultured for 5 h before plunge-freezing. For HHT-treated samples, cells were cultured without HHT for 3 h and subsequently exposed to HHT (Santa Cruz Biotechnology) at a final concentration of 100 µM for 2 h before the plunge-freezing process. The grids were blotted from the backside for 6 s using the Leica EM GP2 plunger under 70% humidity and 37 °C. The grids were rapidly plunged into liquid ethane and stored in liquid nitrogen. Grids were FIB-milled using Aquilos FIB-SEM (Thermo Fisher Scientific). The samples were sputter-coated with an organometallic protective platinum layer using the gas injection system for 15 s. Lamella preparation was performed through a stepwise milling process with gallium ion-beam currents decreasing from 0.5 nA to 30 pA.

    The data acquisition area was focused on the cytoplasmic region within the cell. Tilt series were acquired on a Titan Krios G4 (Thermo Fisher Scientific) operated at 300 kV, and equipped with Selectris X imaging filter and Falcon 4 direct electron detector, at 4,000 × 4,000 pixel dimensions, pixel size of 1.188 Å, a total dose of 120 to 150 e Å−2 per tilt series, 2° tilt increment, tilt range of −60° to 60° and target defocus of −1.5 to −4.5 µm, using SerialEM software51. Tilt series were aligned automatically using the IMOD package52. The alignment files generated from IMOD were used for tomogram reconstruction in Warp53 v.1.0.9.

    Particle localization and refinement

    Template matching was performed similarly to previous studies22,54. For this work, the parameters were set as follows: 5° angular scanning step, low-pass filter radius=20, high-pass filter radius=1, apply_laplacian=0, noise_corelation=1 and calc_ctf=1. The cryo-EM map (EMD-32822)14 of TRiC downloaded from the Electron Microscopy Data Bank (EMDB) was used as the template covered by a sphere mask. The above optimized setting produced distinguished peaks visualized in napari55 (Extended Data Fig. 1b and Supplementary Video 1). To analyse all potential TRiC complexes within the datasets, we extracted the top 1,000 peaks per tomogram. The selection was based on the constrained cross-correlation (CCC) value from template matching, and these chosen coordinates were subsequently extracted as subtomograms in Warp. In total, 360,000 untreated and 352,000 treated subtomograms were extracted. 3D classifications (classes = 4, T = 0.5, iterations = 30, without mask) and refinements (C1 symmetry) were performed in RELION56 v.3.1. In total, 3,353 open TRiC particles and 4,054 closed TRiC particles in the untreated dataset, and 3,785 and 3,418 in the treated dataset were identified. Open TRiC particles from untreated and treated datasets were combined and refined to improve map resolution. Closed TRiC particles were merged from untreated and treated datasets and refined with C1 or D8 symmetry. Actin filaments were manually picked in ten tomograms. In total, 1,490 subtomograms were extracted and refined at bin4. Atomic models obtained from the PDB (7X3J, 7NVN, 7NVO, 7NVL, 7NVM and 8F8P)13,16,57 were fitted into our maps. ChimeraX58,59 was used to visualize EM maps and models.

    Subtomogram classification of TRiC states

    For 3,353 open TRiC particles in the untreated dataset, classification with a sphere mask covering the potential PFD region (classes = 3, T = 3, iterations = 50, C1 symmetry) of one ring (denoted ring1) was performed (Extended Data Fig. 2a), which generated 2,874 particles without PFD and 479 particles with PFD of ring1. Independently, the same classification was performed with a mask focused on the other ring (denoted ring2), which produced 2,791 particles without PFD and 562 particles with PFD of ring2. In total, 2,395 particles without PFD, 875 particles with 1 PFD and 83 particles with 2 PFD were identified by sorting particles based on the above two classifications. The same classification strategy was applied to 3,785 open TRiC particles in the treated dataset, resulting in 2,334 particles without PFD, 1,287 particles with 1 PFD and 164 particles with 2 PFD. The atomic model (PDB: 7WU7)14 was fitted into the maps with PFD. Different classification parameters were evaluated in attempts to resolve the density in the chamber of TRiC, but this did not result in meaningful insights. The densities inside the TRiC chamber were Gaussian filtered (sDev = 2 or 4) for visualization in Figs. 1b and 4d and Extended Data Figs. 3 and 10. For closed TRiC, 3D classification (classes = 4, T = 3, iterations = 35, C1 symmetry) was performed in untreated and treated datasets independently in RELION 3.1, which revealed several classes with different densities occupied in the chamber of the closed TRiC. Further classification with a mask focusing on the substrate position did not produce meaningful results (Supplementary Figs. 4 and 5). Fourier shell correlation (FSC) was calculated in RELION 3.1.

    AlphaFold-Multimer model of the CCT3–CCT1–CCT4–PDCD5 complex

    The structure of human PDCD5 in a complex with human CCT3, CCT1 and CCT4 was predicted using AlphaFold-Multimer31 (v.2.2.0). The prediction was executed using the default setting with AMBER relaxation, and 15 models were generated for each prediction. The same prediction setting was used for PDCD5 with the other CCT combinations. The full-length amino acid sequences of PDCD5 (UniProt: O14737)60 and the equatorial domain of CCT1–CCT8 (the sequences were the same as PDB 7NVO) were used for the above prediction. The monomeric model of PDCD5 (AF-O14737-F1) was downloaded from the AlphaFold Protein Structure Database30.

