Twin-roll casting results in a finer microstructure, where the LPSO phases are more finely distributed and less strongly connected. A transfer of the hot deformation behavior from the as-cast condition to the TRC condition is only possible to a limited extent due to the microstructural differences. Both states show differences in the recrystallization behavior during hot deformation. In the conventional cast state, dynamic recrystallization (DRX) is assumed to be delayed by the occurrence of coarse blocky LPSO phases. Main DRX mechanisms are continuous dynamic recrystallization (CDRX), particle stimulated nucleation (PSN) and twin induced dynamic recrystallization (TDRX). The deformed TRC sample showed pronounced DRX at almost all deformation conditions. Besides the TDRX and the PSN mechanism, kink induced dynamic recrystallization (KDRX) can be observed. Optimum deformation conditions for both states are temperatures from 500 °C to 520 °C, and strain rates ranging from 0.01 s Within the last few years, there has been increased research on magnesium alloys with LPSO (long period stacking ordered) phases. These are known to provide alloys with a good mechanical property profile. The LPSO phases are characterized by excellent strength, unique crystal structure and good thermal stability. The good strength properties are retained even at elevated temperatures due to the high melting temperature of 545 °C. The high strength is mainly attributed to the formation of kink bands in the LPSO phase. Several studies have reported the formation of kink bands during plastic deformation for different initial states, such as as-cast or twin-roll cast. Predominantly, LPSO phases with an 18R or a 14H structure occur after conventional casting as well as after twin-roll casting (TRC) ,4,5,6,7.95.21Zn1.44Y2.86Mn0.49 magnesium alloy after homogenization. They have reported that at low temperatures (250 °C–300 °C), kink band formation in connection with low strain rates (0.01 s−1) twinning are the main deformation mechanisms. The beginning of dynamic recrystallization (DRX) at the original grain boundaries was shown at temperatures between 350 °C and 400 °C, which remains incomplete. Increasing temperatures up to 500 °C results in a complete DRX. Comparable results are presented by Zhang et al. (2018) [98.5Y1Zn0.5 magnesium alloy. The investigations of Li et al. (2019) [−1) during deformation of a Mg-5.6Gd-0.8Zn (wt.%) alloy. Dynamic recrystallization preferably starts at the twin boundaries (TDRX), at temperatures between 350 °C and 400 °C. Increasing temperatures lead to a change in the DRX mechanism, as new grains mainly originate at the original grain boundaries. At 500 °C the degree of DRX is considerably high. Other studies indicate that DRX starts at the primary LPSO phase [Many factors, such as temperature, strain rate and strain, are associated with the hot deformation behavior of metallic metals. For magnesium alloys containing LPSO phases, several papers are focused on the effect of such factors on workability and recrystallization behavior. Li et al. (2020)  investig. (2018)  showed t. (2019)  revealedSO phase . It is rSO phase  indicateSO phase .Based on our previous studies, this paper compares the hot deformation behavior of the Mg-6.8Y-2.5Zn-0.4Zr (WZ73) alloy in the as-cast and the twin-roll cast condition. In addition to the more economical and resource-saving production by reducing the forming and annealing effort during TRC, the quality of the cast strips can basically be improved in terms of homogeneity. The cooling rates  , i.e., aCast ingots of the WZ73 alloy (Mg-6.8Y-.5Zn-0.4Zr) acquired from XI’AN YUECHEN Metal Products Co., Ltd., China, were used in this study. The chemical composition is shown in −1, 0.1 s−1, 1 s−1 and 10 s−1 . The detailed approach can be found in [The plane strain compression tests were performed under process-oriented conditions using a servo-hydraulic hot forming simulator. Samples were heat treated in an air circulation furnace at a temperature of 500 °C for a holding time of 2 h. Subsequent to the heat treatment, samples were compressed or cooled down (in the furnace) to the deformation temperatures between 350 °C and 450 °C and kept at these temperatures for 15 min in order to achieve a homogeneous temperature distribution over the cross section. To reduce friction between the samples surface and the tool, a graphite/oil mixture was used. Isothermal flow curves were obtained based on the recorded force-displacement data. Dissipation energy and friction were considered by the correction function according to Siebel (1932) , with µ d 10 s−1 . The samfound in .λ = 1.540598 Å). Diffraction patterns were recorded within the 2θ-range of 30 to 60 with a step size of 0.026. For phase identification, the ICDD database was used. The diffraction peaks from Mg were identified based on ICDD 01-089-5003. However, as the existence of 18R and 14H LPSO structures cannot be unambiguously confirmed by the ICDD database, the final identification was only supported by ICDD and was evaluated based on the studies form Luo et al. (2000) [From the compressed samples, longitudinal sections across the cross-section were cut and metallographically prepared for microstructural characterization. The samples were ground and then polished with an oxide polishing suspension. Etching was carried out in two steps. After brief rinsing with a 3% initial solution, the sample was etched with a picric acid solution consisting of 70 mL ethanol, 10 mL distilled water, 10 mL glacial acetic acid and 4.2 g picric acid. Light microscopic images were taken on a Keyence VHX 6000 microscope at the Institute of Metal Forming, Freiberg, Germany. Scanning electron microscopic (SEM) evaluation was performed on a ZEISS GeminiSEM 450 device at the Institute of Metal Forming, Freiberg, Germany. Different detectors were used for SEM: angular selective back-scatter (AsB), back-scatter (BSE) and secondary electron detector (SE). X-ray diffraction analysis (XRD) was performed on Empyrean Cu LFF HR at the Academic Centre for Materials and Nanotechnology, University of Science and Technology, Krakow, Poland using CuK radiation (. 2000)  and Wen 00  and W−1) during hot compression. At a deformation temperature of 450 °C (−1), the beginning of the plastic flow is accompanied by higher flow stresses in the as-cast condition, while at 10 s−1 the TRC and as-cast condition exhibit the same flow stress at the beginning of the plastic flow. Maximum flow stress in the as-cast state is higher compared to the TRC state. It is assumed that at the large-scale LPSO phases in the as-cast state the dislocation density is higher because dislocations can hardly overcome or circumvent those LPSO phases compared to the finer phases after TRC. Therefore, in the as-cast condition, the flow curve exhibits higher flow stresses at 450 °C.f 450 °C a the flo−1) and low (0.01 s−1) strain rates.After reaching the peak stress, the flow decreases or remains at a certain level because of softening processes, which take place when plastic deformation is maintained. During softening, dislocation movement is enabled by cross-slip and climbing processes, which result in the annihilation of dislocations and in a decreasing dislocation density. Both lead to a decrease of the flow stress. The flow curves of the as-cast condition reveal only a slight decrease of the flow stress, and then it is in a quasi-steady state, where hardening and softening counteract each other. It is assumed that at 450 °C dynamic recovery is the dominant softening mechanism. Only a small amount of dynamic recrystallization can be observed, indicating that the DRX remains incomplete even at high strains and low strain rates a,c. Afte−1 in 95.21Zn1.44Y2.86Mn0.49 magnesium alloy, which are typical for DRX during hot compression. Main deformation mechanisms were kink deformation and twinning, while dynamic recrystallization occurred along the original grain boundaries and in the vicinity of the LPSO phases. Flow curves of the Mg-5.6GD-0.8Zn (wt.