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  • Bone repair supported by flexible films made using an innovative method

    Bone repair supported by flexible films made using an innovative method

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    • RESEARCH BRIEFINGS

    A scalable strategy for fabricating high-performance films of 2D inorganic materials called MXenes has been developed. The resulting MXene films show excellent performance in guiding bone regeneration and in other applications.

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  • A lipid made by tumour cells reprograms immune cells

    A lipid made by tumour cells reprograms immune cells

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    Nature, Published online: 11 December 2024; doi:10.1038/d41586-024-03855-8

    Cancer cells often become unresponsive to multiple types of therapy. It emerges that these ‘cross-resistant’ tumour cells release lipids that reprogram cells called monocytes to stop them from activating tumour-targeting T cells.

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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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  • Meet the Latina scientists advancing health and policy

    Meet the Latina scientists advancing health and policy

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    Life as a scientist in Latin America isn’t always easy — and this is especially true for women. Latina researchers have had to find creative ways to bypass the gender gap from an early age: at 15, girls are half as likely as boys to expect that they’ll work in a science, technology, engineering and mathematics (STEM) area. According to the United Nations, in 2016, less than half (45%) of Latin America’s workforce in research and development were women. Although this figure is above the world average (38%), it is low compared with graduation rates for women in Latin American countries.

    Nature spoke to four Latin American researchers about the peaks and troughs they have faced in their careers and how they are connecting science and policy.

    ILIANA CURIEL: A translator between cultures

    Paediatrician and researcher at the Colombian Institute of Family Welfare in La Guajira, Colombia.

    A portrait of Iliana Curiel

    Iliana Curiel became a paediatrician to help her local community in La Guajira, Colombia.Credit: Iliana Curiel Arismendy

    I am a mix of Black and Wayuu. I grew up in Uribia, a municipality located in La Guajira, Colombia, at the northernmost tip of South America.

    Health challenges in La Guajira are different from those in the rest of Colombia. Although non-transmittable diseases such as obesity and heart conditions demand a considerable effort from public-health services in large cities, the greatest issue in La Guajira is child malnutrition. A child in the region is 60 times more likely to die from undernourishment than is a child living in Bogotá. Most of La Guajira is a desert and access to water can be limited. In some parts of the region, water services reach less than 10% of the population. Around 40% of the population in the territory is under 19, so there is an immense need for paediatric care there. Like other rural and Indigenous communities in Colombia and South America, this is a place where the ‘multidimensional poverty index’ is high and preventable infant and mother mortality abounds.

    Growing up in La Guajira, I decided to be a paediatrician and help my community. But I also wanted more than that: after my medical degree in the mid-1990s, I went on to study public health and social policy.

    Indigenous communities in La Guajira do not easily accept Western medicine because their cultural practices differ from those in urban settings — and public-health policies rarely meet on common ground with these cultural singularities. So, in 2018, I went back there and, together with my wife, started a non-governmental organization, Los Hijos del Sol (Children of the Sun). Our goal has been to conduct research by listening to Indigenous communities, allowing us to plan more adequate models of health care.

    Once, for example, we needed to care for a severely undernourished boy. But to offer proper care, we needed to take him from the community to a hospital — and to do that, we had to ask for permission from the community leaders. Because we were in a matrilineal-led group — in which the line of descent is considered from the mothers’ side — it was the boy’s maternal uncles, not his parents, who spoke for the child. So we had to contact his uncle first. A health team, unaware of this, might have asked the boy’s mother for authorization and had a hard time gaining it. If the family think a certain disease is rooted in a spell or bad spirits, we can’t say it’s nonsense — we must adapt our approach and find a shared understanding.

    At Los Hijos del Sol, we train Indigenous mothers and midwives to take steps to reduce child mortality. We ask mothers how they know when their child is in trouble, and they come up with the most beautiful analogies. They won’t say the child is “breathing quickly”, but that the child is in a “high tide”, as if the chest and abdomen were moving like restless waves — and they know that it is a sign of alarm.

    Most physicians avoid politics, but public health is a political matter and we must be aware of that if we ever want to change things for the better. I’d tell young Latina researchers to never lose sight of your purpose. The path in science, to women, is one of perseverance and resistance, but also of transformation. The qualities that are said to disqualify us as scientists — such as empathy and creativity — are the ones we should take most pride in.

    Iliana Curiel providing care to a newborn baby

    Child malnutrition in La Guajira is one of the biggest issues in the region, says Iliana Curiel (left).Credit: Organización Los Hijos del Sol

    XÓCHITL CASTAÑEDA: A voice to Latin American immigrants

    Programme director and professor in the School of Public Health at the University of California, Berkeley.

    Around 30 years ago, I moved from Mexico City to the United States for my postdoctoral research and it was here that I first saw the negative health impacts felt by immigrants. In the early 2000s, a large number of the migrant community came from Mexico and Latin America. Although the number of migrants from other countries has grown, Mexicans are still the main immigrant workforce in the United States — we’re about 10.6 million people.

    During my research at the University of California in San Francisco, I visited the fields where farm workers were employed, and it completely changed my life. I saw the terrible conditions in which they were living to perform the most dangerous, belittling and dirty jobs.

    I am a medical anthropologist; in the mid-1990s, I was conducting research on the risks that immigrants faced regarding HIV and AIDS. After witnessing neglect and abuse of migrant workers, I realized I couldn’t just stay in academia — I needed to translate research into public action. And this was the beginning of the Health Initiative of the Americas, a programme on health and migration at the University of California, Berkeley.

    Since its inception in 2001, the programme has relied on around 20,000 volunteers working to grow a grass-roots movement. I was very fortunate to be part of the University of California system: it helped me to knock at the door of the Mexican government. Because of the magnitude of the Mexican diaspora in the United States, the Mexican government has 50 consulates in the United States. The Mexican government partnered with the programme, and this has opened the doors to cooperation with other Latin American countries, such as Guatemala, El Salvador and Honduras. In the United States, health is unfortunately not a human right — it is sometimes seen as a commodity. We want to extend access to health care to immigrants, who are excluded from the health system, to help improve their living conditions.

    We wanted to hold National Health Weeks, just like the ones in Mexico — when the government mobilizes health personnel across the country to knock at houses to give everyone a chance to get vaccinated three times a year. But without accredited health providers, that wouldn’t be possible in the United States. So, we sought out community clinics, and many other organizations started to join: our network has several partners nationwide, including health and cultural institutions and consulates. These are places where immigrants, regardless of their legal status, can access basic health services and advice. Even in remote regions of the United States, they can get vaccines and education about preventive health to improve their overall quality of life.