    Sequence alignment

    Sequence alignment of CCT1–CCT8 (UniProt: P17987, P78371, P49368, P50991, P48643, P40227, Q99832 and P50990) was executed through Clustal Omega61. Sequence alignment of PDCD5 (UniProt: M. maripaludis, A9A8D7; S. pombe, O13929; C. elegans, Q93408; mouse, P56812; bovine, Q2HJH9; and human, O14737) and CCT1 (UniProt: H. volcanii, O30561; S. pombe, O94501; C. elegans, P41988; mouse, P11983; bovine, Q32L40; and human, P17987) were performed with ClustalO in Jalview62. The sequence conservation score of PDCD5 was calculated using the ConSurf server63.

    Spatial analysis of TRiC in situ

    The distance and angle examination of TRiC was performed similarly to as in previous studies22,64,65. For TRiC cluster tracing, the coordinates of TRiC determined by subtomogram averaging were used to localize the particles in the tomograms. The TRiC cluster (containing ≥2 TRiC particles) was defined by the distance between the coordinates of one TRiC and that of its nearest neighbour using a distance cut-off of 20 nm (centre-to-centre distance). As the coordinate represents the centre of the structure, the rotation of the particles would not affect the distance measurement. The particle closest to the previous particle in terms of Euclidean distance was selected as the trailing TRiC within the cluster, provided that it fell within the permitted distance threshold. Various distance thresholds ranging from 15 nm (the minimum centre-to-centre distance between two TRiC) to 40 nm were investigated (Fig. 4b,c). For each specific distance, the threshold was confined within a range of ±0.5 nm (for example, for 17 nm, the permissible distance ranged from 16.5 nm to 17.5 nm). A distance threshold of 20 nm was used to define whether TRiC belongs to the same cluster in this study.

    For the distance of TRiC pair analysis in Extended Data Fig. 9h,i, the number and the mean ± s.d. values were n2 (cluster length = 2) = 326 (17.35 ± 1.18); n3 = 218 (17.44 ± 1.27), n4 = 74 (17.01 ± 1.17), n5 = 35 (17.05 ± 1.16), n6 = 16 (16.87 ± 1.01) and n7 = 4 (17.33 ± 0.89), respectively, in the untreated dataset. The number and the mean ± s.d. were n2 = 195 (17.04 ± 1.28), n3 = 116 (17.42 ± 1.18), n4 = 27 (16.87 ± 0.96), n5 = 7 (17.09 ± 1.25) and n6 = 4 (16.65 ± 1.88), respectively, in the treated dataset. TRiC pairs with distances between 15 and 20 nm were analysed.

    The angle between TRiC and its closest neighbouring TRiC was investigated for particles within clusters in the untreated dataset (Extended Data Fig. 8d). The divided area of the hemisphere contains all points denoting cone rotation, described by Euler angles θ and ψ, of a vector (0, 0, 1). These rotations are projected onto the northern hemisphere (for vectors rotated with a z-coordinate greater than 0) and the southern hemisphere (for vectors rotated with a z-coordinate less than or equal to 0) using stereographic projection. The north pole corresponds to zero rotation, signifying a vector (0, 0, 1). The rotations of the neighbour TRiC were multiplied by the inverse rotations of the respective neighbour particles.

    To calculate the percentage of TRiC clusters with neighbouring actin filaments. The particles from the subtomogram averaging of TRiC and actin filaments were mapped back to tomograms for analysis. The threshold of the neighbouring distance (TRiC centre to the centre of actin dimer) was set to 20 nm.

    Spatial relation between ribosomes and TRiC in cells

    The spatial distribution of TRiC near the ribosome exit tunnel was investigated. The coordinates of ribosome, 60S and 40S determined by subtomogram averaging were used to localize the particles in the tomograms22. The ribosome was rotated to a reference position (zero rotation) through an inverse rotation, which means it was rotated by (−ψ, −θ, −φ)ribosome. Subsequently, TRiC underwent rotation by its respective angles (φ, θ, ψ)TRiC, followed by another rotation of (−ψ, −θ, −φ)ribosome, therefore aligning the ribosome–TRiC within a standard rotation frame (zero rotation of the ribosome), while maintaining their original angular relationship. The coordinates of the ribosome exit tunnel were subtracted from both the ribosome exit tunnel coordinates (setting it to zero) and TRiC coordinates. The new TRiC coordinates were rotated by (−ψ, −θ, −φ)ribosome to illustrate their positioning relative to the zero rotation of the ribosome. For the spatial analysis of ribosome and TRiC, ribosome particles were more abundant than TRiC particles. As a result, the same TRiC can be the nearest neighbour of several ribosomes. Our analysis focused on the ribosomes that acted as the nearest neighbours of TRiC. The mean ± s.d. in Extended Data Fig. 9c,k were as follows: untreated open TRiC in the ribosome ETS (55.1 ± 0.8%); untreated closed TRiC in the ETS (55.3 ± 0.3%); untreated open TRiC in the non-ETS (44.9 ± 0.8%); untreated closed TRiC in the non-ETS (44.7 ± 0.3%); treated open TRiC in the ETS (50.4 ± 0.4%); treated closed TRiC in the ETS (49.7 ± 1.0%); treated open TRiC in the non-ETS (49.6 ± 0.4%); and treated closed TRiC in the non-ETS (50.3 ± 1.0%). Data plotting and statistical analysis were performed using GraphPad Prism (v.10, GraphPad Software).