%) alloy reveal DRX characteristics expect hot deformation at 350 °C and 0.1 s−1 to 1 s−1. At higher strain rates, twinning is an additional deformation mode and DRX takes place at the twin boundaries [−1 to 1 s−1. The material showed a characteristic behavior of strain hardening and flow softening at low temperatures and high strain rates, while the flow stress decreases to a steady state at high temperatures and low strain rates.Decreasing forming temperatures result in increasing flow stress, which is shown in a temperature range from 350 °C to 500 °C and a strain rate of 1 sundaries . Lv et aundaries  investig−1), σ is the flow stress (MPa), A and α are material constants, n is the stress exponent, R is the universal gas constant (8.314 J·K−1mol−1), and T is the absolute deformation temperature (K). Values for A, α and n for the as-cast and the TRC states can be found in The activation energy is an important material parameter for determining the critical conditions for initiating dynamic recrystallization. In determining the model coefficients, it is assumed that the processes taking place during hot deformation are diffusion-controlled and thus strain rate- and temperature-dependent. The calculated flow curves form the basis for determining the dynamic recrystallization processes. From the flow stress maxima determined, the Zener–Hollomon parameter Z is established, considering the model coefficients A, α and n as well as the activation energy Q, using Equation (1) according to Sellars (1966) ,29:(1)ZThe activation energy describes the difficulty of initiating deformation during hot working and is simultaneously affected by dynamic precipitation formation, pinning effects of dislocations, and the occurrence of secondary phases. The activation energy for dynamic recrystallization of LPSO containing magnesium alloys derived by different scientists is very different and varies between 182 kJ/mol and 276 kJ/mol, according to Gonzales et al. (2016) . These vThe LPSO phases occur in different forms, block-like, connected to networks or isolated as well as in lamellar form. Due to their high thermal stability, a high density of LPSO phases remains in the microstructure even after hot deformation at temperatures between 350 °C and 500 °C. In the TRC initial state, the block-like LPSO phase is more finely distributed and, above all, less strongly connected. The deformed specimen shows that the blocky LPSO phase is aligned in the flow direction of the material. At the same time, previous investigations  show thaWhen deformation takes place at 350 °C, hardly any DRX occurs. However, it was also found in previous investigations  that twi−1) in a temperature range of 400 °C to 440 °C for TRC condition, while for as-cast conditions higher temperatures of 450 °C to 470 °C are required for higher power dissipation efficiency. At higher temperatures (500 °C to 520 °C) moderate strain rates of 1 s−1 (TRC condition) and low strain rates of 0.1 s−1 to 0.01 s−1 (as-cast condition) are preferable for hot deformation. Under these conditions, dynamic recrystallisation is assumed to occur preferentially during hot deformation.The DMM processing maps at a moderate strain of 0.4 are shown in −1. The conventionally cast and TRC Mg-6.8Y-2.5Zn-0.4Zr alloy both show flow instabilities in this parameter range. A second instability domain occurs at high temperatures (>500 °C). For the TRC condition unstable flow is restricted to high strain rates (10 s−1), while it is broadened for the as-cast condition to lower strain rates (0.01 s−1). In these zones, where the corresponding power dissipation coefficient is comparatively low, microcracks nucleate and propagate along the interface of the grain boundaries and the network-like LPSO phase. At low temperature and/or high strain rates, a large quantity of dislocations could pile-up at the interface of the magnesium matrix and the LPSO phase. The instability of adiabatic shear bands or local plastic flow tends to occur rather than dynamic recrystallization. Consequently, these instabilities result in the formation of microcracks [−1 to 0.1 s−1 for the as-cast material and a strain rate of 1 s−1 for the TRC material.Literature data on suitable hot deformation conditions for comparable alloys containing LPSO phases are presented in rocracks . OptimumThe microstructure of both conditions mainly consists of the α-magnesium matrix and network-like LPSO phases located along the grain boundaries. LPSO phases could be assigned to 18R or 14H structures. Twin-roll casting leads to a finer microstructure compared to conventional casting. After conventional casting, thin lamellar LPSO phases arise within the α-magnesium grains, which can be assigned to the 14H structure. In the TRC condition, lamellar LPSO phases are not observed.−1) and low (0.01 s−1) strain rates.During hot deformation of the conventionally cast material, it is assumed that at 450 °C dynamic recovery is the dominant softening mechanism. Only a small amount of dynamic recrystallization can be observed, indicating that the DRX remains incomplete even at high strains and low strain rates. The TRC samples showed softening via dynamic recrystallization, where an almost completely recrystallized structure developed at both high (10 sAt higher temperatures (500 °C), DRX occurs in both conditions. The as-cast state exhibits a high amount of network-like and lamellar LPSO phases, which are assumed to be responsible for a delayed DRX. The main recrystallization mechanisms of the as-cast condition are CDRX, PSN and TDRX. Kink-aided dynamic recrystallization can hardly be detected. In the TRC initial state, the block-like LPSO phase is more finely distributed and, above all, less strongly connected. Lamellar LPSO phases are precipitated within the Mg matrix during cooling and different DRX mechanism take place depending on deformation temperature. The fine distribution of the LPSO phase and less network-like nature favor DRX by PSN. Besides this, TDRX and KDRX can be observed.−1 to 0.1 s−1 for the as-cast material as well as a strain rate of 1 s−1 for the TRC material.Optimum deformation conditions are temperatures from 500 °C to 520 °C and strain rates ranging from 0.01 sThis paper compares the hot deformation behavior of the Mg-6.8Y-2.5Zn-0.4Zr (WZ73) alloy in the as-cast and the twin-roll cast condition. The main findings can be summarized as follows:


Secreted class 3 semaphorins (Sema3s) form tripartite complexes with the plexin receptor and neuropilin coreceptor, which are both transmembrane proteins that together mediate semaphorin signal for neuronal axon guidance and other processes. Despite extensive investigations, the overall architecture of and the molecular interactions in the Sema3/plexin/neuropilin complex are incompletely understood. Here we present the cryo-EM structure of a near intact extracellular region complex of Sema3A, PlexinA4 and Neuropilin 1 (Nrp1) at 3.7 Å resolution. The structure shows a large symmetric 2:2:2 assembly in which each subunit makes multiple interactions with others. The two PlexinA4 molecules in the complex do not interact directly, but their membrane proximal regions are close to each other and poised to promote the formation of the intracellular active dimer for signaling. The structure reveals a previously unknown interface between the a2b1b2 module in Nrp1 and the Sema domain of Sema3A. This interaction places the a2b1b2 module at the top of the complex, far away from the plasma membrane where the transmembrane regions of Nrp1 and PlexinA4 embed. As a result, the region following the a2b1b2 module in Nrp1 must span a large distance to allow the connection to the transmembrane region, suggesting an essential role for the long non-conserved linkers and the MAM domain in neuropilin in the semaphorin/plexin/neuropilin complex. Secreted class 3 semaphorins (Sema3s) form tripartite complexes with a plexin receptor and neuropilin co-receptor to transduce signals for neuronal axon guidance and other processes. Here, the authors present the cryo-EM structure of the extracellular Sema3A/PlexinA4/Neuropilin1 complex that provides further insights into the interactions among semaphorin, plexin and neuropilin and reveals long flexible linkers in semaphorin and neuropilin that are important for complex formation. Loss-of-function of semaphorins leads to severe developmental defects in these systems and in some cases embryonic lethality2. Semaphorin-mediated