    Young Latina researchers have the opportunity and the responsibility to contribute to a more equitable world. My advice is to never give up. Even in hard times there is light, and public health is a marvellous instrument to shine that light.

    DENISE LAPA: A fetoscopy pioneer

    Fetal and neonatal surgery programme coordinator at Sabará Child Hospital in São Paulo, Brazil.

    In 1999, I started to develop a technique to treat spina bifida — a pre-birth condition in which the neural tube bulges on the back of the fetus. The condition can damage nerves in the spinal cord and greatly affect a child’s ability to walk or perform day-to-day activities.

    In the late 1990s, Thomas Kohl, who is now head of the German Center for Fetal Surgery and Minimally-Invasive Therapy at the University Medical Center Mannheim, developed a technique to close the gap that forms in the spine. His idea was to stitch the fetus’s spine without opening the mother’s womb. I had been testing a similar technique for a decade when, in 2012, he invited me to Germany. We started an informal exchange.

    The difference between Kohl’s technique and mine was that, instead of stitching all of the layers in the back of a fetus — spinal cord, muscle and skin tissues — my team and I used a biocellulose patch over the spinal tissue to help it self-heal and avoid suturing the fetus’s spinal cord to the tissue above it.

    Throughout my career, I felt I had to prove myself all the time as a woman and, as a Latina researcher, I also had regional prejudice on top of that. To me, it seemed that some people, most of whom were men, felt that if a breakthrough in fetoscopy (fetal endoscopy) was to be made, it wouldn’t be made by a woman and certainly not one from Brazil.

    However, in 2013, after 14 years of testing in animal models, our first fetal surgery at the Samaritan Hospital of São Paulo proved that the technique worked. A decade later, we could see that not only was it viable, but also that it yielded positive long-term results. A study1 following 78 children who had undergone our procedure showed that almost half of them (46%) could walk independently once they reached between 2.5 and 10 years old — and almost all of them (94%) had expected social function. In comparison, a 2020 study2 on the effectiveness of the conventional open-womb surgical technique showed that around 29% of children aged 6 and over who had undergone this surgery could walk independently. Previous studies have shown that the effectiveness of the conventional technique in terms of walking rates is as high as 45%3.

    As well as in Brazil, our technique is now used in Israel, Chile, Uruguay, Italy and parts of the United States. It’s also rising in popularity: more than 300 surgeries have been performed outside Brazil. Everything I did in my life, I accomplished because a man told me I couldn’t. It’s extremely rewarding to see children, whose parents relied on my team, being able not only to walk, but also to jump and play freely — some even go skiing and do ballet.

    My piece of advice to young Latina researchers would be: structural sexism is still not understood by most men. It is up to us, women, to occupy important spaces and teach our daughters a different language of love and respect between men and women.

    YESTER BASMADJIÁN:On the front line against insect-borne diseases

    Head of the Department of Parasitology and Mycology in the Medicine Faculty at the University of the Republic in Montevideo, Uruguay.

    Yester Basmadjián sitting at her desk

    Yester Basmadjián says protecting against misinformation is an important part of her job.Credit: Ramiro Tomasina

    Before the viral disease dengue returned to Uruguay in 2016, the last epidemic had been a century earlier, in 1916. In the late 1950s, the country had eradicated the mosquito vector Aedes aegypti through monitoring populations and their behaviour. But, because the continent never fully got rid of it, the mosquito returned in 1997. Despite heavy public campaigning, the country was unable to eradicate it again. Now we’re seeing a rise in local transmission of dengue, especially in the Montevideo region and Salto on the border with Argentina. There were 48 confirmed cases in 2023, and this year we have seen more than 700.

    Cases of dengue, most of which were imported by travellers from neighbouring countries such as Brazil, Argentina and Paraguay, are now a concern in Montevideo. At the University of the Republic in Montevideo, we have a laboratory in which we can study this and other disease-vector insects more closely. Our lab has the support of Uruguay’s Public Health Ministry, the International Atomic Energy Agency (IAEA) and the Pan American Health Organization, and we have partnered with a number of institutions in Brazil and other Latin American countries.

    We’re using X-rays (hence our partnership with the IAEA) to sterilize male A. aegypti mosquitoes before they become adults, to decrease their overall population. Female mosquitoes mate only once; if they mate with sterile males then they won’t produce offspring. Another advantage is that male mosquitoes generally don’t interact with people and, because they do not feed on blood, they don’t transmit diseases. Our project will not eliminate this insect in Uruguay, but it’s a tool that will add to the fight. It is certainly better than open-air insecticide spraying — we don’t know whether mosquitoes in Uruguay are resistant to certain chemicals. We’re launching close to 30,000 first-generation sterile mosquitoes at the end of this year and are looking forward to good results.

    One of our biggest challenges is ensuring that the new lab remains operational in both the medium and long term — not only by maintaining resources, but also by protecting against a wave of misinformation and conspiracy theories. Many people think that sterilization of mosquitoes is going to cause a change in human bodies (which is not possible even if a male mosquito interacted with a person). At the lab, we try to counter this through outreach with journalists and by promoting workshops in schools.

    Although sterilizing mosquitoes is not a silver bullet to end dengue, it’s an important tool, and the public’s cooperation is essential to fight the mosquito that transmits it.

    My advice to young Latina researchers is that we have to study a lot to adapt to an ever more technological world — but it’s important never to give up when faced with challenges. Always move forward and, at some point, you’ll get to where you want to be.

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  • How the latest materials are taking biosensors to the next level

    How the latest materials are taking biosensors to the next level

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    A CG illustration showing soft blue bottlebrush shapes interspersed with long grey mesh tubes

    A rendering of a bottlebrush elastomer and carbon-nanotube composite that researchers believe has potential use as a brain electrode.Credit: Xu, P. et al. Nature Commun. 14, 623 (2023)/CC BY 4.0

    When Shuai Xu set out to create a wearable biosensor to monitor the vital signs of premature infants and newborns, he faced a major challenge: the skin of these children is so delicate that the adhesive used to attach a sensor could damage it, potentially leading to infection. The stiff device pulling against the skin as the baby moved, and the wires that might pull it in a different direction, added to the problem. The solution was to build a sensor that was soft and stretchable, with flexible circuit boards and thin, 50-millimetre wires, a huge change from the rigid devices that had long been a mainstay of this type of engineering. It was encased in a bendable silicone, transmitted its readings via Bluetooth, and was stuck to the body using a hydrogel, a polymer-based substance made mostly of water. Xu, a dermatologist, helped develop the device as a postdoctoral researcher in the laboratory of John Rogers, an engineer and materials scientist at Northwestern University in Evanston, Illinois, a pioneer in soft materials.