    Reporting summary

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

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  • Formation of a low-mass galaxy from star clusters in a 600-million-year-old Universe

    Formation of a low-mass galaxy from star clusters in a 600-million-year-old Universe

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    Image preparation

    The cluster field MACS J1423.8 + 2404 was observed with JWST/NIRCam imaging using filters F090W, F115W, F150W, F200W, F277W, F356W, F410M and F444W with exposure times of 6.4 ks each, reaching a signal-to-noise ratio between 5 and 10 for an mAB = 29 point source. It was also observed with JWST/NIRISS imaging using filters F115W, F150W and F200W.

    To reduce the imaging data, we use the photometric pipeline that is presented in more detail in ref. 44. Briefly, the raw data has been reduced using the public grism redshift and line analysis software Grizli43, which masks imaging artefacts, provides astrometric calibrations based on the Gaia Data Release 3 catalogue13 and shifts images using Astrodrizzle. The photometric zero-points are applied as described in ref. 34. RGB image created using six filters of NIRCam observation of the Firefly Sparkle is shown in Fig. 1. We used images from which bright cluster galaxies and intracluster light have been removed, as described in ref. 25. The methodology for modelling and removing diffuse light from cluster galaxies and intracluster light (ICL) is presented in ref. 25. The NIRCam depths (0.3′ diameter aperture) for F090W, F115W, F150W, F200W, F277W, F356W, F410M and F444W are 7.2, 6.6, 5.2, 4.4, 3.0, 2.9, 5.5 and 4.3 nJy, respectively, and the NIRISS depths for F115WN, F150WN and F200WN are 3.6, 4.3 and 4.0 nJy, respectively41.

    Photometry of Firefly Sparkle

    We perform photometry in 10 JWST bands (NIRISS: F115WN, F150WN and F200WN; NIRCam: F115W, F150W, F200W, F277W, F356W, F410M and F444W) in which the Firefly Sparkle is detected from their morphological fit with GALFIT. In other JWST and HST filters, the Firefly Sparkle is not or barely detected; hence, we place upper limits for the entire source. As the object is resolved into at least 10 distinct clusters and a diffuse galaxy component, we perform a morphological fit using Galfit10 to extract the photometric information.

    Point spread functions are extracted empirically by median stacking bright, isolated, non-saturated stars following the methodology described in ref. 28. Convolution kernels for homogenizing all data to the F444W resolution are created with photutils.psf.matching using a SplitCosineBellWindow() windowing function to remove high-frequency noise, which results from floating-point imprecision when taking the ratio of Fourier transforms. We optimize the shape of each window function to minimize the median residual between convolved stars from each source filter that is convolved and stars from the target F444W filter.

    For the morphological fit, we create 10″ × 10″ postage stamps in all 10 filters from the BCG-subtracted images. We determine the priors for the centres of the 10 clusters by visual inspection. Although nine out of the ten appear as point sources, FF-4 has an elongated shape and appears unresolved. We first determine the central coordinates of the 10 clusters and the arc by fitting (1) an elliptical Gaussian for FF-4; (2) nine point sources for the other nine clusters; and (3) another elliptical Gaussian with the bending mode turned on for the diffuse arc to the F115W image, which has the highest resolution (smallest PSF). The free parameters are the centres and total fluxes of all the components, the radius and axis ratio of FF-4, and the radius, axis ratio and bending mode (B2) of the arc. The initial guesses for the coordinates were determined by visual inspection of the F115W image. Once we obtain the fitted central coordinates of all the components from F115W, we again fit all 11 components in F444W, which has the highest signal-to-noise ratio for the arc and FF-4, to determine the radius, axis ratio, position angles of the ellipses, and the bending mode B2 of the arc.

    We use the best-fit centre coordinates from F115W as the central coordinates in all the filters. However, instead of fixing the central coordinates, we allow GALFIT to fit for them in every filter within a very narrow range of ±0.5 pixels (0.02″) to account for the uncertainty in the PSF centre. We also fix the bending mode B2 (2.14), ellipse radius (3.9″), axis ratio (0.08) and position angle (−51.8°) of the arc from the F444W fit. We fix the morphology of FF4 also with radius = 0.59″, axis ratio = 0.1 and position angle = −53°.

    We now fit all 11 components in all 10 filters to determine their fluxes. The resulting models and residuals are shown in Extended Data Fig. 1. Residuals from the fits are negligible, as shown by χ2/ν ~ 1 in the GALFIT fits in all filters. This confirms the original visual impression that nine of the ten clusters are unresolved and an additional smooth component is present.

    To derive the uncertainty in our flux estimation, we inject the full Firefly Sparkle model in 100 random locations in our 10″ × 10″ postage stamps (avoiding the edge) and refit with the exact same setting of GALFIT. We find no significant systematic offset between the fitted flux and the injected flux for any of the 11 components, in any of the filters, showing that our photometric technique is robust to background variations across all filters. The uncertainty in the photometry is calculated from the bi-weight scale of the 100 refitted fluxes. The resulting photometry and the RGB image of the model and the residual are shown in Extended Data Fig. 1. The agreement between NIRISS and NIRCam fluxes in the three overlapping filters is another confirmation of the robustness of photometry. We have used updated zero-points34 and corrected for Milky Way extinction using the colour excess E(B − V) = 0.0272 from ref. 6 and assuming the extinction law in ref. 35 using the factor between the extinction coefficient and colour excess RV = 3.1.

    Spectroscopy extraction and spectral fitting

    NIRSpec spectroscopy has been acquired for MACS J1423.8 + 2404 and spectra were obtained for the Firefly Sparkle, FF-BF and FF-NBF. The spectra for the FF-BF were part of the sample in ref. 23, with zspec = 8.2953 ± 0.0005. The spectra were observed using the PRISM/CLEAR disperser and filter, through three Micro-Shutter Assembly (MSA) masks per cluster with a total exposure time of 2.9 ks per MSA configuration.