signal also play important roles in regulating other processes, such as wound healing, immunity, and bone development3.Semaphorins are the largest family of guidance molecules that transduce signal to control cell morphological changes, migration and directional growth. Specifically, semaphorin signaling plays essential roles in the development and functions of the nervous system and cardiovascular system by serving as guidance cues for developing neurons and blood vessels10. In most semaphorins, the Sema domain is followed by a small cysteine-rich plexin-semaphorin-Integrin (PSI) domain and an immunoglobin-like (Ig-like) domain. Semaphorins typically form homodimers and in some cases heterodimers through interactions mediated by the Sema and Ig-like domains10. The region following the Ig-like domain is more diverse among the semaphorin family members. A conserved cysteine residue in the C-terminal tail region of class 3 semaphorins forms an interchain disulfide to covalently link the two subunits of the dimer12.Semaphorins are divided into eight classes based on sequence conservation. They are either cell surface attached or secreted proteins that exert their biological effects through binding and activating their cognate cell surface receptors. The semaphorin family members all contain a N-terminal Sema domain, characterized by a 7-bladed β-propeller fold, that mediates the interaction with the receptor2. Plexins are type I single-pass transmembrane proteins that also contain a Sema domain at the N-terminus of the extracellular region, which binds the Sema domain in semaphorins. The Sema domain in plexins is followed by multiple PSI and IPT  domains, which connect to the transmembrane (TM) helix. The intracellular region of all plexins contains a membrane proximal juxtamembrane region (JM), a Rho GTPase-binding domain (RBD), and GTPase Activating Protein (GAP) domain18. In general, the binding of dimeric semaphorin leads to the formation of the active dimer of plexin, which activates the GAP activity of plexins specific for Rap GTPase that is essential for signaling20. The intracellular region of some plexins can interact with other proteins for signal transduction through additional pathways or intracellular trafficking23.The best characterized receptor of semaphorins are plexins, which are classified into four classes 26. In this case, the transmembrane protein neuropilin (Nrp1 or Nrp2) acts as an essential co-receptor to facilitate the semaphorin/plexin interaction. Neuropilin interacts with both semaphorin and plexin, leading to a stable 2:2:2 complex of semaphorin, plexin and neuropilin that is capable of triggering downstream signaling7. Nrp1 and Nrp2 are also type I transmembrane proteins, composed of a multi-domain extracellular region, a single transmembrane helix and a short cytoplasmic tail. The extracellular region contains from the N- to C-terminal region the a1, a2, b1, b2, and MAM  domains. The a1 and a2 domains belong to the Ca2+-binding CUB domain family, whereas the b1 and b2 are coagulation factor V/VIII homology domains29. The MAM domain exhibits a jellyroll topology and also binds Ca2+.30 The extracellular domains of neuropilin play major roles in stabilizing the semaphorin/plexin/neuropilin complex, while the intracellular region interacts with proteins that contribute to downstream signaling or regulate trafficking of neuropilin2. Another interesting aspect is that some class 3 semaphorins require both neuropilin and the simultaneous presence of two different class A plexins for signaling32. Overexpression of one plexin in some cases could overcome this requirement32. These observations suggest that different plexin family members can heterodimerize and gain extra structural and functional features, the precise mechanisms of which are not well understood at present.An exception to the general mechanism described above is that secreted class 3 semaphorins binds class A plexins very weakly, and therefore cannot induce their activation independently34. While all the plexins use similar mechanisms of activation, there are some important differences for some plexin family members. For example, PlexinC1 has a distinct extracellular architecture, and therefore the overall shape of the semaphorin-induced dimer of PlexinC1 appears quite different from that of class A plexins33. A crystal structure of the a1 domain of Nrp1 in complex with PlexinA2 and Sema3A shows that the Nrp1-a1 domain stabilizes the complex by binding the Sema domains of both semaphorin and plexin simultaneously7. Interestingly, biochemical analyses presented in this study showed that the a1a2b1b2 domains together are required for the semaphorin/plexin/neuropilin complex formation, but the a2b1b2 domains are not resolved in the structure and therefore their roles in the complex remain unclear7. In addition, the C-terminal tail region of class 3 semaphorins contributes to the complex formation by interacting with neuropilin36. Class 3 semaphorins could be cleaved at multiple sites by furin or furin-like proteases, which recognize a degenerate K/R-XX-R  motif37. Of particular interest, furin-cleavage of the C-terminal poly-basic region of class 3 semaphorins results in a C-terminal arginine residue that serves as a high-affinity ligand for a pocket on the b2 domain of neuropilin41. Notably this pocket on the b2 domain of neuropilin has recently been suggested to bind a C-terminal arginine-containing motif as a result of furin-mediated cleavage of the spike protein of SARS-CoV-2, and thereby contributing to viral infection43.Extensive structural analyses of plexin have led to a general model on how dimeric semaphorin binds and induces the dimerization and activation of plexinTaken together, neuropilin interacts with semaphorin and plexin through multiple binding sites. How these multiple sites work together in the context of the 2:2:2 semaphorin/plexin/neuropilin assembly is not known due to lack of structure analyses of an intact complex. We reconstituted the complex using the a1a2b1b2 domains of Nrp1, the near full-length extracellular regions of PlexinA4 and Sema3A. We determined the structure of this complex to 3.7 Å resolution by using cryo-electron microscopy (Cryo-EM) single particle reconstruction. The structure reveals the overall architecture of the 2:2:2 complex and the details of the multiple interfaces among the subunits that stabilize this large assembly. In addition, this architecture reveals previously unappreciated important roles for the long and nonconserved interdomain linker regions in both neuropilin and semaphorin in the complex formation.44. However, thorough in vitro binding assays with surface plasma resonance have shown that the dissociation constants between individual pairs of the Sema-PSI domains of Sema3A, the Sema-PSI1-IPT1-PSI2 domains of PlexinA2 and the a1a2b1b2 domains of Nrp1 range from double-digit micromolar to undetectable7. The binding affinity of PlexinA2 to Sema3A and Nrp1 both immobilized on the same sensor chip is stronger , but still three orders of magnitude weaker than those indicated by the cell-based assays. This discrepancy is in part owing to the fact that the in vitro binding assays used a Sema3A construct lacking the C-terminal poly-basic tail, which is processed by furin-like proteases to generate a motif with a C-terminal arginine residue that binds the b1 domain of Nrp1 with high affinity 39. In our effort of reconstitution of the semaphorin/plexin/neuropilin complexes, we therefore used constructs of semaphorins containing the C-terminal tail but truncated at one of the furin-cleavage sites, equivalent to the furin-processed mature forms -A106K), the MAM-domain truncated version of Nrp1 (Nrp1-a1a2b1b2) and the full-length extracellular region of PlexinA4 was stable in gel filtration chromatography and showed relatively homogenous particles in our cryo-EM screen . The ~20 residues from the two copies of the linker can span the distance (~50 Å) between the C-termini of the two IPT domains in the dimeric complex. The architecture of the Sema3A/PlexinA4/Nrp1 extracellular region complex is therefore poised to induce the formation of the intracellular active dimer of plexin for signaling, in a similar manner as that