    Xu went on to become a founder and chief executive of Sibel Health in Chicago, Illinois, a medical device company that won Nature’s Spinoff Prize in 2020 and sells wearable sensors for monitoring patients. Xu’s challenges are common among researchers trying to develop biosensors and the materials that go into creating them. The devices must be small and lightweight, and must attach to the body with minimum irritation. In some cases, they require long-lasting batteries and circuitry that can handle a growing suite of artificial-intelligence algorithms that make sense of the data they collect.

    According to one estimate, the global market for health sensors was worth an estimated US$42.6 billion in 2023 and expected to grow to US$142.2 billion by 2030. The wrist-worn or finger-worn devices that were designed to count steps can now measure heartbeat and blood-oxygen levels, and they’ve been joined by patches that allow diabetics to perform continuous monitoring of their glucose levels.

    “That’s nothing to sneeze at,” Xu says. “But there are so many other things that are out there, biochemical and biophysical, that we still can’t do in a practical, continuous way.” Figuring out how to measure a variety of physical and chemical signals cheaply and non-invasively could provide diagnostic information that could reshape medicine. And this might go beyond sensors that take mechanical measurements, such as heart rate. Researchers are also working on chemical sensors that can detect biomarkers in blood, sweat and tears, as well as in fluids that surround cells.

    Aida Ebrahimi, a biosensor engineer at Pennsylvania State University in State College, is working on materials that can detect neurotransmitters in saliva or urine such as dopamine, serotonin, adrenaline and noradrenaline, which change in people with diseases such as Parkinson’s or Alzheimer’s. She’s focused on 2D materials, which are only one atomic layer thick, such as molybdenum disulfide. With a material in which, effectively, the “whole thing is surface, you are going to get high sensitivity in the ability to detect a very low concentration of biomolecules”, says Ebrahimi. The material properties of such atomically thin films are also sensitive to surface modification. For example, attaching molecules of manganese gives the material an affinity for dopamine, creating an ultrasensitive detector1.

    The upper torso of a small baby in a NICU cot shown with caring hands in shot and traditional wire monitors; a plaster-like clear wireless biosensor is visible on its' chest

    A soft and stretchable sensor was developed for a newborn’s sensitive skin.Credit: Northwestern University

    Similar materials with different molecules attached could be used as sensors for other chemicals that can provide information about health, says Ebrahimi. Her team built a prototype of the sensor in 2020 that they showed could measure dopamine1, but building it and validating it for use could be several years off.

    One measuring challenge is that a lot of signalling, particularly in the brain, is performed by the movement of ions, whereas most monitoring equipment is designed to detect electrical currents carried by the flow of electrons. Sahika Inal, a bioengineer at KAUST in Thuwal, Saudi Arabia, is using organic electrochemical transistors (OECTs)2, devices that can detect signals from biomolecules, cells and lipid layers and turn them into readings that can be measured by electronic equipment. OECTs can be built using organic mixed ionic–electronic conductors (OMIECs), which have been the focus of much interest in the past few years. OMIECs are polymers that both ions and electrons can flow across easily. When part of the transistor experiences a small change in a property it is measuring, the OMIEC amplifies that signal. Because it’s an organic polymer, the material is much more compatible with the wet environment of the body than a standard electronic transistor, which has to be encapsulated to protect it from fluids. As a result, electronics can be developed “that can be integrated directly with the biological system,” Inal says.

    OECT’s could be printed directly on the skin’s surface to detect biological signals, for instance, or built on top of threads of fabric to create biosensing garments and wraps that could survive washing. They also have the potential to replace the stiff electrodes used in brain implants to control prosthetic devices and monitor electrical activity in seizure patients. Their flexibility and biocompatibility might cause less irritation to brain tissue, which can render the electrodes less sensitive.

    At the University of Toronto, mechanical engineer, Xinyu Liu, and chemical engineer, Helen Tran, have developed another material with the softness and flexibility to be used as a brain electrode3. Dubbed the bottlebrush elastomer, their rubber-like substance is made from a molecule that has a long, stiff spine, which maintains its structure, surrounded by short, flexible bristles, for softness. To give the material electrical conductivity, Liu and Tran add a filler — either carbon nanotubes or a mixture of silver flakes and eutectic gallium indium, a semiconductor in liquid form. They worry, though, that the filler could leech out and have toxic effects, so they’d like to eliminate it. “Ultimately, we would like to design a polymer that is soft and electron-conducting,” Tran says. “These demands are often at odds.”

    Liu’s lab is also working on wearable sensors. One, based on a hydrogel, is designed to conform to the skin and measure strain when a body part, a knee, for example — is bent4. Such a device could be useful in monitoring an athlete’s performance or assessing arthritis.

    Another sensor they are developing places nanowires of zinc oxide on a cotton thread to create electronic textiles that can measure substances such as lactate and sodium in sweat. The material could be woven into a shirt or a sweatband to monitor an athlete’s health5.

    Xu sees a lot of opportunities for new biosensors. “AI is generating new algorithms,” he says, that can then be integrated into sensors to learn from, and react, to measurements they’re recording. That would require developing processors that can work with the limited power available in a sensor. Better batteries might help, as would alternatives such as harvesting power from movement or body heat, he says. Devices that can combine readings — glucose levels with heart rate, for instance — could be transformative, he says. He would also like to be able to detect stress hormones that could be used to monitor fatigue, or drug metabolites to check patients have taken medications.

    Biosensors have the potential to collect a lot of useful information, and to do it in everyday settings that might give a more realistic picture of health than a one-time doctor’s test. “Whether you’re ill or not”, says Xu, people do not spend most of their time in a clinic or hospital. The ability to track health “and use the technology yourself, I think is really important”.

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  • Liquid metal pumps itself out of gels to make artificial vasculature

    Liquid metal pumps itself out of gels to make artificial vasculature

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  • Campbell, J. Complete Casting Handbook: Metal Casting Processes, Metallurgy, Techniques and Design (Butterworth-Heinemann, 2015).