    The NIRSpec data were processed using the STScI JWST pipeline (software v.1.8.4 and jwst_1030.pmap) and the msaexp package31. We used the standard JWST pipeline for the level 1 processing, in which we obtained the rate fits files from the raw data. We enabled the jump step option expand_large_events to mitigate contamination by snowball residuals and used a custom persistence correction that masked out pixels that approach saturation within the following 1,200 s for any readout groups. We then used msaexp for level 2 processing, for which we performed the standard wavelength calibration, flat-fielding, path-loss correction and photometric calibration and obtained the 2D spectrum before background subtraction. As the central and upper shutters contain different clusters (see Fig. 2a to find the shutter positions), we need custom background subtraction to avoid self-subtraction. We did this by building the background 2D spectrum by stacking and smoothing the sky spectrum in the empty pixels and obtained the background subtracted 2D spectrum of Firefly Sparkle. We confirmed that this custom background subtraction method works as well as a standard drizzle background subtraction method used in the literature33, using a well-isolated galaxy spectrum from the CANUCS observation (Asada et al., in prep.). We finally extract the 1D spectrum separately in slit 1 and slit 2, by collapsing the 2D spectrum using an inverse-variance weighted kernel following the prescription in ref. 24. We verified that the uncertainty array of the 1D spectrum has the appropriate normalization by testing the distribution of spectral fluctuations in an empty sky region and finding the fractions of pixels at >1 and >2σ as expected.

    Spectral fitting in Firefly Sparkle slit 1

    The resulting 1D spectrum of Firefly Sparkle in slit 1, dominated by the cluster FF-6, is shown in Fig. 2.

    The spectrum exhibits a Balmer jump at λobs ~ 3.5 μm and a turnover at λobs ≲ 1.4 μm, probably because of two-photon emission. These features suggest that the nebular continuum should dominate over the stellar continuum in the rest frame UV to optical spectrum within slit 1 (as found for a z = 5.9 galaxy in ref. 12). We thus model the continuum of the spectrum with nebular continuum using the photoionization code CLOUDY v.23 (ref. 5). To determine the dust attenuation value in the continuum model fitting, we first measure the Hγ/Hβ ratio by fitting the Gaussian profiles. The ratio agrees well with the case B recombination, and no significant dust attenuation is indicated. Therefore, in the continuum spectral modelling, we use pure hydrogen gas irradiated by an ionizing source having black-body SED without dust attenuation. We vary the effective temperature of the black body (Teff) and the electron temperature of the (ionized) hydrogen gas (\({T}_{{\rm{e}},{{\rm{H}}}^{+}}\)), and search for the best-fitting model continuum by χ2 minimization. In the continuum fitting, we mask out emission line regions and all wavelengths λobs < 1.2 μm at which the Lyman break is seen in the slit 2 spectrum, because this region may be affected by a neutral hydrogen damping wing. The best-fit model has log(Teff/K) = 5.10 and \(\log ({T}_{{\rm{e}},{{\rm{H}}}^{+}}/K)=4.34\), which is fully consistent with the results in ref. 12. The result of continuum fitting does not change if we consider a slight dust attenuation (AV = 0.1 mag) in the fitting. As discussed in ref. 12, the effective temperature of log(Teff/K) = 5.10 is much hotter than typical massive type O stars and is suggestive of this star-forming cluster having a top-heavy IMF. The IMF of this cluster is further discussed in section ‘SED fitting analysis’.

    Note that the UV continuum turnover feature could be because of the absorption from dense neutral hydrogen either in the intergalactic medium (IGM) or in the circumgalactic medium (CGM). However, in the case of slit 1 spectrum, we expect the effect of IGM and CGM damping absorption to be negligible or limited at λobs < 1.2 μm based on the blue continuum and sharp drop-out in the slit 2 spectrum (see section ‘Spectral fitting in Firefly Sparkle slit 2’ for details of slit 2 spectrum). Considering the spatial proximity of the slit 1 and slit 2 regions (Fig. 2), we can assume the absorption feature from line-of-sight neutral hydrogen to be the same in the slit 1 and slit 2 spectra. The slit 2 spectrum is rather blue and has a sharp Lyman break starting at λobs = 1.2 μm, whereas the slit 1 spectrum shows the turnover starting at λobs ~ 1.4 μm. Thus, the turnover feature should not be because of the neutral hydrogen absorption, but rather because of the intrinsic continuum shape of the source. Nevertheless, to avoid the possible effect of the neutral hydrogen absorption, we mask out λobs < 1.2 μm in the nebular continuum fitting above (corresponding to 1,290 Å in the rest frame).

    Having the model continuum, we subtract the underlying model continuum from the observed spectrum and measure the spectroscopic redshift and emission line fluxes by fitting Gaussian profiles. The best-fitting model spectrum with nebular continuum and Gaussian profiles is shown in Fig. 2b (red solid curve). We securely detect emission lines of [O iii]λλ4959, 5007, Hβ, [Oiii]λ4363, Hγ, Hδ and [Neiii]λλ3869, 3889. We do not find significant detection of [Oii]λ3727 and obtain an upper limit for the flux of this line. There is a tentative detection of the blended line of [Oiii]λλ1661 + 1666, although the spectral resolution of the prism is low at this wavelength making this doublet difficult to securely detect and separate from Heiiλ1640. We use these emission line fluxes to estimate the physical parameters in slit 1. We first estimate the dust attenuation based on Balmer decrements. Both the Hγ/Hβ and Hδ/Hβ ratios are consistent with theoretical predictions in case B recombination21 within the uncertainties, suggesting there is no significant dust attenuation (Extended Data Fig. 3, red squares in the left). This result is consistent with the initial measurement before the continuum fitting above and supports the validity of the dust-free assumption in the nebular continuum fitting process. Therefore, we do not correct for dust attenuation in the following measurements of emission line ratios and physical parameters in this section.