seen in PlexinC1 activated by the viral semaphorin-mimic A39R33.The binding mode between the Sema3A dimer and the two PlexinA4 molecules in the complex is similar to those in all the previous structures of semaphorin/plexin complexes, with or without neuropilin  reveals more details of the binding interfaces (see more below). The a2b1b2 domains of Nrp1, adopting the characteristic triangular shape28, is placed on the side of Sema3A above both the a1 domain and PlexinA4. The a2 domain of Nrp1 makes an interface with the Sema domain of Sema3A, which has not been observed before (See more below). Presumably, the b1 domain binds the C-terminal arginine motif of Sema3A, but this interaction is not resolved in the cryo-EM map and therefore not included in the atomic model.Each subunit of the Sema3A dimer binds one PlexinA4 molecule to form a 2:2 complex where the two PlexinA4 molecules do not contact each other Fig. . The 2:230. Therefore, the b2-MAM and MAM-TM linkers must cover ~100 Å distance to allow the formation of the semaphorin/plexin/neuropilin complex on the cell surface  around the Ca2+-binding site in the Nrp1-a1 domain  that forms the interchain disulfide that further stabilizes the Sema3A dimer  adopts a relatively extended conformation and is oriented roughly orthogonal to the connecting β-strands in the a1 and a2 domains respectively , and a mutant with 10-residue deletion in the MAM-TM linker (Δ820–829). A third mutant was generated by combining these two deletions. The results show that these link deletions reduced the percentage of cell collapse to ~50%, significantly lower than that of the wild-type  to the cryo-EM structure based on superimposition of the Sema domain of PlexinA1 and PlexinA4 shows that this conformation of PlexinA1 can form a 2:2:2 active complex similar to that of PlexinA4  results in severe clashes between the two IPT6 domains in the complex, suggesting that these conformations are not compatible with the complex formation or activation of PlexinA1  was subcloned into a modified pETZ-BM vector as described previously33, which includes the N-terminal signal peptide from alkaline phosphatase and a C-terminal His8-tag -A106K, PlexinA4, and Nrp1-a1a2b1b2 could form the complex that remained intact in gel filtration chromatography. Complexes formed with shorter versions of Semaphorin were less stable and tended to dissociate in gel filtration chromatography. Sema3A (21–770)-A106K, PlexinA4, and Nrp1-a1a2b1b2 were co-expressed for convenience as well as taking advantage of the mutual stabilization of the proteins when the complex was formed as soon as they were secreted into the culture medium. During co-expression, Nrp1-a1a2b1b2 expressed at much higher level than Sema3A (21–770)-A106K and PlexinA4, lead to a large peak of Nrp1 in addition to the complex peak in the gel filtration run  into Expi293F cells cultured in suspension in Expi293 Expression medium , with 1000 µg DNA and 4 ml PEI at 1 mg/ml for 1 L cells. For co-expression of the three proteins, the three plasmids at 1:1:1 weight ratio were transfected. Twelve hours post transfection, sodium butyrate with a final concentration of 5 mM were added to Expi293F cells to enhance protein expression. After 7 days expression, medium was collected and exchanged to a buffer containing 25 mM Tris (pH 8.0), 500 mM NaCl, and 25 mM imidazole. The expressed proteins were first purified by Ni-NTA affinity beads using standard protocols. Proteins were further purified by a Superose 6 10/300 gel filtration column  with a buffer containing 20 mM HEPES (pH 7.5) and 150 mM NaCl and 1 mM CaCl2 at 4 °C for 30 min. Various pairs of Nrp1 (wild-type or mutants) and Sema3A (wild-type or mutants), 30 µg each, were added and incubated at 4 °C for an additional hour. In parallel, anti-FLAG resin without FLAG-tagged PlexinA4 bound were incubated with wild-type Sema3A or Nrp1 as controls to ensure that they do not interact with the resin nonspecifically. Resin was pelleted by centrifugation, washed with the buffer containing 20 mM HEPES (pH 7.5), 150 mM NaCl, and 1 mM CaCl2 three times. The samples were boiled and resolved by SDS-PAGE. Protein was stained with Coomassie blue R250.The wild-type or the K343E mutant of FLAG-tagged PlexinA4 (20 µg) was incubated with 50 µl anti-FLAG antibody conjugated resin  in a buffer containing 20 mM HEPES (pH 7.5),150 mM NaCl and 1 mM CaClPlexinA4 with a C-terminal c-myc tag were subcloned into a Lentiviral vector pTY with a puromycin-resistance marker . The cDNA of full-length Nrp1 with a C-terminal FLAG-tag were inserted into a Lentiviral vector pTY with a blasticidin S-resistance marker. The mutants of PlexinA4 and Nrp1 were generated in these constructs by PCR-based methods. The plasmids were transfected into HEK293T cells  to generate Lentiviral vectors, which were then used to infect COS7 cells . COS7 cells were cultured in DMEM medium (Invitrogen) supplemented with 1% (V/V) penicillin/streptomycinb and 10% (V/V) FBS with 5% CO2 at 37 °C. Puromycin (1 ug/ml) and blasticidin S (5 ug/ml) were used together to select for COS7 cells co-expressing PlexinA4 and Nrp1. Cell surface expression of these proteins was confirmed with immuno-staining and fluorescence microscopy. Myc-tagged PlexinA4 was detected by using a rabbit anti-Myc antibody  and an Alexa-488-conjugated anti-Rabbit IgG secondary antibody . FLAG-tagged Nrp1 was detected with a mouse anti-FLAG antibody  and an anti-Mouse IgG secondary antibody conjugated with the Cy3 fluorophore . Nuclei were stained with DAPI . A DeltaVision Core microscope was used to image cells.The cDNA of full-length mouse 6 cells per well and cultured for 12 h. To induce collapse, cells were incubated with Sema3A (21–770) wild-type or mutants at a concentration of 5 nM for 30 min at 37 °C. Cells were then washed with PBS buffer and fixed in 4% paraformaldehyde. Cells were stained for PlexinA4 with an anti-myc antibody  and Alexa-488-coupled anti-mouse IgG secondary antibody . Nuclei were stained with DAPI. Images were taken at ×200 magnification with a ZOE fluorescent cell imager (Bio-Rad). Collapsed cells displayed reduced area and star-like morphology, in contrast to the characteristic large and round shape of normal uncollapsed COS7 cells. Counting of collapsed cells was carried out in blind. For each group, at least three biological repeats were done. The number of cells counted in each experiment for different groups was in the range of 379–3020. Plots of percentage of collapse and calculation of p-values were done in Graphpad Prism 9.COS7 co-expressing various combinations of the wild-type or mutants of PlexinA4 and Nrp1 were seeded in six-well plates with 1 × 1052. The slit width of the GIF-Quantum energy filter was set to 20 eV. Images were recorded in the super-resolution counting mode, dose-fractioned into 30 frames with the dose rate of 1.6 e−/Å/frame. The magnification was set to equivalent of the pixel size of 1.08 Å.The Sema3A/PlexinA4/Nrp1 complex at 2 mg/ml was applied to a glow-discharged Quantifoil R1.2/1.3 300-mesh gold holey carbon grid . The grid was blotted under 100% humidity at 4 °C and plunged into liquid ethane with a Mark IV Vitrobot (FEI). The serial EM software was used to control a 300 kV Titan Krios microscope (FEI) with a K3 Summit direct electron detector (Gatan) for Cryo-EM data collection53. GCTF 1.06 was used for CTF correction54. The rest of the image processing was carried out using RELION 3.155. The initial round of particle picking was carried out using the Laplacian of Gaussian blob detection implemented in Relion. Particles were extracted and subjected to several rounds of 2D classification. A subset of particles from good 2D classes were selected for another around of 2D classification. Nine good 2D classes averages from this step were then used for reference-based particle autopicking in Relion. Particles were then extracted with a binning factor of 4 and subjected to three rounds of 2D classification. Particles in good 2D classes were selected and subjected to 3D classification. The initial model was generated by combining the crystal structures of the partial Sema3A/PlexinA2/Nrp1 complex (PDB ID: 4gza) and the apo-full-length extracellular region of PlexinA4 (PDB ID: 5l5k), and