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  • π-HuB: the proteomic navigator of the human body

    π-HuB: the proteomic navigator of the human body

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  • State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China

    Fuchu He, Cheng Chang, Ying Jiang, Yang Li, Aihua Sun, Liujun Tang, Chanjuan Wang, Xiaowen Wang, Yan Wang, Linhai Xie, Xiao Yang, Lingqiang Zhang, Yunping Zhu, Chenxi Jia, Chaoying Li, Dong Li, Yanchang Li, Zhongyang Liu, Jian Wang, Ping Xu, Wantao Ying & Xiaobo Yu

  • International Academy of Phronesis Medicine (Guangdong), Guangdong, China

    Fuchu He, Yuezhong He, Tianhao Xu & Yu Zi Zheng

  • Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Ruedi Aebersold

  • Macquarie Medical School, Macquarie University, Sydney, New South Wales, Australia

    Mark S. Baker

  • Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing, China

    Xiuwu Bian

  • Institute of Health Service and Transfusion Medicine, Beijing, China

    Xiaochen Bo

  • Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA

    Daniel W. Chan & Daniel W. Chan

  • Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China

    Luonan Chen

  • Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China

    Xiangmei Chen

  • Institute of Chemistry, Academia Sinica, Taipei, China

    Yu-Ju Chen

  • National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China

    Heping Cheng

  • School of Biological Sciences, Queen’s University of Belfast, Belfast, UK

    Ben C. Collins

  • Functional Proteomics Laboratory, Centro Nacional de Biotecnología-CSIC, Madrid, Spain

    Fernando Corrales

  • Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany

    Jürgen Cox

  • AI for Science Institute, Beijing, China

    Weinan E & Weinan E

  • Center for Machine Learning Research, Peking University, Beijing, China

    Weinan E & Weinan E

  • Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA

    Jennifer E. Van Eyk & Jennifer E. Van Eyk

  • Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China

    Jia Fan & Qiang Gao

  • Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia

    Pouya Faridi

  • Monash Proteomics and Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia

    Pouya Faridi

  • School of Pharmaceutical Sciences and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Daniel Figeys

  • The D. H. Chen School of Universal Health, Zhejiang University, Hangzhou, China

    George Fu Gao

  • Pengcheng Laboratory, Shenzhen, China

    Wen Gao

  • School of Electronic Engineering and Computer Science, Peking University, Beijing, China

    Wen Gao

  • Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Zu-Hua Gao & Yu Zi Zheng

  • Department of Chemistry, University of Tokyo, Tokyo, Japan

    Keisuke Goda

  • Department of Bioengineering, University of California, Los Angeles, California, USA

    Keisuke Goda

  • Institute of Technological Sciences, Wuhan University, Wuhan, Hubei, China

    Keisuke Goda

  • Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Wilson Wen Bin Goh

  • School of Medicine, Southern University of Science and Technology, Shenzhen, China

    Dongfeng Gu

  • Department of Nutrition, Tianjin Institute of Environmental and Operational Medicine, Tianjin, China

    Changjiang Guo & Xinxing Wang

  • School of Medicine, Westlake University, Hangzhou, China

    Tiannan Guo & Yi Zhu

  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

    Tiannan Guo & Yi Zhu

  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China

    Tiannan Guo & Yi Zhu

  • Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands

    Albert J. R. Heck

  • Netherlands Proteomics Center, Utrecht, the Netherlands

    Albert J. R. Heck

  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK

    Henning Hermjakob & Juan Antonio Vizcaíno

  • Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

    Tony Hunter

  • Department of Head & Neck Surgery, Division of Surgery & Surgical Oncology, Division of Medical Sciences, National Cancer Centre Singapore, Singapore, Singapore

    Narayanan Gopalakrishna Iyer

  • OncoProteomics Laboratory, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

    Connie R. Jimenez

  • Advanced Glycoscience Research Cluster, School of Biological and Chemical Sciences, University of Galway, Galway, Ireland

    Lokesh Joshi

  • Departments of Molecular Biosciences, Departments of Chemistry, Northwestern University, Evanston, IL, USA

    Neil L. Kelleher

  • David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada

    Ming Li

  • Central China Institute of Artificial Intelligence, Henan, China

    Ming Li

  • Department of Biological Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore

    Qingsong Lin

  • CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

    Cui Hua Liu

  • Department of Structural Biology, Leibniz-Forschungsinstitut für MolekularePharmakologie (FMP), Berlin, Germany

    Fan Liu

  • State Key Laboratory of Membrane Biology, Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Guang-Hui Liu

  • Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, USA

    Yansheng Liu

  • State Key Laboratory of Molecular Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Zhihua Liu

  • UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia

    Teck Yew Low

  • Department of Critical Care Medicine and Hematology, The Third Xiangya Hospital, Central South University; Department of Hematology and Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China

    Ben Lu

  • Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Matthias Mann

  • School of Life Sciences, Tsinghua University, Tsinghua-Peking Center for Life Sciences, Beijing, China

    Anming Meng & Wei Xie

  • Institute for Systems Biology, Seattle, WA, USA

    Robert L. Moritz

  • Clinical Biomarker Discovery and Validation, Monash University, Clayton, Victoria, Australia

    Edouard Nice

  • Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Guang Ning

  • Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai, China

    Guang Ning

  • Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Guang Ning

  • Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Gilbert S. Omenn

  • Department of Oral Biological and Medical Sciences, University of British Columbia, Vancouver, British Columbia, Canada

    Christopher M. Overall

  • Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea

    Christopher M. Overall

  • Glycoproteomics Laboratory, Department of Parasitology, University of São Paulo, Sao Paulo, Brazil

    Giuseppe Palmisano

  • Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Yaojin Peng

  • Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China

    Yaojin Peng

  • University of the Chinese Academy of Sciences, Beijing, China

    Yaojin Peng

  • Institut de Recherche en Santé Environnement et Travail, Univ. Rennes, Inserm, EHESP, Irset, Rennes, France

    Charles Pineau

  • Pilot Laboratory, MOE Frontier Science Centre for Precision Oncology, Centre for Precision Medicine Research and Training, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China

    Terence Chuen Wai Poon

  • Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

    Anthony W. Purcell

  • State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China

    Jie Qiao & Liying Yan

  • ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia

    Roger R. Reddel, Phillip J. Robinson & Qing Zhong

  • Department of Health Sciences, University Magna Græcia of Catanzaro, Catanzaro, Italy