    We next measure the electron temperature using temperature-sensitive emission line ratios: [Oiii]4959+5007/[Oiii]4363 and [Oiii]5007/[Oiii]1661+1666. We assume the electron density to be ne = 103 cm−3, which is consistent with recent JWST observations of similarly high-z galaxies7 and obtain consistent independent temperature measurements within the uncertainties (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}={4.0}_{-0.9}^{+2.6}\,{\rm{K}}\) and \({2.9}_{-0.4}^{+0.7}\times 1{0}^{4}\,{\rm{K}}\), respectively; Extended Data Fig. 3 (right)). Note that because the [Oiii]λλ1661 + 1666 detection is tentative and potentially blended with Heiiλ1640, we consider [Oiii]λ4363 to be more reliable.

    We note that in ref. 16, the authors measured a similar ratio of [Oiii]4959+5007/[Oiii]4363 in the z = 6 galaxy RXCJ2248-ID to that of slit 1. In ref. 16, medium resolution spectroscopy was used to determine the electron density directly. They found that when using lines with higher ionization potential than O+, the electron density was higher (ne ~ 105 cm−3) than is typically found from [Oii]λ3727 (ref. 7). This high electron density leads to a lower electron temperature for their galaxy of \({T}_{{\rm{e}},{{\rm{O}}}^{++}}=2.5\times 1{0}^{4}\,{\rm{K}}\). Similarly, if we assume the electron density of ne = 105 cm−3 instead for our slit 1 spectrum, the electron temperature from [Oiii]λ4363 becomes \({T}_{{\rm{e}},{{\rm{O}}}^{++}}={3.2}_{-0.96}^{+1.6}\), which is in between the two measurements based on [Oiii]λλ1661 + 1666 and [Oiii]λ4363 when assuming ne ~ 103 cm−3 above. To consider the possibility of a somewhat higher electron density in the highly ionized region, we adopt the mean value of our two electron temperature measurements (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}=3.5\times 1{0}^{4}\,{\rm{K}}\)) as our fiducial value and propagate the full range of the two measurement uncertainties into the following metallicity measurement.

    Based on the electron temperature measurement, we obtained the oxygen abundance from [Oiii]4959+5007/Hβ and [Oii]3727/Hβ ratios, following the prescription in ref. 8. We assume the electron density to be ne = 103 cm−3. The total oxygen abundance is calculated from O++/H+ and O+/H+, and the higher ionizing state oxygen is ignored30. As the [Oii]λ3727 emission line is undetected, we can obtain only an upper limit for O+/H+, but the upper limit for the abundance of the singly ionized oxygen is negligibly small as compared with the doubly ionized oxygen. We thus derived the total oxygen abundance from O++/H+, yielding \(12+\log ({\rm{O/H}})=7.0{5}_{-0.37}^{+0.22}\) (\({Z}_{{\rm{gas}}}/{Z}_{\odot }=0.0{2}_{-0.01}^{+0.04}\) assuming the solar abundance to be 8.69; ref. 38).

    We also derive the ionization parameters using the ionization-sensitive emission line ratios: [Oiii]5007/[Oii]3727 and [Neiii]3869/[Oii]3727. Following the prescription in refs. 45,46, we obtain the lower limit for the ionization parameters (log  U) from these two ratios. Both ratios provide a similar limit of log U > −2.0.

    All the emission line flux measurements and the derived physical parameters in Firefly Sparkle slit 1 are presented in Extended Data Table 1. We also compare the diagnostic emission line ratios in Firefly Sparkle with those in other galaxy population in Fig. 2d. We use the ionization-sensitive line ratio O32 ([Oiii]5007/[Oii]3727) and the temperature-sensitive line ratio RO3 ([Oiii]4959+5007/[Oiii]4363) and compare these line ratios with other [Oiii]λ4363-detected galaxies at z = 2–9 from previous JWST observations2 and those in the local universe from SDSS observations14. Extended Data Fig. 3 (middle) presents a similar comparison but uses another ionization-sensitive line ratio Ne3O2 ([Neiii]3869/[Oii]3727) instead of O32.

    Spectral fitting in Firefly Sparkle slit 2

    In contrast to slit 1, the extracted 1D spectrum in Firefly Sparkle slit 2 does not show nebular continuum features, and the blue continuum is rather smooth with a sharp drop-out because of the Lyman break at λobs ~ 1.2 μm. We thus derive the emission line fluxes from the slit 2 spectrum by fitting Gaussian profiles with the continuum being modelled by a constant offset around each emission line. We detect [Oiii]λλ4959,5007, Hβ, Hγ, Hδ, [Neiii]λ3869 and [Oii]λ3727 emission lines in the slit 2 spectrum but do not detect [Oiii]λ4363.

    We then derive the physical properties in the same way as done for Firefly Sparkle slit 1 spectrum. We measure the dust attenuation from Balmer decrement, Hγ/Hβ and Hδ/Hβ, and find both line ratios agree well with the predicted ratios under case B recombination (blue squares in Extended Data Fig. 3 (left)). This suggests that the dust attenuation is negligible in the slit 2 spectrum as well, and we do not make a dust attenuation correction.