converting it to a density map low-pass filtered to 30 Å resolution. A total of 137,836 particles from two good 3D classes were re-extracted to the original pixel size and subjected to 3D refinement the C2 symmetry. The resulted 3D reconstruction reached 4.0 Å overall resolution, but the density for the peripheral parts of the complex was much weaker than the center portion. A round of 3D classification without image alignment of the refined particles led to one class showing stronger density for the peripheral parts. Particles in this class (26741 particles) were selected and subjected to further 3D refinement, CTF refinement, leading to a final 3D reconstruction of resolution 3.7 Å , and the a1a2b1b2 domains Nrp1 (PDB ID: 2qqk). Manual building was carried out using the program Coot 0.8956. The final model does not include sidechain for many residues, the C-terminal tail of Sem3A and several interdomain loops of the three proteins due to lack of clear density. The model was refined by using the real-space refinement module in the Phenix package (V1.8)57. Restraints on secondary structure, backbone Ramachandran angels, residue sidechain rotamers were used during the refinement to improve the geometry of the model. MolProbity 4.5 as a part of the Phenix validation tools was used for model validation  or Chimera 1.359. Sequence alignments are rendered with JalView 2.11.1.360.Model building was initiated by rigid-body docking of individual domains from the crystal structures of the mouse PlexinA4 extracellular region (PDB ID: 5l5k), Sema3A (PDB ID: Further information on research design is available in the Supplementary informationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Reporting Summary


Chromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been characterized only in a few cell types and systematic comparisons across multiple tissues and conditions are missing. Here, we present Calder, an algorithmic approach that enables the identification of multi-scale sub-compartments at variable data resolution. Calder allows to infer and compare chromatin sub-compartments and compartment domains in >100 cell lines. Our results reveal sub-compartments enriched for poised chromatin states and undergoing spatial repositioning during lineage differentiation and oncogenic transformation. Computational algorithms to infer chromatin sub-compartments and compartment domains require high-resolution Hi-C maps. Here the authors present Calder, an algorithm that can infer sub-compartments and compartment domains with variable resolution Hi-C data, and they apply it to more than a hundred Hi-C experiments to study sub-compartment repositioning. Hi-C technologies have allowed to quantify and computationally model such interactions to unravel chromatin spatial organization6. The formation of chromatin SEs is driven by two major mechanisms: loop extrusion of chromatin fibers mediated by CTCF and cohesin, and chromatin compartmentalization, which segregates chromatin regions with different patterns of histone acetylation and methylation7. Loop extrusion has been associated with the formation of topologically associating domains (TADs) and structural loops10, whereas chromatin compartmentalization segregates the chromatin into spatial compartments12 and compartment domains10. At a broad scale, chromatin segregates into two major compartments, one preferentially localized at the core of the nucleus and exhibiting high transcriptional activity (A compartment), and another localized closer to the nuclear lamina and enriched for repressed and gene-depleted chromatin (B compartment). Within each chromosome, DNA regions belonging to a given compartment are defined as compartment domains. Interestingly, recent experiments based on CTCF and cohesin depletion have shown that although often overlapping and sometime coincident, TADs and compartment domains are in fact distinct SEs13. Compartment domains from the same compartment preferentially interact among each other and, in Hi-C contact correlation maps, the alternance of A and B compartment domains generates a chessboard or “plaid” pattern reflecting enrichment or depletion of Hi-C interactions4.In interphase, the chromatin is packaged into a hierarchy of three-dimensional (3D) structural elements (SEs) emerging from interactions and insulation of distinct DNA regions5 and phenotypic changes are more frequently associated with compartment repositioning of a given genomic region than with structural disruption of loops or TADs14. Importantly, A and B compartments have been shown enriched for distinct histone modifications, which are consistent with the observed transcriptional activity. However, chromatin activity encompasses multiple states15, some of which can only be captured through a more refined subcompartmentalization2. It was previously proposed that the A and B dichotomy might not be sufficient to explain chromatin compartmentalization16. Indeed, up to 6 subcompartments have been inferred by clustering interchromosomal interactions using a Gaussian Hidden Markov Model (GHMM)10 and, recently, an intermediate compartment was characterized using Hi-C and imaging techniques in colorectal tumor samples17. Computational inference of subcompartments has so far relied on interchromosomal contacts and, thus, it has been possible only for high-resolution experiments. For example, the GHMM approach was exclusively applied to the GM12878 cell line (4.9 billion read pairs). Machine learning-based imputation of Hi-C contacts has been used to enhance data resolution and allow subcompartment inference in eight additional Hi-C experiments18. However, as we will show, this approach frequently fails to correctly infer subcompartments when challenged with relatively low-resolution experiments or when patterns of interchromosomal interactions deviate significantly from the training dataset. As a consequence, the identification of chromatin subcompartments and compartment domains remains unfeasible for the vast majority of available Hi-C datasets. Here, we propose an algorithm able to infer compartment domains and hierarchies of subcompartments in Hi-C contact maps with highly variable total number of reads. By inferring chromatin subcompartments across multiple cell types and states, we could study repositioning of compartment domains and its association with cell phenotypes.Computational inference of A and B compartments has been performed across multiple cell types and it showed, for example, that compartments are less conserved across cell types than TADs21, which invariably preserved TAD 1D-proximity, i.e., two contiguous TADs along the genome sequence will also be contiguous in the inferred hierarchy. We called our approach Calder to draw a suggestive analogy between the nonstatic nature of compartment domain hierarchies and the mobile sculptures of Alexander Calder  data for seven histone marks. We found that eight subcompartments were sufficient to capture significant changes of these histone modifications in all cell lines  and A.1.1-A.1.2 and between heterochromatin and B.2.1-B.2.2 . Although the original GHMM approach was applied only to the GM12878 Hi-C map, a Hi-C contact imputation approach called SNIPER18 was later proposed to enhance data resolution in five additional Hi-C maps2  and infer five subcompartments in these models. Subcompartments inferred by Calder and SNIPER in these six cell lines were highly concordant  approach, originally used to detect three subcompartments24, compartment domain boundaries have been simply inferred as genomic positions where a change of A/B compartment or subcompartment occurs10 or as positions where there is a switch of contact propensity towards either A or B compartments25. We obtained compartment domain boundaries using these approaches in the GM12878 cell line, based on available subcompartment annotations2. On the one hand, compartment domain boundaries inferred by Calder covered 92–93% of boundaries determined based on these annotations and approaches  from TAD boundaries inferred by TopDom or arrowhead, but this percentage increased to 75–85% for nested domain boundaries  in lymphoblastoid cells (GM12878), where the gene is highly expressed, and in lung fibroblasts (IMR90), where