    Paola Roncada

  • Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Chris Sander

  • Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Chris Sander

  • State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China

    Jiahao Sha & Xuejiang Guo

  • Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China

    Erwei Song & Shicheng Su

  • Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China

    Erwei Song & Shicheng Su

  • Indian Institute of Technology Bombay, Mumbai, India

    Sanjeeva Srivastava

  • Department of Health Sciences, Faculty of Applied Health Sciences, Brock University, St. Catharines, Ontario, Canada

    Siu Kwan Sze

  • Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Chao Tang

  • Department of Chemistry, Southern University of Science and Technology, Shenzhen, China

    Ruijun Tian & Chris Soon Heng Tan

  • State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China

    Chen Wang, Yushun Gao, Jie He & Catherine C. L. Wong

  • Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, China-Japan Friendship Hospital, Beijing, China

    Chen Wang

  • Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland

    Tobias Weiss

  • Technical University of Munich, Freising, Germany

    Mathias Wilhelm & Bernhard Kuster

  • Advanced Genomics Unit, Center for Research and Advanced Studies, Irapuato, Mexico

    Robert Winkler

  • Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

    Bernd Wollscheid

  • Department of Computer Science, National University of Singapore, Singapore, Singapore

    Limsoon Wong

  • Department of Pathology, National University of Singapore, Singapore, Singapore

    Limsoon Wong

  • Guangzhou National Laboratory, Guangzhou, China

    Tao Xu, Jing Yang & Nan-Shan Zhong

  • School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China

    Tao Xu & Tao Xu

  • The Scripps Research Institute, La Jolla, CA, USA

    John Yates

  • China Science and Technology Exchange Center, Beijing, China

    Tao Yun

  • CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Qiwei Zhai

  • Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA

    Bing Zhang

  • Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Bing Zhang

  • Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Hui Zhang

  • State Key Laboratory of Medical Proteomics, National Chromatography R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China

    Lihua Zhang, Yukui Zhang, Hongqiang Qin & Mingliang Ye

  • School of Mathematical Sciences, Peking University, Beijing, China

    Pingwen Zhang

  • Wuhan University, Wuhan, China

    Pingwen Zhang

  • Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Mingxia Gao, Haojie Lu, Liming Wei, Ying Zhang & Feng Zhou

  • Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

    Jun He & Xiaofei Zhang

  • College of Life Science and Technology, Jinan University, Guangzhou, China

    Qing-Yu He & Tong Wang

  • Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China

    Jinlin Hou

  • State Key Laboratory of Biotherapy, West China Hospital, and West China School of Basic Sciences & Forensic Medicine, Sichuan University, Chengdu, China

    Canhua Huang

  • Peking University Cancer Hospital & Institute, Beijing, China

    Yan Li, Lin Shen & Qimin Zhan

  • BGI Group, Shenzhen, China

    Siqi Liu, Yan Ren & Huanming Yang

  • Xijing Hospital, Fourth Military Medical University, Xi’an, China

    Xiaonan Liu, Ya Liu, Yongzhan Nie & Jianjun Yang

  • Institute for Protein Research, Osaka University, Osaka, Japan

    Mariko Okada

  • Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China

    Guojun Qian & Feng Shen

  • School of Pharmaceutical Sciences, Tsinghua University, Beijing, China

    Yu Rao

  • School of Medicine, Tsinghua University, Beijing, China

    Zihe Rao

  • Changping Laboratory, Beijing, China

    Xianwen Ren, Xiaoliang Sunney Xie & Zemin Zhang

  • Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Yan Ren

  • State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Minjia Tan

  • School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen, China

    Ben Zhong Tang

  • Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China

    Sheng-Ce Tao

  • Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China

    Xiaoliang Sunney Xie & Zemin Zhang

  • Department of Liver Surgery, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

    Li Xu

  • Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Yaxiang Yuan

  • Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Qingcun Zeng

  • Peking University International Cancer Institute, Beijing, China

    Qimin Zhan

  • Department of Urology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China

    Xu Zhang

  • State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Nan-Shan Zhong

  • F.H. conceived the concept of π-HuB and designed its scientific goals, and contributed ideas for phronesis medicine with L.X., F.H., R.A., M.S.B., X.W.B., X.C.B., D.W.C., C.C., L.C., X.C., H.C., F.C., W.E., J.F., P.F., D.F., G.F.G., W.G., Z.-H.G., K.G., W.W.B.G., D.G., C.G., T.G., A.J.R.H., H.H., T.H., N.G.I., Y.J., C.R.J., L.J., N.L.K., M.L., Y.L., Q.L., C.H.L., F.L., G.-H.L., Y.S.L., Z.L., T.Y.L., B.L., M.M., A.M., R.L.M., E.N., G.N., G.S.O., G.P., Y.P., C.P., T.C.W.P., A.P., J.Q., R.R., P.J.R., P.R., C.S., J.S., E.S., S.S., A.S., S.K.S., C.T., L.T., R.T., J.V.E., J.A.V., C.W., X.W.W., X.X.W., Y.W., T.W., M.W., R.W., B.W., L.W., L.X., W.X., Tao Xu, L.Y., J.Y., X.Y., J.R.Y., Q.W.Z., L.H.Z., L.Q.Z., Y.K.Z., Q.Z. and Y.P.Z. contributed ideas and suggestions for the conception and design of this project. T.G., L.T. and Y.W. contributed coordination of the π-HuB Consortium. J.Y. wrote the first draft of the manuscript, and created the figures with F.H., T.G., Y.L. and L.X. F.H., R.A., M.S.B., F.C., P.F., D.F., Z.-H.G., K.G., W.W.B.G., T.G., H.H., T.H., N.G.I., C.R.J., L.J., M.L., Q.L., F.L., Y.S.L., T.Y.L., R.L.M., G.S.O., T.C.W.P., A.P., R.R., P.J.R., C.S., S.K.S., J.A.V., T.W., R.W., B.W., L.W., J.Y., J.R.Y. and Q.Z. provided important edits to the manuscript. All authors contributed to review and editing of the manuscript. The π-HuB Consortium contributed to the discussion of strategic π-HuB research plans.

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  • Strong-field quantum control in the extreme ultraviolet domain using pulse shaping

    Strong-field quantum control in the extreme ultraviolet domain using pulse shaping

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    Strong-field phenomena play an important part in our understanding of the quantum world. Light–matter interactions beyond the perturbative limit can substantially distort the energy landscape of a quantum system, which forms the basis of many strong-field effects8 and provides opportunities for efficient quantum control schemes11. Moreover, resonant strong coupling induces rapid Rabi cycling of the level populations12, enabling complete population transfer to a target state2. The development of intense extreme ultraviolet (XUV) and X-ray light sources has recently led to the investigation of related phenomena beyond valence electron dynamics, in highly excited, multi-electron and inner-shell electron states9,10,13,14,15,16,17. Yet in most of these studies, the dressing of the quantum systems was induced by intense infrared fields overlapping with the XUV and X-ray pulses. In contrast, the alteration of energy levels directly by short-wavelength radiation is more difficult. So far, only a few studies have reported XUV-induced AC-Stark shifts of moderate magnitude (100 meV), difficult to resolve experimentally9,18,19,20.