    As we do not detect the temperature-sensitive emission lines of [Oiii]λ1666 or [Oiii]λ4363 in the slit 2 spectrum, we cannot measure the electron temperature and the metallicity from the direct-temperature method. We thus obtain only the upper limit for the electron temperature (\({T}_{{\rm{e}},{{\rm{O}}}^{++}}\)) from the non-detection of [Oiii]λ4363. The electron temperature in Firefly Sparkle slit 2 is shown to be \({T}_{{\rm{e}},{{\rm{O}}}^{++}} < 1.8\times 1{0}^{4}\,{\rm{K}}\) (1σ) or <4.5 × 104 K (3σ). To visualize the difference in physical properties in slit 1 and slit 2, we show the diagnostic emission line ratios of Firefly Sparkle slit 2 in Fig. 2d and Extended Data Fig. 3 (middle) as well.

    SED fitting analysis

    SEDs derived from our photometry were analysed using a slightly modified version of the Dense Basis method18,47 to determine non-parametric SFHs, masses and ages for our sources in Firefly Sparkle. We adopt the Calzetti attenuation law48 and a Kroupa IMF32 with a flat prior for the high-mass slope α ∈ [1., 4.]. We run fits using both the MILES stellar libraries29 and MESA Isochrones and Stellar Tracks (MIST; ref.  17), as well as the Binary Population and Spectral Synthesis (BPASS; refs. 26,36) models to consider for the presence of binary populations. As the latest BPASS version in FSPS (-bin-imf135all 100) assumes a Salpeter IMF with an upper mass cutoff of 100M⊙ and does not allow for a varying IMF, we only vary the top-heavy slope of the Kroupa IMF in the MILES + MIST runs with an upper mass cutoff of 120M⊙. This should be considered while comparing the physical properties from the two runs, as allowing for a varying IMF based on the MILES + MIST configuration results in lower stellar masses for those runs because they are preferentially fit with top-heavy SSPs with a greater fraction of light coming from more massive stars. We fix the redshift to that found from the NIRSpec Prism spectroscopy by the [Oiii] λ4959 line at zspec = 8.296 ± 0.001. All other parameters are left free. We run the SED fits in two configurations to account for different possibilities of the nature of the individual clusters:

    1. 1.

      SSP fits: to account for the possibility that the individual clumps are star clusters, which is likely given the physical scales of the clusters and the extreme emission lines in the spectra, we modify the code to fit for instantaneous bursts of star formation, described by SSPs. In this case, we assume a flat prior in the log age of the SSP from 105 years to the age of the universe at zspec = 8.296 ± 0.001 instead of the non-parametric defaults for the SFH in Dense Basis.

    2. 2.

      Non-parametric SFH fits (Dense Basis): to fit the diffuse body of the galaxy and to account for the possibility that the clusters are nuclear star clusters or remnants of previous mergers, we also run fits with non-parametric SFHs with a Dirichlet prior. The main advantage of using Dense Basis with non-parametric SFHs is that they allow us to account for flexible stellar populations, which is important at these redshifts49 because star formation is expected to be stochastic and may be underestimated if fit using traditional parametric assumptions39,50.

    We perform our fitting in two stages—we initially perform a joint spectrophotometric fit to the NIRSpec Prism spectrum along with the HST + NIRISS + NIRCam photometry in the slits in which both exist (Extended Data Fig. 4). We correct for slit loss considering two factors—the amount of light lost due to the changing PSF as a function of wavelength and an overall multiplicative correction to match the spectrum against the photometric measurements. We modify the default Dense Basis method in this stage to additionally fit for the slope at the massive end of the IMF, the gas-phase metallicity and the ionization parameter, using the relevant parameters from FSPS (imf3, gas_logz and gas_logu). Doing so allows us to substantially constrain priors on star formation rate, IMF, dust, ionization parameter and metallicity that we then use to fit the photometry. We find that the fits are consistent with negligible dust attenuation, consistent with our estimates from measuring the Balmer decrement. We also find that our fits rule out the part of parameter space consistent with the canonical Chabrier-like or Kroupa-like IMF (with the high-mass slope ≈ 2.3) in favour of more top-heavy slopes of about \({1.5}_{-0.6}^{+0.7}\) for slit 1, which contains portions of clusters 3, 4, 5 and 6. We find weaker constraints from the spectrum for slit 2, which still skews towards top-heaviness but with large uncertainties of about \({1.7}_{-0.7}^{+0.9}\). Finally, we find estimates of both stellar and gas-phase metallicities to be sub-solar, consistent with estimates from the line ratios.

    Using our photometry (Extended Data Table 4), we now determine the stellar properties of each individual component by running a second set of fits using the same set of parameters that are used to fit the spectrophotometry. Although parameters such as the metallicity and ionization parameter are only loosely constrained by these fits, we obtain parameter estimates for the stellar masses, star formation rates and ages of the individual star clusters with uncertainties that marginalize over the variations in the other parameters.

    Both photometry and corresponding fits to the SED fit are shown in Extended Data Fig. 5, with variations in the stellar mass, age and reduced χ2 of the fits for each of the four scenarios (SSPs fits with MILES + MIST and BPASS, and Dense Basis fits with MILES + MIST and BPASS) shown in Extended Data Table 2. All 10 components have intrinsic (corrected for magnification) stellar masses of about 105–106M⊙ and sSFR of 10−7 yr−1. Although the error bars are large, the distinct colours of the clusters hint at different formation times. Although the smooth component contains a large fraction of the stellar mass, the bulk (about 57%) lies in the clusters. Extended Data Table 3 lists the physical properties of the individual components as well as the full Firefly Sparkle, BF and NBF galaxies.