the gene is silenced  and GM12878 . Conversely, we did not observe any repositioning for the domain containing the OSBP gene   Fig. , which ines Fig. . CD20 recus Fig. . Notablycus Fig. .Fig. 3Sun = 8) assignments for each bin  were significantly enriched for fundamental cellular processes, such as protein transport, localization, and phosphorylation, RNA processing and metabolism, and cell cycle  were significantly enriched for cell differentiation, development, and morphogenesis , we examined which regions were found in the same subcompartment across multiple cell lines (low entropy) and which instead were frequently repositioned (high entropy). The overall cle Fig. —top. Vicsis Fig. . To explsis Fig. . OverallFOXO1, which has been frequently associated with tumor suppressive functions28. The FOXO1 locus was in A.1.1 or A.1.2 in all normal cells and it shifted towards intermediate and inactive compartments in cancer cell lines , we found that FOXO1 was downregulated in the vast majority of human breast, prostate, and pancreatic tumors . To verify that subcompartment repositioning was effectively associated with modified 3D-proximity to other domains, we analyzed intrachromosomal contact frequencies between the FOXO1-containing compartment domain and other compartment domains that remained in the same subcompartment in normal and cancer cell lines  compartments and compartment domainsWe showed that subcompartment differences are extensive between different cell lines, even when domain boundaries are preserved, and these differences were often associated with epigenetic and transcriptional heterogeneity among tissue types, differentiation stages, and malignant states. These results caution against assuming that subcompartments partitions derived from one cell line can be applied to other samples. Intriguingly, the most different subcompartments  were the most stable across cell lines, whereas regions in intermediate subcompartments were rarely the same across samples. These findings indicate a different degree of chromatin plasticity along the genome. Future studies should investigate the biological relevance of this plasticity and whether it reflects the chromatin conformation of individual cells or the average conformation of a heterogeneous cell population.33 or lead to changes in chromatin compartmentalization34. In this context, robust inference of chromatin subcompartments and compartment domains will be crucial to appreciate the effect of altered epigenetic states on chromatin compartmentalization.Moreover, we found that subcompartment repositioning was frequently associated with changes of gene expression and enrichment for different histone marks. Although the chain of causality of these events needs further investigation, recent evidence indicated that altering histone modifications is sufficient to increase and decrease the frequency of chromatin interactionsOverall, the identification of chromatin subcompartments across a variety of cell types and conditions opens the possibility to their systematic comparison and quantification of spatial repositioning to aid the understanding of the link between structure and phenotype.35 with default parameters or obtained from processed public data. Data sources are provided in Supplementary Data Hi-C intrachromosomal contact maps (or matrices) were either generated from raw FASTQ files using Juicer toolshttps://www.encodeproject.org/] or GEO [https://www.ncbi.nlm.nih.gov/geo/]. We also processed H3K27ac for BT474 from publicly available raw sequencing data. FASTQ files were aligned to hg19 reference genome by bowtie236 version 2.3.4.3 with local alignment option. Peaks of H3K27ac enrichment compared to the corresponding input control were identified using MACS237 version 2.1.1. Extreme values, defined as those greater than the upper 0.999 quantile of each chromosome, were replaced with the 0.999 quantile. Data sources are provided in Supplementary Data Processed ChIP-seq value  were retrieved from ENCODE . We used TPM (transcripts per million) as a measure of gene expression level for these samples. Data sources are provided in Supplementary Data Gene expression values at mRNA level for cell lines were quantified by FPKM (fragments per kilobase per million mapped fragments) and were retrieved from ENCODE or processed from publicly available raw FASTQ files using RSEMth row of A* represents the total contacts between bini and every 10 bins. If the bin size is ≥40 kb we omit this step thus A* = A. The purpose of this step is to sum up contact values to reduce data sparsity for small bin size, thus, to allow applying our method to bin size up to 10 kb.Providing an intrachromosomal contact matrix A, we generated an aggregated matrix A* if the bin size is smaller than 40 kb, by compressing every 10 columns into a single column, thus the i2A.A correlation matrix ρA was computed storing all pairwise correlations of the rows of A*. To enhance the correlation signals, we computed pairwise column correlations again from ρA to generate a second correlation matrix ρ2A was transformed into a similarity matrix defined as sA = arctanh(ρ2A). The function arctanh was chosen such that correlations with absolute value greater than ~0.3 have a greater amplification ratio. This is also commonly referred to as Fisher z-transformation, which the converted correlation score is approximately normally distributed.ρFinally, we called compartment domain boundaries separating regions having high intraregion values and low inter-region values. To this purpose, we used the same strategy proposed in the TopDom algorithm applied to the sA matrix (instead of the Hi-C contact matrix).We defined compartment domains as consecutive bins having high intradomain similarity while low interdomain similarity, where the similarity is measured based on whole-chromosome interaction pattern. Identification of compartment domains proceeds through the following steps  × k, to summarize the plaid pattern commonly observed in the Hi-C contact matrix and that reflects compartmentalization. Values of Tm are defined as: Tm = 1 if the mean contact value between bins in domain i and in domain j + m is greater than the mean contact value between bins in domain i and bins in domain j (thus indicating enrichment of contacts); otherwise Tm = 0 (depletion of contacts). These four trend matrices were concatenated by rows to form a combined trend matrix T. A correlation matrix ρT was computed storing all the pairwise correlations of the columns of T and then converted to a score matrix sT using arctanh transform. Although a single raw trend matrix (m = 1) can also be used to compute the correlation in this step, we found the concatenation of multiple matrices can increase the correlation signals, however the marginal gain is negligible when the number if greater than 4.First, we computed 4 raw trend matrices T1) was the most associated with chromatin compartmentalization , in the analyses presented in this manuscript, we set w = 1 for PC1 and w = 0.25 for all other PCs. We chose the first 10 PCs in this step as we observed the marginal variance explained by each additional PC is negligible.Next, we used the first 10 principal components (PCs) of sT to perform a divisive hierarchical clustering. Starting from the complete set of compartment domains, we iteratively split a set into two sub-sets using k-means (k = 2), until each compartment domain is on a separate set. PC values can be rescaled by a factor w, to give different weight to different PCs. Given the first PC (PC1 such that it correlates with gene density (gene density is expected to be higher in A than in B compartment).Given the sign of PCs is arbitrary, we fixed the sign of PC1 and PC2 using loess regression, i.e. loess(PC2 ~ PC1), and projected each data point, i.e., domain,  to this major axis.Then we derived the non-linear major axis of the point cloud (each point represent one domain) formed by PCz based on its ranking j of this major axis: j > 1, which is the Euclidean distance of between each pair of adjacent domains along this axis.To each domain we assigned a score z value than the branch on the left side, for any sibling branch pair.Finally, we ordered the branches of the dendrogram such