    Another important step in exploring and mastering the quantum world is the active control of quantum dynamics with tailored light fields21,22,23. At long wavelengths, sophisticated pulse-shaping techniques facilitate the precise quantum control and even the adaptive-feedback control of many light-induced processes, in both weak- and strong-field regimes24,25,26,27,28. Several theoretical studies have pointed out the potential of pulse shaping in XUV and X-ray experiments29,30,31. As an experimental step in this direction, phase-locked monochromatic and polychromatic pulse sequences have been generated32,33,34,35. Using this tool, coherent control demonstrations in the perturbative limit32,35,36 and the generation of intense attosecond pulses were achieved37. Moreover, ultrafast polarization shaping at XUV wavelengths38 and chirp control for the temporal compression of XUV pulses39 were recently demonstrated. However, spectral phase shaping, which forms the core of pulse-shaping techniques, has not been demonstrated for the control of quantum phenomena at short wavelengths. Here we establish spectral phase shaping of intense XUV laser pulses and demonstrate high-fidelity quantum control of the Rabi and photoionization dynamics in helium.

    In the experiment, He atoms are dressed and ionized by intense coherent XUV pulses (I > 1014 W cm−2) delivered by the seeded FEL FERMI (Fig. 1a). The high radiation intensity causes a strong dressing of both the bound states in He and the photoelectron continuum, whereas the dynamics of the quantum system are still in the multiphoton regime (Keldysh parameter γ = 11). By contrast, the dynamics of a system dressed with near-infrared (NIR) radiation of comparable intensity would be dominated by tunnel and above barrier ionization (γ = 0.35) (ref. 8). Hence, the use of short-wavelength radiation provides access to a unique regime, in which the interplay between strongly dressed bound states and a strongly dressed continuum can be studied.

    Fig. 1: XUV strong-field coherent control scheme.
    figure 1

    a, Intense XUV pulses dress the He 1s2, 1s2p states and the electron continuum. E± labels indicate the bound dressed states correlated to the 1s2p bare state. Mixing of p- and d-waves in the dressed continuum results in different coupling strengths to the dressed bound states (indicated by the thickness of the arrows). b,c, In the time domain, the AT splitting follows the intensity profile of the XUV field (middle). The dressed-state populations are monitored in the photoelectron eKE distributions (top). XUV pulse shaping enables the control of the non-perturbative quantum dynamics (bottom). For a flat phase ϕ (no chirp), both the excited dressed states are equally populated. For a positive phase curvature (up chirp), the population is predominantly transferred to the lower dressed state and the upper state is depleted, whereas for negative curvature (down chirp), the situation is reversed. d, Principle of XUV pulse shaping at the FEL FERMI. Intense seed laser pulses overlap spatially and temporally with the relativistic electron bunch in the modulator section of the FEL, leading to a modulation in the electron phase space. The induced energy modulations are converted into electron-density oscillations on passing a dispersive magnet section. The micro-bunched electrons then propagate through a section of radiator undulators, producing a coherent XUV pulse. In this process, the phase function of the seed pulse is coherently transferred to the XUV pulse, resulting in precise XUV phase shaping. The FEL pulses are focused on the interaction volume, exciting and ionizing He atoms. The photoelectrons are detected with a magnetic bottle electron spectrometer (MBES).

    To dress the He atoms, we induce rapid Rabi cycling of the 1s2 → 1s2p atomic resonance with a near-resonant field E(t). The generalized Rabi frequency of this process is \(\varOmega ={\hbar }^{-1}\sqrt{{(\mu E)}^{2}+{\delta }^{2}}\), where μ denotes the transition dipole moment of the atomic resonance, δ the energy detuning and \(\hbar \) the reduced Planck constant. In the dressed-state formalism, the eigenenergies of the bound states depend on the field intensity and show the characteristic Autler–Townes (AT) energy splitting ΔE = ħΩ (ref. 40). The observation of this phenomenon requires the mapping of the transiently dressed level structure of He while perturbed by the external field41. This is achieved by immediate photoionization over the course of the femtosecond pulses, thus projecting the time-integrated energy level shifts onto the electron kinetic energy (eKE) distribution (Fig. 1b).

    Analogous to the bound-state description, the dressed continuum states are obtained by diagonalization of the corresponding Hamiltonian. The hybrid electron–photon eigenstates consist of a mixing of partial waves with different angular momenta, which alters the coupling strength to the dressed bound states of the He atoms (Fig. 1a).

    Figure 2 demonstrates experimentally the dressing of the He atoms. The build-up of the AT doublet is visible in the raw photoelectron spectra as the XUV intensity increases (Fig. 2a). The evolution of the AT doublet splitting is in good agreement with the expected square-root dependence on the XUV intensity \(\Delta E=\mu \sqrt{2{I}_{{\rm{eff}}}/({{\epsilon }}_{0}c)}\). Here, Ieff denotes an effective peak intensity, accounting for the spatially averaged intensity distribution in the interaction volume, ϵ0 denotes the vacuum permittivity and c denotes the speed of light. The data can be thus used for gauging the XUV intensity in the interaction volume, a parameter otherwise difficult to determine. At the maximum XUV intensity, the photoelectron spectrum shows an energy splitting exceeding 1 eV, indicative of substantial AC-Stark shifts in the atomic level structure. The large AT splitting further implies that a Rabi flopping within 2 fs is achieved, offering a perspective for rapid population transfer outpacing possible competing intra- and inter-atomic decay mechanisms, which are ubiquitous in XUV and X-ray applications.

    Fig. 2: Build-up of the AT splitting in He atoms.
    figure 2

    a, Detected photoelectron eKE distribution (raw data) as a function of the XUV intensity (FEL photon energy: 21.26 eV, GDD = 135 fs2). Dashed lines show the calculated AT splitting for an effective XUV peak intensity Ieff accounting for the spatial averaging in the interaction volume. b,c, Photoelectron spectra as a function of photon energy recorded for high XUV intensity (Ieff = 2.92(18) × 1014 W cm−2) (b) and for lower intensity (Ieff ≈ 1013 W cm2) (c). In b, an avoided crossing between the lower and higher AT band is visible directly in the raw photoelectron spectra. The photoelectron distribution peaking at eKE = 17.9 eV in a and b is ascribed to He atoms excited by lower XUV intensity (see text).