    We find that the SSP fits are generally less massive compared with the Dense Basis fits, because the light from the SED is modelled by a single epoch of star formation instead of an extended episode. As light from the massive stars responsible for young star formation are much brighter than older stellar populations, they can describe the observed SED with a lower mass. However, the SSP fits often cannot capture both the UV slopes and the nebular emission in the rest-optical, as seen for clusters 1, 3, 7 and 8 in Extended Data Fig. 5 and often approximate it using a Balmer break, leading to posteriors consistent with much older ages than the median values.

    Although the tage from SSP and t50 from Dense Basis fits (Extended Data Table 2) may seem inconsistent, it is important to note that the Dense Basis fits for most star clusters indicate a sharp burst of star formation within the past 10 million years (Extended Data Fig. 6). By design, an SSP is biased towards this recent burst, whereas a non-parametric SFH can accommodate extended episodes of star formation. However, with our current data, we cannot distinguish between extended SFH in the star clusters and the contribution of light from the diffused arc.

    The masses of the clusters also scale with the top-heaviness of the high-mass end of the IMF in the MILES + MIST fits, with lower masses for more top-heavy IMF values as that scale the amount of light from massive stars. In comparison, the BPASS fits in the current setup are done at the canonical Kroupa IMF, leading to higher masses for those fits. At the same IMF slope, the masses are comparable within uncertainties for the different SPS models, and the sSFR and age/t50 values are consistent even marginalizing over the IMF posteriors. Given the observational constraints and the χ2 from the fits in Extended Data Table 2, it is not currently possible to definitively rule out any of the current fitting approaches.

    Lens modelling

    We use Lenstool9 to build a strong lensing model of the MACS 1423 cluster, to be fully presented in Desprez et al. (manuscript in preparation). This model is constrained with the three multiple image systems that were leveraged in ref. 3, for which we provide additional information obtained from the CANUCS data. The first two systems are those presented in ref. 27, one at z = 2.84 for which we account for the two clusters visible in the four images of the objects, and the second one with three images at z = 1.779 for which we identify another cluster in the two northernmost images for improved constraints. The last system is composed of five images11 for which we provide a new spectroscopic redshift measurement of z = 1.781 that is in agreement with photometric and geometric redshifts previously measured.

    The different mass components are parameterised as double Pseudo-Isothermal Elliptical (dPIE) profiles4. The model is composed of a large cluster scale mass halo, an independent galaxy scale centred on the brightest cluster galaxy and small galaxy scale mass components to account for the contribution of all cluster members that follow a mass–luminosity scaling relation22. For all galaxies, their positions, ellipticities and orientations have been fixed to these measured from the images. The final best model manages to reproduce the position of the input multiple images with a distance rms of 0.46″.

    Magnifications are obtained by generating convergence and shear maps around the Firefly Sparkle with a size of 20″ and a resolution of 10 milli-arcsec per pixel. Uncertainties in the magnifications are computed from 100 randomly selected models from the optimization of Lenstool after its convergence around the minimum χ2. The numbers provided in Extended Data Table 3 are the median and ±1σ limits on the distribution of the 100 values obtained at the position of each cluster. We measured the average magnification of the FF-arc by using the GALFIT model of the arc (in F200W) and selecting all pixels with flux >10% of maximum flux. We then computed the best magnification value for all selected pixels and computed the mean and standard deviation values for these to find the magnification of the arc (μ = 24.4 ± 6.0).

    The source plane reconstruction is made using the best GALFIT model to compute the source plane positions and magnification for the 10 star clusters. We use Lenstool to generate a source plane image reconstruction of the diffuse light of the galaxy with a smooth PSF-deconvolved model of its light profile. We use GALFIT to add 10 point sources convolved with the appropriate PSFs to the diffused source plane model at the source plane positions of the star clusters with the demagnified fluxes. This process is repeated to generate source plane models in all filters. We also generate a mass map using the same prescription, replacing the demagnified fluxes with the demagnified masses. The resulting source plane RGB image and mass map are shown in Fig. 4c,d.

    Size and surface density of star clusters

    We now investigate the spatial properties of the star clusters. Nine out of the ten star clusters are unresolved even in our highest resolution F115W NIRCam image. FF-4 has a slightly elongated shape visually but has a best-fit major axis size (0.01) smaller than the smallest PSF, making the size estimate unreliable. Hence, we use the half-width half-max of the NIRCam F115W PSF (0.02) to set an upper limit on the size of all 10 star clusters. To determine the upper limits of the sizes of unresolved sources, we use the tangential eigenvalue of magnification 1/∣λt∣, which ranges between 14 and 24. This results in a size upper limit between 4 pc and 7 pc. The central star clusters have the highest magnification and the smallest upper limits, whereas the ones near the two ends of the arc have the lowest. We use the upper limit on sizes and the demagnified stellar masses to calculate the lower limit on stellar surface densities as shown in Fig. 3b.