that the branch on the right side should have a greater average Fianlly, since there are multiple possible ways to order the dendrogram branches without disrupting its structure, we sought a consistent ordering among chromosomes reflecting chromatin compartmentalization.Given The left and right branch at the top level of the ordered dendrogram were labelled as B and A respectively. For a sub-branch X, its left child branch was labelled as X.2 and right child branch as X.1. The chromatin domain ranks were derived from the order of the domains in the final dendrogram and normalized by the total number of chromatin domains in that dendrogram.X = 0) = 1; Finding the best-fitting dendrogram: We assume the contact probability between bins from two sibling domains in the dendrogram D follows a mixture log-normal distribution with parameter Given the contact matrix We computed the best-fitting dendrogram and corresponding parameters using maximum likelihood estimationwhich can be computed via dynamic programming and has a computation complexity of oe to eliminate distance-bias of the contact matrix Aoe is defined as the ration between the contact maps A and E, where E is the expected contact matrix with:First, we compute the observed vs. expected contact matrix Aoe within a given domain is split into four parts: A: the triangle delimited by the off-diagonal corner of the domain and the two off-diagonal corners of the candidate nested domains; B: the triangle comprising the contacts between the two nested domains that are not included in (a); C–D: contacts within the two nested domains  contacts in C and D were significantly higher than contacts in B. Contact differences are tested using a one-tailed Wilcoxon test and considered as significant if p-value < 0.05 and oe is 1).Finally, a given split was accepted if (1) contacts within Trimming the dendrogram to get meaningful nested domains: Given the best-fitting dendrogram D, we extracted meaningful nested domains by splitting iteratively a domain into subdomains as described below.A stochastic approach was developed to call nested subdomains within each compartment domain exclusively based on short-range contacts. The basic idea is to determine whether a hierarchical structure of smaller domains can be further inferred from local contact patterns within a given compartment domain. In this step of the Calder (and unlike what is done in the previous step) hierarchies of nested domains contained in a domain are anchored to the genome sequence, i.e., adjacent nested domain in the hierarchy are also contiguous along the genome sequence. This step can be split into two major tasks: (1) finding the best-fitting dendrogram D; (2) trimming D to get meaningful nested domains.The above procedures result in a trimmed dendrogram representing nested subdomains in a compartment domain.For each histone mark, we first log-transformed (ln(x + 1)) bin-level ChIP-seq intensity values and took the mean of these for each chromatin domain.1,…,xn} and Y = {y1,…,ym} and overall mean μ(X) > μ(Y), we defined the effect size Δμ as the maximum possible value such that μ(X)-Δμ is significantly greater than μ(Y) .Next, given two sibling compartments in the domain hierarchy with mean intensity values X = {xMean intensity values of two adjacent compartments were considered as significantly different if the median Δμ of all 22 chromosomes was above a threshold γ. To penalize large number of compartments that did not lead to high gains in terms of explained variance , we used progressive thresholds: To determine the number of compartments that is sufficient to explain the chromatin epigenetic pattern variation, we proceed as follow:We performed this test for each histone mark starting from the top (A/B compartment) and iteratively on lower levels of the hierarchy until no significant differences were reached. The number of compartments corresponding right before the stopping step was retained as the minimal number of compartment sufficient to explain epigenetic pattern variation.2, then predict five compartments  of a new dataset using the new interchromosomal contact pattern. The author provided five trained models for different downsample ratios . We chose the downsample ratio that best matches the observed ratio between a given dataset and the training dataset of GM12878 . We also applied Adapted k-means on 7 Hi-C datasets , using 1 Mb bin size as specified in the original paper. We set k = 8 with the aim to identify the same number of subcompartments as Calder. We ordered the inferred subcompartments based on the average H3Kme3 intensity of each subcompartment .We applied SNIPER on 38 Hi-C datasets at the resolution of 100 kb. SNIPER is a machine learning-based method which first learned the relation between the interchromosomal contact pattern and compartment annotation of 10, or as positions where there is a switch of contact propensity with A/B compartments25. For the first approach, we retrieved compartment domain boundaries based on the publicly available subcompartment annotation from2. For the second approach, we computed for each 10 kb bin the average distance-corrected contact intensity with A or B compartment. An A-B index was defined as the difference between the A/B intensity. Compartment domain boundaries were retrieved at positions where a switch of the sign (±) of A-B index occurs. Switches that occupy only 1 bin were considered as noise and omitted. To compare boundaries identified by these approaches or Calder, we considered two boundaries as overlapping if they are less than 100 kb apart (100 kb was the resolution of subcompartment annotation from2).Compartment domain boundaries have been simply inferred as genomic positions where a change of A/B compartment or subcompartment occurs2 and TopDom26 to the Hi-C map of GM12878 at 10 kb bin size to call TADs, using default parameters. Two boundaries were considered as overlapping if they are less than 40 kb apart.We also applied Arrowheadmax_pcs = 50, bad_frac = 0 and centromere_search = FALSE.We applied TADpole to the Hi-C data of GM12878 binned at 40 kb to derive genome sequence-dependent hierarchies, for the purpose to compare with Calder’s genome sequence-independent hierarchies as shown in Supplementary Fig. b as:j is the frequency with which b is assigned to compartment j across the 114 datasets. The normalizing factor ln(8) ensures that S is in the  range. We defined the distribution of entropy values for all bins of the genome as chromatin entropy. To estimate the expected chromatin entropy, we performed 1000 permutations of compartment labels. In each permutation, we randomly shuffled the compartment labels of each bin, within each dataset independently. Chromatin entropies computed from each permutation were aggregated to derive the overall random distribution.Given the 8-compartment model, we defined the Shannon’s information entropy of a bin 2-transformed ratio between the median value in the compartment and the overall median value.For each histone mark, mean intensity values for each chromatin domain were computed. Next, we determined the enrichment of these values within a given compartment (8-compartment model) as the loghttp://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHmm]To determine the enrichment of ChromHMM states in each compartment, we used the 15-state definition reported in:  and retaining the top 100 solutions with adjusted p-value < 0.01. In the geneset enrichment analysis of genes with high entropy, we noticed strong enrichment for GO categories associated with cell adhesion (Supplementary Data Coding genes located in genomic regions of low entropy (S < 0.15, Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7