    Figure 2b,c shows the photoelectron yield as a function of excitation photon energy. For high XUV intensity (Fig. 2b), the photoelectron spectra show an avoided level crossing of the dressed He states as they are mapped to the electron continuum (see also Fig. 4). Accordingly, at lower XUV intensity (Fig. 2c), the avoided crossing is not visible anymore. In the latter, the eKE distribution centres at 17.9 eV. In Fig. 2b, a similar contribution appears at the same kinetic energy that overlays the photoelectrons emitted from the strongly dressed atoms. Likewise, a notable portion of photoelectrons at eKE ≈ 17.9 eV in Fig. 2a does not show a discernible AT splitting. We conclude that a fraction of He atoms in the ionization volume are excited by much lower FEL intensity, which is consistent with the aberrated intensity profile of the FEL measured in the ionization volume (Extended Data Fig. 1). This overlapping lower intensity contribution does not influence the interpretation of the results in this work. For better visibility of the main features, we thus subtract this contribution from the data shown in Figs. 3 and 4.

    Fig. 3: Strong-field quantum control of dressed He populations.
    figure 3

    a, Photoelectron spectra obtained for phase-shaped XUV pulses (see labels for GDD values; photon energy = 21.25 eV; Ieff = 2.8(2) × 1,014 W cm−2). The control of the dressed-state populations is directly reflected in the relative change of amplitude in the photoelectron bands. The small peak at 18.13 eV results from imperfect removal of the lower intensity contribution from the aberrated focus. b, Calculations of the time-dependent Schrödinger equation for a single active electron (TDSE-SAE) and a single laser intensity corresponding to the experimental Ieff = 2.8 × 1014 W cm2 (dark colours). Spectral fringes reflect here the temporal progression of the Rabi frequency during the light–matter interaction. The broadened photoelectron spectra (light colours) account for experimental broadening effects caused by the focal intensity averaging and the instrument response function. a.u., arbitrary units.

    Fig. 4: Energy-domain representation of the quantum control scheme.
    figure 4

    a, Photoelectron spectra as a function of energy detuning for different GDD values as labelled (Ieff = 2.92(18) × 1014 W cm2). b, TDSE-SAE calculations. Broadening by the instrument response function is omitted in the model. c, Amplitude ratio between the upper and lower photoelectron bands evaluated at the 1s2 → 1s2p resonance; hence, δ = 0. Experimental data (red), TDSE-SAE model treating the bound and continuum dynamics non-perturbatively (blue) and TDSE-SAE model applied to the bound-state dynamics, but treating the continuum perturbatively (yellow). d, Dependence of the He ionization rate on the spectral phase of the driving field. Data (red) and TDSE-SAE model (blue). a.u., arbitrary units.

    The demonstrated dressing of He atoms provides the prerequisite for implementing the strong-field quantum control scheme (Fig. 1b,c). The main mechanism underlying the control scheme is described in the framework of the selective population of dressed states (SPODS), which is well established in the NIR spectral domain28. Here, we extend SPODS to the XUV domain and include a new physical aspect—that is, the transition of the bound atomic system into a strongly dressed continuum. In SPODS, a flat phase leads to an equal population of both dressed states in the excited state manifold of helium; a positive phase curvature results in a predominant population of the lower dressed state and a negative phase curvature results in a predominant population of the upper dressed state (Fig. 1c). The scheme has been experimentally demonstrated with long-wavelength radiation42, in which pulse-shaping techniques are readily available. However, the opportunities for pulse-shaping technologies are largely unexplored for XUV and X-ray radiation.

    We solve this problem by exploiting the potential of seeded FELs to allow for the accurate control of XUV pulse properties39,43. These demonstrations have been so far limited to applications of temporal compression and amplification of the FEL pulses. By contrast, the deterministic control of quantum dynamics in a material system involves many more degrees of freedom, which makes the situation considerably more complex. The seeded FEL FERMI operation is based on the high-gain harmonic generation (HGHG) principle44, in which the phase of an intense seed laser pulse is imprinted into a relativistic electron pulse to precondition the coherent XUV emission at harmonics of the seed laser (Fig. 1d). For FEL operation in the linear amplification regime, the phase ϕnH(t) of the FEL pulses emitted at the n’th harmonic of the seed laser follows the relationship39

    $${\phi }_{n{\rm{H}}}(t)\approx n[{\phi }_{{\rm{s}}}(t)+{\phi }_{{\rm{e}}}(t)]+{\phi }_{{\rm{a}}}.$$

    (1)

    Here, ϕs denotes the phase of the seed laser pulses, which can be tuned with standard pulse-shaping technology at long wavelengths (Methods); ϕe accounts for the possible phase shifts caused by the energy dispersion of the electron beam through the dispersive magnet and is negligible for the parameters used in the experiment; and ϕa accounts for the FEL phase distortion due to the amplification and saturation in the radiator and has been kept negligibly small by properly tuning the FEL (Methods). Although complex phase shapes may be implemented with this scheme, for the current objective of controlling the strong-field induced dynamics in He atoms, shaping the quadratic phase term (group delay dispersion (GDD)) is sufficient42. Therefore, we focus on the GDD control in the following discussion.

    Figure 3 demonstrates the quantum control of the dressed He populations. The eKE distribution shows a pronounced dependence on the GDD of the XUV pulses (Fig. 3a). At minimum chirp (GDD = 135 fs2), we observe an almost even amplitude in the AT doublet, whereas for GDD < 0, the higher energy photoelectron band dominates; for GDD  > 0, the situation is reversed. These changes directly reflect the control of the relative populations in the upper and lower dressed states of the He atoms. We obtain an excellent control contrast and the results are highly robust (Extended Data Fig. 2), which is remarkable given the complex experimental setup.