    Abundance matching for MW and M31 progenitors

    To estimate the range of stellar masses of progenitors of both MW-mass and M31-mass galaxies at higher redshift, we adopt a semi-empirical approach combining both simulations and observations. We assume an evolving co-moving number density with redshift, as determined by the abundance matching code from ref. 20, with z = 0 number densities of \(\log (n/{{\rm{Mpc}}}^{3})=-\,2.95\) and \(\log (n/{{\rm{Mpc}}}^{3})=-\,3.4\), respectively, for MW and M31 mass analogues. The code calculates a past median galaxy number density at z2, given an initial number density at z1, using peak halo mass functions. As the merger rate per unit halo per unit Δz is roughly constant, the evolution of the cumulative number density of progenitors of any given galaxy is a power law, with the change described by (0.16Δz) dex.

    In ref. 20, peak halo mass functions are used because the resultant median number densities are less affected by the scatter in stellar mass and luminosity. However, this scatter does affect the 1σ errors in cumulative number density. The 1σ or 68 percentile range grows with increasing redshift, but this growth is also higher for more massive galaxies.

    As the code from ref. 20 does not calculate stellar masses, we obtain the stellar mass ranges of the progenitor populations of MW and M31 analogues using stellar mass functions (SMFs) from various surveys15,19,40. We take the median cumulative number densities at each Δz to find the stellar mass associated with that number density from the corresponding SMF. Moreover, the 1σ errors on the given number density for each redshift are then used to determine the 1σ errors on the stellar mass of the progenitors. At z = 8.3, the median stellar mass of MW progenitors is \(\log ({M}_{\star }/{M}_{\odot })=6.4\pm 0.7\) and the median stellar mass of M31 progenitors is \(\log ({M}_{\star }/{M}_{\odot })=6.9\pm 0.8\). The Firefly Sparkle with a stellar mass of \(\log ({M}_{\star }/{M}_{\odot })={7.0}_{-0.3}^{+1.0}\) is definitely within 1σ stellar mass range of both Milky Way and M31 progenitors. More details on the progenitor matching technique can be found in ref. 37.

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  • Distant sparkles hint at how the Milky Way formed

    Distant sparkles hint at how the Milky Way formed

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    The JWST has captured images of the Firefly Sparkle — a galaxy that resembles an early Milky Way — in the process of being assembled from star clusters. The discovery could help to reveal how the Milky Way formed in the early Universe.

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    Mitochondria are mighty ― especially those of birds that fly thousands of kilometres each year. Investigation into mitochondria, the powerhouses of the cell, shows that they undergo extensive transformation in the muscle tissues of migratory birds in preparation for the long journey1.

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    Competing Interests

    S.J.R.M. received research grants, research support, consultancy fees and lecture fees from diagnostic companies, including Roche Diagnostics, Abbott Laboratories and Werfen, all paid to his employer (Maastricht University Medical Center), and unrelated to the topic discussed in this manuscript. K.M.A. has served on advisory boards for Roche Diagnostics, Radiometer, Siemens Healthineers and SpinChip, and received consultant honoraria from CardiNor, lecturing honorarium from Siemens Healthineers, Roche Diagnostics, Mindray and Snibe Diagnostics and research grants from Siemens Healthineers and Roche Diagnostics. K.M.A. is also Associate Editor of Clinical Biochemistry and Chair of the IFCC Committee of Clinical Application of Cardiac Bio-markers.

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  • Targeted mRNA therapy tackles deadly pregnancy condition in mice

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    In this episode:

    00:45 A potential treatment for pre-eclampsia

    Researchers have shown in mice experiments that an mRNA-based therapy can reverse the underlying causes of pre-eclampsia, a deadly complication of pregnancy for which treatment options are limited. Inspired by the success of mRNA vaccines, the team behind the work designed a method to deliver the genomic instructions for a blood-vessel growth factor directly into mouse placentas. This stimulated the production of extra blood vessels reducing the very high-blood pressure associated with the condition. Pre-eclampsia causes 15% of maternal deaths and 25% of foetal and newborn deaths worldwide and although the work is early and human trials will be required, the team hope that this work demonstrates the potential of using this approach to treat pre-eclampsia.

    Research Article: Swingle et al.

    News and Views: Lipid-delivery system could treat life-threatening pregnancy complication

    11:00 Research Highlights

    Stacks of mass-produced bowls suggest that people founded but then abandoned an ancient Mesopotamian civilization, and analysis of Venus’s gases suggests that the planet was always dry.

    Research Highlight: Ancient stacks of dishes tell tale of society’s dissolution

    Research Highlight: Has Venus ever had an ocean? Its volcanoes hint at an answer

    13:29 Programmable cellular switches

    A team of scientists have created cellular switches on the surface of cells, allowing them to control custom behaviours. Creating these switches has been a long-term goal for synthetic biologists — one target has been a group of proteins called G-protein-coupled receptors that already control many cellular processes. However, engineering these proteins has been challenging, as modifications can ruin their function. Instead, the team added another molecular component that blocked the receptors activity, but could be removed in response to specific signals. This allowed the researchers to activate these receptors on command, potentially opening up a myriad of new ways to control cell behaviour, such as controlling when neurons fire.

    Research Article: Kalogriopoulos et al.

    19:35 Google reaches a milestone in quantum computing

    A team at Google has shown it is possible to create a quantum computer that becomes more accurate as it scales up, a goal researchers have been trying to achieve for decades. Quantum computing could potentially open up applications beyond the capabilities of classical computers, but these systems are error-prone, making it difficult to scale them up without introducing errors into calculations. The team showed that by increasing the quality of all the components in a quantum computer they could create a system with fewer errors, and that this trend of improvement continued as the system became larger. This breakthrough could mean that quantum computers are getting very close to realising the useful applications that their proponents have long promised.

    Nature: ‘A truly remarkable breakthrough’: Google’s new quantum chip achieves accuracy milestone

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