However, numerous planar dislocation arrays and microbands can be observed, and these substructures may be a reason for multi-peak strain hardening behaviors at 4 K. They can also provide certain strain hardening capacity, and a relatively high total elongation of ~48% can be obtained at 4 K. In addition, it was found that the yield strength (YS) and ultimate tensile strength (UTS) linearly increases with the lowering of the deformation temperature from 298 K to 4 K, and the increment in YS and UTS was estimated to be 2.13 and 2.43 MPa per 1 K reduction, respectively.The deformation structure and its contribution to strain hardening of a high manganese austenitic steel were investigated after tensile deformation at 298 K, 77 K and 4 K by means of electron backscatter diffraction and transmission electron microscopy, exhibiting a strong dependence of strain hardening and deformation structure on deformation temperature. It was demonstrated that sufficient twinning indeed provides a high and stable strain hardening capacity, leading to a simultaneous increase in strength and ductility at 77 K compared with the tensile deformation at 298 K. Moreover, although the SFE of the steel is ~34.4 mJ/m For a long time, high manganese austenitic steels, such as high manganese twinning-induced plasticity (TWIP), transformation-induced plasticity (TRIP) and low-density steels, have attracted much interest due to their potential applications in the automotive industry ,2,3,4,5.2. The TRIP effect can occur, and extremely low temperature mechanical properties are deteriorated. Hence, we designed a Fe–0.6C–0.5Si–24.2Mn–4.9Al (in wt%) steel with a relatively high SFE at room temperature to suppress martensite transformation at 77 K and 4 K. In addition, the deformation mechanisms are somewhat unclear at 4 K. Moreover, it is also unknown whether sufficient twinning can occur for a twinning SFE at 4 K.In addition, the mechanical twinning, as one of the secondary plastic deformation mechanisms ,2,13, plHence, we investigate the temperature dependence of deformation behaviors, mainly focusing on mechanical twinning and planar dislocation slipping. The present study will provide a better understanding of the role of mechanical twining in the enhancement of the strain hardening rate and deformation mechanism at a temperature as low as 4 K.The high manganese steel was alloyed by ~4.9 wt% Al to increase the SFE, with the aim of suppressing martensite transformation at 4 K. The chemical compositions and SFEs, which were estimated via a thermodynamic model , are givStandard cylindrical tensile samples with a gauge diameter of 6 mm and a gauge length of 50 mm were machined along the rolling direction. The uniaxial tensile testing was performed on an AG-X plus pc-controlled tensile machine  at 298 and 77 K at a cross speed of 5 mm/min. The 4 K tensile testing was performed on an MTS-SANS CMT5000 pc-controlled tensile machine (MTS SYSTEMS (CHINA) Co. Ltd., Shanghai, China) equipped with a CryoLab cryogenic system (4.2~300 K)  at a cross speed of 5 mm/min. Moreover, the steels with tensile deformation at 298, 77 and 4 K were designated as the 298 K, 77 K and 4 K steels, respectively.Metallographic specimens were electron-discharge machined from the hot rolled steel and tensile fractured samples with a maximum uniform strain. These specimens were mechanically polished using silicon papers and a polishing machine with the help of polishing paste. Subsequently, they were further polished using a three ion beam polishing instrument  to remove the surface strain layers. The microstructure characteristics before and after tensile deformation were analyzed using a Zeiss Ultra 55  field-emission scanning electron microscope (FE-SEM) equipped with an Electron backscatter diffraction (EBSD) attachment. Moreover, a scanning step of 5 μm, which is small enough to obtain a clear morphology, was utilized during EBSD data collection. The analyzed area of ~2.3 × ~1.7 mm was sufficiently large. These EBSD data were post-processed by a Tango procedure using HKL CHANNEL 5 software .2 F20  field-emission transmission electron microscope (FE-TEM) operated at 200 kV.Some square slices with a thickness of ~600 μm were electron-discharge machined from 298 K, 77 K and 4 K steels. The square slices were mechanically thinned to ~50 μm from both sides to avoid sample bending, which commonly causes curved extinction fringes. Subsequently, they were punched to obtain thin foils with a diameter of 3 mm using a gatan 659 puncher. These thin foils were further thinned using a twin-jet electrolytic polisher  to acquire thin regions, which surround a hole in the thin foil and are thin enough to cause the electron beam to penetrate. Mechanical twins, dislocation configurations and deformation bands were observed using an FEI Tecnai GThe uniaxial tensile engineering stress–strain and corresponding strain hardening rate (SHR) curves are shown in In addition, the strain hardening rate was derived from the true stress–strain curves, as shown in The EBSD IPF and kernel average misorientation (KAM) maps of the steels, subjected to a maximum uniform tensile deformation at 298, 77 and 4 K, are shown in 2 at 298 K, numerous mechanical twins can be observed in the 298 K steel, and the SHR curve also shows that the mechanical twinning has occurred. In general, the mechanical twinning can be activated for the SFE ranging from 15~20 to 40~50 mJ/m2 50 mJ/m2 ,33,34, ig stress .Besides mechanical twinning, 2 at 298 K, this value decreases to ~38.6 mJ/m2 at 77 K. Thus, the mechanical twinning can be sufficiently activated according to the SFE range for mechanical twinning. Numerous mechanical twins are indeed observed in the 77 K steel. Most mechanical twins show a relatively small thickness of 10~20 nm or large thickness of 50~100 nm, and there are some thicken mechanical twins with a thickness greater than 300 nm, as shown in When the deformation temperature is decreased to 77 K, the deformed microstructure is very different from the 298 K steel, as shown in ) planes ,38, lead) planes . Figure ) planes ,38.2 at 4 K and this value is slightly less than ~38.6 mJ/m2, a lot of mechanical twins, such as those in the 77 K steel, are not observed, seeming to indicate that the mechanical twinning is hard to activate at 4 K. Compared with the 77 K steel, a pronounced decrease in TEL may be due to few mechanical twins. However, compared with the 298 K steel, there is only a small decrease in TEL, indicating that the formation of planar dislocation arrays and microbands can also enhance SHR, and a relatively high TEL can be obtained. In addition, the serrations in the stress–strain curves of metals tested at 4 K are commonly observed, and some mechanisms were proposed to explain this phenomenon. We think that the adiabatic deformation is the main reason. Furthermore, the two-variant planar dislocation arrays in the 4 K steel may be also a reason for the formation of serrations. The pre-formed planar dislocation arrays can act strong barriers for dislocation slipping along another variant slip plane, and dislocation pinning and depinning can occur, generating bursts of increased and decreased stress.With the further lowering of the deformation temperature to 4 K, few mechanical twins can be observed. Instead, the dislocation configurations are characterized by planar dislocation arrays. Although the SFE of the steel is ~34.4 mJ/mIt is normal that both YS and UTS increased with the lowering of deformation temperature, whereas the TEL exhibited an obvious increase at 77 K and only a slight decrease in TEL at 4 K compared with the one at 298 K, thus overcoming the long-standing strength–ductility trade-off.In the main deformation stage, the SHR of the 298 K steel was typical for high manganese TWIP steels. A high and constant SHR led to a high ductility in the 77 K steel. However, the 4 K steel exhibited multi-peak strain hardening behaviors.With the lowering of deformation temperature, the major change in the deformation mechanism was as follows: certain twinning → sufficient twinning → little twinning. This change is consistent with the change in TEL, indicating that the mechanical twinning indeed provided a high and stable strain hardening capacity. Thus, a high TEL could be obtained.2 at 4 K, sufficient twinning was not observed. Instead, numerous planar dislocation arrays and microbands could be observed. This may be also a reason for the formation of serrations.Interestingly, it was found that although the SFE of the steel was ~34.4 mJ/mIn the present work, the tensile deformation behaviors of a high manganese austenitic steel were investigated at 298, 77 and 4 K. The deformed microstructures were characterized by means of EBSD and TEM. Some conclusions can be drawn.