    The experiment is in good agreement with the theoretical model (Fig. 3b) numerically solving the time-dependent Schrödinger equation for a single active electron (TDSE-SAE; Methods). To account for experimental broadening effects, we calculated the photoelectron spectra for a single intensity (corresponding to the experimental Ieff) and including the focal intensity average present in the experiment (Methods). All salient features of the experiment are well reproduced. The control of the dressed-state populations is in very good qualitative agreement. The different widths and shapes of the photoelectron peaks are qualitatively well-matched between the experiment and the calculations. The difference in the AT energy splitting between the experiment (ΔEexp ≈ 1.02 eV) and theory (ΔEtheo = 0.74 eV) is in good agreement with the fact that the model underestimates the transition dipole moment of the 1s2 → 1s2p transition by a factor of 1.4 (Methods).

    The high reproducibility, the excellent control contrast and the good agreement with theory confirm the feasibility of precise pulse shaping in the XUV domain and of quantum control applications, even of transient strong-field phenomena. This is an important achievement in view of quantum optimal control applications at short wavelengths.

    The implemented control scheme is not restricted to adiabatic processes28. In our experiment, the dynamics are adiabatic only for the largest frequency chirp (GDD = −1,127 fs2) (Extended Data Fig. 3). However, this also shows that the condition for rapid adiabatic passage2 can be generally reached with our approach, offering a perspective on efficient population transfer in the XUV and potentially in the soft X-ray regime.

    The active control of quantum dynamics with tailored light fields is an asset of pulse shaping. As another asset, systematic studies with shaped laser pulses can be used to uncover underlying physical mechanisms that are otherwise hidden. Here, we demonstrate this concept for pulse shaping in the XUV domain. The high XUV intensities used in our study lead to a peculiar scenario in which both bound and continuum states are dressed and a complex interplay between their dynamics arises. Hence, for a comprehensive understanding of the strong-field physics taking place, the bound-state dynamics and the non-perturbative photoionization have to be considered. This is in contrast to the strong-field control at long wavelengths, for which the continuum could be described perturbatively42.

    Figure 4a,b shows the avoided crossing of the photoelectron bands for different spectral phase curvatures applied to the XUV pulses. The experimental data show a clear dependence of the AT doublet amplitudes on the detuning and the GDD of the driving field, in good agreement with the theory. In the strong dressing regime, the bound–continuum coupling marks a third factor that influences the photoelectron spectrum. As predicted by theory, the strong-field-induced mixing of continuum states (Fig. 1a) leads to different photoionization probabilities for the upper and lower dressed states of the bound system45. This is in agreement with the prevalent asymmetry of the AT doublet amplitudes observed in our data and calculations (Fig. 4a,b). An analogous effect is observed for the strong-field bound–continuum coupling in solid state systems46.

    To disentangle this strong-field effect from the influence of the detuning and spectral phase of the driving field, we evaluate the amplitude ratio between the upper and lower photoelectron bands at detuning δ = 0 eV (Fig. 4c). Interpolation to GDD = 0 fs2 isolates the asymmetry solely caused by the strong-field bound–continuum coupling. We find reasonable agreement with our model when including the dressing of the ionization continuum (blue curve), in stark contrast to the same model but treating the continuum perturbatively (yellow curve). Hence, the dressing of the He atoms provides a probe of the strong-field dynamics in the continuum. This property is otherwise difficult to access and becomes available through our systematic study of the spectral phase dependence on the photoelectron spectrum.

    Another possible mechanism for a general asymmetry in the AT doublet amplitudes could be the interference between ionization pathways through resonant and near-resonant bound states as recently suggested for the dressing of He atoms with XUV20,47 and for alkali atoms with bichromatic NIR fields48. In our experiment, we study the energetically well-isolated transition 1s2 → 1s2p, in which the contributions from neighbouring optically active states should be negligible. This provides us with a clean two-level system and greatly simplifies the data interpretation. For confirmation, we performed a calculation with a modified model in which any two-photon ionization through near-resonant states (except for the 1s2p state) was suppressed and, thus, possible photoionization interference effects were eliminated. Still, we observe a pronounced asymmetry in the AT doublet amplitudes (Extended Data Fig. 4). Moreover, owing to the large Keldysh parameter (γ = 11) and the low ponderomotive potential (Up < 100 meV) in our study, other strong-field effects are expected to play a negligible part in the observed dynamics. We thus assign the experimental observation to the coupling of the dressed atom dynamics with a dressed ionization continuum induced by intense XUV driving fields.

    A comprehensive understanding of the strong-field-induced dynamics in the system lays the basis for another quantum control effect, that is, the suppression of the ionization rate of the system, as proposed theoretically45. The excitation probability for one-photon transitions is generally independent of the chirp direction of the driving field. However, if driving a quantum system in the strong-field limit, its quasi-resonant two-photon ionization rate may become sensitive to the chirp direction. We demonstrate the effect experimentally in Fig. 4d. A substantial reduction of the He ionization rate by 64% is achieved, solely by tuning the chirp of the FEL pulses while keeping the pulse area constant. The good agreement with the TDSE-SAE calculations confirms the mechanism. This control scheme exploits the interplay between the bound-state dynamics and the above-discussed selective coupling of the upper and lower dressed states to the ionization continuum. We note a stabilization mechanism of the dressed states in He was recently proposed, effectively causing also a suppression of the ionization rate47. This mechanism requires, however, extreme pulse parameters, difficult to achieve experimentally. By contrast, our approach based on shaped pulses is more feasible and applies to a broader parameter range.

    With this work, we have established a new tool for the manipulation and control of matter using XUV light sources. The demonstrated concept offers a wide pulse shaping window regarding pulse duration, photon energy and more complex phase shapes. In particular, the recent progress in echo-enabled harmonic generation49,50 promises to extend the pulse-shaping concept to the soft X-ray domain (up to the 600 eV range) in which localized core electron states can be addressed. As such, we expect our work will stimulate other experimental and theoretical activities exploring the exciting possibilities offered by XUV and soft X-ray pulse shaping: first theory proposals in this direction have already been made29,30,31. The demonstrated scheme already sets the basis for highly efficient adiabatic population transfer1,2 and an extension to cubic or sinusoidal phase shaping would open up many more interesting control schemes26,27. This may find applications, for example, in valence-core-stimulated Raman scattering or efficient and fast qubit manipulation with XUV and soft X-ray light. Furthermore, selective control schemes may reduce the influence of competing ionization processes ubiquitous in XUV and X-ray spectroscopy and imaging experiments, for which our work provides experimental demonstration. The generation of coherent attosecond pulse trains, with independent control of amplitude and phases, has been demonstrated at seeded FELs37, bringing pulse shaping applications on the attosecond time scale within reach. This paves the way for the quantum control of molecular and solid state systems with chemical selectivity and on attosecond time scales.

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